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Sample records for method based artificial

  1. Convergence of a residual based artificial viscosity finite element method

    KAUST Repository

    Nazarov, Murtazo

    2013-02-01

    We present a residual based artificial viscosity finite element method to solve conservation laws. The Galerkin approximation is stabilized by only residual based artificial viscosity, without any least-squares, SUPG, or streamline diffusion terms. We prove convergence of the method, applied to a scalar conservation law in two space dimensions, toward an unique entropy solution for implicit time stepping schemes. © 2012 Elsevier B.V. All rights reserved.

  2. Improved artificial bee colony algorithm based gravity matching navigation method.

    Science.gov (United States)

    Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang

    2014-07-18

    Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.

  3. Control Systems for Hyper-Redundant Robots Based on Artificial Potential Method

    Directory of Open Access Journals (Sweden)

    Mihaela Florescu

    2015-06-01

    Full Text Available This paper presents the control method of hyper-redundant robots based on the artificial potential approach. The principles of this method are shown and a suggestive example is offered. Then, the artificial potential method is applied to the case of a tentacle robot starting from the dynamic model of the robot. In addition, a series of results that are obtained through simulation is presented.

  4. Convergence of a residual based artificial viscosity finite element method

    KAUST Repository

    Nazarov, Murtazo

    2013-01-01

    . We prove convergence of the method, applied to a scalar conservation law in two space dimensions, toward an unique entropy solution for implicit time stepping schemes. © 2012 Elsevier B.V. All rights reserved.

  5. Discrete simulation system based on artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Futo, I; Szeredi, J

    1982-01-01

    A discrete event simulation system based on the AI language Prolog is presented. The system called t-Prolog extends the traditional possibilities of simulation languages toward automatic problem solving by using backtrack in time and automatic model modification depending on logical deductions. As t-Prolog is an interactive tool, the user has the possibility to interrupt the simulation run to modify the model or to force it to return to a previous state for trying possible alternatives. It admits the construction of goal-oriented or goal-seeking models with variable structure. Models are defined in a restricted version of the first order predicate calculus using Horn clauses. 21 references.

  6. Nuclear power plant monitoring and fault diagnosis methods based on the artificial intelligence technique

    International Nuclear Information System (INIS)

    Yoshikawa, S.; Saiki, A.; Ugolini, D.; Ozawa, K.

    1996-01-01

    The main objective of this paper is to develop an advanced diagnosis system based on the artificial intelligence technique to monitor the operation and to improve the operational safety of nuclear power plants. Three different methods have been elaborated in this study: an artificial neural network local diagnosis (NN ds ) scheme that acting at the component level discriminates between normal and abnormal transients, a model-based diagnostic reasoning mechanism that combines a physical causal network model-based knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge. Although the three methods have been developed and verified independently, they are highly correlated and, when connected together, form a effective and robust diagnosis and monitoring tool. (authors)

  7. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  8. Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system

    Directory of Open Access Journals (Sweden)

    A.Y. Hatata

    2018-03-01

    Full Text Available Sandia frequency shift (SFS is one of the active anti-islanding detection methods that depend on frequency drift to detect an islanding condition for inverter-based distributed generation. The non-detection zone (NDZ of the SFS method depends to a great extent on its parameters. Improper adjusting of these parameters may result in failure of the method. This paper presents a proposed artificial immune system (AIS-based technique to obtain optimal parameters of SFS anti-islanding detection method. The immune system is highly distributed, highly adaptive, and self-organizing in nature, maintains a memory of past encounters, and has the ability to continually learn about new encounters. The proposed method generates less total harmonic distortion (THD than the conventional SFS, which results in faster island detection and better non-detection zone. The performance of the proposed method is derived analytically and simulated using Matlab/Simulink. Two case studies are used to verify the proposed method. The first case includes a photovoltaic (PV connected to grid and the second includes a wind turbine connected to grid. The deduced optimized parameter setting helps to achieve the “non-islanding inverter” as well as least potential adverse impact on power quality. Keywords: Anti-islanding detection, Sandia frequency shift (SFS, Non-detection zone (NDZ, Total harmonic distortion (THD, Artificial immune system (AIS, Clonal selection algorithm

  9. The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem

    Science.gov (United States)

    Chen, Zhong; Liu, June; Li, Xiong

    2017-01-01

    A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP). The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization problem by using scalar approach, and then the whole efficient set of the BLBOP is derived by the proposed two-stage ANN for exploring the induced set. In order to illustrate the proposed method, seven numerical examples are tested and compared with results in the classical literature. Finally, a practical problem is solved by the proposed algorithm. PMID:29312446

  10. Intelligent Evaluation Method of Tank Bottom Corrosion Status Based on Improved BP Artificial Neural Network

    Science.gov (United States)

    Qiu, Feng; Dai, Guang; Zhang, Ying

    According to the acoustic emission information and the appearance inspection information of tank bottom online testing, the external factors associated with tank bottom corrosion status are confirmed. Applying artificial neural network intelligent evaluation method, three tank bottom corrosion status evaluation models based on appearance inspection information, acoustic emission information, and online testing information are established. Comparing with the result of acoustic emission online testing through the evaluation of test sample, the accuracy of the evaluation model based on online testing information is 94 %. The evaluation model can evaluate tank bottom corrosion accurately and realize acoustic emission online testing intelligent evaluation of tank bottom.

  11. Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Pengyu Gao

    2016-03-01

    Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

  12. Risk assessment for pipelines with active defects based on artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Anghel, Calin I. [Department of Chemical Engineering, Faculty of Chemistry and Chemical Engineering, University ' Babes-Bolyai' , Cluj-Napoca (Romania)], E-mail: canghel@chem.ubbcluj.ro

    2009-07-15

    The paper provides another insight into the pipeline risk assessment for in-service pressure piping containing defects. Beside of the traditional analytical approximation methods or sampling-based methods safety index and failure probability of pressure piping containing defects will be obtained based on a novel type of support vector machine developed in a minimax manner. The safety index or failure probability is carried out based on a binary classification approach. The procedure named classification reliability procedure, involving a link between artificial intelligence and reliability methods was developed as a user-friendly computer program in MATLAB language. To reveal the capacity of the proposed procedure two comparative numerical examples replicating a previous related work and predicting the failure probabilities of pressured pipeline with defects were presented.

  13. Risk assessment for pipelines with active defects based on artificial intelligence methods

    International Nuclear Information System (INIS)

    Anghel, Calin I.

    2009-01-01

    The paper provides another insight into the pipeline risk assessment for in-service pressure piping containing defects. Beside of the traditional analytical approximation methods or sampling-based methods safety index and failure probability of pressure piping containing defects will be obtained based on a novel type of support vector machine developed in a minimax manner. The safety index or failure probability is carried out based on a binary classification approach. The procedure named classification reliability procedure, involving a link between artificial intelligence and reliability methods was developed as a user-friendly computer program in MATLAB language. To reveal the capacity of the proposed procedure two comparative numerical examples replicating a previous related work and predicting the failure probabilities of pressured pipeline with defects were presented.

  14. Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

    Science.gov (United States)

    Furferi, Rocco; Governi, Lapo; Volpe, Yary

    2016-11-01

    Color matching of fabric blends is a key issue for the textile industry, mainly due to the rising need to create high-quality products for the fashion market. The process of mixing together differently colored fibers to match a desired color is usually performed by using some historical recipes, skillfully managed by company colorists. More often than desired, the first attempt in creating a blend is not satisfactory, thus requiring the experts to spend efforts in changing the recipe with a trial-and-error process. To confront this issue, a number of computer-based methods have been proposed in the last decades, roughly classified into theoretical and artificial neural network (ANN)-based approaches. Inspired by the above literature, the present paper provides a method for accurate estimation of spectrophotometric response of a textile blend composed of differently colored fibers made of different materials. In particular, the performance of the Kubelka-Munk (K-M) theory is enhanced by introducing an artificial intelligence approach to determine a more consistent value of the nonlinear function relationship between the blend and its components. Therefore, a hybrid K-M+ANN-based method capable of modeling the color mixing mechanism is devised to predict the reflectance values of a blend.

  15. The method in γ spectrum analysis with artificial neural network based on MATLAB

    International Nuclear Information System (INIS)

    Bai Lixin; Zhang Yiyun; Xu Jiayun; Wu Liping

    2003-01-01

    Analyzing γ spectrum with artificial neural network have the advantage of using the information of whole spectrum and having high analyzing precision. A convenient realization based on MATLAB was present in this

  16. A stereo remote sensing feature selection method based on artificial bee colony algorithm

    Science.gov (United States)

    Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

    2014-05-01

    To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

  17. Preconditioned characteristic boundary conditions based on artificial compressibility method for solution of incompressible flows

    Science.gov (United States)

    Hejranfar, Kazem; Parseh, Kaveh

    2017-09-01

    The preconditioned characteristic boundary conditions based on the artificial compressibility (AC) method are implemented at artificial boundaries for the solution of two- and three-dimensional incompressible viscous flows in the generalized curvilinear coordinates. The compatibility equations and the corresponding characteristic variables (or the Riemann invariants) are mathematically derived and then applied as suitable boundary conditions in a high-order accurate incompressible flow solver. The spatial discretization of the resulting system of equations is carried out by the fourth-order compact finite-difference (FD) scheme. In the preconditioning applied here, the value of AC parameter in the flow field and also at the far-field boundary is automatically calculated based on the local flow conditions to enhance the robustness and performance of the solution algorithm. The code is fully parallelized using the Concurrency Runtime standard and Parallel Patterns Library (PPL) and its performance on a multi-core CPU is analyzed. The incompressible viscous flows around a 2-D circular cylinder, a 2-D NACA0012 airfoil and also a 3-D wavy cylinder are simulated and the accuracy and performance of the preconditioned characteristic boundary conditions applied at the far-field boundaries are evaluated in comparison to the simplified boundary conditions and the non-preconditioned characteristic boundary conditions. It is indicated that the preconditioned characteristic boundary conditions considerably improve the convergence rate of the solution of incompressible flows compared to the other boundary conditions and the computational costs are significantly decreased.

  18. Infrared thermography based on artificial intelligence as a screening method for carpal tunnel syndrome diagnosis.

    Science.gov (United States)

    Jesensek Papez, B; Palfy, M; Mertik, M; Turk, Z

    2009-01-01

    This study further evaluated a computer-based infrared thermography (IRT) system, which employs artificial neural networks for the diagnosis of carpal tunnel syndrome (CTS) using a large database of 502 thermal images of the dorsal and palmar side of 132 healthy and 119 pathological hands. It confirmed the hypothesis that the dorsal side of the hand is of greater importance than the palmar side when diagnosing CTS thermographically. Using this method it was possible correctly to classify 72.2% of all hands (healthy and pathological) based on dorsal images and > 80% of hands when only severely affected and healthy hands were considered. Compared with the gold standard electromyographic diagnosis of CTS, IRT cannot be recommended as an adequate diagnostic tool when exact severity level diagnosis is required, however we conclude that IRT could be used as a screening tool for severe cases in populations with high ergonomic risk factors of CTS.

  19. Cognitive Artificial Intelligence Method for Interpreting Transformer Condition Based on Maintenance Data

    Directory of Open Access Journals (Sweden)

    Karel Octavianus Bachri

    2017-07-01

    Full Text Available A3S(Arwin-Adang-Aciek-Sembiring is a method of information fusion at a single observation and OMA3S(Observation Multi-time A3S is a method of information fusion for time-series data. This paper proposes OMA3S-based Cognitive Artificial-Intelligence method for interpreting Transformer Condition, which is calculated based on maintenance data from Indonesia National Electric Company (PLN. First, the proposed method is tested using the previously published data, and then followed by implementation on maintenance data. Maintenance data are fused to obtain part condition, and part conditions are fused to obtain transformer condition. Result shows proposed method is valid for DGA fault identification with the average accuracy of 91.1%. The proposed method not only can interpret the major fault, it can also identify the minor fault occurring along with the major fault, allowing early warning feature. Result also shows part conditions can be interpreted using information fusion on maintenance data, and the transformer condition can be interpreted using information fusion on part conditions. The future works on this research is to gather more data, to elaborate more factors to be fused, and to design a cognitive processor that can be used to implement this concept of intelligent instrumentation.

  20. Log-linear model based behavior selection method for artificial fish swarm algorithm.

    Science.gov (United States)

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

    Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  1. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Zhehuang Huang

    2015-01-01

    Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  2. Residential building energy estimation method based on the application of artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Marshall, S.; Kajl, S.

    1999-07-01

    The energy requirements of a residential building five to twenty-five stories high can be measured using a newly proposed analytical method based on artificial intelligence. The method is fast and provides a wide range of results such as total energy consumption values, power surges, and heating or cooling consumption values. A series of database were created to take into account the particularities which influence the energy consumption of a building. In this study, DOE-2 software was created for use in 8 apartment models. A total of 27 neural networks were used, 3 for the estimation of energy consumption in the corridor, and 24 for inside the apartments. Three user interfaces were created to facilitate the estimation of energy consumption. These were named the Energy Estimation Assistance System (EEAS) interfaces and are only accessible using MATLAB software. The input parameters for EEAS are: climatic region, exterior wall resistance, roofing resistance, type of windows, infiltration, number of storeys, and corridor ventilation system operating schedule. By changing the parameters, the EEAS can determine annual heating, cooling and basic energy consumption levels for apartments and corridors. 2 tabs., 2 figs.

  3. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    Science.gov (United States)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-12-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  4. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    International Nuclear Information System (INIS)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-01-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis

  5. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Stoitsis, John [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)]. E-mail: stoitsis@biosim.ntua.gr; Valavanis, Ioannis [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Mougiakakou, Stavroula G. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Golemati, Spyretta [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Nikita, Alexandra [University of Athens, Medical School 152 28 Athens (Greece); Nikita, Konstantina S. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)

    2006-12-20

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  6. Based on Short Motion Paths and Artificial Intelligence Method for Chinese Chess Game

    Directory of Open Access Journals (Sweden)

    Chien-Ming Hung

    2017-08-01

    Full Text Available The article develops the decision rules to win each set of the Chinese chess game using evaluation algorithm and artificial intelligence method, and uses the mobile robot to be instead of the chess, and presents the movement scenarios using the shortest motion paths for mobile robots. Player can play the Chinese chess game according to the game rules with the supervised computer. The supervised computer decides the optimal motion path to win the set using artificial intelligence method, and controls mobile robots according to the programmed motion paths of the assigned chesses moving on the platform via wireless RF interface. We uses enhance A* searching algorithm to solve the shortest path problem of the assigned chess, and solve the collision problems of the motion paths for two mobile robots moving on the platform simultaneously. We implement a famous set to be called lwild horses run in farmr using the proposed method. First we use simulation method to display the motion paths of the assigned chesses for the player and the supervised computer. Then the supervised computer implements the simulation results on the chessboard platform using mobile robots. Mobile robots move on the chessboard platform according to the programmed motion paths and is guided to move on the centre line of the corridor, and avoid the obstacles (chesses, and detect the cross point of the platform using three reflective IR modules.

  7. Effectiveness of Context-Aware Character Input Method for Mobile Phone Based on Artificial Neural Network

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    Masafumi Matsuhara

    2012-01-01

    Full Text Available Opportunities and needs are increasing to input Japanese sentences on mobile phones since performance of mobile phones is improving. Applications like E-mail, Web search, and so on are widely used on mobile phones now. We need to input Japanese sentences using only 12 keys on mobile phones. We have proposed a method to input Japanese sentences on mobile phones quickly and easily. We call this method number-Kanji translation method. The number string inputted by a user is translated into Kanji-Kana mixed sentence in our proposed method. Number string to Kana string is a one-to-many mapping. Therefore, it is difficult to translate a number string into the correct sentence intended by the user. The proposed context-aware mapping method is able to disambiguate a number string by artificial neural network (ANN. The system is able to translate number segments into the intended words because the system becomes aware of the correspondence of number segments with Japanese words through learning by ANN. The system does not need a dictionary. We also show the effectiveness of our proposed method for practical use by the result of the evaluation experiment in Twitter data.

  8. Stability monitoring for BWR based on singular value decomposition method using artificial neural network

    International Nuclear Information System (INIS)

    Tsuji, Masashi; Shimazu, Yoichiro; Michishita, Hiroshi

    2005-01-01

    A new method for evaluating the decay ratios in a boiling water reactor (BWR) using the singular value decomposition (SVD) method had been proposed. In this method, a signal component closely related to the BWR stability can be extracted from independent components of the neutron noise signal decomposed by the SVD method. However, real-time stability monitoring by the SVD method requires an efficient procedure for screening such components. For efficient screening, an artificial neural network (ANN) with three layers was adopted. The trained ANN was actually applied to decomposed components of local power range monitor (LPRM) signals that were measured in stability experiments conducted in the Ringhals-1 BWR. In each LPRM signal, multiple candidates were screened from the decomposed components. However, decay ratios could be estimated by introducing appropriate criterions for selecting the most suitable component among the candidates. The estimated decay ratios are almost identical to those evaluated by visual screening in a previous study. The selected components commonly have the largest singular value, the largest decay ratio and the least squared fitting error among the candidates. By virtue of excellent screening performance of the trained ANN, the real-time stability monitoring by the SVD method can be applied in practice. (author)

  9. A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images.

    Science.gov (United States)

    Rocha, José Celso; Passalia, Felipe José; Matos, Felipe Delestro; Takahashi, Maria Beatriz; Ciniciato, Diego de Souza; Maserati, Marc Peter; Alves, Mayra Fernanda; de Almeida, Tamie Guibu; Cardoso, Bruna Lopes; Basso, Andrea Cristina; Nogueira, Marcelo Fábio Gouveia

    2017-08-09

    Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.4%. Interestingly, when the same embryologists evaluated a sub-sample (10%) of the dataset, there was only 54.0% agreement with the standard (mode for grades). However, when using the ANN to assess this sub-sample, there was 87.5% agreement with the modal values obtained by the evaluators. The presented methodology is covered by National Institute of Industrial Property (INPI) and World Intellectual Property Organization (WIPO) patents and is currently undergoing a commercial evaluation of its feasibility.

  10. Computer vision-based method for classification of wheat grains using artificial neural network.

    Science.gov (United States)

    Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

    2017-06-01

    A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  11. Reinforcement Learning Based Artificial Immune Classifier

    Directory of Open Access Journals (Sweden)

    Mehmet Karakose

    2013-01-01

    Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

  12. Artificial Inductance Concept to Compensate Nonlinear Inductance Effects in the Back EMF-Based Sensorless Control Method for PMSM

    DEFF Research Database (Denmark)

    Lu, Kaiyuan; Lei, Xiao; Blaabjerg, Frede

    2013-01-01

    The back EMF-based sensorless control method is very popular for permanent magnet synchronous machines (PMSMs) in the medium- to high-speed operation range due to its simple structure. In this speed range, the accuracy of the estimated position is mainly affected by the inductance, which varies...... at different loading conditions due to saturation effects. In this paper, a new concept of using a constant artificial inductance to replace the actual varying machine inductance for position estimation is introduced. This facilitates greatly the analysis of the influence of inductance variation...

  13. Proposal of a New Method for Neutron Dosimetry Based on Spectral Information Obtained by Application of Artificial Neural Networks

    International Nuclear Information System (INIS)

    Fehrenbacher, G.; Schuetz, R.; Hahn, K.; Sprunck, M.; Cordes, E.; Biersack, J.P.; Wahl, W.

    1999-01-01

    A new method for the monitoring of neutron radiation is proposed. It is based on the determination of spectral information on the neutron field in order to derive dose quantities like the ambient dose equivalent, the dose equivalent, or other dose quantities which depend on the neutron energy. The method uses a multi-element system consisting of converter type silicon detectors. The unfolding procedure is based on an artificial neural network (ANN). The response function of each element is determined by a computational model considering the neutron interaction with the dosemeter layers and the subsequent transport of produced ions. An example is given for a multi-element system. The ANN is trained by a given set of neutron spectra and then applied to count responses obtained in neutron fields. Four examples of spectra unfolded using the ANN are presented. (author)

  14. Artificial intelligence methods for diagnostic

    International Nuclear Information System (INIS)

    Dourgnon-Hanoune, A.; Porcheron, M.; Ricard, B.

    1996-01-01

    To assist in diagnosis of its nuclear power plants, the Research and Development Division of Electricite de France has been developing skills in Artificial Intelligence for about a decade. Different diagnostic expert systems have been designed. Among them, SILEX for control rods cabinet troubleshooting, DIVA for turbine generator diagnosis, DIAPO for reactor coolant pump diagnosis. This know how in expert knowledge modeling and acquisition is direct result of experience gained during developments and of a more general reflection on knowledge based system development. We have been able to reuse this results for other developments such as a guide for auxiliary rotating machines diagnosis. (authors)

  15. Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Chun-tian Cheng

    2015-07-01

    Full Text Available Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO, ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.

  16. Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

    Science.gov (United States)

    Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

    2017-04-01

    In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

  17. Computerized detection of multiple sclerosis candidate regions based on a level set method using an artificial neural network

    International Nuclear Information System (INIS)

    Kuwazuru, Junpei; Magome, Taiki; Arimura, Hidetaka; Yamashita, Yasuo; Oki, Masafumi; Toyofuku, Fukai; Kakeda, Shingo; Yamamoto, Daisuke

    2010-01-01

    Yamamoto et al. developed the system for computer-aided detection of multiple sclerosis (MS) candidate regions. In a level set method in their proposed method, they employed the constant threshold value for the edge indicator function related to a speed function of the level set method. However, it would be appropriate to adjust the threshold value to each MS candidate region, because the edge magnitudes in MS candidates differ from each other. Our purpose of this study was to develop a computerized detection of MS candidate regions in MR images based on a level set method using an artificial neural network (ANN). To adjust the threshold value for the edge indicator function in the level set method to each true positive (TP) and false positive (FP) region, we constructed the ANN. The ANN could provide the suitable threshold value for each candidate region in the proposed level set method so that TP regions can be segmented and FP regions can be removed. Our proposed method detected MS regions at a sensitivity of 82.1% with 0.204 FPs per slice and similarity index of MS candidate regions was 0.717 on average. (author)

  18. Measurement correction method for force sensor used in dynamic pressure calibration based on artificial neural network optimized by genetic algorithm

    Science.gov (United States)

    Gu, Tingwei; Kong, Deren; Shang, Fei; Chen, Jing

    2017-12-01

    We present an optimization algorithm to obtain low-uncertainty dynamic pressure measurements from a force-transducer-based device. In this paper, the advantages and disadvantages of the methods that are commonly used to measure the propellant powder gas pressure, the applicable scope of dynamic pressure calibration devices, and the shortcomings of the traditional comparison calibration method based on the drop-weight device are firstly analysed in detail. Then, a dynamic calibration method for measuring pressure using a force sensor based on a drop-weight device is introduced. This method can effectively save time when many pressure sensors are calibrated simultaneously and extend the life of expensive reference sensors. However, the force sensor is installed between the drop-weight and the hammerhead by transition pieces through the connection mode of bolt fastening, which causes adverse effects such as additional pretightening and inertia forces. To solve these effects, the influence mechanisms of the pretightening force, the inertia force and other influence factors on the force measurement are theoretically analysed. Then a measurement correction method for the force measurement is proposed based on an artificial neural network optimized by a genetic algorithm. The training and testing data sets are obtained from calibration tests, and the selection criteria for the key parameters of the correction model is discussed. The evaluation results for the test data show that the correction model can effectively improve the force measurement accuracy of the force sensor. Compared with the traditional high-accuracy comparison calibration method, the percentage difference of the impact-force-based measurement is less than 0.6% and the relative uncertainty of the corrected force value is 1.95%, which can meet the requirements of engineering applications.

  19. Research on method of nuclear power plant operation fault diagnosis based on a combined artificial neural network

    International Nuclear Information System (INIS)

    Liu Feng; Yu Ren; Li Fengyu; Zhang Meng

    2007-01-01

    To solve the online real-time diagnosis problem of the nuclear power plant in operating condition, a method based on a combined artificial neural network is put forward in the paper. Its main principle is: using the BP neural network for the fast group diagnosis, and then using the RBF neural network for distinguishing and verifying the diagnostic result. The accuracy of the method is verified using the simulation values of the key parameters in normal status and malfunction status of a nuclear power plant. The results show that the method combining the advantages of the two neural networks can not only diagnose the learned faults in similar power level of the nuclear power plant quickly and accurately, but also can identify the faults in different power status, as well as the unlearned faults. The outputs of the diagnosis system are in form of the reliability of the faults, and are changing with the lasting of the operation time of the plant. This makes the diagnosis results be more acceptable to operators. (authors)

  20. A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

    Science.gov (United States)

    Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

    2017-07-01

    As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

  1. Artificial Leaf Based on Artificial Photosynthesis for Solar Fuel Production

    Science.gov (United States)

    2017-06-30

    collect light energy and separate charge for developing new types of nanobiodevices to construct ”artificial leaf” from solar to fuel. or Concept of...AFRL-AFOSR-JP-TR-2017-0054 Artificial Leaf Based on Artificial Photosynthesis for Solar Fuel Production Mamoru Nango NAGOYA INSTITUTE OF TECHNOLOGY...display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY)      30-06-2017 2

  2. An Object-Based Image Analysis Method for Monitoring Land Conversion by Artificial Sprawl Use of RapidEye and IRS Data

    Directory of Open Access Journals (Sweden)

    Maud Balestrat

    2012-02-01

    Full Text Available In France, in the peri-urban context, urban sprawl dynamics are particularly strong with huge population growth as well as a land crisis. The increase and spreading of built-up areas from the city centre towards the periphery takes place to the detriment of natural and agricultural spaces. The conversion of land with agricultural potential is all the more worrying as it is usually irreversible. The French Ministry of Agriculture therefore needs reliable and repeatable spatial-temporal methods to locate and quantify loss of land at both local and national scales. The main objective of this study was to design a repeatable method to monitor land conversion characterized by artificial sprawl: (i We used an object-based image analysis to extract artificial areas from satellite images; (ii We built an artificial patch that consists of aggregating all the peripheral areas that characterize artificial areas. The “artificialized” patch concept is an innovative extension of the urban patch concept, but differs in the nature of its components and in the continuity distance applied; (iii The diachronic analysis of artificial patch maps enables characterization of artificial sprawl. The method was applied at the scale of four departments (similar to provinces along the coast of Languedoc-Roussillon, in the South of France, based on two satellite datasets, one acquired in 1996–1997 (Indian Remote Sensing and the other in 2009 (RapidEye. In the four departments, we measured an increase in artificial areas of from 113,000 ha in 1997 to 133,000 ha in 2009, i.e., an 18% increase in 12 years. The package comes in the form of a 1/15,000 valid cartography, usable at the scale of a commune (the smallest territorial division used for administrative purposes in France that can be adapted to departmental and regional scales. The method is reproducible in homogenous spatial-temporal terms, so that it could be used periodically to assess changes in land conversion

  3. Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

    Directory of Open Access Journals (Sweden)

    Mustafa Serter Uzer

    2013-01-01

    Full Text Available This paper offers a hybrid approach that uses the artificial bee colony (ABC algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

  4. Comparison of pixel -based and artificial neural networks classification methods for detecting forest cover changes in Malaysia

    International Nuclear Information System (INIS)

    Deilmai, B R; Rasib, A W; Ariffin, A; Kanniah, K D

    2014-01-01

    According to the FAO (Food and Agriculture Organization), Malaysia lost 8.6% of its forest cover between 1990 and 2005. In forest cover change detection, remote sensing plays an important role. A lot of change detection methods have been developed, and most of them are semi-automated. These methods are time consuming and difficult to apply. One of the new and robust methods for change detection is artificial neural network (ANN). In this study, (ANN) classification scheme is used to detect the forest cover changes in the Johor state in Malaysia. Landsat Thematic Mapper images covering a period of 9 years (2000 and 2009) are used. Results obtained with ANN technique was compared with Maximum likelihood classification (MLC) to investigate whether ANN can perform better in the tropical environment. Overall accuracy of the ANN and MLC techniques are 75%, 68% (2000) and 80%, 75% (2009) respectively. Using the ANN method, it was found that forest area in Johor decreased as much as 1298 km2 between 2000 and 2009. The results also showed the potential and advantages of neural network in classification and change detection analysis

  5. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  6. An Integrated Start-Up Method for Pumped Storage Units Based on a Novel Artificial Sheep Algorithm

    Directory of Open Access Journals (Sweden)

    Zanbin Wang

    2018-01-01

    Full Text Available Pumped storage units (PSUs are an important storage tool for power systems containing large-scale renewable energy, and the merit of rapid start-up enable PSUs to modulate and stabilize the power system. In this paper, PSU start-up strategies have been studied and a new integrated start-up method has been proposed for the purpose of achieving swift and smooth start-up. A two-phase closed-loop startup strategy, composed of switching Proportion Integration (PI and Proportion Integration Differentiation (PID controller is designed, and an integrated optimization scheme is proposed for a synchronous optimization of the parameters in the strategy. To enhance the optimization performance, a novel meta-heuristic called Artificial Sheep Algorithm (ASA is proposed and applied to solve the optimization task after a sufficient verification with seven popular meta-heuristic algorithms and 13 typical benchmark functions. Simulation model has been built for a China’s PSU and comparative experiments are conducted to evaluate the proposed integrated method. Results show that the start-up performance could be significantly improved on both indices on overshoot and start-up, and up to 34%-time consumption has been reduced under different working condition. The significant improvements on start-up of PSU is interesting and meaning for further application on real unit.

  7. Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

    Science.gov (United States)

    Li, Mengshan; Zhang, Huaijing; Chen, Bingsheng; Wu, Yan; Guan, Lixin

    2018-03-05

    The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

  8. Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model.

    Science.gov (United States)

    Stefanović, Stefica Cerjan; Bolanča, Tomislav; Luša, Melita; Ukić, Sime; Rogošić, Marko

    2012-02-24

    This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

    Science.gov (United States)

    Eslamizadeh, Gholamhossein; Barati, Ramin

    2017-05-01

    Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Glytube: a conical tube and parafilm M-based method as a simplified device to artificially blood-feed the dengue vector mosquito, Aedes aegypti.

    Directory of Open Access Journals (Sweden)

    André Luis Costa-da-Silva

    Full Text Available Aedes aegypti, the main vector of dengue virus, requires a blood meal to produce eggs. Although live animals are still the main blood source for laboratory colonies, many artificial feeders are available. These feeders are also the best method for experimental oral infection of Ae. aegypti with Dengue viruses. However, most of them are expensive or laborious to construct. Based on principle of Rutledge-type feeder, a conventional conical tube, glycerol and Parafilm-M were used to develop a simple in-house feeder device. The blood feeding efficiency of this apparatus was compared to a live blood source, mice, and no significant differences (p = 0.1189 were observed between artificial-fed (51.3% of engorgement and mice-fed groups (40.6%. Thus, an easy to assemble and cost-effective artificial feeder, designated "Glytube" was developed in this report. This simple and efficient feeding device can be built with common laboratory materials for research on Ae. aegypti.

  11. Forming of the regional core transport network taking into account the allocation of alternative energy sources based on artificial intelligence methods

    Directory of Open Access Journals (Sweden)

    Marina ZHURAVSKAYA

    2014-12-01

    Full Text Available In the modern world the alternative energy sources, which considerably depend on a region, play more and more significant role. However, the transition of regions to new energy sources lead to the change of transport and logistic network configuration. The formation of optimal core transport network today is a guarantee of the successful economic development of a region tomorrow. The present article studies the issue of advanced core transport network development in a region based on the experience of European and Asian countries and the opportunity to adapt the best foreign experience to Russian conditions. On the basis of artificial intelligence methods for forest industry complex of Sverdlovskaya Oblast the algorithm of problem solution of an optimal logistic infrastructure allocation is offered and some results of a regional transport network are presented. These methods allowed to solve the set task in the conditions of information uncertainty. There are suggestions on the improvement of transport and logistic network in the territory of Sverdlovskaya Oblast. Traditionally the logistics of mineral fuel plays main role in regions development. Actually it is required to develop logistic strategic plans to be able to provide different possibilities of power-supply, flexible enough to change with the population density, transport infrastructure and demographics of different regions. The problem of logistic centers allocation was studied by many authors. The approach, offered by the authors of this paper is to solve the set of tasks by applying artificial intelligence methods, such as fuzzy set theory and genetic algorithms.

  12. Review of Artificial Abrasion Test Methods for PV Module Technology

    Energy Technology Data Exchange (ETDEWEB)

    Miller, David C. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Muller, Matt T. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Simpson, Lin J. [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2016-08-01

    This review is intended to identify the method or methods--and the basic details of those methods--that might be used to develop an artificial abrasion test. Methods used in the PV literature were compared with their closest implementation in existing standards. Also, meetings of the International PV Quality Assurance Task Force Task Group 12-3 (TG12-3, which is concerned with coated glass) were used to identify established test methods. Feedback from the group, which included many of the authors from the PV literature, included insights not explored within the literature itself. The combined experience and examples from the literature are intended to provide an assessment of the present industry practices and an informed path forward. Recommendations toward artificial abrasion test methods are then identified based on the experiences in the literature and feedback from the PV community. The review here is strictly focused on abrasion. Assessment methods, including optical performance (e.g., transmittance or reflectance), surface energy, and verification of chemical composition were not examined. Methods of artificially soiling PV modules or other specimens were not examined. The weathering of artificial or naturally soiled specimens (which may ultimately include combined temperature and humidity, thermal cycling and ultraviolet light) were also not examined. A sense of the purpose or application of an abrasion test method within the PV industry should, however, be evident from the literature.

  13. A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering

    Directory of Open Access Journals (Sweden)

    Li Ma

    2015-01-01

    Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA. The proposed algorithm combines artificial fish swarm algorithm (AFSA with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM.

  14. A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Sunil Tyagi

    2017-04-01

    Full Text Available A classification technique using Support Vector Machine (SVM classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

  15. Exploration of mineral resource deposits based on analysis of aerial and satellite image data employing artificial intelligence methods

    Science.gov (United States)

    Osipov, Gennady

    2013-04-01

    We propose a solution to the problem of exploration of various mineral resource deposits, determination of their forms / classification of types (oil, gas, minerals, gold, etc.) with the help of satellite photography of the region of interest. Images received from satellite are processed and analyzed to reveal the presence of specific signs of deposits of various minerals. Course of data processing and making forecast can be divided into some stages: Pre-processing of images. Normalization of color and luminosity characteristics, determination of the necessary contrast level and integration of a great number of separate photos into a single map of the region are performed. Construction of semantic map image. Recognition of bitmapped image and allocation of objects and primitives known to system are realized. Intelligent analysis. At this stage acquired information is analyzed with the help of a knowledge base, which contain so-called "attention landscapes" of experts. Used methods of recognition and identification of images: a) combined method of image recognition, b)semantic analysis of posterized images, c) reconstruction of three-dimensional objects from bitmapped images, d)cognitive technology of processing and interpretation of images. This stage is fundamentally new and it distinguishes suggested technology from all others. Automatic registration of allocation of experts` attention - registration of so-called "attention landscape" of experts - is the base of the technology. Landscapes of attention are, essentially, highly effective filters that cut off unnecessary information and emphasize exactly the factors used by an expert for making a decision. The technology based on denoted principles involves the next stages, which are implemented in corresponding program agents. Training mode -> Creation of base of ophthalmologic images (OI) -> Processing and making generalized OI (GOI) -> Mode of recognition and interpretation of unknown images. Training mode

  16. Structural Reliability: An Assessment Using a New and Efficient Two-Phase Method Based on Artificial Neural Network and a Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    Naser Kazemi Elaki

    2016-06-01

    Full Text Available In this research, a two-phase algorithm based on the artificial neural network (ANN and a harmony search (HS algorithm has been developed with the aim of assessing the reliability of structures with implicit limit state functions. The proposed method involves the generation of datasets to be used specifically for training by Finite Element analysis, to establish an ANN model using a proven ANN model in the reliability assessment process as an analyzer for structures, and finally estimate the reliability index and failure probability by using the HS algorithm, without any requirements for the explicit form of limit state function. The proposed algorithm is investigated here, and its accuracy and efficiency are demonstrated by using several numerical examples. The results obtained show that the proposed algorithm gives an appropriate estimate for the assessment of reliability of structures.

  17. Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: Offering a new approach for lagged ETo data-based modeling

    Science.gov (United States)

    Mehdizadeh, Saeid

    2018-04-01

    Evapotranspiration (ET) is considered as a key factor in hydrological and climatological studies, agricultural water management, irrigation scheduling, etc. It can be directly measured using lysimeters. Moreover, other methods such as empirical equations and artificial intelligence methods can be used to model ET. In the recent years, artificial intelligence methods have been widely utilized to estimate reference evapotranspiration (ETo). In the present study, local and external performances of multivariate adaptive regression splines (MARS) and gene expression programming (GEP) were assessed for estimating daily ETo. For this aim, daily weather data of six stations with different climates in Iran, namely Urmia and Tabriz (semi-arid), Isfahan and Shiraz (arid), Yazd and Zahedan (hyper-arid) were employed during 2000-2014. Two types of input patterns consisting of weather data-based and lagged ETo data-based scenarios were considered to develop the models. Four statistical indicators including root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE) were used to check the accuracy of models. The local performance of models revealed that the MARS and GEP approaches have the capability to estimate daily ETo using the meteorological parameters and the lagged ETo data as inputs. Nevertheless, the MARS had the best performance in the weather data-based scenarios. On the other hand, considerable differences were not observed in the models' accuracy for the lagged ETo data-based scenarios. In the innovation of this study, novel hybrid models were proposed in the lagged ETo data-based scenarios through combination of MARS and GEP models with autoregressive conditional heteroscedasticity (ARCH) time series model. It was concluded that the proposed novel models named MARS-ARCH and GEP-ARCH improved the performance of ETo modeling compared to the single MARS and GEP. In addition, the external

  18. A Streamlined Artificial Variable Free Version of Simplex Method

    OpenAIRE

    Inayatullah, Syed; Touheed, Nasir; Imtiaz, Muhammad

    2015-01-01

    This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method. For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP. This method follows the same pivoting sequence as of simplex phase 1 without showing any explicit description of artificial variables which also makes it space efficient. Later in this paper, a dual version of the new ...

  19. Development of an Origin Trace Method based on Bayesian Inference and Artificial Neural Network for Missing or Stolen Nuclear Materials

    Energy Technology Data Exchange (ETDEWEB)

    Bin, Yim Ho; Min, Lee Seung; Min, Kim Kyung; Jeong, Hong Yoon; Kim, Jae Kwang [Nuclear Security Div., Daejeon (Korea, Republic of)

    2014-05-15

    Thus, 'to put nuclear materials under control' is an important issue for prosperity mankind. Unfortunately, numbers of illicit trafficking of nuclear materials have been increased for decades. Consequently, security of nuclear materials is recently spotlighted. After the 2{sup nd} Nuclear Security Summit in Seoul in 2012, the president of Korea had showed his devotion to nuclear security. One of the main responses for nuclear security related interest of Korea was to develop a national nuclear forensic support system. International Atomic Energy Agency (IAEA) published the document of Nuclear Security Series No.2 'Nuclear Forensics Support' in 2006 to encourage international cooperation of all IAEA member states for tracking nuclear attributions. There are two main questions related to nuclear forensics to answer in the document. The first question is 'what type of material is it?', and the second one is 'where did the material come from?' Korea Nuclear Forensic Library (K-NFL) and mathematical methods to trace origins of missing or stolen nuclear materials (MSNMs) are being developed by Korea Institute of Nuclear Non-proliferation and Control (KINAC) to answer those questions. Although the K-NFL has been designed to perform many functions, K-NFL is being developed to effectively trace the origin of MSNMs and tested to validate suitability of trace methods. New fuels and spent fuels need each trace method because of the different nature of data acquisition. An inductive logic was found to be appropriate for new fuels, which had values as well as a bistable property. On the other hand, machine learning was suitable for spent fuels, which were unable to measure, and thus needed simulation.

  20. Artificial urinary conduit construction using tissue engineering methods.

    Science.gov (United States)

    Kloskowski, Tomasz; Pokrywczyńska, Marta; Drewa, Tomasz

    2015-01-01

    Incontinent urinary diversion using an ileal conduit is the most popular method used by urologists after bladder cystectomy resulting from muscle invasive bladder cancer. The use of gastrointestinal tissue is related to a series of complications with the necessity of surgical procedure extension which increases the time of surgery. Regenerative medicine together with tissue engineering techniques gives hope for artificial urinary conduit construction de novo without affecting the ileum. In this review we analyzed history of urinary diversion together with current attempts in urinary conduit construction using tissue engineering methods. Based on literature and our own experience we presented future perspectives related to the artificial urinary conduit construction. A small number of papers in the field of tissue engineered urinary conduit construction indicates that this topic requires more attention. Three main factors can be distinguished to resolve this topic: proper scaffold construction along with proper regeneration of both the urothelium and smooth muscle layers. Artificial urinary conduit has a great chance to become the first commercially available product in urology constructed by regenerative medicine methods.

  1. Empirical estimation of the grades of hearing impairment among industrial workers based on new artificial neural networks and classical regression methods.

    Science.gov (United States)

    Farhadian, Maryam; Aliabadi, Mohsen; Darvishi, Ebrahim

    2015-01-01

    Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers. A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software. Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively. Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

  2. Artificial muscles based on liquid crystal elastomers.

    Science.gov (United States)

    Li, Min-Hui; Keller, Patrick

    2006-10-15

    This paper presents our results on liquid crystal (LC) elastomers as artificial muscle, based on the ideas proposed by de Gennes. In the theoretical model, the material consists of a repeated series of main-chain nematic LC polymer blocks, N, and conventional rubber blocks, R, based on the lamellar phase of a triblock copolymer RNR. The motor for the contraction is the reversible macromolecular shape change of the chain, from stretched to spherical, that occurs at the nematic-to-isotropic phase transition in the main-chain nematic LC polymers. We first developed a new kind of muscle-like material based on a network of side-on nematic LC homopolymers. Side-on LC polymers were used instead of main-chain LC polymers for synthetic reasons. The first example of these materials was thermo-responsive, with a typical contraction of around 35-45% and a generated force of around 210 kPa. Subsequently, a photo-responsive material was developed, with a fast photochemically induced contraction of around 20%, triggered by UV light. We then succeeded in preparing a thermo-responsive artificial muscle, RNR, with lamellar structure, using a side-on nematic LC polymer as N block.Micrometre-sized artificial muscles were also prepared. This paper illustrates the bottom-up design of stimuli-responsive materials, in which the overall material response reflects the individual macromolecular response, using LC polymer as building block.

  3. Reasoning methods in medical consultation systems: artificial intelligence approaches.

    Science.gov (United States)

    Shortliffe, E H

    1984-01-01

    It has been argued that the problem of medical diagnosis is fundamentally ill-structured, particularly during the early stages when the number of possible explanations for presenting complaints can be immense. This paper discusses the process of clinical hypothesis evocation, contrasts it with the structured decision making approaches used in traditional computer-based diagnostic systems, and briefly surveys the more open-ended reasoning methods that have been used in medical artificial intelligence (AI) programs. The additional complexity introduced when an advice system is designed to suggest management instead of (or in addition to) diagnosis is also emphasized. Example systems are discussed to illustrate the key concepts.

  4. Artificial organic networks artificial intelligence based on carbon networks

    CERN Document Server

    Ponce-Espinosa, Hiram; Molina, Arturo

    2014-01-01

    This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        class...

  5. Estimation of mechanical properties of nanomaterials using artificial intelligence methods

    Science.gov (United States)

    Vijayaraghavan, V.; Garg, A.; Wong, C. H.; Tai, K.

    2014-09-01

    Computational modeling tools such as molecular dynamics (MD), ab initio, finite element modeling or continuum mechanics models have been extensively applied to study the properties of carbon nanotubes (CNTs) based on given input variables such as temperature, geometry and defects. Artificial intelligence techniques can be used to further complement the application of numerical methods in characterizing the properties of CNTs. In this paper, we have introduced the application of multi-gene genetic programming (MGGP) and support vector regression to formulate the mathematical relationship between the compressive strength of CNTs and input variables such as temperature and diameter. The predictions of compressive strength of CNTs made by these models are compared to those generated using MD simulations. The results indicate that MGGP method can be deployed as a powerful method for predicting the compressive strength of the carbon nanotubes.

  6. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    Science.gov (United States)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  7. A streamlined artificial variable free version of simplex method.

    Directory of Open Access Journals (Sweden)

    Syed Inayatullah

    Full Text Available This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method. For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP. This method follows the same pivoting sequence as of simplex phase 1 without showing any explicit description of artificial variables which also makes it space efficient. Later in this paper, a dual version of the new method has also been presented which provides a way to easily implement the phase 1 of traditional dual simplex method. For a problem having an initial basis which is both primal and dual infeasible, our methods provide full freedom to the user, that whether to start with primal artificial free version or dual artificial free version without making any reformulation to the LP structure. Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.

  8. A streamlined artificial variable free version of simplex method.

    Science.gov (United States)

    Inayatullah, Syed; Touheed, Nasir; Imtiaz, Muhammad

    2015-01-01

    This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method. For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP. This method follows the same pivoting sequence as of simplex phase 1 without showing any explicit description of artificial variables which also makes it space efficient. Later in this paper, a dual version of the new method has also been presented which provides a way to easily implement the phase 1 of traditional dual simplex method. For a problem having an initial basis which is both primal and dual infeasible, our methods provide full freedom to the user, that whether to start with primal artificial free version or dual artificial free version without making any reformulation to the LP structure. Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.

  9. Artificial terraced field extraction based on high resolution DEMs

    Science.gov (United States)

    Na, Jiaming; Yang, Xin; Xiong, Liyang; Tang, Guoan

    2017-04-01

    With the increase of human activities, artificial landforms become one of the main terrain features with special geographical and hydrological value. Terraced field, as the most important artificial landscapes of the loess plateau, plays an important role in conserving soil and water. With the development of digital terrain analysis (DTA), there is a current and future need in developing a robust, repeatable and cost-effective research methodology for terraced fields. In this paper, a novel method using bidirectional DEM shaded relief is proposed for terraced field identification based on high resolution DEM, taking Zhifanggou watershed, Shannxi province as the study area. Firstly, 1m DEM is obtained by low altitude aerial photogrammetry using Unmanned Aerial Vehicle (UAV), and 0.1m DOM is also obtained as the test data. Then, the positive and negative terrain segmentation is done to acquire the area of terraced field. Finally, a bidirectional DEM shaded relief is simulated to extract the ridges of each terraced field stages. The method in this paper can get not only polygon feature of the terraced field areas but also line feature of terraced field ridges. The accuracy is 89.7% compared with the artificial interpretation result from DOM. And additional experiment shows that this method has a strong robustness as well as high accuracy.

  10. A new method of artificial latent fingerprint creation using artificial sweat and inkjet printer.

    Science.gov (United States)

    Hong, Sungwook; Hong, Ingi; Han, Aleum; Seo, Jin Yi; Namgung, Juyoung

    2015-12-01

    In order to study fingerprinting in the field of forensic science, it is very important to have two or more latent fingerprints with identical chemical composition and intensity. However, it is impossible to obtain identical fingerprints, in reality, because fingerprinting comes out slightly differently every time. A previous research study had proposed an artificial fingerprint creation method in which inkjet ink was replaced with amino acids and sodium chloride solution: the components of human sweat. But, this method had some drawbacks: divalent cations were not added while formulating the artificial sweat solution, and diluted solutions were used for creating weakly deposited latent fingerprint. In this study, a method was developed for overcoming the drawbacks of the methods used in the previous study. Several divalent cations were added in this study because the amino acid-ninhydrin (or some of its analogues) complex is known to react with divalent cations to produce a photoluminescent product; and, similarly, the amino acid-1,2-indanedione complex is known to be catalyzed by a small amount of zinc ions to produce a highly photoluminescent product. Also, in this study, a new technique was developed which enables to adjust the intensity when printing the latent fingerprint patterns. In this method, image processing software is used to control the intensity of the master fingerprint patterns, which adjusts the printing intensity of the latent fingerprints. This new method opened the way to produce a more realistic artificial fingerprint in various strengths with one artificial sweat working solution. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  11. Image Signal Transfer Method in Artificial Retina using Laser

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, I.Y.; Lee, B.H.; Kim, S.J. [Seoul National University, Seoul (Korea)

    2002-05-01

    Recently, the research on artificial retina for the blind is active. In this paper a new optical link method for the retinal prosthesis is proposed. Laser diode system was chosen to transfer image into the eye in this project and the new optical system was designed and evaluated. The use of laser diode array in artificial retina system makes system simple for lack of signal processing part inside of the eyeball. Designed optical system is enough to focus laser diode array on photodiode array in 20X20 application. (author). 11 refs., 7 figs., 2 tabs.

  12. Artificial nanomachines based on interlocked molecules

    International Nuclear Information System (INIS)

    Credi, Alberto

    2006-01-01

    The extension of the concept of machine to the molecular level is of great interest for the growth of nanoscience and the development of nanotechnology. A molecular machine can be defined as an assembly of a discrete number of molecular components (that is, a supramolecular structure) designed to perform a function through the mechanical movements of its components, which occur under appropriate external stimulation. Hence, molecular machines contain a motor part, that is a device capable of converting energy into mechanical work. Molecular motors and machines operate via nuclear rearrangements and, like their macroscopic counterparts, are characterized by the kind of energy input supplied to make them work, the manner in which their operation can be monitored, the possibility to repeat the operation at will, i.e., establishing a cyclic process, the timescale needed to complete a cycle of operation, and the performed function. Owing to the progresses made in several branches of chemistry, and to the better understanding of the operation mechanisms of molecular machines of the biological world, it has become possible to design and construct simple prototypes of artificial molecular motors and machines. Some examples based on rotaxanes, catenanes, and related interlocked molecules will be described

  13. A MITE-based genotyping method to reveal hundreds of DNA polymorphisms in an animal genome after a few generations of artificial selection

    Directory of Open Access Journals (Sweden)

    Tetreau Guillaume

    2008-10-01

    Full Text Available Abstract Background For most organisms, developing hundreds of genetic markers spanning the whole genome still requires excessive if not unrealistic efforts. In this context, there is an obvious need for methodologies allowing the low-cost, fast and high-throughput genotyping of virtually any species, such as the Diversity Arrays Technology (DArT. One of the crucial steps of the DArT technique is the genome complexity reduction, which allows obtaining a genomic representation characteristic of the studied DNA sample and necessary for subsequent genotyping. In this article, using the mosquito Aedes aegypti as a study model, we describe a new genome complexity reduction method taking advantage of the abundance of miniature inverted repeat transposable elements (MITEs in the genome of this species. Results Ae. aegypti genomic representations were produced following a two-step procedure: (1 restriction digestion of the genomic DNA and simultaneous ligation of a specific adaptor to compatible ends, and (2 amplification of restriction fragments containing a particular MITE element called Pony using two primers, one annealing to the adaptor sequence and one annealing to a conserved sequence motif of the Pony element. Using this protocol, we constructed a library comprising more than 6,000 DArT clones, of which at least 5.70% were highly reliable polymorphic markers for two closely related mosquito strains separated by only a few generations of artificial selection. Within this dataset, linkage disequilibrium was low, and marker redundancy was evaluated at 2.86% only. Most of the detected genetic variability was observed between the two studied mosquito strains, but individuals of the same strain could still be clearly distinguished. Conclusion The new complexity reduction method was particularly efficient to reveal genetic polymorphisms in Ae. egypti. Overall, our results testify of the flexibility of the DArT genotyping technique and open new

  14. Space Environment Modelling with the Use of Artificial Intelligence Methods

    Science.gov (United States)

    Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

    1996-12-01

    Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore

  15. Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

    Directory of Open Access Journals (Sweden)

    Ebtehaj Isa

    2016-09-01

    Full Text Available A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP, a hybrid method based on decision trees (DT (MLP-DT, to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036. The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

  16. Artificial intelligence methods applied for quantitative analysis of natural radioactive sources

    International Nuclear Information System (INIS)

    Medhat, M.E.

    2012-01-01

    Highlights: ► Basic description of artificial neural networks. ► Natural gamma ray sources and problem of detections. ► Application of neural network for peak detection and activity determination. - Abstract: Artificial neural network (ANN) represents one of artificial intelligence methods in the field of modeling and uncertainty in different applications. The objective of the proposed work was focused to apply ANN to identify isotopes and to predict uncertainties of their activities of some natural radioactive sources. The method was tested for analyzing gamma-ray spectra emitted from natural radionuclides in soil samples detected by a high-resolution gamma-ray spectrometry based on HPGe (high purity germanium). The principle of the suggested method is described, including, relevant input parameters definition, input data scaling and networks training. It is clear that there is satisfactory agreement between obtained and predicted results using neural network.

  17. An Artificial Intelligence-Based Environment Quality Analysis System

    OpenAIRE

    Oprea , Mihaela; Iliadis , Lazaros

    2011-01-01

    Part 20: Informatics and Intelligent Systems Applications for Quality of Life information Services (ISQLIS) Workshop; International audience; The paper describes an environment quality analysis system based on a combination of some artificial intelligence techniques, artificial neural networks and rule-based expert systems. Two case studies of the system use are discussed: air pollution analysis and flood forecasting with their impact on the environment and on the population health. The syste...

  18. Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants

    International Nuclear Information System (INIS)

    Reifman, J.

    1997-01-01

    A comprehensive survey of computer-based systems that apply artificial intelligence methods to detect and identify component faults in nuclear power plants is presented. Classification criteria are established that categorize artificial intelligence diagnostic systems according to the types of computing approaches used (e.g., computing tools, computer languages, and shell and simulation programs), the types of methodologies employed (e.g., types of knowledge, reasoning and inference mechanisms, and diagnostic approach), and the scope of the system. The major issues of process diagnostics and computer-based diagnostic systems are identified and cross-correlated with the various categories used for classification. Ninety-five publications are reviewed

  19. Artificially lengthened and constricted vocal tract in vocal training methods.

    Science.gov (United States)

    Bele, Irene Velsvik

    2005-01-01

    It is common practice in vocal training to make use of vocal exercise techniques that involve partial occlusion of the vocal tract. Various techniques are used; some of them form an occlusion within the front part of the oral cavity or at the lips. Another vocal exercise technique involves lengthening the vocal tract; for example, the method of phonation into small tubes. This essay presents some studies made on the effects of various vocal training methods that involve an artificially lengthened and constricted vocal tract. The influence of sufficient acoustic impedance on vocal fold vibration and economical voice production is presented.

  20. A Developed Artificial Bee Colony Algorithm Based on Cloud Model

    Directory of Open Access Journals (Sweden)

    Ye Jin

    2018-04-01

    Full Text Available The Artificial Bee Colony (ABC algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

  1. Artificial Neural Network Based Model of Photovoltaic Cell

    Directory of Open Access Journals (Sweden)

    Messaouda Azzouzi

    2017-03-01

    Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 

  2. Principle-based ethics and nurses' attitudes towards artificial feeding.

    Science.gov (United States)

    Day, L; Drought, T; Davis, A J

    1995-02-01

    Nurses often institute artificial feeding for patients who would otherwise starve. Recently, the courts in the United States have favoured withholding or withdrawing feedings from patients who currently refuse or previously gave some indication they would refuse artificial nutrition and hydration. This paper investigates under what circumstances nurses feel justified in withholding artificial nutrition and hydration. Structured interviews were conducted with 40 cancer care nurses from two sites, and 40 dementia care nurses from two sites. The interviews were based on two vignettes, one involving an alert patient with terminal cancer, the other a patient suffering end-stage Alzheimer's dementia, and were analysed for themes coinciding with principles of deontological ethics. Investigators found that autonomy, beneficence and non-maleficence most often guided nurses' decisions to withhold or implement artificial feeding.

  3. Bond strength test of acrylic artificial teeth with prosthetic base

    Directory of Open Access Journals (Sweden)

    Erna Kurnikasari

    2008-07-01

    Full Text Available Denture consists of acrylic artificial teeth and acrylic prothesis base bond chemically with a bond strength of 315 kgF/cm2. Most of the commercial acrylic artificial teeth do not specify their specifications and all of those acrylic artificial teeth do not include mechanical data (bond strength. The aim of this study is to discover which acrylic artificial teeth meet ADA specification no. 15. This study is a descriptive analytic study performed to 5 acrylic artificial teeth posterior brands commonly used by dentists and technicians. From each brand, 3 sample teeth were taken. The acrylic artificial teeth were prepared into a rectangular shape and were attached between acrylic prothesis base simulation and jigs. The sample was given tensile load using a Universal Testing Machine. The amount of force that causes the teeth to be fractured was recorded and the bond strength was calculated. The results of the study show that the average value for the five acrylic artificial teeth for the five brands were as followed: Brand A, 125.993 kgF/cm2; B, 188.457 kgF/cm2; C, 175.880 kgF/cm2; D, 153.373 kgF/cm2; E, 82.839 kgF/cm2. The data can be tested statistically by using One Way ANOVA test and Dunnett test (alpha = 0.05. From the study, it is concluded that the five acrylic artificial teeth have a bond strength below the ADA specification no. 15.

  4. Fabrication of photonic crystal microprisms based on artificial opals

    Science.gov (United States)

    Fenollosa, Roberto; Ibisate, Marta; Rubio, Silvia; Lopez, Ceferino; Meseguer, Francisco; Sanchez-Dehesa, Jose

    2002-04-01

    This paper reports a new method for faceting artificial opals based on micromanipulation techniques. By this means it was possible to fabricate an opal prism in a single domain with different faces: (111), (110) and (100), which were characterized by Scanning Electron Microscopy and Optical Reflectance Spectroscopy. Their spectra exhibit different characteristics depending on the orientation of the facet. While (111)-oriented face gives rise to a high Bragg reflection peak at about a/(lambda) equals 0.66 (where a is the lattice parameter), (110) and (100) faces show much less intense peaks corresponding to features in the band structure at a/(lambda) equals 1.12 and a/(lambda) equals 1.07 respectively. Peaks at higher energies have less obvious explanation.

  5. Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

    Directory of Open Access Journals (Sweden)

    Gregorius Satia Budhi

    2015-07-01

    Full Text Available Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.

  6. SOLVING TRANSPORT LOGISTICS PROBLEMS IN A VIRTUAL ENTERPRISE THROUGH ARTIFICIAL INTELLIGENCE METHODS

    OpenAIRE

    PAVLENKO, Vitaliy; PAVLENKO, Tetiana; MOROZOVA, Olga; KUZNETSOVA, Anna; VOROPAI, Olena

    2017-01-01

    The paper offers a solution to the problem of material flow allocation within a virtual enterprise by using artificial intelligence methods. The research is based on the use of fuzzy relations when planning for optimal transportation modes to deliver components for manufactured products. The Fuzzy Logic Toolbox is used to determine the optimal route for transportation of components for manufactured products. The methods offered have been exemplified in the present research. The authors have b...

  7. Artificial tongue based on metal-biomolecule coordination polymer nanoparticles.

    Science.gov (United States)

    Pu, Fang; Ran, Xiang; Ren, Jinsong; Qu, Xiaogang

    2016-02-25

    We construct an array-based recognition system (the so-called artificial tongue) through the self-assembly of nucleotides, dyes and lanthanide ions. Metal ions are selected as model analytes for verifying its discrimination ability. The work provides valuable insights into the application and development of biomolecule-based materials.

  8. Methods for estimating residential building energy consumption by application of artificial intelligence; Methode d'estimation energetique des batiments d'habitation basee sur l'application de l'intelligence artificielle

    Energy Technology Data Exchange (ETDEWEB)

    Kajl, S.; Roberge, M-A. [Quebec Univ., Ecole de technologie superieure, Montreal, PQ (Canada)

    1999-02-01

    A method for estimating energy requirements in buildings five to twenty-five stories in height using artificial intelligence techniques is proposed. In developing this technique, the pre-requisites specified were rapid execution, the ability to generate a wide range of results, including total energy consumption, power demands, heating and cooling consumption, and accuracy comparable to that of a detailed building energy simulation software. The method proposed encompasses (1) the creation of various databases such as classification of the parameters used in the energy simulation, modelling using the Department of Energy (DOE)-2 software and validation of the DOE-2 models; (2) application of the neural networks inclusive of teaching the neural network and validation of the neural network's learning; (3) designing an energy estimate assessment (EEA) system for residential buildings; and (4) validation of the EEA system. The system has been developed in the MATLAB software environment, specifically for the climate in the Ottawa region. For use under different climatic conditions appropriate adjustments need to be made for the heating and cooling consumption. 12 refs., tabs., figs., 2 appendices.

  9. A REVIEW OF VIBRATION MACHINE DIAGNOSTICS BY USING ARTIFICIAL INTELLIGENCE METHODS

    Directory of Open Access Journals (Sweden)

    Grover Zurita

    2016-09-01

    Full Text Available In the industry, gears and rolling bearings failures are one of the foremost causes of breakdown in rotating machines, reducing availability time of the production and resulting in costly systems downtime. Therefore, there are growing demands for vibration condition based monitoring of gears and bearings, and any method in order to improve the effectiveness, reliability, and accuracy of the bearing faults diagnosis ought to be evaluated. In order to perform machine diagnosis efficiently, researchers have extensively investigated different advanced digital signal processing techniques and artificial intelligence methods to accurately extract fault characteristics from vibration signals. The main goal of this article is to present the state-of-the-art development in vibration analysis for machine diagnosis based on artificial intelligence methods.

  10. Screening therapeutics according to their uptake across the blood-brain barrier: A high throughput method based on immobilized artificial membrane liquid chromatography-diode-array-detection coupled to electrospray-time-of-flight mass spectrometry.

    Science.gov (United States)

    Russo, Giacomo; Grumetto, Lucia; Szucs, Roman; Barbato, Francesco; Lynen, Frederic

    2018-02-07

    The Blood-Brain Barrier (BBB) plays an essential role in protecting the brain tissues against possible injurious substances. In the present work, 79 neutral, basic, acidic and amphoteric structurally unrelated analytes were considered and their chromatographic retention coefficients on immobilized artificial membrane (IAM) stationary phase were determined employing a mass spectrometry (MS) -compatible buffer based on ammonium acetate. Their BBB passage predictive strength was evaluated and the statistical models based on IAM indexes and in silico physico-chemical descriptors showed solid statistics (r 2 (n-1) = 0.78). The predictive strength of the indexes achieved by the MS-compatible method was comparable to that achieved by employing the more "biomimetic" Dulbecco's phosphate buffered saline, even if some differences in the elution order were observed. The method was transferred to the MS, employing a diode-array-detection coupled to an electrospray ionization source and a time-of-flight analyzer. This setup allowed the simultaneous analysis of up to eight analytes, yielding a remarkable acceleration of the analysis time. Copyright © 2018. Published by Elsevier B.V.

  11. An evaluation of an organically bound tritium measurement method in artificial and natural urine

    International Nuclear Information System (INIS)

    Trivedi, A.; Duong, T.

    1993-03-01

    The accurate measurement of tritium in urine in the form of tritiated water (HTO) as well as in organic forms (organically bound tritium (OBT)) is an essential step in assessing tritium exposures correctly. Exchange between HTO and OBT, arising intrinsically in the separation of HTO from urine samples, is a source of error in determining the concentration of OBT using the low-temperature distillation (LTD) bioassay method. The accuracy and precision of OBT measurements using the LTD method was investigated using spiked natural and artificial urine samples. The relative bias for most of the measurements was less than 25%. The choice of testing matrix, artificial urine versus human urine, made little difference: the precisions for each urine type were similar. The appropriateness of the use of artificial urine for testing purposes was judged using a ratio of performance indices. Based on this evaluation, the artificial urine is a suitable test matrix for intercomparisons of OBT in urine measurements. It is further concluded that the LTD method is reliable for measuring OBT in urine samples. (author). 7 refs., 6 tabs

  12. An evaluation of an organically bound tritium measurement method in artificial and natural urine

    Energy Technology Data Exchange (ETDEWEB)

    Trivedi, A; Duong, T

    1993-03-01

    The accurate measurement of tritium in urine in the form of tritiated water (HTO) as well as in organic forms (organically bound tritium (OBT)) is an essential step in assessing tritium exposures correctly. Exchange between HTO and OBT, arising intrinsically in the separation of HTO from urine samples, is a source of error in determining the concentration of OBT using the low-temperature distillation (LTD) bioassay method. The accuracy and precision of OBT measurements using the LTD method was investigated using spiked natural and artificial urine samples. The relative bias for most of the measurements was less than 25%. The choice of testing matrix, artificial urine versus human urine, made little difference: the precisions for each urine type were similar. The appropriateness of the use of artificial urine for testing purposes was judged using a ratio of performance indices. Based on this evaluation, the artificial urine is a suitable test matrix for intercomparisons of OBT in urine measurements. It is further concluded that the LTD method is reliable for measuring OBT in urine samples. (author). 7 refs., 6 tabs.

  13. Crack identification based on synthetic artificial intelligent technique

    International Nuclear Information System (INIS)

    Shim, Mun Bo; Suh, Myung Won

    2001-01-01

    It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a Continuous Evolutionary Algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising

  14. Forming of the regional core transport network taking into account the allocation of alternative energy sources based on artificial intelligence methods

    OpenAIRE

    Marina ZHURAVSKAYA; Vladimir TARASYAN

    2014-01-01

    In the modern world the alternative energy sources, which considerably depend on a region, play more and more significant role. However, the transition of regions to new energy sources lead to the change of transport and logistic network configuration. The formation of optimal core transport network today is a guarantee of the successful economic development of a region tomorrow. The present article studies the issue of advanced core transport network development in a region based on the ...

  15. Pneumatic Artificial Muscles Based on Biomechanical Characteristics of Human Muscles

    Directory of Open Access Journals (Sweden)

    N. Saga

    2006-01-01

    Full Text Available This article reports the pneumatic artificial muscles based on biomechanical characteristics of human muscles. A wearable device and a rehabilitation robot that assist a human muscle should have characteristics similar to those of human muscle. In addition, since the wearable device and the rehabilitation robot should be light, an actuator with a high power to weight ratio is needed. At present, the McKibben type is widely used as an artificial muscle, but in fact its physical model is highly nonlinear. Therefore, an artificial muscle actuator has been developed in which high-strength carbon fibres have been built into the silicone tube. However, its contraction rate is smaller than the actual biological muscles. On the other hand, if an artificial muscle that contracts axially is installed in a robot as compactly as the robot hand, big installing space is required. Therefore, an artificial muscle with a high contraction rate and a tendon-driven system as a compact actuator were developed, respectively. In this study, we report on the basic structure and basic characteristics of two types of actuators.

  16. [Artificial intelligence--the knowledge base applied to nephrology].

    Science.gov (United States)

    Sancipriano, G P

    2005-01-01

    The idea that efficacy efficiency, and quality in medicine could not be reached without sorting the huge knowledge of medical and nursing science is very common. Engineers and computer scientists have developed medical software with great prospects for success, but currently these software applications are not so useful in clinical practice. The medical doctor and the trained nurse live the 'information age' in many daily activities, but the main benefits are not so widespread in working activities. Artificial intelligence and, particularly, export systems charm health staff because of their potential. The first part of this paper summarizes the characteristics of 'weak artificial intelligence' and of expert systems important in clinical practice. The second part discusses medical doctors' requirements and the current nephrologic knowledge bases available for artificial intelligence development.

  17. An Artificial Intelligence-Based Distance Education System: Artimat

    Science.gov (United States)

    Nabiyev, Vasif; Karal, Hasan; Arslan, Selahattin; Erumit, Ali Kursat; Cebi, Ayca

    2013-01-01

    The purpose of this study is to evaluate the artificial intelligence-based distance education system called ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed…

  18. Prediction of soil urea conversion and quantification of the importance degrees of influencing factors through a new combinatorial model based on cluster method and artificial neural network.

    Science.gov (United States)

    Lei, Tao; Guo, Xianghong; Sun, Xihuan; Ma, Juanjuan; Zhang, Shaowen; Zhang, Yong

    2018-05-01

    Quantitative prediction of soil urea conversion is crucial in determining the mechanism of nitrogen transformation and understanding the dynamics of soil nutrients. This study aimed to establish a combinatorial prediction model (MCA-F-ANN) for soil urea conversion and quantify the relative importance degrees (RIDs) of influencing factors with the MCA-F-ANN method. Data samples were obtained from laboratory culture experiments, and soil nitrogen content and physicochemical properties were measured every other day. Results showed that when MCA-F-ANN was used, the mean-absolute-percent error values of NH 4 + -N, NO 3 - -N, and NH 3 contents were 3.180%, 2.756%, and 3.656%, respectively. MCA-F-ANN predicted urea transformation under multi-factor coupling conditions more accurately than traditional models did. The RIDs of reaction time (RT), electrical conductivity (EC), temperature (T), pH, nitrogen application rate (F), and moisture content (W) were 32.2%-36.5%, 24.0%-28.9%, 12.8%-15.2%, 9.8%-12.5%, 7.8%-11.0%, and 3.5%-6.0%, respectively. The RIDs of the influencing factors in a descending order showed the pattern RT > EC > T > pH > F > W. RT and EC were the key factors in the urea conversion process. The prediction accuracy of urea transformation process was improved, and the RIDs of the influencing factors were quantified. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Glucose Synthesis in a Protein-Based Artificial Photosynthesis System.

    Science.gov (United States)

    Lu, Hao; Yuan, Wenqiao; Zhou, Jack; Chong, Parkson Lee-Gau

    2015-09-01

    The objective of this study was to understand glucose synthesis of a protein-based artificial photosynthesis system affected by operating conditions, including the concentrations of reactants, reaction temperature, and illumination. Results from non-vesicle-based glyceraldehyde-3-phosphate (GAP) and glucose synthesis showed that the initial concentrations of ribulose-1,5-bisphosphate (RuBP) and adenosine triphosphate (ATP), lighting source, and temperature significantly affected glucose synthesis. Higher initial concentrations of RuBP and ATP significantly enhanced GAP synthesis, which was linearly correlated to glucose synthesis, confirming the proper functions of all catalyzing enzymes in the system. White fluorescent light inhibited artificial photosynthesis and reduced glucose synthesis by 79.2 % compared to in the dark. The reaction temperature of 40 °C was optimum, whereas lower or higher temperature reduced glucose synthesis. Glucose synthesis in the vesicle-based artificial photosynthesis system reconstituted with bacteriorhodopsin, F 0 F 1 ATP synthase, and polydimethylsiloxane-methyloxazoline-polydimethylsiloxane triblock copolymer was successfully demonstrated. This system efficiently utilized light-induced ATP to drive glucose synthesis, and 5.2 μg ml(-1) glucose was synthesized in 0.78-ml reaction buffer in 7 h. Light-dependent reactions were found to be the bottleneck of the studied artificial photosynthesis system.

  20. Reviewing the development of an artificial intelligence based risk program

    International Nuclear Information System (INIS)

    Dixon, B.W.; Hinton, M.F.

    1985-01-01

    A successful application of nonconventional programming methods has been achieved in computer-assisted probabilistic risk assessment (PRA). The event tree sequence importance calculator, SQUIMP, provides for prompted data entry, generic expansion, on-line pruning, boolean reductions, and importance factor selection. SQUIMP employs constructs typically found in artificial intelligence (AI) programs. The development history of SQUIMP is outlined and its internal structure described as background for a discussion on the applicability of symbolic programming methods in PRA

  1. Artificial Intelligence based technique for BTS placement

    Science.gov (United States)

    Alenoghena, C. O.; Emagbetere, J. O.; Aibinu, A. M.

    2013-12-01

    The increase of the base transceiver station (BTS) in most urban areas can be traced to the drive by network providers to meet demand for coverage and capacity. In traditional network planning, the final decision of BTS placement is taken by a team of radio planners, this decision is not fool proof against regulatory requirements. In this paper, an intelligent based algorithm for optimal BTS site placement has been proposed. The proposed technique takes into consideration neighbour and regulation considerations objectively while determining cell site. The application will lead to a quantitatively unbiased evaluated decision making process in BTS placement. An experimental data of a 2km by 3km territory was simulated for testing the new algorithm, results obtained show a 100% performance of the neighbour constrained algorithm in BTS placement optimization. Results on the application of GA with neighbourhood constraint indicate that the choices of location can be unbiased and optimization of facility placement for network design can be carried out.

  2. Artificial Intelligence based technique for BTS placement

    International Nuclear Information System (INIS)

    Alenoghena, C O; Emagbetere, J O; 1 Minna (Nigeria))" data-affiliation=" (Department of Telecommunications Engineering, Federal University of Techn.1 Minna (Nigeria))" >Aibinu, A M

    2013-01-01

    The increase of the base transceiver station (BTS) in most urban areas can be traced to the drive by network providers to meet demand for coverage and capacity. In traditional network planning, the final decision of BTS placement is taken by a team of radio planners, this decision is not fool proof against regulatory requirements. In this paper, an intelligent based algorithm for optimal BTS site placement has been proposed. The proposed technique takes into consideration neighbour and regulation considerations objectively while determining cell site. The application will lead to a quantitatively unbiased evaluated decision making process in BTS placement. An experimental data of a 2km by 3km territory was simulated for testing the new algorithm, results obtained show a 100% performance of the neighbour constrained algorithm in BTS placement optimization. Results on the application of GA with neighbourhood constraint indicate that the choices of location can be unbiased and optimization of facility placement for network design can be carried out

  3. Artificial Neural Network Based Optical Character Recognition

    OpenAIRE

    Vivek Shrivastava; Navdeep Sharma

    2012-01-01

    Optical Character Recognition deals in recognition and classification of characters from an image. For the recognition to be accurate, certain topological and geometrical properties are calculated, based on which a character is classified and recognized. Also, the Human psychology perceives characters by its overall shape and features such as strokes, curves, protrusions, enclosures etc. These properties, also called Features are extracted from the image by means of spatial pixel-...

  4. Artificial Intelligence (AI) Based Tactical Guidance for Fighter Aircraft

    Science.gov (United States)

    McManus, John W.; Goodrich, Kenneth H.

    1990-01-01

    A research program investigating the use of Artificial Intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within Visual Range (WVR) air combat engagements is discussed. The application of AI programming and problem solving methods in the development and implementation of the Computerized Logic For Air-to-Air Warfare Simulations (CLAWS), a second generation TDG, is presented. The Knowledge-Based Systems used by CLAWS to aid in the tactical decision-making process are outlined in detail, and the results of tests to evaluate the performance of CLAWS versus a baseline TDG developed in FORTRAN to run in real-time in the Langley Differential Maneuvering Simulator (DMS), are presented. To date, these test results have shown significant performance gains with respect to the TDG baseline in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify and maintain than the baseline FORTRAN TDG programs. Alternate computing environments and programming approaches, including the use of parallel algorithms and heterogeneous computer networks are discussed, and the design and performance of a prototype concurrent TDG system are presented.

  5. Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

    International Nuclear Information System (INIS)

    Sadeghi-Goughari, M; Mojra, A; Sadeghi, S

    2016-01-01

    Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors. (paper)

  6. Thermophysical properties of cement based composites and their changes after artificial ageing

    Science.gov (United States)

    Šín, Peter; Pavlendová, Gabriela; Lukovičová, Jozefa; Kopčok, Michal

    2017-07-01

    The usage of recycled plastic materials in concrete mix gained increased attention. The behaviour of such environmental friendly material is studied. In this paper an investigation of the thermophysical properties of cement based composites containing plastic waste particles with different percentage is presented. Measurements were carried out using pulse transient method before and after artificial ageing in climatic chamber BINDER MKF (E3).

  7. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

  8. A study on measurement on artificial radiation dose rate using the response matrix method

    International Nuclear Information System (INIS)

    Kidachi, Hiroshi; Ishikawa, Yoichi; Konno, Tatsuya

    2004-01-01

    We examined accuracy and stability of estimated artificial dose contribution which is distinguished from natural background gamma-ray dose rate using Response Matrix method. Irradiation experiments using artificial gamma-ray sources indicated that there was a linear relationship between observed dose rate and estimated artificial dose contribution, when irradiated artificial gamma-ray dose rate was higher than about 2 nGy/h. Statistical and time-series analyses of long term data made it clear that estimated artificial contribution showed almost constant values under no artificial influence from the nuclear power plants. However, variations of estimated artificial dose contribution were infrequently observed due to of rainfall, detector maintenance operation and occurrence of calibration error. Some considerations on the factors to these variations were made. (author)

  9. Continuous surveillance of transformers using artificial intelligence methods; Surveillance continue des transformateurs: application des methodes d'intelligence artificielle

    Energy Technology Data Exchange (ETDEWEB)

    Schenk, A.; Germond, A. [Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland); Boss, P.; Lorin, P. [ABB Secheron SA, Geneve (Switzerland)

    2000-07-01

    The article describes a new method for the continuous surveillance of power transformers based on the application of artificial intelligence (AI) techniques. An experimental pilot project on a specially equipped, strategically important power transformer is described. Traditional surveillance methods and the use of mathematical models for the prediction of faults are described. The article describes the monitoring equipment used in the pilot project and the AI principles such as self-organising maps that are applied. The results obtained from the pilot project and methods for their graphical representation are discussed.

  10. Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support.

    Science.gov (United States)

    Elkin, Peter L; Schlegel, Daniel R; Anderson, Michael; Komm, Jordan; Ficheur, Gregoire; Bisson, Leslie

    2018-04-01

    Evoking strength is one of the important contributions of the field of Biomedical Informatics to the discipline of Artificial Intelligence. The University at Buffalo's Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis of knee problems and to thereby facilitate referral to the right orthopedic subspecialist. They had two independent sports medicine physicians review 469 cases. A board-certified orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a gold standard diagnosis was reached. For each case, the patients entered 126 potential answers to 26 questions into a Web interface. These were modeled by an expert sports medicine physician and the answers were reviewed by L.B. For each finding, the clinician specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic evoking strength (ES). Heuristics are methods of reasoning with only partial evidence. An expert system was constructed that reflected the posttest odds of disease-ranked list for each case. We compare the accuracy of using Sp to that of using ES (original model, p  < 0.0008; term importance * disease importance [DItimesTI] model, p  < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI model was superior to the use of ES. By the fifth diagnosis, the advantage was lost and so there is no difference between the techniques when serving as a reminder system. Schattauer GmbH Stuttgart.

  11. Using artificial intelligence methods to design new conducting polymers

    Directory of Open Access Journals (Sweden)

    Ronaldo Giro

    2003-12-01

    Full Text Available In the last years the possibility of creating new conducting polymers exploring the concept of copolymerization (different structural monomeric units has attracted much attention from experimental and theoretical points of view. Due to the rich carbon reactivity an almost infinite number of new structures is possible and the procedure of trial and error has been the rule. In this work we have used a methodology able of generating new structures with pre-specified properties. It combines the use of negative factor counting (NFC technique with artificial intelligence methods (genetic algorithms - GAs. We present the results for a case study for poly(phenylenesulfide phenyleneamine (PPSA, a copolymer formed by combination of homopolymers: polyaniline (PANI and polyphenylenesulfide (PPS. The methodology was successfully applied to the problem of obtaining binary up to quinternary disordered polymeric alloys with a pre-specific gap value or exhibiting metallic properties. It is completely general and can be in principle adapted to the design of new classes of materials with pre-specified properties.

  12. Alteration of blue pigment in artificial iris in ocular prosthesis: effect of paint, drying method and artificial aging.

    Science.gov (United States)

    Goiato, Marcelo Coelho; Fernandes, Aline Úrsula Rocha; dos Santos, Daniela Micheline; Hadadd, Marcela Filié; Moreno, Amália; Pesqueira, Aldiéris Alves

    2011-02-01

    The artificial iris is the structure responsible for the dissimulation and aesthetics of ocular prosthesis. The objective of the present study was to evaluate the color stability of artificial iris of microwaveable polymerized ocular prosthesis, as a function of paint type, drying method and accelerated aging. A total of 40 discs of microwaveable polymerized acrylic resin were fabricated, and divided according to the blue paint type (n = 5): hydrosoluble acrylic, nitrocellulose automotive, hydrosoluble gouache and oil paints. Paints where dried either at natural or at infrared light bulb method. Each specimen was constituted of one disc in colorless acrylic resin and another colored with a basic sclera pigment. Painting was performed in one surface of one of the discs. The specimens were submitted to an artificial aging chamber under ultraviolet light, during 1008 h. A reflective spectrophotometer was used to evaluate color changes. Data were evaluated by 3-way repeated-measures ANOVA and the Tukey HSD test (α = 0.05). All paints suffered color alteration. The oil paint presented the highest color resistance to artificial aging regardless of drying method. Copyright © 2010 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.

  13. Three-dimensional magnetophotonic crystals based on artificial opals

    Science.gov (United States)

    Baryshev, A. V.; Kodama, T.; Nishimura, K.; Uchida, H.; Inoue, M.

    2004-06-01

    We fabricated and experimentally investigated three-dimensional magnetophotonic crystals (3D MPCs) based on artificial opals. Opal samples with three-dimensional dielectric lattices were impregnated with different types of magnetic material. Magnetic and structural properties of 3D MPCs were studied by field emission scanning electron microscopy, x-ray diffraction analysis, and vibrating sample magnetometer. We have shown that magnetic materials synthesized in voids of opal lattices and the composites obtained have typical magnetic properties.

  14. Three-dimensional magnetophotonic crystals based on artificial opals

    International Nuclear Information System (INIS)

    Baryshev, A.V.; Kodama, T.; Nishimura, K.; Uchida, H.; Inoue, M.

    2004-01-01

    We fabricated and experimentally investigated three-dimensional magnetophotonic crystals (3D MPCs) based on artificial opals. Opal samples with three-dimensional dielectric lattices were impregnated with different types of magnetic material. Magnetic and structural properties of 3D MPCs were studied by field emission scanning electron microscopy, x-ray diffraction analysis, and vibrating sample magnetometer. We have shown that magnetic materials synthesized in voids of opal lattices and the composites obtained have typical magnetic properties

  15. Artificial Immune Ecosystems: the role of expert-based learning in artificial cognition

    Directory of Open Access Journals (Sweden)

    Pierre Parrend

    2018-03-01

    Full Text Available The rapid evolution of IT ecosystems significantly challenges the security models our infrastructures rely on. Beyond the old dichotomy between open and closed systems, it is now necessary to handle securely the interaction between heterogeneous devices building dynamic ecosystems. To this regard, bio-inspired approaches provide a rich set of conceptual tools, but they have failed to lay the basis for robust and efficient solutions. Our research effort intends to revisit the contribution of artificial immune system research to bring immune properties: security, resilience, distribution, memory, into IT infrastructures. Artificial immune ecosystems support a comprehensive model for anomaly detection and characterization, but their cognitive capacity are limited by the state of the art in machine learning and the rapid evolution of cybersecurity threats so far. We therefore propose to enrich the cognitive process with expert-based learning for reinforcement, classification and investigation. Application to system supervision using system logs and supervision time series confirms the relevance and performance of this model.

  16. SOLVING TRANSPORT LOGISTICS PROBLEMS IN A VIRTUAL ENTERPRISE THROUGH ARTIFICIAL INTELLIGENCE METHODS

    Directory of Open Access Journals (Sweden)

    Vitaliy PAVLENKO

    2017-06-01

    Full Text Available The paper offers a solution to the problem of material flow allocation within a virtual enterprise by using artificial intelligence methods. The research is based on the use of fuzzy relations when planning for optimal transportation modes to deliver components for manufactured products. The Fuzzy Logic Toolbox is used to determine the optimal route for transportation of components for manufactured products. The methods offered have been exemplified in the present research. The authors have built a simulation model for component transportation and delivery for manufactured products using the Simulink graphical environment for building models.

  17. Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation

    Science.gov (United States)

    Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

    2016-08-01

    In the present research, three artificial intelligence methods including Gene Expression Programming (GEP), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as, 48 empirical equations (10, 12 and 26 equations were temperature-based, sunshine-based and meteorological parameters-based, respectively) were used to estimate daily solar radiation in Kerman, Iran in the period of 1992-2009. To develop the GEP, ANN and ANFIS models, depending on the used empirical equations, various combinations of minimum air temperature, maximum air temperature, mean air temperature, extraterrestrial radiation, actual sunshine duration, maximum possible sunshine duration, sunshine duration ratio, relative humidity and precipitation were considered as inputs in the mentioned intelligent methods. To compare the accuracy of empirical equations and intelligent models, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and determination coefficient (R2) indices were used. The results showed that in general, sunshine-based and meteorological parameters-based scenarios in ANN and ANFIS models presented high accuracy than mentioned empirical equations. Moreover, the most accurate method in the studied region was ANN11 scenario with five inputs. The values of RMSE, MAE, MARE and R2 indices for the mentioned model were 1.850 MJ m-2 day-1, 1.184 MJ m-2 day-1, 9.58% and 0.935, respectively.

  18. Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

    Directory of Open Access Journals (Sweden)

    Aleksandar Sabljic

    2004-12-01

    Full Text Available This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

  19. De novo formed satellite DNA-based mammalian artificial chromosomes and their possible applications.

    Science.gov (United States)

    Katona, Robert L

    2015-02-01

    Mammalian artificial chromosomes (MACs) are non-integrating, autonomously replicating natural chromosome-based vectors that may carry a vast amount of genetic material, which in turn enable potentially prolonged, safe, and regulated therapeutic transgene expression and render MACs as attractive genetic vectors for "gene replacement" or for controlling differentiation pathways in target cells. Satellite-DNA-based artificial chromosomes (SATACs) can be made by induced de novo chromosome formation in cells of different mammalian and plant species. These artificially generated accessory chromosomes are composed of predictable DNA sequences, and they contain defined genetic information. SATACs have already passed a number of obstacles crucial to their further development as gene therapy vectors, including large-scale purification, transfer of purified artificial chromosomes into different cells and embryos, generation of transgenic animals and germline transmission with purified SATACs, and the tissue-specific expression of a therapeutic gene from an artificial chromosome in the milk of transgenic animals. SATACs could be used in cell therapy protocols. For these methods, the most versatile target cell would be one that was pluripotent and self-renewing to address multiple disease target cell types, thus making multilineage stem cells, such as adult derived early progenitor cells and embryonic stem cells, as attractive universal host cells.

  20. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    Science.gov (United States)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

  1. Experiments with microcomputer-based artificial intelligence environments

    Science.gov (United States)

    Summers, E.G.; MacDonald, R.A.

    1988-01-01

    The U.S. Geological Survey (USGS) has been experimenting with the use of relatively inexpensive microcomputers as artificial intelligence (AI) development environments. Several AI languages are available that perform fairly well on desk-top personal computers, as are low-to-medium cost expert system packages. Although performance of these systems is respectable, their speed and capacity limitations are questionable for serious earth science applications foreseen by the USGS. The most capable artificial intelligence applications currently are concentrated on what is known as the "artificial intelligence computer," and include Xerox D-series, Tektronix 4400 series, Symbolics 3600, VAX, LMI, and Texas Instruments Explorer. The artificial intelligence computer runs expert system shells and Lisp, Prolog, and Smalltalk programming languages. However, these AI environments are expensive. Recently, inexpensive 32-bit hardware has become available for the IBM/AT microcomputer. USGS has acquired and recently completed Beta-testing of the Gold Hill Systems 80386 Hummingboard, which runs Common Lisp on an IBM/AT microcomputer. Hummingboard appears to have the potential to overcome many of the speed/capacity limitations observed with AI-applications on standard personal computers. USGS is a Beta-test site for the Gold Hill Systems GoldWorks expert system. GoldWorks combines some high-end expert system shell capabilities in a medium-cost package. This shell is developed in Common Lisp, runs on the 80386 Hummingboard, and provides some expert system features formerly available only on AI-computers including frame and rule-based reasoning, on-line tutorial, multiple inheritance, and object-programming. ?? 1988 International Association for Mathematical Geology.

  2. Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

    OpenAIRE

    Lehký , David; Slowik , Ondřej; Novák , Drahomír

    2014-01-01

    Part 7: Genetic Algorithms; International audience; The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation. Efficie...

  3. Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

    Science.gov (United States)

    Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

    2014-07-01

    Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

  4. A comparison of methods for demonstrating artificial bone lesions; conventional versus computer tomography

    International Nuclear Information System (INIS)

    Heller, M.; Wenk, M.; Jend, H.H.

    1984-01-01

    Conventional tomography (T) and computer tomography (CT) were used for examining 97 artificial bone lesions at various sites. The purpose of the study was to determine how far CT can replace T in the diagnosis of skeletal abnormalities. The results have shown that modern CT, particularly in its high resolution form, equals T and provides additional information (substrate of a lesion, its relationship to neighbouring tissues, simultaneous demonstration of soft tissue etc.). These cannot be shown successfully by T. It follows that CT is indicated as the primary method of examination for lesions of the facial skeleton, skull base, spine, pelvis and, to some extent, extremities. (orig.) [de

  5. Method of mobile robot indoor navigation by artificial landmarks with use of computer vision

    Science.gov (United States)

    Glibin, E. S.; Shevtsov, A. A.; Enik, O. A.

    2018-05-01

    The article describes an algorithm of the mobile robot indoor navigation based on the use of visual odometry. The results of the experiment identifying calculation errors in the distance traveled on a slip are presented. It is shown that the use of computer vision allows one to correct erroneous coordinates of the robot with the help of artificial landmarks. The control system utilizing the proposed method has been realized on the basis of Arduino Mego 2560 controller and a single-board computer Raspberry Pi 3. The results of the experiment on the mobile robot navigation with the use of this control system are presented.

  6. Dynamics and Thermodynamics of Artificial Muscles Based on Nematic Gels

    Science.gov (United States)

    Hébert, M.; Kant, R.; de Gennes, P.-G.

    1997-07-01

    A scheme based on nemato-mechanical conversion has been proposed for potential artificial muscle applications (de Gennes P.-G., Hébert M. and Kant R., to appear in Macromol. Symp. (1996)). As the temperature in a nematic gel is reduced through the transition temperature, strong uniaxial deformation is encountered. We briefly expose the dynamics of contraction/elongation in this system. Work and dissipative losses are calculated for an operating cycle to get an approximative expression of the ratio work/losses, which can then be compared with real muscular efficiencies.

  7. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  8. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    International Nuclear Information System (INIS)

    Nedic, Vladimir; Despotovic, Danijela; Cvetanovic, Slobodan; Despotovic, Milan; Babic, Sasa

    2014-01-01

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L eq . Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model

  9. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    Energy Technology Data Exchange (ETDEWEB)

    Nedic, Vladimir, E-mail: vnedic@kg.ac.rs [Faculty of Philology and Arts, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac (Serbia); Despotovic, Danijela, E-mail: ddespotovic@kg.ac.rs [Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, 34000 Kragujevac (Serbia); Cvetanovic, Slobodan, E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs [Faculty of Economics, University of Niš, Trg kralja Aleksandra Ujedinitelja, 18000 Niš (Serbia); Despotovic, Milan, E-mail: mdespotovic@kg.ac.rs [Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac (Serbia); Babic, Sasa, E-mail: babicsf@yahoo.com [College of Applied Mechanical Engineering, Trstenik (Serbia)

    2014-11-15

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.

  10. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  11. Random fiber laser based on artificially controlled backscattering fibers.

    Science.gov (United States)

    Wang, Xiaoliang; Chen, Daru; Li, Haitao; She, Lijuan; Wu, Qiong

    2018-01-10

    The random fiber laser (RFL), which is a milestone in laser physics and nonlinear optics, has attracted considerable attention recently. Most previously reported RFLs are based on distributed feedback of Rayleigh scattering amplified through the stimulated Raman-Brillouin scattering effect in single-mode fibers, which require long-distance (tens of kilometers) single-mode fibers and high threshold, up to watt level, due to the extremely small Rayleigh scattering coefficient of the fiber. We proposed and demonstrated a half-open-cavity RFL based on a segment of an artificially controlled backscattering single-mode fiber with a length of 210 m, 310 m, or 390 m. A fiber Bragg grating with a central wavelength of 1530 nm and a segment of artificially controlled backscattering single-mode fiber fabricated by using a femtosecond laser form the half-open cavity. The proposed RFL achieves thresholds of 25 mW, 30 mW, and 30 mW, respectively. Random lasing at a wavelength of 1530 nm and extinction ratio of 50 dB is achieved when a segment of 5 m erbium-doped fiber is pumped by a 980 nm laser diode in the RFL. A novel RFL with many short cavities has been achieved with low threshold.

  12. Comparison of the accuracy of SST estimates by artificial neural networks (ANN) and other quantitative methods using radiolarian data from the Antarctic and Pacific Oceans

    Digital Repository Service at National Institute of Oceanography (India)

    Gupta, S.M.; Malmgren, B.A.

    ) regression, the maximum likelihood (ML) method, and artificial neural networks (ANNs), based on radiolarian faunal abundance data from surface sediments from the Antarctic and Pacific Oceans. Recent studies have suggested that ANNs may represent one...

  13. Evolution of an artificial neural network based autonomous land vehicle controller.

    Science.gov (United States)

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.

  14. Gross domestic product estimation based on electricity utilization by artificial neural network

    Science.gov (United States)

    Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

    2018-01-01

    The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

  15. Novel method to load multiple genes onto a mammalian artificial chromosome.

    Science.gov (United States)

    Tóth, Anna; Fodor, Katalin; Praznovszky, Tünde; Tubak, Vilmos; Udvardy, Andor; Hadlaczky, Gyula; Katona, Robert L

    2014-01-01

    Mammalian artificial chromosomes are natural chromosome-based vectors that may carry a vast amount of genetic material in terms of both size and number. They are reasonably stable and segregate well in both mitosis and meiosis. A platform artificial chromosome expression system (ACEs) was earlier described with multiple loading sites for a modified lambda-integrase enzyme. It has been shown that this ACEs is suitable for high-level industrial protein production and the treatment of a mouse model for a devastating human disorder, Krabbe's disease. ACEs-treated mutant mice carrying a therapeutic gene lived more than four times longer than untreated counterparts. This novel gene therapy method is called combined mammalian artificial chromosome-stem cell therapy. At present, this method suffers from the limitation that a new selection marker gene should be present for each therapeutic gene loaded onto the ACEs. Complex diseases require the cooperative action of several genes for treatment, but only a limited number of selection marker genes are available and there is also a risk of serious side-effects caused by the unwanted expression of these marker genes in mammalian cells, organs and organisms. We describe here a novel method to load multiple genes onto the ACEs by using only two selectable marker genes. These markers may be removed from the ACEs before therapeutic application. This novel technology could revolutionize gene therapeutic applications targeting the treatment of complex disorders and cancers. It could also speed up cell therapy by allowing researchers to engineer a chromosome with a predetermined set of genetic factors to differentiate adult stem cells, embryonic stem cells and induced pluripotent stem (iPS) cells into cell types of therapeutic value. It is also a suitable tool for the investigation of complex biochemical pathways in basic science by producing an ACEs with several genes from a signal transduction pathway of interest.

  16. Novel method to load multiple genes onto a mammalian artificial chromosome.

    Directory of Open Access Journals (Sweden)

    Anna Tóth

    Full Text Available Mammalian artificial chromosomes are natural chromosome-based vectors that may carry a vast amount of genetic material in terms of both size and number. They are reasonably stable and segregate well in both mitosis and meiosis. A platform artificial chromosome expression system (ACEs was earlier described with multiple loading sites for a modified lambda-integrase enzyme. It has been shown that this ACEs is suitable for high-level industrial protein production and the treatment of a mouse model for a devastating human disorder, Krabbe's disease. ACEs-treated mutant mice carrying a therapeutic gene lived more than four times longer than untreated counterparts. This novel gene therapy method is called combined mammalian artificial chromosome-stem cell therapy. At present, this method suffers from the limitation that a new selection marker gene should be present for each therapeutic gene loaded onto the ACEs. Complex diseases require the cooperative action of several genes for treatment, but only a limited number of selection marker genes are available and there is also a risk of serious side-effects caused by the unwanted expression of these marker genes in mammalian cells, organs and organisms. We describe here a novel method to load multiple genes onto the ACEs by using only two selectable marker genes. These markers may be removed from the ACEs before therapeutic application. This novel technology could revolutionize gene therapeutic applications targeting the treatment of complex disorders and cancers. It could also speed up cell therapy by allowing researchers to engineer a chromosome with a predetermined set of genetic factors to differentiate adult stem cells, embryonic stem cells and induced pluripotent stem (iPS cells into cell types of therapeutic value. It is also a suitable tool for the investigation of complex biochemical pathways in basic science by producing an ACEs with several genes from a signal transduction pathway of interest.

  17. Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

    2004-01-01

    Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

  18. An artificial nonlinear diffusivity method for supersonic reacting flows with shocks

    Science.gov (United States)

    Fiorina, B.; Lele, S. K.

    2007-03-01

    A computational approach for modeling interactions between shocks waves, contact discontinuities and reactions zones with a high-order compact scheme is investigated. To prevent the formation of spurious oscillations around shocks, artificial nonlinear viscosity [A.W. Cook, W.H. Cabot, A high-wavenumber viscosity for high resolution numerical method, J. Comput. Phys. 195 (2004) 594-601] based on high-order derivative of the strain rate tensor is used. To capture temperature and species discontinuities a nonlinear diffusivity based on the entropy gradient is added. It is shown that the damping of 'wiggles' is controlled by the model constants and is largely independent of the mesh size and the shock strength. The same holds for the numerical shock thickness and allows a determination of the L2 error. In the shock tube problem, with fluids of different initial entropy separated by the diaphragm, an artificial diffusivity is required to accurately capture the contact surface. Finally, the method is applied to a shock wave propagating into a medium with non-uniform density/entropy and to a CJ detonation wave. Multi-dimensional formulation of the model is presented and is illustrated by a 2D oblique wave reflection from an inviscid wall, by a 2D supersonic blunt body flow and by a Mach reflection problem.

  19. AN ARTIFICIAL INTELLIGENCE-BASED DISTANCE EDUCATION SYSTEM: Artimat

    Directory of Open Access Journals (Sweden)

    Vasif NABIYEV

    2013-04-01

    Full Text Available The purpose of this study is to evaluate the artificial intelligence-based distance education system called as ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed with 4 teachers and 59 students in 10th grade in an Anatolian High School in Trabzon. Many institutions and organizations in the world approach seriously to distance education besides traditional education. It is inevitable to use the distance education in teaching the problem solving skills in this different dimension of the education. In the studies in Turkey and abroad in the field of mathematics teaching, problem solving skills are generally stated not to be at the desired level and often expressed to have difficulty in teaching. For this reason, difficulties of the students in problem solving have initially been evaluated and the system has been prepared utilizing artificial intelligence algorithms according to the obtained results. In the evaluation of the findings obtained from the application, it has been concluded that the system is responsive to the needs of the students and is successful in general, but that conceptual changes should be made in order that students adapt to the system quickly.

  20. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  1. Kinetic parametric estimation in animal PET molecular imaging based on artificial immune network

    International Nuclear Information System (INIS)

    Chen Yuting; Ding Hong; Lu Rui; Huang Hongbo; Liu Li

    2011-01-01

    Objective: To develop an accurate,reliable method without the need of initialization in animal PET modeling for estimation of the tracer kinetic parameters based on the artificial immune network. Methods: The hepatic and left ventricular time activity curves (TACs) were obtained by drawing ROIs of liver tissue and left ventricle on dynamic 18 F-FDG PET imaging of small mice. Meanwhile, the blood TAC was analyzed by sampling the tail vein blood at different time points after injection. The artificial immune network for parametric optimization of pharmacokinetics (PKAIN) was adapted to estimate the model parameters and the metabolic rate of glucose (K i ) was calculated. Results: TACs of liver,left ventricle and tail vein blood were obtained.Based on the artificial immune network, K i in 3 mice was estimated as 0.0024, 0.0417 and 0.0047, respectively. The average weighted residual sum of squares of the output model generated by PKAIN was less than 0.0745 with a maximum standard deviation of 0.0084, which indicated that the proposed PKAIN method can provide accurate and reliable parametric estimation. Conclusion: The PKAIN method could provide accurate and reliable tracer kinetic modeling in animal PET imaging without the need of initialization of model parameters. (authors)

  2. Developing energy forecasting model using hybrid artificial intelligence method

    Institute of Scientific and Technical Information of China (English)

    Shahram Mollaiy-Berneti

    2015-01-01

    An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

  3. Development of hybrid artificial intelligent based handover decision algorithm

    Directory of Open Access Journals (Sweden)

    A.M. Aibinu

    2017-04-01

    Full Text Available The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k-step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k-step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k-step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques.

  4. Random fiber lasers based on artificially controlled backscattering fibers

    Science.gov (United States)

    Chen, Daru; Wang, Xiaoliang; She, Lijuan; Qiang, Zexuan; Yu, Zhangwei

    2017-10-01

    The random fiber laser (RFL) which is a milestone in laser physics and nonlinear optics, has attracted considerable attention recently. Most previous RFLs are based on distributed feedback of Rayleigh scattering amplified through stimulated Raman/Brillouin scattering effect in single mode fibers, which required long-distance (tens of kilometers) single mode fibers and high threshold up to watt-level due to the extremely small Rayleigh scattering coefficient of the fiber. We proposed and demonstrated a half-open cavity RFL based on a segment of a artificially controlled backscattering SMF(ACB-SMF) with a length of 210m, 310m or 390m. A fiber Bragg grating with the central wavelength of 1530nm and a segment of ACB-SMF forms the half-open cavity. The proposed RFL achieves the threshold of 25mW, 30mW and 30mW, respectively. Random lasing at the wavelength of 1530nm and the extinction ratio of 50dB is achieved when a segment of 5m EDF is pumped by a 980nm LD in the RFL. Another half-open cavity RFL based on a segment of a artificially controlled backscattering EDF(ACBS-EDF) is also demonstrated without an ACB-SMF. The 3m ACB-EDF is fabricated by using the femtosecond laser with pulse energy of 0.34mJ which introduces about 50 reflectors in the EDF. Random lasing at the wavelength of 1530nm is achieved with the output power of 7.5mW and the efficiency of 1.88%. Two novel RFLs with much short cavities have been achieved with low threshold and high efficiency.

  5. Artificial Neural Network Method at PT Buana Intan Gemilang

    Directory of Open Access Journals (Sweden)

    Shadika

    2017-01-01

    Full Text Available The textile industry is one of the industries that provide high export value by occupying the third position in Indonesia. The process of inspection on traditional textile enterprises by relying on human vision that takes an average scanning time of 19.87 seconds. Each roll of cloth should be inspected twice to avoid missed defects. This inspection process causes the buildup at the inspection station. This study proposes the automation of inspection systems using the Artificial Neural Network (ANN. The input for ANN comes from GLCM extraction. The automation system on the defect inspection resulted in a detection time of 0.56 seconds. The degree of accuracy gained in classifying the three types of defects is 88.7%. Implementing an automated inspection system results in faster processing time.

  6. Spatial capture-recapture: a promising method for analyzing data collected using artificial cover objects

    Science.gov (United States)

    Sutherland, Chris; Munoz, David; Miller, David A.W.; Grant, Evan H. Campbell

    2016-01-01

    Spatial capture–recapture (SCR) is a relatively recent development in ecological statistics that provides a spatial context for estimating abundance and space use patterns, and improves inference about absolute population density. SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus, collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m2) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m2. Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.

  7. Pressure distribution-based texture sensing by using a simple artificial mastication system.

    Science.gov (United States)

    Yamamoto, Takeshi; Higashimori, Mitsuru; Nakauma, Makoto; Nakao, Satomi; Ikegami, Akira; Ishihara, Sayaka

    2014-01-01

    This paper proposes a novel texture sensing method for nursing-care gel by using an artificial mastication system, in which not only mechanical characteristics but also geometrical ones are objectively and quantitatively evaluated. When human masticates gel food, she or he perceives the changes of the shape and contact force simultaneously. Based on the impressions, they evaluate the texture. For reproducing such a procedure, the pressure distribution of gel is measured in the simple artificial mastication, and the information associated to both the geometrical and mechanical characteristics is simultaneously acquired. The relationship between the value of sensory evaluation (i.e. impression human perceives), and the pressure distribution data is numerically modeled by applying the image texture analysis. Experimental results show that the proposed method succeeds in estimating the values of sensory evaluation of nine kinds of gel with the coefficient of determination greater than 0.93.

  8. Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies

    Directory of Open Access Journals (Sweden)

    Mingsheng Tang

    2014-08-01

    Full Text Available Emergence is a common phenomenon, and it is also a general and important concept in complex dynamic systems like artificial societies. Usually, artificial societies are used for assisting in resolving several complex social issues (e.g., emergency management, intelligent transportation system with the aid of computer science. The levels of an emergence may have an effect on decisions making, and the occurrence and degree of an emergence are generally perceived by human observers. However, due to the ambiguity and inaccuracy of human observers, to propose a quantitative method to measure emergences in artificial societies is a meaningful and challenging task. This article mainly concentrates upon three kinds of emergences in artificial societies, including emergence of attribution, emergence of behavior, and emergence of structure. Based on information entropy, three metrics have been proposed to measure emergences in a quantitative way. Meanwhile, the correctness of these metrics has been verified through three case studies (the spread of an infectious influenza, a dynamic microblog network, and a flock of birds with several experimental simulations on the Netlogo platform. These experimental results confirm that these metrics increase with the rising degree of emergences. In addition, this article also has discussed the limitations and extended applications of these metrics.

  9. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

    Science.gov (United States)

    Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

    2009-08-01

    SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

  10. ANALYSIS DATA SETS USING HYBRID TECHNIQUES APPLIED ARTIFICIAL INTELLIGENCE BASED PRODUCTION SYSTEMS INTEGRATED DESIGN

    Directory of Open Access Journals (Sweden)

    Daniel-Petru GHENCEA

    2017-06-01

    Full Text Available The paper proposes a prediction model of behavior spindle from the point of view of the thermal deformations and the level of the vibrations by highlighting and processing the characteristic equations. This is a model analysis for the shaft with similar electro-mechanical characteristics can be achieved using a hybrid analysis based on artificial intelligence (genetic algorithms - artificial neural networks - fuzzy logic. The paper presents a prediction mode obtaining valid range of values for spindles with similar characteristics based on measured data sets from a few spindles test without additional measures being required. Extracting polynomial functions of graphs resulting from simultaneous measurements and predict the dynamics of the two features with multi-objective criterion is the main advantage of this method.

  11. Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2014-01-01

    Full Text Available The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

  12. Development of a linear induction motor based artificial muscle system.

    Science.gov (United States)

    Gruber, A; Arguello, E; Silva, R

    2013-01-01

    We present the design of a linear induction motor based on electromagnetic interactions. The engine is capable of producing a linear movement from electricity. The design consists of stators arranged in parallel, which produce a magnetic field sufficient to displace a plunger along its axial axis. Furthermore, the winding has a shell and cap of ferromagnetic material that amplifies the magnetic field. This produces a force along the length of the motor that is similar to that of skeletal muscle. In principle, the objective is to use the engine in the development of an artificial muscle system for prosthetic applications, but it could have multiple applications, not only in the medical field, but in other industries.

  13. Prediction of flow boiling curves based on artificial neural network

    International Nuclear Information System (INIS)

    Wu Junmei; Xi'an Jiaotong Univ., Xi'an; Su Guanghui

    2007-01-01

    The effects of the main system parameters on flow boiling curves were analyzed by using an artificial neural network (ANN) based on the database selected from the 1960s. The input parameters of the ANN are system pressure, mass flow rate, inlet subcooling, wall superheat and steady/transition boiling, and the output parameter is heat flux. The results obtained by the ANN show that the heat flux increases with increasing inlet sub cooling for all heat transfer modes. Mass flow rate has no significant effects on nucleate boiling curves. The transition boiling and film boiling heat fluxes will increase with an increase of mass flow rate. The pressure plays a predominant role and improves heat transfer in whole boiling regions except film boiling. There are slight differences between the steady and the transient boiling curves in all boiling regions except the nucleate one. (authors)

  14. Design of alluvial Egyptian irrigation canals using artificial neural networks method

    Directory of Open Access Journals (Sweden)

    Hassan Ibrahim Mohamed

    2013-06-01

    Full Text Available In the present study, artificial neural networks method (ANNs is used to estimate the main parameters which used in design of stable alluvial channels. The capability of ANN models to predict the stable alluvial channels dimensions is investigated, where the flow rate and sediment mean grain size were considered as input variables and wetted perimeter, hydraulic radius, and water surface slope were considered as output variables. The used ANN models are based on a back propagation algorithm to train a multi-layer feed-forward network (Levenberg Marquardt algorithm. The proposed models were verified using 311 data sets of field data collected from 61 manmade canals and drains. Several statistical measures and graphical representation are used to check the accuracy of the models in comparison with previous empirical equations. The results of the developed ANN model proved that this technique is reliable in such field compared with previously developed methods.

  15. Comparison of two solution ways of district heating control: Using analysis methods, using artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Balate, J.; Sysala, T. [Technical Univ., Zlin (Czech Republic). Dept. of Automation and Control Technology

    1997-12-31

    The District Heating Systems - DHS (Centralized Heat Supply Systems - CHSS) are being developed in large cities in accordance with their growth. The systems are formed by enlarging networks of heat distribution to consumers and at the same time they interconnect the heat sources gradually built. The heat is distributed to the consumers through the circular networks, that are supplied by several cooperating heat sources, that means by power and heating plants and heating plants. The complicated process of heat production technology and supply requires the system approach when solving the concept of automatized control. The paper deals with comparison of the solution way using the analysis methods and using the artificial intelligence methods. (orig.)

  16. Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

    Science.gov (United States)

    Arotaritei, D.; Rotariu, C.

    2015-09-01

    In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).

  17. Analysis of micro-failure behaviors in artificial muscles based on fishing line and sewing thread

    Science.gov (United States)

    Xu, J. B.; Cheng, K. F.; Tu, S. L.; He, X. M.; Ma, C.; Jin, Y. Z.; Kang, X. N.; Sun, T.; Zhang, Y.

    2017-06-01

    The aim of the present study was to discuss a new and effective method for testing artificial muscles based on micro-failure behaviors analysis. Thermo-mechanical actuators based on fishing line and sewing thread, also, the capability of responding to ambient temperature variations producing a large amount of shrinkage ratio of a resulting variation in longitudinal length. The minimum micro-failure value is 0.02μm and the maximum value is 1.72μm with nylon twist pattern. The discovery of an innovative effective testing of artificial muscles based on polymeric fibers specimens on micro-failure, rupture, slippage, etc. This research finds out a micro-failure behavior analysis of thermo-mechanical actuators based on fishing line and sewing thread. The specimens show large deformations when heated together with warping performance in terms of shrinkage of energy and densities. With the purpose of providing useful analysis data for the further technology applications, we attempt micrometre-sized artificial muscles which were also tested was readily accessible and also can be applied to other polymeric fibers. Effective use of this technique achievement relies on rotate speed, temperature and tensile direction. The results of the tensile testing experiments were outstanding with respect to some important issues related to the response of micro-structure, twisted polymeric fibers and shrinkage ratio.

  18. Aragonite coating solutions (ACS) based on artificial seawater

    International Nuclear Information System (INIS)

    Tas, A. Cuneyt

    2015-01-01

    Graphical abstract: - Highlights: • Developed completely inorganic solutions for the deposition of monolayers of aragonite spherules (or ooids). • Solutions mimicked the artificial seawater. • Biomimetic crystallization was performed at the tropical sea surface temperature of 30 °C. - Abstract: Aragonite (CaCO 3 , calcium carbonate) is an abundant biomaterial of marine life. It is the dominant inorganic phase of coral reefs, mollusc bivalve shells and the stalactites or stalagmites of geological sediments. Inorganic and initially precipitate-free aragonite coating solutions (ACS) of pH 7.4 were developed in this study to deposit monolayers of aragonite spherules or ooids on biomaterial (e.g., UHMWPE, ultrahigh molecular weight polyethylene) surfaces soaked in ACS at 30 °C. The ACS solutions of this study have been developed for the surface engineering of synthetic biomaterials. The abiotic ACS solutions, enriched with calcium and bicarbonate ions at different concentrations, essentially mimicked the artificial seawater composition and started to deposit aragonite after a long (4 h) incubation period at the tropical sea surface temperature of 30 °C. While numerous techniques for the solution deposition of calcium hydroxyapatite (Ca 10 (PO 4 ) 6 (OH) 2 ), of low thermodynamic solubility, on synthetic biomaterials have been demonstrated, procedures related to the solution-based surface deposition of high solubility aragonite remained uncommon. Monolayers of aragonite ooids deposited at 30 °C on UHMWPE substrates soaked in organic-free ACS solutions were found to possess nano-structures similar to the mortar-and-brick-type botryoids observed in biogenic marine shells. Samples were characterized using SEM, XRD, FTIR, ICP-AES and contact angle goniometry

  19. Aragonite coating solutions (ACS) based on artificial seawater

    Energy Technology Data Exchange (ETDEWEB)

    Tas, A. Cuneyt, E-mail: c_tas@hotmail.com

    2015-03-01

    Graphical abstract: - Highlights: • Developed completely inorganic solutions for the deposition of monolayers of aragonite spherules (or ooids). • Solutions mimicked the artificial seawater. • Biomimetic crystallization was performed at the tropical sea surface temperature of 30 °C. - Abstract: Aragonite (CaCO{sub 3}, calcium carbonate) is an abundant biomaterial of marine life. It is the dominant inorganic phase of coral reefs, mollusc bivalve shells and the stalactites or stalagmites of geological sediments. Inorganic and initially precipitate-free aragonite coating solutions (ACS) of pH 7.4 were developed in this study to deposit monolayers of aragonite spherules or ooids on biomaterial (e.g., UHMWPE, ultrahigh molecular weight polyethylene) surfaces soaked in ACS at 30 °C. The ACS solutions of this study have been developed for the surface engineering of synthetic biomaterials. The abiotic ACS solutions, enriched with calcium and bicarbonate ions at different concentrations, essentially mimicked the artificial seawater composition and started to deposit aragonite after a long (4 h) incubation period at the tropical sea surface temperature of 30 °C. While numerous techniques for the solution deposition of calcium hydroxyapatite (Ca{sub 10}(PO{sub 4}){sub 6}(OH){sub 2}), of low thermodynamic solubility, on synthetic biomaterials have been demonstrated, procedures related to the solution-based surface deposition of high solubility aragonite remained uncommon. Monolayers of aragonite ooids deposited at 30 °C on UHMWPE substrates soaked in organic-free ACS solutions were found to possess nano-structures similar to the mortar-and-brick-type botryoids observed in biogenic marine shells. Samples were characterized using SEM, XRD, FTIR, ICP-AES and contact angle goniometry.

  20. Variable camber wing based on pneumatic artificial muscles

    Science.gov (United States)

    Yin, Weilong; Liu, Libo; Chen, Yijin; Leng, Jinsong

    2009-07-01

    As a novel bionic actuator, pneumatic artificial muscle has high power to weight ratio. In this paper, a variable camber wing with the pneumatic artificial muscle is developed. Firstly, the experimental setup to measure the static output force of pneumatic artificial muscle is designed. The relationship between the static output force and the air pressure is investigated. Experimental result shows the static output force of pneumatic artificial muscle decreases nonlinearly with increasing contraction ratio. Secondly, the finite element model of the variable camber wing is developed. Numerical results show that the tip displacement of the trailing-edge increases linearly with increasing external load and limited with the maximum static output force of pneumatic artificial muscles. Finally, the variable camber wing model is manufactured to validate the variable camber concept. Experimental result shows that the wing camber increases with increasing air pressure and that it compare very well with the FEM result.

  1. The use of artificial intelligence methods for visual analysis of properties of surface layers

    Directory of Open Access Journals (Sweden)

    Tomasz Wójcicki

    2014-12-01

    Full Text Available [b]Abstract[/b]. The article presents a selected area of research on the possibility of automatic prediction of material properties based on the analysis of digital images. Original, holistic model of forecasting properties of surface layers based on a multi-step process that includes the selected methods of processing and analysis of images, inference with the use of a priori knowledge bases and multi-valued fuzzy logic, and simulation with the use of finite element methods is presented. Surface layers characteristics and core technologies of their production processes such as mechanical, thermal, thermo-mechanical, thermo-chemical, electrochemical, physical are discussed. Developed methods used in the model for the classification of images of the surface layers are shown. The objectives of the use of selected methods of processing and analysis of digital images, including techniques for improving the quality of images, segmentation, morphological transformation, pattern recognition and simulation of physical phenomena in the structures of materials are described.[b]Keywords[/b]: image analysis, surface layer, artificial intelligence, fuzzy logic

  2. Comparison between Two Methods for Diagnosis of Trichinellosis: Trichinoscopy and Artificial Digestion

    Directory of Open Access Journals (Sweden)

    María Laura Vignau

    1997-09-01

    Full Text Available Two direct methods for the diagnosis of trichinellosis were compared: trichinoscopy and artificial digestion. Muscles from 17 wistar rats, orally infected with 500 Trichinella spiralis encysted larvae were examined. From each of the following muscles: diaphragm, tongue, masseters, intercostals, triceps brachialis and cuadriceps femoralis, 648,440 larvae from 1 g samples were recovered. The linear correlation between trichinoscopy and artificial digestion was very high and significant (r=0.94, p< 0.0001, showing that both methods for the detection of muscular larvae did not differ significantly. In both methods, significant differences were found in the distribution of larvae per gramme of muscle

  3. Upwind methods for the Baer–Nunziato equations and higher-order reconstruction using artificial viscosity

    International Nuclear Information System (INIS)

    Fraysse, F.; Redondo, C.; Rubio, G.; Valero, E.

    2016-01-01

    This article is devoted to the numerical discretisation of the hyperbolic two-phase flow model of Baer and Nunziato. A special attention is paid on the discretisation of intercell flux functions in the framework of Finite Volume and Discontinuous Galerkin approaches, where care has to be taken to efficiently approximate the non-conservative products inherent to the model equations. Various upwind approximate Riemann solvers have been tested on a bench of discontinuous test cases. New discretisation schemes are proposed in a Discontinuous Galerkin framework following the criterion of Abgrall and the path-conservative formalism. A stabilisation technique based on artificial viscosity is applied to the high-order Discontinuous Galerkin method and compared against classical TVD-MUSCL Finite Volume flux reconstruction.

  4. Upwind methods for the Baer–Nunziato equations and higher-order reconstruction using artificial viscosity

    Energy Technology Data Exchange (ETDEWEB)

    Fraysse, F., E-mail: francois.fraysse@rs2n.eu [RS2N, St. Zacharie (France); E. T. S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Madrid (Spain); Redondo, C.; Rubio, G.; Valero, E. [E. T. S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Madrid (Spain)

    2016-12-01

    This article is devoted to the numerical discretisation of the hyperbolic two-phase flow model of Baer and Nunziato. A special attention is paid on the discretisation of intercell flux functions in the framework of Finite Volume and Discontinuous Galerkin approaches, where care has to be taken to efficiently approximate the non-conservative products inherent to the model equations. Various upwind approximate Riemann solvers have been tested on a bench of discontinuous test cases. New discretisation schemes are proposed in a Discontinuous Galerkin framework following the criterion of Abgrall and the path-conservative formalism. A stabilisation technique based on artificial viscosity is applied to the high-order Discontinuous Galerkin method and compared against classical TVD-MUSCL Finite Volume flux reconstruction.

  5. EEG signal classification based on artificial neural networks and amplitude spectra features

    Science.gov (United States)

    Chojnowski, K.; FrÄ czek, J.

    BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

  6. An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

    Directory of Open Access Journals (Sweden)

    Yao Junyang

    2014-06-01

    Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

  7. Artificial emotional model based on finite state machine

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-mei; WU Wei-guo

    2008-01-01

    According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

  8. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  9. A neutron spectrum unfolding computer code based on artificial neural networks

    Science.gov (United States)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2014-02-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  10. Artificial ligamentous joints:Methods, materials and characteristics

    OpenAIRE

    Hockings, Nick; Iravani, Pejman; Bowen, Chris

    2014-01-01

    This paper presents a novel method for making ligamentous articulations for robots. Ligamentous joints are widely found in animals, but they have been of limited appli- cation in robotics due to lack of analogous synthetic materials. The method presented combines 3D printing, tow laying and thermoplastic welding which enables manufacturing of this type of structure.

  11. Porphyrin and fullerene-based artificial photosynthetic materials for photovoltaics

    International Nuclear Information System (INIS)

    Imahori, Hiroshi; Kashiwagi, Yukiyasu; Hasobe, Taku; Kimura, Makoto; Hanada, Takeshi; Nishimura, Yoshinobu; Yamazaki, Iwao; Araki, Yasuyuki; Ito, Osamu; Fukuzumi, Shunichi

    2004-01-01

    We have developed artificial photosynthetic systems in which porphyrins and fullerenes are self-assembled as building blocks into nanostructured molecular light-harvesting materials and photovoltaic devices. Multistep electron transfer strategy has been combined with our finding that porphyrin and fullerene systems have small reorganization energies, which are suitable for the construction of light energy conversion systems as well as artificial photosynthetic models. Highly efficient photosynthetic electron transfer reactions have been realized at ITO electrodes modified with self-assembled monolayers of porphyrin oligomers as well as porphyrin-fullerene linked systems. Porphyrin-modified gold nanoclusters have been found to have potential as artificial photosynthetic materials. These results provide basic information for the development of nanostructured artificial photosynthetic systems

  12. Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

    Science.gov (United States)

    Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Zhu, Shengying

    2018-04-01

    The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

  13. Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

    Science.gov (United States)

    Liong, Shie-Yui; He, Shan

    2010-05-01

    Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

  14. Neuro-Based Artificial Intelligence Model for Loan Decisions

    OpenAIRE

    Shorouq F. Eletter; Saad G. Yaseen; Ghaleb A. Elrefae

    2010-01-01

    Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an enabling tool for evaluating credit applications to support loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpr...

  15. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    Science.gov (United States)

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

  16. Application of artificial intelligence methods for prediction of steel mechanical properties

    Directory of Open Access Journals (Sweden)

    Z. Jančíková

    2008-10-01

    Full Text Available The target of the contribution is to outline possibilities of applying artificial neural networks for the prediction of mechanical steel properties after heat treatment and to judge their perspective use in this field. The achieved models enable the prediction of final mechanical material properties on the basis of decisive parameters influencing these properties. By applying artificial intelligence methods in combination with mathematic-physical analysis methods it will be possible to create facilities for designing a system of the continuous rationalization of existing and also newly developing industrial technologies.

  17. Photoredox-Based Actuation of an Artificial Molecular Muscle.

    Science.gov (United States)

    Liles, Kevin P; Greene, Angelique F; Danielson, Mary K; Colley, Nathan D; Wellen, Andrew; Fisher, Jeremy M; Barnes, Jonathan C

    2018-01-24

    The use of light to actuate materials is advantageous because it represents a cost-effective and operationally straightforward way to introduce energy into a stimuli-responsive system. Common strategies for photoinduced actuation of materials typically rely on light irradiation to isomerize azobenzene or spiropyran derivatives, or to induce unidirectional rotation of molecular motors incorporated into a 3D polymer network. Although interest in photoredox catalysis has risen exponentially in the past decade, there are far fewer examples where photoinduced electron transfer (PET) processes are employed to actuate materials. Here, a novel mode of actuation in a series of redox-responsive hydrogels doped with a visible-light-absorbing ruthenium-based photocatalyst is reported. The hydrogels are composed primarily of polyethylene glycol and low molar concentrations of a unimolecular electroactive polyviologen that is activated through a PET mechanism. The rate and degree of contraction of the hydrogels are measured over several hours while irradiating with blue light. Likewise, the change in mechanical properties-determined through oscillatory shear rheology experiments-is assessed as a function of polyviologen concentration. Finally, an artificial molecular muscle is fabricated using the best-performing hydrogel composition, and its ability to perform work, while irradiated, is demonstrated by lifting a small weight. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Aragonite coating solutions (ACS) based on artificial seawater

    Science.gov (United States)

    Tas, A. Cuneyt

    2015-03-01

    Aragonite (CaCO3, calcium carbonate) is an abundant biomaterial of marine life. It is the dominant inorganic phase of coral reefs, mollusc bivalve shells and the stalactites or stalagmites of geological sediments. Inorganic and initially precipitate-free aragonite coating solutions (ACS) of pH 7.4 were developed in this study to deposit monolayers of aragonite spherules or ooids on biomaterial (e.g., UHMWPE, ultrahigh molecular weight polyethylene) surfaces soaked in ACS at 30 °C. The ACS solutions of this study have been developed for the surface engineering of synthetic biomaterials. The abiotic ACS solutions, enriched with calcium and bicarbonate ions at different concentrations, essentially mimicked the artificial seawater composition and started to deposit aragonite after a long (4 h) incubation period at the tropical sea surface temperature of 30 °C. While numerous techniques for the solution deposition of calcium hydroxyapatite (Ca10(PO4)6(OH)2), of low thermodynamic solubility, on synthetic biomaterials have been demonstrated, procedures related to the solution-based surface deposition of high solubility aragonite remained uncommon. Monolayers of aragonite ooids deposited at 30 °C on UHMWPE substrates soaked in organic-free ACS solutions were found to possess nano-structures similar to the mortar-and-brick-type botryoids observed in biogenic marine shells. Samples were characterized using SEM, XRD, FTIR, ICP-AES and contact angle goniometry.

  19. Optical-Based Artificial Palpation Sensors for Lesion Characterization

    Directory of Open Access Journals (Sweden)

    Hee-Jun Park

    2013-08-01

    Full Text Available Palpation techniques are widely used in medical procedures to detect the presence of lumps or tumors in the soft breast tissues. Since these procedures are very subjective and depend on the skills of the physician, it is imperative to perform detailed a scientific study in order to develop more efficient medical sensors to measure and generate palpation parameters. In this research, we propose an optical-based, artificial palpation sensor for lesion characterization. This has been developed using a multilayer polydimethylsiloxane optical waveguide. Light was generated at the critical angle to reflect totally within the flexible and transparent waveguide. When a waveguide was compressed by an external force, its contact area would deform and cause the light to scatter. The scattered light was captured by a high-resolution camera and saved as an image format. To test the performance of the proposed system, we used a realistic tissue phantom with embedded hard inclusions. The experimental results show that the proposed sensor can detect inclusions and provide the relative value of size, depth, and Young’s modulus of an inclusion.

  20. Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

    Science.gov (United States)

    Gao, Lingyun; Ye, Mingquan; Wu, Changrong

    2017-11-29

    Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

  1. A time-domain method to generate artificial time history from a given reference response spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Gang Sik [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of); Song, Oh Seop [Dept. of Mechanical Engineering, Chungnam National University, Daejeon (Korea, Republic of)

    2016-06-15

    Seismic qualification by test is widely used as a way to show the integrity and functionality of equipment that is related to the overall safety of nuclear power plants. Another means of seismic qualification is by direct integration analysis. Both approaches require a series of time histories as an input. However, in most cases, the possibility of using real earthquake data is limited. Thus, artificial time histories are widely used instead. In many cases, however, response spectra are given. Thus, most of the artificial time histories are generated from the given response spectra. Obtaining the response spectrum from a given time history is straightforward. However, the procedure for generating artificial time histories from a given response spectrum is difficult and complex to understand. Thus, this paper presents a simple time-domain method for generating a time history from a given response spectrum; the method was shown to satisfy conditions derived from nuclear regulatory guidance.

  2. A time-domain method to generate artificial time history from a given reference response spectrum

    International Nuclear Information System (INIS)

    Shin, Gang Sik; Song, Oh Seop

    2016-01-01

    Seismic qualification by test is widely used as a way to show the integrity and functionality of equipment that is related to the overall safety of nuclear power plants. Another means of seismic qualification is by direct integration analysis. Both approaches require a series of time histories as an input. However, in most cases, the possibility of using real earthquake data is limited. Thus, artificial time histories are widely used instead. In many cases, however, response spectra are given. Thus, most of the artificial time histories are generated from the given response spectra. Obtaining the response spectrum from a given time history is straightforward. However, the procedure for generating artificial time histories from a given response spectrum is difficult and complex to understand. Thus, this paper presents a simple time-domain method for generating a time history from a given response spectrum; the method was shown to satisfy conditions derived from nuclear regulatory guidance

  3. Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods

    International Nuclear Information System (INIS)

    Almonacid, F.; Rus, C.; Hontoria, L.; Munoz, F.J.

    2010-01-01

    The presence of PV modules made with new technologies and materials is increasing in PV market, in special Thin Film Solar Modules (TFSM). They are ready to make a substantial contribution to the world's electricity generation. Although Si wafer-based cells account for the most of increase, technologies of thin film have been those of the major growth in last three years. During 2007 they grew 133%. On the other hand, manufacturers provide ratings for PV modules for conditions referred to as Standard Test Conditions (STC). However, these conditions rarely occur outdoors, so the usefulness and applicability of the indoors characterisation in standard test conditions of PV modules is a controversial issue. Therefore, to carry out a correct photovoltaic engineering, a suitable characterisation of PV module electrical behaviour is necessary. The IDEA Research Group from Jaen University has developed a method based on artificial neural networks (ANNs) to electrical characterisation of PV modules. An ANN was able to generate V-I curves of si-crystalline PV modules for any irradiance and module cell temperature. The results show that the proposed ANN introduces a good accurate prediction for si-crystalline PV modules performance when compared with the measured values. Now, this method is going to be applied for electrical characterisation of PV CIS modules. Finally, a comparative study with other methods, of electrical characterisation, is done. (author)

  4. [A method of recognizing biology surface spectrum using cascade-connection artificial neural nets].

    Science.gov (United States)

    Shi, Wei-Jie; Yao, Yong; Zhang, Tie-Qiang; Meng, Xian-Jiang

    2008-05-01

    A method of recognizing the visible spectrum of micro-areas on the biological surface with cascade-connection artificial neural nets is presented in the present paper. The visible spectra of spots on apples' pericarp, ranging from 500 to 730 nm, were obtained with a fiber-probe spectrometer, and a new spectrum recognition system consisting of three-level cascade-connection neural nets was set up. The experiments show that the spectra of rotten, scar and bumped spot on an apple's pericarp can be recognized by the spectrum recognition system, and the recognition accuracy is higher than 85% even when noise level is 15%. The new recognition system overcomes the disadvantages of poor accuracy and poor anti-noise with the traditional system based on single cascade neural nets. Finally, a new method of expression of recognition results was proved. The method is based on the conception of degree of membership in fuzzing mathematics, and through it the recognition results can be expressed exactly and objectively.

  5. New Method for Leakage Detection by Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Attari

    2018-03-01

    Full Text Available Nowadays water loss has been turned into a global concern and on the other hand the demand for water is increasing. This problem has made the demand management and consumption pattern reform necessary. One of the most important methods for managing water consumption is to decrease the water loss. In this study by using neural networks, a new method is presented to specify the location and quantity of leakages in water distribution networks.  In this method, by producing the training data and applying it to neural network, the network is able to determine approximate location and quantity of nodal leakage with receiving the nodal pressure. Production of training data is carried out by applying assumed leakage to specific nodes in the network and calculating the new nodal pressures. The results show that by minimum use of hydraulic data taken from pressures, not only this method can determine the location of nodal leakages, but also it can specify the amount of leakage on each node with reasonable accuracy.

  6. Efficacy of Blood Sources and Artificial Blood Feeding Methods in Rearing of Aedes aegypti (Diptera: Culicidae) for Sterile Insect Technique and Incompatible Insect Technique Approaches in Sri Lanka

    OpenAIRE

    Nayana Gunathilaka; Tharaka Ranathunge; Lahiru Udayanga; Wimaladharma Abeyewickreme

    2017-01-01

    Introduction Selection of the artificial membrane feeding technique and blood meal source has been recognized as key considerations in mass rearing of vectors. Methodology Artificial membrane feeding techniques, namely, glass plate, metal plate, and Hemotek membrane feeding method, and three blood sources (human, cattle, and chicken) were evaluated based on feeding rates, fecundity, and hatching rates of Aedes aegypti. Significance in the variations among blood feeding was investigated by one...

  7. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

  8. Analysis and control of a parallel lower limb based on pneumatic artificial muscles

    Directory of Open Access Journals (Sweden)

    Feilong Jiang

    2016-12-01

    Full Text Available Most robots that are actuated by antagonistic pneumatic artificial muscles are controlled by various control algorithms that cannot adequately imitate the actual muscle distribution of human limbs. Other robots in which the distribution of pneumatic artificial muscle is similar to that of human limbs can only analyze the position of the robot using perceptual data instead of rational knowledge. In order to better imitate the movement of a human limb, the article proposes a humanoid lower limb in the form of a parallel mechanism where muscle is unevenly distributed. Next, the kinematic and dynamic movements of bionic hip joint are analyzed, where the joint movement is controlled by an observer-based fuzzy adaptive control algorithm as a whole rather than each individual pneumatic artificial muscle and parameters that are optimized by a neural network. Finally, experimental results are provided to confirm the effectiveness of the proposed method. We also document the role of muscle in trajectory tracking for the piriformis and musculi obturator internus in isobaric processes.

  9. Artificial neuron-glia networks learning approach based on cooperative coevolution.

    Science.gov (United States)

    Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

    2015-06-01

    Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

  10. Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Suryanita Reni

    2017-01-01

    Full Text Available The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1, Immediate Occupancy (2, Life Safety (3 or in a condition of Collapse Prevention (4. According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network

  11. Artificial neural network based pulse-shape analysis for cryogenic detectors operated in CRESST-II

    Energy Technology Data Exchange (ETDEWEB)

    Zoeller, Andreas [Physik-Department and Excellence Cluster Universe, Technische Universitaet Muenchen, D-85747 Garching (Germany); Collaboration: CRESST-Collaboration

    2016-07-01

    In this talk we report on results of a pulse-shape analysis of cryogenic detectors based on artificial neural networks. To train the neural network a large amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets will be explained. The presented analysis shows an excellent discrimination performance even down to the energy threshold. The method is applied to several detectors, among them is the module with the lowest threshold (307eV) operated in CRESST-II phase 2. The performed blind analysis of this module confirms the substantially enhanced sensitivity for light dark matter published in 2015.

  12. A neutron spectrum unfolding computer code based on artificial neural networks

    International Nuclear Information System (INIS)

    Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

    2014-01-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding

  13. Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks

    International Nuclear Information System (INIS)

    Almonacid, F.; Rus, C.; Perez-Higueras, P.; Hontoria, L.

    2011-01-01

    The use of photovoltaics for electricity generation purposes has recorded one of the largest increases in the field of renewable energies. The energy production of a grid-connected PV system depends on various factors. In a wide sense, it is considered that the annual energy provided by a generator is directly proportional to the annual radiation incident on the plane of the generator and to the installed nominal power. However, a range of factors is influencing the expected outcome by reducing the generation of energy. The aim of this study is to compare the results of four different methods for estimating the annual energy produced by a PV generator: three of them are classical methods and the fourth one is based on an artificial neural network developed by the R and D Group for Solar and Automatic Energy at the University of Jaen. The results obtained shown that the method based on an artificial neural network provides better results than the alternative classical methods in study, mainly due to the fact that this method takes also into account some second order effects, such as low irradiance, angular and spectral effects. -- Research highlights: → It is considered that the annual energy provided by a PV generator is directly proportional to the annual radiation incident on the plane of the generator and to the installed nominal power. → A range of factors are influencing the expected outcome by reducing the generation of energy (mismatch losses, dirt and dust, Ohmic losses,.). → The aim of this study is to compare the results of four different methods for estimating the annual energy produced by a PV generator: three of them are classical methods and the fourth one is based on an artificial neural network. → The results obtained shown that the method based on an artificial neural network provides better results than the alternative classical methods in study. While classical methods have only taken into account temperature losses, the method based in

  14. A relaxation-projection method for compressible flows. Part II: Artificial heat exchanges for multiphase shocks

    International Nuclear Information System (INIS)

    Petitpas, Fabien; Franquet, Erwin; Saurel, Richard; Le Metayer, Olivier

    2007-01-01

    the 'pseudoviscosity' introduced by Von Neumann and Richtmyer [J. Von Neumann, R.D. Richtmyer, A method for the numerical calculation of hydrodynamic shocks, J. Appl. Phys. 21 (1950) 232-237]. The present Lagrangian numerical scheme thus combines Riemann solvers and artificial heat exchanges. An Eulerian variant is then obtained by using the relaxation-projection method developed earlier by the authors for the Euler equations. The method is validated against exact solutions based on the multiphase shock relations as well as exact solutions of the Euler equations in the context of interface problems. The method is able to solve interfaces separating pure fluids or heterogeneous mixtures with very large density ratio and with very strong shocks

  15. A relaxation-projection method for compressible flows. Part II: Artificial heat exchanges for multiphase shocks

    Science.gov (United States)

    Petitpas, Fabien; Franquet, Erwin; Saurel, Richard; Le Metayer, Olivier

    2007-08-01

    the 'pseudoviscosity' introduced by Von Neumann and Richtmyer [J. Von Neumann, R.D. Richtmyer, A method for the numerical calculation of hydrodynamic shocks, J. Appl. Phys. 21 (1950) 232-237]. The present Lagrangian numerical scheme thus combines Riemann solvers and artificial heat exchanges. An Eulerian variant is then obtained by using the relaxation-projection method developed earlier by the authors for the Euler equations. The method is validated against exact solutions based on the multiphase shock relations as well as exact solutions of the Euler equations in the context of interface problems. The method is able to solve interfaces separating pure fluids or heterogeneous mixtures with very large density ratio and with very strong shocks.

  16. Work behaviors of artificial muscle based on cation driven polypyrrole.

    Science.gov (United States)

    Fujisue, Hisashi; Sendai, Tomokazu; Yamato, Kentaro; Takashima, Wataru; Kaneto, Keiichi

    2007-06-01

    A soft actuator mimicking natural muscles (artificial muscle) has been developed using a flexible conducting polymer of polypyrrole films, which were driven by electrical stimulus in a saline solution. The work characteristics were studied under various load stresses and found to behave like natural muscles. The artificial muscles shrunk and stiffened by the positive electrical stimulus by 2-3% at the maximum force of 5 MPa, and relaxed by application of negative voltages. At larger load stresses, the artificial muscle shrunk slowly as natural muscles do. The driving current also lasted longer at larger loads, indicating that the muscle sensed the magnitude of the load stress. During contraction of the muscle, the conversion efficiency from the electrical input and mechanical output energies was estimated to be around 0.06%. The maximum volumetric work was approximately estimated to be 100 kJ m(-3). These figures are unexpectedly small compared with those of natural muscles.

  17. Fluxonium-Based Artificial Molecule with a Tunable Magnetic Moment

    Science.gov (United States)

    Kou, A.; Smith, W. C.; Vool, U.; Brierley, R. T.; Meier, H.; Frunzio, L.; Girvin, S. M.; Glazman, L. I.; Devoret, M. H.

    2017-07-01

    Engineered quantum systems allow us to observe phenomena that are not easily accessible naturally. The LEGO®-like nature of superconducting circuits makes them particularly suited for building and coupling artificial atoms. Here, we introduce an artificial molecule, composed of two strongly coupled fluxonium atoms, which possesses a tunable magnetic moment. Using an applied external flux, one can tune the molecule between two regimes: one in which the ground-excited state manifold has a magnetic dipole moment and one in which the ground-excited state manifold has only a magnetic quadrupole moment. By varying the applied external flux, we find the coherence of the molecule to be limited by local flux noise. The ability to engineer and control artificial molecules paves the way for building more complex circuits for quantum simulation and protected qubits.

  18. A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

    Science.gov (United States)

    Ju, Chunhua; Xu, Chonghuan

    2013-01-01

    Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

  19. A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Chunhua Ju

    2013-01-01

    Full Text Available Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users’ preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

  20. Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

    Directory of Open Access Journals (Sweden)

    Ye Tian

    2014-01-01

    Full Text Available An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL of lithium-ion (Li-ion batteries based on artificial fish swarm algorithm (AFSA and particle filter (PF, which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.

  1. Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

    Science.gov (United States)

    Kim, Seongjung; Kim, Jongman; Ahn, Soonjae; Kim, Youngho

    2018-04-18

    Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

  2. Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

    Science.gov (United States)

    Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

    2007-12-01

    The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

  3. Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models.

    Science.gov (United States)

    Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Abdullah, Sharifah Mastura Syed; El-Shafie, Ahmed

    2018-05-01

    Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

  4. Artificial Neural Networks for SCADA Data based Load Reconstruction (poster)

    NARCIS (Netherlands)

    Hofemann, C.; Van Bussel, G.J.W.; Veldkamp, H.

    2011-01-01

    If at least one reference wind turbine is available, which provides sufficient information about the wind turbine loads, the loads acting on the neighbouring wind turbines can be predicted via an artificial neural network (ANN). This research explores the possibilities to apply such a network not

  5. Role-based Rights in Artificial Social Systems

    NARCIS (Netherlands)

    G. Boella (Guido); L.W.N. van der Torre (Leon)

    2005-01-01

    htmlabstract In this paper we use normative systems to introduce roles and rights in the game-theoretic artificial social systems developed by Shoham and Tennenholtz. We model normative systems as socially constructed agents whose behavior is determined by a set of role playing agents. Roles are

  6. Artificial-neural-network-based failure detection and isolation

    Science.gov (United States)

    Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

    1998-03-01

    This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

  7. Artificial intelligence-based speed control of DTC induction motor drives - A comparative study

    Energy Technology Data Exchange (ETDEWEB)

    Gadoue, S.M.; Giaouris, D.; Finch, J.W. [School of Electrical, Electronic and Computer Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU (United Kingdom)

    2009-01-15

    The design of the speed controller greatly affects the performance of an electric drive. A common strategy to control an induction machine is to use direct torque control combined with a PI speed controller. These schemes require proper and continuous tuning and therefore adaptive controllers are proposed to replace conventional PI controllers to improve the drive's performance. This paper presents a comparison between four different speed controller design strategies based on artificial intelligence techniques; two are based on tuning of conventional PI controllers, the third makes use of a fuzzy logic controller and the last is based on hybrid fuzzy sliding mode control theory. To provide a numerical comparison between different controllers, a performance index based on speed error is assigned. All methods are applied to the direct torque control scheme and each control strategy has been tested for its robustness and disturbance rejection ability. (author)

  8. Dynamic route guidance algorithm based algorithm based on artificial immune system

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.

  9. Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given......In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable...... solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned...

  10. Artificial neural networks versus conventional methods for boiling water reactor stability monitoring

    International Nuclear Information System (INIS)

    Hagen, T.H.J.J. van der

    1995-01-01

    The application of an artificial neural network (ANN) for boiling water reactor (BWR) stability monitoring was studied. A three-layer perceptron was trained on synthetic autocorrelation functions to estimate the decay ratio and the resonance frequency from measured neutron noise. Training of the ANN was improved by adding noise to the training patterns and by applying nonconventional error definitions in the generalized delta rule. The performance of the developed ANN was compared with those of conventional stability monitoring techniques. Explicit care was taken for generating unbiased test data. It is found that the trained ANN is capable of monitoring the stability of the Dodewaard BWR for four specific cases. By comparing properties such as the false alarm ratio, the alarm failure ratio, and the average time to alarm, it is shown that it performs worse than model-based methods in stability monitoring of exact second-order systems but that it is more robust (better resistant to corruptions of the input data and to deviations of the system at issue from an exact second-order system) than other methods. The latter explains its good performance on the Dodewaard BWR and is promising for the application of an ANN for stability monitoring of other reactors and for other operating conditions

  11. QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

    Directory of Open Access Journals (Sweden)

    Adi Syahputra

    2014-03-01

    Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

  12. Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

    Science.gov (United States)

    Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

    2015-01-01

    Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

  13. In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting.

    Science.gov (United States)

    DeJournett, Leon; DeJournett, Jeremy

    2016-11-01

    Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers. We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis. For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (artificial pancreas system for use in the ICU setting. © 2016 Diabetes Technology Society.

  14. Forecast for Artificial Muscle Tremor Behavior Based on Dynamic Additional Grey Catastrophe Prediction

    Directory of Open Access Journals (Sweden)

    Yu Fu

    2018-02-01

    Full Text Available Recently, bio-inspired artificial muscles based on ionic polymers have shown a bright perspective in engineering and medical research, but the inherent tremor behavior can cause instability of output response. In this paper, dynamic additional grey catastrophe prediction (DAGCP is proposed to forecast the occurrence time of tremor behavior, providing adequate preparation time for the suppression of the chitosan-based artificial muscles. DAGCP constructs various dimensions of time subsequence models under different starting points based on the threshold of tremor occurrence times and peak-to-peak values in unit time. Next, the appropriate subsequence is selected according to grey correlation degree and prediction accuracy, then it is updated with the newly generated values to achieve a real-time forecast of forthcoming tremor time. Compared with conventional grey catastrophe prediction (GCP, the proposed method has the following advantages: (1 the degradation of prediction accuracy caused by the immobilization of original parameters is prevented; (2 the dynamic input, real-time update and gradual forecast of time sequence are incorporated into the model. The experiment results show that the novel DAGCP can predict forthcoming tremor time earlier and more accurately than the conventional GCP. The generation mechanism of tremor behavior is illustrated as well.

  15. New technology-based recruitment methods

    OpenAIRE

    Oksanen, Reija

    2018-01-01

    The transformation that recruitment might encounter due to big data analytics and artificial intelligence (AI) is particularly fascinating which is why this thesis focuses on the changes recruitment processes are and will be facing as new technological solutions are emerging. The aim and main objective of this study is to widen knowledge about new technology-based recruitment methods, focusing on how they are utilized by Finnish recruitment professionals and how the opportunities and risks th...

  16. Carbon Nanotube Yarn-Based Glucose Sensing Artificial Muscle.

    Science.gov (United States)

    Lee, Junghan; Ko, Sachan; Kwon, Cheong Hoon; Lima, Márcio D; Baughman, Ray H; Kim, Seon Jeong

    2016-04-01

    Boronic acid (BA), known to be a reversible glucose-sensing material, is conjugated to a nanogel (NG) derived from hyaluronic acid biopolymer and used as a guest material for a carbon multiwalled nanotube (MWNT) yarn. By exploiting the swelling/deswelling of the NG that originates from the internal anionic charge changes resulting from BA binding to glucose, a NG MWNT yarn artificial muscle is obtained that provides reversible torsional actuation that can be used for glucose sensing. This actuator shows a short response time and high sensitivity (in the 5-100 × 10(-3) m range) for monitoring changes in glucose concentration in physiological buffer, without using any additional auxiliary substances or an electrical power source. It may be possible to apply the glucose-sensing MWNT yarn muscles as implantable glucose sensors that automatically release drugs when needed or as an artificial pancreas. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    Joorabian, M.

    1999-05-01

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  18. Challenges facing the distribution of an artificial-intelligence-based system for nursing.

    Science.gov (United States)

    Evans, S

    1985-04-01

    The marketing and successful distribution of artificial-intelligence-based decision-support systems for nursing face special barriers and challenges. Issues that must be confronted arise particularly from the present culture of the nursing profession as well as the typical organizational structures in which nurses predominantly work. Generalizations in the literature based on the limited experience of physician-oriented artificial intelligence applications (predominantly in diagnosis and pharmacologic treatment) must be modified for applicability to other health professions.

  19. Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

    Science.gov (United States)

    Broderick, Ron

    1997-01-01

    The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network

  20. Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

    Science.gov (United States)

    Radziszewski, Kacper

    2017-10-01

    The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

  1. Artificial intelligence-based condition monitoring for practical electrical drives

    OpenAIRE

    Ashari, Djoni; Pislaru, Crinela; Ball, Andrew; Gu, Fengshou

    2012-01-01

    The main types of existing Condition Monitoring methods (MCSA, GA, IAS) for electrical drives are\\ud described. Then the steps for the design of expert systems are presented: problem identification and analysis, system specification, development tool selection, knowledge based, prototyping and testing. The employment of SOMA (Self-Organizing Migrating Algorithm) used for the optimization of ambient\\ud vibration energy harvesting is analyzed. The power electronics devices are becoming smaller ...

  2. Identification of Age, Temperature and Radiation Effect on Ferritic Steel Microstructure Based on Artificial Intelligence

    International Nuclear Information System (INIS)

    Mike Susmikanti; Entin Hartini; Antonius Sitompul

    2008-01-01

    In the construction of nuclear installation, it is important to know the material condition used on it. Considering mechanical properties of these materials, there are some material change affected by ageing, temperature and radiation. For some years, austenitic stainless steel are used as a fuel cladding in fast breeder reactor. However this material will not sufficiently competitive from economic point of view for the next year. Experiment result on ferritic steel gave information of stronger structural properties compared to austenitic stainless steel. Modeling and simulation will support further identification of this material changing caused by such effects. Pattern recognition of these changes base on artificial intelligence is expected to support the research and development activities on nuclear structure materials. Material structure pattern of these materials, observed by SEM, are converted using image processing system. Its characteristic is then analyzed with principal component using perception method, which usually used on identifying and learning neural network system based on artificial intelligence. Specific design and input are needed to identify the change of material structure pattern before and after any applied effect. In this paper, simulation of changing identification on three types ferritic steel F17(17 Cr), EM 12 (9 CR-2 MoNbV), and EMI 0 (9 Cr-I Mo) were done. The microstructure data before and after effect are taken from some references. The whole pattern recognition process are done using MATLAB software package. (author)

  3. Fault detection and analysis in nuclear research facility using artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Ghazali, Abu Bakar, E-mail: Abakar@uniten.edu.my [Department of Electronics & Communication, College of Engineering, Universiti Tenaga Nasional, 43009 Kajang, Selangor (Malaysia); Ibrahim, Maslina Mohd [Instrumentation Program, Malaysian Nuclear Agency, Bangi (Malaysia)

    2016-01-22

    In this article, an online detection of transducer and actuator condition is discussed. A case study is on the reading of area radiation monitor (ARM) installed at the chimney of PUSPATI TRIGA nuclear reactor building, located at Bangi, Malaysia. There are at least five categories of abnormal ARM reading that could happen during the transducer failure, namely either the reading becomes very high, or very low/ zero, or with high fluctuation and noise. Moreover, the reading may be significantly higher or significantly lower as compared to the normal reading. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are good methods for modeling this plant dynamics. The failure of equipment is based on ARM reading so it is then to compare with the estimated ARM data from ANN/ ANFIS function. The failure categories in either ‘yes’ or ‘no’ state are obtained from a comparison between the actual online data and the estimated output from ANN/ ANFIS function. It is found that this system design can correctly report the condition of ARM equipment in a simulated environment and later be implemented for online monitoring. This approach can also be extended to other transducers, such as the temperature profile of reactor core and also to include other critical actuator conditions such as the valves and pumps in the reactor facility provided that the failure symptoms are clearly defined.

  4. Method and apparatus for optimizing operation of a power generating plant using artificial intelligence techniques

    Science.gov (United States)

    Wroblewski, David [Mentor, OH; Katrompas, Alexander M [Concord, OH; Parikh, Neel J [Richmond Heights, OH

    2009-09-01

    A method and apparatus for optimizing the operation of a power generating plant using artificial intelligence techniques. One or more decisions D are determined for at least one consecutive time increment, where at least one of the decisions D is associated with a discrete variable for the operation of a power plant device in the power generating plant. In an illustrated embodiment, the power plant device is a soot cleaning device associated with a boiler.

  5. Measurement of the activity of an artificial neutrino source based on 37Ar

    International Nuclear Information System (INIS)

    Abdurashitov, D. N.; Veretenkin, E. P.; Gavrin, V. N.; Gorbachev, V. V.; Ibragimova, T. V.; Kalikhov, A. V.; Mirmov, I. N.; Shikhin, A. A.; Yants, V. E.; Barsanov, V. I.; Dzhanelidze, A. A.; Zlokazov, S. B.; Markov, S. Yu.; Shakirov, Z. N.; Cleveland, B. T.

    2007-01-01

    The activity of an artificial neutrino source based on 37 Ar was measured by a specially developed method of directly counting 37 Ar decays in a proportional counter. This source was used to irradiate the target of the SAGE radiochemical gallium-germanium neutrino telescope at the Baksan Neutrino Observatory (Institute for Nuclear Research, Russian Academy of Sciences, Moscow), whereupon the measurements were performed at the Institute of Reactor Materials (Zarechny, Sverdlovsk oblast, Russia). The method used to prepare gaseous samples for measurements in proportional counters and the counting procedure are described. The measured activity of the 37 Ar neutrino source is 405.1 ± 3.7 kCi (corrected for decays that occurred within the period between the instant of activity measurement and the commencement of the irradiation of Ga target at 04:00 Moscow time, 30.04.2004)

  6. Condition Monitoring for DC-link Capacitors Based on Artificial Neural Network Algorithm

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    hardware will reduce the cost, and therefore could be more promising for industry applications. A condition monitoring method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implementation of the ANN to the DC-link capacitor condition monitoring in a back......In power electronic systems, capacitor is one of the reliability critical components . Recently, the condition monitoring of capacitors to estimate their health status have been attracted by the academic research. Industry applications require more reliable power electronics products...... with preventive maintenance. However, the existing capacitor condition monitoring methods suffer from either increased hardware cost or low estimation accuracy, being the challenges to be adopted in industry applications. New development in condition monitoring technology with software solutions without extra...

  7. Artificial intelligence systems based on texture descriptors for vaccine development.

    Science.gov (United States)

    Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

    2011-02-01

    The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors. The matlab implementation of our approaches is available at http://bias.csr.unibo.it/nanni/TexturePeptide.zip.

  8. Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets

    International Nuclear Information System (INIS)

    Canizes, Bruno; Soares, João; Faria, Pedro; Vale, Zita

    2013-01-01

    Highlights: • Ancillary services market management. • Ancillary services requirements forecast based on Artificial Neural Network. • Ancillary services clearing mechanisms without complex bids and with complex bids. - Abstract: Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids

  9. An artificial flexible robot arm based on pneumatic muscle actuators

    Directory of Open Access Journals (Sweden)

    Renn Jyh-Chyang

    2017-01-01

    Full Text Available The purpose of this paper is to develop a novel human-friendly artificial flexible robot arm using four parallel-connected pneumatic muscle actuators (PMAs. The PMA is a flexible silicone rubber actuator which has some behaviors nearest to the real biological muscle including translational and rotational motions. An inverse kinematic model for the motion control is also developed. Finally, from experiment results, it is proved that not only the axial contraction control of a single PMA but also the attitude control of the whole pneumatic flexible robot arm using PID controller are satisfactory.

  10. Laser Doppler Vibrometer Based Examination of the Efficiency of Introducing Artificial Delaminations into Composite Shells

    Directory of Open Access Journals (Sweden)

    Kustroń Kamila

    2015-09-01

    Full Text Available During its operation, the laminate shell of the watercraft hull can be exposed to local stability losses caused by the appearance and development of delaminations. The sources of these delaminations are discontinuities, created both in the production process and as a result of bumps of foreign bodies into the hull in operation. In the environment of fatigue loads acting on the hull, the delaminations propagate and lead to the loss of load capacity of the hull structure. There is a need to improve diagnostic systems used in Structural Health Monitoring (SHM of laminate hull elements to detect and monitor the development of the delaminations. Effective diagnostic systems used for delamination assessment base on expert systems. Along with other tools, the expert diagnostic advisory systems make use of the non-destructive examination method which consists in generating elastic waves in the hull shell structure and observing their changes by comparing the recorded signal with damage patterns collected in the expert system database. This system requires introducing certain patterns to its knowledge base, based on the results of experimental examinations performed on specimens with implemented artificial delaminations. The article presents the results of the examination oriented on assessing the delaminations artificially generated in the structure of glass- and carbon-epoxy laminates by introducing local non-adhesive layers with the aid of thin polyethylene film, teflon insert, or thin layer of polyvinyl alcohol. The efficiency of each method was assessed using laser vibrometry. The effect of the depth of delamination position in the laminate on the efficiency of the applied method is documented as well.

  11. Feasibility of gas-discharge and optical methods of creating artificial ozone layers of the earth

    International Nuclear Information System (INIS)

    Batanov, G.M.; Kossyi, I.A.; Matveev, A.A.; Silakov, V.P.

    1996-01-01

    Gas-discharge (microwave) and optical (laser) methods of generating large-scale artificial ozone layers in the stratosphere are analyzed. A kinetic model is developed to calculate the plasma-chemical consequences of discharges localized in the stratosphere. Computations and simple estimates indicate that, in order to implement gas-discharge and optical methods, the operating power of ozone-producing sources should be comparable to or even much higher than the present-day power production throughout the world. Consequently, from the engineering and economic standpoints, microwave and laser methods cannot be used to repair large-scale ozone 'holes'

  12. Artificial neural network based particle size prediction of polymeric nanoparticles.

    Science.gov (United States)

    Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

    2017-10-01

    Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China

    Directory of Open Access Journals (Sweden)

    Ruijing Gan

    2015-01-01

    Full Text Available Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM and back propagation artificial neural networks (BP-ANN to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method’s feasibility. The results showed that the proposal method has advantages over GM (1, 1 and GM (2, 1 in all the evaluation indexes.

  14. Development of classification and prediction methods of critical heat flux using fuzzy theory and artificial neural networks

    International Nuclear Information System (INIS)

    Moon, Sang Ki

    1995-02-01

    This thesis applies new information techniques, artificial neural networks, (ANNs) and fuzzy theory, to the investigation of the critical heat flux (CHF) phenomenon for water flow in vertical round tubes. The work performed are (a) classification and prediction of CHF based on fuzzy clustering and ANN, (b) prediction and parametric trends analysis of CHF using ANN with the introduction of dimensionless parameters, and (c) detection of CHF occurrence using fuzzy rule and spatiotemporal neural network (STN). Fuzzy clustering and ANN are used for classification and prediction of the CHF using primary system parameters. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulted clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanisms. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. Parametric trends of the CHF are analyzed by applying artificial neural networks to a CHF data base for water flow in uniformly heated vertical round tubes. The analyses are performed from three viewpoints, i.e., for fixed inlet conditions, for fixed exit conditions, and based on local conditions hypothesis. In order to remove the necessity of data classification, Katto and Groeneveld et al.'s dimensionless parameters are introduced in training the ANNs with the experimental CHF data. The trained ANNs predict the CHF better than any other conventional correlations, showing RMS error of 8.9%, 13.1%, and 19.3% for fixed inlet conditions, for fixed exit conditions, and for local

  15. Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

    Science.gov (United States)

    Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

    2018-02-01

    In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

  16. ANALYSIS DATA SETS USING HYBRID TECHNIQUES APPLIED ARTIFICIAL INTELLIGENCE BASED PRODUCTION SYSTEMS INTEGRATED DESIGN

    OpenAIRE

    Daniel-Petru GHENCEA; Miron ZAPCIU; Claudiu-Florinel BISU; Elena-Iuliana BOTEANU; Elena-Luminiţa OLTEANU

    2017-01-01

    The paper proposes a prediction model of behavior spindle from the point of view of the thermal deformations and the level of the vibrations by highlighting and processing the characteristic equations. This is a model analysis for the shaft with similar electro-mechanical characteristics can be achieved using a hybrid analysis based on artificial intelligence (genetic algorithms - artificial neural networks - fuzzy logic). The paper presents a prediction mode obtaining valid range of values f...

  17. A study on the advanced methods for on-line signal processing by using artificial intelligence in nuclear power plants

    International Nuclear Information System (INIS)

    Kim, Wan Joo

    1993-02-01

    In order to assist the operators at the transient states of a nuclear power plant, the automation of signal processing is needed. This study has the objective to process the signals from a nuclear power plant for the purpose of advising the operator. To meet this objective, in this study, two kinds of on-line signal processing system based on AI techniques are developed for the nuclear power plant application with on-line signals. First, an artificial neural network for signal prediction is developed for the adequate countermoves at transient states. The steam generator water level is adopted as the example and the outputs of a simulation program for the dynamics of steam generator combined with noises are used as the training patterns. For the training of the artificial neural network, the modified backpropagation algorithm is proposed for escaping quickly from local minima. The modified algorithm is different from the ordinary backpropagation algorithm in the aspect that the training rate coefficient is repeatedly adjusted randomly and taken when the training is improved. This trial has an effect to search for an adequate magnitude of a training rate coefficient. The comparison result shows that the modified algorithm enables the neural network to be trained more quickly. The simulation result shows that the outputs of the artificial neural network are not sensitive to noises. Using the artificial neural networks proposed in this thesis, the operators can predict the next status of a plant and can take actions to maintain the stability of plant. Second, the multi sensor integration system has been developed for the identification of transient states. The developed system is divided into two parts; pre-processors and a fusion part. An artificial neural network is adopted in the fusion part to include the knowledge about the identification and to make a decision of the transient state. The developed pre-processors play a role of classifying the trend types of

  18. Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Ke Wu

    2016-08-01

    Full Text Available In recent years, a novel matching classification strategy inspired by the artificial deoxyribonucleic acid (DNA technology has been proposed for hyperspectral remote sensing imagery. Such a method can describe brightness and shape information of a spectrum by encoding the spectral curve into a DNA strand, providing a more comprehensive way for spectral similarity comparison. However, it suffers from two problems: data volume is amplified when all of the bands participate in the encoding procedure and full-band comparison degrades the importance of bands carrying key information. In this paper, a new multi-probe based artificial DNA encoding and matching (MADEM method is proposed. In this method, spectral signatures are first transformed into DNA code words with a spectral feature encoding operation. After that, multiple probes for interesting classes are extracted to represent the specific fragments of DNA strands. During the course of spectral matching, the different probes are compared to obtain the similarity of different types of land covers. By computing the absolute vector distance (AVD between different probes of an unclassified spectrum and the typical DNA code words from the database, the class property of each pixel is set as the minimum distance class. The main benefit of this strategy is that the risk of redundant bands can be deeply reduced and critical spectral discrepancies can be enlarged. Two hyperspectral image datasets were tested. Comparing with the other classification methods, the overall accuracy can be improved from 1.22% to 10.09% and 1.19% to 15.87%, respectively. Furthermore, the kappa coefficient can be improved from 2.05% to 15.29% and 1.35% to 19.59%, respectively. This demonstrated that the proposed algorithm outperformed other traditional classification methods.

  19. Cargo flows distribution over the loading sites of enterprises by using methods of artificial intelligence

    Directory of Open Access Journals (Sweden)

    Олександр Павлович Кіркін

    2017-06-01

    Full Text Available Development of information technologies and market requirements in effective control over cargo flows, forces enterprises to look for new ways and methods of automated control over the technological operations. For rail transportation one of the most complicated tasks of automation is the cargo flows distribution over the sites of loading and unloading. In this article the solution with the use of one of the methods of artificial intelligence – a fuzzy inference has been proposed. The analysis of the last publications showed that the fuzzy inference method is effective for the solution of similar tasks, it makes it possible to accumulate experience, it is stable to temporary impacts of the environmental conditions. The existing methods of the cargo flows distribution over the sites of loading and unloading are too simplified and can lead to incorrect decisions. The purpose of the article is to create a distribution model of cargo flows of the enterprises over the sites of loading and unloading, basing on the fuzzy inference method and to automate the control. To achieve the objective a mathematical model of the cargo flows distribution over the sites of loading and unloading has been made using fuzzy logic. The key input parameters of the model are: «number of loading sites», «arrival of the next set of cars», «availability of additional operations». The output parameter is «a variety of set of cars». Application of the fuzzy inference method made it possible to reduce loading time by 15% and to reduce costs for preparatory operations before loading by 20%. Thus this method is an effective means and holds the greatest promise for railway competitiveness increase. Interaction between different types of transportation and their influence on the cargo flows distribution over the sites of loading and unloading hasn’t been considered. These sites may be busy transshipping at that very time which is characteristic of large enterprises

  20. The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

    Directory of Open Access Journals (Sweden)

    Gulshan Kumar

    2012-01-01

    Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.

  1. Fractured reservoir history matching improved based on artificial intelligent

    Directory of Open Access Journals (Sweden)

    Sayyed Hadi Riazi

    2016-12-01

    Full Text Available In this paper, a new robust approach based on Least Square Support Vector Machine (LSSVM as a proxy model is used for an automatic fractured reservoir history matching. The proxy model is made to model the history match objective function (mismatch values based on the history data of the field. This model is then used to minimize the objective function through Particle Swarm Optimization (PSO and Imperialist Competitive Algorithm (ICA. In automatic history matching, sensitive analysis is often performed on full simulation model. In this work, to get new range of the uncertain parameters (matching parameters in which the objective function has a minimum value, sensitivity analysis is also performed on the proxy model. By applying the modified ranges to the optimization methods, optimization of the objective function will be faster and outputs of the optimization methods (matching parameters are produced in less time and with high precision. This procedure leads to matching of history of the field in which a set of reservoir parameters is used. The final sets of parameters are then applied for the full simulation model to validate the technique. The obtained results show that the present procedure in this work is effective for history matching process due to its robust dependability and fast convergence speed. Due to high speed and need for small data sets, LSSVM is the best tool to build a proxy model. Also the comparison of PSO and ICA shows that PSO is less time-consuming and more effective.

  2. Formations of Robotic Swarm: An Artificial Force Based Approach

    Directory of Open Access Journals (Sweden)

    Samitha W. Ekanayake

    2009-03-01

    Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

  3. Formations of Robotic Swarm: An Artificial Force Based Approach

    Directory of Open Access Journals (Sweden)

    Samitha W. Ekanayake

    2010-09-01

    Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

  4. Formations of Robotic Swarm: An Artificial Force Based Approach

    Directory of Open Access Journals (Sweden)

    Samitha W. Ekanayake

    2009-03-01

    Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter- member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

  5. Formations of Robotic Swarm: An Artificial Force Based Approach

    Directory of Open Access Journals (Sweden)

    Samitha W. Ekanayake

    2010-09-01

    Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter- member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

  6. An Artificial Nose Based on Microcantilever Array Sensors

    International Nuclear Information System (INIS)

    Lang, H P; Ramseyer, J P; Grange, W; Braun, T; Schmid, D; Hunziker, P; Jung, C; Hegner, M; Gerber, C

    2007-01-01

    We used microfabricated cantilever array sensors for an artificial nose setup. Each cantilever is coated on its top surface with a polymer layer. Volatile gaseous analytes are detected by tracking the diffusion process of the molecules into the polymer layers, resulting in swelling of the polymer layers and therewith bending of the cantilevers. From the bending pattern of all cantilevers in the array, a characteristic 'fingerprint' of the analyte is obtained, which is evaluated using principal component analysis. In a flow of dry nitrogen gas, the bending of the cantilevers is reverted to its initial state before exposure to the analyte, which allows reversible and reproducible operation of the sensor. We show examples of detection of solvents, perfume essences and beverage flavors. In a medical application, the setup provides indication of presence of diseases in patient's breath samples

  7. An Artificial Nose Based on Microcantilever Array Sensors

    Energy Technology Data Exchange (ETDEWEB)

    Lang, H P [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Ramseyer, J P [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Grange, W [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Braun, T [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Schmid, D [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Hunziker, P [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Jung, C [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Hegner, M [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland); Gerber, C [National Center of Competence in Research for Nanoscale Science, Institute of Physics of Univesity of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland)

    2007-03-15

    We used microfabricated cantilever array sensors for an artificial nose setup. Each cantilever is coated on its top surface with a polymer layer. Volatile gaseous analytes are detected by tracking the diffusion process of the molecules into the polymer layers, resulting in swelling of the polymer layers and therewith bending of the cantilevers. From the bending pattern of all cantilevers in the array, a characteristic 'fingerprint' of the analyte is obtained, which is evaluated using principal component analysis. In a flow of dry nitrogen gas, the bending of the cantilevers is reverted to its initial state before exposure to the analyte, which allows reversible and reproducible operation of the sensor. We show examples of detection of solvents, perfume essences and beverage flavors. In a medical application, the setup provides indication of presence of diseases in patient's breath samples.

  8. Development of artificial muscles based on electroactive ionomeric polymer-metal composites.

    Science.gov (United States)

    Hirano, Laos A; Escote, Márcia T; Martins-Filho, Luiz S; Mantovani, Gerson L; Scuracchio, Carlos H

    2011-05-01

    This work contextualizes the research of materials that can be applied as artificial muscles. The main motivation of this research is the importance of the development of mechatronic systems for the replacement of traditional devices of actuation and motion based on rotational electrical motors by other devices that reproduce biological muscle movements. Electroactive polymers (EAPs) are materials that respond to electric stimuli with shape and/or dimension changes, and accomplish movements that are smooth enough to mimic biological muscles. Among EAPs, the ionomeric polymer-metal composites (IPMCs) are an interesting alternative to biomimetic devices due to large displacements when submitted to low applied voltage. This article presents a brief review of IPMCs, a sample preparation procedure, and some electromechanical experimental results. We also discuss the applicability of this technology in medical devices and as artificial muscles. © 2011, Copyright the Authors. Artificial Organs © 2011, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  9. Satellite DNA-based artificial chromosomes for use in gene therapy.

    Science.gov (United States)

    Hadlaczky, G

    2001-04-01

    Satellite DNA-based artificial chromosomes (SATACs) can be made by induced de novo chromosome formation in cells of different mammalian species. These artificially generated accessory chromosomes are composed of predictable DNA sequences and they contain defined genetic information. Prototype human SATACs have been successfully constructed in different cell types from 'neutral' endogenous DNA sequences from the short arm of the human chromosome 15. SATACs have already passed a number of hurdles crucial to their further development as gene therapy vectors, including: large-scale purification; transfer of purified artificial chromosomes into different cells and embryos; generation of transgenic animals and germline transmission with purified SATACs; and the tissue-specific expression of a therapeutic gene from an artificial chromosome in the milk of transgenic animals.

  10. Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhijian Liu

    2015-08-01

    Full Text Available The determinations of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs and support vector machines (SVM to predict the heat collection rate and heat loss coefficient without a direct determination. Parameters that can be easily obtained by “portable test instruments” were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat loss coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for training and testing the models. Results show that the multilayer feed-forward neural network (MLFN with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN is the best model for the prediction of heat loss coefficient due to their low root mean square (RMS errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively.

  11. Estimating Penetration Resistance in Agricultural Soils of Ardabil Plain Using Artificial Neural Network and Regression Methods

    Directory of Open Access Journals (Sweden)

    Gholam Reza Sheykhzadeh

    2017-02-01

    Full Text Available Introduction: Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance is time consuming and difficult because of high temporal and spatial variability. Therefore, many different regressions and artificial neural network pedotransfer functions have been proposed to estimate penetration resistance from readily available soil variables such as particle size distribution, bulk density (Db and gravimetric water content (θm. The lands of Ardabil Province are one of the main production regions of potato in Iran, thus, obtaining the soil penetration resistance in these regions help with the management of potato production. The objective of this research was to derive pedotransfer functions by using regression and artificial neural network to predict penetration resistance from some soil variations in the agricultural soils of Ardabil plain and to compare the performance of artificial neural network with regression models. Materials and methods: Disturbed and undisturbed soil samples (n= 105 were systematically taken from 0-10 cm soil depth with nearly 3000 m distance in the agricultural lands of the Ardabil plain ((lat 38°15' to 38°40' N, long 48°16' to 48°61' E. The contents of sand, silt and clay (hydrometer method, CaCO3 (titration method, bulk density (cylinder method, particle density (Dp (pychnometer method, organic carbon (wet oxidation method, total porosity(calculating from Db and Dp, saturated (θs and field soil water (θf using the gravimetric method were measured in the laboratory. Mean geometric diameter (dg and standard deviation (σg of soil particles were computed using the percentages of sand, silt and clay. Penetration resistance was measured in situ using cone penetrometer (analog model at 10

  12. Unidirectional Expiratory Valve Method to Assess Maximal Inspiratory Pressure in Individuals without Artificial Airway.

    Directory of Open Access Journals (Sweden)

    Samantha Torres Grams

    Full Text Available Maximal Inspiratory Pressure (MIP is considered an effective method to estimate strength of inspiratory muscles, but still leads to false positive diagnosis. Although MIP assessment with unidirectional expiratory valve method has been used in patients undergoing mechanical ventilation, no previous studies investigated the application of this method in subjects without artificial airway.This study aimed to compare the MIP values assessed by standard method (MIPsta and by unidirectional expiratory valve method (MIPuni in subjects with spontaneous breathing without artificial airway. MIPuni reproducibility was also evaluated.This was a crossover design study, and 31 subjects performed MIPsta and MIPuni in a random order. MIPsta measured MIP maintaining negative pressure for at least one second after forceful expiration. MIPuni evaluated MIP using a unidirectional expiratory valve attached to a face mask and was conducted by two evaluators (A and B at two moments (Tests 1 and 2 to determine interobserver and intraobserver reproducibility of MIP values. Intraclass correlation coefficient (ICC[2,1] was used to determine intraobserver and interobserver reproducibility.The mean values for MIPuni were 14.3% higher (-117.3 ± 24.8 cmH2O than the mean values for MIPsta (-102.5 ± 23.9 cmH2O (p<0.001. Interobserver reproducibility assessment showed very high correlation for Test 1 (ICC[2,1] = 0.91, and high correlation for Test 2 (ICC[2,1] = 0.88. The assessment of the intraobserver reproducibility showed high correlation for evaluator A (ICC[2,1] = 0.86 and evaluator B (ICC[2,1] = 0.77.MIPuni presented higher values when compared with MIPsta and proved to be reproducible in subjects with spontaneous breathing without artificial airway.

  13. Analysis of multicriteria models application for selection of an optimal artificial lift method in oil production

    Directory of Open Access Journals (Sweden)

    Crnogorac Miroslav P.

    2016-01-01

    Full Text Available In the world today for the exploitation of oil reservoirs by artificial lift methods are applied different types of deep pumps (piston, centrifugal, screw, hydraulic, water jet pumps and gas lift (continuous, intermittent and plunger. Maximum values of oil production achieved by these exploitation methods are significantly different. In order to select the optimal exploitation method of oil well, the multicriteria analysis models are used. In this paper is presented an analysis of the multicriteria model's application known as VIKOR, TOPSIS, ELECTRE, AHP and PROMETHEE for selection of optimal exploitation method for typical oil well at Serbian exploration area. Ranking results of applicability of the deep piston pumps, hydraulic pumps, screw pumps, gas lift method and electric submersible centrifugal pumps, indicated that in the all above multicriteria models except in PROMETHEE, the optimal method of exploitation are deep piston pumps and gas lift.

  14. EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

    Directory of Open Access Journals (Sweden)

    David Camarena-Martinez

    2014-01-01

    Full Text Available This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification- based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.

  15. Based on Artificial Neural Network to Realize K-Parameter Analysis of Vehicle Air Spring System

    Science.gov (United States)

    Hung, San-Shan; Hsu, Chia-Ning; Hwang, Chang-Chou; Chen, Wen-Jan

    2017-10-01

    In recent years, because of the air-spring control technique is more mature, that air- spring suspension systems already can be used to replace the classical vehicle suspension system. Depend on internal pressure variation of the air-spring, thestiffnessand the damping factor can be adjusted. Because of air-spring has highly nonlinear characteristic, therefore it isn’t easy to construct the classical controller to control the air-spring effectively. The paper based on Artificial Neural Network to propose a feasible control strategy. By using offline way for the neural network design and learning to the air-spring in different initial pressures and different loads, offline method through, predict air-spring stiffness parameter to establish a model. Finally, through adjusting air-spring internal pressure to change the K-parameter of the air-spring, realize the well dynamic control performance of air-spring suspension.

  16. EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

    Science.gov (United States)

    Amezquita-Sanchez, Juan P.; Romero-Troncoso, Rene J.; Osornio-Rios, Roque A.; Garcia-Perez, Arturo

    2014-01-01

    This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals. PMID:24683346

  17. Development of a diagnostic expert system for eddy current data analysis using applied artificial intelligence methods

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Yan, W.; Henry, G.

    1999-01-01

    A diagnostic expert system that integrates database management methods, artificial neural networks, and decision-making using fuzzy logic has been developed for the automation of steam generator eddy current test (ECT) data analysis. The new system, known as EDDYAI, considers the following key issues: (1) digital eddy current test data calibration, compression, and representation; (2) development of robust neural networks with low probability of misclassification for flaw depth estimation; (3) flaw detection using fuzzy logic; (4) development of an expert system for database management, compilation of a trained neural network library, and a decision module; and (5) evaluation of the integrated approach using eddy current data. The implementation to field test data includes the selection of proper feature vectors for ECT data analysis, development of a methodology for large eddy current database management, artificial neural networks for flaw depth estimation, and a fuzzy logic decision algorithm for flaw detection. A large eddy current inspection database from the Electric Power Research Institute NDE Center is being utilized in this research towards the development of an expert system for steam generator tube diagnosis. The integration of ECT data pre-processing as part of the data management, fuzzy logic flaw detection technique, and tube defect parameter estimation using artificial neural networks are the fundamental contributions of this research. (orig.)

  18. Development of a diagnostic expert system for eddy current data analysis using applied artificial intelligence methods

    Energy Technology Data Exchange (ETDEWEB)

    Upadhyaya, B.R.; Yan, W. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering; Behravesh, M.M. [Electric Power Research Institute, Palo Alto, CA (United States); Henry, G. [EPRI NDE Center, Charlotte, NC (United States)

    1999-09-01

    A diagnostic expert system that integrates database management methods, artificial neural networks, and decision-making using fuzzy logic has been developed for the automation of steam generator eddy current test (ECT) data analysis. The new system, known as EDDYAI, considers the following key issues: (1) digital eddy current test data calibration, compression, and representation; (2) development of robust neural networks with low probability of misclassification for flaw depth estimation; (3) flaw detection using fuzzy logic; (4) development of an expert system for database management, compilation of a trained neural network library, and a decision module; and (5) evaluation of the integrated approach using eddy current data. The implementation to field test data includes the selection of proper feature vectors for ECT data analysis, development of a methodology for large eddy current database management, artificial neural networks for flaw depth estimation, and a fuzzy logic decision algorithm for flaw detection. A large eddy current inspection database from the Electric Power Research Institute NDE Center is being utilized in this research towards the development of an expert system for steam generator tube diagnosis. The integration of ECT data pre-processing as part of the data management, fuzzy logic flaw detection technique, and tube defect parameter estimation using artificial neural networks are the fundamental contributions of this research. (orig.)

  19. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

    Science.gov (United States)

    Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

    2013-04-01

    Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

  20. Artificial Neural Network-Based Monitoring of the Fuel Assembly Temperature Sensor and FPGA Implementation

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2015-07-01

    Numerous methods have been developed around the world to model the dynamic behavior and detect a faulty operating mode of a temperature sensor. In this context, we present in this study a new method based on the dependence between the fuel assembly temperature profile on control rods positions, and the coolant flow rate in a nuclear reactor. This seems to be possible since the insertion of control rods at different axial positions and variations in flow rate of the reactor coolant results in different produced thermal power in the reactor. This is closely linked to the instant fuel rod temperature profile. In a first step, we selected parameters to be used and confirmed the adequate correlation between the chosen parameters and those to be estimated by the proposed monitoring system. In the next step, we acquired and de-noised the data of corresponding parameters, the qualified data is then used to design and train the artificial neural network. The effective data denoising was done by using the wavelet transform to remove a various kind of artifacts such as inherent noise. With the suitable choice of wavelet level and smoothing method, it was possible for us to remove all the non-required artifacts with a view to verify and analyze the considered signal. In our work, several potential mother wavelet functions (Haar, Daubechies, Bi-orthogonal, Reverse Bi-orthogonal, Discrete Meyer and Symlets) were investigated to find the most similar function with the being processed signals. To implement the proposed monitoring system for the fuel rod temperature sensor (03 wire RTD sensor), we used the Bayesian artificial neural network 'BNN' technique to model the dynamic behavior of the considered sensor, the system correlate the estimated values with the measured for the concretization of the proposed system we propose an FPGA (field programmable gate array) implementation. The monitoring system use the correlation. (authors)

  1. A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

    Directory of Open Access Journals (Sweden)

    Zhongbin Wang

    2016-01-01

    Full Text Available In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN and support vector machine (SVM methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.

  2. Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm.

    Science.gov (United States)

    Devikanniga, D; Joshua Samuel Raj, R

    2018-04-01

    Osteoporosis is a life threatening disease which commonly affects women mostly after their menopause. It primarily causes mild bone fractures, which on advanced stage leads to the death of an individual. The diagnosis of osteoporosis is done based on bone mineral density (BMD) values obtained through various clinical methods experimented from various skeletal regions. The main objective of the authors' work is to develop a hybrid classifier model that discriminates the osteoporotic patient from healthy person, based on BMD values. In this Letter, the authors propose the monarch butterfly optimisation-based artificial neural network classifier which helps in earlier diagnosis and prevention of osteoporosis. The experiments were conducted using 10-fold cross-validation method for two datasets lumbar spine and femoral neck. The results were compared with other similar hybrid approaches. The proposed method resulted with the accuracy, specificity and sensitivity of 97.9% ± 0.14, 98.33% ± 0.03 and 95.24% ± 0.08, respectively, for lumbar spine dataset and 99.3% ± 0.16%, 99.2% ± 0.13 and 100, respectively, for femoral neck dataset. Further, its performance is compared using receiver operating characteristics analysis and Wilcoxon signed-rank test. The results proved that the proposed classifier is efficient and it outperformed the other approaches in all the cases.

  3. Installation and evaluation of a nuclear power plant operator advisor based on artificial intelligence technology

    International Nuclear Information System (INIS)

    Hajek, B.K.; Miller, D.W.

    1989-01-01

    This report discusses the following topics on a Nuclear Power Plant operator advisor based on artificial Intelligence Technology; Workstation conversion; Software Conversion; V ampersand V Program Development Development; Simulator Interface Development; Knowledge Base Expansion; Dynamic Testing; Database Conversion; Installation at the Perry Simulator; Evaluation of Operator Interaction; Design of Man-Machine Interface; and Design of Maintenance Facility

  4. Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method

    CERN Document Server

    Quiza, Ramón; Davim, J Paulo

    2012-01-01

    Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

  5. Nonlinear estimation-based dipole source localization for artificial lateral line systems

    International Nuclear Information System (INIS)

    Abdulsadda, Ahmad T; Tan Xiaobo

    2013-01-01

    As a flow-sensing organ, the lateral line system plays an important role in various behaviors of fish. An engineering equivalent of a biological lateral line is of great interest to the navigation and control of underwater robots and vehicles. A vibrating sphere, also known as a dipole source, can emulate the rhythmic movement of fins and body appendages, and has been widely used as a stimulus in the study of biological lateral lines. Dipole source localization has also become a benchmark problem in the development of artificial lateral lines. In this paper we present two novel iterative schemes, referred to as Gauss–Newton (GN) and Newton–Raphson (NR) algorithms, for simultaneously localizing a dipole source and estimating its vibration amplitude and orientation, based on the analytical model for a dipole-generated flow field. The performance of the GN and NR methods is first confirmed with simulation results and the Cramer–Rao bound (CRB) analysis. Experiments are further conducted on an artificial lateral line prototype, consisting of six millimeter-scale ionic polymer–metal composite sensors with intra-sensor spacing optimized with CRB analysis. Consistent with simulation results, the experimental results show that both GN and NR schemes are able to simultaneously estimate the source location, vibration amplitude and orientation with comparable precision. Specifically, the maximum localization error is less than 5% of the body length (BL) when the source is within the distance of one BL. Experimental results have also shown that the proposed schemes are superior to the beamforming method, one of the most competitive approaches reported in literature, in terms of accuracy and computational efficiency. (paper)

  6. Dual sensing-actuation artificial muscle based on polypyrrole-carbon nanotube composite

    Science.gov (United States)

    Schumacher, J.; Otero, Toribio F.; Pascual, Victor H.

    2017-04-01

    Dual sensing artificial muscles based on conducting polymer are faradaic motors driven by electrochemical reactions, which announce the development of proprioceptive devices. The applicability of different composites has been investigated with the aim to improve the performance. Addition of carbon nanotubes may reduce irreversible reactions. We present the testing of a dual sensing artificial muscle based on a conducting polymer and carbon nanotubes composite. Large bending motions (up to 127 degrees) in aqueous solution and simultaneously sensing abilities of the operation conditions are recorded. The sensing and actuation equations are derived for incorporation into a control system.

  7. Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

    Science.gov (United States)

    Kemp, Z. D. C.

    2018-04-01

    Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

  8. Interchange Recognition Method Based on CNN

    Directory of Open Access Journals (Sweden)

    HE Haiwei

    2018-03-01

    Full Text Available The identification and classification of interchange structures in OSM data can provide important information for the construction of multi-scale model, navigation and location services, congestion analysis, etc. The traditional method of interchange identification relies on the low-level characteristics of artificial design, and cannot distinguish the complex interchange structure with interference section effectively. In this paper, a new method based on convolutional neural network for identification of the interchange is proposed. The method combines vector data with raster image, and uses neural network to learn the fuzzy characteristics of the interchange, and classifies the complex interchange structure in OSM. Experiments show that this method has strong anti-interference, and has achieved good results in the classification of complex interchange shape, and there is room for further improvement with the expansion of the case base and the optimization of neural network model.

  9. A novel artificial bee colony based clustering algorithm for categorical data.

    Science.gov (United States)

    Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang

    2015-01-01

    Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

  10. Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Gilberto Bojorquez

    2007-08-01

    Full Text Available The development of smart sensors involves the design of reconfigurable systemscapable of working with different input sensors. Reconfigurable systems ideally shouldspend the least possible amount of time in their calibration. An autocalibration algorithmfor intelligent sensors should be able to fix major problems such as offset, variation of gainand lack of linearity, as accurately as possible. This paper describes a new autocalibrationmethodology for nonlinear intelligent sensors based on artificial neural networks, ANN.The methodology involves analysis of several network topologies and training algorithms.The proposed method was compared against the piecewise and polynomial linearizationmethods. Method comparison was achieved using different number of calibration points,and several nonlinear levels of the input signal. This paper also shows that the proposedmethod turned out to have a better overall accuracy than the other two methods. Besides,experimentation results and analysis of the complete study, the paper describes theimplementation of the ANN in a microcontroller unit, MCU. In order to illustrate themethod capability to build autocalibration and reconfigurable systems, a temperaturemeasurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

  11. A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

    Directory of Open Access Journals (Sweden)

    Zhendong Yin

    2013-01-01

    Full Text Available Artificial Bee Colony (ABC algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD is proposed and implemented in direct-sequence ultra-wideband (DS-UWB systems under the additive white Gaussian noise (AWGN channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

  12. Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

    Directory of Open Access Journals (Sweden)

    Namık KılıÇ

    2015-06-01

    Full Text Available Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods (FEM in this research field. The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort, therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time. This study aims to apply a hybrid method using FEM simulation and artificial neural network (ANN analysis to approximate ballistic limit thickness for armor steels. To achieve this objective, a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition. In this methodology, the FEM simulations are used to create training cases for Multilayer Perceptron (MLP three layer networks. In order to validate FE simulation methodology, ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569. Afterwards, the successfully trained ANN(s is used to predict the ballistic limit thickness of 500 HB high hardness steel armor. Results show that even with limited number of data, FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.

  13. Artificial phototropism based on a photo-thermo-responsive hydrogel

    Science.gov (United States)

    Gopalakrishna, Hamsini

    Solar energy is leading in renewable energy sources and the aspects surrounding the efforts to harvest light are gaining importance. One such aspect is increasing the light absorption, where heliotropism comes into play. Heliotropism, the ability to track the sun across the sky, can be integrated with solar cells for more efficient photon collection and other optoelectronic systems. Inspired by plants, which optimize incident sunlight in nature, several researchers have made artificial heliotropic and phototropic systems. This project aims to design, synthesize and characterize a material system and evaluate its application in a phototropic system. A gold nanoparticle (Au NP) incorporated poly(N-isopropylacrylamide) (PNIPAAm) hydrogel was synthesized as a photo-thermo-responsive material in our phototropic system. The Au NPs generate heat from the incident via plasmonic resonance to induce a volume phase change of the thermo-responsive hydrogel PNIPAAm. PNIPAAm shrinks or swells at temperature above or below 32°C. Upon irradiation, the Au NP-PNIPAAm micropillar actuates, specifically bending toward the incident light and precisely following the varying incident angle. Swelling ratio tests, bending angle tests with a static incident light and bending tests with varying angles were carried out on hydrogel samples with varying Au NP concentrations. Swelling ratios ranging from 1.45 to 2.9 were recorded for pure hydrogel samples and samples with very low Au NP concentrations. Swelling ratios of 2.41 and 3.37 were calculated for samples with low and high concentrations of Au NPs, respectively. A bending of up to 88° was observed in Au NP-hydrogel pillars with a low Au NP concentration with a 90° incident angle. The light tracking performance was assessed by the slope of the pillar Bending angle (response angle) vs. Incident light angle plot. A slope of 1 indicates ideal tracking with top of the pillar being normal to the incident light, maximizing the photon

  14. Determination of Electron Optical Properties for Aperture Zoom Lenses Using an Artificial Neural Network Method.

    Science.gov (United States)

    Isik, Nimet

    2016-04-01

    Multi-element electrostatic aperture lens systems are widely used to control electron or charged particle beams in many scientific instruments. By means of applied voltages, these lens systems can be operated for different purposes. In this context, numerous methods have been performed to calculate focal properties of these lenses. In this study, an artificial neural network (ANN) classification method is utilized to determine the focused/unfocused charged particle beam in the image point as a function of lens voltages for multi-element electrostatic aperture lenses. A data set for training and testing of ANN is taken from the SIMION 8.1 simulation program, which is a well known and proven accuracy program in charged particle optics. Mean squared error results of this study indicate that the ANN classification method provides notable performance characteristics for electrostatic aperture zoom lenses.

  15. Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

    Science.gov (United States)

    Maleki, Shahoo; Moradzadeh, Ali; Riabi, Reza Ghavami; Gholami, Raoof; Sadeghzadeh, Farhad

    2014-06-01

    Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR) and Back-Propagation Neural Network (BPNN). Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

  16. Monitoring of operation with artificial intelligence methods; Betriebsueberwachung mit Verfahren der Kuenstlichen Intelligenz

    Energy Technology Data Exchange (ETDEWEB)

    Bruenninghaus, H. [DMT-Gesellschaft fuer Forschung und Pruefung mbH, Essen (Germany). Geschaeftsbereich Systemtechnik

    1999-03-11

    Taking the applications `early detection of fires` and `reduction of burst of messages` as an example, the usability of artificial intelligence (AI) methods in the monitoring of operation was checked in a R and D project. The contribution describes the concept, development and evaluation of solutions to the specified problems. A platform, which made it possible to investigate different AI methods (in particular artificial neuronal networks), had to be creaated as a basis for the project. At the same time ventilation data had to be acquired and processed by the networks for the classification. (orig.) [Deutsch] Am Beispiel der Anwendungsfaelle `Brandfrueherkennung` und `Meldungsschauerreduzierung` wurde im Rahmen eines F+E-Vorhabens die Einsetzbarkeit von Kuenstliche-Intelligenz-Methoden (KI) in der Betriebsueberwachung geprueft. Der Beitrag stellt Konzeption, Entwicklung und Bewertung von Loesungsansaetzen fuer die genannten Aufgabenstellungen vor. Als Grundlage fuer das Vorhaben wurden anhand KI-Methoden (speziell: Kuenstliche Neuronale Netze -KNN) auf der Grundlage gewonnener und aufbereiteter Wetterdaten die Beziehungen zwischen den Wettermessstellen im Laufe des Wetterwegs klassifiziert. (orig.)

  17. Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

    Directory of Open Access Journals (Sweden)

    Shahoo Maleki

    2014-06-01

    Full Text Available Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR and Back-Propagation Neural Network (BPNN. Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

  18. Comparative study of landslides susceptibility mapping methods: Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN)

    Science.gov (United States)

    Salleh, S. A.; Rahman, A. S. A. Abd; Othman, A. N.; Mohd, W. M. N. Wan

    2018-02-01

    As different approach produces different results, it is crucial to determine the methods that are accurate in order to perform analysis towards the event. This research aim is to compare the Rank Reciprocal (MCDM) and Artificial Neural Network (ANN) analysis techniques in determining susceptible zones of landslide hazard. The study is based on data obtained from various sources such as local authority; Dewan Bandaraya Kuala Lumpur (DBKL), Jabatan Kerja Raya (JKR) and other agencies. The data were analysed and processed using Arc GIS. The results were compared by quantifying the risk ranking and area differential. It was also compared with the zonation map classified by DBKL. The results suggested that ANN method gives better accuracy compared to MCDM with 18.18% higher accuracy assessment of the MCDM approach. This indicated that ANN provides more reliable results and it is probably due to its ability to learn from the environment thus portraying realistic and accurate result.

  19. Modeling of the Cutting Forces in Turning Process Using Various Methods of Cooling and Lubricating: An Artificial Intelligence Approach

    Directory of Open Access Journals (Sweden)

    Djordje Cica

    2013-01-01

    Full Text Available Cutting forces are one of the inherent phenomena and a very significant indicator of the metal cutting process. The work presented in this paper is an investigation of the prediction of these parameters in turning using soft computing techniques. During the experimental research focus is placed on the application of various methods of cooling and lubricating of the cutting zone. On this occasion were used the conventional method of cooling and lubricating, high pressure jet assisted machining, and minimal quantity lubrication technique. The data obtained by experiment are used to create two different models, namely, artificial neural network and adaptive networks based fuzzy inference systems for prediction of cutting forces. Furthermore, both models are compared with the experimental data and results are indicated.

  20. Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

    International Nuclear Information System (INIS)

    Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

    2010-01-01

    Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)

  1. [Design of an artificial sphincter system with bio-feedback function based on MSP430].

    Science.gov (United States)

    Wang, Yong-kan; Yan, De-tian

    2005-11-01

    In this paper, we advance a new treating method for rectectomy postoperative anus incontinence, which is called "artificial sphincter system with biofeedback-function". The system simulates the function of human's sphincter and has entered into a stage of simulation experiments on animals.

  2. Artificial intelligence-based modeling and control of fluidized bed combustion

    Energy Technology Data Exchange (ETDEWEB)

    Ikonen, E.; Leppaekoski, K. (Univ. of Oulu, Dept. of Process and Environmental Engineering (Finland)). email: enso.ikonen@oulu.fi

    2009-07-01

    AI-inspired techniques have a lot to offer when developing methods for advanced identification, monitoring, control and optimization of industrial processes, such as power plants. Advanced control methods have been extensively examined in the research of the Power Plant Automation group at the Systems Engineering Laboratory, e.g., in fuel inventory modelling, combustion power control, modelling and control of flue gas oxygen, drum control, modelling and control of superheaters, or in optimization of flue-gas emissions. Most engineering approaches to artificial intelligence (AI) are characterized by two fundamental properties: the ability to learn from various sources and the ability to deal with plant complexity. Learning systems that are able to operate in uncertain environments based on incomplete information are commonly referred to as being intelligent. A number of other approaches exist, characterized by these properties, but not easily categorized as AI-systems. Advanced control methods (adaptive, predictive, multivariable, robust, etc.) are based on the availability of a model of the process to be controlled. Hence identification of processes becomes a key issue, leading to the use of adaptation and learning techniques. A typical learning control system concerns a selection of learning techniques applied for updating a process model, which in turn is used for the controller design. When design of learning control systems is complemented with concerns for dealing with uncertainties or vaguenesses in models, measurements, or even objectives, particularly close connections exist between advanced process control and methods of artificial intelligence and machine learning. Needs for advanced techniques are typically characterized by the desire to properly handle plant non-linearities, the multivariable nature of the dynamic problems, and the necessity to adapt to changing plant conditions. In the field of fluidized bed combustion (FBC) control, the many promising

  3. An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

    Directory of Open Access Journals (Sweden)

    Jing Shao

    Full Text Available China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining "replace" and "alter" operations, and the third is a "swap" strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China, and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

  4. Development of methods for remediation of artificial polluted soils and improvement of soils for ecologically clean agricultural production systems

    International Nuclear Information System (INIS)

    Bogachev, V.; Adrianova, G.; Zaitzev, V.; Kalinin, V.; Kovalenko, E.; Makeev, A.; Malikova, L.; Popov, Yu.; Savenkov, A.; Shnyakina, V.

    1996-01-01

    The purpose of the research: Development of methods for the remediation of artificial polluted soils and the improvement of polluted lands to ecologically clean agricultural production.The following tasks will be implemented in this project to achieve viable practical solutions: - To determine the priority pollutants, their ecological pathways, and sources of origin. - To form a supervised environmental monitoring data bank throughout the various geo system conditions. - To evaluate the degree of the bio geo system pollution and the influence on the health of the local human populations. - To establish agricultural plant tolerance levels to the priority pollutants. - To calculate the standard concentrations of the priority pollutants for main agricultural plant groups. - To develop a soil remediation methodology incorporating the structural, functional geo system features. - To establish a territory zone division methodology in consideration of the degree of component pollution, plant tolerance to pollutants, plant production conditions, and human health. - Scientific grounding of the soil remediation proposals and agricultural plant material introductions with soil pollution levels and relative plant tolerances to pollutants. Technological Means, Methods, and Approaches Final proposed solutions will be based upon geo system and ecosystem approaches and methodologies. The complex ecological valuation methods of the polluted territories will be used in this investigation. Also, laboratory culture in vitro, application work, and multi-factor field experiments will be conducted. The results will be statistically analyzed using appropriate methods. Expected Results Complex biogeochemical artificial province assessment according to primary pollutant concentrations. Development of agricultural plant tolerance levels relative to the priority pollutants. Assessment of newly introduced plant materials that may possess variable levels of pollution tolerance. Remediation

  5. Differences of Streptococcus mutans adhesion between artificial mouth systems: a dinamic and static methods

    Directory of Open Access Journals (Sweden)

    Aryan Morita

    2016-06-01

    Full Text Available Background: Various materials have been used for treating dental caries. Dental caries is a disease that attacks hard tissues of the teeth. The initial phase of caries is a formation of bacterial biofilm, called as dental plaque. Dental restorative materials are expected for preventing secondary caries formation initiated by dental plaque. Initial bacterial adhesion is assumed to be an important stage of dental plaque formation. Bacteria that recognize the receptor for binding to the pellicle on tooth surface are known as initial bacterial colonies. One of the bacteria that plays a role in the early stage of dental plaque formation is Streptococcus mutans (S. mutans. Artificial mouth system (AMS used in bacterial biofilm research on the oral cavity provides the real condition of oral cavity and continous and intermittent supply of nutrients for bacteria. Purpose: This study aimed to compare the profile of S. mutans bacterial adhesion as the primary etiologic agent for dental caries between using static method and using artificial mouth system, a dinamic. method (AMS. Method: The study was conducted at Faculty of Dentistry and Integrated Research and testing laboratory (LPPT in Universitas Gadjah Mada from April to August 2015. Composite resin was used as the subject of this research. Twelve composite resins with a diameter of 5 mm and a width of 2 mm were divided into two groups, namely group using static method and group using dynamic method. Static method was performed by submerging the samples into a 100µl suspension of 1.5 x 108 CFU/ml S. mutans and 200µl BHI broth. Meanwhile AMS method was carried out by placing the samples at the AMS tube drained with 20 drops/minute of bacterial suspension and sterile aquadest. After 72 hours, five samples from each group were calculated for their biofilm mass using 1% crystal violet and read by a spectrofotometer with a wavelength of 570 nm. Meanwhile, one sample from each group was taken for its

  6. Application of hybrid artificial fish swarm algorithm based on similar fragments in VRP

    Science.gov (United States)

    Che, Jinnuo; Zhou, Kang; Zhang, Xueyu; Tong, Xin; Hou, Lingyun; Jia, Shiyu; Zhen, Yiting

    2018-03-01

    Focused on the issue that the decrease of convergence speed and the precision of calculation at the end of the process in Artificial Fish Swarm Algorithm(AFSA) and instability of results, a hybrid AFSA based on similar fragments is proposed. Traditional AFSA enjoys a lot of obvious advantages in solving complex optimization problems like Vehicle Routing Problem(VRP). AFSA have a few limitations such as low convergence speed, low precision and instability of results. In this paper, two improvements are introduced. On the one hand, change the definition of the distance for artificial fish, as well as increase vision field of artificial fish, and the problem of speed and precision can be improved when solving VRP. On the other hand, mix artificial bee colony algorithm(ABC) into AFSA - initialize the population of artificial fish by the ABC, and it solves the problem of instability of results in some extend. The experiment results demonstrate that the optimal solution of the hybrid AFSA is easier to approach the optimal solution of the standard database than the other two algorithms. In conclusion, the hybrid algorithm can effectively solve the problem that instability of results and decrease of convergence speed and the precision of calculation at the end of the process.

  7. Mechanical properties of silorane-based and methacrylate-based composite resins after artificial aging.

    Science.gov (United States)

    de Castro, Denise Tornavoi; Lepri, César Penazzo; Valente, Mariana Lima da Costa; dos Reis, Andréa Cândido

    2016-01-01

    The aim of this study was to compare the compressive strength of a silorane-based composite resin (Filtek P90) to that of conventional composite resins (Charisma, Filtek Z250, Fill Magic, and NT Premium) before and after accelerated artificial aging (AAA). For each composite resin, 16 cylindrical specimens were prepared and divided into 2 groups. One group underwent analysis of compressive strength in a universal testing machine 24 hours after preparation, and the other was subjected first to 192 hours of AAA and then the compressive strength test. Data were analyzed by analysis of variance, followed by the Tukey HSD post hoc test (α = 0.05). Some statistically significant differences in compressive strength were found among the commercial brands (P aging. Comparison of each material before and after AAA revealed that the aging process did not influence the compressive strength of the tested resins (P = 0.785).

  8. A Game Based e-Learning System to Teach Artificial Intelligence in the Computer Sciences Degree

    Science.gov (United States)

    de Castro-Santos, Amable; Fajardo, Waldo; Molina-Solana, Miguel

    2017-01-01

    Our students taking the Artificial Intelligence and Knowledge Engineering courses often encounter a large number of problems to solve which are not directly related to the subject to be learned. To solve this problem, we have developed a game based e-learning system. The elected game, that has been implemented as an e-learning system, allows to…

  9. Artificial intelligence in process control: Knowledge base for the shuttle ECS model

    Science.gov (United States)

    Stiffler, A. Kent

    1989-01-01

    The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

  10. Overconfident investors in the LLS agent-based artificial financial market

    NARCIS (Netherlands)

    Lovric, M.; Kaymak, U.; Spronk, J.

    2009-01-01

    Agent-based artificial financial markets are bottom-up models of financial markets which explore the mapping from the micro level of individual investor behavior into the macro level of aggregate market phenomena. It has been recently recognized in the literature that such (agentbased) models are

  11. Coating membranes for a sorbent-based artificial liver: adsorption characteristics

    NARCIS (Netherlands)

    de Koning, H. W.; Chamuleau, R. A.; Bantjes, A.

    1982-01-01

    Techniques are described for the coating of sorbents to be used in an artificial liver support system based on mixed sorbent bed hemoperfusion. Activated charcoal has been coated with cellulose acetate (CA) by solvent evaporation. With Amberlite XAD-4, the Wurster technique was used for coating with

  12. Artificial sensory hairs based on the flow sensitive receptor hairs of crickets

    NARCIS (Netherlands)

    Dijkstra, Marcel; van Baar, J.J.J.; Wiegerink, Remco J.; Lammerink, Theodorus S.J.; de Boer, J.H.; Krijnen, Gijsbertus J.M.

    2005-01-01

    This paper presents the modelling, design, fabrication and characterization of flow sensors based on the wind-receptor hairs of crickets. Cricket sensory hairs are highly sensitive to drag-forces exerted on the hair shaft. Artificial sensory hairs have been realized in SU-8 on suspended SixNy

  13. A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

    Science.gov (United States)

    Zhao, Weixiang; Davis, Cristina E.

    2011-01-01

    Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for

  14. Demand Forecasting Methods in Accommodation Establishments: A Research with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ebru ULUCAN

    2018-05-01

    Full Text Available As it being seen in every sector, demand forecasting in tourism is been conducted with various qualitative and quantitative methods. In recent years, artificial neural network models, which have been developed as an alternative to these forecasting methods, give the nearest values in forecasting with the smallest failure percentage. This study aims to reveal that accomodation establishments can use the neural network models as an alternative while forecasting their demand. With this aim, neural network models have been tested by using the sold room values between the period of 2013-2016 of a five star hotel in Istanbul and it is found that the results acquired from the testing models are the nearest values comparing the realized figures. In the light of these results, tourism demand of the hotel for 2017 and 2018 has been forecasted.

  15. A neutron spectrum unfolding code based on generalized regression artificial neural networks

    International Nuclear Information System (INIS)

    Ortiz R, J. M.; Martinez B, M. R.; Castaneda M, R.; Solis S, L. O.; Vega C, H. R.

    2015-10-01

    The most delicate part of neutron spectrometry, is the unfolding process. Then derivation of the spectral information is not simple because the unknown is not given directly as result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, as the optimum selection of the network topology and the long training time. Compared to BPNN, is usually much faster to train a generalized regression neural network (GRNN). That is mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum. In addition, often are more accurate than BPNN in prediction. These characteristics make GRNN be of great interest in the neutron spectrometry domain. In this work is presented a computational tool based on GRNN, capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. (Author)

  16. Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

    Science.gov (United States)

    Tigges, P; Kathmann, N; Engel, R R

    1997-07-01

    Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

  17. A neutron spectrum unfolding code based on generalized regression artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M.; Martinez B, M. R.; Castaneda M, R.; Solis S, L. O. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Av. Ramon Lopez Velarde 801, Col. Centro, 98000 Zacatecas, Zac. (Mexico); Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas, Zac. (Mexico)

    2015-10-15

    The most delicate part of neutron spectrometry, is the unfolding process. Then derivation of the spectral information is not simple because the unknown is not given directly as result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, as the optimum selection of the network topology and the long training time. Compared to BPNN, is usually much faster to train a generalized regression neural network (GRNN). That is mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum. In addition, often are more accurate than BPNN in prediction. These characteristics make GRNN be of great interest in the neutron spectrometry domain. In this work is presented a computational tool based on GRNN, capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a {sup 6}LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. (Author)

  18. Fertility response of artificial insemination methods in sheep with fresh and frozen-thawed semen.

    Science.gov (United States)

    Masoudi, Reza; Zare Shahneh, Ahmad; Towhidi, Armin; Kohram, Hamid; Akbarisharif, Abbas; Sharafi, Mohsen

    2017-02-01

    The aim of this study was to evaluate the fertility response of artificial insemination (AI) methods with fresh and frozen sperm in sheep. In experiment 1, one hundred and fifty fat tailed Zandi ewes were assigned into 3 equal groups and inseminated with three AI methods consisting of vaginal, laparoscopic and trans-cervical AI with fresh semen. In experiment 2, a factorial study (3 AI methods × 2 extenders) was used to analyze the effects of three AI methods and two freezing extenders containing soybean lecithin (SL) or Egg yolk (EY) on reproductive performance of 300 fat tailed Zandi ewes. Also, total motility, progressive motility, viability and lipid peroxidation of semen were evaluated after freeze-thawing in two extenders. In result, there was no significant difference among three AI methods when fresh semen was used. In experiment 2, the highest percentage of pregnancy rate, parturition rate and lambing rate were obtained in laparoscopic AI group (P semen, trans-cervical AI was more efficient than vaginal method when frozen-thawed semen was used, but its efficiency was not as high as laparoscopic method. Also, SL extender can be an efficient alternative extender to preserve ram sperm during cryopreservation procedure without adverse effects of EY. Copyright © 2016. Published by Elsevier Inc.

  19. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

  20. Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach

    Science.gov (United States)

    Ahmed, Rounaq; Srinivasa Pai, P.; Sriram, N. S.; Bhat, Vasudeva

    2018-02-01

    Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to noise ratio (SNR), reduces root mean square error (RMSE) and is effective to denoise the gear vibration signals. The extracted signals have to be denoised by selecting a proper denoising scheme in order to prevent the loss of signal information along with noise. An approach has been made in this work to show the effectiveness of Principal Component Analysis (PCA) to denoise gear vibration signal. In this regard three selected wavelet based denoising schemes namely PCA, Empirical Mode Decomposition (EMD), Neighcoeff Coefficient (NC), has been compared with Adaptive Threshold (AT) an extensively used wavelet based denoising scheme for gear vibration signal. The vibration signals acquired from a customized gear test rig were denoised by above mentioned four denoising schemes. The fault identification capability as well as SNR, Kurtosis and RMSE for the four denoising schemes have been compared. Features extracted from the denoised signals have been used to train and test artificial neural network (ANN) models. The performances of the four denoising schemes have been evaluated based on the performance of the ANN models. The best denoising scheme has been identified, based on the classification accuracy results. PCA is effective in all the regards as a best denoising scheme.

  1. Prediction of the Thermal Conductivity of Refrigerants by Computational Methods and Artificial Neural Network.

    Science.gov (United States)

    Ghaderi, Forouzan; Ghaderi, Amir H; Ghaderi, Noushin; Najafi, Bijan

    2017-01-01

    Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density. The thermal conductivity of six refrigerants, R12, R14, R32, R115, R143, and R152 was predicted by these methods and the effectiveness of models was specified and compared. Results: The results show that the computational method is a usable method for predicting thermal conductivity at low levels of density. However, the efficiency of this model is considerably reduced in the mid-range of density. It means that this model cannot be used at density levels which are higher than 6. On the other hand, the ANN approach is a reliable method for thermal conductivity prediction in all ranges of density. The best accuracy of ANN is achieved when the number of units is increased in the hidden layer. Conclusion: The results of the computational method indicate that the regular dependence between thermal conductivity and density at higher densities is eliminated. It can develop a nonlinear problem. Therefore, analytical approaches are not able to predict thermal conductivity in wide ranges of density. Instead, a nonlinear approach such as, ANN is a valuable method for this purpose.

  2. Macroprudential policies in an agent-based artificial economy

    OpenAIRE

    Raberto, Marco; Teglio, Andrea; Cincotti, Silvano

    2012-01-01

    Basel III is a recently-agreed regulatory standard for bank capital adequacy with focus on the macroprudential dimension of banking regulation, i.e., the system- wide implications of banks’ lending and risk. An important Basel III provision is to reduce procyclicality of present banking regulation and promote countercyclical capital buffers for banks. The Eurace agent-based macroeconomic model and sim- ulator has been recently showed to be able to reproduce a credit-fueled boom-bust dynamics ...

  3. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  4. Alternate mutation based artificial immune algorithm for step fixed charge transportation problem

    Directory of Open Access Journals (Sweden)

    Mahmoud Moustafa El-Sherbiny

    2012-07-01

    Full Text Available Step fixed charge transportation problem (SFCTP is considered as a special version of the fixed-charge transportation problem (FCTP. In SFCTP, the fixed cost is incurred for every route that is used in the solution and is proportional to the amount shipped. This cost structure causes the value of the objective function to behave like a step function. Both FCTP and SFCTP are considered to be NP-hard problems. While a lot of research has been carried out concerning FCTP, not much has been done concerning SFCTP. This paper introduces an alternate Mutation based Artificial Immune (MAI algorithm for solving SFCTPs. The proposed MAI algorithm solves both balanced and unbalanced SFCTP without introducing a dummy supplier or a dummy customer. In MAI algorithm a coding schema is designed and procedures are developed for decoding such schema and shipping units. MAI algorithm guarantees the feasibility of all the generated solutions. Due to the significant role of mutation function on the MAI algorithm’s quality, 16 mutation functions are presented and their performances are compared to select the best one. For this purpose, forty problems with different sizes have been generated at random and then a robust calibration is applied using the relative percentage deviation (RPD method. Through two illustrative problems of different sizes the performance of the MAI algorithm has been compared with most recent methods.

  5. A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

    Directory of Open Access Journals (Sweden)

    Zahra Bahramian

    2017-01-01

    Full Text Available Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user’s feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning with k-nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.

  6. COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE

    Science.gov (United States)

    Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian

    2015-01-01

    Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies. PMID:27027010

  7. Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Dezhi Wang

    2018-02-01

    Full Text Available In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms—decision tree (DT, support vector machine (SVM, and random forest (RF—were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%; for object-based image analysis, RF could achieve the highest overall accuracy (82.40%, and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar’s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1 as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT could well be identified. However, for complex and easily

  8. Estimation of interfacial heat transfer coefficient in inverse heat conduction problems based on artificial fish swarm algorithm

    Science.gov (United States)

    Wang, Xiaowei; Li, Huiping; Li, Zhichao

    2018-04-01

    The interfacial heat transfer coefficient (IHTC) is one of the most important thermal physical parameters which have significant effects on the calculation accuracy of physical fields in the numerical simulation. In this study, the artificial fish swarm algorithm (AFSA) was used to evaluate the IHTC between the heated sample and the quenchant in a one-dimensional heat conduction problem. AFSA is a global optimization method. In order to speed up the convergence speed, a hybrid method which is the combination of AFSA and normal distribution method (ZAFSA) was presented. The IHTC evaluated by ZAFSA were compared with those attained by AFSA and the advanced-retreat method and golden section method. The results show that the reasonable IHTC is obtained by using ZAFSA, the convergence of hybrid method is well. The algorithm based on ZAFSA can not only accelerate the convergence speed, but also reduce the numerical oscillation in the evaluation of IHTC.

  9. A ferrofluid based artificial tactile sensor with magnetic field control

    Energy Technology Data Exchange (ETDEWEB)

    Volkova, T.I., E-mail: tatiana.volkova@tu-ilmenau.de [Technical Mechanics Group, Faculty of Mechanical Engineering, Technische Universität Ilmenau, Ilmenau D-98684 (Germany); Böhm, V., E-mail: valter.boehm@tu-ilmenau.de [Technical Mechanics Group, Faculty of Mechanical Engineering, Technische Universität Ilmenau, Ilmenau D-98684 (Germany); Naletova, V.A., E-mail: naletova@imec.msu.ru [Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, 119991 Moscow (Russian Federation); Kaufhold, T., E-mail: tobias.kaufhold@tu-ilmenau.de [Technical Mechanics Group, Faculty of Mechanical Engineering, Technische Universität Ilmenau, Ilmenau D-98684 (Germany); Becker, F., E-mail: felix.becker@tu-ilmenau.de [Technical Mechanics Group, Faculty of Mechanical Engineering, Technische Universität Ilmenau, Ilmenau D-98684 (Germany); Zeidis, I., E-mail: igor.zeidis@tu-ilmenau.de [Technical Mechanics Group, Faculty of Mechanical Engineering, Technische Universität Ilmenau, Ilmenau D-98684 (Germany); Zimmermann, K., E-mail: klaus.zimmermann@tu-ilmenau.de [Technical Mechanics Group, Faculty of Mechanical Engineering, Technische Universität Ilmenau, Ilmenau D-98684 (Germany)

    2017-06-01

    The paper deals with a tactile sensor inspired by biological hairs of mammals. The working principle is based on the effect of the magnetic force exerted on a paramagnetic body submerged into a ferrofluid volume under the influence of a nonuniform magnetic field. The deflection of the sensor's rod caused by external mechanical stimuli may be unambiguously identified by the distortion of the magnetic field, which occurs due to the motion of the attached body in the ferrofluid. The magnetic force acting on the body is evaluated experimentally and theoretically for the nonuniform magnetic field of a permanent magnet. The controlled oscillations of the rod are realised by applying a nonuniform magnetic field of periodically altering direction. - Highlights: • A design approach of a tactile sensor inspired by special mammalian hairs is presented. • The working principle is based on magnetic properties of a ferrofluid in magnetic fields. • The magnetic force acting on a body submerged into a ferrofluid volume is evaluated. • External mechanical stimuli may be identified by the distortion of the magnetic field. • The controlled whisking-like oscillations of the sensor's rod are realised experimentally.

  10. A ferrofluid based artificial tactile sensor with magnetic field control

    International Nuclear Information System (INIS)

    Volkova, T.I.; Böhm, V.; Naletova, V.A.; Kaufhold, T.; Becker, F.; Zeidis, I.; Zimmermann, K.

    2017-01-01

    The paper deals with a tactile sensor inspired by biological hairs of mammals. The working principle is based on the effect of the magnetic force exerted on a paramagnetic body submerged into a ferrofluid volume under the influence of a nonuniform magnetic field. The deflection of the sensor's rod caused by external mechanical stimuli may be unambiguously identified by the distortion of the magnetic field, which occurs due to the motion of the attached body in the ferrofluid. The magnetic force acting on the body is evaluated experimentally and theoretically for the nonuniform magnetic field of a permanent magnet. The controlled oscillations of the rod are realised by applying a nonuniform magnetic field of periodically altering direction. - Highlights: • A design approach of a tactile sensor inspired by special mammalian hairs is presented. • The working principle is based on magnetic properties of a ferrofluid in magnetic fields. • The magnetic force acting on a body submerged into a ferrofluid volume is evaluated. • External mechanical stimuli may be identified by the distortion of the magnetic field. • The controlled whisking-like oscillations of the sensor's rod are realised experimentally.

  11. A Method of Effective Quarry Water Purifying Using Artificial Filtering Arrays

    Science.gov (United States)

    Tyulenev, M.; Garina, E.; Khoreshok, A.; Litvin, O.; Litvin, Y.; Maliukhina, E.

    2017-01-01

    The development of open pit mining in the large coal basins of Russia and other countries increases their negative impact on the environment. Along with the damage of land and air pollution by dust and combustion gases of blasting, coal pits have a significant negative impact on water resources. Polluted quarry water worsens the ecological situation on a much larger area than covered by air pollution and land damage. This significantly worsens the conditions of people living in cities and towns located near the coal pits, and complicates the subsequent restoration of the environment, irreversibly destroying the nature. Therefore, the research of quarry wastewater purifying is becoming an important mater for scholars of technical colleges and universities in the regions with developing open-pit mining. This paper describes the method of determining the basic parameters of the artificial filtering arrays formed on coal pits of Kuzbass (Western Siberia, Russia), and gives recommendations on its application.

  12. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  13. Artificial intelligence based decision support for trumpeter swan management

    Science.gov (United States)

    Sojda, Richard S.

    2002-01-01

    -2000. Applying the Matched Pairs Multivariate Permutation Test as a statistical tool was a new approach for comparing flyway distributions of waterfowl over time that seemed to work well. Based on this approach, the empirical evidence that I gathered led me to conclude that the base queuing model does accurately simulate swan distributions in the flyway. The system was insensitive to almost all model parameters tested. That remains perplexing, but might result from the base queuing model, itself, being particularly effective at representing the actual ecological diversity in the world of Rocky Mountain trumpeter swans, both spatial and temporally.

  14. An artificial neural network method for lumen and media-adventitia border detection in IVUS.

    Science.gov (United States)

    Su, Shengran; Hu, Zhenghui; Lin, Qiang; Hau, William Kongto; Gao, Zhifan; Zhang, Heye

    2017-04-01

    Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Artificial neural network and classical least-squares methods for neurotransmitter mixture analysis.

    Science.gov (United States)

    Schulze, H G; Greek, L S; Gorzalka, B B; Bree, A V; Blades, M W; Turner, R F

    1995-02-01

    Identification of individual components in biological mixtures can be a difficult problem regardless of the analytical method employed. In this work, Raman spectroscopy was chosen as a prototype analytical method due to its inherent versatility and applicability to aqueous media, making it useful for the study of biological samples. Artificial neural networks (ANNs) and the classical least-squares (CLS) method were used to identify and quantify the Raman spectra of the small-molecule neurotransmitters and mixtures of such molecules. The transfer functions used by a network, as well as the architecture of a network, played an important role in the ability of the network to identify the Raman spectra of individual neurotransmitters and the Raman spectra of neurotransmitter mixtures. Specifically, networks using sigmoid and hyperbolic tangent transfer functions generalized better from the mixtures in the training data set to those in the testing data sets than networks using sine functions. Networks with connections that permit the local processing of inputs generally performed better than other networks on all the testing data sets. and better than the CLS method of curve fitting, on novel spectra of some neurotransmitters. The CLS method was found to perform well on noisy, shifted, and difference spectra.

  16. Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

    OpenAIRE

    Yumin, Dong; Li, Zhao

    2014-01-01

    Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the a...

  17. Artificial intelligence methods applied in the controlled synthesis of polydimethilsiloxane - poly (methacrylic acid) copolymer networks with imposed properties

    Science.gov (United States)

    Rusu, Teodora; Gogan, Oana Marilena

    2016-05-01

    This paper describes the use of artificial intelligence method in copolymer networks design. In the present study, we pursue a hybrid algorithm composed from two research themes in the genetic design framework: a Kohonen neural network (KNN), path (forward problem) combined with a genetic algorithm path (backward problem). The Tabu Search Method is used to improve the performance of the genetic algorithm path.

  18. Comparing and validating methods of reading instruction using behavioural and neural findings in an artificial orthography.

    Science.gov (United States)

    Taylor, J S H; Davis, Matthew H; Rastle, Kathleen

    2017-06-01

    There is strong scientific consensus that emphasizing print-to-sound relationships is critical when learning to read alphabetic languages. Nevertheless, reading instruction varies across English-speaking countries, from intensive phonic training to multicuing environments that teach sound- and meaning-based strategies. We sought to understand the behavioral and neural consequences of these differences in relative emphasis. We taught 24 English-speaking adults to read 2 sets of 24 novel words (e.g., /buv/, /sig/), written in 2 different unfamiliar orthographies. Following pretraining on oral vocabulary, participants learned to read the novel words over 8 days. Training in 1 language was biased toward print-to-sound mappings while training in the other language was biased toward print-to-meaning mappings. Results showed striking benefits of print-sound training on reading aloud, generalization, and comprehension of single words. Univariate analyses of fMRI data collected at the end of training showed that print-meaning relative to print-sound relative training increased neural effort in dorsal pathway regions involved in reading aloud. Conversely, activity in ventral pathway brain regions involved in reading comprehension was no different following print-meaning versus print-sound training. Multivariate analyses validated our artificial language approach, showing high similarity between the spatial distribution of fMRI activity during artificial and English word reading. Our results suggest that early literacy education should focus on the systematicities present in print-to-sound relationships in alphabetic languages, rather than teaching meaning-based strategies, in order to enhance both reading aloud and comprehension of written words. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  19. Artificial Muscles Based on Electroactive Polymers as an Enabling Tool in Biomimetics

    Science.gov (United States)

    Bar-Cohen, Y.

    2007-01-01

    Evolution has resolved many of nature's challenges leading to working and lasting solutions that employ principles of physics, chemistry, mechanical engineering, materials science, and many other fields of science and engineering. Nature's inventions have always inspired human achievements leading to effective materials, structures, tools, mechanisms, processes, algorithms, methods, systems, and many other benefits. Some of the technologies that have emerged include artificial intelligence, artificial vision, and artificial muscles, where the latter is the moniker for electroactive polymers (EAPs). To take advantage of these materials and make them practical actuators, efforts are made worldwide to develop capabilities that are critical to the field infrastructure. Researchers are developing analytical model and comprehensive understanding of EAP materials response mechanism as well as effective processing and characterization techniques. The field is still in its emerging state and robust materials are still not readily available; however, in recent years, significant progress has been made and commercial products have already started to appear. In the current paper, the state-of-the-art and challenges to artificial muscles as well as their potential application to biomimetic mechanisms and devices are described and discussed.

  20. Artificial Synapses Based on in-Plane Gate Organic Electrochemical Transistors.

    Science.gov (United States)

    Qian, Chuan; Sun, Jia; Kong, Ling-An; Gou, Guangyang; Yang, Junliang; He, Jun; Gao, Yongli; Wan, Qing

    2016-10-05

    Realization of biological synapses using electronic devices is regarded as the basic building blocks for neuromorphic engineering and artificial neural network. With the advantages of biocompatibility, low cost, flexibility, and compatible with printing and roll-to-roll processes, the artificial synapse based on organic transistor is of great interest. In this paper, the artificial synapse simulation by ion-gel gated organic field-effect transistors (FETs) with poly(3-hexylthiophene) (P3HT) active channel is demonstrated. Key features of the synaptic behaviors, such as paired-pulse facilitation (PPF), short-term plasticity (STP), self-tuning, the spike logic operation, spatiotemporal dentritic integration, and modulation are successfully mimicked. Furthermore, the interface doping processes of electrolyte ions between the active P3HT layer and ion gels is comprehensively studied for confirming the operating processes underlying the conductivity and excitatory postsynaptic current (EPSC) variations in the organic synaptic devices. This study represents an important step toward building future artificial neuromorphic systems with newly emerged ion gel gated organic synaptic devices.

  1. Development of nylon-based artificial muscles for the usage in robotic prosthetic limb

    Science.gov (United States)

    Atikah, Nurul Anis; Weng, Leong Yeng; Anuar, Adzly; Fat, Chau Chien; Abidin, Izham Zainal; Sahari, Khairul Salleh Mohamed

    2017-09-01

    This paper describes the development of nylon-based artificial muscles that is intended to be used in prosthetic limb for young amputees. Prosthetic limbs are very expensive and this situation is further compounded for young amputees who are very quickly out-grow their prosthesis. The proposed artificial muscles are made of nylon fishing strings from various size such as 0.45mm, 0.55mm, 0.65mm and 1.00mm. These fishing strings were twisted into coils to create Super Coiled Polymers (SCP) and tested using hot air blower. These artificial muscles react counterintuitively, where when it is exposed to heat, contracts, and when cooled, expands. Peltier devices, when switched-on acts as heat pump, where one side is hot and the other is cold. This phenomenon, when affixed in between 2 SCP's, creates tandem motion similar to triceps and biceps. As initial study, the hot side of the Peltier module was tested using these artificial muscles. The string was measured for both its force production, length contraction, the initial results were promising.

  2. Development of a novel artificial medium based on utilization of algal photosynthetic metabolites by symbiotic heterotrophs.

    Science.gov (United States)

    Watanabe, K; Imase, M; Aoyagi, H; Ohmura, N; Saiki, H; Tanaka, H

    2008-09-01

    (i) Quantitative and qualitative analyses of photosynthetic metabolites of Chlorella sorokiniana and elucidation of the mechanism of their utilization by algal symbionts. (ii) Development of artificial medium that imitates photoautotroph-heterotroph interaction and investigation of its suitability for isolation of novel microbes from the environment. Various components, including free dissolved carbohydrates, nitrogenous compounds and vitamin, were detected and together contributed 11.1% (as carbon content) of the total photosynthetic metabolites in the medium. Utilization of these photosynthetic metabolites in algal culture broth by algal symbionts was studied. Many symbionts showed specific utilization patterns. A novel artificial extracellular released organic carbon medium, which imitated the nutritional conditions surrounding algae, was developed based on the pattern of utilization of the algal metabolites by the symbiotic heterotrophs. About 42.9% of the isolates were closely related to photoautotrophic-dependent and oligotrophic bacteria. With the novel artificial medium, it was possible to selectively isolate some bacterial strains. Synthetic bacterial growth medium is an important and basic tool for bacterial isolation from environmental samples. The current study shows that preferential separation of typical bacterial subset can be achieved by using artificial medium that mimics photosynthetic metabolites.

  3. Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network.

    Science.gov (United States)

    Liu, Ming; Zhao, Jing; Lu, XiaoZuo; Li, Gang; Wu, Taixia; Zhang, LiFu

    2018-05-10

    With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate. Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation. To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively. Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

  4. Capacitance Estimation for DC-link Capacitors in a Back-to-Back Converter Based on Artificial Neural Network Algorithm

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Blaabjerg, Frede

    2016-01-01

    of the aforementioned challenges and shortcomings. In this paper, a pure software condition monitoring method based on Artificial Neural Network (ANN) algorithm is proposed. The implemented ANN estimates the capacitance of the dc-link capacitor in a back-to-back converter. The error analysis of the estimated results......The reliability of dc-link capacitors in power electronic converters is one of the critical aspects to be considered in modern power converter design. The observation of their ageing process and the estimation of their health status have been an attractive subject for the industrial field and hence...

  5. Combining ground-based and airborne EM through Artificial Neural Networks for modelling glacial till under saline groundwater conditions

    DEFF Research Database (Denmark)

    Gunnink, J.L.; Bosch, A.; Siemon, B.

    2012-01-01

    Airborne electromagnetic (AEM) methods supply data over large areas in a cost-effective way. We used ArtificialNeural Networks (ANN) to classify the geophysical signal into a meaningful geological parameter. By using examples of known relations between ground-based geophysical data (in this case...... electrical conductivity, EC, from electrical cone penetration tests) and geological parameters (presence of glacial till), we extracted learning rules that could be applied to map the presence of a glacial till using the EC profiles from the airborne EM data. The saline groundwater in the area was obscuring...

  6. Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

    Directory of Open Access Journals (Sweden)

    Chao Yang

    2016-01-01

    Full Text Available The artificial bee colony (ABC algorithm is a recently introduced optimization method in the research field of swarm intelligence. This paper presents an improved ABC algorithm named as OGABC based on opposition-based learning (OBL and global best search equation to overcome the shortcomings of the slow convergence rate and sinking into local optima in the process of inversion of atmospheric duct. Taking the inversion of the surface duct using refractivity from clutter (RFC technique as an example to validate the performance of the proposed OGABC, the inversion results are compared with those of the modified invasive weed optimization (MIWO and ABC. The radar sea clutter power calculated by parabolic equation method using the simulated and measured refractivity profile is utilized to carry out the inversion of the surface duct, respectively. The comparative investigation results indicate that the performance of OGABC is superior to that of MIWO and ABC in terms of stability, accuracy, and convergence rate during the process of inversion.

  7. Soft computing in artificial intelligence

    CERN Document Server

    Matson, Eric

    2014-01-01

    This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...

  8. Infrared thermography based on artificial intelligence for carpal tunnel syndrome diagnosis.

    Science.gov (United States)

    Jesensek Papez, B; Palfy, M; Turk, Z

    2008-01-01

    Thermography for the measurement of surface temperatures is well known in industry, although is not established in medicine despite its safety, lack of pain and invasiveness, easy reproducibility, and low running costs. Promising results have been achieved in nerve entrapment syndromes, although thermography has never represented a real alternative to electromyography. Here an attempt is described to improve the diagnosis of carpal tunnel syndrome with thermography using a computer-based system employing artificial neural networks to analyse the images. Method reliability was tested on 112 images (depicting the dorsal and palmar sides of 26 healthy and 30 pathological hands), with the hand divided into 12 segments and compared relative to a reference. Palmar segments appeared to have no beneficial influence on classification outcome, whereas dorsal segments gave improved outcome with classification success rates near to or over 80%, and finger segments influenced by the median nerve appeared to be of greatest importance. These are preliminary results from a limited number of images and further research will be undertaken as our image database grows.

  9. Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

    Science.gov (United States)

    Islam, Kh Tohidul; Raj, Ram Gopal

    2017-01-01

    Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications. PMID:28406471

  10. Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network.

    Science.gov (United States)

    Islam, Kh Tohidul; Raj, Ram Gopal

    2017-04-13

    Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

  11. Nanobioarchitectures based on chlorophyll photopigment, artificial lipid bilayers and carbon nanotubes

    Directory of Open Access Journals (Sweden)

    Marcela Elisabeta Barbinta-Patrascu

    2014-12-01

    Full Text Available In the last decade, building biohybrid materials has gained considerable interest in the field of nanotechnology. This paper describes an original design for bionanoarchitectures with interesting properties and potential bioapplications. Multilamellar lipid vesicles (obtained by hydration of a dipalmitoyl phosphatidylcholine thin film with and without cholesterol were labelled with a natural photopigment (chlorophyll a, which functioned as a sensor to detect modifications in the artificial lipid bilayers. These biomimetic membranes were used to build non-covalent structures with single-walled carbon nanotubes. Different biophysical methods were employed to characterize these biohybrids such as: UV–vis absorption and emission spectroscopy, zeta potential measurements, AFM and chemiluminescence techniques. The designed, carbon-based biohybrids exhibited good physical stability, good antioxidant and antimicrobial properties, and could be used as biocoating materials. As compared to the cholesterol-free samples, the cholesterol-containing hybrid structures demonstrated better stability (i.e., their zeta potential reached the value of −36.4 mV, more pronounced oxygen radical scavenging ability (affording an antioxidant activity of 73.25% and enhanced biocidal ability, offering inhibition zones of 12.4, 11.3 and 10.2 mm in diameter, against Escherichia coli, Staphylococcus aureus and Enterococcus faecalis, respectively.

  12. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    Science.gov (United States)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

  13. Development of canine herpesvirus based antifertility vaccines for foxes using bacterial artificial chromosomes.

    Science.gov (United States)

    Strive, Tanja; Hardy, Christopher M; French, Nigel; Wright, John D; Nagaraja, Nitin; Reubel, Gerhard H

    2006-02-13

    Using bacterial artificial chromosome (BAC) technology, a canine herpesvirus (CHV)-based recombinant vaccine vector was produced for the development of an antifertility vaccine for foxes. Infectious viruses were recovered following transfection of canid cells with a BAC plasmid carrying the complete CHV genome. In vitro growth characteristics of BAC-derived viruses were similar to that of wildtype (wt)-CHV. Two recombinant antigens, fox zona pellucida protein subunit 3 (fZPC) and enhanced green fluorescent protein (EGFP) as control antigen, were inserted into thymidine kinase (TK) locus of the CHV genome and shown to be efficiently expressed in vitro. Inoculation of foxes with transgenic CHVs induced CHV specific antibodies, but was innocuous and failed to elicit transgene-specific antibody responses. Infectious virus or viral DNA was not detected in mucosal secretions or tissues of vaccinated foxes. The CHV-BAC system proved to be a quick and reliable method to manipulate the CHV genome. It will help to readily apply changes in the vector design in order to improve virus replication in vivo.

  14. A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea.

    Science.gov (United States)

    Sun, Lei Ming; Chiu, Hung-Wen; Chuang, Chih Yuan; Liu, Li

    2011-09-01

    Obstructive sleep apnea (OSA) is a major concern in modern medicine; however, it is difficult to diagnose. Screening questionnaires such as the Berlin questionnaire, Rome questionnaire, and BASH'IM score are used to identify patients with OSA. However, the sensitivity and specificity of these tools are not satisfactory. We aim to introduce an artificial intelligence method to screen moderate to severe OSA patients (apnea-hypopnea index ≧15). One hundred twenty patients were asked to complete a newly developed questionnaire before undergoing an overnight polysomnography (PSG) study. One hundred ten validated questionnaires were enrolled in this study. Genetic algorithm (GA) was used to build the five best models based on these questionnaires. The same data were analyzed with logistic regression (LR) for comparison. The sensitivity of the GA models varied from 81.8% to 88.0%, with a specificity of 95% to 97%. On the other hand, the sensitivity and specificity of the LR model were 55.6% and 57.9%, respectively. GA provides a good solution to build models for screening moderate to severe OSA patients, who require PSG evaluation and medical intervention. The questionnaire did not require any special biochemistry data and was easily self-administered. The sensitivity and specificity of the GA models are satisfactory and may improve when more patients are recruited.

  15. Study on the identifying of meat's visible spectrum based on BP artificial neural network

    Science.gov (United States)

    Li, Xiaotian; Zhang, Tieqiang; Li, Bo; Jiang, Yongheng; Liu, Binghui; Li, Zhaokai

    2006-01-01

    A method to identify different meat by the visible and reflected spectra of meat with BP artificial neural net (BP-ANN) was introduced in this paper. The visible and reflected spectra (from 420 to 535nm) of different meat (beef and pork) were measured with fiber sensor spectrometer. A kind of ANN with a double-hidden layer was created to identify the different meat automatically. Its right ratio reaches 92.71%.

  16. Real power transfer allocation method with the application of artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Mustafa, M.W.; Khalid, S.N.; Shareef, H.; Khairuddin, A. [Technological Univ. of Malaysia, Skudai, Johor Bahru (Malaysia). Dept. of Electrical Power Enginering

    2008-07-01

    This paper presented a newly modified nodal equations method for identifying the real power transfer between generators and load. The objective was to represent each load current as a function of the generator's current and load voltages. The modified admittance matrix of a circuit was used to decompose the load voltage dependent term into components of generator dependent terms. By using these two decompositions of current and voltage terms, the real power transfer between loads and generators was obtained. The robustness of the proposed method was demonstrated on the modified IEEE 30-bus system. An appropriate Artificial Neural Network (ANN) was also created to solve the same problem in a simpler and faster manner with very good accuracy. For this purpose, supervised learning paradigm and feedforward architecture were chosen for the proposed ANN power transfer allocation technique. The method could be adapted to other larger systems by modifying the neural network structure. This technique can be used to solve some of the difficult real power pricing and costing issues and to ensure fairness and transparency in the deregulated environment of power system operation. 22 refs., 5 tabs., 8 figs.

  17. Orbit Determination from Tracking Data of Artificial Satellite Using the Method of Differential Correction

    Directory of Open Access Journals (Sweden)

    Byoung-Sun Lee

    1988-06-01

    Full Text Available The differential correction process determining osculating orbital elements as correct as possible at a given instant of time from tracking data of artificial satellite was accomplished. Preliminary orbital elements were used as an initial value of the differential correction procedure and iterated until the residual of real observation(O and computed observation(C was minimized. Tracking satellite was NOAA-9 or TIROS-N series. Two types of tracking data were prediction data precomputed from mean orbital elements of TBUS and real data obtained from tracking 1.707GHz HRPT signal of NOAA-9 using 5 meter auto-track antenna in Radio Research Laboratory. According to tracking data either Gauss method or Herrick-Gibbs method was applied to preliminary orbit determination. In the differential correction stage we used both of the Escobal(1975's analytical method and numerical ones are nearly consistent. And the differentially corrected orbit converged to the same value in spite of the differences between preliminary orbits of each time span.

  18. Attribute-Based Methods

    Science.gov (United States)

    Thomas P. Holmes; Wiktor L. Adamowicz

    2003-01-01

    Stated preference methods of environmental valuation have been used by economists for decades where behavioral data have limitations. The contingent valuation method (Chapter 5) is the oldest stated preference approach, and hundreds of contingent valuation studies have been conducted. More recently, and especially over the last decade, a class of stated preference...

  19. Cleavage of influenza RNA by using a human PUF-based artificial RNA-binding protein–staphylococcal nuclease hybrid

    International Nuclear Information System (INIS)

    Mori, Tomoaki; Nakamura, Kento; Masaoka, Keisuke; Fujita, Yusuke; Morisada, Ryosuke; Mori, Koichi; Tobimatsu, Takamasa; Sera, Takashi

    2016-01-01

    Various viruses infect animals and humans and cause a variety of diseases, including cancer. However, effective methodologies to prevent virus infection have not yet been established. Therefore, development of technologies to inactivate viruses is highly desired. We have already demonstrated that cleavage of a DNA virus genome was effective to prevent its replication. Here, we expanded this methodology to RNA viruses. In the present study, we used staphylococcal nuclease (SNase) instead of the PIN domain (PilT N-terminus) of human SMG6 as an RNA-cleavage domain and fused the SNase to a human Pumilio/fem-3 binding factor (PUF)-based artificial RNA-binding protein to construct an artificial RNA restriction enzyme with enhanced RNA-cleavage rates for influenzavirus. The resulting SNase-fusion nuclease cleaved influenza RNA at rates 120-fold greater than the corresponding PIN-fusion nuclease. The cleaving ability of the PIN-fusion nuclease was not improved even though the linker moiety between the PUF and RNA-cleavage domain was changed. Gel shift assays revealed that the RNA-binding properties of the PUF derivative used was not as good as wild type PUF. Improvement of the binding properties or the design method will allow the SNase-fusion nuclease to cleave an RNA target in mammalian animal cells and/or organisms. - Highlights: • A novel RNA restriction enzyme using SNase was developed tor cleave viral RNA. • Our enzyme cleaved influenza RNA with rates >120-fold higher rates a PIN-fusion one. • Our artificial enzyme with the L5 linker showed the highest RNA cleavage rate. • Our artificial enzyme site-selectively cleaved influenza RNA in vitro.

  20. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

    Science.gov (United States)

    Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-Wing

    2018-08-01

    Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg -1 and 66,112.94MJkg -1 , respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.

  1. Artificial Intelligence in Civil Engineering

    OpenAIRE

    Lu, Pengzhen; Chen, Shengyong; Zheng, Yujun

    2012-01-01

    Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applicati...

  2. Design and construction of miniature artificial ecosystem based on dynamic response optimization

    Science.gov (United States)

    Hu, Dawei; Liu, Hong; Tong, Ling; Li, Ming; Hu, Enzhu

    The miniature artificial ecosystem (MAES) is a combination of man, silkworm, salad and mi-croalgae to partially regenerate O2 , sanitary water and food, simultaneously dispose CO2 and wastes, therefore it have a fundamental life support function. In order to enhance the safety and reliability of MAES and eliminate the influences of internal variations and external dis-turbances, it was necessary to configure MAES as a closed-loop control system, and it could be considered as a prototype for future bioregenerative life support system. However, MAES is a complex system possessing large numbers of parameters, intricate nonlinearities, time-varying factors as well as uncertainties, hence it is difficult to perfectly design and construct a prototype through merely conducting experiments by trial and error method. Our research presented an effective way to resolve preceding problem by use of dynamic response optimiza-tion. Firstly the mathematical model of MAES with first-order nonlinear ordinary differential equations including parameters was developed based on relevant mechanisms and experimental data, secondly simulation model of MAES was derived on the platform of MatLab/Simulink to perform model validation and further digital simulations, thirdly reference trajectories of de-sired dynamic response of system outputs were specified according to prescribed requirements, and finally optimization for initial values, tuned parameter and independent parameters was carried out using the genetic algorithm, the advanced direct search method along with parallel computing methods through computer simulations. The result showed that all parameters and configurations of MAES were determined after a series of computer experiments, and its tran-sient response performances and steady characteristics closely matched the reference curves. Since the prototype is a physical system that represents the mathematical model with reason-able accuracy, so the process of designing and

  3. Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography

    Directory of Open Access Journals (Sweden)

    Koivistoinen Teemu

    2007-01-01

    Full Text Available As we know, singular value decomposition (SVD is designed for computing singular values (SVs of a matrix. Then, if it is used for finding SVs of an -by-1 or 1-by- array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD.'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal. This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs for ballistocardiogram (BCG data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

  4. Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography

    Directory of Open Access Journals (Sweden)

    Alpo Värri

    2007-01-01

    Full Text Available As we know, singular value decomposition (SVD is designed for computing singular values (SVs of a matrix. Then, if it is used for finding SVs of an m-by-1 or 1-by-m array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ‘‘time-frequency moments singular value decomposition (TFM-SVD.’’ In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal. This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs for ballistocardiogram (BCG data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

  5. Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography

    Science.gov (United States)

    Akhbardeh, Alireza; Junnila, Sakari; Koivuluoma, Mikko; Koivistoinen, Teemu; Värri, Alpo

    2006-12-01

    As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an [InlineEquation not available: see fulltext.]-by-1 or 1-by- [InlineEquation not available: see fulltext.] array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD).'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

  6. MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

    OpenAIRE

    Alexandridis, Konstantinos T.; Pijanowski, Bryan C.

    2002-01-01

    The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving g...

  7. How People Interact with Technology based on Natural and Artificial Intelligence

    OpenAIRE

    Vasile MAZILESCU

    2017-01-01

    This paper aims to analyse the different forms of intelligence within organizations in a systemic and inclusive vision, in order to design an integrated environment based on Artificial Intelligence (AI) and Collective Intelligence (CI). This way we effectively shift the classical approaches of connecting people with people using collaboration tools (which allow people to work together, such as videoconferencing or email, groupware in virtual space, forums, workflow), of connecting people with...

  8. Cyclone track forecasting based on satellite images using artificial neural networks

    OpenAIRE

    Kovordanyi, Rita; Roy, Chandan

    2009-01-01

    Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on sate...

  9. Control system for solar tracking based on artificial vision; Sistema de control para seguimiento solar basado en vision artificial

    Energy Technology Data Exchange (ETDEWEB)

    Pacheco Ramirez, Jesus Horacio; Anaya Perez, Maria Elena; Benitez Baltazar, Victor Hugo [Universidad de onora, Hermosillo, Sonora (Mexico)]. E-mail: jpacheco@industrial.uson.mx; meanaya@industrial.uson.mx; vbenitez@industrial.uson.mx

    2010-11-15

    This work shows how artificial vision feedback can be applied to control systems. The control is applied to a solar panel in order to track the sun position. The algorithms to calculate the position of the sun and the image processing are developed in LabView. The responses obtained from the control show that it is possible to use vision for a control scheme in closed loop. [Spanish] El presente trabajo muestra la manera en la cual un sistema de control puede ser retroalimentado mediante vision artificial. El control es aplicado en un panel solar para realizar el seguimiento del sol a lo largo del dia. Los algoritmos para calcular la posicion del sol y para el tratamiento de la imagen fueron desarrollados en LabView. Las respuestas obtenidas del control muestran que es posible utilizar vision para un esquema de control en lazo cerrado.

  10. A neutron spectrum unfolding code based on generalized regression artificial neural networks

    International Nuclear Information System (INIS)

    Rosario Martinez-Blanco, Ma. del

    2016-01-01

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a "6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. - Highlights: • Main drawback of neutron spectrometry with BPNN is network topology optimization. • Compared to BPNN, it’s usually much faster to train a (GRNN). • GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest. • This computational code, automates the pre-processing, training

  11. Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    Haidong Xu; Mingyan Jiang; Kun Xu

    2015-01-01

    The artificial bee colony (ABC) algorithm is a com-petitive stochastic population-based optimization algorithm. How-ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in-sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA cal ed Archimedean copula estima-tion of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench-mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen-tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.

  12. Efficacy of Blood Sources and Artificial Blood Feeding Methods in Rearing of Aedes aegypti (Diptera: Culicidae for Sterile Insect Technique and Incompatible Insect Technique Approaches in Sri Lanka

    Directory of Open Access Journals (Sweden)

    Nayana Gunathilaka

    2017-01-01

    Full Text Available Introduction. Selection of the artificial membrane feeding technique and blood meal source has been recognized as key considerations in mass rearing of vectors. Methodology. Artificial membrane feeding techniques, namely, glass plate, metal plate, and Hemotek membrane feeding method, and three blood sources (human, cattle, and chicken were evaluated based on feeding rates, fecundity, and hatching rates of Aedes aegypti. Significance in the variations among blood feeding was investigated by one-way ANOVA, cluster analysis of variance (ANOSIM, and principal coordinates (PCO analysis. Results. Feeding rates of Ae. aegypti significantly differed among the membrane feeding techniques as suggested by one-way ANOVA (p0.05. Conclusions. Metal plate method could be recommended as the most effective membrane feeding technique for mass rearing of Ae. aegypti, due to its high feeding rate and cost effectiveness. Cattle blood could be recommended for mass rearing Ae. aegypti.

  13. Comparison of three artificial digestion methods for detection of non-encapsulated Trichinella pseudospiralis larvae in pork

    DEFF Research Database (Denmark)

    Nockler, K.; Reckinger, S.; Szabo, I.

    2009-01-01

    In a ring trial involving five laboratories (A, B, C, D, and E), three different methods of artificial digestion were compared for the detection of non-encapsulated Trichinella pseudospiralis larvae in minced meat. Each sample panel consisted often 1 g minced pork samples. All samples in each panel...... were derived from a bulk meat preparation with a nominal value of either 7 or 17 larvae per g (Ipg). Samples were tested for the number of muscle larvae using the magnetic stirrer method (labs A, B, and E), stomacher method (lab B), and Trichomatic 35 (R) (labs C and D). T. pseudospiralis larvae were...... by using the magnetic stirrer method (22%), followed by the stomacher method (25%), and Trichomatic 35 (R) (30%). Results revealed that T. pseudospiralis larvae in samples with a nominal value of 7 and 17 Ipg can be detected by all three methods of artificial digestion....

  14. [Percutaneous catheter-based implantation of artificial pulmonary valves in patients with congenital heart defects].

    Science.gov (United States)

    Wyller, Vegard Bruun; Aaberge, Lars; Thaulow, Erik; Døhlen, Gaute

    2011-07-01

    Percutaneous catheter-based implantation of artificial heart valves is a new technique that may supplement surgery and which may be used more in the future. We here report our first experience with implantation of artificial pulmonary valves in children with congenital heart defects. Eligible patients were those with symptoms of heart failure combined with stenosis and/or insufficiency in an established artificial right ventricular outflow tract. The valve was inserted through a catheter from a vein in the groin or neck. Symptoms, echocardiography, invasive measurements and angiography were assessed for evaluation of treatment effect. Our treatment results are reported for the period April 2007-September 2009. Ten patients (seven men and three women, median age 17 years) were assessed. The procedure reduced pressure in the right ventricle (p = 0.008) and resolved the pulmonary insufficiency in all patients. The median time in hospital was two days. No patients had complications that were directly associated with the implantation procedure. One patient developed a pseudoaneurysm in the femoral artery, another had a short-lasting fever two days after the procedure and one patient experienced a stent fracture that required surgery 9 months after the implantation. After 6 months all patients had a reduced pressure gradient in the right ventricular outflow tract (p = 0.008), the pulmonary insufficiency had improved (p = 0.006) and they all reported improval of symptoms. These results persisted for at least 24 months for the four patients who were monitored until then. Percutaneous catheter-based implantation of artificial pulmonary valves improves hemodynamics in the right ventricle of selected patients with congenital heart defects. A randomized controlled study should be undertaken to provide a stronger evidence-base for usefulness of this procedure.

  15. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-07-05

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  16. Predicting IVF Outcome: A Proposed Web-based System Using Artificial Intelligence.

    Science.gov (United States)

    Siristatidis, Charalampos; Vogiatzi, Paraskevi; Pouliakis, Abraham; Trivella, Marialenna; Papantoniou, Nikolaos; Bettocchi, Stefano

    2016-01-01

    To propose a functional in vitro fertilization (IVF) prediction model to assist clinicians in tailoring personalized treatment of subfertile couples and improve assisted reproduction outcome. Construction and evaluation of an enhanced web-based system with a novel Artificial Neural Network (ANN) architecture and conformed input and output parameters according to the clinical and bibliographical standards, driven by a complete data set and "trained" by a network expert in an IVF setting. The system is capable to act as a routine information technology platform for the IVF unit and is capable of recalling and evaluating a vast amount of information in a rapid and automated manner to provide an objective indication on the outcome of an artificial reproductive cycle. ANNs are an exceptional candidate in providing the fertility specialist with numerical estimates to promote personalization of healthcare and adaptation of the course of treatment according to the indications. Copyright © 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  17. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  18. A red-light running prevention system based on artificial neural network and vehicle trajectory data.

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  19. Tissue engineering and cell-based therapy toward integrated strategy with artificial organs.

    Science.gov (United States)

    Gojo, Satoshi; Toyoda, Masashi; Umezawa, Akihiro

    2011-09-01

    Research in order that artificial organs can supplement or completely replace the functions of impaired or damaged tissues and internal organs has been underway for many years. The recent clinical development of implantable left ventricular assist devices has revolutionized the treatment of patients with heart failure. The emerging field of regenerative medicine, which uses human cells and tissues to regenerate internal organs, is now advancing from basic and clinical research to clinical application. In this review, we focus on the novel biomaterials, i.e., fusion protein, and approaches such as three-dimensional and whole-organ tissue engineering. We also compare induced pluripotent stem cells, directly reprogrammed cardiomyocytes, and somatic stem cells for cell source of future cell-based therapy. Integrated strategy of artificial organ and tissue engineering/regenerative medicine should give rise to a new era of medical treatment to organ failure.

  20. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems. PMID:25435870

  1. Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

    International Nuclear Information System (INIS)

    Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem

    2015-01-01

    The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately

  2. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  3. Thermodynamic Optimization of a Geothermal- Based Organic Rankine Cycle System Using an Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Osman Özkaraca

    2017-10-01

    Full Text Available Geothermal energy is a renewable form of energy, however due to misuse, processing and management issues, it is necessary to use the resource more efficiently. To increase energy efficiency, energy systems engineers carry out careful energy control studies and offer alternative solutions. With this aim, this study was conducted to improve the performance of a real operating air-cooled organic Rankine cycle binary geothermal power plant (GPP and its components in the aspects of thermodynamic modeling, exergy analysis and optimization processes. In-depth information is obtained about the exergy (maximum work a system can make, exergy losses and destruction at the power plant and its components. Thus the performance of the power plant may be predicted with reasonable accuracy and better understanding is gained for the physical process to be used in improving the performance of the power plant. The results of the exergy analysis show that total exergy production rate and exergy efficiency of the GPP are 21 MW and 14.52%, respectively, after removing parasitic loads. The highest amount of exergy destruction occurs, respectively, in condenser 2, vaporizer HH2, condenser 1, pumps 1 and 2 as components requiring priority performance improvement. To maximize the system exergy efficiency, the artificial bee colony (ABC is applied to the model that simulates the actual GPP. Under all the optimization conditions, the maximum exergy efficiency for the GPP and its components is obtained. Two of these conditions such as Case 4 related to the turbine and Case 12 related to the condenser have the best performance. As a result, the ABC optimization method provides better quality information than exergy analysis. Based on the guidance of this study, the performance of power plants based on geothermal energy and other energy resources may be improved.

  4. Beyond AI: Artificial Dreams Conference

    CERN Document Server

    Zackova, Eva; Kelemen, Jozef; Beyond Artificial Intelligence : The Disappearing Human-Machine Divide

    2015-01-01

    This book is an edited collection of chapters based on the papers presented at the conference “Beyond AI: Artificial Dreams” held in Pilsen in November 2012. The aim of the conference was to question deep-rooted ideas of artificial intelligence and cast critical reflection on methods standing at its foundations.  Artificial Dreams epitomize our controversial quest for non-biological intelligence, and therefore the contributors of this book tried to fully exploit such a controversy in their respective chapters, which resulted in an interdisciplinary dialogue between experts from engineering, natural sciences and humanities.   While pursuing the Artificial Dreams, it has become clear that it is still more and more difficult to draw a clear divide between human and machine. And therefore this book tries to portrait such an image of what lies beyond artificial intelligence: we can see the disappearing human-machine divide, a very important phenomenon of nowadays technological society, the phenomenon which i...

  5. Hall et al., 2016 Artificial Turf Surrogate Surface Methods Paper Data File

    Data.gov (United States)

    U.S. Environmental Protection Agency — Mercury dry deposition data quantified via static water surrogate surface (SWSS) and artificial turf surrogate surface (ATSS) collectors. This dataset is associated...

  6. On the Need for Artificial Intelligence and Advanced Test and Evaluation Methods for Space Exploration

    Science.gov (United States)

    Scheidt, D. H.; Hibbitts, C. A.; Chen, M. H.; Paxton, L. J.; Bekker, D. L.

    2017-02-01

    Implementing mature artificial intelligence would create the ability to significantly increase the science return from a mission, while potentially saving costs in mission and instrument operations, and solving currently intractable problems.

  7. Application of artificial intelligence (AI) methods for designing and analysis of reconfigurable cellular manufacturing system (RCMS)

    CSIR Research Space (South Africa)

    Xing, B

    2009-12-01

    Full Text Available This work focuses on the design and control of a novel hybrid manufacturing system: Reconfigurable Cellular Manufacturing System (RCMS) by using Artificial Intelligence (AI) approach. It is hybrid as it combines the advantages of Cellular...

  8. Structural refinement of artificial superlattices by the X-ray diffraction method

    CERN Document Server

    Ishibashi, Y; Tsurumi, T

    1999-01-01

    This paper reports a structural refinement of BaTiO sub 3 (BTO)/SrTiO sub 3 (STO) artificially superstructured thin films. The refinement was achieved by taking into account the effect of interdiffusion between BTO and STO. The samples were prepared by a molecular-beam epitaxy method on SrTiO sub 3 (001) substrate at 600 .deg. C. The phonon model was employed to simulate the X-ray diffraction (XRD) profiles. A discrepancy was observed in the intensities of the satellite peaks when the effect of the interdiffusion between BTO and STO was not incorporated in the simulation. In successive simulations, the concentration profile due to the interdiffusion was first calculated according to Fick's second law, and then the coefficients of the Fourier series describing the lattice distortion and the modulation of the structure factor were determined. The XRD profiles thus simulated almost completely agreed with those observed. This indicates that XRD analysis with the calculation process proposed in this study will ena...

  9. Artificial Intelligence Mechanisms on Interactive Modified Simplex Method with Desirability Function for Optimising Surface Lapping Process

    Directory of Open Access Journals (Sweden)

    Pongchanun Luangpaiboon

    2014-01-01

    Full Text Available A study has been made to optimise the influential parameters of surface lapping process. Lapping time, lapping speed, downward pressure, and charging pressure were chosen from the preliminary studies as parameters to determine process performances in terms of material removal, lap width, and clamp force. The desirability functions of the-nominal-the-best were used to compromise multiple responses into the overall desirability function level or D response. The conventional modified simplex or Nelder-Mead simplex method and the interactive desirability function are performed to optimise online the parameter levels in order to maximise the D response. In order to determine the lapping process parameters effectively, this research then applies two powerful artificial intelligence optimisation mechanisms from harmony search and firefly algorithms. The recommended condition of (lapping time, lapping speed, downward pressure, and charging pressure at (33, 35, 6.0, and 5.0 has been verified by performing confirmation experiments. It showed that the D response level increased to 0.96. When compared with the current operating condition, there is a decrease of the material removal and lap width with the improved process performance indices of 2.01 and 1.14, respectively. Similarly, there is an increase of the clamp force with the improved process performance index of 1.58.

  10. Artificial Bee Colony Algorithm Combined with Grenade Explosion Method and Cauchy Operator for Global Optimization

    Directory of Open Access Journals (Sweden)

    Jian-Guo Zheng

    2015-01-01

    Full Text Available Artificial bee colony (ABC algorithm is a popular swarm intelligence technique inspired by the intelligent foraging behavior of honey bees. However, ABC is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. To improve the performance of ABC, a novel ABC combined with grenade explosion method (GEM and Cauchy operator, namely, ABCGC, is proposed. GEM is embedded in the onlooker bees’ phase to enhance the exploitation ability and accelerate convergence of ABCGC; meanwhile, Cauchy operator is introduced into the scout bees’ phase to help ABCGC escape from local optimum and further enhance its exploration ability. Two sets of well-known benchmark functions are used to validate the better performance of ABCGC. The experiments confirm that ABCGC is significantly superior to ABC and other competitors; particularly it converges to the global optimum faster in most cases. These results suggest that ABCGC usually achieves a good balance between exploitation and exploration and can effectively serve as an alternative for global optimization.

  11. Robustly Fitting and Forecasting Dynamical Data With Electromagnetically Coupled Artificial Neural Network: A Data Compression Method.

    Science.gov (United States)

    Wang, Ziyin; Liu, Mandan; Cheng, Yicheng; Wang, Rubin

    2017-06-01

    In this paper, a dynamical recurrent artificial neural network (ANN) is proposed and studied. Inspired from a recent research in neuroscience, we introduced nonsynaptic coupling to form a dynamical component of the network. We mathematically proved that, with adequate neurons provided, this dynamical ANN model is capable of approximating any continuous dynamic system with an arbitrarily small error in a limited time interval. Its extreme concise Jacobian matrix makes the local stability easy to control. We designed this ANN for fitting and forecasting dynamic data and obtained satisfied results in simulation. The fitting performance is also compared with those of both the classic dynamic ANN and the state-of-the-art models. Sufficient trials and the statistical results indicated that our model is superior to those have been compared. Moreover, we proposed a robust approximation problem, which asking the ANN to approximate a cluster of input-output data pairs in large ranges and to forecast the output of the system under previously unseen input. Our model and learning scheme proposed in this paper have successfully solved this problem, and through this, the approximation becomes much more robust and adaptive to noise, perturbation, and low-order harmonic wave. This approach is actually an efficient method for compressing massive external data of a dynamic system into the weight of the ANN.

  12. Geochemical and isotopic methods for management of artificial recharge in mazraha station (Damascus)

    International Nuclear Information System (INIS)

    Abou Zakhem, B.; Hafez, R.; Kadkoy, N.

    2009-11-01

    Artificial recharge of shallow groundwater at specially designed facilities is an attractive option increasing the storage capacity of potable water in arid and semi arid region such as Syria, Damascus Oasis. This operation needs integral management and detailed knowledge of groundwater dynamics and quantity and quality development of water. The objective of this study is to determine the temporal and spatial variations of chemical and environmental isotopic characteristics of groundwater during injection and recovery process. The geochemical and environmental isotope techniques are ideally suited for these investigations. 400 to 500 x10 3 m 3 of spring water were injected annually into the ambient groundwater in Mazraha station, Damascus Oasis, which is used later for drinking purpose. Native groundwater and injected water are calcium bicarbonate type with EC of about 850±100 μS/cm and 300±50 μS/cm respectively. The injected water is under saturated with respect to calcite, while ambient groundwater is over saturated and the mixed water is in equilibrium after injection. It was observed that The injection process created a dilution cloud decreasing chemical concentrations progressively that improve the groundwater quality. After completed injection, the dilution center moved about 200 m during 85 days to the south southeast according to the ambient groundwater flow path. Based on this observation, the hydraulic conductivity of the aquifer is estimated about 7.5±1.3x10 -4 m/s. The spatial distribution maps of CFC-11 and CFC-12, after injection, showed the same shape and flow direction of the spatial distribution of chemical elements. The effective diameter of artificial recharge is limited to about 250 m from the injection wells, as EC, Cl- and NO 3 - concentrations are effected significantly. Mixing ratio of 30% is required in order to lower nitrate concentration to less than 50 mg/l in native groundwater for potable water. Depending on pumping rate, the

  13. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network.

    Science.gov (United States)

    Xing, Jida; Chen, Jie

    2015-06-23

    In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous

  14. Artificial intelligent methods for thermodynamic evaluation of ammonia-water refrigeration systems

    International Nuclear Information System (INIS)

    Sencan, Arzu

    2006-01-01

    In this paper, Linear Regression and M5'Rules models within Data Mining Process and Artificial Neural Network (ANN) model for thermodynamic evaluation of ammonia-water absorption refrigeration systems was carried out. A new formulation based on ANN model is presented for the analysis of ammonia-water absorption refrigeration systems (AWRS) because the optimal result was obtained by using ANN Model. Thermodynamic analysis of the AWRS is very complex because of analytic functions used for calculating the properties of fluid couples and simulation programs. Therefore, it is extremely difficult to perform analysis of this system. COP and f are estimated depending on the temperatures of system component and concentration values. Using the weights obtained from the trained network a new formulation is presented for the calculation of the COP and f; the use of ANN is proliferating with high speed in simulation. The R 2 -values obtained when unknown data were used to the networks was 0.9996 and 0.9873 for the circulation ratio and COP respectively which is very satisfactory. The use of this new formulation, which can be employed with any programming language or spreadsheet program for the estimation of the circulation ratio and COP of AWRS, as described in this paper, may make the use of dedicated ANN software unnecessary

  15. Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.

    Science.gov (United States)

    Opitz, Bertram; Hofmann, Juliane

    2015-03-01

    A current theoretical debate regards whether rule-based or similarity-based learning prevails during artificial grammar learning (AGL). Although the majority of findings are consistent with a similarity-based account of AGL it has been argued that these results were obtained only after limited exposure to study exemplars, and performance on subsequent grammaticality judgment tests has often been barely above chance level. In three experiments the conditions were investigated under which rule- and similarity-based learning could be applied. Participants were exposed to exemplars of an artificial grammar under different (implicit and explicit) learning instructions. The analysis of receiver operating characteristics (ROC) during a final grammaticality judgment test revealed that explicit but not implicit learning led to rule knowledge. It also demonstrated that this knowledge base is built up gradually while similarity knowledge governed the initial state of learning. Together these results indicate that rule- and similarity-based mechanisms concur during AGL. Moreover, it could be speculated that two different rule processes might operate in parallel; bottom-up learning via gradual rule extraction and top-down learning via rule testing. Crucially, the latter is facilitated by performance feedback that encourages explicit hypothesis testing. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Artificial Intelligence in Civil Engineering

    Directory of Open Access Journals (Sweden)

    Pengzhen Lu

    2012-01-01

    Full Text Available Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

  17. Larvas output and influence of human factor in reliability of meat inspection by the method of artificial digestion

    OpenAIRE

    Đorđević Vesna; Savić Marko; Vasilev Saša; Đorđević Milovan

    2013-01-01

    On the basis of the performed analyses of the factors that contributed the infected meat reach food chain, we have found out that the infection occurred after consuming the meat inspected by the method of collective samples artificial digestion by using a magnetic stirrer (MM). In this work there are presented assay results which show how modifications of the method, on the level of final sedimentation, influence the reliability of Trichinella larvas detect...

  18. [Suitable investigation method of exploration and suggestions for investigating Chinese materia medica resources from wetland and artificial water of Hongze Lake region].

    Science.gov (United States)

    Liu, Rui; Yan, Hui; Duan, Jin-Ao; Liu, Xing-Jian; Ren, Quan-Jin; Li, Hui-Wei; Bao, Bei-Hua; Zhang, Zhao-Hui

    2016-08-01

    According to the technology requirements of the fourth national survey of Chinese Materia Medica resources (pilot), suitable investigation method of exploration and suggestions for investigating Chinese Materia Medica resources was proposed based on the type of wetland and artificial water of Hongze Lake region. Environment of Hongze Lake and overview of wetland, present situation of ecology and vegetation and vegetation distribution were analyzed. Establishment of survey plan, selection of sample area and sample square and confirmation of representative water area survey plan were all suggested. The present study provide references for improving Chinese materia medica resources survey around Hongze Lake, and improving the technical specifications. It also provide references for investigating Chinese Materia Medica resources survey on similar ecological environment under the condition of artificial intervention. Copyright© by the Chinese Pharmaceutical Association.

  19. Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

    Science.gov (United States)

    Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

    2017-09-01

    Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

  20. Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system.

    Science.gov (United States)

    Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari Hormozi, Shahram; Wah, Teh Ying; Aghabozorgi, Saeed; Pourhoseingholi, Mohamad Amin; Olariu, Teodora

    2015-04-01

    Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden's Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden's Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for

  1. Methods for Creation and Detection of Ultra-Strong Artificial Ionization in the Upper Atmosphere (Invited)

    Science.gov (United States)

    Bernhardt, P. A.; Siefring, C. L.; Briczinski, S. J.; Kendall, E. A.; Watkins, B. J.; Bristow, W. A.; Michell, R.

    2013-12-01

    The High Frequency Active Auroral Research Program (HAARP) transmitter in Alaska has been used to produce localized regions of artificial ionization at altitudes between 150 and 250 km. High power radio waves tuned near harmonics of the electron gyro frequency were discovered by Todd Pederson of the Air Force Research Laboratory to produce ionosonde traces that looked like artificial ionization layers below the natural F-region. The initial regions of artificial ionization (AI) were not stable but had moved down in altitude over a period of 15 minutes. Recently, artificial ionization has been produced by the 2nd, 3rd, 4th and 6th harmonics transmissions by the HAARP. In march 2013, the artificial ionization clouds were sustained for more the 5 hours using HAARP tuned to the 4 fce at the full power of 3.6 Mega-Watts with a twisted-beam antenna pattern. Frequency selection with narrow-band sweeps and antenna pattern shaping has been employed for optimal generation of AI. Recent research at HAARP has produced the longest lived and denser artificial ionization clouds using HF transmissions at the harmonics of the electron cyclotron frequency and ring-shaped radio beams tailored to prevent the descent of the clouds. Detection of artificial ionization employs (1) ionosonde echoes, (2) coherent backscatter from the Kodiak SuperDARN radar, (3) enhanced ion and plasma line echoes from the HAARP MUIR radar at 400 MHz, (4) high resolution optical image from ground sites, and (5) unique stimulated electromagnetic emissions, and (6) strong UHF and L-Band scintillation induced into trans-ionospheric signals from satellite radio beacons. Future HAARP experiments will determine the uses of long-sustained AI for enhanced HF communications.

  2. A neural network based artificial vision system for licence plate recognition.

    Science.gov (United States)

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

  3. Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

    OpenAIRE

    Jing Wang; Yourui Huang

    2013-01-01

    In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...

  4. An independent evaluation of a new method for automated interpretation of lung scintigrams using artificial neural networks

    International Nuclear Information System (INIS)

    Holst, H.; Jaerund, A.; Evander, E.; Taegil, K.; Edenbrandt, L.; Maare, K.; Aastroem, K.; Ohlsson, M.

    2001-01-01

    The purpose of this study was to evaluate a new automated method for the interpretation of lung perfusion scintigrams using patients from a hospital other than that where the method was developed, and then to compare the performance of the technique against that of experienced physicians. A total of 1,087 scintigrams from patients with suspected pulmonary embolism comprised the training group. The test group consisted of scintigrams from 140 patients collected in a hospital different to that from which the training group had been drawn. An artificial neural network was trained using 18 automatically obtained features from each set of perfusion scintigrams. The image processing techniques included alignment to templates, construction of quotient images based on the perfusion/template images, and finally calculation of features describing segmental perfusion defects in the quotient images. The templates represented lungs of normal size and shape without any pathological changes. The performance of the neural network was compared with that of three experienced physicians who read the same test scintigrams according to the modified PIOPED criteria using, in addition to perfusion images, ventilation images when available and chest radiographs for all patients. Performances were measured as area under the receiver operating characteristic curve. The performance of the neural network evaluated in the test group was 0.88 (95% confidence limits 0.81-0.94). The performance of the three experienced experts was in the range 0.87-0.93 when using the perfusion images, chest radiographs and ventilation images when available. Perfusion scintigrams can be interpreted regarding the diagnosis of pulmonary embolism by the use of an automated method also in a hospital other than that where it was developed. The performance of this method is similar to that of experienced physicians even though the physicians, in addition to perfusion images, also had access to ventilation images for

  5. An Examination of a Music Appreciation Method Incorporating Tactile Sensations from Artificial Vibrations

    Science.gov (United States)

    Ideguchi, Tsuyoshi; Yoshida, Ryujyu; Ooshima, Keita

    We examined how test subject impressions of music changed when artificial vibrations were incorporated as constituent elements of a musical composition. In this study, test subjects listened to several music samples in which different types of artificial vibration had been incorporated and then subjectively evaluated any resulting changes to their impressions of the music. The following results were obtained: i) Even if rhythm vibration is added to a silent component of a musical composition, it can effectively enhance musical fitness. This could be readily accomplished when actual sounds that had been synchronized with the vibration components were provided beforehand. ii) The music could be listened to more comfortably by adding not only a natural vibration extracted from percussion instruments but also artificial vibration as tactile stimulation according to intentional timing. Furthermore, it was found that the test subjects' impression of the music was affected by a characteristic of the artificial vibration. iii) Adding vibration to high-frequency areas can offer an effective and practical way of enhancing the appeal of a musical composition. iv) The movement sensations of sound and vibration could be experienced when the strength of the sound and vibration are modified in turn. These results suggest that the intentional application of artificial vibration could result in a sensitivity amplification factor on the part of a listener.

  6. Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

    Science.gov (United States)

    Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

    2014-06-01

    This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

  7. Design of Artificial Neural Network-Based pH Estimator

    Directory of Open Access Journals (Sweden)

    Shebel A. Alsabbah

    2010-10-01

    Full Text Available Taking into consideration the cost, size and drawbacks might be found with real hardware instrument for measuring pH values such that the complications of the wiring, installing, calibrating and troubleshooting the system, would make a person look for a cheaper, accurate, and alternative choice to perform the measuring operation, Where’s hereby, a feedforward artificial neural network-based pH estimator has to be proposed. The proposed estimator has been designed with multi- layer perceptrons. One input which is a measured base stream and two outputs represent pH values at strong base and strong/weak acids for a titration process. The created data base has been obtained with consideration of temperature variation. The final numerical results ensure the effectiveness and robustness of the design neural network-based pH estimator.

  8. Temporal anomaly detection: an artificial immune approach based on T cell activation, clonal size regulation and homeostasis.

    Science.gov (United States)

    Antunes, Mário J; Correia, Manuel E

    2010-01-01

    This paper presents an artificial immune system (AIS) based on Grossman's tunable activation threshold (TAT) for temporal anomaly detection. We describe the generic AIS framework and the TAT model adopted for simulating T Cells behaviour, emphasizing two novel important features: the temporal dynamic adjustment of T Cells clonal size and its associated homeostasis mechanism. We also present some promising results obtained with artificially generated data sets, aiming to test the appropriateness of using TAT in dynamic changing environments, to distinguish new unseen patterns as part of what should be detected as normal or as anomalous. We conclude by discussing results obtained thus far with artificially generated data sets.

  9. Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults

    International Nuclear Information System (INIS)

    Reifman, J.; Wei, T.Y.C.; Vitela, J.E.; Applequist, C. A.; Chasensky, T.M.

    1996-01-01

    Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL) are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA), project to perform feasibility studies on a novel approach to Artificial Intelligence (Al) based diagnostics for component faults in nuclear power plants. Investigations are being performed in the construction of a first-principles physics-based plant level process diagnostic expert system (ES) and the identification of component-level fault patterns through operating component characteristics using artificial neural networks (ANNs). The purpose of the proof-of-concept project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant conditions. The proposed computer-based system will use thermal hydraulic (T-H) signals complemented by other non-T-H signals available in the data stream to provide the process operator with the component which most likely caused the observed process disturbance.To demonstrate the scale-up feasibility of the proposed diagnostic system it is being developed for use with the Chemical Volume Control System (CVCS) of a nuclear power plant. A full-scope operator training simulator representing the Commonwealth Edison Braidwood nuclear power plant is being used both as the source of development data and as the means to evaluate the advantages of the proposed diagnostic system. This is an ongoing multi-year project and this paper presents the results to date of the CRADA phase

  10. Comparative performance analysis of the artificial-intelligence-based thermal control algorithms for the double-skin building

    International Nuclear Information System (INIS)

    Moon, Jin Woo

    2015-01-01

    This study aimed at developing artificial-intelligence-(AI)-theory-based optimal control algorithms for improving the indoor temperature conditions and heating energy efficiency of the double-skin buildings. For this, one conventional rule-based and four AI-based algorithms were developed, including artificial neural network (ANN), fuzzy logic (FL), and adaptive neuro fuzzy inference systems (ANFIS), for operating the surface openings of the double skin and the heating system. A numerical computer simulation method incorporating the matrix laboratory (MATLAB) and the transient systems simulation (TRNSYS) software was used for the comparative performance tests. The analysis results revealed that advanced thermal-environment comfort and stability can be provided by the AI-based algorithms. In particular, the FL and ANFIS algorithms were superior to the ANN algorithm in terms of providing better thermal conditions. The ANN-based algorithm, however, proved its potential to be the most energy-efficient and stable strategy among the four AI-based algorithms. It can be concluded that the optimal algorithm can be differently determined according to the major focus of the strategy. If comfortable thermal condition is the principal interest, then the FL or ANFIS algorithm could be the proper solution, and if energy saving for space heating and system operation stability is the main concerns, then the ANN-based algorithm may be applicable. - Highlights: • Integrated control algorithms were developed for the heating system and surface openings. • AI theories were applied to the control algorithms. • ANN, FL, and ANFIS were the applied AI theories. • Comparative performance tests were conducted using computer simulation. • AI algorithms presented superior temperature environment.

  11. Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

    Science.gov (United States)

    Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

    To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

  12. Two-phase flow simulation inside a tubing string with artificial lift system PCP based; Simulacao do escoamento bifasico em uma coluna de producao com sistema de elevacao artificial por BCP

    Energy Technology Data Exchange (ETDEWEB)

    Vidal, F.J.T.; Salazar, A.O.; Maitelli, A.L. [Rio Grande do Norte Univ., Natal, RN (Brazil). Programa de Pos-graduacao em Engenharia Eletrica]. E-mail: francisco@dca.ufrn.br; andres@dca.ufrn.br; maitelli@dca.ufrn.br; Assmann, B.W. [PETROBRAS S.A., Natal/Fortaleza, RN/CE (Brazil). Unidade de Negocios]. E-mail: benno@petrobras.com.br; Lima, J.A. [Rio Grande do Norte Univ., Natal, RN (Brazil). Dept. de Engenharia Mecanica. Programa de Pos-graduacao em Engenharia Mecanica]. E-mail: jalima@dem.ufrn.br

    2005-07-01

    The main goal of the present work is the computational simulation of the vertical two-phase flow within a tubing string in the production of oil equipped with a PCP artificial lift system (Progressive Cavity Pumping). By initially adopting the homogeneous model for the two-phase mixture (oil and gas), the fields of velocity and pressure are evaluated for prediction of pressure loss along the tubing, as well as the spatial and temporal behavior of typical parameters as gas-oil ratio, bubble pressure, solubility ratio, void fraction, gas and oil formation volume factors, among others. Prediction of these properties is an integral part of pressure loss calculations, as well as they constitute essential parameters for optimization of any artificial lift system. The numerical simulation is based on the transport equations (continuity and momentum equations) for a pseudo-fluid through the finite difference method, and the mixture properties are evaluated by employing the black-oil fluid model. Behavior analyses of the main flow variables are made and results for a typical artificial lift system PCP based are discussed. (author)

  13. Distinguishing of artificial irradiation by α dose: a method of discriminating imitations of ancient pottery

    International Nuclear Information System (INIS)

    Wang Weida; Xia Junding; Zhou Zhixin; Leung, P.L.

    2003-01-01

    If a modern pottery is artificially irradiated by γ-rays of 60 Co source, the modern will become ancient when the pottery is dated by the thermoluminescence technique. For distinguishing artificial irradiation a study was made. Meanwhile the 'fine-grain' and 'pre-dose' techniques were used respectively for measurement of the paleodose in a fine-grain sample from the same pottery. If the paleodose measured by the fine-grain technique is greater than that by the pre-dose techniques, we can affirm that the difference between two paleodoses is due to α dose and this paleodose containing α component results from natural radiation, the pottery therefore is ancient. If two paleodoses are equal approximately, i.e. α dose is not included in the paleodose, the paleodose comes from artificial γ irradiation and the pottery is an imitation

  14. Effects of Coulomb screening and disorder on artificial graphene based on nanopatterned semiconductor

    OpenAIRE

    Tkachenko, O. A.; Tkachenko, V. A.; Terekhov, I. S.; Sushkov, O. P.

    2014-01-01

    A residual disorder in the gate system is the main problem on the way to create artificial graphene based on two-dimensional electron gas. The disorder can be significantly screened/reduced due to the many-body effects. To analyse the screening/disorder problem we consider AlGaAs/GaAs/AlGaAs heterostructure with two metallic gates. We demonstrate that the design least susceptible to the disorder corresponds to the weak coupling regime (opposite to tight binding) which is realised via system o...

  15. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

  16. Evaluation of dna extraction methods of the Salmonella sp. bacterium in artificially infected chickens eggs

    Directory of Open Access Journals (Sweden)

    Ana Cristina dos Reis Ferreira

    2015-06-01

    Full Text Available ABSTRACT. Ferreira A.C.dosR. & dos Santos B.M. [Evaluation of dna extraction methods of the Salmonella sp. bacterium in artificially infected chickens eggs.] Avaliação de três métodos de extração de DNA de Salmonella sp. em ovos de galinhas contaminados artificialmente. Revista Brasileira de Medicina Veterinária, 37(2:115-119, 2015. Departamento de Veterinária, Universidade Federal de Viçosa, Campus Universitário, Av. Peter Henry Rolfs, s/n, Viçosa, MG 36571-000, Brasil. E-mail: bmsantos@ufv.br The present study evaluated the efficiency of different protocols for the genomic DNA extraction of Salmonella bacteria in chicken eggs free of specific pathogens – SPF. Seventy-five eggs were used and divided into five groups with fifteen eggs each. Three of the five groups of eggs were inoculated with enteric Salmonella cultures. One of the five groups was inoculated with Escherichia coli bacterium culture. And another group of eggs was the negative control that received saline solution 0.85% infertile. The eggs were incubated on a temperature that varied from 20 to 25°C during 24, 48 and 72 hours. Five yolks of each group were collected every 24 hours. These yolks were homogenized and centrifuged during 10 minutes. The supernatant was rejected. After the discard, PBS ph 7.2 was added and centrifuged again. The sediment obtained of each group was used for the extraction of bacterial genomic DNA. Silica particles and a commercial kit were utilized as the extraction methods. The extracted DNA was kept on a temperature of 20°C until the evaluation through PCR. The primers utilized were related with the invA gene and they were the following: 5’ GTA AAA TTA TCG CCA CGT TCG GGC AA 3’ and 5’ TCA TCG CAC CGT CAA AGG AAC C 3’. The amplification products were visualized in transilluminator with ultraviolet light. The obtained results through the bacterial DNA extractions demonstrated that the extraction method utilizing silica particles was

  17. Methods in Logic Based Control

    DEFF Research Database (Denmark)

    Christensen, Georg Kronborg

    1999-01-01

    Desing and theory of Logic Based Control systems.Boolean Algebra, Karnaugh Map, Quine McClusky's algorithm. Sequential control design. Logic Based Control Method, Cascade Control Method. Implementation techniques: relay, pneumatic, TTL/CMOS,PAL and PLC- and Soft_PLC implementation. PLC...

  18. Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic

    Science.gov (United States)

    Jain, G. Panka; Gurupur, Varadraj P.; Schroeder, Jennifer L.; Faulkenberry, Eileen D.

    2014-01-01

    In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of…

  19. A development framework for artificial intelligence based distributed operations support systems

    Science.gov (United States)

    Adler, Richard M.; Cottman, Bruce H.

    1990-01-01

    Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself.

  20. Algorithm of formation of touristic product based on natural and artificial attractions

    Directory of Open Access Journals (Sweden)

    L. E. Glagoleva

    2016-01-01

    Full Text Available At present potential touristic resources are the objects of numerous investigations. For using the touristic resource in the most effective way it’s necessary to transform it into a touristic product. Various types of attraction play an important part in this process, the combination of natural and artificial attractions being the most productive. Considering touristic resources of Voronezh region we can distinguish Voronezh state biosphere reserve named after V.M. Peskov as the object possessing the combination of the two attractions. For formation of touristic product based on natural and artificial attractions the question of developing the algorithm of sequence of actions is of great importance. As an example of touristic product developed according to this algorithm there performed the creation of the tour “V gostyakh u skazki” whose central idea is the route “Zapovednaya skazka” with the demonstration of objects united by the concept of Russian fairy-tales. The territory of the reserve provides every facility for accommodation, recreation, having meals and entertaining of guests. As a target audience there were chosen parents with 3–14-year-old children and schoolchildren with their teachers. During the mentioned tour cognitive excursions are proposed. Demonstrated objects are the following: the beaver farm, Beaver museum, interactive house of Fire, Tolshevsky monastery. The algorithm used for the creation of the tour is based on the idea of developing the touristic product and presence or forming the objects of artificial attraction which in its turn combining with natural attraction becomes the effective tool for attracting tourists.

  1. Activity based costing (ABC Method

    Directory of Open Access Journals (Sweden)

    Prof. Ph.D. Saveta Tudorache

    2008-05-01

    Full Text Available In the present paper the need and advantages are presented of using the Activity BasedCosting method, need arising from the need of solving the information pertinence issue. This issue has occurreddue to the limitation of classic methods in this field, limitation also reflected by the disadvantages ofsuch classic methods in establishing complete costs.

  2. Prediction of enthalpy of fusion of pure compounds using an Artificial Neural Network-Group Contribution method

    International Nuclear Information System (INIS)

    Gharagheizi, Farhad; Salehi, Gholam Reza

    2011-01-01

    Highlights: → An Artificial Neural Network-Group Contribution method is presented for prediction of enthalpy of fusion of pure compounds at their normal melting point. → Validity of the model is confirmed using a large evaluated data set containing 4157 pure compounds. → The average percent error of the model is equal to 2.65% in comparison with the experimental data. - Abstract: In this work, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to estimate the enthalpy of fusion of pure chemical compounds at their normal melting point. 4157 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the Squared Correlation Coefficient (R 2 ) of 0.999, Root Mean Square Error of 0.82 kJ/mol, and average absolute deviation lower than 2.65% for the estimated properties from existing experimental values.

  3. Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

    Science.gov (United States)

    Wang, Geng; Zhou, Kexin; Zhang, Yeming

    2018-04-01

    The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

  4. Classification of Weed Species Using Artificial Neural Networks Based on Color Leaf Texture Feature

    Science.gov (United States)

    Li, Zhichen; An, Qiu; Ji, Changying

    The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

  5. Facile control of silica nanoparticles using a novel solvent varying method for the fabrication of artificial opal photonic crystals

    International Nuclear Information System (INIS)

    Gao, Weihong; Rigout, Muriel; Owens, Huw

    2016-01-01

    In this work, the Stöber process was applied to produce uniform silica nanoparticles (SNPs) in the meso-scale size range. The novel aspect of this work was to control the produced silica particle size by only varying the volume of the solvent ethanol used, whilst fixing the other reaction conditions. Using this one-step Stöber-based solvent varying (SV) method, seven batches of SNPs with target diameters ranging from 70 to 400 nm were repeatedly reproduced, and the size distribution in terms of the polydispersity index (PDI) was well maintained (within 0.1). An exponential equation was used to fit the relationship between the particle diameter and ethanol volume. This equation allows the prediction of the amount of ethanol required in order to produce particles of any target diameter within this size range. In addition, it was found that the reaction was completed in approximately 2 h for all batches regardless of the volume of ethanol. Structurally coloured artificial opal photonic crystals (PCs) were fabricated from the prepared SNPs by self-assembly under gravity sedimentation.

  6. Facile control of silica nanoparticles using a novel solvent varying method for the fabrication of artificial opal photonic crystals

    Science.gov (United States)

    Gao, Weihong; Rigout, Muriel; Owens, Huw

    2016-12-01

    In this work, the Stöber process was applied to produce uniform silica nanoparticles (SNPs) in the meso-scale size range. The novel aspect of this work was to control the produced silica particle size by only varying the volume of the solvent ethanol used, whilst fixing the other reaction conditions. Using this one-step Stöber-based solvent varying (SV) method, seven batches of SNPs with target diameters ranging from 70 to 400 nm were repeatedly reproduced, and the size distribution in terms of the polydispersity index (PDI) was well maintained (within 0.1). An exponential equation was used to fit the relationship between the particle diameter and ethanol volume. This equation allows the prediction of the amount of ethanol required in order to produce particles of any target diameter within this size range. In addition, it was found that the reaction was completed in approximately 2 h for all batches regardless of the volume of ethanol. Structurally coloured artificial opal photonic crystals (PCs) were fabricated from the prepared SNPs by self-assembly under gravity sedimentation.

  7. Facile control of silica nanoparticles using a novel solvent varying method for the fabrication of artificial opal photonic crystals

    Energy Technology Data Exchange (ETDEWEB)

    Gao, Weihong [The University of Manchester, School of Materials (United Kingdom); Rigout, Muriel [University of Leeds, School of Design (United Kingdom); Owens, Huw, E-mail: Huw.Owens@manchester.ac.uk [The University of Manchester, School of Materials (United Kingdom)

    2016-12-15

    In this work, the Stöber process was applied to produce uniform silica nanoparticles (SNPs) in the meso-scale size range. The novel aspect of this work was to control the produced silica particle size by only varying the volume of the solvent ethanol used, whilst fixing the other reaction conditions. Using this one-step Stöber-based solvent varying (SV) method, seven batches of SNPs with target diameters ranging from 70 to 400 nm were repeatedly reproduced, and the size distribution in terms of the polydispersity index (PDI) was well maintained (within 0.1). An exponential equation was used to fit the relationship between the particle diameter and ethanol volume. This equation allows the prediction of the amount of ethanol required in order to produce particles of any target diameter within this size range. In addition, it was found that the reaction was completed in approximately 2 h for all batches regardless of the volume of ethanol. Structurally coloured artificial opal photonic crystals (PCs) were fabricated from the prepared SNPs by self-assembly under gravity sedimentation.

  8. Comparison of in-situ gamma ray spectrometry measurements with conventional methods in determination natural and artificial nuclides in soil

    International Nuclear Information System (INIS)

    Al-Masri, M. S.; Doubal, A. W.

    2010-12-01

    Two nuclear analytical techniques (In-Situ Gamma ray spectrometry and laboratory gamma ray spectrometry) for determination of natural and artificial radionuclides in soil have been validated. The first technique depends on determination of radioactivity content of representative samples of the studied soil after laboratory preparation, while the second technique is based on direct determination of radioactivity content of soil using in-situ gamma-ray spectrometer. Analytical validation parameter such as detection limits, repeatability, reproducibility in addition to measurement uncertainties were estimated and compared for both techniques. Comparison results have shown that the determination of radioactivity in soil should apply the two techniques together where each of techniques is characterized by its low detection limit and uncertainty suitable for defined application of measurement. Radioactive isotopes in various locations were determined using the two methods by measuring 40 k, 238 U,and 137 Cs. The results showed that there are differences in attenuation factors due to soil moisture content differences; wet weight corrections should be applied when the two techniques are compared. (author)

  9. A new method for making artificially weathered stone specimens for testing of conservation treatments

    NARCIS (Netherlands)

    Lubelli, B.A.; Hees, R.P.J. van; Nijland, T.G.; Bolhuis, J.

    2015-01-01

    tThe application of new consolidating products on the surface of weathered materials is a common inter-vention technique in conservation practice. Due to the difficulty of producing artificially weatheredsubstrates in a reproducible way, the effect of consolidating products in laboratory is

  10. New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method

    Science.gov (United States)

    Mofavvaz, Shirin; Sohrabi, Mahmoud Reza; Nezamzadeh-Ejhieh, Alireza

    2017-07-01

    In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300 nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R2), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. Moreover, the proposed methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them.

  11. Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jorjani, E.; Poorali, H.A.; Sam, A.; Chelgani, S.C.; Mesroghli, S.; Shayestehfar, M.R. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering

    2009-10-15

    In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) in (ash), volatile matter and moisture (b) in (ash), in (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R{sup 2}) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.

  12. Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    M. Kumar

    2016-01-01

    Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

  13. Artificial snowmaking possibilities and climate change based on regional climate modeling in the Southern Black Forest

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, Philipp; Matzarakis, Andreas [Freiburg Univ. (Germany). Meteorological Inst.; Steiger, Robert [alpS - Centre for Climate Change Adaptation Technologies, Innsbruck (Austria)

    2012-04-15

    Winter sport, especially ski tourism - is one of those sectors of tourism that will be affected by climate change. Ski resorts across the Alps and in the adjacent low mountain ranges react to warm winter seasons by investing in artificial snowmaking. But snowmaking in warm winter seasons is fraught with risk, because sufficiently low air temperature will become less frequent in the future. The present study deals with the ski resort Feldberg, which has 14 ski lifts and 16 ski slopes which is the biggest ski resort in the German Federal state Baden-Wuerttemberg. The impact of climate change in this region is extraordinary important because winter tourism is the main source of revenue for the whole area around the ski resort. The study area is in altitudinal range of 850 to 1450 meters above sea level. At the moment, it is possible to supply one third of the whole area with artificial snow, but there is plan for artificial snowmaking of the whole Feldberg area by the year 2020. Based on this, more detailed investigations of season length and the needed volume of produced snow are necessary. A ski season simulation model (SkiSim 2.0) was applied in order to assess potential impacts of climate change on the Feldberg ski area for the A1B and B1 emission scenarios based on the ECHAM5 GCM downscaled by the REMO RCM. SkiSim 2.0 calculates daily snow depth (natural and technically produced snow) and the required amount of artificial snow for 100 m altitudinal bands. Analysing the development of the number of potential skiing days, it can be assessed whether ski operation is cost covering or not. Model results of the study show a more pronounced and rapid shortening of the ski season in the lower ranges until the year 2100 in each climate scenario. In both the A1B and B1 scenario runs of REMO, a cost-covering ski season of 100 days cannot be guaranteed in every altitudinal range even if snowmaking is considered. In this context, the obtained high-resolution snow data can

  14. Corrosion in artificial saliva of a Ni-Cr-based dental alloy joined by TIG welding and conventional brazing.

    Science.gov (United States)

    Matos, Irma C; Bastos, Ivan N; Diniz, Marília G; de Miranda, Mauro S

    2015-08-01

    Fixed prosthesis and partial dental prosthesis frameworks are usually made from welded Ni-Cr-based alloys. These structures can corrode in saliva and have to be investigated to establish their safety. The purpose of this study was to evaluate the corrosion behavior of joints joined by tungsten inert gas (TIG) welding and conventional brazing in specimens made of commercial Ni-Cr alloy in Fusayama artificial saliva at 37°C (pH 2.5 and 5.5). Eighteen Ni-Cr base metal specimens were cast and welded by brazing or tungsten inert gas methods. The specimens were divided into 3 groups (base metal, 2 welded specimens), and the composition and microstructure were qualitatively evaluated. The results of potential corrosion and corrosion current density were analyzed with a 1-way analysis of variance and the Tukey test for pairwise comparisons (α=.05). Base metal and tungsten inert gas welded material showed equivalent results in electrochemical corrosion tests, while the air-torched specimens exhibited low corrosion resistance. The performance was worst at pH 2.5. These results suggest that tungsten inert gas is a suitable welding process for use in dentistry, because the final microstructure does not reduce the corrosion resistance in artificial saliva at 37°C, even in a corrosion-testing medium that facilitates galvanic corrosion processes. Moreover, the corrosion current density of brazed Ni-Cr alloy joints was significantly higher (P<.001) than the base metal and tungsten inert gas welded joints. Copyright © 2015 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

  15. Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System

    Directory of Open Access Journals (Sweden)

    Litian Duan

    2016-11-01

    Full Text Available In the multiple-reader environment (MRE of radio frequency identification (RFID system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.

  16. How People Interact with Technology based on Natural and Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Vasile MAZILESCU

    2017-04-01

    Full Text Available This paper aims to analyse the different forms of intelligence within organizations in a systemic and inclusive vision, in order to design an integrated environment based on Artificial Intelligence (AI and Collective Intelligence (CI. This way we effectively shift the classical approaches of connecting people with people using collaboration tools (which allow people to work together, such as videoconferencing or email, groupware in virtual space, forums, workflow, of connecting people with a series of content management knowledge (taxonomies and documents classification, ontologies or thesauri, search engines, portals, to the current approaches of connecting people on the use (automatic of operational knowledge to solve problems and make decisions based on intellectual cooperation. Few technologies have the big potential to review how we live, move, and work. Artificial intelligence (AI is nowdays equivalent of electricity and the Internet. AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans.

  17. Artificial intelligence (AI)-based relational matching and multimodal medical image fusion: generalized 3D approaches

    Science.gov (United States)

    Vajdic, Stevan M.; Katz, Henry E.; Downing, Andrew R.; Brooks, Michael J.

    1994-09-01

    A 3D relational image matching/fusion algorithm is introduced. It is implemented in the domain of medical imaging and is based on Artificial Intelligence paradigms--in particular, knowledge base representation and tree search. The 2D reference and target images are selected from 3D sets and segmented into non-touching and non-overlapping regions, using iterative thresholding and/or knowledge about the anatomical shapes of human organs. Selected image region attributes are calculated. Region matches are obtained using a tree search, and the error is minimized by evaluating a `goodness' of matching function based on similarities of region attributes. Once the matched regions are found and the spline geometric transform is applied to regional centers of gravity, images are ready for fusion and visualization into a single 3D image of higher clarity.

  18. Elements of decisional dynamics: An agent-based approach applied to artificial financial market

    Science.gov (United States)

    Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

    2018-02-01

    This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

  19. Comparison of three artificial digestion methods for detection of non-encapsulated Trichinella pseudospiralis larvae in pork.

    Science.gov (United States)

    Nöckler, K; Reckinger, S; Szabó, I; Maddox-Hyttel, C; Pozio, E; van der Giessen, J; Vallée, I; Boireau, P

    2009-02-23

    In a ring trial involving five laboratories (A, B, C, D, and E), three different methods of artificial digestion were compared for the detection of non-encapsulated Trichinella pseudospiralis larvae in minced meat. Each sample panel consisted of ten 1g minced pork samples. All samples in each panel were derived from a bulk meat preparation with a nominal value of either 7 or 17 larvae per g (lpg). Samples were tested for the number of muscle larvae using the magnetic stirrer method (labs A, B, and E), stomacher method (lab B), and Trichomatic 35 (labs C and D). T. pseudospiralis larvae were found in all 120 samples tested. For samples with 7 lpg, larval recoveries were significantly higher using the stomacher method versus the magnetic stirrer method, but there were no significant differences for samples with 17 lpg. In comparing laboratory results irrespective of the method used, lab B detected a significantly higher number of larvae than lab E for samples with 7 lpg, and lab E detected significantly less larvae than labs A, B, and D in samples with 17 lpg. The lowest overall variation for quantitative results (i.e. larval recoveries which were outside the tolerance range) was achieved by using the magnetic stirrer method (22%), followed by the stomacher method (25%), and Trichomatic 35 (30%). Results revealed that T. pseudospiralis larvae in samples with a nominal value of 7 and 17 lpg can be detected by all three methods of artificial digestion.

  20. The deconvolution of complex spectra by artificial immune system

    Science.gov (United States)

    Galiakhmetova, D. I.; Sibgatullin, M. E.; Galimullin, D. Z.; Kamalova, D. I.

    2017-11-01

    An application of the artificial immune system method for decomposition of complex spectra is presented. The results of decomposition of the model contour consisting of three components, Gaussian contours, are demonstrated. The method of artificial immune system is an optimization method, which is based on the behaviour of the immune system and refers to modern methods of search for the engine optimization.

  1. Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

    International Nuclear Information System (INIS)

    Kapur, G.S.; Sastry, M.I.S.; Jaiswal, A.K.; Sarpal, A.S.

    2004-01-01

    The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using 1 H and 13 C nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their 1 H NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the 'measured' and 'estimated' RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only 1 H NMR data) also showed high correlation between the 'measured' and 'estimated' RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets

  2. Artificial Leg Design and Control Research of a Biped Robot with Heterogeneous Legs Based on PID Control Algorithm

    Directory of Open Access Journals (Sweden)

    Hualong Xie

    2015-04-01

    Full Text Available A biped robot with heterogeneous legs (BRHL is proposed to provide an ideal test-bed for intelligent bionic legs (IBL. To make artificial leg gait better suited to a human, a four-bar mechanism is used as its knee joint, and a pneumatic artificial muscle (PAM is used as its driving source. The static mathematical model of PAM is established and the mechanical model of a single degree of freedom of a knee joint driven by PAM is analyzed. A control simulation of an artificial leg based on PID control algorithm is carried out and the simulation results indicate that the artificial leg can simulate precisely a normal human walking gait.

  3. Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night

    Directory of Open Access Journals (Sweden)

    Davinia Font

    2015-04-01

    Full Text Available This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV. The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1 the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2 the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and −17%, respectively.

  4. Artificial life and Piaget.

    Science.gov (United States)

    Mueller, Ulrich; Grobman, K H.

    2003-04-01

    Artificial life provides important theoretical and methodological tools for the investigation of Piaget's developmental theory. This new method uses artificial neural networks to simulate living phenomena in a computer. A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework. We contrast artificial life with traditional cognitivist approaches, discuss the role of innateness in development, and examine the relation between physiological and psychological explanations of intelligent behaviour.

  5. Application of Classical and Lie Transform Methods to Zonal Perturbation in the Artificial Satellite

    Science.gov (United States)

    San-Juan, J. F.; San-Martin, M.; Perez, I.; Lopez-Ochoa, L. M.

    2013-08-01

    A scalable second-order analytical orbit propagator program is being carried out. This analytical orbit propagator combines modern perturbation methods, based on the canonical frame of the Lie transform, and classical perturbation methods in function of orbit types or the requirements needed for a space mission, such as catalog maintenance operations, long period evolution, and so on. As a first step on the validation of part of our orbit propagator, in this work we only consider the perturbation produced by zonal harmonic coefficients in the Earth's gravity potential, so that it is possible to analyze the behaviour of the perturbation methods involved in the corresponding analytical theories.

  6. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jida Xing

    2015-06-01

    Full Text Available In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared

  7. Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC, to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

  8. Research on Flow Field Perception Based on Artificial Lateral Line Sensor System

    Directory of Open Access Journals (Sweden)

    Guijie Liu

    2018-03-01

    Full Text Available In nature, the lateral line of fish is a peculiar and important organ for sensing the surrounding hydrodynamic environment, preying, escaping from predators and schooling. In this paper, by imitating the mechanism of fish lateral canal neuromasts, we developed an artificial lateral line system composed of micro-pressure sensors. Through hydrodynamic simulations, an optimized sensor structure was obtained and the pressure distribution models of the lateral surface were established in uniform flow and turbulent flow. Carrying out the corresponding underwater experiment, the validity of the numerical simulation method is verified by the comparison between the experimental data and the simulation results. In addition, a variety of effective research methods are proposed and validated for the flow velocity estimation and attitude perception in turbulent flow, respectively and the shape recognition of obstacles is realized by the neural network algorithm.

  9. Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

    Science.gov (United States)

    Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

    2016-10-01

    An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

  10. Modified three-dimensional skull base model with artificial dura mater, cranial nerves, and venous sinuses for training in skull base surgery: technical note.

    Science.gov (United States)

    Mori, Kentaro; Yamamoto, Takuji; Oyama, Kazutaka; Ueno, Hideaki; Nakao, Yasuaki; Honma, Keiichirou

    2008-12-01

    Experience with dissection of the cavernous sinus and the temporal bone is essential for training in skull base surgery, but the opportunities for cadaver dissection are very limited. A modification of a commercially available prototype three-dimensional (3D) skull base model, made by a selective laser sintering method and incorporating surface details and inner bony structures such as the inner ear structures and air cells, is proposed to include artificial dura mater, cranial nerves, venous sinuses, and the internal carotid artery for such surgical training. The transpetrosal approach and epidural cavernous sinus surgery (Dolenc's technique) were performed on this modified model using a high speed drill or ultrasonic bone curette under an operating microscope. The model could be dissected in almost the same way as a real cadaver. The modified 3D skull base model provides a good educational tool for training in skull base surgery.

  11. Silk fibroin based biomimetic artificial extracellular matrix for hepatic tissue engineering applications

    International Nuclear Information System (INIS)

    Kasoju, Naresh; Bora, Utpal

    2012-01-01

    Hepatic tissue engineering, which aims to construct artificial liver tissues, requires a suitable extracellular matrix (ECM) for growth and proliferation of metabolically active hepatocytes. The current paper describes the development of a biomimetic artificial ECM, for hepatic tissue engineering applications, by mimicking the architectural features and biochemical composition of native ECM. Electrospinning was chosen as the fabrication technique of choice, while regenerated silk fibroin (RSF) and galactosylated chitosan (GalCS) were chosen as materials of choice. Poly(ethylene oxide) was used as a processing aid. Methodical optimization studies were performed to obtain smooth and continuous nanofibers with homogenous size distribution. Extensive characterization studies were performed to determine its morphological, physical, chemical/structural, thermal and cytotoxicity properties. Subsequently, detailed in vitro hepatocyte compatibility studies were performed using HepG2 cell line. Remarkably, the studies revealed that the growth, viability, metabolic activity and proliferation of hepatocytes were relatively superior on RSF–GalCS scaffold than on pure RSF and pure GalCS. In summary, the electrospun nanofibrous RSF–GalCS scaffold tries to mimic both architectural and biochemical features of native ECM, and hence could be an appropriate scaffold for in vitro engineering of hepatic tissue. However, additional experiments are needed to confirm the superiority in characteristic functionality of hepatocytes growing on RSF–GalCS scaffold in relation to RSF and GalCS scaffolds, and to test its behavior in vivo. (paper)

  12. Automatic system for radar echoes filtering based on textural features and artificial intelligence

    Science.gov (United States)

    Hedir, Mehdia; Haddad, Boualem

    2017-10-01

    Among the very popular Artificial Intelligence (AI) techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been retained to process Ground Echoes (GE) on meteorological radar images taken from Setif (Algeria) and Bordeaux (France) with different climates and topologies. To achieve this task, AI techniques were associated with textural approaches. We used Gray Level Co-occurrence Matrix (GLCM) and Completed Local Binary Pattern (CLBP); both methods were largely used in image analysis. The obtained results show the efficiency of texture to preserve precipitations forecast on both sites with the accuracy of 98% on Bordeaux and 95% on Setif despite the AI technique used. 98% of GE are suppressed with SVM, this rate is outperforming ANN skills. CLBP approach associated to SVM eliminates 98% of GE and preserves precipitations forecast on Bordeaux site better than on Setif's, while it exhibits lower accuracy with ANN. SVM classifier is well adapted to the proposed application since the average filtering rate is 95-98% with texture and 92-93% with CLBP. These approaches allow removing Anomalous Propagations (APs) too with a better accuracy of 97.15% with texture and SVM. In fact, textural features associated to AI techniques are an efficient tool for incoherent radars to surpass spurious echoes.

  13. Artificial Intelligence Based Control Power Optimization on Tailless Aircraft. [ARMD Seedling Fund Phase I

    Science.gov (United States)

    Gern, Frank; Vicroy, Dan D.; Mulani, Sameer B.; Chhabra, Rupanshi; Kapania, Rakesh K.; Schetz, Joseph A.; Brown, Derrell; Princen, Norman H.

    2014-01-01

    Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling large arrays of control devices on innovative air vehicles. Artificial neutral networks are inspired by biological nervous systems and neurocomputing has successfully been applied to a variety of complex optimization problems. This project investigates the potential of applying neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while keeping system weights within acceptable limits. The main objective of this project is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces. A Nastran aeroservoelastic finite element model is used to generate a learning database of hinge moment and actuation power characteristics for an array of flight conditions and control surface deflections. An artificial neural network incorporating a genetic algorithm then uses this training data to perform control allocation optimization for the investigated aircraft configuration. The phase I project showed that optimization results for the sum of required hinge moments are improved by more than 12% over the best Nastran solution by using the neural network optimization process.

  14. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  15. An artificial muscle model unit based on inorganic nanosheet sliding by photochemical reaction.

    Science.gov (United States)

    Nabetani, Yu; Takamura, Hazuki; Hayasaka, Yuika; Sasamoto, Shin; Tanamura, Yoshihiko; Shimada, Tetsuya; Masui, Dai; Takagi, Shinsuke; Tachibana, Hiroshi; Tong, Zhiwei; Inoue, Haruo

    2013-04-21

    From the viewpoint of developing photoresponsive supramolecular systems in microenvironments to exhibit more sophisticated photo-functions even at the macroscopic level, inorganic/organic hybrid compounds based on clay or niobate nanosheets as the microenvironments were prepared, characterized, and examined for their photoreactions. We show here a novel type of artificial muscle model unit having much similarity with that in natural muscle fibrils. Upon photoirradiation, the organic/inorganic hybrid nanosheets reversibly slide horizontally on a giant scale, and the interlayer spaces in the layered hybrid structure shrink and expand vertically. In particular, our layered hybrid molecular system exhibits a macroscopic morphological change on a giant scale (~1500 nm) compared with the molecular size of ~1 nm, based on a reversible sliding mechanism.

  16. Promise of a low power mobile CPU based embedded system in artificial leg control.

    Science.gov (United States)

    Hernandez, Robert; Zhang, Fan; Zhang, Xiaorong; Huang, He; Yang, Qing

    2012-01-01

    This paper presents the design and implementation of a low power embedded system using mobile processor technology (Intel Atom™ Z530 Processor) specifically tailored for a neural-machine interface (NMI) for artificial limbs. This embedded system effectively performs our previously developed NMI algorithm based on neuromuscular-mechanical fusion and phase-dependent pattern classification. The analysis shows that NMI embedded system can meet real-time constraints with high accuracies for recognizing the user's locomotion mode. Our implementation utilizes the mobile processor efficiently to allow a power consumption of 2.2 watts and low CPU utilization (less than 4.3%) while executing the complex NMI algorithm. Our experiments have shown that the highly optimized C program implementation on the embedded system has superb advantages over existing PC implementations on MATLAB. The study results suggest that mobile-CPU-based embedded system is promising for implementing advanced control for powered lower limb prostheses.

  17. The prediction of the residual life of electromechanical equipment based on the artificial neural network

    Science.gov (United States)

    Zhukovskiy, Yu L.; Korolev, N. A.; Babanova, I. S.; Boikov, A. V.

    2017-10-01

    This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.

  18. Research on reactor power controller based on artificial immune P and PID cascade control technology

    International Nuclear Information System (INIS)

    Cheng Shouyu; Peng Minjun; Liu Xinkai

    2014-01-01

    The Reactor Power control system usually adopts the traditional PID controller, the traditional PID controller can meet the operating requirements, but the control effect is not very good. In order to improve this condition, the paper proposes an immune P and PID cascade controller which based the immune mechanism of B-cell co-operating with T-cell, the nuclear power controller based on artificial immune is less reported. In order to verify and validate the control strategy, the designed controller debugs with the full-scope real-time simulation system of nuclear power plants. The simulation results shows that the immune controller can effectively improve the dynamic operating characteristics of the reactor system, and the immune controller is superior to the traditional PID controller in control performance. (authors)

  19. A biologically inspired artificial muscle based on fiber-reinforced and electropneumatic dielectric elastomers

    Science.gov (United States)

    Liu, Lei; Zhang, Chi; Luo, Meng; Chen, Xi; Li, Dichen; Chen, Hualing

    2017-08-01

    Dielectric elastomers (DEs) have great potential for use as artificial muscles because of the following characteristics: electrical activity, fast and large deformation under stimuli, and softness as natural muscles. Inspired by the traditional McKibben actuators, in this study, we developed a cylindrical soft fiber-reinforced and electropneumatic DE artificial muscle (DEAM) by mimicking the spindle shape of natural muscles. Based on continuum mechanics and variation principle, the inhomogeneous actuation of DEAMs was theoretically modeled and calculated. Prototypes of DEAMs were prepared to validate the design concept and theoretical model. The theoretical predictions are consistent with the experimental results; they successfully predicted the evolutions of the contours of DEAMs with voltage. A pneumatically supported high prestretch in the hoop direction was achieved by our DEAM prototype without buckling the soft fibers sandwiched by the DE films. Besides, a continuously tunable prestretch in the actuation direction was achieved by varying the supporting pressure. Using the theoretical model, the failure modes, maximum actuations, and critical voltages were analyzed; they were highly dependent on the structural parameters, i.e., the cylinder aspect ratio, prestretch level, and supporting pressure. The effects of structural parameters and supporting pressure on the actuation performance were also investigated to optimize the DEAMs.

  20. Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach

    Directory of Open Access Journals (Sweden)

    Ying Liu

    2017-07-01

    Full Text Available We apply the concept of artificial and controlled interference in a two-hop relay network with an untrusted relay, aiming at enhancing the wireless communication secrecy between the source and the destination node. In order to shield the square quadrature amplitude-modulated (QAM signals transmitted from the source node to the relay, the destination node designs and transmits artificial noise (AN symbols to jam the relay reception. The objective of our considered AN design is to degrade the error probability performance at the untrusted relay, for different types of channel state information (CSI at the destination. By considering perfect knowledge of the instantaneous CSI of the source-to-relay and relay-to-destination links, we first present an analytical expression for the symbol error rate (SER performance at the relay. Based on the assumption of an average power constraint at the destination node, we then derive the optimal phase and power distribution of the AN that maximizes the SER at the relay. Furthermore, we obtain the optimal AN design for the case where only statistical CSI is available at the destination node. For both cases, our study reveals that the Gaussian distribution is generally not optimal to generate AN symbols. The presented AN design takes into account practical parameters for the communication links, such as QAM signaling and maximum likelihood decoding.

  1. A Game Theoretic Framework for Incentive-Based Models of Intrinsic Motivation in Artificial Systems

    Directory of Open Access Journals (Sweden)

    Kathryn Elizabeth Merrick

    2013-10-01

    Full Text Available An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players’ optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

  2. A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems.

    Science.gov (United States)

    Merrick, Kathryn E; Shafi, Kamran

    2013-01-01

    An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players' optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

  3. An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system

    International Nuclear Information System (INIS)

    Shao, Meng; Zhu, Xin-Jian; Cao, Hong-Fei; Shen, Hai-Feng

    2014-01-01

    The commercial viability of PEMFC (proton exchange membrane fuel cell) systems depends on using effective fault diagnosis technologies in PEMFC systems. However, many researchers have experimentally studied PEMFC (proton exchange membrane fuel cell) systems without considering certain fault conditions. In this paper, an ANN (artificial neural network) ensemble method is presented that improves the stability and reliability of the PEMFC systems. In the first part, a transient model giving it flexibility in application to some exceptional conditions is built. The PEMFC dynamic model is built and simulated using MATLAB. In the second, using this model and experiments, the mechanisms of four different faults in PEMFC systems are analyzed in detail. Third, the ANN ensemble for the fault diagnosis is built and modeled. This model is trained and tested by the data. The test result shows that, compared with the previous method for fault diagnosis of PEMFC systems, the proposed fault diagnosis method has higher diagnostic rate and generalization ability. Moreover, the partial structure of this method can be altered easily, along with the change of the PEMFC systems. In general, this method for diagnosis of PEMFC has value for certain applications. - Highlights: • We analyze the principles and mechanisms of the four faults in PEMFC (proton exchange membrane fuel cell) system. • We design and model an ANN (artificial neural network) ensemble method for the fault diagnosis of PEMFC system. • This method has high diagnostic rate and strong generalization ability

  4. Comparison of standard resampling methods for performance estimation of artificial neural network ensembles

    OpenAIRE

    Green, Michael; Ohlsson, Mattias

    2007-01-01

    Estimation of the generalization performance for classification within the medical applications domain is always an important task. In this study we focus on artificial neural network ensembles as the machine learning technique. We present a numerical comparison between five common resampling techniques: k-fold cross validation (CV), holdout, using three cutoffs, and bootstrap using five different data sets. The results show that CV together with holdout $0.25$ and $0.50$ are the best resampl...

  5. Orbit Determination from Tracking Data of Artificial Satellite Using the Method of Differential Correction

    OpenAIRE

    Byoung-Sun Lee; Jung-Hyun Jo; Sang-Young Park; Kyu-Hong Choi; Chun-Hwey Kim

    1988-01-01

    The differential correction process determining osculating orbital elements as correct as possible at a given instant of time from tracking data of artificial satellite was accomplished. Preliminary orbital elements were used as an initial value of the differential correction procedure and iterated until the residual of real observation(O) and computed observation(C) was minimized. Tracking satellite was NOAA-9 or TIROS-N series. Two types of tracking data were prediction data precomputed fro...

  6. Event classification and optimization methods using artificial intelligence and other relevant techniques: Sharing the experiences

    Science.gov (United States)

    Mohamed, Abdul Aziz; Hasan, Abu Bakar; Ghazali, Abu Bakar Mhd.

    2017-01-01

    Classification of large data into respected classes or groups could be carried out with the help of artificial intelligence (AI) tools readily available in the market. To get the optimum or best results, optimization tool could be applied on those data. Classification and optimization have been used by researchers throughout their works, and the outcomes were very encouraging indeed. Here, the authors are trying to share what they have experienced in three different areas of applied research.

  7. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    OpenAIRE

    Sukarno, Pudjo; Sidarto, Kuntjoro Adji; Trisnobudi, Amoranto; Setyoadi, Delint Ira; Rohani, Nancy; Darmadi, Darmadi

    2007-01-01

    Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained ...

  8. METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS

    Directory of Open Access Journals (Sweden)

    L. V. Serebryanaya

    2016-01-01

    Full Text Available The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.

  9. The optimal design support system for shell components of vehicles using the methods of artificial intelligence

    Science.gov (United States)

    Szczepanik, M.; Poteralski, A.

    2016-11-01

    The paper is devoted to an application of the evolutionary methods and the finite element method to the optimization of shell structures. Optimization of thickness of a car wheel (shell) by minimization of stress functional is considered. A car wheel geometry is built from three surfaces of revolution: the central surface with the holes destined for the fastening bolts, the surface of the ring of the wheel and the surface connecting the two mentioned earlier. The last one is subjected to the optimization process. The structures are discretized by triangular finite elements and subjected to the volume constraints. Using proposed method, material properties or thickness of finite elements are changing evolutionally and some of them are eliminated. As a result the optimal shape, topology and material or thickness of the structures are obtained. The numerical examples demonstrate that the method based on evolutionary computation is an effective technique for solving computer aided optimal design.

  10. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

    1998-01-01

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

  11. A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Sébastien Martin

    Full Text Available Electrical Impedance Tomography (EIT is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time. Several nonlinear approaches have been proposed as a replacement for the linear solver, but in practice very few are capable of stable, high-quality, and real-time EIT imaging because of their very low robustness to errors and inaccurate modeling, or because they require considerable computational effort.In this paper, a post-processing technique based on an artificial neural network (ANN is proposed to obtain a nonlinear solution to the inverse problem, starting from a linear solution. While common reconstruction methods based on ANNs estimate the solution directly from the measured data, the method proposed here enhances the solution obtained from a linear solver.Applying a linear reconstruction algorithm before applying an ANN reduces the effects of noise and modeling errors. Hence, this approach significantly reduces the error associated with solving 2D inverse problems using machine-learning-based algorithms.This work presents radical enhancements in the stability of nonlinear methods for biomedical EIT applications.

  12. Comparison of Boolean analysis and standard phylogenetic methods using artificially evolved and natural mt-tRNA sequences from great apes.

    Science.gov (United States)

    Ari, Eszter; Ittzés, Péter; Podani, János; Thi, Quynh Chi Le; Jakó, Eena

    2012-04-01

    Boolean analysis (or BOOL-AN; Jakó et al., 2009. BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction. Mol. Phylogenet. Evol. 52, 887-97.), a recently developed method for sequence comparison uses the Iterative Canonical Form of Boolean functions. It considers sequence information in a way entirely different from standard phylogenetic methods (i.e. Maximum Parsimony, Maximum-Likelihood, Neighbor-Joining, and Bayesian analysis). The performance and reliability of Boolean analysis were tested and compared with the standard phylogenetic methods, using artificially evolved - simulated - nucleotide sequences and the 22 mitochondrial tRNA genes of the great apes. At the outset, we assumed that the phylogeny of Hominidae is generally well established, and the guide tree of artificial sequence evolution can also be used as a benchmark. These offer a possibility to compare and test the performance of different phylogenetic methods. Trees were reconstructed by each method from 2500 simulated sequences and 22 mitochondrial tRNA sequences. We also introduced a special re-sampling method for Boolean analysis on permuted sequence sites, the P-BOOL-AN procedure. Considering the reliability values (branch support values of consensus trees and Robinson-Foulds distances) we used for simulated sequence trees produced by different phylogenetic methods, BOOL-AN appeared as the most reliable method. Although the mitochondrial tRNA sequences of great apes are relatively short (59-75 bases long) and the ratio of their constant characters is about 75%, BOOL-AN, P-BOOL-AN and the Bayesian approach produced the same tree-topology as the established phylogeny, while the outcomes of Maximum Parsimony, Maximum-Likelihood and Neighbor-Joining methods were equivocal. We conclude that Boolean analysis is a promising alternative to existing methods of sequence comparison for phylogenetic reconstruction and congruence analysis. Copyright © 2012 Elsevier Inc. All

  13. Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

    Science.gov (United States)

    Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

    2018-05-17

    Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

  14. Development and evaluation of a novel lossless image compression method (AIC: artificial intelligence compression method) using neural networks as artificial intelligence

    International Nuclear Information System (INIS)

    Fukatsu, Hiroshi; Naganawa, Shinji; Yumura, Shinnichiro

    2008-01-01

    This study was aimed to validate the performance of a novel image compression method using a neural network to achieve a lossless compression. The encoding consists of the following blocks: a prediction block; a residual data calculation block; a transformation and quantization block; an organization and modification block; and an entropy encoding block. The predicted image is divided into four macro-blocks using the original image for teaching; and then redivided into sixteen sub-blocks. The predicted image is compared to the original image to create the residual image. The spatial and frequency data of the residual image are compared and transformed. Chest radiography, computed tomography (CT), magnetic resonance imaging, positron emission tomography, radioisotope mammography, ultrasonography, and digital subtraction angiography images were compressed using the AIC lossless compression method; and the compression rates were calculated. The compression rates were around 15:1 for chest radiography and mammography, 12:1 for CT, and around 6:1 for other images. This method thus enables greater lossless compression than the conventional methods. This novel method should improve the efficiency of handling of the increasing volume of medical imaging data. (author)

  15. Comparison of Piezo-material based Energy Transduction Systems for Artificial Nanoswimmer

    Science.gov (United States)

    Nain, S.; Rathore, J. S.; Sharma, N. N.

    2018-04-01

    The energy harnessing is a process of obtaining energy from the surrounding environment and converting into electrical energy. In the last two decades, there has been a plenteous study in energy harnessing. Now a day, energy harnessing using piezoelectric materials has drawn attention of researchers due to low cost, flexibility and light weight. The benefits of piezoelectric material can be utilized by designing a self-powered device for artificial nanoswimmer. Some of the ceramics which displays the piezoelectric effect are lead-zirconate-titanate (PZT), lead-titanate (PbTiO2), lead-zirconate (PbZrO3) and Barium Titanate (BaTiO3). PZT is most extensively used piezoelectric material in the field of energy harnessing but it is brittle in nature. Lead based piezoelectric materials are toxic in nature and may not suitable for in-vivo biomedical applications. To eradicate this problem, researchers are interested in synthesizing lead free piezoelectric material such as Aluminium Nitride (AIN), Barium Titanate (BaTiO3) and Polyvinylidenefluoride (PVDF). The biocompatibility of PVDF makes it appropriate to be used for energy harnessing in human body for applications like on board powering of nanoswimmer for various disease detection and drug delivery. In this paper, a cantilever beam is being simulated in COMSOL to study electric potential generated on the surface of beam made of different piezoelectric materials such as AIN, PVDF and PZT due to fluidic pressure, which will be utilized as energy for actuation of artificial nanoswimmer. Piezo-based cantilever beams have been compared and maximum electric potential is being observed in PVDF based beam. PVDF seems most promising piezoelectric material for in-vivo biomedical application and it is readily available.

  16. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  17. An example of the use of the DELPHI method: future prospects of artificial heart techniques in France

    International Nuclear Information System (INIS)

    Derian, Jean-Claude; Morize, Francoise; Vernejoul, Pierre de; Vial, Renee

    1971-01-01

    The artificial heart is still only a research project surrounded by numerous uncertainties which make it very difficult to estimate, at the moment, the possibilities for future development of this technique in France. A systematic analysis of the hazards which characterize this project has been undertaken in the following report: restricting these uncertainties has required a taking into account of opinions of specialists concerned with type of research or its upshot. We have achieved this by adapting an investigation technique which is still unusual in France, the DELPHI method. This adaptation has allowed the confrontation and statistical aggregation of the opinions given by a body of a hundred experts who were consulted through a program of sequential interrogations which studied in particular, the probable date of the research issue, the clinical cases which require the use of an artificial heart, as well as the probable future needs. After having taken into account the economic constraints, we can deduce from these results the probable amount of plutonium 238 needed in the hypothesis where isotopic generator would be retained for the energetics feeding of the artificial heart [fr

  18. Long-time stability effects of quadrature and artificial viscosity on nodal discontinuous Galerkin methods for gas dynamics

    Science.gov (United States)

    Durant, Bradford; Hackl, Jason; Balachandar, Sivaramakrishnan

    2017-11-01

    Nodal discontinuous Galerkin schemes present an attractive approach to robust high-order solution of the equations of fluid mechanics, but remain accompanied by subtle challenges in their consistent stabilization. The effect of quadrature choices (full mass matrix vs spectral elements), over-integration to manage aliasing errors, and explicit artificial viscosity on the numerical solution of a steady homentropic vortex are assessed over a wide range of resolutions and polynomial orders using quadrilateral elements. In both stagnant and advected vortices in periodic and non-periodic domains the need arises for explicit stabilization beyond the numerical surface fluxes of discontinuous Galerkin spectral elements. Artificial viscosity via the entropy viscosity method is assessed as a stabilizing mechanism. It is shown that the regularity of the artificial viscosity field is essential to its use for long-time stabilization of small-scale features in nodal discontinuous Galerkin solutions of the Euler equations of gas dynamics. Supported by the Department of Energy Predictive Science Academic Alliance Program Contract DE-NA0002378.

  19. An empirical model of the Earth's bow shock based on an artificial neural network

    Science.gov (United States)

    Pallocchia, Giuseppe; Ambrosino, Danila; Trenchi, Lorenzo

    2014-05-01

    All of the past empirical models of the Earth's bow shock shape were obtained by best-fitting some given surfaces to sets of observed crossings. However, the issue of bow shock modeling can be addressed by means of artificial neural networks (ANN) as well. In this regard, here it is presented a perceptron, a simple feedforward network, which computes the bow shock distance along a given direction using the two angular coordinates of that direction, the bow shock predicted distance RF79 (provided by Formisano's model (F79)) and the upstream alfvénic Mach number Ma. After a brief description of the ANN architecture and training method, we discuss the results of the statistical comparison, performed over a test set of 1140 IMP8 crossings, between the prediction accuracies of ANN and F79 models.

  20. Bidding strategy based on artificial intelligence for a competitive electric market

    International Nuclear Information System (INIS)

    Hong, Y.-Y.; Tsai, S.-W.; Weng, M.-T.

    2001-01-01

    A bidding strategy using a fuzzy-c-mean (FCM) algorithm and the artificial neural network (ANN) was developed for competitive electric markets. The nodal price information was assumed to be released into the market. The FCM was used, first, to classify the daily load pattern into peak, medium-peak and off-peak levels and, secondly, to classify the competitive generation companies (gencos) into less-menacing, possible-menacing and menacing gencos. The back-propagation ANN was used for determining the bidding price for a genco. The FCM results aided in lessening the training data and reducing the ANN input nodes. The IEEE 30-busbar system was used for illustrating the applicability of the proposed method. (Author)

  1. Bidding strategy based on artificial intelligence for a competitive electric market

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Y.-Y.; Tsai, S.-W.; Weng, M.-T. [Chung Yuan Univ., Dept. of Electrical Engineering, Chung Li (China)

    2001-03-01

    A bidding strategy using a fuzzy-c-mean (FCM) algorithm and the artificial neural network (ANN) was developed for competitive electric markets. The nodal price information was assumed to be released into the market. The FCM was used, first, to classify the daily load pattern into peak, medium-peak and off-peak levels and, secondly, to classify the competitive generation companies (gencos) into less-menacing, possible-menacing and menacing gencos. The back-propagation ANN was used for determining the bidding price for a genco. The FCM results aided in lessening the training data and reducing the ANN input nodes. The IEEE 30-busbar system was used for illustrating the applicability of the proposed method. (Author)

  2. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Directory of Open Access Journals (Sweden)

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  3. A Comparison Between Heliosat-2 and Artificial Neural Network Methods for Global Horizontal Irradiance Retrievals over Desert Environments

    Science.gov (United States)

    Ghedira, H.; Eissa, Y.

    2012-12-01

    Global horizontal irradiance (GHI) retrievals at the surface of any given location could be used for preliminary solar resource assessments. More accurately, the direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) are also required to estimate the global tilt irradiance, mainly used for fixed flat plate collectors. Two different satellite-based models for solar irradiance retrievals have been applied over the desert environment of the United Arab Emirates (UAE). Both models employ channels of the SEVIRI instrument, onboard the geostationary satellite Meteosat Second Generation, as their main inputs. The satellite images used in this study have a temporal resolution of 15-min and a spatial resolution of 3-km. The objective of this study is to compare between the GHI retrieved using the Heliosat-2 method and an artificial neural network (ANN) ensemble method over the UAE. The high-resolution visible channel of SEVIRI is used in the Heliosat-2 method to derive the cloud index. The cloud index is then used to compute the cloud transmission, while the cloud-free GHI is computed from the Linke turbidity factor. The product of the cloud transmission and the cloud-free GHI denotes the estimated GHI. A constant underestimation is observed in the estimated GHI over the dataset available in the UAE. Therefore, the cloud-free DHI equation in the model was recalibrated to fix the bias. After recalibration, results over the UAE show a root mean square error (RMSE) value of 10.1% and a mean bias error (MBE) of -0.5%. As for the ANN approach, six thermal channels of SEVIRI were used to estimate the DHI and the total optical depth of the atmosphere (δ). An ensemble approach is employed to obtain a better generalizability of the results, as opposed to using one single weak network. The DNI is then computed from the estimated δ using the Beer-Bouguer-Lambert law. The GHI is computed from the DNI and DHI estimates. The RMSE for the estimated GHI obtained over an

  4. Spray-coated carbon nanotube carpets for creeping reduction of conducting polymer based artificial muscles

    Science.gov (United States)

    Simaite, Aiva; Delagarde, Aude; Tondu, Bertrand; Souères, Philippe; Flahaut, Emmanuel; Bergaud, Christian

    2017-01-01

    During cyclic actuation, conducting polymer based artificial muscles are often creeping from the initial movement range. One of the likely reasons of such behaviour is unbalanced charging during conducting polymer oxidation and reduction. To improve the actuation reversibility and subsequently the long time performance of ionic actuators, we suggest using spray-coated carbon nanotube (CNT) carpets on the surface of the conducting polymer electrodes. We show that carbon nanotubes facilitate a conducting polymer redox reaction and improve its reversibility. Consequently, in the long term, charge accumulation in the polymer film is avoided leading to a significantly improved lifetime performance during cycling actuation. To our knowledge, it is the first time a simple solution to an actuator creeping problem has been suggested.

  5. Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

    Science.gov (United States)

    Wilson, Eric Lee

    Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

  6. Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kemal Fidanboylu

    2009-09-01

    Full Text Available Artificial neural network (ANN based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP with different training algorithms, Radial Basis Function (RBF network and General Regression Neural Network (GRNN are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.

  7. Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    Guo Jiansheng; Wang Zutong; Zheng Mingfa; Wang Ying

    2014-01-01

    Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

  8. Powered Upper Limb Orthosis Actuation System Based on Pneumatic Artificial Muscles

    Science.gov (United States)

    Chakarov, Dimitar; Veneva, Ivanka; Tsveov, Mihail; Venev, Pavel

    2018-03-01

    The actuation system of a powered upper limb orthosis is studied in the work. To create natural safety in the mutual "man-robot" interaction, an actuation system based on pneumatic artificial muscles (PAM) is selected. Experimentally obtained force/contraction diagrams for bundles, consisting of different number of muscles are shown in the paper. The pooling force and the stiffness of the pneumatic actuators is assessed as a function of the number of muscles in the bundle and the supply pressure. Joint motion and torque is achieved by antagonistic actions through pulleys, driven by bundles of pneumatic muscles. Joint stiffness and joint torques are determined on condition of a power balance, as a function of the joint position, pressure, number of muscles and muscles

  9. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

  10. Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars

    Directory of Open Access Journals (Sweden)

    M. P. Jarabo-Amores

    2010-01-01

    Full Text Available The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs. In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships.

  11. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

    International Nuclear Information System (INIS)

    Almonacid, F.; Pérez-Higueras, P.J.; Fernández, Eduardo F.; Hontoria, L.

    2014-01-01

    Highlights: • The output of the majority of renewables energies depends on the variability of the weather conditions. • The short-term forecast is going to be essential for effectively integrating solar energy sources. • A new method based on artificial neural network to predict the power output of a PV generator one hour ahead is proposed. • This new method is based on dynamic artificial neural network to predict global solar irradiance and the air temperature. • The methodology developed can be used to estimate the power output of a PV generator with a satisfactory margin of error. - Abstract: One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy

  12. Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran

    Science.gov (United States)

    Fijani, Elham; Nadiri, Ata Allah; Asghari Moghaddam, Asghar; Tsai, Frank T.-C.; Dixon, Barnali

    2013-10-01

    Contamination of wells with nitrate-N (NO3-N) poses various threats to human health. Contamination of groundwater is a complex process and full of uncertainty in regional scale. Development of an integrative vulnerability assessment methodology can be useful to effectively manage (including prioritization of limited resource allocation to monitor high risk areas) and protect this valuable freshwater source. This study introduces a supervised committee machine with artificial intelligence (SCMAI) model to improve the DRASTIC method for groundwater vulnerability assessment for the Maragheh-Bonab plain aquifer in Iran. Four different AI models are considered in the SCMAI model, whose input is the DRASTIC parameters. The SCMAI model improves the committee machine artificial intelligence (CMAI) model by replacing the linear combination in the CMAI with a nonlinear supervised ANN framework. To calibrate the AI models, NO3-N concentration data are divided in two datasets for the training and validation purposes. The target value of the AI models in the training step is the corrected vulnerability indices that relate to the first NO3-N concentration dataset. After model training, the AI models are verified by the second NO3-N concentration dataset. The results show that the four AI models are able to improve the DRASTIC method. Since the best AI model performance is not dominant, the SCMAI model is considered to combine the advantages of individual AI models to achieve the optimal performance. The SCMAI method re-predicts the groundwater vulnerability based on the different AI model prediction values. The results show that the SCMAI outperforms individual AI models and committee machine with artificial intelligence (CMAI) model. The SCMAI model ensures that no water well with high NO3-N levels would be classified as low risk and vice versa. The study concludes that the SCMAI model is an effective model to improve the DRASTIC model and provides a confident estimate of the

  13. An Effective Scheduling-Based RFID Reader Collision Avoidance Model and Its Resource Allocation via Artificial Immune Network

    Directory of Open Access Journals (Sweden)

    Shanjin Wang

    2016-01-01

    Full Text Available Radio frequency identification, that is, RFID, is one of important technologies in Internet of Things. Reader collision does impair the tag identification efficiency of an RFID system. Many developed methods, for example, the scheduling-based series, that are used to avoid RFID reader collision, have been developed. For scheduling-based methods, communication resources, that is, time slots, channels, and power, are optimally assigned to readers. In this case, reader collision avoidance is equivalent to an optimization problem related to resource allocation. However, the existing methods neglect the overlap between the interrogation regions of readers, which reduces the tag identification rate (TIR. To resolve this shortage, this paper attempts to build a reader-to-reader collision avoidance model considering the interrogation region overlaps (R2RCAM-IRO. In addition, an artificial immune network for resource allocation (RA-IRO-aiNet is designed to optimize the proposed model. For comparison, some comparative numerical simulations are arranged. The simulation results show that the proposed R2RCAM-IRO is an effective model where TIR is improved significantly. And especially in the application of reader-to-reader collision avoidance, the proposed RA-IRO-aiNet outperforms GA, opt-aiNet, and PSO in the total coverage area of readers.

  14. Accretion rate in mangroves sediment at Sungai Miang, Pahang, Malaysia: 230Thexcess versus artificial horizon marker method

    International Nuclear Information System (INIS)

    Kamaruzzaman Yunus; Jamil Tajam; Hasrizal Shaari; Noor Azhar Mohd Shazili; Misbahul Mohd Amin

    2008-01-01

    Mangroves have enormous ecological value and one of their important role is to act as an efficient sediment trappers which dominantly supplied by rivers and the atmosphere to the oceans. Applying the 230 Th excess method, an average accretion rate of 0.54 cm yr -1 was obtained. this is comparable to that of an artificial horizon marker method giving an average of 0.54 cm yr -1 . The 230 Th excess method provides a rapid and simple method of evaluating 230 Th excess accumulation histories in sediment cores. Sample preparation is also significantly simplified, thus providing a relatively quick and easy method for the determination of the accretion rate in mangrove area. (author)

  15. Estimation of the groundwater recharge in laterita using the artificial tritium method

    International Nuclear Information System (INIS)

    Castro Rubio Poli, D. de; Kimmelman e Silva, A.A.; Pfisterer, U.

    1990-01-01

    An estimation of the groundwater recharge was made, for the first time, in laterita, which is a alteration of dunite. This work was carried out at the city of Cajati-Jacupiranga, situated in the Ribeira Valley, state of Sao Paulo. The moisture migration in unsaturated zones was analized using water tagget with artificial tritium. In the place studied, an annual recharge of 1070mm was estimated. This value corresponds to 65% of local precipitation (1650 mm/year). The difference can be considered as a loss through evaporation, evapotranspiration and run off. (author) [pt

  16. Assessment and in vitro experiment of artificial anal sphincter system based on rebuilding the rectal sensation function.

    Science.gov (United States)

    Zan, Peng; Liu, Jinding; Jiang, Enyu; Wang, Hua

    2014-05-01

    In this paper, a novel artificial anal sphincter (AAS) system based on rebuilding the rectal sensation function is proposed to treat human fecal incontinence. The executive mechanism of the traditional AAS system was redesigned and integrated for a simpler structure and better durability. The novel executive mechanism uses a sandwich structure to simulate the basic function of the natural human anal sphincter. To rebuild the lost rectal sensation function caused by fecal incontinence, we propose a novel method for rebuilding the rectal sensation function based on an Optimal Wavelet Packet Basis (OWPB) using the Davies-Bouldin (DB) index and a support vector machine (SVM). OWPB using a DB index is used for feature vector extraction, while a SVM is adopted for pattern recognition.Furthermore, an in vitro experiment with the AAS system based on rectal sensation function rebuilding was carried out. Experimental results indicate that the novel executive mechanism can simulate the basic function of the natural human anal sphincter, and the proposed method is quite effective for rebuilding rectal sensation in patients.

  17. Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm-artificial neural network method

    Science.gov (United States)

    Darvishvand, Leila; Kamkari, Babak; Kowsary, Farshad

    2018-03-01

    In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.

  18. Cooperative research and development for artificial intelligence based reactor diagnostic system

    International Nuclear Information System (INIS)

    Reifman, J.; Wei, T.Y.C.; Abboud, R.G.; Chasensky, T.M.

    1994-01-01

    Artificial Intelligence (AI) techniques in the form of knowledge-based Expert Systems (ESs) have been proposed to provide on-line decision-making support for plant operators during both normal and emergency conditions. However, in spite of the great interest in these advanced techniques, their application in the diagnosis of large-scale processes has not yet reached its full potential because of limitations of the knowledge base. These limitations include problems with knowledge acquisition and the use of an event-oriented approach for process diagnosis. The knowledge base of process diagnosis ESs is generally acquired in a heuristic fashion through empirical associations between plant symptoms and component malfunctions with no reliance on fundamental physical principles. This nonsystematic construction of the knowledge base causes, among other problems, the encoded information to be biased and limited towards the developer's own experience and judgmental knowledge. The use of an event-oriented approach for process diagnosis requires the developer of the knowledge base to anticipate and formulate rules to cover every conceivable plant situation. In addition to yielding a large knowledge base, an undesirable characteristic for an on-line real-time advisory system, an event-oriented approach for diagnosis of large and complex thermal-hydraulic (T-H) based processes cannot guarantee functional completeness and is likely to fail under unanticipated circumstances. Hence, these limitations preclude an effective verification and validation of the knowledge base which is required in industrial applications. In contrast to the heuristic construction of a rigid knowledge base that uses an event-oriented approach for process diagnosis, the authors propose a different approach that involves the systematic construction of a hierarchical knowledge base with two levels

  19. SEAI: Social Emotional Artificial Intelligence Based on Damasio’s Theory of Mind

    Directory of Open Access Journals (Sweden)

    Lorenzo Cominelli

    2018-02-01

    Full Text Available A socially intelligent robot must be capable to extract meaningful information in real time from the social environment and react accordingly with coherent human-like behavior. Moreover, it should be able to internalize this information, to reason on it at a higher level, build its own opinions independently, and then automatically bias the decision-making according to its unique experience. In the last decades, neuroscience research highlighted the link between the evolution of such complex behavior and the evolution of a certain level of consciousness, which cannot leave out of a body that feels emotions as discriminants and prompters. In order to develop cognitive systems for social robotics with greater human-likeliness, we used an “understanding by building” approach to model and implement a well-known theory of mind in the form of an artificial intelligence, and we tested it on a sophisticated robotic platform. The name of the presented system is SEAI (Social Emotional Artificial Intelligence, a cognitive system specifically conceived for social and emotional robots. It is designed as a bio-inspired, highly modular, hybrid system with emotion modeling and high-level reasoning capabilities. It follows the deliberative/reactive paradigm where a knowledge-based expert system is aimed at dealing with the high-level symbolic reasoning, while a more conventional reactive paradigm is deputed to the low-level processing and control. The SEAI system is also enriched by a model that simulates the Damasio’s theory of consciousness and the theory of Somatic Markers. After a review of similar bio-inspired cognitive systems, we present the scientific foundations and their computational formalization at the basis of the SEAI framework. Then, a deeper technical description of the architecture is disclosed underlining the numerous parallelisms with the human cognitive system. Finally, the influence of artificial emotions and feelings, and their link

  20. The criteria for selecting a method for unfolding neutron spectra based on the information entropy theory

    International Nuclear Information System (INIS)

    Zhu, Qingjun; Song, Fengquan; Ren, Jie; Chen, Xueyong; Zhou, Bin

    2014-01-01

    To further expand the application of an artificial neural network in the field of neutron spectrometry, the criteria for choosing between an artificial neural network and the maximum entropy method for the purpose of unfolding neutron spectra was presented. The counts of the Bonner spheres for IAEA neutron spectra were used as a database, and the artificial neural network and the maximum entropy method were used to unfold neutron spectra; the mean squares of the spectra were defined as the differences between the desired and unfolded spectra. After the information entropy of each spectrum was calculated using information entropy theory, the relationship between the mean squares of the spectra and the information entropy was acquired. Useful information from the information entropy guided the selection of unfolding methods. Due to the importance of the information entropy, the method for predicting the information entropy using the Bonner spheres' counts was established. The criteria based on the information entropy theory can be used to choose between the artificial neural network and the maximum entropy method unfolding methods. The application of an artificial neural network to unfold neutron spectra was expanded. - Highlights: • Two neutron spectra unfolding methods, ANN and MEM, were compared. • The spectrum's entropy offers useful information for selecting unfolding methods. • For the spectrum with low entropy, the ANN was generally better than MEM. • The spectrum's entropy was predicted based on the Bonner spheres' counts

  1. Securing mobile ad hoc networks using danger theory-based artificial immune algorithm.

    Science.gov (United States)

    Abdelhaq, Maha; Alsaqour, Raed; Abdelhaq, Shawkat

    2015-01-01

    A mobile ad hoc network (MANET) is a set of mobile, decentralized, and self-organizing nodes that are used in special cases, such as in the military. MANET properties render the environment of this network vulnerable to different types of attacks, including black hole, wormhole and flooding-based attacks. Flooding-based attacks are one of the most dangerous attacks that aim to consume all network resources and thus paralyze the functionality of the whole network. Therefore, the objective of this paper is to investigate the capability of a danger theory-based artificial immune algorithm called the mobile dendritic cell algorithm (MDCA) to detect flooding-based attacks in MANETs. The MDCA applies the dendritic cell algorithm (DCA) to secure the MANET with additional improvements. The MDCA is tested and validated using Qualnet v7.1 simulation tool. This work also introduces a new simulation module for a flooding attack called the resource consumption attack (RCA) using Qualnet v7.1. The results highlight the high efficiency of the MDCA in detecting RCAs in MANETs.

  2. Entropy-based benchmarking methods

    NARCIS (Netherlands)

    Temurshoev, Umed

    2012-01-01

    We argue that benchmarking sign-volatile series should be based on the principle of movement and sign preservation, which states that a bench-marked series should reproduce the movement and signs in the original series. We show that the widely used variants of Denton (1971) method and the growth

  3. Comparison of Artificial Neural Networks and GIS Based Solar Analysis for Solar Potential Estimation

    Science.gov (United States)

    Konakoǧlu, Berkant; Usta, Ziya; Cömert, Çetin; Gökalp, Ertan

    2016-04-01

    Nowadays, estimation of solar potential plays an important role in planning process for sustainable cities. The use of solar panels, which produces electricity directly from the sun, has become popular in accordance with developing technologies. Since the use of solar panels enables the users to decrease costs and increase yields, the use of solar panels will be more popular in the future. Production of electricity is not convenient for all circumstances. Shading effects, massive clouds and rainy weather are some factors that directly affect the production of electricity from solar energy. Hence, before the installation of solar panels, it is crucial to conduct spatial analysis and estimate the solar potential of the place that the solar panel will be installed. There are several approaches to determine the solar potential. Examination of the applications in the literature reveals that the applications conducted for determining the solar potential are divided into two main categories. Solar potential is estimated either by using artificial neural network approach in which statistical parameters such as the duration of sun shine, number of clear days, solar radiation etc. are used, or by spatial analysis conducted in GIS approaches in which spatial parameters such as, latitude, longitude, slope, aspect etc. are used. In the literature, there are several studies that use both approaches but the literature lacks of a study related to the comparison of these approaches. In this study, Karadeniz Technical University campus has been selected as study area. Monthly average values of the number of clear sky days, air temperature, atmospheric pressure, relative humidity, sunshine duration and solar radiation parameters obtained for the years between 2005 and 2015 will be used to perform artificial neural network analysis to estimate the solar potential of the study area. The solar potential will also be estimated by using GIS-based solar analysis modules. The results of

  4. Screening of a new cadmium hyperaccumulator, Galinsoga parviflora, from winter farmland weeds using the artificially high soil cadmium concentration method.

    Science.gov (United States)

    Lin, Lijin; Jin, Qian; Liu, Yingjie; Ning, Bo; Liao, Ming'an; Luo, Li

    2014-11-01

    A new method, the artificially high soil cadmium (Cd) concentration method, was used to screen for Cd hyperaccumulators among winter farmland weeds. Galinsoga parviflora was the most promising remedial plant among 5 Cd accumulators or hyperaccumulators. In Cd concentration gradient experiments, as soil Cd concentration increased, root and shoot biomass decreased, and their Cd contents increased. In additional concentration gradient experiments, superoxide dismutase and peroxidase activities increased with soil Cd concentrations up to 75 mg kg(-1) , while expression of their isoenzymes strengthened. Catalase (CAT) activity declined and CAT isoenzyme expression weakened at soil Cd concentrations less than 50 mg kg(-1) . The maxima of Cd contents in shoots and roots were 137.63 mg kg(-1) and 105.70 mg kg(-1) , respectively, at 100 mg kg(-1) Cd in soil. The root and shoot bioconcentration factors exceeded 1.0, as did the translocation factor. In a field experiment, total extraction of Cd by shoots was 1.35 mg m(-2) to 1.43 mg m(-2) at soil Cd levels of 2.04 mg kg(-1) to 2.89 mg kg(-1) . Therefore, the artificially high soil Cd concentration method was effective for screening Cd hyperaccumulators. Galinsoga parviflora is a Cd hyperaccumulator that could be used to efficiently remediate Cd-contaminated farmland soil. © 2014 SETAC.

  5. Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

    Science.gov (United States)

    Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

    2015-01-01

    The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

  6. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  7. Fracture resistance of zirconia-based implant abutments after artificial long-term aging.

    Science.gov (United States)

    Alsahhaf, Abdulaziz; Spies, Benedikt Christopher; Vach, Kirstin; Kohal, Ralf-Joachim

    2017-02-01

    To investigate the survival rate, fracture strength, bending moments, loading to fracture and fracture modes of different designs of zirconia abutments after dynamic loading with thermocycling, and compare these values to titanium abutments. A total of 80 abutment samples were divided into 5 test groups of 16 samples in each group. The study included the following groups, "Group 1" CAD/CAM produced all-zirconia abutments, "Group 2" titanium abutments, "Group 3" zirconia-abutments adhesively luted to a titanium base, "Group 4" prefabricated all-zirconia abutments and "Group 5" zirconia-abutments glass soldered to a titanium base. Half the number of samples in each group was exposed to 1.2 million loading cycles (5-years simulation) in the chewing simulator. The samples that survived the artificial aging were later tested for fracture strength in a universal testing machine. The remaining 8 samples of the group were directly tested for fracture strength. All samples exposed to the 5-years artificial aging survived except of six samples in one group (Group 1). The surviving samples were later fracture tested in the universal testing machine. The bending moments (Ncm) values were as follow: Exposed groups: "Group 1" 94.5Ncm; "Group 2" 599.2Ncm; "Group 3" 477.5Ncm; "Group 4" 314.4Ncm; "Group 5" 509.4Ncm. Non-exposed groups: "Group 1" 269.3Ncm; "Group 2" 474.2Ncm; "Group 3" 377.6Ncm; "Group 4" 265.4Ncm; "Group 5" 372.4Ncm. Except in Group 1, the values were higher in the exposed groups, although, statistically there was no difference (p>0.05). The one-piece ZrO2-abutment group (Group 1 and Group 4) exhibited lower values, while the two-piece ZrO2-abutment groups (Group 3 and Group 5) showed similar values and fracture modes like the titanium abutment group. The titanium abutment group showed the highest values of bending moments among all groups. The implant-abutment connection area appeared to influence the bending moment value and the fracture mode of the tested

  8. Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter.

    Science.gov (United States)

    Tang, Fengna; Wang, Youqing

    2017-11-01

    Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

  9. Autonomously Generating Operations Sequences for a Mars Rover Using Artificial Intelligence-Based Planning

    Science.gov (United States)

    Sherwood, R.; Mutz, D.; Estlin, T.; Chien, S.; Backes, P.; Norris, J.; Tran, D.; Cooper, B.; Rabideau, G.; Mishkin, A.; Maxwell, S.

    2001-07-01

    This article discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from high-level science and engineering activities. This prototype is based on ASPEN, the Automated Scheduling and Planning Environment. This artificial intelligence (AI)-based planning and scheduling system will automatically generate a command sequence that will execute within resource constraints and satisfy flight rules. An automated planning and scheduling system encodes rover design knowledge and uses search and reasoning techniques to automatically generate low-level command sequences while respecting rover operability constraints, science and engineering preferences, environmental predictions, and also adhering to hard temporal constraints. This prototype planning system has been field-tested using the Rocky 7 rover at JPL and will be field-tested on more complex rovers to prove its effectiveness before transferring the technology to flight operations for an upcoming NASA mission. Enabling goal-driven commanding of planetary rovers greatly reduces the requirements for highly skilled rover engineering personnel. This in turn greatly reduces mission operations costs. In addition, goal-driven commanding permits a faster response to changes in rover state (e.g., faults) or science discoveries by removing the time-consuming manual sequence validation process, allowing rapid "what-if" analyses, and thus reducing overall cycle times.

  10. A Review and Performance Investigation of NPCC Based UPQC by Using Various Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Venkata Rami Reddy K

    2017-03-01

    Full Text Available This paper presents a comprehensive review and performance investigation of Neutral Point Clamped Converter (NPCC based Unified Power Quality Conditioner (UPQC by using Artificial Intelligent (AI techniques. A Novel application of various levels of Diode Clamped Multi-Level Inverters [DCMLI] with Anti Phase Opposition and Disposition (APOD Pulse Width Modulation (PWM Scheme to Unified Power Quality Conditioner (UPQC. The Power Quality problem became a burning issues since the starting of high voltage AC transmission system. Hence, in this article it has been discussed to mitigate the PQ issues in high voltage AC systems through a three phase four wire Unified Power Quality Conditioner (UPQC under non-linear loads. The emphasised PQ problems such as voltage and current harmonics along with voltage sags and swells have also been discussed with improved performance. Also, it proposes to control the DCMLI based UPQC through conventional control schemes. Thus application of these control technique makes the system performance in par with the standards and also compared with existing system. The simulation results based on MATLAB/Simulink are discussed in detail to support the concept developed in the paper.

  11. Parametric trends analysis of the critical heat flux based on artificial neural networks

    International Nuclear Information System (INIS)

    Moon, S.K.; Baek, W.P.; Chang, S.H.

    1996-01-01

    Parametric trends of the critical heat flux (CHF) are analyzed by applying artificial neural networks (ANNs) to a CHF data base for upward flow of water in uniformly heated vertical round tubes. The analyses are performed from three viewpoints, i.e., for fixed inlet conditions, for fixed exit conditions, and based on local conditions hypothesis. Katto's and Groeneveld et al. dimensionless parameters are used to train the ANNs with the experimental CHF data. The trained ANNs predict the CHF better than any other conventional correlations, showing RMS errors of 8.9%, 13.1% and 19.3% for fixed inlet conditions, for fixed exit conditions, and for local conditions hypothesis, respectively. The parametric trends of the CHF obtained from those trained ANNs show a general agreement with previous understanding. In addition, this study provides more comprehensive information and indicates interesting points for the effects of the tube diameter, the heated length, and the mass flux. It is expected that better understanding of the parametric trends is feasible with an extended data base. (orig.)

  12. Perspective on the Ongoing Replacement of Artificial and Animal-Based Dyes with Alternative Natural Pigments in Foods and Beverages.

    Science.gov (United States)

    Schweiggert, Ralf M

    2018-03-28

    This perspective highlights current trends, advances, and challenges related to the replacement of artificial dyes and the insect-based carmine with alternative natural pigments. Briefly reviewing the history of food coloration, key publications and public events leading to diverse concerns about artificial dyes and carmine will be summarized. An overview about promising alternatives in the market and those under development is provided, including a separate section on coloring foodstuffs. The perspective aims at supporting readers to keep abreast with the enormous efforts undertaken by the food and beverage industry to replace certain food dyes.

  13. Efficacy of Blood Sources and Artificial Blood Feeding Methods in Rearing of Aedes aegypti (Diptera: Culicidae) for Sterile Insect Technique and Incompatible Insect Technique Approaches in Sri Lanka.

    Science.gov (United States)

    Gunathilaka, Nayana; Ranathunge, Tharaka; Udayanga, Lahiru; Abeyewickreme, Wimaladharma

    2017-01-01

    Selection of the artificial membrane feeding technique and blood meal source has been recognized as key considerations in mass rearing of vectors. Artificial membrane feeding techniques, namely, glass plate, metal plate, and Hemotek membrane feeding method, and three blood sources (human, cattle, and chicken) were evaluated based on feeding rates, fecundity, and hatching rates of Aedes aegypti . Significance in the variations among blood feeding was investigated by one-way ANOVA, cluster analysis of variance (ANOSIM), and principal coordinates (PCO) analysis. Feeding rates of Ae. aegypti significantly differed among the membrane feeding techniques as suggested by one-way ANOVA ( p feeding technique. Blood feeding rate of Ae. aegypti was higher with human blood followed by cattle and chicken blood, respectively. However, no significant difference was observed from the mosquitoes fed with cattle and human blood, in terms of fecundity, oviposition rate, and fertility as suggested by one-way ANOVA ( p > 0.05). Metal plate method could be recommended as the most effective membrane feeding technique for mass rearing of Ae. aegypti , due to its high feeding rate and cost effectiveness. Cattle blood could be recommended for mass rearing Ae. aegypti .

  14. Artificial senses for characterization of food quality

    Institute of Scientific and Technical Information of China (English)

    HUANG Yan-bo; LAN Yu-bin; R.E. Lacey

    2004-01-01

    Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: vision, hearing, smell, taste and touch.In the characterization of food quality, people assess the samples sensorially and differentiate "good" from "bad" on a continuum.However, the human sensory system is subjective, with mental and physical inconsistencies, and needs time to work. Artificial senses such as machine vision, the electronic ear, electronic nose, electronic tongue, artificial mouth and even artificial the head have been developed that mimic the human senses. These artificial senses are coordinated individually or collectively by a pattern recognition technique, typically artificial neural networks, which have been developed based on studies of the mechanism of the human brain. Such a structure has been used to formulate methods for rapid characterization of food quality. This research presents and discusses individual artificial sensing systems. With the concept of multi-sensor data fusion these sensor systems can work collectively in some way. Two such fused systems, artificial mouth and artificial head, are described and discussed. It indicates that each of the individual systems has their own artificially sensing ability to differentiate food samples. It further indicates that with a more complete mimic of human intelligence the fused systems are more powerful than the individual systems in differentiation of food samples.

  15. Use of Synergistic Interactions to Fabricate Strong, Tough, and Conductive Artificial Nacre Based on Graphene Oxide and Chitosan.

    Science.gov (United States)

    Wan, Sijie; Peng, Jingsong; Li, Yuchen; Hu, Han; Jiang, Lei; Cheng, Qunfeng

    2015-10-27

    Graphene is the strongest and stiffest material, leading to the development of promising applications in many fields. However, the assembly of graphene nanosheets into macrosized nanocomposites for practical applications remains a challenge. Nacre in its natural form sets the "gold standard" for toughness and strength, which serves as a guide to the assembly of graphene nanosheets into high-performance nanocomposites. Here we show the strong, tough, conductive artificial nacre based on graphene oxide through synergistic interactions of hydrogen and covalent bonding. Tensile strength and toughness was 4 and 10 times higher, respectively, than that of natural nacre. The exceptional integrated strong and tough artificial nacre has promising applications in aerospace, artificial muscle, and tissue engineering, especially for flexible supercapacitor electrodes due to its high electrical conductivity. The use of synergistic interactions is a strategy for the development of high-performance nanocomposites.

  16. Artificial intelligence/fuzzy logic method for analysis of combined signals from heavy metal chemical sensors

    International Nuclear Information System (INIS)

    Turek, M.; Heiden, W.; Riesen, A.; Chhabda, T.A.; Schubert, J.; Zander, W.; Krueger, P.; Keusgen, M.; Schoening, M.J.

    2009-01-01

    The cross-sensitivity of chemical sensors for several metal ions resembles in a way the overlapping sensitivity of some biological sensors, like the optical colour receptors of human retinal cone cells. While it is difficult to assign crisp classification values to measurands based on complex overlapping sensory signals, fuzzy logic offers a possibility to mathematically model such systems. Current work goes into the direction of mixed heavy metal solutions and the combination of fuzzy logic with heavy metal-sensitive, silicon-based chemical sensors for training scenarios of arbitrary sensor/probe combinations in terms of an electronic tongue. Heavy metals play an important role in environmental analysis. As trace elements as well as water impurities released from industrial processes they occur in the environment. In this work, the development of a new fuzzy logic method based on potentiometric measurements performed with three different miniaturised chalcogenide glass sensors in different heavy metal solutions will be presented. The critical validation of the developed fuzzy logic program will be demonstrated by means of measurements in unknown single- and multi-component heavy metal solutions. Limitations of this program and a comparison between calculated and expected values in terms of analyte composition and heavy metal ion concentration will be shown and discussed.

  17. Artificial intelligence/fuzzy logic method for analysis of combined signals from heavy metal chemical sensors

    Energy Technology Data Exchange (ETDEWEB)

    Turek, M. [Institute of Nano- and Biotechnologies (INB), Aachen University of Applied Sciences, Campus Juelich, Juelich (Germany); Institute of Bio- and Nanosystems (IBN), Research Centre Juelich GmbH, Juelich (Germany); Heiden, W.; Riesen, A. [Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin (Germany); Chhabda, T.A. [Institute of Nano- and Biotechnologies (INB), Aachen University of Applied Sciences, Campus Juelich, Juelich (Germany); Schubert, J.; Zander, W. [Institute of Bio- and Nanosystems (IBN), Research Centre Juelich GmbH, Juelich (Germany); Krueger, P. [Institute of Biochemistry and Molecular Biology, RWTH Aachen, Aachen (Germany); Keusgen, M. [Institute for Pharmaceutical Chemistry, Philipps-University Marburg, Marburg (Germany); Schoening, M.J. [Institute of Nano- and Biotechnologies (INB), Aachen University of Applied Sciences, Campus Juelich, Juelich (Germany); Institute of Bio- and Nanosystems (IBN), Research Centre Juelich GmbH, Juelich (Germany)], E-mail: m.j.schoening@fz-juelich.de

    2009-10-30

    The cross-sensitivity of chemical sensors for several metal ions resembles in a way the overlapping sensitivity of some biological sensors, like the optical colour receptors of human retinal cone cells. While it is difficult to assign crisp classification values to measurands based on complex overlapping sensory signals, fuzzy logic offers a possibility to mathematically model such systems. Current work goes into the direction of mixed heavy metal solutions and the combination of fuzzy logic with heavy metal-sensitive, silicon-based chemical sensors for training scenarios of arbitrary sensor/probe combinations in terms of an electronic tongue. Heavy metals play an important role in environmental analysis. As trace elements as well as water impurities released from industrial processes they occur in the environment. In this work, the development of a new fuzzy logic method based on potentiometric measurements performed with three different miniaturised chalcogenide glass sensors in different heavy metal solutions will be presented. The critical validation of the developed fuzzy logic program will be demonstrated by means of measurements in unknown single- and multi-component heavy metal solutions. Limitations of this program and a comparison between calculated and expected values in terms of analyte composition and heavy metal ion concentration will be shown and discussed.

  18. Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

    Science.gov (United States)

    Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

    2013-01-01

    As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

  19. An Integrated Conceptual Environment based on Collective Intelligence and Distributed Artificial Intelligence for Connecting People on Problem Solving

    Directory of Open Access Journals (Sweden)

    Vasile MAZILESCU

    2012-12-01

    Full Text Available This paper aims to analyze the different forms of intelligence within organizations in a systemic and inclusive vision, in order to conceptualize an integrated environment based on Distributed Artificial Intelligence (DAI and Collective Intelligence (CI. In this way we effectively shift the classical approaches of connecting people with people using collaboration tools (which allow people to work together, such as videoconferencing or email, groupware in virtual space, forums, workflow, of connecting people with a series of content management knowledge (taxonomies and documents classification, ontologies or thesauri, search engines, portals, to the current approaches of connecting people on the use (automatic of operational knowledge to solve problems and make decisions based on intellectual cooperation. The best way to use collective intelligence is based on knowledge mobilization and semantic technologies. We must not let computers to imitate people but to support people think and develop their ideas within a group. CI helps people to think together, while DAI tries to support people so as to limit human error. Within an organization, to manage CI is to combine instruments like Semantic Technologies (STs, knowledge mobilization methods for developing Knowledge Management (KM strategies, and the processes that promote connection and collaboration between individual minds in order to achieve collective objectives, to perform a task or to solve increasingly economic complex problems.

  20. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Directory of Open Access Journals (Sweden)

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.