WorldWideScience

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. An Improved Minimum Distance Method Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    MDM (minimum distance method) is a very popular algorithm in state recognition. But it has a presupposition, that is, the distance within one class must be shorter enough than the distance between classes. When this presupposition is not satisfied, the method is no longer valid. In order to overcome the shortcomings of MDM, an improved mi nimum distance method (IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstrate that IMDM has two advantages, that is, the rate of recognition is faster and the accuracy of recognition is higher compared with MDM.

  3. WLAN indoor location method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Zhou Mu; Sun Ying; Xu Yubin; Deng Zhian; Meng Weixiao

    2010-01-01

    WLAN indoor location method based on artificial neural network (ANN) is analyzed.A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase.According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances.Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor (KNN) and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.

  4. Study on optimization control method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SUN Shao-guang; XU Zhen-Iiang

    2005-01-01

    In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

  5. Artificial neural network based inverse design method for circular sliding slopes

    Institute of Scientific and Technical Information of China (English)

    丁德馨; 张志军

    2004-01-01

    Current design method for circular sliding slopes is not so reasonable that it often results in slope sliding. As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

  6. Training a Feed-Forward Neural Network with Artificial Bee Colony based Backpropagation Method

    Directory of Open Access Journals (Sweden)

    Sudarshan Nandy

    2012-09-01

    Full Text Available Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feedforward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-freesolution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristicalgorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and thisalgorithm is implemented in several applications for an improved optimized outcome. The proposedmethod in this paper includes an improved artificial bee colony algorithm based back-propagation neuralnetwork training method for fast and improved convergence rate of the hybrid neural network learningmethod. The result is analysed with the genetic algorithm based back-propagation method, and it isanother hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the lightof efficiency of proposed method in terms of convergence speed and rate.

  7. Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

    Science.gov (United States)

    Leong, Harrison Monfook

    1988-01-01

    General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. A New Method Based on Multi Agent System and Artificial Immune System for Systematic Maintenance

    Directory of Open Access Journals (Sweden)

    Adel Abdelhadi

    2014-05-01

    Full Text Available This study propose a novel method for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling. The proposed approach is inspired by the behavior of the human body. We have implemented a problem-solving approach for optimizing the processing time, methods based on Métaheuristiques. This hybridization is between a Multi agent system and inspirations of the human body, especially artificial immune system. The effectiveness of our approach has been demonstrated repeatedly in this study. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

  13. 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.

  14. A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Honglu Zhu

    2015-12-01

    Full Text Available The power prediction for photovoltaic (PV power plants has significant importance for their grid connection. Due to PV power’s periodicity and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable. This paper presents a method combining the advantages of the wavelet decomposition (WD and artificial neural network (ANN to solve this problem. With the ability of ANN to address nonlinear relationships, theoretical solar irradiance and meteorological variables are chosen as the input of the hybrid model based on WD and ANN. The output power of the PV plant is decomposed using WD to separated useful information from disturbances. The ANNs are used to build the models of the decomposed PV output power. Finally, the outputs of the ANN models are reconstructed into the forecasted PV plant power. The presented method is compared with the traditional forecasting method based on ANN. The results shows that the method described in this paper needs less calculation time and has better forecasting precision.

  15. a Detection Method of Artificial Area from High Resolution Remote Sensing Images Based on Multi Scale and Multi Feature Fusion

    Science.gov (United States)

    Li, P.; Hu, X.; Hu, Y.; Ding, Y.; Wang, L.; Li, L.

    2017-05-01

    In order to solve the problem of automatic detection of artificial objects in high resolution remote sensing images, a method for detection of artificial areas in high resolution remote sensing images based on multi-scale and multi feature fusion is proposed. Firstly, the geometric features such as corner, straight line and right angle are extracted from the original resolution, and the pseudo corner points, pseudo linear features and pseudo orthogonal angles are filtered out by the self-constraint and mutual restraint between them. Then the radiation intensity map of the image with high geometric characteristics is obtained by the linear inverse distance weighted method. Secondly, the original image is reduced to multiple scales and the visual saliency image of each scale is obtained by adaptive weighting of the orthogonal saliency, the local brightness and contrast which are calculated at the corresponding scale. Then the final visual saliency image is obtained by fusing all scales' visual saliency images. Thirdly, the visual saliency images of artificial areas based on multi scales and multi features are obtained by fusing the geometric feature energy intensity map and visual saliency image obtained in previous decision level. Finally, the artificial areas can be segmented based on the method called OTSU. Experiments show that the method in this paper not only can detect large artificial areas such as urban city, residential district, but also detect the single family house in the countryside correctly. The detection rate of artificial areas reached 92 %.

  16. OPTIMIZATION OF OPERATING PARAMETERS FOR EDM PROCESS BASED ON THE TAGUCHI METHOD AND ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    A.Thillaivanan,

    2010-12-01

    Full Text Available In this paper the complexity of electrical discharge machining process which is very difficult to determine optimal cutting parameters for improving cutting performance has been reported. Optimization of operating parameters is an important step in machining, particularly for operating unconventional machiningprocedure like EDM. A suitable selection of machining parameters for the electrical discharge machining process relies heavily on the operators’ technologies and experience because of their numerous and diverse range. Machining parameters tables provided by the machine tool builder can not meet the operators’ requirements, since for anarbitrary desired machining time for a particular job, they do not provide the optimal machining conditions. An approach to determine parameters setting is proposed. Based on the Taguchi parameter design method and the analysis of variance, the significant factors affecting the machining performance such as total machining time, oversize and taper for a hole machined by EDM process, are determined.Artificial neural networks are highly flexible modeling tools with an ability to learn the mapping between input variables and output feature spaces. The superiority of using artificial neural networks inmodeling machining processes make easier to model the EDM process with dimensional input and output spaces. On the basis of the developed neural network model, for a required total machining time, oversize and taper the corresponding process parameters to be set in EDM by using the developed and trained ANN are determined.

  17. Polarimetric synthetic aperture radar image unsupervised classification method based on artificial immune system

    Science.gov (United States)

    Jie, Yu; Gang, Wang; Teng, Zhu; Xiaojuan, Li; Qin, Yan

    2014-01-01

    An unsupervised classification method based on the H/α classifier and artificial immune system (AIS) is proposed to overcome the inefficiencies that arise when traditional classification methods deal with polarimetric synthetic aperture radar (PolSAR) data having large numbers of overlapping pixels and excess polarimetric information. The method is composed of two steps. First, Cloude-Pottier decomposition is used to obtain the entropy H and the scattering angle α. The classification result based on the H/α plane is used to initialize the AIS algorithm. Second, to obtain accurate results, the AIS clonal selection algorithm is used to perform an iterative calculation. As a self-organizing, self-recognizing, and self-optimizing algorithm, the AIS is able to obtain a global optimal solution and better classification results by making use of both the scattering mechanism of ground features and polarimetric scattering characteristics. The effectiveness and feasibility of this method are demonstrated by experiments using a NASA-JPL PolSAR image and a high-resolution PolSAR image of Lingshui autonomous county in Hainan Province.

  18. Artificial Neural Network Based Method to Mitigate Temporary Over-voltages

    Directory of Open Access Journals (Sweden)

    Iman Sadeghkhani

    2011-09-01

    Full Text Available Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching over-voltages. The most effective method for the limitation of the switching over-voltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch.‎ We introduce a harmonic index that its minimum value is corresponding to the best-case switching time.‎ Also, this paper ‎presents an Artificial Neural Network (ANN-based approach to ‎estimate the optimum switching instants for real time applications. In the proposed ANN, second order Levenberg–Marquardt ‎ method is used to train the multilayer perceptron. ANN training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.

  19. Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.

  20. [A method of hyperspectral quantificational identification of minerals based on infrared spectral artificial immune calculation].

    Science.gov (United States)

    Liu, Qing-Jie; Jing, Lin-Hai; Li, Xin-Wu; Bi, Jian-Tao; Wang, Meng-Fei; Lin, Qi-Zhong

    2013-04-01

    Rapid identification of minerals based on near infrared (NIR) and shortwave infrared (SWIR) hyperspectra is vital to remote sensing mine exploration, remote sensing minerals mapping and field geological documentation of drill core, and have leaded to many identification methods including spectral angle mapping (SAM), spectral distance mapping (SDM), spectral feature fitting(SFF), linear spectral mixture model (LSMM), mathematical combination feature spectral linear inversion model(CFSLIM) etc. However, limitations of these methods affect their actual applications. The present paper firstly gives a unified minerals components spectral inversion (MCSI) model based on target sample spectrum and standard endmember spectral library evaluated by spectral similarity indexes. Then taking LSMM and SAM evaluation index for example, a specific formulation of unified MCSI model is presented in the form of a kind of combinatorial optimization. And then, an artificial immune colonial selection algorithm is used for solving minerals feature spectral linear inversion model optimization problem, which is named ICSFSLIM. Finally, an experiment was performed to use ICSFSLIM and CFSLIM to identify the contained minerals of 22 rock samples selected in Baogutu in Xinjiang China. The mean value of correctness and validness identification of ICSFSLIM are 34.22% and 54.08% respectively, which is better than that of CFSLIM 31.97% and 37.38%; the correctness and validness variance of ICSFSLIM are 0.11 and 0.13 smaller than that of CFSLIM, 0.15 and 0.25, indicating better identification stability.

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

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    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. 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.

  3. Applying Convolution-Based Processing Methods To A Dual-Channel, Large Array Artificial Olfactory Mucosa

    Science.gov (United States)

    Taylor, J. E.; Che Harun, F. K.; Covington, J. A.; Gardner, J. W.

    2009-05-01

    Our understanding of the human olfactory system, particularly with respect to the phenomenon of nasal chromatography, has led us to develop a new generation of novel odour-sensitive instruments (or electronic noses). This novel instrument is in need of new approaches to data processing so that the information rich signals can be fully exploited; here, we apply a novel time-series based technique for processing such data. The dual-channel, large array artificial olfactory mucosa consists of 3 arrays of 300 sensors each. The sensors are divided into 24 groups, with each group made from a particular type of polymer. The first array is connected to the other two arrays by a pair of retentive columns. One channel is coated with Carbowax 20 M, and the other with OV-1. This configuration partly mimics the nasal chromatography effect, and partly augments it by utilizing not only polar (mucus layer) but also non-polar (artificial) coatings. Such a device presents several challenges to multi-variate data processing: a large, redundant dataset, spatio-temporal output, and small sample space. By applying a novel convolution approach to this problem, it has been demonstrated that these problems can be overcome. The artificial mucosa signals have been classified using a probabilistic neural network and gave an accuracy of 85%. Even better results should be possible through the selection of other sensors with lower correlation.

  4. 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.

  5. AGENT-BASED SIMULATION FOR KANSEI ENGINEERING: TESTING A FUZZY LINEAR QUANTIFICATION METHOD IN AN ARTIFICIAL WORLD

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper argues that agent-based simulation can be used as a way for testing Kansei Engineering methods which deal with the human reaction from sensory to mental state, that is, sensitivity, sense,sensibility, feeling, esthetics, emotion affection and intuition. A new fuzzy linear quantification method is tested in an artificial world by agent-based modeling and simulations, and the performance of the fuzzy linear method is compared with that of a genetic algorithm. The simulations can expand people's imagination and enhance people's intuition that the new fuzzy linear quantification method is effective.

  6. An Endmember Extraction Method Based on Artificial Bee Colony Algorithms for Hyperspectral Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Xu Sun

    2015-12-01

    Full Text Available Mixed pixels are common in hyperspectral remote sensing images. Endmember extraction is a key step in spectral unmixing. The linear spectral mixture model (LSMM constitutes a geometric approach that is commonly used for this purpose. This paper introduces the use of artificial bee colony (ABC algorithms for spectral unmixing. First, the objective function of the external minimum volume model is improved to enhance the robustness of the results, and then, the ABC-based endmember extraction process is presented. Depending on the characteristics of the objective function, two algorithms, Artificial Bee Colony Endmember Extraction-RMSE (ABCEE-R and ABCEE-Volume (ABCEE-V are proposed. Finally, two sets of experiment using synthetic data and one set of experiments using a real hyperspectral image are reported. Comparative experiments reveal that ABCEE-R and ABCEE-V can achieve better endmember extraction results than other algorithms when processing data with a low signal-to-noise ratio (SNR. ABCEE-R does not require high accuracy in the number of endmembers, and it can always obtain the result with the best root mean square error (RMSE; when the number of endmembers extracted and the true number of endmembers does not match, the RMSE of the ABCEE-V results is usually not as good as that of ABCEE-R, but the endmembers extracted using the former algorithm are closer to the true endmembers.

  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. A Comparison of natural (english) and artificial (esperanto) languages. A Multifractal method based analysis

    CERN Document Server

    Gillet, J

    2008-01-01

    We present a comparison of two english texts, written by Lewis Carroll, one (Alice in wonderland) and the other (Through a looking glass), the former translated into esperanto, in order to observe whether natural and artificial languages significantly differ from each other. We construct one dimensional time series like signals using either word lengths or word frequencies. We use the multifractal ideas for sorting out correlations in the writings. In order to check the robustness of the methods we also write the corresponding shuffled texts. We compare characteristic functions and e.g. observe marked differences in the (far from parabolic) f(alpha) curves, differences which we attribute to Tsallis non extensive statistical features in the ''frequency time series'' and ''length time series''. The esperanto text has more extreme vallues. A very rough approximation consists in modeling the texts as a random Cantor set if resulting from a binomial cascade of long and short words (or words and blanks). This leads...

  9. Hyperspectral image clustering method based on artificial bee colony algorithm and Markov random fields

    Science.gov (United States)

    Sun, Xu; Yang, Lina; Gao, Lianru; Zhang, Bing; Li, Shanshan; Li, Jun

    2015-01-01

    Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC-MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC-MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC-MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC-MRF-cluster showed good stability.

  10. ARTIFICIAL NEURAL NETWORK BASED METHOD OF ASSESSMENT OF STUDENTS` FOREIGN LANGUAGE COMPETENCE BY THE GROUP OF EXPERTS

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    Olha V. Zastelo

    2015-09-01

    Full Text Available In this article the method of the integral assessment of the level of students` foreign language communicative competence by the group of experts through the complex test in a foreign language is considered. The use of mathematical methods and modern specialized software during complex testing of students significantly improves the expert methods, particularly in the direction of increasing the reliability of the assessment. Capitalizing analytical software environment realizes the simulation of non-linear generalizations based on artificial neural networks, which increases the accuracy of the estimate and allows further efficient use of the competent experts` experience gained in the model.

  11. Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor

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    Sepehr Sadighi

    2013-12-01

    Full Text Available An artificial neural network (ANN and kinetic-based models for a pilot scale vacuum gas oil (VGO hydrocracking plant are presented in this paper. Reported experimental data in the literature were used to develop, train, and check these models. The proposed models are capable of predicting the yield of all main hydrocracking products including dry gas, light naphtha, heavy naphtha, kerosene, diesel, and unconverted VGO (residue. Results showed that kinetic-based and artificial neural models have specific capabilities to predict yield of hydrocracking products. The former is able to accurately predict the yield of lighter products, i.e. light naphtha, heavy naphtha and kerosene. However, ANN model is capable of predicting yields of diesel and residue with higher precision. The comparison shows that the ANN model is superior to the kinetic-base models.  © 2013 BCREC UNDIP. All rights reservedReceived: 9th April 2013; Revised: 13rd August 2013; Accepted: 18th August 2013[How to Cite: Sadighi, S., Zahedi, G.R. (2013. Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor. Bulletin of Chemical Reaction Engineering & Catalysis, 8 (2: 125-136. (doi:10.9767/bcrec.8.2.4722.125-136][Permalink/DOI: http://dx.doi.org/10.9767/bcrec.8.2.4722.125-136

  12. 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.

  13. Real-time tumor tracking with an artificial neural networks-based method: a feasibility study.

    Science.gov (United States)

    Seregni, Matteo; Pella, Andrea; Riboldi, Marco; Orecchia, Roberto; Cerveri, Pietro; Baroni, Guido

    2013-01-01

    The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy. This motion compensation strategy is currently applied clinically only in conventional photon radiotherapy but not in particle therapy, as greater accuracy in dose delivery is required. We proposed a tracking method that exploits artificial neural networks to estimate the internal tumor trajectory as a function of external surrogate signals. The developed algorithm was tested by means of a retrospective clinical data analysis in 20 patients, who were treated with state of the art infra-red motion tracking for photon radiotherapy, which is used as a benchmark. Integration into a hardware platform for motion tracking in particle therapy was performed and then tested on a moving phantom, specifically developed for this purpose. Clinical data show that a median tracking error reduction up to 0.7 mm can be achieved with respect to state of the art technologies. The phantom study demonstrates that a real-time tumor position estimation is feasible when the external signals are acquired at 60 Hz. The results of this work show that neural networks can be considered a valuable tool for the implementation of high accuracy real-time tumor tracking methodologies.

  14. Numerical model of nonhydrostatic ocean dynamics based on methods of artificial compressibility and multicomponent splitting

    Science.gov (United States)

    Zalesny, V. B.; Gusev, A. V.; Fomin, V. V.

    2016-11-01

    An algorithm is proposed for solving three-dimensional ocean hydrodynamics equations without hydrostatic approximation and traditional simplification of Coriolis acceleration. It is based on multicomponent splitting of the modified model with artificial compressibility. The original system of equations is split into two subsystems describing the transport of three velocity components and adjustment of the density and velocity fields. At the adjustment stage, the horizontal velocity components are represented as a sum of the depth means and deviations; the two corresponding subsystems are derived. For barotropic dynamics, the compressibility effect is represented as the boundary condition at the free surface, while for the baroclinic subsystem, it is introduced as ɛ-regularization of the continuity equation. Then, the baroclinic equations are split into two subsystems describing the hydrostatic and nonhydrostatic dynamics. The nonhydrostatic dynamics is computed at a separate splitting stage. The algorithm is included into the Institute of Numerical Mathematics of the Russian Academy of Sciences model based on "primitive" equations and verified by solving the hydrodynamics problem for the Sea of Marmara.

  15. 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.

  16. 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...... on the estimated position error, and gives a deep insight into this problem. It also provides a simple approach to achieve a globally minimized position error. A proper choice of the artificial machine inductance may reduce the maximum position error by 50% without considering the actual inductance variation...

  17. Discontinuous Galerkin finite element method for the nonlinear hyperbolic problems with entropy-based artificial viscosity stabilization

    Science.gov (United States)

    Zingan, Valentin Nikolaevich

    This work develops a discontinuous Galerkin finite element discretization of non- linear hyperbolic conservation equations with efficient and robust high order stabilization built on an entropy-based artificial viscosity approximation. The solutions of equations are represented by elementwise polynomials of an arbitrary degree p > 0 which are continuous within each element but discontinuous on the boundaries. The discretization of equations in time is done by means of high order explicit Runge-Kutta methods identified with respective Butcher tableaux. To stabilize a numerical solution in the vicinity of shock waves and simultaneously preserve the smooth parts from smearing, we add some reasonable amount of artificial viscosity in accordance with the physical principle of entropy production in the interior of shock waves. The viscosity coefficient is proportional to the local size of the residual of an entropy equation and is bounded from above by the first-order artificial viscosity defined by a local wave speed. Since the residual of an entropy equation is supposed to be vanishingly small in smooth regions (of the order of the Local Truncation Error) and arbitrarily large in shocks, the entropy viscosity is almost zero everywhere except the shocks, where it reaches the first-order upper bound. One- and two-dimensional benchmark test cases are presented for nonlinear hyperbolic scalar conservation laws and the system of compressible Euler equations. These tests demonstrate the satisfactory stability properties of the method and optimal convergence rates as well. All numerical solutions to the test problems agree well with the reference solutions found in the literature. We conclude that the new method developed in the present work is a valuable alternative to currently existing techniques of viscous stabilization.

  18. Study on the comprehensive advantage evaluation method of high-tech enterprises based on RBF artificial neural network

    Institute of Scientific and Technical Information of China (English)

    WANG Hong-qi; WANG Xue-yuan; TANG Yu

    2005-01-01

    This paper designs an intelligent evaluation approach using a Radial Basis Function (RBF) Artificial Neural Network. We based our approach on establishing a comprehensive advantage evaluating index system that offers scientific substance for creating a development plan and the strategic management of high-tech industry and regional cluslers of high-tech enterprises. Furthermore, this paper selects some typical high-tech enterprises' data to make comprehensive training on the network system. Meanwhile, the paper chooses some enterprises as testing samples to test the method, the result of which proves that this method is truly effective. The research of this paper provides a comprehensive advantage evaluating and managing method for high-tech enterprise.

  19. Tuning fractional PID controllers for a Steward platform based on frequency domain and artificial intelligence methods

    Science.gov (United States)

    Copot, Cosmin; Zhong, Yu; Ionescu, Clara; Keyser, Robin

    2013-06-01

    In this paper, two methods to tune a fractional-order PI λ D μ controller for a mechatronic system are presented. The first method is based on a genetic algorithm to obtain the parameter values for the fractionalorder PI λ D μ controller by global optimization. The second method used to design the fractional-order PI λ D μ controller relies on an auto-tuning approach by meeting some specifications in the frequency domain. The real-time experiments are conducted using a Steward platform which consists of a table tilted by six servo-motors with a ball on the top of the table. The considered system is a 6 degrees of freedom (d.o.f.) motion platform. The feedback on the position of the ball is obtained from images acquired by a visual sensor mounted above the platform. The fractional-order controllers were implemented and the performances of the steward platform are analyzed.

  20. A Hybrid Model For Phrase Chunking Employing Artificial Immunity System And Rule Based Methods

    Directory of Open Access Journals (Sweden)

    Bindu.M.S

    2011-11-01

    Full Text Available Natural language Understanding (NLU, an important field of Artificial Intelligence (AI is concerned with the speech and language understanding between human and computer. Understanding language means knowing what concept a word or phrase stands for and how to link them to form meaningful sentence. Identification of phrases or phrase chunking is an important step in natural language understanding (NLU. Chunker identifies and divides sentences into syntactically correlated word groups. Question Answering (QA systems, another important application of Artificial Intelligence (AImostly requires retrieval of nouns or noun phrases as answers to the questions raised by the users. Also Chunking is an important preprocessing step in full parsing. Due to high ambiguity of natural language, exact parsing of text may become very complex. This ambiguity may be partially resolved by using chunking as an intermediate step. To the best of our knowledge no known work or tag set is available for phrase chunking in Malayalam. To separate the chunks in a document it must be labeled with parts-ofspeech (POS tags. POS Tagging is a difficult task in Malayalam as it is a complex and compounding language. In this paper we describe the application of artificial immunity system (AIS for chunking which is implemented and obtained an accurate output with 96% precision and 93% recall. This system istested on corpuses collected from reputed news papers and magazines. These corpuses contained documents from five different domains such as sports, health, agriculture, science and politics and each document contained sentences –simple, compound, complex-of various levels of complexity. POS tag set with 52 tags is developed for preparing the tagged corpus for Malayalam. The phrase tag set contains 20 phrase tags.

  1. 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

    2016-01-01

    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.

  2. 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.

  3. 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.

  4. Independent component analysis based on adaptive artificial bee colony

    National Research Council Canada - National Science Library

    Shi Zhang; Chao-Wei Bao; Hai-Bin Shen

    2016-01-01

    .... An independent component analysis method based on adaptive artificial bee colony algorithm is proposed in this paper, aiming at the problems of slow convergence and low computational precision...

  5. Economic modeling using artificial intelligence methods

    CERN Document Server

    Marwala, Tshilidzi

    2013-01-01

    This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.

  6. Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities

    Science.gov (United States)

    Kousari, Mohammad Reza; Hosseini, Mitra Esmaeilzadeh; Ahani, Hossein; Hakimelahi, Hemila

    2015-09-01

    An effective forecast of the drought definitely gives lots of advantages in regard to the management of water resources being used in agriculture, industry, and households consumption. To introduce such a model applying simple data inputs, in this study a regional drought forecast method on the basis of artificial intelligence capabilities (artificial neural networks) and Standardized Precipitation Index (SPI in 3, 6, 9, 12, 18, and 24 monthly series) has been presented in Fars Province of Iran. The precipitation data of 41 rain gauge stations were applied for computing SPI values. Besides, weather signals including Multivariate ENSO Index (MEI), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), NINO1+2, anomaly NINO1+2, NINO3, anomaly NINO3, NINO4, anomaly NINO4, NINO3.4, and anomaly NINO3.4 were also used as the predictor variables for SPI time series forecast the next 12 months. Frequent testing and validating steps were considered to obtain the best artificial neural networks (ANNs) models. The forecasted values were mapped in verification sector then they were compared with the observed maps at the same dates. Results showed considerable spatial and temporal relationships even among the maps of different SPI time series. Also, the first 6 months forecasted maps showed an average of 73 % agreements with the observed ones. The most important finding and the strong point of this study was the fact that although drought forecast in each station and time series was completely independent, the relationships between spatial and temporal predictions remained. This strong point mainly referred to frequent testing and validating steps in order to explore the best drought forecast models from plenty of produced ANNs models. Finally, wherever the precipitation data are available, the practical application of the presented method is possible.

  7. Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities

    Science.gov (United States)

    Kousari, Mohammad Reza; Hosseini, Mitra Esmaeilzadeh; Ahani, Hossein; Hakimelahi, Hemila

    2017-01-01

    An effective forecast of the drought definitely gives lots of advantages in regard to the management of water resources being used in agriculture, industry, and households consumption. To introduce such a model applying simple data inputs, in this study a regional drought forecast method on the basis of artificial intelligence capabilities (artificial neural networks) and Standardized Precipitation Index (SPI in 3, 6, 9, 12, 18, and 24 monthly series) has been presented in Fars Province of Iran. The precipitation data of 41 rain gauge stations were applied for computing SPI values. Besides, weather signals including Multivariate ENSO Index (MEI), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), NINO1+2, anomaly NINO1+2, NINO3, anomaly NINO3, NINO4, anomaly NINO4, NINO3.4, and anomaly NINO3.4 were also used as the predictor variables for SPI time series forecast the next 12 months. Frequent testing and validating steps were considered to obtain the best artificial neural networks (ANNs) models. The forecasted values were mapped in verification sector then they were compared with the observed maps at the same dates. Results showed considerable spatial and temporal relationships even among the maps of different SPI time series. Also, the first 6 months forecasted maps showed an average of 73 % agreements with the observed ones. The most important finding and the strong point of this study was the fact that although drought forecast in each station and time series was completely independent, the relationships between spatial and temporal predictions remained. This strong point mainly referred to frequent testing and validating steps in order to explore the best drought forecast models from plenty of produced ANNs models. Finally, wherever the precipitation data are available, the practical application of the presented method is possible.

  8. AN INDOOR POSITIONING TECHNIQUE BASED ON A FEED-FORWARD ARTIFICIAL NEURAL NETWORK USING LEVENBERG-MARQUARDT LEARNING METHOD

    Directory of Open Access Journals (Sweden)

    P. Pahlavani

    2017-09-01

    Full Text Available This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF artificial neural networks (ANN. Most of the indoor received signal strength (RSS-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration phase and the online (estimation phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening. Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg–Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

  9. 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

  10. 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.

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

    Science.gov (United States)

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

    2014-02-01

    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.

  12. 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.

  13. Anharmonic osillators energies via artificial perturbation method

    CERN Document Server

    Mustafa, O; Mustafa, Omar; Odeh, Maen

    2000-01-01

    A new pseudoperturbative (artificial in nature) methodical proposal [15] is used to solve for Schrodinger equation with a class of phenomenologically useful and methodically challenging anharmonice oscillator potentials V(q)=\\alpha_o q^2 + \\alpha q^4. The effect of the [4,5] Pade' approximant on the leading eigenenergy term is studied. Comparison with results from numerical (exact) and several eligible (approximation) methods is made.

  14. 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.

  15. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

    Directory of Open Access Journals (Sweden)

    Ali Salmasnia

    2012-01-01

    Full Text Available An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables, called a multiple response optimization (MRO problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM, due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.

  16. 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.

  17. 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.

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

    Science.gov (United States)

    Costa-da-Silva, André Luis; Navarrete, Flávia Rosa; Salvador, Felipe Scassi; Karina-Costa, Maria; Ioshino, Rafaella Sayuri; Azevedo, Diego Soares; Rocha, Desirée Rafaela; Romano, Camila Malta; Capurro, Margareth Lara

    2013-01-01

    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.

  19. 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.

  20. 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.

  1. Distance Concentration-Based Artificial Immune Algorithm

    Institute of Scientific and Technical Information of China (English)

    LIU Tao; WANG Yao-cai; WANG Zhi-jie; MENG Jiang

    2005-01-01

    The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentration-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune algorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the problem of precocity,holding the diversity of antibody, and enhancing convergence rate.

  2. Spatiotemporal realization of an artificial retina model and performance evaluation through ISI- and spike count-based image reconstruction methods

    OpenAIRE

    2014-01-01

    Development of an artificial retina model that can mimic the biologic retina is a highly challenging task and this task is an important step in the development of a visual prosthesis. The receptive field structure of the retina layer is usually modeled as a 2D difference of Gaussian (DOG) filter profile. In the present study, as a different approach, a retina model including a 3D 2-stage DOG filter (3D-ADOG) that has an adaptively changing bandwidth with respect to the local image stati...

  3. 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

  4. Artificial Neural Network Based Controller for Speed Control of An Induction Motor (IM using Indirect Vector Control Method

    Directory of Open Access Journals (Sweden)

    Ashutosh Mishra

    2012-10-01

    Full Text Available

    In this paper, an implementation of intelligent controller for speed control of an induction motor (IM using indirect vector control method has been developed and analyzed in detail. The project is complete mathematical model of field orientation control (FOC induction motor is described and simulated in MATLAB for studies a 50 HP(37KW, cage type induction motor has been considered .The comparative  performance of PI, Fuzzy and Neural network control techniques have been  presented and analyzed in this work.  The present approach avoids the use of flux and speed sensor which increase the installation cost and mechanical robustness .The neural network based controller is found to be a very useful technique to obtain a high performance speed control. The scheme consist of neural network controller, reference modal, an algorithm for changing the neural network weight in order that  speed of the derive can track performance speed.  The indirect vector controlled induction motor drive involve decoupling of the stator current in to torque and flux producing components.

  5. 基于人工鱼群算法的彩色图像检索方法%Color image retrieval method based on artificial fish algorithm

    Institute of Scientific and Technical Information of China (English)

    薛亚娣; 阮文惠

    2016-01-01

    针对传统的彩色图像检索方法计算过程复杂,相似图像匹配度低,检索过程无法实现最优组合的问题,提出一种基于人工鱼群算法的彩色图像检索方法。通过介绍人工鱼群算法,完成彩色图像的颜色特征提取,进行彩色图像鱼群算法的相似度匹配,实现人工鱼群算法图像检索,完成人工鱼群算法检索图像权重的优化。仿真实验表明,提出的人工鱼群算法具备有效性,在实际环境中有检索优势。%The traditional color image retrieval method has complex calculation process,low similar image matching degree,and it unable to achieve the optimal combination in retrieval process.A color image retrieval method based on artificial fish swarm algorithm is thus pro-posed.By introducing the algorithm,the color feature extraction of color image is completed, similarity matching of fish algorithm conducted,the image retrieval realized,and retrieved im-age weight optimization complished.The simulation results show that the proposed artificial fish swarm algorithm is effective,and it has a certain advantage in the actual environment.

  6. Research on Artificial Neural Network Method for Credit Application

    Institute of Scientific and Technical Information of China (English)

    MingxingLi; PingHeng; PeiwuDong

    2004-01-01

    Considering our country's present situation, in this paper we provide ten evaluation indexes of the credit application management, which is used as the input vector of neural network. Then we set up a three-layer back propagation model for the credit application evaluation based on the artificial neural network. We also analyzed the model using the real data; the testing result indicates that the model is a good method and a good tool.

  7. Multithreshold Segmentation Based on Artificial Immune Systems

    Directory of Open Access Journals (Sweden)

    Erik Cuevas

    2012-01-01

    Full Text Available Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS technique, also known as theclonal selection algorithm (CSA. It follows the clonal selection principle (CSP from the human immune system which basically generates a response according to the relationship between antigens (Ag, that is, patterns to be recognized and antibodies (Ab, that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.

  8. Preserving GABAergic interneurons in acute brain slices of mice using the N-methyl-D-glucamine-based artificial cerebrospinal fluid method.

    Science.gov (United States)

    Pan, Geng; Li, Yue; Geng, Hong-Yan; Yang, Jian-Ming; Li, Ke-Xin; Li, Xiao-Ming

    2015-04-01

    Defects in the function and development of GABAergic interneurons have been linked to psychiatric disorders, so preservation of these interneurons in brain slices is important for successful electrophysiological recording in various ex vivo methods. However, it is difficult to maintain the activity and morphology of neurons in slices from mice of >30 days old. Here we evaluated the N-methyl-D-glucamine (NMDG)-based artificial cerebrospinal fluid (aCSF) method for the preservation of interneurons in slices from mice of up to ∼6 months old and discussed the steps that may affect their quality during slicing. We found that the NMDG-aCSF method rescued more cells than sucrose-aCSF and successfully preserved different types of interneurons including parvalbumin- and somatostatin-positive interneurons. In addition, both the chemical and electrical synaptic signaling of interneurons were maintained. These results demonstrate that the NMDG-aCSF method is suitable for the preservation of interneurons, especially in studies of gap junctions.

  9. Cobaloxime-based artificial hydrogenases.

    Science.gov (United States)

    Bacchi, Marine; Berggren, Gustav; Niklas, Jens; Veinberg, Elias; Mara, Michael W; Shelby, Megan L; Poluektov, Oleg G; Chen, Lin X; Tiede, David M; Cavazza, Christine; Field, Martin J; Fontecave, Marc; Artero, Vincent

    2014-08-01

    Cobaloximes are popular H2 evolution molecular catalysts but have so far mainly been studied in nonaqueous conditions. We show here that they are also valuable for the design of artificial hydrogenases for application in neutral aqueous solutions and report on the preparation of two well-defined biohybrid species via the binding of two cobaloxime moieties, {Co(dmgH)2} and {Co(dmgBF2)2} (dmgH2 = dimethylglyoxime), to apo Sperm-whale myoglobin (SwMb). All spectroscopic data confirm that the cobaloxime moieties are inserted within the binding pocket of the SwMb protein and are coordinated to a histidine residue in the axial position of the cobalt complex, resulting in thermodynamically stable complexes. Quantum chemical/molecular mechanical docking calculations indicated a coordination preference for His93 over the other histidine residue (His64) present in the vicinity. Interestingly, the redox activity of the cobalt centers is retained in both biohybrids, which provides them with the catalytic activity for H2 evolution in near-neutral aqueous conditions.

  10. 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.

  11. A Hybrid Monte Carlo Method Based Artificial Neural Networks Approach for Rock Boundaries Identification: A Case Study from the KTB Bore Hole

    Science.gov (United States)

    Maiti, Saumen; Tiwari, R. K.

    2009-11-01

    Identification of rock boundaries and structural features from well log response is a fundamental problem in geological field studies. However, in a complex geologic situation, such as in the presence of crystalline rocks where metamorphisms lead to facies changes, it is not easy to discern accurate information from well log data using conventional artificial neural network (ANN) methods. Moreover inferences drawn by such methods are also found to be ambiguous because of the strong overlapping of well log signals, which are generally tainted with deceptive noise. Here, we have developed an alternative ANN approach based on Bayesian statistics using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) inversion scheme for modeling the German Continental Deep Drilling Program (KTB) well log data. MCMC algorithm draws an independent and identically distributed (i.i.d) sample by Markov Chain simulation technique from posterior probability distribution using the principle of statistical mechanics in Hamiltonian dynamics. In this algorithm, each trajectory is updated by approximating the Hamiltonian differential equations through a leapfrog discrimination scheme. We examined the stability and efficiency of the HMC-based approach on “noisy” data assorted with different levels of colored noise. We also perform uncertainty analysis by estimating standard deviation (STD) error map of a posteriori covariance matrix at the network output of three types of lithofacies over the entire length of the litho section of KTB. Our analyses demonstrate that the HMC-based approach renders robust means for classification of complex lithofacies successions from the KTB borehole noisy signals, and hence may provide a useful guide for understanding the crustal inhomogeneity and structural discontinuity in many other tectonically critical and complex regions.

  12. Magnetic skyrmion-based artificial neuron device

    Science.gov (United States)

    Li, Sai; Kang, Wang; Huang, Yangqi; Zhang, Xichao; Zhou, Yan; Zhao, Weisheng

    2017-08-01

    Neuromorphic computing, inspired by the biological nervous system, has attracted considerable attention. Intensive research has been conducted in this field for developing artificial synapses and neurons, attempting to mimic the behaviors of biological synapses and neurons, which are two basic elements of a human brain. Recently, magnetic skyrmions have been investigated as promising candidates in neuromorphic computing design owing to their topologically protected particle-like behaviors, nanoscale size and low driving current density. In one of our previous studies, a skyrmion-based artificial synapse was proposed, with which both short-term plasticity and long-term potentiation functions have been demonstrated. In this work, we further report on a skyrmion-based artificial neuron by exploiting the tunable current-driven skyrmion motion dynamics, mimicking the leaky-integrate-fire function of a biological neuron. With a simple single-device implementation, this proposed artificial neuron may enable us to build a dense and energy-efficient spiking neuromorphic computing system.

  13. Method based on artificial excitation of characteristic radiation by an electron beam for remote X-ray spectral elemental analysis of surface rocks on atmosphereless celestial bodies

    Science.gov (United States)

    Kolesnikov, E. K.

    2016-11-01

    This article, like our previous one [1], is devoted to advanced space technology concepts. It evaluates the potential for developing active systems to conduct a remote elemental analysis of surface rocks on an atmosphereless celestial body. The analysis is based on the spectrometry of characteristic X-rays (CXR) artificially excited in the surface soil layer. It has been proposed to use an electron beam injected from aboard a spacecraft orbiting the celestial body (or moving in a flyby trajectory) to excite the CXR elements contained in surface rocks. The focus is on specifying technical requirements to the parameters of payloads for a global mapping of the composition of lunar rocks from aboard of a low-orbiting lunar satellite. This article uses the results obtained in [2], our first study that shows the potential to develop an active system for a remote elemental analysis of lunar surface rocks using the above method. Although there has been interest in our research on the part of leading national academic institutions and space technology developers in the Soviet Union, the studies were discontinued because of the termination of the Soviet lunar program and the completion of the American Apollo program.

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

    Directory of Open Access Journals (Sweden)

    Maryam Farhadian

    2015-01-01

    Full Text Available Background: 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. Materials and Methods: 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. Results: 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. Conclusion: 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.

  15. Comparison of artificial inoculation methods for studying ...

    African Journals Online (AJOL)

    divya

    2013-05-01

    May 1, 2013 ... Establishment of disease by artificial inoculation is essential for studies of various aspects of plant pathology. Keeping this in mind .... three to four days under cool white fluorescent light (2,000 lux) followed by 2 days ... and placed in growth chambers at 25°C and 70% relative humidity. The Petri plates were ...

  16. 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.

  17. Heterogeneous information-based artificial stock market

    Science.gov (United States)

    Pastore, S.; Ponta, L.; Cincotti, S.

    2010-05-01

    In this paper, an information-based artificial stock market is considered. The market is populated by heterogeneous agents that are seen as nodes of a sparsely connected graph. Agents trade a risky asset in exchange for cash. Besides the amount of cash and assets owned, each agent is characterized by a sentiment. Moreover, agents share their sentiments by means of interactions that are identified by the graph. Interactions are unidirectional and are supplied with heterogeneous weights. The agent's trading decision is based on sentiment and, consequently, the stock price process depends on the propagation of information among the interacting agents, on budget constraints and on market feedback. A central market maker (clearing house mechanism) determines the price process at the intersection of the demand and supply curves. Both closed- and open-market conditions are considered. The results point out the validity of the proposed model of information exchange among agents and are helpful for understanding the role of information in real markets. Under closed market conditions, the interaction among agents' sentiments yields a price process that reproduces the main stylized facts of real markets, e.g. the fat tails of the returns distributions and the clustering of volatility. Within open-market conditions, i.e. with an external cash inflow that results in asset price inflation, also the unitary root stylized fact is reproduced by the artificial stock market. Finally, the effects of model parameters on the properties of the artificial stock market are also addressed.

  18. Artificial patinas analyzed with PIXE method

    Energy Technology Data Exchange (ETDEWEB)

    Campos, P.H.O.V. de; Rizzutto, M.A. [Universidade de Sao Paulo (USP), SP (Brazil). Inst. de Fisica. Dept. de Fisica Nuclear]. E-mail: rizzutto@if.usp.br; Neiva, A.C.; Bendezu H, R. del P. [Universidade de Sao Paulo (USP), SP (Brazil). Escola Politecnica. Dept. de Engenharia Quimica]. E-mail: acneiva@usp.br

    2007-07-01

    Aiming at the restoration and conservation of the archaeological metallic objects, the artificial patinas can be used to simulate the natural patinas (corrosion products in metal and its alloys), permitting the characterization and corrosion mechanisms studies. The natural patinas formation is difficult to study because of the long corrosion production process in materials which take years to be formed. On the other hand the artificial patinas can be easily produced in a shorter time, moreover, they can be used as simulation of the corrosion process and in substitution of monuments and old art objects, damaged for some reason. In our study artificial patinas were produced over pellets of copper and bronze with sulfate, chloride and nitrate solutions and were analyzed with PIXE (Proton Induced X-Ray Emission) technique to supply qualitative and quantitative information of the corrosion elements. The quantitative PIXE analysis takes into account the incident ion beam absorption and the emergent X-ray of the sample, as well as the patina layer and the backing. The PIXE results have shown the presence of S, Cl and Fe and some other elements already known form the backings, such as Cu, Sn, etc. PIXE measurements were also realized in reference metallic materials. (author)

  19. 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.

  20. 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...

  1. Artificial intelligence methods in deregulated power systems operations

    Science.gov (United States)

    Ilic, Jovan

    With the introduction of the power systems deregulation, many classical power transmission and distribution optimization tools became inadequate. Optimal Power Flow and Unit Commitment are common computer programs used in the regulated power industry. This work is addressing the Optimal Power Flow and Unit Commitment in the new deregulated environment. Optimal Power Flow is a high dimensional, non-linear, and non-convex optimization problem. As such, it is even now, after forty years since its introduction, a research topic without a widely accepted solution able to encompass all areas of interest. Unit Commitment is a high dimensional, combinatorial problem which should ideally include the Optimal Power Flow in its solution. The dimensionality of a typical Unit Commitment problem is so great that even the enumeration of all the combinations would take too much time for any practical purposes. This dissertation attacks the Optimal Power Flow problem using non-traditional tools from the Artificial Intelligence arena. Artificial Intelligence optimization methods are based on stochastic principles. Usually, stochastic optimization methods are successful where all other classical approaches fail. We will use Genetic Programming optimization for both Optimal Power Flow and Unit Commitment. Long processing times will also be addressed through supervised machine learning.

  2. 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.

  3. 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.

  4. Artificial Boundary Method for Calculating Ship Wave Resistance

    Institute of Scientific and Technical Information of China (English)

    文新; 韩厚德

    2003-01-01

    The calculation of wave resistance for a ship moving at constant speed near a free surface is considered. This wave resistance is calculated with a linearized steady potential model. To deal with the unboundedness of the physical domain in the potential flow problem, we introduce one vertical side as an artificial upstream boundary and two vertical sides as the artificial downstream boundaries. On the artificial boundaries, a sequence of high-order global artificial boundary conditions are given. Then the potential flow problem is reduced to a problem defined on a finite computational domain, which is equivalent to a variational problem. The solution of the variational problem by the finite element method gives the numerical approximation of the potential flow around the ship, which was used to calculate the wave resistance. The numerical examples show the accuracy and efficiency of the proposed numerical scheme.

  5. To fabricate artificial nerves with tissue engineering methods

    Institute of Scientific and Technical Information of China (English)

    程飚; 陈峥嵘

    2002-01-01

    To fabricate artificial nerves with tissue engineering methods in vitro. Methods: Schwann cells (SCs) were cultured and seeded on polyglactin 910 fibers wrapped by biomembrane coated with rat tail glue and laminin for 2 weeks. The absorbability on the scaffolds, growth and migration of SCs were assessed with a light microscope, a scanning electron microscope and a transmission electron microscope. Results: SCs could migrate and proliferate on polyglactin 910 fibers. They were well distributed between scaffolds and absorbed on surface of scaffolds and formed a bungner band, on which SCs produced more matrices. SCs seeded on the biomembrane could also grow well. Axon regeneration in the distal nerve stump was observed at 8 weeks. Conclusions: Adult SCs can be expanded on coated fibers and biomembrane. Three-dimensional scaffold of SCs has the basic characteristics of artificial nerves. These findings offer a novel method to fabricate artificial nerves with tissue engineering methods for repairing defected long nerves.

  6. Artificial cells based on biodegradable polymersomes

    NARCIS (Netherlands)

    Meng, Fenghua

    2003-01-01

    The aim of the research that is described in this thesis is to develop biodegradable, biocompatible polymersomes that can serve as a basis for artificial cells. These artificial cells should be able to be delivered to the circulation, interact with specific sites where they perform their function, a

  7. [Design of artificial foetor flatus based on bacterial volatile compounds].

    Science.gov (United States)

    Justesen, Ulrik Stenz

    2016-12-12

    Excessive flatulence can be a huge social problem. The purpose of this study was to design artificial flatus from bacterial volatile compounds to stimulate research into neutralizing measures. Anaerobic bacteria, representing a broad spectrum, from a recognized international culture collection were included. The strains were incubated in an anaerobic jar. After 24 hours the lid was removed, and the odour was evaluated by a specialist in clinical microbiology. Four different anaerobic strains were chosen for further studies based on their individual odours. In total, seven different combinations of two or three strains were tested. The combination of Bacteroides fragilis ATCC 25285, Clostridium difficile ATCC 700057 and Fusobacterium necrophorum ATCC 25286 was chosen as it had a suitably foul odour. It is possible to design artificial flatus from bacterial volatile compounds. The method is easy and inexpensive and can stimulate further research into neutralizing measures. none. none.

  8. [Heart rate measurement algorithm based on artificial intelligence].

    Science.gov (United States)

    Chengxian, Cai; Wei, Wang

    2010-01-01

    Based on the heart rate measurement method using time-lapse image of human cheek, this paper proposes a novel measurement algorithm based on Artificial Intelligence. The algorithm combining with fuzzy logic theory acquires the heart beat point by using the defined fuzzy membership function of each sampled point. As a result, it calculates the heart rate by counting the heart beat points in a certain time period. Experiment shows said algorithm satisfies in operability, accuracy and robustness, which leads to constant practical value.

  9. New directions for Artificial Intelligence (AI) methods in optimum design

    Science.gov (United States)

    Hajela, Prabhat

    1989-01-01

    Developments and applications of artificial intelligence (AI) methods in the design of structural systems is reviewed. Principal shortcomings in the current approach are emphasized, and the need for some degree of formalism in the development environment for such design tools is underscored. Emphasis is placed on efforts to integrate algorithmic computations in expert systems.

  10. 防波堤的人工神经网络Monte Carlo法可靠性分析%Application of Artificial Neural Network-Based Monte Carlo Method in Breakwater Reliability Analysis

    Institute of Scientific and Technical Information of China (English)

    张向东; 董胜; 张磊; 张国伟

    2012-01-01

    The construction cost of breakwaters is large. Once destroyed, the consequences would be very serious. Therefore, correctly calculating breakwater reliability has great significance. With the rapid development of artificial neural network theory, the application of artificial neural network theory in breakwater reliability is gradually attracting more and more attentions. The probabilistic meaning is definite u-sing the artificial neural network-based Monte Carlo method to calculate the failure probability of the vertical breakwaters. The breakwater in Qinhuangdao is taken as an example to inspect and verify the artificial neural network-based Monte Carlo method. All parameters in the sliding failure limit state function and the overturning limit state function are taken as variables. The failure probability and reliability index are calculated using numerical artificial neural network-based Monte Carlo method. The calculation results are compared with those calculated using variable-independent JC method and Monte Carlo simulation (in- ? Eluding direct sampling method and importance sampling method of Monte Carlo simulation). It can be concluded that the reliability indexes calculated using the artificial neural network - based Monte Carlo method are similar to those calculated using the Monte Carlo simulation, but are slightly lower than those calculated using the variable-independent JC method.%防波堤建设费用巨大,且一旦遭到破坏,后果甚为严重,因此,如何准确地计算防波堤的可靠性意义重大.随着人工神经网络理论的快速发展,人工神经网络方法在结构可靠性分析中的应用逐渐得到重视.基于神经网络的Monte Carlo法计算直立式防波堤的可靠性,概率意义明确.以秦皇岛典型直立堤为算例,采用基于神经网络的Monte Carlo法对直立式防波堤进行可靠性分析时,将直立堤滑动破坏和倾覆破坏的极限状态方程中的所有参数均作为变量

  11. 人工皮革材质鉴定方法%Material Identification Method of Artificial Leather

    Institute of Scientific and Technical Information of China (English)

    段晓霞; 孙世彧; 黄仕明

    2012-01-01

    The material indentification method of artificial leather was researched. First, the cross section of artificial leather was observed by three-dimensional microscope in order to define the exact position of the base material of artificial leather, then the definite base material was analysed by infrared spectrum, last according to the test infrared spectrums and the characteristic absorption bands, the material of unknown artificial leather was identified.%研究了人工皮革材质鉴定方法.首先利用三维显微镜观察人工皮革的横截面,确定人工皮革基材的准确位置,然后对所确定的基材进行红外光谱分析,根据所测得的红外图谱中主要特征峰的归属,分析确定未知人工皮革材质的种类.

  12. Artificial nanomachines based on interlocked molecules

    Energy Technology Data Exchange (ETDEWEB)

    Credi, Alberto [Dipartimento di Chimica ' G Ciamician' , Universita di Bologna, via Selmi 2, 40126 Bologna (Italy)

    2006-08-23

    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. Prediction of enzyme binding: human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures.

    Science.gov (United States)

    Mlinsek, G; Novic, M; Hodoscek, M; Solmajer, T

    2001-01-01

    Thrombin is a serine protease which plays important roles in the human body, the key one being the control of thrombus formation. The inhibition of thrombin has become a target for new antithrombotics. The aim of our work was to (i) construct a model which would enable us to predict Ki values for the binding of an inhibitor into the active site of thrombin based on a database of known X-ray structures of inhibitor-enzyme complexes and (ii) to identify the structural and electrostatic characteristics of inhibitor molecules crucially important to their effective binding. To retain as much of the 3D structural information of the bound inhibitor as possible, we implemented the quantum mechanical/molecular mechanical (QM/MM) procedure for calculating the molecular electrostatic potential (MEP) at the van der Waals surfaces of atoms in the protein's active site. The inhibitor was treated quantum mechanically, while the rest of the complex was treated by classical means. The obtained MEP values served as inputs into the counter-propagation artificial neural network (CP-ANN), and a genetic algorithm was subsequently used to search for the combination of atoms that predominantly influences the binding. The constructed CP-ANN model yielded Ki values predictions with a correlation coefficient of 0.96, with Ki values extended over 7 orders of magnitude. Our approach also shows the relative importance of the various amino acid residues present in the active site of the enzyme for inhibitor binding. The list of residues selected by our automatic procedure is in good correlation with the current consensus regarding the importance of certain crucial residues in thrombin's active site.

  14. 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

  15. Artificial Neural Reliability Calculating Method Based on Uniform Test Network%基于均匀试验人工神经网络的实用可靠性方法

    Institute of Scientific and Technical Information of China (English)

    高博

    2012-01-01

    This paper presents a design of a new reliability calculation method based on uniform test, which organically combines artificial intelligence network. Artificial neural networks abandons the finite element method (FEM) and applies this method,because the new law can greatly reduce the amount of computation. First, according to the distribution of the random variable,limited samples are extracted by uniform test, which are taken as for the input parameters finite element analysis. Secondly, based on the finite element analysis results, the limited training samples are used to construct the optimal artificial neural network. Optimal artificial neural network generalization ability is applied to obtain a valid response, and then the reliability index of the structural system is calculated. Finally, this calculation method provides a new attempt for reliability analysis in the actual testing of complex systems, which is proved to be practical and effective.%根据均匀试验的设计提出了一种全新的可靠性计算方法,它有机地综合了人工智能网络.人工神经网络弃用有限单元法(FEM)而改用该方法,一个根本原因在于它能极大地减少计算量.首先,根据随机变量分布情况,通过均匀试验提取出有限样本,将它们看作是有限元法分析的输入参数.其次,基于有限元分析结果利用这些有限的训练样本构建最优的人工神经网络.利用最优人工神经网络的泛化能力,随机得到一个有效的响应值,然后计算出结构系统的可靠性指数.最后,这一计算方法还为进行可靠性分析提供新尝试,在对复杂系统进行实际试验时该方法表现得切实可行且有效.

  16. Artificial atoms based on correlated materials

    Science.gov (United States)

    Mannhart, J.; Boschker, H.; Kopp, T.; Valentí, R.

    2016-08-01

    Low-dimensional electron systems fabricated from quantum matter have in recent years become available and are being explored with great intensity. This article gives an overview of the fundamental properties of such systems and summarizes the state of the field. We furthermore present and consider the concept of artificial atoms fabricated from quantum materials, anticipating remarkable scientific advances and possibly important applications of this new field of research. The surprising properties of these artificial atoms and of molecules or even of solids assembled from them are presented and discussed.

  17. A Survey of Artificial Immune System Based Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Hua Yang

    2014-01-01

    Full Text Available In the area of computer security, Intrusion Detection (ID is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS. The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs. This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.

  18. An Artificial Glowworm Swarm Optimization Algorithm Based on Powell Local Optimization Method%一种用Powell方法局部优化的人工萤火虫算法

    Institute of Scientific and Technical Information of China (English)

    张军丽; 周永权

    2011-01-01

    In order to overcome the shortcomings of artificial glowworm swarm optimization ( GSO) algorithm including slow convergence speed, easily falling into local optimum value, low computational accuracy and low success rate of convergence, an artificial GSO algorithm based on Powell local optimization method is proposed. It adopts the powerful local optimization ability of Powell method and embeds it into GSO as a local search operator. Experimental results of 8 typical functions show that the proposed algorithm is superior to GSO in convergence efficiency, computational precision and stability.%针对人工萤火虫算法在寻找函数全局最优值时,存在着收敛速度慢、易陷入局部最优、收敛成功率和求解精度低等不足,利用Powell方法强大的局部优化能力,将其作为一局部搜索算子嵌入到人工萤火虫算法,提出一种用Powell方法局部优化的人工萤火虫算法.最后,8个标准函数测试结果表明,改进后人工萤火虫算法在收敛速度、精度和稳定性方面都优于人工萤火虫算法.

  19. Finite Element - Artificial Transmitting Boundary Method for Acoustical Field on Tapered Waveguide

    Institute of Scientific and Technical Information of China (English)

    J.; S.; Yang; G; F.; Fan; J.; P.; Zhu; C.K.; Sun; Y.; H.; Zhu

    2003-01-01

    In earlier approach, the 2-D acoustical field profiles on the substrate region are often calculated with BPM. In this paper, we present a new approach based on the finite element -artificial transmitting boundary method and calculate acoustical field on the substrate region.

  20. 基于等级加权法的海上机械采油方式优选方法%Optimizal Method of Offshore Oil Artificial Lift Modes Based on Hierarchical Grade-Weighted Method

    Institute of Scientific and Technical Information of China (English)

    杨阳; 曹砚锋; 隋先富; 于继飞; 欧阳铁兵

    2016-01-01

    目前海上油田总体开发方案中,机械采油方式优选方法单一,以定性指标为主,缺乏必要理论支撑与数据支持。为此,综合考虑技术指标、经济指标、工程指标的影响,以机械采油方式优选为目标,采用等级加权法对技术、经济和工程3个指标的影响因素进行赋值以及分配权重,建立了海上机械采油方式优选方法。利用该优选方法对海上X稠油油田的机械采油方式进行了优选,开发初期产液量为40~108 m3/d时,推荐采用螺杆泵开采;开发后期产液量达到200 m3/d时,考虑X油田为稠油油田,应采取降黏措施,推荐采用电潜泵+掺稀/化学降黏方式开采。优选结果与该油田总体开发方案中的机械采油方式一致,且该油田机械采油方式与优选结果一致的井,都正常生产。这表明基于等级加权法的机械采油方式优选方法可以指导海上油田选用合理、经济、对海洋工程影响较小的机械采油方式,从而提高海上油田的开发效益。%Currently ,the method used for optimizing mechanical artificial lift methods in offshore oil fields is developed during the overall development plan .Generally speaking ,there is insufficient theoretical support or data available in these processes .For that reason ,it is important to develop a new analytical ap-proach that incorporates all relevant technical ,economic ,and engineering factors .To improves the process , a hierarchical ,grade-weighted method can be deployed to assign these three indicators for establishing a model of optimal offshore artificial lift .The newly developed method has been deployed for identification of optimal artificial lifts in X Oilfield for heavy oil development .The result shows that in the early stages of development ,liquid production was 40 108 m3/d ,and accordingly ,a screw pump was proposed for its devel-opment .In the late stages of development ,with liquid

  1. Continuous Control Artificial Potential Function Methods and Optimal Control

    Science.gov (United States)

    2014-03-27

    Method, namely r̈VDSVAPF = −K̇SKR∇φ−KSK̇R∇φ−KSKRH(φ)ṙ −KD (KSKR∇φ+ ṙ) . The above dynamics are very nonlinear due to the trigonometric functions (inside...constraints (on KS and θ) and the deletion of trigonometric functions . The suspected reasons for the larger computa- tional expense are twofold. First, this...Continuous Control Artificial Potential Function Methods and Optimal Control THESIS R. Andrew Fields, Civ, USAF AFIT-ENY-14-M-20 DEPARTMENT OF THE

  2. The selection of artificial corner reflectors based on RCS analysis

    Science.gov (United States)

    Li, Chengfan; Yin, Jingyuan; Zhao, Junjuan; Zhang, Guifang; Shan, Xinjian

    2012-02-01

    Artificial corner reflectors (ACRs) are widely applicable in monitoring terrain change via interferometric synthetic aperture radar (InSAR) remote sensing techniques. Many different types are available. The choice of the most appropriate ones has recently attracted scholarly attentions. Based on physical optics methods, via calculating the radar cross section (RCS) values (the higher the value, the better the detectability), the current study tested three ACRs, i.e., triangular pyramidal, rectangular pyramidal and square trihedral ACRs. Our calculation suggests that the square trihedral ACR produces the largest RCS but least tolerance towards incident radar ray's deviation from optimal angle. The triangular pyramidal trihedral ACR is the most geometrically stable ACR, and has the highest tolerance towards incident radar ray's deviation. Its RCS values, however, are the least of the three. Due to the high cost of deploying ACRs in the fields, the physical optics method seems to provide a viable way to choose appropriate ACRs.

  3. 基于梯度蜂群混合算法的电力系统最优潮流计算%Hybrid Artificial bee Colony Algorithm Based on Gradient Method for Optimal Power Flower Calculation System

    Institute of Scientific and Technical Information of China (English)

    杨琳; 孔峰

    2011-01-01

    Aiming at optimal power flow calculation problem in power system, this paper presents a new method of hybrid artificial bee colony algorithm based on gradient method GABC. Firstly, the new algorithm used quickness searching of gradient method to obtain a local minimum. then by utilizing the abilities of global searching of artificial bee colony algorithm, it escaped from trapping this local minimum. At last, the global minimum was achieved through iterative computation. Simulation experiments of IEEE5 system show that the improved algorithm can be better dealt with optimal flow constraints in dealing with the issue of optimal power flow. This method can also find preferable results and its correctness and validity is proven by a series of tests and computation, and that the algorithms can be widely applied to the areas of power system planning and operation.%针对电力系统最优潮流计算的问题提出一种基于梯度蜂群混合算法GABC.利用梯度算法的快速寻优特性得到某一局部极值,然后采用蜂群算法的全局寻优能力跳出该局部极值,并经过反复交替迭代最终找到问题的最优解.通过对IEEE5节点系统的计算结果表明改进后的人工蜂群算法可较好的处理最优潮流约束条件,有效提高基本蜂群算法的全局寻优能力和收敛精度.在处理最优潮流问题上具有一定的有效性和优越性.

  4. Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict Kd of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors.

    Science.gov (United States)

    Maleki, Afshin; Daraei, Hiua; Alaei, Loghman; Faraji, Aram

    2014-01-01

    Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.

  5. An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

    OpenAIRE

    Mengling Zhao; Hongwei Liu

    2015-01-01

    As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new class...

  6. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion. 1. ... Recently, some methods based on artificial intelligence have been applied In order to improve ..... The effect is the reduction of.

  7. Hybrid Heuristic-Based Artificial Immune System for Task Scheduling

    CERN Document Server

    sanei, Masoomeh

    2011-01-01

    Task scheduling problem in heterogeneous systems is the process of allocating tasks of an application to heterogeneous processors interconnected by high-speed networks, so that minimizing the finishing time of application as much as possible. Tasks are processing units of application and have precedenceconstrained, communication and also, are presented by Directed Acyclic Graphs (DAGs). Evolutionary algorithms are well suited for solving task scheduling problem in heterogeneous environment. In this paper, we propose a hybrid heuristic-based Artificial Immune System (AIS) algorithm for solving the scheduling problem. In this regard, AIS with some heuristics and Single Neighbourhood Search (SNS) technique are hybridized. Clonning and immune-remove operators of AIS provide diversity, while heuristics and SNS provide convergence of algorithm into good solutions, that is balancing between exploration and exploitation. We have compared our method with some state-of-the art algorithms. The results of the experiments...

  8. 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.

  9. Collision avoidance planning in multi-robot system based on improved artificial potential field and rules

    Institute of Scientific and Technical Information of China (English)

    YUAN Xin; ZHU Qi-dan; YAN Yong-jie

    2009-01-01

    For real-time and distributed features of multi-robot system, the strategy of combining the improved artificial potential field method and the mles based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method, so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots, collisions between robots can be detected, thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-basod simulated environment.

  10. 基于人工神经网络方法的钢筋混凝土梁式桥损伤鉴识研究%Reinforced Concrete Beam Bridge's Damage Forensic Research Based on Artificial Neural Network Method

    Institute of Scientific and Technical Information of China (English)

    林立

    2012-01-01

    越来越多的桥梁需要进行损伤维护,面对的首要问题即是如何快速高效地进行损伤鉴定。本文探讨了人工神经网络方法在损伤鉴识方面的可行性,通过对68座钢筋混凝土梁式桥破坏信息的收集整理,建立仿真样本库,训练网络。经过比较选择泛化能力优越的RBF网络,开发了“桥梁工程鉴识专家系统”。利用该系统对一座损伤桥梁进行鉴识分析,良好的效果说明了该系统的有效性以及人工神经网络方法在解决工程鉴识问题方面的卓越能力。%More and more damaged bridges need maintenance. The quick and efficient damage forensic became the most important issue. In this paper, the feasibility of artificial neural network method applicated to the damage forensics was investigated. 68 reinforced concrete girder bridges' damage information was collected for establishment the simulation sample library and training multiple networks. Basing on the compare, the RBF network, because of its generalization, was selected for developing Bridge Engineering Forensic Expert System. The network was used for a damage bridges' forensic. The excellent results demonstrate the effectiveness of the network, and the outstanding ability of artificial neural network method in solving engineering forensic problems.

  11. 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.

  12. Path planning of mobile robot by mixing experience with modified artificial potential field method

    Directory of Open Access Journals (Sweden)

    Huasong Min

    2015-12-01

    Full Text Available In this article, a new method is proposed to help the mobile robot to avoid many kinds of collisions effectively, which combined past experience with modified artificial potential field method. In the process of the actual global obstacle avoidance, system will invoke case-based reasoning algorithm using its past experience to achieve obstacle avoidance when obstacles are recognized as known type; otherwise, it will invoke the modified artificial potential field method to solve the current problem and the new case will also be retained into the case base. In case-based reasoning, we innovatively consider that all the complex obstacles are retrieved by two kinds of basic build-in obstacle models (linear obstacle and angle-type obstacle. Our proposed experience mixing with modified artificial potential field method algorithm has been simulated in MATLAB and implemented on actual mobile robot platform successfully. The result shows that the proposed method is applicable to the dynamic real-time obstacle avoidance under unknown and unstructured environment and greatly improved the performances of robot path planning not only to reduce the time consumption but also to shorten the moving distance.

  13. Research on the Method of Web Text Automatic Summarization Based on Artificial Immune%基于人工免疫的Web文本自动摘要方法研究

    Institute of Scientific and Technical Information of China (English)

    李士勇

    2013-01-01

      Web text searching is an important aspect of user implementation in the Internet environment. Presents a new Web text automatic summarization method based on artificial immune by com-paring with the traditional methods, the result of experiment shows that the method is high self-adaptive and has evolution ability, it can provide a new research approach to the field of Web text summarization.%  如何在数量巨大的Internet中快速准确搜索到符合要求的Web页是一个值得探讨的重要课题。构造一种能够根据句式和词频对Web文本自动模型,运用人工免疫算法使该模型具有较高的聚类精度和自发现能力,实验结果表明,该模型不仅能够有效对各类Web文本进行,保持较低的错误肯定率和错误否定率,还具有很强的自适应性和更新能力,在算法复杂度上也具备一定优势。

  14. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  15. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  16. Neuro-Based Artificial Intelligence Model for Loan Decisions

    Directory of Open Access Journals (Sweden)

    Shorouq F. Eletter

    2010-01-01

    Full Text Available 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 backpropagation learning algorithm was used to build up the proposed model. Results: Different representative cases of loan applications were considered based on the guidelines of different banks in Jordan, to validate the neural network model. Conclusion: The results indicated that artificial neural networks are a successful technology that can be used in loan application evaluation in the Jordanian commercial banks.

  17. [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.

  18. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  19. 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…

  20. Does Artificial Tutoring Foster Inquiry Based Learning?

    Science.gov (United States)

    Schmoelz, Alexander; Swertz, Christian; Forstner, Alexandra; Barberi, Alessandro

    2014-01-01

    This contribution looks at the Intelligent Tutoring Interface for Technology Enhanced Learning, which integrates multistage-learning and inquiry-based learning in an adaptive e-learning system. Based on a common pedagogical ontology, adaptive e-learning systems can be enabled to recommend learning objects and activities, which follow inquiry-based…

  1. 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.

  2. Research on measurement method of optical transmittance of the artificial fog

    Science.gov (United States)

    Hu, Jianghua; Jian, Chaochao; Cui, Guangzhen; Lv, Xuliang; Rong, Xianhui

    2016-10-01

    The attenuation of light was a common result that the light was absorbed and scattered by the artificial fog particles when it transmitted in the artificial fog. The absorbing attenuation process of light transmission in the artificial fog was that the artificial fog converting incident light energy into other forms of internal energy (such as heat energy). The scattering attenuation process of light transmission in the artificial fog was that the artificial fog particles intercepting incident radiation energy to form infrasonic waves and to radiate peripherally so that the incident light energy was reduced on the original direction of transmission. The mechanism of light transmission attenuation in the artificial fog was analyzed. The formation method of the artificial fog was expounded and the measuring principle of the artificial fog transmittance was described. A simple and reliable measurement method of the optical transmittance of the artificial fog in the fog chamber was proposed. The optical transmittance measurement system of the artificial fog was built by using incandescent lamp, power with steady current and voltage, lens, selenium photocell, micro-galvanometer, optical bench, hygrothermograph, humidifier, etc. Under different conditions of humidity, the optical transmittance of the artificial fog was obtained on the basis of measuring the photocurrent before the fog was formed in the fog chamber. The test results show that the measurement system is stable and reliable. During the 43 minutes after the artificial fog was formed, the optical transmittance of the artificial fog was averagely less than 5 percent and the optical transmittance increased gradually with the extension of time. In addition, the optical transmittance of artificial fog didn't produce obvious change while air humidity increased from 68.7% to 85%. The measurement system can be used to measure transmittance of smoke screen, water mist and other aerosol.

  3. 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.

  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. Cooperative Traffic Control based on the Artificial Bee Colony

    Directory of Open Access Journals (Sweden)

    Jinjian Li

    2016-12-01

    Full Text Available This paper studies the traffic control problem in an isolated intersection without traffic lights and phase, because the right-of-way is distributed to each vehicle individually based on connection of the Vehicle-to-Infrastructure (V2I, and the compatible streams are dynamically combined according to the arrival vehicles in each traffic flows. The control objective in the proposed algorithm is to minimize the time delay, which is defined as the difference between the travel time in real state and that in free flow state. In order to realize this target, a cooperative control structure with a two-way communications is proposed. First of all, once the vehicle enters the communication zone, it sends its information to the intersection. Then the passing sequence is optimized in the intersection with the heuristic algorithm of the Artificial Bee Colony, based on the arrival interval of the vehicles. At last, each vehicle plans its speed profile to meet the received passing sequence by V2I. The simulation results show that each vehicle can finish the entire travel trip with a near free flow speed in the proposed method.

  6. Path planning for mobile robot based on improved Artificial Potential Field method in complex environment%复杂环境下改进APF的机器人路径规划

    Institute of Scientific and Technical Information of China (English)

    卢恩超; 张万绪

    2013-01-01

    When the obstacles are large, or the complex environment space is relatively narrow, Artificial Potential Field method (APF)is prone to appear repeated shocks, long time planning and obstacle avoidance of difficulty nearby the large obstacles. This paper presents an adaptive dynamic step length adjustment method based on the APF path planning which is combined with the edge detection method to overcome the proposed defects of APF, achieving mobile robot smooth path planning in the complex environment. Hence it can not only improve APF algorithm convergence speed and the safety of path planning, but at the same time ensure the approximate optimum path. Experiments are carried out by simulation to verify the effectiveness of the afore-mentioned methods.%针对移动机器人在有大型障碍物和运动空间相对狭窄的复杂环境中,人工势场法(APF)容易出现反复震荡、路径规划时间较长以及大型障碍物附近避障困难的问题,提出了在结合边缘探测法的APF路径规划基础上,加入自适应动态步长调整算法来克服APF的上述缺陷,实现移动机器人在复杂环境下的平滑路径规划,在确保路径近似最优的同时提高APF算法的收敛速度和路经规划的避障性能。实验结果证明了上述方法的有效性。

  7. An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

    Directory of Open Access Journals (Sweden)

    Mengling Zhao

    2015-01-01

    Full Text Available As a computational intelligence method, artificial immune network (AIN algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN. The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC, and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

  8. Nanoscale Probe of Magnetism Based on Artificial Atoms in Diamond

    Science.gov (United States)

    2014-07-18

    AFRL-OSR-VA-TR-2014-0165 ( YIP 11) Nanoscale probe of magnetism based on artificial atoms in diamond Ania Bleszynski Jayich UNIVERSITY OF CALIFORNIA...center Ania Bleszynski Jayihc (805) 893 8089 AFOSR   YIP  Report     Ania  Bleszynski  Jayich   Nanoscale probe of magnetism based on...dramatically affected by proximal Gd ions. Gd ions are commonly used spin labels for biological imaging. AFOSR   YIP  Report     Ania

  9. Application of Global Dynamic Reconfiguration in Artificial Neural Network System based on Field Programmable Gate Array

    Institute of Scientific and Technical Information of China (English)

    LI Wei; WANG Wei; MA Yi-mei; WANG Jin-hai

    2008-01-01

    Presented is a global dynamic reconfiguration design of an artificial neural network based on field programmable gate array(FPGA). Discussed are the dynamic reconfiguration principles and methods. Proposed is a global dynamic reconfiguration scheme using Xilinx FPGA and platform flash. Using the revision capabilities of Xilinx XCF32P platform flash, an artificial neural network based on Xilinx XC2V30P Virtex-Ⅱ can be reconfigured dynamically from back propagation(BP) learning algorithms to BP network testing algorithms. The experimental results indicate that the scheme is feasible, and that, using dynamic reconfiguration technology, FPGA resource utilization can be reduced remarkably.

  10. Methods of artificial enlargement of the training set for statistical shape models.

    Science.gov (United States)

    Koikkalainen, Juha; Tölli, Tuomas; Lauerma, Kirsi; Antila, Kari; Mattila, Elina; Lilja, Mikko; Lötjönen, Jyrki

    2008-11-01

    Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.

  11. Mechatronic Hydraulic Drive with Regulator, Based on Artificial Neural Network

    Science.gov (United States)

    Burennikov, Y.; Kozlov, L.; Pyliavets, V.; Piontkevych, O.

    2017-06-01

    Mechatronic hydraulic drives, based on variable pump, proportional hydraulics and controllers find wide application in technological machines and testing equipment. Mechatronic hydraulic drives provide necessary parameters of actuating elements motion with the possibility of their correction in case of external loads change. This enables to improve the quality of working operations, increase the capacity of machines. The scheme of mechatronic hydraulic drive, based on the pump, hydraulic cylinder, proportional valve with electrohydraulic control and programmable controller is suggested. Algorithm for the control of mechatronic hydraulic drive to provide necessary pressure change law in hydraulic cylinder is developed. For the realization of control algorithm in the controller artificial neural networks are used. Mathematical model of mechatronic hydraulic drive, enabling to create the training base for adjustment of artificial neural networks of the regulator is developed.

  12. 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...

  13. 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).

  14. 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.

  15. 基于神经网络逆系统方法的链式STATCOM线性化解耦控制%Linearization decoupling control of cascaded STATCOM based on the method of artificial neural networks inverse system

    Institute of Scientific and Technical Information of China (English)

    刘庆锋; 粟时平; 刘桂英; 吕超

    2016-01-01

    Aiming at the control problem of cascaded STATCOM to compensate the load reactive current and stabilize the grid voltage, this paper establishes dynamic mathematics model of cascaded STATCOM, and obtains the relation-ship of the two variablesδ、M and current, then proposes a control method based on artificial neural networks inverse system.The linearization decoupling control of active and reactive current of cascaded STATCOM is realized through the verification of reversibility, construction of artificial neural networks as well as the design of control system for the system of cascaded STATCOM.Finally, the simulation results show that the control strategy acquires good dynamic effect and static effect when switching inductive load into capacitive load or in turn, which makes the device possess better performance to resist parameters change and load disturbance, thereby, it illustrates the effectiveness and the feasibility of the control strategy.%针对链式STATCOM补偿负载无功电流以及稳定电网电压的控制问题,建立了链式STATCOM动态数学模型,得出了双变量δ、M(移相角与调制比)和电流的关系式,提出了一种基于神经网络逆系统控制方法,通过对该链式STATCOM系统可逆的验证、神经网络的构建以及控制系统的设计,实现了链式STATCOM输出的有功-无功电流的线性化解耦控制。仿真结果表明,在感性负载与容性负载进行切换以时,该控制策略取得了良好的动态效果以及稳态效果,使得装置具有较好的抗参数变化、抗负载扰动性能,从而验证了该控制策略的有效性及可行性。

  16. A Smartphone Malware Detection Framework Based on Artificial Immunology

    Directory of Open Access Journals (Sweden)

    Min Zhao

    2013-02-01

    Full Text Available With the sharp increase in the number of smartphones, the Android platform pose to becoming a market leader that makes the need for malware analysis on this platform an urgent issue. The current Artificial Immune-Based malware detection systems research focus on traditional computers that uses information from OS or network, but the smartphone software behavior has its own structure and semantics. Current research cannot detect malware in smartphone exactly and efficiently. To address these problems, in this paper, we capitalize on earlier approaches for dynamic analysis of application behavior as a means for detecting malware in the smartphone. An Artificial Immune-Based Smartphone Malware Detection Framework is brought forwards and a prototype system is implemented, the experiment result show that the system can obtain higher detection rate and decrease the false positive rate.

  17. THE ELLIPSOID ARTIFICIAL BOUNDARY METHOD FOR THREE-DIMENSIONAL UNBOUNDED DOMAINS

    Institute of Scientific and Technical Information of China (English)

    Hongying Huang; Dehao Yu

    2009-01-01

    The artificial boundary method is applied to solve three-dimensional exterior problems.Two kind of rotating ellipsoids are chosen as the artificial boundaries and the exact artificial boundary conditions are derived explicitly in terms of an infinite series. Then the well-posedness of the coupled variational problem is obtained. It is found that error estimates derived depend on the mesh size, truncation term and the location of the artificial boundary. Three numerical examples are presented to demonstrate the effectiveness and accuracy of the proposed method.

  18. Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

    Science.gov (United States)

    Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun

    2013-02-15

    This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

  19. Analytical study on aseismic base isolation in new offshore location. For practical location for thermal power plants by soft-landing artificial ground construction method; Atarashii kaijo ricchi ni okeru menshinsei ni kansuru kaisekiteki kento. Nanchaku jinko jiban koho ni yoru karyoku hatsuden setsubi ricchi jitsuyoka ni mukete

    Energy Technology Data Exchange (ETDEWEB)

    Kawamura, T.; Sakakibara, T. [Tokyo Electric Power Co. Inc., Tokyo (Japan); Ishikawa, M. [Nikken Sekkei Ltd., Osaka (Japan)

    1996-09-05

    Assuming offshore location of thermal power plants by soft-landing artificial ground construction method, its effect on uneven settlement of facility unit group and its seismic behavior were studied. In soft-landing artificial ground, the total weight and buoyancy of a caisson body with interior space are balanced by controlling water level in a tank to reduce landing pressure. Artificial ground resists lateral seismic external force by friction with submarine bedrock. Settlement analysis and seismic behavior analysis were conducted by 2-D FEM. As a result, bottom RC honeycomb structure was sufficient to resist uneven settlement during construction even under fixed junction condition between units. In soft-landing artificial ground, the maximum acceleration was reduced to nearly 70% by aseismic base isolation as compared with conventional methods. In addition, its sliding manner in earthquake was different from that of rigid bodies, and the maximum stress acceleration transmitted to the bottom of artificial ground changed even under the same waveform. 4 refs., 13 figs., 4 tabs.

  20. Artificial immunity-based induction motor bearing fault diagnosis

    OpenAIRE

    Hakan ÇALIŞ; ÇAKIR, Abdülkadir; Emre DANDIL

    2013-01-01

    In this study, the artificial immunity of the negative selection algorithm is used for bearing fault detection. It is implemented in MATLAB-based graphical user interface software. The developed software uses amplitudes of the vibration signal in the time and frequency domains. Outer, inner, and ball defects in the bearings of the induction motor are detected by anomaly monitoring. The time instants of the fault occurrence and fault level are determined according to the number of a...

  1. 基于改进人工蜂群算法的高光谱图像端元提取方法%An endmember extraction method for hyperspectral remote sensing imagery based on improved artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    李冰; 孙辉; 孙宁; 王坤

    2015-01-01

    To solve the problem of endmember extraction for hyperspectral remote sensing imagery,a new endmember extraction method based on improved artificial bee colony algorithm is proposed. First,the weighted generated bee guided search strategy is used to balance the exploration and exploitation in ABC, and a new algorithm named IABC is proposed. Experiments are carried out on 8 benchmark functions,and the results show that the performance of the new algorithm is significantly improved. Then,the core idea and the main steps of the IABC-based extraction are introduced. The results show that the new algorithm has better applicability compared with ABC and conventional extraction algorithm in the simulation and real hyperspectral data.%针对高光谱图像中端元提取的问题,提出了一种基于改进人工蜂群算法的提取方法。首先,为平衡人工蜂群算法全局和局部搜索能力,研究了加权构造蜂引导的搜索策略,构造了改进人工蜂群算法。在8个基准测试函数中进行实验,验证了新算法的性能有明显提升。然后,介绍了基于IABC端元提取的核心思想与主要步骤,与ABC和常规提取算法在模拟和真实高光谱遥感数据中进行实验对比,结果表明了新算法具有更好的适用性。

  2. 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.

  3. Comparative evaluation of tensile bond strength of a polyvinyl acetate-based resilient liner following various denture base surface pre-treatment methods and immersion in artificial salivary medium: An in vitro study

    Directory of Open Access Journals (Sweden)

    Jacob M Philip

    2012-01-01

    Full Text Available Background and Aim: This study was formulated to evaluate and estimate the influence of various denture base resin surface pre-treatments (chemical and mechanical and combinations upon tensile bond strength between a poly vinyl acetate-based denture liner and a denture base resin. Materials and Methods: A universal testing machine was used for determining the bond strength of the liner to surface pre-treated acrylic resin blocks. The data was analyzed by one-way analysis of variance and the t-test (α =.05. Results: This study infers that denture base surface pre-treatment can improve the adhesive tensile bond strength between the liner and denture base specimens. The results of this study infer that chemical, mechanical, and mechano-chemical pre-treatments will have different effects on the bond strength of the acrylic soft resilient liner to the denture base. Conclusion: Among the various methods of pre-treatment of denture base resins, it was inferred that the mechano-chemical pre-treatment method with air-borne particle abrasion followed by monomer application exhibited superior bond strength than other methods with the resilient liner. Hence, this method could be effectively used to improve bond strength between liner and denture base and thus could minimize delamination of liner from the denture base during function.

  4. 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.

  5. Reactive underwater object inspection based on artificial electric sense.

    Science.gov (United States)

    Lebastard, Vincent; Boyer, Frédéric; Lanneau, Sylvain

    2016-07-26

    Weakly electric fish can perform complex cognitive tasks based on extracting information from blurry electric images projected from their immediate environment onto their electro-sensitive skin. In particular they can be trained to recognize the intrinsic properties of objects such as their shape, size and electric nature. They do this by means of novel perceptual strategies that exploit the relations between the physics of a self-generated electric field, their body morphology and the ability to perform specific movement termed probing motor acts (PMAs). In this article we artificially reproduce and combine these PMAs to build an autonomous control strategy that allows an artificial electric sensor to find electrically contrasted objects, and to orbit around them based on a minimum set of measurements and simple reactive feedback control laws of the probe's motion. The approach does not require any simulation models and could be implemented on an autonomous underwater vehicle (AUV) equipped with artificial electric sense. The AUV has only to satisfy certain simple geometric properties, such as bi-laterally (left/right) symmetrical electrodes and possess a reasonably high aspect (length/width) ratio.

  6. Monitoring Artificial Pancreas Trials Through Agent-based Technologies

    Science.gov (United States)

    Scarpellini, Stefania; Di Palma, Federico; Toffanin, Chiara; Del Favero, Simone; Magni, Lalo; Bellazzi, Riccardo

    2014-01-01

    The increase in the availability and reliability of network connections lets envision systems supporting a continuous remote monitoring of clinical parameters useful either for overseeing chronic diseases or for following clinical trials involving outpatients. We report here the results achieved by a telemedicine infrastructure that has been linked to an artificial pancreas platform and used during a trial of the AP@home project, funded by the European Union. The telemedicine infrastructure is based on a multiagent paradigm and is able to deliver to the clinic any information concerning the patient status and the operation of the artificial pancreas. A web application has also been developed, so that the clinic staff and the researchers involved in the design of the blood glucose control algorithms are able to follow the ongoing experiments. Albeit the duration of the experiments in the trial discussed in the article was limited to only 2 days, the system proved to be successful for monitoring patients, in particular overnight when the patients are sleeping. Based on that outcome we can conclude that the infrastructure is suitable for the purpose of accomplishing an intelligent monitoring of an artificial pancreas either during longer trials or whenever that system will be used as a routine treatment. PMID:24876570

  7. Techniques of Image Processing Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    LI Wei-qing; WANG Qun; WANG Cheng-biao

    2006-01-01

    This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue,saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram,were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.

  8. 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.

  9. Optimizing Mining Association Rules for Artificial Immune System based Classification

    Directory of Open Access Journals (Sweden)

    SAMEER DIXIT

    2011-08-01

    Full Text Available The primary function of a biological immune system is to protect the body from foreign molecules known as antigens. It has great pattern recognition capability that may be used to distinguish between foreigncells entering the body (non-self or antigen and the body cells (self. Immune systems have many characteristics such as uniqueness, autonomous, recognition of foreigners, distributed detection, and noise tolerance . Inspired by biological immune systems, Artificial Immune Systems have emerged during the last decade. They are incited by many researchers to design and build immune-based models for a variety of application domains. Artificial immune systems can be defined as a computational paradigm that is inspired by theoretical immunology, observed immune functions, principles and mechanisms. Association rule mining is one of the most important and well researched techniques of data mining. The goal of association rules is to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in thetransaction databases or other data repositories. Association rules are widely used in various areas such as inventory control, telecommunication networks, intelligent decision making, market analysis and risk management etc. Apriori is the most widely used algorithm for mining the association rules. Other popular association rule mining algorithms are frequent pattern (FP growth, Eclat, dynamic itemset counting (DIC etc. Associative classification uses association rule mining in the rule discovery process to predict the class labels of the data. This technique has shown great promise over many other classification techniques. Associative classification also integrates the process of rule discovery and classification to build the classifier for the purpose of prediction. The main problem with the associative classification approach is the discovery of highquality association rules in a very large space of

  10. Product Assembly Cost Estimation Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks (ANN). It can support the de signers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a compari son, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.

  11. An artificially generated atmosphere near a lunar base

    Science.gov (United States)

    Burns, Jack O.; Fernini, Ilias; Sulkanen, Martin; Duric, Nebojsa; Taylor, G. Jeffrey; Johnson, Stewart W.

    1992-09-01

    We discuss the formation of an artificial atmosphere generated by vigorous lunar base activity in this paper. We developed an analytical, steady-state model for a lunar atmosphere based upon previous investigations of the Moon's atmosphere from Apollo. Constant gas-injection rates, ballistic trajectories, and a Maxwellian particle distribution for an oxygen-like gas are assumed. Even for the extreme case of continuous He-3 mining of the lunar regolith, we find that the lunar atmosphere would not significantly degrade astronomical observations beyond about 10 km from the mining operation.

  12. FORCE FEEDBACK DATAGLOVE BASED ON PNEUMATIC ARTIFICIAL MUSCLES

    Institute of Scientific and Technical Information of China (English)

    BAO Gang; SUN Zhongsheng; WANG Zuwen

    2006-01-01

    An exoskeleton force feedback dataglove is developed, which uses the pneumatic artificial muscles as actuators. On the basis of the simplified hand model, the motion equation is deduced according to the theory of Denavit-Hartenberg. The model of the equivalent contact forces exerted by the object on the finger is proposed. By the principle of virtual work, the static equilibrium of finger is established. The force Jacobian matrix of finger is calculated, and then the joint torques of the finger when grasping objects are obtained. The theory and structure of the force feedback datagolve are introduced. Based on the theory of motion stabilization of four-bar linkage, the flexion angles of joints are measured. The torques on finger joints caused by the output forces of pneumatic artificial muscles are calculated. The output forces of pneumatic artificial muscle, whose values are controlled by its inner pressure, can be calculated by the joint torques ofthe finger when grasping objects. The arms of force, driving torques and the needed output forces of pneumatic muscle are calculated for each joint of the index finger. The criterion of output force of pneumatic muscle is given.

  13. Rule Extraction from Trained Artificial Neural Network Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Wen-jian; ZHANG Li-xia

    2002-01-01

    This paper discusses how to extract symbolic rules from trained artificial neural network (ANN) in domains involving classification using genetic algorithms (GA). Previous methods based on an exhaustive analysis of network connections and output values have already been demonstrated to be intractable in that the scale-up factor increases with the number of nodes and connections in the network.Some experiments explaining effectiveness of the presented method are given as well.

  14. 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 wi...... detection upon a generalized-likelihood-test. An upper and a lower control bounds are established for x and y respectively, given a minimum false alarm probability η based on the statistical characteristics of the data....

  15. 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.

  16. 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.

  17. Surname Inherited Algorithm Research Based on Artificial Immune System

    Directory of Open Access Journals (Sweden)

    Jing Xie

    2013-06-01

    Full Text Available To keep the diversity of antibodies in artificial immune system evolution process, this paper puts forward a kind of increase simulation surname inheritance algorithm based on the clonal selection algorithm, and identification and forecast the Vibration Data about CA6140 horizontal  lathe machining slender shaft workpiece prone . The results show that the algorithm has the characteristics of flexible application, strong adaptability, an effective approach to improve efficiency of the algorithm, a good performance of global searching and broad application prospect.

  18. 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.

  19. Method of Measurement of Capacitance and Dielectric Loss Factor Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Roj Jerzy

    2015-06-01

    Full Text Available A novel method of dielectric loss factor measuring has been described. It is based on a quasi-balanced method for the capacitance measurement. These AC circuits allow to measure only one component of the impedance. However, after analyzing a quasi-balanced circuit's processing equation, it is possible to derive a novel method of dielectric loss factor measuring. Dielectric loss factor can be calculated after detuning the circuit from its quasi-equilibrium state. There are two possible ways of measuring the dielectric loss factor. In the first, the quasi-balancing of the circuit is necessary. However, it is possible to measure capacitance of an object under test. In the second method, the capacitance cannot be measured. Use of an artificial neural network minimizes errors of the loss factor determining. Simulations showed that the appropriate choice of the range of the detuning can minimize errors as well.

  20. Bio-inspired artificial iriodphores based on capillary origami

    Science.gov (United States)

    Manakasettharn, Supone; Taylor, J. Ashley; Krupenkin, Tom

    2011-03-01

    Many marine organisms have evolved complex optical mechanisms of dynamic skin color control that allow them to drastically change their visual appearance. In particular, cephalopods have developed especially effective dynamic color control mechanism based on the mechanical actuation of the micro-scale optical structures, which produce either variable degrees of area coverage by a given color (chromatophores) or variations in spatial orientation of the reflective and diffractive surfaces (iridophores). In this work we describe bio-inspired artificial iridophores based on electrowetting-controlled capillary origami. We describe the developed microfabrication approach, characterize mechanical and optical properties of the obtained microstructures and discuss their electrowetting-based actuation. The obtained experimental results are in good agreement with a simple theoretical model based on electrocapillarity and elasticity theory. The results of the work can enable a broad range of novel optical devices.

  1. Forecast Method of City Water Consumption Per Hour Based on Artificial Fish-Swarm Neural Network%基于人工鱼群神经网络的城市时用水量预测方法

    Institute of Scientific and Technical Information of China (English)

    刘洪波; 郑博一; 蒋博龄

    2015-01-01

    The forecast precision of city water consumption per hour has great effect on the city water supply system. The traditional forecast method of back-propagation(BP)neural network tends to offer local values and requires a lot of data training. The artificial fish-swarm algorithm(AFSA)has better global convergence ability and higher optimiza-tion speed. AFSA was adopted to optimize the initial setting weights and thresholds of BP neural network. Then anew forecast model of water consumption per hour was built and was applied to forecast the water consumption per hour of a city in North China. Results show that the mean square error of the artificial fish-swarm neural network algorithm is lower than that of BP neural network algorithm by 5%. It has been verified by instances that the artificial fish-swarm neural network has better forecast precision and higher convergence speed than BP neural network. Artificial fish-swarm neural network algorithm can be used to forecast the short-term water consumption.%城市供水时用水量预测精度对城市供水系统具有重要影响。传统的反向传播(back-propaganda,BP)神经网络预测方法容易陷入局部解,并且需要大量的训练数据。人工鱼群算法具有较优的全局收敛能力及较快的寻优速度。为此,利用人工鱼群算法对 BP 神经网络的初始权值和阈值进行优化,建立了一种新的时用水量预测模型。将该模型应用到华北某市时用水量的预测中,预测结果表明人工鱼群神经网络算法的均方差比 BP 神经网络算法的均方差小5%。实例证明,人工鱼群神经网络比 BP 神经网络的预测精度更高,收敛速度更快。人工鱼群神经网络算法可用于短期水量预测。

  2. 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.

  3. 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.

  4. Determination of type and concentration of DNA nitrogenous bases by Raman spectroscopy using artificial neural networks

    Science.gov (United States)

    Laptinskiy, Kirill A.; Burikov, Sergey A.; Sarmanova, Olga E.; Dolenko, Sergey A.; Dolenko, Tatiana A.

    2016-04-01

    In this article the results of solution of two-parametrical inverse problems of laser Raman spectroscopy of identification and determination of concentration of DNA nitrogenous bases in two-component solutions are presented. Elaboration of methods of control of reactions with DNA strands in remote real-time mode is necessary for solution of one of the basic problems of creation of biocomputers - increase of reliability of molecular DNA-computations. The comparative analysis of two used methods of solution of stated problems has demonstrated convincing advantages of technique of artificial neural networks. Use of artificial neural networks allowed to reach the accuracy of determination of concentration of each base in two-component solutions 0.2-0.3 g/l.

  5. Path Planning for Mobile Robots using Iterative Artificial Potential Field Method

    Directory of Open Access Journals (Sweden)

    Hossein Adeli

    2011-07-01

    Full Text Available In this paper, a new algorithm is proposed for solving the path planning problem of mobile robots. The algorithm is based on Artificial Potential Field (APF methods that have been widely used for path planning related problems for more than two decades. While keeping the simplicity of traditional APF methods, our algorithm is built upon new potential functions based on the distances from obstacles, destination point and start point. The algorithm uses the potential field values iteratively to find the optimum points in the workspace in order to form the path from start to destination. The number of iterations depends on the size and shape of the workspace. The performance of the proposed algorithm is tested by conducting simulation experiments.

  6. 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.

  7. 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.

  8. Optimization Design of Artificial Bee Colony Algorithm in Automobile Structure Based on Sequential Response Surface Method%基于序列响应面法的汽车结构件蜂群优化设计

    Institute of Scientific and Technical Information of China (English)

    陈黎明; 陈文亮

    2013-01-01

    The artificial bee colony (ABC) algorithm, a relatively recent bio—inspired approach mimicking the behavior of real bee colony, was applied to deal with the optimization problems of automobile structure. The metamodel of objective and constrains were gotten through combination of design of experiment and sequential response surface method. Then,the optimum design was obtained by the modified artificial bee colony algorithm. It can reduce the computing cost by the metamodeling techniques. Finally.a typical example was selected to proof this method. The comparison results between the simulated and experimental values show that this method has enough precision and satisfies the engineering practical demands.%将蜂群算法应用于汽车结构件的优化问题.先由试验设计和序列响应面法构建目标函数及约束条件的代理模型,再应用改进的蜂群算法求解最优设计.在优化过程中调用的是代理模型,显著减少了有限元计算次数,提高了优化效率.最后,选取典型实例对该算法进行验证,比较预期值与实际值的结果表明,该算法具备了足够的求解精度,能够满足工程实际要求.

  9. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    Energy Technology Data Exchange (ETDEWEB)

    Zoeller, Andreas [Physik Department E15, Technische Universitaet Muenchen, 85748 Garching (Germany); Collaboration: CRESST-Collaboration

    2015-07-01

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  10. 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.

  11. UAV path planning using artificial potential field method updated by optimal control theory

    Science.gov (United States)

    Chen, Yong-bo; Luo, Guan-chen; Mei, Yue-song; Yu, Jian-qiao; Su, Xiao-long

    2016-04-01

    The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.

  12. 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.

  13. 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.

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

    Science.gov (United States)

    Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib

    2010-01-01

    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.

  15. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    Science.gov (United States)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  16. Assembly Quality Prediction Based on Back-propagation Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    He Yong-yi

    2013-07-01

    Full Text Available Because of the severe geometrical distortion induced by the optical system and the limited kinetic accuracy of mechanical system in the vision-based mobile-phone lens’s assembly system, the nonlinear, perspective distortion errors and the kinematics errors generally exist in the assembly process of the mobile-phone lens. It is necessary to predict the assembly quality of the vision-based mobile-phone lens’s pick-and-place system so as to eliminate the immediate effect on the assembling process before extracting quantitative assembling. Comparison with current research methods, the back-propagation artificial neural network is applied to predict the assembly quality of the vision-based mobile-phone lens’s pick-and-place system. Firstly, the mobile-phone lens’s assembly quality characteristics are defined and sampled; Secondly, a back-propagation artificial neural network of the mobile-phone lens’s assembly quality prediction is presented; Finally apply some training samples obtained from the experiments to train and test this back-propagation artificial neural network. The results show that the proposed method is effective to predict the assembly quality of the vision-based mobile-phone lens’s pick-and-place system with high accuracy and high reliability.  

  17. PID Active Queue Management Method Based on Artificial Bee Colony and Wavelet Transform%基于人工蜂群和小波变换的PID主动队列管理方法

    Institute of Scientific and Technical Information of China (English)

    鄢树

    2013-01-01

    针对日益严重的网络拥塞问题,基于PID控制器建立了一种新的主动队列管理方法(PID active queue management algorithm based-artificial bee colony and wavelet,PIDAW).该方法首先利用小波技术对到达流量进行变换,并且采用人工蜂群算法来计算小波系数,同时通过重构方式获得实际队列长度,以此优化丢包概率.最后,以实际数据进行仿真实验,深入分析了该方法与PID、RED方法之间的性能,结果表明PIDAW具有较好的适应性.

  18. 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.

  19. Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology

    Directory of Open Access Journals (Sweden)

    Hossam Meshref

    2013-01-01

    Full Text Available In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful models to overcome its complexity such as the artificial neural networks. Common concepts between the two systems were investigated to design a simple, yet a robust, model of artificial immune networks. Three artificial neural networks learning models were available to choose from in the research design: supervised, unsupervised, and reinforcement learning models. However, it was found that the reinforcement model is the most suitable model. Research results examined network parameters, and appropriate relations between concentration ranges and their dependent parameters as well as the expected reward during network learning. In conclusion, it is recommended the use of the designed model by other researchers in different applications such as controlling robots in hazardous environment to save human lives as well as using it on image retrieval in general to help the police department identify suspects.

  20. 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

    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.

  1. 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.

  2. 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.

  3. Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hong-liang; ZOU Zhong; LI Jie; CHEN Xiang-tao

    2008-01-01

    Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.

  4. 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.

  5. 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.)

  6. Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

    Science.gov (United States)

    Dashtban, M; Balafar, Mohammadali

    2017-03-01

    Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Artificial Intelligence Based Three-Phase Unified Power Quality Conditioner

    Directory of Open Access Journals (Sweden)

    Moleykutty George

    2007-01-01

    Full Text Available Power quality is an important measure of the performance of an electrical power system. This paper discusses the topology, control strategies using artificial intelligent (AI based controllers and the performance of a unified power quality conditioner (UPQC for power quality improvement. UPQC is an integration of shunt and series compensation to limit the harmonic contamination within 5 %, the limit imposed by IEEE-519 standard. The novelty of this paper lies in the application of neural network control (NNC algorithms such as model reference control (MRC, and nonlinear autoregressive-moving average (NARMA–L2 control to generate switching signals for the series compensator of the UPQC system. The entire system has been modeled using MATLAB 7.0 toolbox. Simulation results demonstrate the applicability of MRC and NARMA-L2 controllers for the control of UPQC.

  8. A Recommender System based on Idiotypic Artificial Immune Networks

    CERN Document Server

    Cayzer, Steve

    2008-01-01

    The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

  9. 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.

  10. Visual system based on artificial retina for motion detection.

    Science.gov (United States)

    Barranco, Francisco; Díaz, Javier; Ros, Eduardo; del Pino, Begoña

    2009-06-01

    We present a bioinspired model for detecting spatiotemporal features based on artificial retina response models. Event-driven processing is implemented using four kinds of cells encoding image contrast and temporal information. We have evaluated how the accuracy of motion processing depends on local contrast by using a multiscale and rank-order coding scheme to select the most important cues from retinal inputs. We have also developed some alternatives by integrating temporal feature results and obtained a new improved bioinspired matching algorithm with high stability, low error and low cost. Finally, we define a dynamic and versatile multimodal attention operator with which the system is driven to focus on different target features such as motion, colors, and textures.

  11. XDANNG: XML based Distributed Artificial Neural Network with Globus Toolkit

    CERN Document Server

    Mahini, Hamidreza; Ghofrani, Javad

    2009-01-01

    Artificial Neural Network is one of the most common AI application fields. This field has direct and indirect usages most sciences. The main goal of ANN is to imitate biological neural networks for solving scientific problems. But the level of parallelism is the main problem of ANN systems in comparison with biological systems. To solve this problem, we have offered a XML-based framework for implementing ANN on the Globus Toolkit Platform. Globus Toolkit is well known management software for multipurpose Grids. Using the Grid for simulating the neuron network will lead to a high degree of parallelism in the implementation of ANN. We have used the XML for improving flexibility and scalability in our framework.

  12. 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%).

  13. Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association

    Directory of Open Access Journals (Sweden)

    Curtis David

    2007-07-01

    Full Text Available Abstract Background Debate remains as to the optimal method for utilising genotype data obtained from multiple markers in case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs, whose performance compared favourably to single-marker methods. Here, the perfomance of ANN analysis is compared with other multi-marker methods, comprising different haplotype-based analyses and locus-based analyses. Results Of several methods studied and applied to simulated SNP datasets, heterogeneity testing of estimated haplotype frequencies using asymptotic p values rather than permutation testing had the lowest power of the methods studied and ANN analysis had the highest power. The difference in power to detect association between these two methods was statistically significant (p = 0.001 but other comparisons between methods were not significant. The raw t statistic obtained from ANN analysis correlated highly with the empirical statistical significance obtained from permutation testing of the ANN results and with the p value obtained from the heterogeneity test. Conclusion Although ANN analysis was more powerful than the standard haplotype-based test it is unlikely to be taken up widely. The permutation testing necessary to obtain a valid p value makes it slow to perform and it is not underpinned by a theoretical model relating marker genotypes to disease phenotype. Nevertheless, the superior performance of this method does imply that the widely-used haplotype-based methods for detecting association with multiple markers are not optimal and efforts could be made to improve upon them. The fact that the t statistic obtained from ANN analysis is highly correlated with the statistical significance does suggest a possibility to use ANN analysis in situations where large numbers of markers have been genotyped, since the t value could be used as a proxy for the p value in

  14. Bio-inspired artificial iridophores based on capillary origami: Fabrication and device characterization

    Science.gov (United States)

    Manakasettharn, Supone; Ashley Taylor, J.; Krupenkin, Tom N.

    2011-10-01

    Cephalopods have evolved complex optical mechanisms of dynamic skin color control based on mechanical actuation of micro-scale optical structures such as iridophores and chromatophores. In this work, we describe the design, fabrication, and characterization of bio-inspired artificial iridophores, which resemble microflowers with flexible reflective petals, based on capillary origami microstructures. Two methods of petal actuation have been demonstrated—one based on the electrowetting process and the other by volume change of the liquid droplet. These results were in good agreement with a model derived to characterize the actuation dynamics.

  15. 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.

  16. Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

    Science.gov (United States)

    2010-01-01

    Background All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences. Results The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%. Conclusions This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant

  17. 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.

  18. Application of Artificial Intelligence Methods of Tool Path Optimization in CNC Machines: A Review

    Directory of Open Access Journals (Sweden)

    Khashayar Danesh Narooei

    2014-08-01

    Full Text Available Today, in most of metal machining process, Computer Numerical Control (CNC machine tools have been very popular due to their efficiencies and repeatability to achieve high accuracy positioning. One of the factors that govern the productivity is the tool path travel during cutting a work piece. It has been proved that determination of optimal cutting parameters can enhance the machining results to reach high efficiency and minimum the machining cost. In various publication and articles, scientist and researchers adapted several Artificial Intelligence (AI methods or hybrid method for tool path optimization such as Genetic Algorithms (GA, Artificial Neural Network (ANN, Artificial Immune Systems (AIS, Ant Colony Optimization (ACO and Particle Swarm Optimization (PSO. This study presents a review of researches in tool path optimization with different types of AI methods that show the capability of using different types of optimization methods in CNC machining process.

  19. 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.

  20. 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

  1. Comparison between two methods for diagnosis of trichinellosis: trichinoscopy and artificial digestion.

    Science.gov (United States)

    Vignau, M L; del Valle Guardis, M; Risso, M A; Eiras, D F

    1997-01-01

    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.

  2. Do educational methods affect students' ability to remove artificial carious dentine? A randomised controlled trial.

    Science.gov (United States)

    Rosa, Q F; Barcelos, T M; Kaizer, M R; Montagner, A F; Sarkis-Onofre, R; Masotti, A S; Jardim, P S; Pereira-Cenci, T; Oliveira, E F; Cenci, M S

    2013-08-01

    This trial aimed to evaluate the influence of two educational methods on students' ability to remove artificial carious dentine. Traditional lecture and lecture plus a live demonstration of artificial carious tissue removal were compared in a blind two-parallel-group design. Twenty-six students were randomly divided into two groups, and their skills were evaluated according to the following criteria: time spent on the dentine excavation procedure (in min), students' perceived confidence in conducting the procedure (graded assessed on a scale from 0 to 10), and the outcome of artificial carious tissue removal, evaluated by measuring the residual dyed artificial carious dentine layer (in μm). Statistical analyses were carried out using a t-test to compare the students' confidence and time spent on the procedure, and a two-way ANOVA was used to compare residual artificial decayed dentine with educational methods and tooth region (incisal, medium, and cervical thirds) as factors. There were no differences between the methods regarding excavation time (P = 0.898) and students' confidence (P = 0.382). The residual artificial carious dentine results showed that the educational method (P < 0.001) and cavity region (P < 0.001) were statistically significant, as was their interaction (P = 0.040). The lecture plus live demonstration group presented the best results for artificial caries removal. Although there were no differences between the two groups for the cervical region, the best results for the lecture plus live demonstration group was in the other two-thirds of the tooth. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  3. Multi-level Threshold Image Segmentation Based on PSNR using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Cao Yun-Fei

    2012-01-01

    Full Text Available Image segmentation is still a crucial problem in image processing. It hasn yet been solved very well. In this study, we propose a novel multi-level thresholding image segmentation method based on PSNR using artificial bee colony algorithm (ABCA. PSNR is considered as an objective function of ABCA. The multi-level thresholds (t*1, t*2 ,...., t*n-1, t*n are those maximizing the PSNR. We compare entropy and PSNR in segmenting gray-level images. The experiments results demonstrate proposed method is effective and efficient.

  4. Approximate analytic method for high-apogee twelve-hour orbits of artificial Earth's satellites

    Science.gov (United States)

    Vashkovyaka, M. A.; Zaslavskii, G. S.

    2016-09-01

    We propose an approach to the study of the evolution of high-apogee twelve-hour orbits of artificial Earth's satellites. We describe parameters of the motion model used for the artificial Earth's satellite such that the principal gravitational perturbations of the Moon and Sun, nonsphericity of the Earth, and perturbations from the light pressure force are approximately taken into account. To solve the system of averaged equations describing the evolution of the orbit parameters of an artificial satellite, we use both numeric and analytic methods. To select initial parameters of the twelve-hour orbit, we assume that the path of the satellite along the surface of the Earth is stable. Results obtained by the analytic method and by the numerical integration of the evolving system are compared. For intervals of several years, we obtain estimates of oscillation periods and amplitudes for orbital elements. To verify the results and estimate the precision of the method, we use the numerical integration of rigorous (not averaged) equations of motion of the artificial satellite: they take into account forces acting on the satellite substantially more completely and precisely. The described method can be applied not only to the investigation of orbit evolutions of artificial satellites of the Earth; it can be applied to the investigation of the orbit evolution for other planets of the Solar system provided that the corresponding research problem will arise in the future and the considered special class of resonance orbits of satellites will be used for that purpose.

  5. A Robust Damage Detection Method Developed for Offshore Jacket Platforms Using Modified Artificial Immune System Algorithm

    Institute of Scientific and Technical Information of China (English)

    Mojtahedi,A.; Lotfollahi Yaghin,M.A.; Hassanzadeh,Y.; Abbasidoust,F.; Ettefagh,M.M.; Aminfar,M.H.

    2012-01-01

    Steel jacket-type platforms are the common kind of the offshore structures and health monitoring is an important issue in their safety assessment.In the present study,a new damage detection method is adopted for this kind of structures and inspected experimentally by use of a laboratory model.The method is investigated for developing the robust damage detection technique which is less sensitive to both measurement and analytical model uncertainties.For this purpose,incorporation of the artificial immune system with weighted attributes (AISWA) method into finite element (FE) model updating is proposed and compared with other methods for exploring its effectiveness in damage identification.Based on mimicking immune recognition,noise simulation and attributes weighting,the method offers important advantages and has high success rates.Therefore,it is proposed as a suitable method for the detection of the failures in the large civil engineering structures with complicated structural geometry,such as the considered case study.

  6. 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jianzhong Wang

    2016-06-01

    Full Text Available Gaze estimation methods play an important role in a gaze tracking system. A novel 2D gaze estimation method based on the pupil-glint vector is proposed in this paper. First, the circular ring rays location (CRRL method and Gaussian fitting are utilized for pupil and glint detection, respectively. Then the pupil-glint vector is calculated through subtraction of pupil and glint center fitting. Second, a mapping function is established according to the corresponding relationship between pupil-glint vectors and actual gaze calibration points. In order to solve the mapping function, an improved artificial neural network (DLSR-ANN based on direct least squares regression is proposed. When the mapping function is determined, gaze estimation can be actualized through calculating gaze point coordinates. Finally, error compensation is implemented to further enhance accuracy of gaze estimation. The proposed method can achieve a corresponding accuracy of 1.29°, 0.89°, 0.52°, and 0.39° when a model with four, six, nine, or 16 calibration markers is utilized for calibration, respectively. Considering error compensation, gaze estimation accuracy can reach 0.36°. The experimental results show that gaze estimation accuracy of the proposed method in this paper is better than that of linear regression (direct least squares regression and nonlinear regression (generic artificial neural network. The proposed method contributes to enhancing the total accuracy of a gaze tracking system.

  7. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram

    2016-05-01

    Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

  8. SMS Spam Filtering Technique Based on Artificial Immune System

    Directory of Open Access Journals (Sweden)

    Tarek M Mahmoud

    2012-03-01

    Full Text Available The Short Message Service (SMS have an important economic impact for end users and service providers. Spam is a serious universal problem that causes problems for almost all users. Several studies have been presented, including implementations of spam filters that prevent spam from reaching their destination. Nave Bayesian algorithm is one of the most effective approaches used in filtering techniques. The computational power of smart phones are increasing, making increasingly possible to perform spam filtering at these devices as a mobile agent application, leading to better personalization and effectiveness. The challenge of filtering SMS spam is that the short messages often consist of few words composed of abbreviations and idioms. In this paper, we propose an anti-spam technique based on Artificial Immune System (AIS for filtering SMS spam messages. The proposed technique utilizes a set of some features that can be used as inputs to spam detection model. The idea is to classify message using trained dataset that contains Phone Numbers, Spam Words, and Detectors. Our proposed technique utilizes a double collection of bulk SMS messages Spam and Ham in the training process. We state a set of stages that help us to build dataset such as tokenizer, stop word filter, and training process. Experimental results presented in this paper are based on iPhone Operating System (iOS. The results applied to the testing messages show that the proposed system can classify the SMS spam and ham with accurate compared with Nave Bayesian algorithm.

  9. Parameter tuning of PVD process based on artificial intelligence technique

    Science.gov (United States)

    Norlina, M. S.; Diyana, M. S. Nor; Mazidah, P.; Rusop, M.

    2016-07-01

    In this study, an artificial intelligence technique is proposed to be implemented in the parameter tuning of a PVD process. Due to its previous adaptation in similar optimization problems, genetic algorithm (GA) is selected to optimize the parameter tuning of the RF magnetron sputtering process. The most optimized parameter combination obtained from GA's optimization result is expected to produce the desirable zinc oxide (ZnO) thin film from the sputtering process. The parameters involved in this study were RF power, deposition time and substrate temperature. The algorithm was tested to optimize the 25 datasets of parameter combinations. The results from the computational experiment were then compared with the actual result from the laboratory experiment. Based on the comparison, GA had shown that the algorithm was reliable to optimize the parameter combination before the parameter tuning could be done to the RF magnetron sputtering machine. In order to verify the result of GA, the algorithm was also been compared to other well known optimization algorithms, which were, particle swarm optimization (PSO) and gravitational search algorithm (GSA). The results had shown that GA was reliable in solving this RF magnetron sputtering process parameter tuning problem. GA had shown better accuracy in the optimization based on the fitness evaluation.

  10. 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.

  11. Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

    OpenAIRE

    2013-01-01

    Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor netw...

  12. 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.

  13. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  14. Application of Electronic Tongue in Edible Oil Detection with Cluster Algorithm based on Artificial Fish Swarm Improvement

    Directory of Open Access Journals (Sweden)

    Pingyan Zhang

    2013-04-01

    Full Text Available Methods for edible oil quality detection mainly have conductivity and peroxide acid value, but as for currently blending edible detection, the effect of these methods is not satisfied. In this study, we propose a cluster algorithm based on artificial fish swarm improvement to detect edible oil by voltammetric electronic tongue. It can optimize the cluster centers value and the initial value. The results show that this is significant to classification and detection of edible oil using the artificial fish swarm cluster algorithm in voltammetric electronic tongue system.

  15. 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.

  16. Neutron spectrum unfolding using artificial neural network and modified least square method

    Science.gov (United States)

    Hosseini, Seyed Abolfazl

    2016-09-01

    In the present paper, neutron spectrum is reconstructed using the Artificial Neural Network (ANN) and Modified Least Square (MLSQR) methods. The detector's response (pulse height distribution) as a required data for unfolding of energy spectrum is calculated using the developed MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Unlike the usual methods that apply inversion procedures to unfold the energy spectrum from the Fredholm integral equation, the MLSQR method uses the direct procedure. Since liquid organic scintillators like NE-213 are well suited and routinely used for spectrometry of neutron sources, the neutron pulse height distribution is simulated/measured in the NE-213 detector. The response matrix is calculated using the MCNPX-ESUT computational code through the simulation of NE-213 detector's response to monoenergetic neutron sources. For known neutron pulse height distribution, the energy spectrum of the neutron source is unfolded using the MLSQR method. In the developed multilayer perception neural network for reconstruction of the energy spectrum of the neutron source, there is no need for formation of the response matrix. The multilayer perception neural network is developed based on logsig, tansig and purelin transfer functions. The developed artificial neural network consists of two hidden layers of type hyperbolic tangent sigmoid transfer function and a linear transfer function in the output layer. The motivation of applying the ANN method may be explained by the fact that no matrix inversion is needed for energy spectrum unfolding. The simulated neutron pulse height distributions in each light bin due to randomly generated neutron spectrum are considered as the input data of ANN. Also, the randomly generated energy spectra are considered as the output data of the ANN. Energy spectrum of the neutron source is identified with high accuracy using both MLSQR and ANN methods. The results obtained from

  17. Adaptive Artificial Intelligence Based Model Base Controller: Applied to Surgical Endoscopy Telemanipulator

    Directory of Open Access Journals (Sweden)

    Farzin Piltan

    2013-08-01

    Full Text Available This research involved developing a surgical robot assistant using an articulated PUMA robot running on a linear or nonlinear axis. The research concentrated on studying the artificial intelligence based switching computed torque controller to localization of an endoscopic tool. Results show that the switching artificial nonlinear control algorithm is capable to design a stable controller. For this system, error was used as the performance metric. Positioning of the endoscopic manipulator relative to the world coordinate frame was possible to within 0.05 inch. Error in maintaining a constant point in space is evident during repositioning however this was caused by limitations in the robot arm.

  18. Quantum-based algorithm for optimizing artificial neural networks.

    Science.gov (United States)

    Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang

    2013-08-01

    This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

  19. Environmentally Conscious Polishing System Based on Robotics and Artificial Vision

    Directory of Open Access Journals (Sweden)

    J. A. Dieste

    2015-02-01

    Full Text Available Polishing process is one of the manufacturing issues that are essential in the production flow, but it generates the major amount of defects on parts. Finishing tasks in which polishing is included are performed in the final steps of the manufacturing sequence. Any defect in these steps impliesrejection of the part, generating a big amount of scrap and generating a huge amount of energy consumption, emission, and time to manufacture and replace the rejected part. Traditionally polishing process has not evolved during the last 30 years, while other manufacturing processes have been automated and technologically improved. Finishing processes (grinding and polishing, are still manually performed, especially in freeform surface parts, but to be sustainable some development and automation have to be introduced. This research proposes a novel polishing system based on robotics and artificial vision. The application of this novel system has allowed reducing the failed parts due to finishing process down to zero percent from 28% of rejected parts with manual polishing process. The reduction in process time consumption, and amount of scrapped parts, has reduced the energy consumption up to 30% in finishing process and 20% in whole manufacturing process for an injection moulded aluminium part for automotive industry with high production volumes.

  20. 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.

  1. A Hybrid Artificial Neural Network-based Scheduling Knowledge Acquisition Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Weida; WANG Wei; LIU Wenjian

    2006-01-01

    It is a key issue that constructing successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. Therefore, a hybrid artificial neural network (ANN)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search. The hybrid method was utilized to resolve the optimal attributes subset of manufacturing system and determine the optimal topology and parameters of ANN under different scheduling objectives; ANN was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal attributes subset, optimal ANN's topology and parameters. The experimental results demonstrate that the proposed algorithm produces significant performance improvements over other machine learning-based algorithms.

  2. Artificial intelligence methods in diagnostics of analog systems

    Directory of Open Access Journals (Sweden)

    Bilski Piotr

    2014-06-01

    Full Text Available The paper presents the state of the art and advancement of artificial intelligence methods in analog systems diagnostics. Firstly, the diagnostic domain is introduced and its problems explained. Then, computational intelligence approaches usable for fault detection and identification are reviewed. Particular groups of methods are presented in detail, explaining their usefulness and drawbacks. Examples, such as the induction motor or the electronic filter, are provided to show the applicability of the presented approaches for monitoring the state of analog objects from engineering domains. The discussion section reviews the presented approaches, their future prospects and problems to be solved.

  3. BP artificial neural network based wave front correction for sensor-less free space optics communication

    Science.gov (United States)

    Li, Zhaokun; Zhao, Xiaohui

    2017-02-01

    The sensor-less adaptive optics (AO) is one of the most promising methods to compensate strong wave front disturbance in free space optics communication (FSO). The back propagation (BP) artificial neural network is applied for the sensor-less AO system to design a distortion correction scheme in this study. This method only needs one or a few online measurements to correct the wave front distortion compared with other model-based approaches, by which the real-time capacity of the system is enhanced and the Strehl Ratio (SR) is largely improved. Necessary comparisons in numerical simulation with other model-based and model-free correction methods proposed in Refs. [6,8,9,10] are given to show the validity and advantage of the proposed method.

  4. Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

    This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource’s behaviour under c...

  5. Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

    This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource’s behaviour under...

  6. 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...

  7. 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.

  8. An artificial aqueous humor as a standard matrix to assess drug concentration in the anterior chamber by high performance liquid chromatography methods.

    Science.gov (United States)

    Macri, Angelo; Marini, Valeria; Sangalli, Giorgio; Fucile, Carmen; Iester, Michele; Mattioli, Francesca

    2015-01-01

    The aim of the study was to recreate in-vitro artificial aqueous humor with the same physico-chemical properties of human aqueous humor to be used as a standard matrix in chromatography to assess drug concentration in the anterior and posterior chamber of the human eye. The artificial aqueous humor was prepared according to the human aqueous humor chemical compositions reported in the literature. The artificial matrix was then analysed via the HPLC-UV method and compared with aqueous humor from 15 patients who underwent cataract surgery. Known concentrations of widely-used ophthalmological drugs were added to the artificial aqueous humor in order to assess whether it can be used to explore ocular disposition towards topically or systemically administered drugs. No significant differences were found between the two examined aqueous humor types. There were no significant qualitative differences between examined fluids in terms of presence of ophthalmological drugs. The composition of artificial, in-vitro recreated aqueous humor was similar to that of the human kind. The absence of significant differences in the analysis of tested drugs both in the artificial and in human aqueous humor indicates that artificial aqueous humor may be used to generate a matrix-based standard curve for analytical method validation.

  9. A NEW METHOD FOR STABILITY ANALYSIS OF UNDERGROUND OPENING USING ARTIFICIAL NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    杨英杰; 张清

    1996-01-01

    The stability of underground opening is strongly constrained by a variety of factors.These include initial stress, structure of rock mass, and underground water, etc. A new approach proposed in this paper aims at revealing the degree of significance of them in order to catch the key factors. This new approach utilizes the mapping potentiality of artificial neural network and works out the sophisticated interactions among those factors from sample set. As a demonstration, an example is given for the application of this method to an underground opening. All results of this paper prove the efficiency of artificial neural network in stability analysis of underground opening.

  10. ARTIFICIAL BOUNDARY METHOD FOR THE THREE-DIMENSIONAL EXTERIOR PROBLEM OF ELASTICITY

    Institute of Scientific and Technical Information of China (English)

    Hou-de Han; Chun-xiong Zheng

    2005-01-01

    The exact boundary condition on a spherical artificial boundary is derived for thethree-dimensional exterior problem of linear elasticity in this paper. After this boundary condition is imposed on the artificial boundary, a reduced problem only defined in a bounded domain is obtained. A series of approximate problems with increasing accuracy can be derived if one truncates the series term in the variational formulation, which is equivalent to the reduced problem. An error estimate is presented to show how the error depends on the finite element discretization and the accuracy of the approximate problem.In the end, a numerical example is given to demonstrate the performance of the proposed method.

  11. An artificial compressibility CBS method for modelling heat transfer and fluid flow in heterogeneous porous materials

    CSIR Research Space (South Africa)

    Malan, AG

    2011-08-01

    Full Text Available This work is concerned with the development of an artificial compressibility version of the characteristicbased split (CBS) method proposed by Zienkiewicz and Codina (Int. J. Numer. Meth. Fluids 1995; 20:869–885). The technique is applied...

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

    OpenAIRE

    Crnogorac, Miroslav P.; Danilović, Dušan Š.; Karović-Maričić, Vesna D.; Leković, Branko A.

    2016-01-01

    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 multi...

  13. Generalized in vitro-in vivo relationship (IVIVR model based on artificial neural networks

    Directory of Open Access Journals (Sweden)

    Mendyk A

    2013-03-01

    Full Text Available Aleksander Mendyk,1 Pawel Tuszynski,1 Sebastian Polak,2 Renata Jachowicz1 1Department of Pharmaceutical Technology and Biopharmaceutics, 2Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland Background: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC/IVIVR. Methods: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. Keywords: artificial neural networks

  14. 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.

  15. Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method

    Science.gov (United States)

    Zhang, Liqiang; Li, Luoxing; Wang, Shiuping; Zhu, Biwu

    2012-04-01

    In this article, the low-pressure die-cast (LPDC) process parameters of aluminum alloy thin-walled component with permanent mold are optimized using a combining artificial neural network and genetic algorithm (ANN/GA) method. In this method, an ANN model combining learning vector quantization (LVQ) and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component with 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared and no obvious defects such as shrinkage, gas porosity, distortion, and crack were found in the component. The results indicate that the combining ANN/GA method is an effective tool for the process optimization of LPDC, and they also provide valuable reference on choosing the right process parameters for LPDC thin-walled aluminum alloy casting.

  16. Advanced Taste Sensors Based on Artificial Lipids with Global Selectivity to Basic Taste Qualities and High Correlation to Sensory Scores

    Directory of Open Access Journals (Sweden)

    Yoshikazu Kobayashi

    2010-04-01

    Full Text Available Effective R&D and strict quality control of a broad range of foods, beverages, and pharmaceutical products require objective taste evaluation. Advanced taste sensors using artificial-lipid membranes have been developed based on concepts of global selectivity and high correlation with human sensory score. These sensors respond similarly to similar basic tastes, which they quantify with high correlations to sensory score. Using these unique properties, these sensors can quantify the basic tastes of saltiness, sourness, bitterness, umami, astringency and richness without multivariate analysis or artificial neural networks. This review describes all aspects of these taste sensors based on artificial lipid, ranging from the response principle and optimal design methods to applications in the food, beverage, and pharmaceutical markets.

  17. 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.

  18. Rough set-based feature selection method

    Institute of Scientific and Technical Information of China (English)

    ZHAN Yanmei; ZENG Xiangyang; SUN Jincai

    2005-01-01

    A new feature selection method is proposed based on the discern matrix in rough set in this paper. The main idea of this method is that the most effective feature, if used for classification, can distinguish the most number of samples belonging to different classes. Experiments are performed using this method to select relevant features for artificial datasets and real-world datasets. Results show that the selection method proposed can correctly select all the relevant features of artificial datasets and drastically reduce the number of features at the same time. In addition, when this method is used for the selection of classification features of real-world underwater targets,the number of classification features after selection drops to 20% of the original feature set, and the classification accuracy increases about 6% using dataset after feature selection.

  19. [Working Temperature Predication of Artificial Heart Based on Neural Network].

    Science.gov (United States)

    Li, Qilei; Yang, Ming; Ou, Wenchu; Meng, Fan; Xu, Zihao; Xu, Liang

    2015-03-01

    The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as well as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetically optimized BP network, by which indicates that the probability is 1.84% with the margin of error more than 1%.

  20. Ablation Properties of the Carbon-Based Composites Used in Artificial Heat Source Under Fire Accident

    Institute of Scientific and Technical Information of China (English)

    TANG; Xian; HUANG; Jin-ming; ZHOU; Shao-jian; LUO; Zhi-fu

    2012-01-01

    <正>The ablation properties of the carbon-based composites used in artificial heat source under fire accident were investigated by the arc heater. In this work, we tested the carbon-based composites referring to Fig. 1. Their linear/mass ablation ratio and ablation morphologies were studied. The results showed that the carbon-based composites used in artificial heat source behaved well

  1. A Rapid Aerodynamic Design Procedure Based on Artificial Neural Networks

    Science.gov (United States)

    Rai, Man Mohan

    2001-01-01

    An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.

  2. Glyphosate detection by voltammetric techniques. A comparison between statistical methods and an artificial neural network

    OpenAIRE

    2012-01-01

    Glyphosate quantification methods are complex and expensive, and its control in natural water bodies is getting more important year after year. In order to find a new system that facilitates the detection of glyphosate, we present a comparison between two models to predict glyphosate concentration in aqueous dissolutions. One of them is done by an artificial neural network (ANN) embedded in a microcontroller and the other one is done by statistic methods (Partial Least Squares) in a computer...

  3. A method of comparing the wear resistance of various materials used for artificial teeth

    OpenAIRE

    SILVA,Claudia Maria Zarpelon; EDUARDO,José Virgílio de Paula; Milton Edson MIRANDA; BASTING, Roberta Tarkany; Olivieri, Karina Andrea Novaes

    2014-01-01

    OBJECTIVE: Abrasion resistance of three commercial brands of acrylic resin for artificial teeth, available on the domestic market (Biotone(r), Plus(r)and Vipi Dent Artiplus(r)) was evaluated by a new methodology called "Pirassununga Method ".METHODS: The Manufacturers produced the maxillary central incisor-shaped incisal resin specimens (n=27). These were embedded in chemically activated acrylic resin in an insulated metal matrix to fabricate test specimens, which were flattened on a lathe an...

  4. 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.

  5. 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.

  6. 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.

  7. Analogue spin-orbit torque device for artificial-neural-network-based associative memory operation

    Science.gov (United States)

    Borders, William A.; Akima, Hisanao; Fukami, Shunsuke; Moriya, Satoshi; Kurihara, Shouta; Horio, Yoshihiko; Sato, Shigeo; Ohno, Hideo

    2017-01-01

    We demonstrate associative memory operations reminiscent of the brain using nonvolatile spintronics devices. Antiferromagnet-ferromagnet bilayer-based Hall devices, which show analogue-like spin-orbit torque switching under zero magnetic fields and behave as artificial synapses, are used. An artificial neural network is used to associate memorized patterns from their noisy versions. We develop a network consisting of a field-programmable gate array and 36 spin-orbit torque devices. An effect of learning on associative memory operations is successfully confirmed for several 3 × 3-block patterns. A discussion on the present approach for realizing spintronics-based artificial intelligence is given.

  8. Simulation of game analysis based on an agent-based artificial stock market re-examined

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This work re-examined the simulation result of game analysis (Joshi et al., 2000) based on an agent-based model,Santa Fe Institute Artificial Stock Market. Allowing for recent research work on this artificial model, this paper's modified game simulations found that the dividend amplitude parameter is a crucial factor and that the original conclusion still holds in a not long period, but only when the dividend amplitude is large enough. Our explanation of this result is that the dividend amplitude parameter is a measurement of market uncertainty. The greater the uncertainty, the greater the price volatility, and so is the risk of investing in the stock market. The greater the risk, the greater the advantage of including technical rules.

  9. A novel artificial anal sphincter system based on transcutaneous energy transmission

    Institute of Scientific and Technical Information of China (English)

    Zan Peng; Yan Guozheng; Liu Hua

    2008-01-01

    For controlling anal incontinence, a new artificial anal sphincter system (AASS) with sensor feedback based on transcutaneous energy transmission is developed. The device mainly comprises an artificial anal sphincter (AAS), a wireless power supply subsystem, and a communication subsystem. The artificial anal sphincter comprises a front cuff and a sensor cuff placed around the rectum, a reservoir sited in abdominal cavity and a micropump controlling inflation and deflation of the front cuff. There are two pressure sensors in the artificial anal sphincter. One can measure the pressure in the front cuff to clamp the rectum, the other in the sensor cuff can measure the pressure of the rectum. Wireless power supply subsystem includes a resonance transmit coil to transmit an alternating magnetic field and a secondary coil to receive the power. Wireless communication subsystem can transmit the pressure information of the artificial anal sphincter to the monitor, or send the control commands to the artificial anal sphincter. A prototype is designed and the basic function of the artificial anal sphincter system has been tested through experiments. The results demonstrate that the artificial anal sphincter system can control anal incontinence effectively.

  10. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    National Research Council Canada - National Science Library

    Chen, Hui; Xiong, Shenghua; Ren, Xuan

    2014-01-01

    ... 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...

  11. The Artificial Reverberation Real-time Processing System Based On DSP

    Institute of Scientific and Technical Information of China (English)

    WEN Chuan-xue; ZHENG Sheng-lin; ZHANG Cheng-yun

    2008-01-01

    A real-time process system of artificial reverberation based on fixed-point DSP is presented in this paper. This paper dis- cusses the hardware interface and software between TMS320VC5509 DSP chip and TLV320AIC23 cedee chip. Based on this, it intro- duces the design and working of the artificial reverberation algorithm. At last, the paper discusses the sticking point in realization of real-time process.

  12. Artificial photosynthesis of oxalate and oxalate-based polymer by a photovoltaic reactor

    Science.gov (United States)

    Nong, Guangzai; Chen, Shan; Xu, Yuanjin; Huang, Lijie; Zou, Qingsong; Li, Shiqiang; Mo, Haitao; Zhu, Pingchuan; Cen, Weijian; Wang, Shuangfei

    2014-01-01

    A photovoltaic reactor was designed for artificial photosynthesis, based on the reactions involved in high energy hydrogen atoms, which were produced from water electrolysis. Water and CO2, under the conditions studied, were converted to oxalate (H2C2O4) and a polymer. This was the first time that the oxalates and oxalate-based polymer were produced from the artificial photosynthesis process.

  13. A Novel Parser Design Algorithm Based on Artificial Ants

    CERN Document Server

    Maiti, Deepyaman; Konar, Amit; Ramadoss, Janarthanan

    2008-01-01

    This article presents a unique design for a parser using the Ant Colony Optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. The scheme presented here uses a bottom-up approach and the parsing program can directly use ambiguous or redundant grammars. We allocate a node corresponding to each production rule present in the given grammar. Each node is connected to all other nodes (representing other production rules), thereby establishing a completely connected graph susceptible to the movement of artificial ants. Each ant tries to modify this sentential form by the production rule present in the node and upgrades its position until the sentential form reduces to the start symbol S. Successful ants deposit pheromone on the links that they have traversed through. Eventually, the optimum path is discovered by the links carrying maximum amount of pheromone concentration. The design is simple, versatile, robust and effective and obviates ...

  14. 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...... is also studied. The developed ANN algorithm has been implemented in a Digital Signal Processor (DSP) in order to have a proof of concept of the proposed method....

  15. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  16. 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.

  17. Comparisons of Short Term Load Forecasting using Artificial Neural Network and Regression Method

    Directory of Open Access Journals (Sweden)

    Rajesh Deshmukh

    2011-12-01

    Full Text Available In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peaking units. On the other hand, over prediction of STLF leads to the unnecessarily large reserve capacity, which is also related to high operating cost. the research work in this area is still a challenge to the electrical engineering scholars because of its high complexity. How to estimate the future load with the historical data has remained a difficulty up to now, especially for the load forecasting of holidays, days with extreme weather and other anomalous days. With the recent development of new mathematical, data mining and artificial intelligence tools, it is potentially possible to improve the forecasting result. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. And its results compare to regression method.

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

    Science.gov (United States)

    Ghazali, Abu Bakar; Ibrahim, Maslina Mohd

    2016-01-01

    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.

  19. MGRO Recognition Algorithm-Based Artificial Potential Field for Mobile Robot Navigation

    Directory of Open Access Journals (Sweden)

    Ming Pang

    2016-01-01

    Full Text Available This paper describes a novel recognition algorithm which includes mean filter, Gaussian filter, Retinex enhancement method, and Ostu threshold segmentation method (MGRO for the navigation of mobile robots with visual sensors. The approach includes obstacle visual recognition and navigation path planning. In the first part, a three-stage method for obstacle visual recognition is constructed. Stage 1 combines mean filtering and Gaussian filtering to remove random noise and Gauss noise in the environmental image. Stage 2 increases image contrast by using the Retinex enhancement method. Stage 3 uses the Ostu threshold segmentation method to achieve obstacle feature extraction. A navigation method based on the auxiliary visual information is constructed in the second part. The method is based on the artificial potential field (APF method and is able to avoid falling into local minimum by changing the repulsion field function. Experimental results confirm that obstacle features can be extracted accurately and the mobile robot can avoid obstacles safely and arrive at target positions correctly.

  20. Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network.

    Science.gov (United States)

    Meng, Hailin; Wang, Jianfeng; Xiong, Zhiqiang; Xu, Feng; Zhao, Guoping; Wang, Yong

    2013-01-01

    Accurate and controllable regulatory elements such as promoters and ribosome binding sites (RBSs) are indispensable tools to quantitatively regulate gene expression for rational pathway engineering. Therefore, de novo designing regulatory elements is brought back to the forefront of synthetic biology research. Here we developed a quantitative design method for regulatory elements based on strength prediction using artificial neural network (ANN). One hundred mutated Trc promoter & RBS sequences, which were finely characterized with a strength distribution from 0 to 3.559 (relative to the strength of the original sequence which was defined as 1), were used for model training and test. A precise strength prediction model, NET90_19_576, was finally constructed with high regression correlation coefficients of 0.98 for both model training and test. Sixteen artificial elements were in silico designed using this model. All of them were proved to have good consistency between the measured strength and our desired strength. The functional reliability of the designed elements was validated in two different genetic contexts. The designed parts were successfully utilized to improve the expression of BmK1 peptide toxin and fine-tune deoxy-xylulose phosphate pathway in Escherichia coli. Our results demonstrate that the methodology based on ANN model can de novo and quantitatively design regulatory elements with desired strengths, which are of great importance for synthetic biology applications.

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

    Science.gov (United States)

    Wroblewski, David; Katrompas, Alexander M.; Parikh, Neel J.

    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.

  2. 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

    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...... 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...

  3. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    Science.gov (United States)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

  4. Term Structure of Interest Rates Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.

  5. MINUTIAE EXTRACTION BASED ON ARTIFICIAL NEURAL NETWORKS FOR AUTOMATIC FINGERPRINT RECOGNITION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Necla ÖZKAYA

    2007-01-01

    Full Text Available Automatic fingerprint recognition systems are utilised for personal identification with the use of comparisons of local ridge characteristics and their relationships. Critical stages in personal identification are to extract features automatically, fast and reliably from the input fingerprint images. In this study, a new approach based on artificial neural networks to extract minutiae from fingerprint images is developed and introduced. The results have shown that artificial neural networks achieve the minutiae extraction from fingerprint images with high accuracy.

  6. A novel protein refolding method integrating ion exchange chromatography with artificial molecular chaperone

    Institute of Scientific and Technical Information of China (English)

    Qin Ming Zhang; Chao Zhan Wang; Jiang Feng Liu; Li Li Wang

    2008-01-01

    Artificial molecular chaperone (AMC) and ion exchange chromatography (IEC) were integrated, thus a new refolding method,artificial molecular chaperone-ion exchange chromatography (AMC-IEC) was developed. Compared with AMC and IEC, theactivity recovery of lysozyme obtained by AMC-IEC was much higher in the investigated range of initial protein concentrations,and the results show that AMC-IEC is very efficient for protein refolding at high concentrations. When the initial concentration oflysozyme is 180 mg/mL, its activity recovery obtained by AMC-IEC is still as high as 76.6%, while the activity recoveries obtainedby AMC and IEC are 45.6% and 42.4%, respectively.2008 Chao Zhan Wang. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.

  7. 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.

  8. 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.

  9. Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines

    Directory of Open Access Journals (Sweden)

    Anamika Jain

    2013-01-01

    Full Text Available This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits. Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.

  10. 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.

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

    Science.gov (United States)

    Grams, Samantha Torres; Kimoto, Karen Yumi Mota; Azevedo, Elen Moda de Oliveira; Lança, Marina; de Albuquerque, André Luis Pereira; de Brito, Christina May Moran; Yamaguti, Wellington Pereira

    2015-01-01

    Introduction 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. Objectives 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. Methods 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. Results 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). Conclusions MIPuni presented higher values when compared with MIPsta and proved to be reproducible in subjects with spontaneous breathing without artificial airway. PMID:26360255

  12. 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

  13. 一种基于BP神经网络的WSNs链路质量预测方法%A Link Quality Prediction Method for WSNs Based on BP Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    刘琳岚; 樊佑磊; 舒坚; 臧超

    2011-01-01

    Link quality prediction can provide the basis for the upper layer protocol of wireless sensor networks (WSNs) choosing the path to transport data so as to improve the data transfer rate and save the energy. The prediction method of link quality of WSNs based on BP neural network is proposed which uses BP neural network to predict the sequence of PRR. It includes the setting of prediction windows and the design of BP neural network. The results show that the link quality prediction based on BP neural network has higher prediction accuracy compared with EWMA alone.%链路质量预测可以为无线传感器网络上层协议选择路径进行数据传输提供依据从而达到提高数据传输率、节省能量的目的.提出一种基于BP人工神经网络的WSNs链路质量预测方法,使用BP神经网络对PRR的序列进行预测,包括预测窗口的设置和BP神经网络的设计两个阶段.实验结果表明,基于BP神经网络的链路质量预测方法与单独使用EWMA方法相比具有预测精度高的优点.

  14. 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.

  15. A distributed optical fiber sensing system for dynamic strain measurement based on artificial reflector

    Science.gov (United States)

    Sun, Zhenhong; Shan, Yuanyuan; Li, Yanting; Zhang, Yixin; Zhang, Xuping

    2016-10-01

    Phase sensitive optical time domain reflectometry (Φ-OTDR) has been widely used in many applications for its distributed sensing ability on weak disturbance all along the sensing fiber. However, traditional Φ-OTDR cannot make quantitative measurement on the external disturbance due to the randomly distributed position and reflectivity of scatters within the optical fiber. Recently, some methods have been proposed to realize quantitative measurement of dynamic strain. In these literatures, the fiber with or without FBGs in practice was easily damaged and with difficulty of maintenance. PZT is employed to generate strain event in the fiber. There is a large gap compared with the real detecting environment, which will not reveal the full performance of the sensing system. In this paper, a distributed optical fiber sensing (DOFS) system for dynamic strain measurement based on artificial reflector is proposed and demonstrated experimentally. The fiber under test (FUT) is composed by four 20-meter long single mode optical fiber patch cords (OFPCs), which are cascaded with ferrule contactor/physical contact (FC/PC) connectors via fiber flanges. The fiber facet of FC/PC connector forms an artificial reflector. When the interval between the two reflectors is changed, the phase of the interference signal will also be changed. A symmetric 3×3 coupler with table-look-up scheme is introduced to discriminate the phase change through interference intensity. In our experiment, the center 10m section of the second OFPC is attached to the bottom of an aluminum alloy plate. An ordinary loudspeaker box was located on the top of the aluminum alloy plate. The dynamic strain generated by the loudspeaker box is transmitted from the aluminum alloy plate to the OFPC. Experimental results show that the proposed method has a good frequency response characteristic up to 3.2 kHz and a linear intensity response of R2=0.9986 while the optical probe pulse width and repetition rate were 100ns

  16. High power fuel cell simulator based on artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Chavez-Ramirez, Abraham U.; Munoz-Guerrero, Roberto [Departamento de Ingenieria Electrica, CINVESTAV-IPN. Av. Instituto Politecnico Nacional No. 2508, D.F. CP 07360 (Mexico); Duron-Torres, S.M. [Unidad Academica de Ciencias Quimicas, Universidad Autonoma de Zacatecas, Campus Siglo XXI, Edif. 6 (Mexico); Ferraro, M.; Brunaccini, G.; Sergi, F.; Antonucci, V. [CNR-ITAE, Via Salita S. Lucia sopra Contesse 5-98126 Messina (Italy); Arriaga, L.G. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica S.C., Parque Tecnologico Queretaro, Sanfandila, Pedro Escobedo, Queretaro (Mexico)

    2010-11-15

    Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (author)

  17. 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.

  18. 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.

  19. Seabird mortality induced by land-based artificial lights.

    Science.gov (United States)

    Rodríguez, Airam; Holmes, Nick D; Ryan, Peter G; Wilson, Kerry-Jayne; Faulquier, Lucie; Murillo, Yovana; Raine, André F; Penniman, Jay F; Neves, Verónica; Rodríguez, Beneharo; Negro, Juan J; Chiaradia, André; Dann, Peter; Anderson, Tracy; Metzger, Benjamin; Shirai, Masaki; Deppe, Lorna; Wheeler, Jennifer; Hodum, Peter; Gouveia, Catia; Carmo, Vanda; Carreira, Gilberto P; Delgado-Alburqueque, Luis; Guerra-Correa, Carlos; Couzi, François-Xavier; Travers, Marc; Corre, Matthieu Le

    2017-10-01

    Artificial lights at night cause high mortality of seabirds, one of the most endangered groups of birds globally. Fledglings of burrow-nesting seabirds, and to a lesser extent adults, are attracted to and then grounded (i.e., forced to land) by lights when they fly at night. We reviewed the current state of knowledge of seabird attraction to light to identify information gaps and propose measures to address the problem. Although species in families such as Alcidae and Anatidae can be grounded by artificial light, the most affected seabirds are petrels and shearwaters (Procellariiformes). At least 56 species of Procellariiformes, more than one-third of them (24) threatened, are subject to grounding by lights. Seabirds grounded by lights have been found worldwide, mainly on oceanic islands but also at some continental locations. Petrel breeding grounds confined to formerly uninhabited islands are particularly at risk from light pollution due to tourism and urban sprawl. Where it is impractical to ban external lights, rescue programs of grounded birds offer the most immediate and employed mitigation to reduce the rate of light-induced mortality and save thousands of birds every year. These programs also provide useful information for seabird management. However, these data are typically fragmentary, biased, and uncertain and can lead to inaccurate impact estimates and poor understanding of the phenomenon of seabird attraction to lights. We believe the most urgently needed actions to mitigate and understand light-induced mortality of seabirds are estimation of mortality and effects on populations; determination of threshold light levels and safe distances from light sources; documentation of the fate of rescued birds; improvement of rescue campaigns, particularly in terms of increasing recovery rates and level of care; and research on seabird-friendly lights to reduce attraction. © 2017 Society for Conservation Biology.

  20. 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.

  1. 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.

  2. Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Antonio J. Sanchez-Esguevillas

    2013-03-01

    Full Text Available Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc., which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN that performs Short-Term Load Forecasting (STLF. In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.

  3. Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach

    Directory of Open Access Journals (Sweden)

    Maya L. Pai

    2014-06-01

    Full Text Available The purpose of this study is to forecast Southwest Indian Monsoon rainfall based on sea surface temperature, sea level pressure, humidity and zonal (u and meridional (v winds. With the aforementioned parameters given as input to an Artificial Neural Network (ANN, the rainfall within 10x10 grids of southwest Indian regions is predicted by means of one of the most efficient clustering methods, namely the Kohonen Self-Organizing Maps (SOM. The ANN is trained with input parameters spanning for 36 years (1960-1995 and tested and validated for a period of 9 years (1996-2004. It is further used to predict the rainfall for 6 years (2005-2010. The results show reasonably good accuracy for the summer monsoon periods June, July, August and September (JJAS of the validation years.

  4. EEMD-MUSIC-based analysis for natural frequencies identification of structures using artificial and natural excitations.

    Science.gov (United States)

    Camarena-Martinez, David; Amezquita-Sanchez, Juan P; Valtierra-Rodriguez, Martin; 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.

  5. Estimation of relative humidity based on artificial neural network approach in the Aegean Region of Turkey

    Science.gov (United States)

    Yasar, Abdulkadir; Simsek, Erdoğan; Bilgili, Mehmet; Yucel, Ahmet; Ilhan, Ilhami

    2012-01-01

    The aim of this study is to estimate the monthly mean relative humidity (MRH) values in the Aegean Region of Turkey with the help of the topographical and meteorological parameters based on artificial neural network (ANN) approach. The monthly MRH values were calculated from the measurement in the meteorological observing stations established in Izmir, Mugla, Aydin, Denizli, Usak, Manisa, Kutahya and Afyonkarahisar provinces between 2000 and 2006. Latitude, longitude, altitude, precipitation and months of the year were used in the input layer of the ANN network, while the MRH was used in output layer of the network. The ANN model was developed using MATLAB software, and then actual values were compared with those obtained by ANN and multi-linear regression methods. It seemed that the obtained values were in the acceptable error limits. It is concluded that the determination of relative humidity values is possible at any target point of the region where the measurement cannot be performed.

  6. NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    WANG Weizhong; ZHAO Jie; GAO Yongsheng; CAI Hegao

    2006-01-01

    A novel approach for collision-free path planning of a multiple degree-of-freedom (DOF)articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point (VNP), a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise fiom initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

  7. 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.

  8. A Hybrid Approach Towards Intrusion Detection Based on Artificial Immune System and Soft Computing

    CERN Document Server

    Sanyal, Sugata

    2012-01-01

    A number of works in the field of intrusion detection have been based on Artificial Immune System and Soft Computing. Artificial Immune System based approaches attempt to leverage the adaptability, error tolerance, self- monitoring and distributed nature of Human Immune Systems. Whereas Soft Computing based approaches are instrumental in developing fuzzy rule based systems for detecting intrusions. They are computationally intensive and apply machine learning (both supervised and unsupervised) techniques to detect intrusions in a given system. A combination of these two approaches could provide significant advantages for intrusion detection. In this paper we attempt to leverage the adaptability of Artificial Immune System and the computation intensive nature of Soft Computing to develop a system that can effectively detect intrusions in a given network.

  9. Mixed Finite Element Method and Higher-Order Local Artificial Boundary Conditions for Exterior 3-D Poisson Equation

    Institute of Scientific and Technical Information of China (English)

    韩厚德; 郑春雄

    2002-01-01

    The mixed finite element method is used to solve the exterior Poisson equations with higher-order local artificial boundary conditions in 3-D space. New unknowns are introduced to reduce the order of the derivatives of the unknown to two. The result is an equivalent mixed variational problem which was solved using bilinear finite elements. The primary advantage is that special finite elements are not needed on the adjacent layer of the artificial boundary for the higher-order derivatives. Error estimates are obtained for some local artificial boundary conditions with prescibed orders. A numerical example demonstrates the effectiveness of this method.

  10. An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Runoff

    Science.gov (United States)

    Ashraf Vaghefi, S.; Mousavi, S. J.; Abbaspour, K. C.; Yang, H.

    2012-04-01

    Modeling of rainfall-runoff relationship is important in view of many uses of water resources. Artificial Neural Networks (ANNs) are able to extract the relation between the rainfall and runoff without addressing the physics behind the process. Using back propagation (BP) method to train weights of ANNs may lead to problems in predicting low flows. This paper provides a procedure for application of artificial neural networks trained by Imperialist Competitive Algorithm (ICA) to flow forecasting in Karkheh watershed in southwest of Iran. The monthly hydrometric and climatic data in ANN existed for the period of 1982 to 2002. The results of this study indicated that ANNs rainfall-runoff models trained by ICA predicted daily flow more accurately than those trained by BP. Coefficient of determination for predicted runoffs in training and validating phases in ICA method were 0.97 and 0.93, respectively, while 0.93 and 0.91 were obtained in BP method. The mean squared error of the networks (MSE) for both ICA and BP methods were measured for training and testing data. The accuracy of the model performance was acceptable in both methods, although ICA's results were slightly more accurate.

  11. Implementation Method of Circuit Evolution Based on Artificial Neural Network Model%基于类神经网络模型的电路演化实现方法

    Institute of Scientific and Technical Information of China (English)

    崔新风; 娄建安; 褚杰; 原亮; 丁国良

    2011-01-01

    为解决目前数字型演化硬件研究中存在的电路编码困难问题,提出一个可用矩阵形式描述组合电路的类神经网络门级电路模型,讨论在此模型上进行电路编码的具体方法.根据编码矩阵特点,对标准遗传算法进行改进,设计遗传操作算子、适应度评估方法等.通过无刷直流电动机电子换相电路的成功演化实例,验证了采用矩阵编码和改进遗传算法实现数字电路演化的可行性.%For the purpose of solving the encoding problem harassed the digital Evolvable Hardware(EHW) researchers, a gate-level circuit model which is based on the similarities between combinatorial circuit and neural network is proposed, on which the matrix encoding scheme of combinatorial circuit is discussed. An improved genetic algorithm is used to evolve the encoding matrix, genetic operators and fitness evaluation method are designed according to the characteristics of circuit encoding. The implementation of the commutation circuit of brushless direct current motor proves the feasibility of the implementation method of digital EHW by the using of matrix encoding scheme and the improved genetic algorithm.

  12. Artificial Intelligence Based Alum Dosage Control in Water Treatment Plant

    Directory of Open Access Journals (Sweden)

    P Poongodi

    2013-08-01

    Full Text Available Supplying good quality of drinking water is a challenging task during the rainy season and floods. During this period water becomes highly polluted with suspended solids which increase the water turbidity. Alum is used to reduce the turbidity of the water. Typically in water treatment plants alum dosage is decided by the Jar test and the desired alum dosage is added manually. This research proposes an automatic alum dosage mixing process. The alum dosage is controlled by an intelligent controller which consists of a dosage predictor, an inverse model of the dosage pump and a Pulse Width Modulation (PWM controller. The optimal alum dosage is predicted by the dosage predictor. The PWM controller controls the flow rate of the alum dosing pump. This proposed method has been implemented in a laboratory based water treatment plant and it ensures the automation in water treatment plant to supply good quality drinking water.

  13. Artificial Force Induced Reaction (AFIR) Method for Exploring Quantum Chemical Potential Energy Surfaces.

    Science.gov (United States)

    Maeda, Satoshi; Harabuchi, Yu; Takagi, Makito; Taketsugu, Tetsuya; Morokuma, Keiji

    2016-10-01

    In this account, a technical overview of the artificial force induced reaction (AFIR) method is presented. The AFIR method is one of the automated reaction-path search methods developed by the authors, and has been applied extensively to a variety of chemical reactions, such as organocatalysis, organometallic catalysis, and photoreactions. There are two modes in the AFIR method, i.e., a multicomponent mode and a single-component mode. The former has been applied to bimolecular and multicomponent reactions and the latter to unimolecular isomerization and dissociation reactions. Five numerical examples are presented for an Aldol reaction, a Claisen rearrangement, a Co-catalyzed hydroformylation, a fullerene structure search, and a nonradiative decay path search in an electronically excited naphthalene molecule. Finally, possible applications of the AFIR method are discussed.

  14. 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)

  15. 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.

  16. [A rapid dialysis method for analysis of artificial sweeteners in food].

    Science.gov (United States)

    Tahara, Shoichi; Fujiwara, Takushi; Yasui, Akiko; Hayafuji, Chieko; Kobayashi, Chigusa; Uematsu, Yoko

    2014-01-01

    A simple and rapid dialysis method was developed for the extraction and purification of four artificial sweeteners, namely, sodium saccharin (Sa), acesulfame potassium (AK), aspartame (APM), and dulcin (Du), which are present in various foods. Conventional dialysis uses a membrane dialysis tube approximately 15 cm in length and is carried out over many hours owing to the small membrane area and owing to inefficient mixing. In particular, processed cereal products such as cookies required treatment for 48 hours to obtain satisfactory recovery of the compounds. By increasing the tube length to 55 cm and introducing efficient mixing by inversion at half-hour intervals, the dialysis times of the four artificial sweeteners, spiked at 0.1 g/kg in the cookie, were shortened to 4 hours. Recovery yields of 88.9-103.2% were obtained by using the improved method, whereas recovery yields were low (65.5-82.0%) by the conventional method. Recovery yields (%) of Sa, AK, APM, and Du, spiked at 0.1 g/kg in various foods, were 91.6-100.1, 93.9-100.1, 86.7-100.0 and 88.7-104.7 using the improved method.

  17. A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems

    Science.gov (United States)

    Hussain Mutlag, Ammar; Mohamed, Azah; Shareef, Hussain

    2016-03-01

    Maximum power point tracking (MPPT) is normally required to improve the performance of photovoltaic (PV) systems. This paper presents artificial intelligent-based maximum power point tracking (AI-MPPT) by considering three artificial intelligent techniques, namely, artificial neural network (ANN), adaptive neuro fuzzy inference system with seven triangular fuzzy sets (7-tri), and adaptive neuro fuzzy inference system with seven gbell fuzzy sets. The AI-MPPT is designed for the 25 SolarTIFSTF-120P6 PV panels, with the capacity of 3 kW peak. A complete PV system is modelled using 300,000 data samples and simulated in the MATLAB/SIMULINK. The AI-MPPT has been tested under real environmental conditions for two days from 8 am to 18 pm. The results showed that the ANN based MPPT gives the most accurate performance and then followed by the 7-tri-based MPPT.

  18. Quickprop method to speed up learning process of Artificial Neural Network in money's nominal value recognition case

    Science.gov (United States)

    Swastika, Windra

    2017-03-01

    A money's nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton's method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E'). In our system, we use 5 types of money's nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our results shows that the use of Quickprop method is successfully reduce the learning process compared to Back Propagation method. For 40 input patterns, Quickprop method successfully reached error below 0.1 for only 20 iterations, while Back Propagation method required 2000 iterations. The prediction accuracy for both method is higher than 90%.

  19. 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.

  20. An artificial cell based on gene expression in vesicle

    Science.gov (United States)

    Noireaux, Vincent

    2006-03-01

    A new experimental approach is presented to build an artificial cell using the translation machinery of a cell-free expression system as the hardware and a DNA synthetic program as the software. Cytoplasmic extracts, encapsulated in phospholipid vesicles, are used to assemble custom-made genetic circuits to develop the functions of a minimal cell. The objective is to understand how a DNA algorithm can be designed to build an operating system that has some of the properties of life. We show how a long-lived bioreactor is built to carry out in vitro transcription and translation in cell-sized vesicles. To develop the synthetic membrane into an active interface, a few amphipathic peptides and an insertion mechanism of integral membrane proteins have been tested. With vesicles composed of different phospholipids, the fusion protein alpha-hemolysin-eGFP can be expressed to reveal patterns on the membrane. Finally, specific degradation mechanisms are introduced to create a sink for the synthesized messengers and proteins. Perspectives and limitations of this approach will be discussed.

  1. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

    Science.gov (United States)

    Soltani, Mahmoud; Omid, Mahmoud; Alimardani, Reza

    2015-05-01

    Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

  2. [Diagnosis and prognosis of cerebral ischemic disturbances course using a method of artificial neuronal networks].

    Science.gov (United States)

    Ivanov, Iu S; Semin, G F

    2004-01-01

    Based on the data of examination of 224 patients with different stages of cerebral ischemic disturbances (CID) and 84 age-matched controls, an artificial neuronal network was constructed and tried in differential diagnosis of CID stages according to the data of transcranial ultrasonic dopplerography. Diagnostic efficacy of the network was 80% for sensitivity, 100% for specificity and 82.7% for reliability. A modeling of the influence of the main risk factors for cerebral ischemia and of the reserve state of cerebral hemodynamics for establishing the stage of CID was performed.

  3. Artificial-intelligence-based hospital-acquired infection control.

    Science.gov (United States)

    Adlassnig, Klaus-Peter; Blacky, Alexander; Koller, Walter

    2009-01-01

    Nosocomial or hospital-acquired infections (NIs) are a frequent complication in hospitalized patients. The growing availability of computerized patient records in hospitals permits automated identification and extended monitoring for signs of NIs. A fuzzy- and knowledge-based system to identify and monitor NIs at intensive care units (ICUs) according to the European Surveillance System HELICS (NI definitions derived from the Centers of Disease Control and Prevention (CDC) criteria) was developed and put into operation at the Vienna General Hospital. This system, named Moni, for monitoring of nosocomial infections contains medical knowledge packages (MKPs) to identify and monitor various infections of the bloodstream, pneumonia, urinary tract infections, and central venous catheter-associated infections. The MKPs consist of medical logic modules (MLMs) in Arden syntax, a medical knowledge representation scheme, whose definition is part of the HL7 standards. These MLM packages together with the Arden software are well suited to be incorporated in medical information systems such as hospital information or intensive-care patient data management systems, or in web-based applications. In terms of method, Moni contains an extended data-to-symbol conversion with several layers of abstraction, until the top level defining NIs according to HELICS is reached. All included medical concepts such as "normal", "increased", "decreased", or similar ones are formally modeled by fuzzy sets, and fuzzy logic is used to process the interpretations of the clinically observed and measured patient data through an inference network. The currently implemented cockpit surveillance connects 96 ICU beds with Moni and offers the hospital's infection control department a hitherto unparalleled NI infection survey.

  4. Integrating artificial neural network and classical methods for unsupervised classification of optical remote sensing data

    Science.gov (United States)

    Tahir, Ahmed AK

    2012-12-01

    A novel system named unsupervised multiple classifier system (UMCS) for unsupervised classification of optical remote sensing data is presented. The system is based on integrating two or more individual classifiers. A new dynamic selection-based method is developed for integrating the decisions of the individual classifiers. It is based on competition distance arranged in a table named class-distance map (CDM) associated to each individual classifier. These maps are derived from the class-to-class-distance measures which represent the distances between each class and the remaining classes for each individual classifier. Three individual classifiers are used for the development of the system, K-means and K-medians clustering of the classical approach and Kohonen network of the artificial neural network approach. The system is applied to ETM + images of an area North to Mosul dam in northern part of Iraq. To show the significance of increasing the number of individual classifiers, the application covered three modes, UMCS@, UMCS#, and UMCS*. In UMCS@, K-means and Kohonen are used as individual classifiers. In UMCS#, K-medians and Kohonen are used as individual classifiers. In UMCS*, K-means, K-medians and Kohonen are used as individual classifiers. The performance of the system for the three modes is evaluated by comparing the outputs of individual classifiers to the outputs of UMCSs using test data extracted by visual interpretation of color composite images. The evaluation has shown that the performance of the system with all three modes outrages the performance of the individual classifiers. However, the improvement in the class and average accuracy for UMCS* was significant compared to the improvements made by UMCS@, and UMCS#. For UMCS*, the accuracy of all classes were improved over the accuracy achieved by each of the individual classifiers and the average improvements reached (4.27, 3.70, and 6.41%) over the average accuracy achieved by K-means, K-medians and

  5. 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.

  6. Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes

    Directory of Open Access Journals (Sweden)

    Jin Woo Moon

    2014-11-01

    Full Text Available This study aims at developing an indoor temperature control method that could provide comfortable thermal conditions by integrating heating system control and the opening conditions of building envelopes. Artificial neural network (ANN-based temperature control logic was developed for the control of heating systems and openings at the building envelopes in a predictive and adaptive manner. Numerical comparative performance tests for the ANN-based temperature control logic and conventional non-ANN-based counterpart were conducted for single skin enveloped and double skin enveloped buildings after the simulation program was validated by comparing the simulation and the field measurement results. Analysis results revealed that the ANN-based control logic improved the indoor temperature environment with an increased comfortable temperature period and decreased overshoot and undershoot of temperatures outside of the operating range. The proposed logic did not show significant superiority in energy efficiency over the conventional logic. The ANN-based temperature control logic was able to maintain the indoor temperature more comfortably and with more stability within the operating range due to the predictive and adaptive features of ANN models.

  7. 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.

  8. Network modeling of membrane-based artificial cellular systems

    Science.gov (United States)

    Freeman, Eric C.; Philen, Michael K.; Leo, Donald J.

    2013-04-01

    Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.

  9. 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.

  10. A multiuser detector based on artificial bee colony algorithm for DS-UWB systems.

    Science.gov (United States)

    Yin, Zhendong; Liu, Xiaohui; Wu, Zhilu

    2013-01-01

    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.

  11. 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.

  12. Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    A. T. Tzallas

    2007-01-01

    Full Text Available The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN, which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.

  13. 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.

  14. Application of an Artificial Intelligence Method for Velocity Calibration and Events Location in Microseismic Monitoring

    Science.gov (United States)

    Yang, Y.; Chen, X.

    2013-12-01

    Good quality hydraulic fracture maps are heavily dependent upon the best possible velocity structure. Particle Swarm Optimization inversion scheme, an artificial intelligence technique for velocity calibration and events location could serve as a viable option, able to produce high quality data. Using perforation data to recalibrate the 1D isotropic velocity model derived from dipole sonic logs (or even without them), we are able to get the initial velocity model used for consequential events location. Velocity parameters can be inverted, as well as the thickness of the layer, through an iterative procedure. Performing inversion without integrating available data is unlikely to produce reliable results; especially if there are only one perforation shot and a single poor-layer-covered array along with low signal/noise ratio signal. The inversion method was validated via simulations and compared to the Fast Simulated Annealing approach and the Conjugate Gradient method. Further velocity model refinement can be accomplished while calculating events location during the iterative procedure minimizing the residuals from both sides. This artificial intelligence technique also displays promising application to the joint inversion of large-scale seismic activities data.

  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. 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.

  17. 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.)

  18. Modeling and Optimization Technique of a Chilled Water AHU Using Artificial Neural Network Methods

    Science.gov (United States)

    Talib, Rand Issa

    Heating, ventilation, and air conditioning (HVAC) systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The chilled water system is one Heating, ventilation, and air conditioning systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The design of these systems constitutes a large impact on the energy usage and operating cost of buildings they serve. The ability to accurately predict the performance of these systems is integral to designing more energy efficient and sustainable building systems. In this thesis the modeling of a chilled water air handling units using Artificial Neural Networks model is proposed. The Artificial neural network model was built using four inputs (1) Chilled water temperature (CHWT), (2) Chilled water valve position (CWVLV), (3) Mixed air temperature (MAT), and (4) Supply air flow (SAF). The output of the model is to predict supply air temperature. Moreover, another model was constructed to predict the fan power as a function of the fan air flow and fan speed. The data that were collected from a real building in a span of three months were processed. The ANN model was trained using the measured data and different model structure were then tested with various time delay, feedback time, and number of neurons to determine the best structure. In addition, an optimization method is developed to automate the process of finding the best model structure that can produce the best accurate prediction against the actual data. The Coefficient of variances which was used to determine the error value was recorded to be as low as 1.22 for the best model structure. The obtained results validate the Artificial neural network model created as an accurate tool for predicting the performance of a chilled water air handling unit.

  19. 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.

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

    Science.gov (United States)

    Shao, Jing; Yang, Lina; Peng, Ling; Chi, Tianhe; Wang, Xiaomeng

    2015-01-01

    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.

  1. Artificial intelligence library for html5 based games: DignityAI

    Directory of Open Access Journals (Sweden)

    Berkan Uslu

    2017-02-01

    Full Text Available Today, acceleration of internet and common use of web pages, revealed the necessity of work with any browser smoothly for each application without of requirement of any plug-in. Generally, HTML5 is a new body of standards which is formed with the combination of CSS and JavaScript. In this context, by analysing game engines developed for HTML5, their features and advantages are investigated. Although, these game engines are close to catch up with the level of popular game engines, it is seen that none of artificial intelligence library was developed for HTML5 based games up to now. In this study, DignityAI artificial intelligence library is developed to fill this deficiency. Developed library has ability to be integrated to all HTML5 games independently from game engine and to add artificial intelligence dynamics to these games.

  2. 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.

  3. Estimation of Reynolds number for flows around cylinders with lattice Boltzmann methods and artificial neural networks

    Science.gov (United States)

    Carrillo, Mauricio; Que, Ulices; González, José A.

    2016-12-01

    The present work investigates the application of artificial neural networks (ANNs) to estimate the Reynolds (Re) number for flows around a cylinder. The data required to train the ANN was generated with our own implementation of a lattice Boltzmann method (LBM) code performing simulations of a two-dimensional flow around a cylinder. As results of the simulations, we obtain the velocity field (v ⃗) and the vorticity (∇ ⃗×v ⃗ ) of the fluid for 120 different values of Re measured at different distances from the obstacle and use them to teach the ANN to predict the Re. The results predicted by the networks show good accuracy with errors of less than 4 % in all the studied cases. One of the possible applications of this method is the development of an efficient tool to characterize a blocked flowing pipe.

  4. A comparison of methods for estimating fish assemblages associated with estuarine artificial reefs

    Directory of Open Access Journals (Sweden)

    Michael Lowry

    2011-01-01

    Full Text Available Monitoring strategies which adequately represent the entire community associated with artificial structures will enable more informed decisions regarding the broader effects of artificial structures and their role in the management of fisheries resources. Despite the widespread application of a range of in situ visual monitoring methodologies used in the assessment of artificial structures, the relative biases associated with each method have not been critically examined and remain poorly understood. Estimates of fish abundance on six estuarine artificial reefs carried out by divers using underwater visual census techniques (UVC were compared with estimates of relative abundance determined by baited remote underwater video (BRUV. It was found that when combined, both methods provided a more comprehensive description of the species associated with estuarine artificial reefs. However, the difference in the number of species detected and the frequency of detection varied between methods. Results indicated that the differences in rates of detection between UVC and BRUV methodologies were primarily related to the ecological niche and behaviour of the species in question. UVC provided better estimates of the rare or cryptic reef associated species. BRUV sampled a smaller proportion of species overall but did identify key recreational species such as Acanthopagrus australis, Pagrus auratus and Rhabdosargus sarba with increased frequency. Correlation of abundance indices for species classified as "permanent" identified interspecific interactions that may act as a source of bias associated with BRUV observations.O monitoramento biológico da comunidade associada a substratos artificiais permite a tomada de decisões corretas em relação ao uso e o papel dos novos habitats no manejo de recursos pesqueiros. Apesar da enorme aplicação das técnicas de censo visual no estudo da ictiofauna em recifes artificiais, os erros relativos de cada metodologia

  5. Two-phase Method without Any Artificial Variable%不需加人工变量的两阶段法

    Institute of Scientific and Technical Information of China (English)

    梁平; 张旭利; 张相斌

    2008-01-01

    A method is provided to achieve an initial basic feasible solution of a linear programming in this paper.This method dose not need introducing any artificial variable, but needs only solving an auxiliary linear programming.Compared with the traditional two-phase method, it has advantages of saving the memories and reducing the computational efforts.

  6. A Space-time Smooth Artificial Viscosity Method For Nonlinear Conservation Laws

    CERN Document Server

    Reisner, Jon; Shkoller, Steve

    2012-01-01

    We introduce the $C$-method, a simple scheme for adding localized, space-time smooth, artificial viscosity to nonlinear systems of conservation laws which propagate shock waves, rarefactions, and contact discontinuities. In particular, we focus our attention on the compressible Euler equations which form a 3x3 system in one space dimension. The novel feature of our approach involves the coupling of a linear scalar reaction diffusion equation to our system of conservation laws, whose solution $C(x,t)$ is the coefficient to an additional (and artificial) term added to the flux, which determines both the location and strength of the added viscosity. Near shock discontinuities, $C(x,t)$ is large and localized, and transitions smoothly in space-time to zero away from the shock. This simple approach has two fundamental features: (1) our regularization is at the continuum level--i.e., the level of he partial differential equations (PDE)-- so that any higher-order numerical discretization scheme can be employed, and ...

  7. 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)

  8. [Outstanding scientist-investigator, S. S. Brukhonenko--founder of artificial circulation method and developer of first in the world autoejector].

    Science.gov (United States)

    Pavlovskiĭ, L N

    2009-01-01

    The article presents data about well-known Russian physician-physiologist and researcher Sergey Sergeeviche Brukhonenko. The hard way passed by the scientist-researcher from completion of artificial breath technique to the method of artificial blood circulation developed by him and development of the first in the world--artificial blood circulation device--autoejector is shown in the article.

  9. 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...

  10. Heidegger and artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Diaz, G.

    1987-01-01

    The discipline of Artificial Intelligence, in its quest for machine intelligence, showed great promise as long as its areas of application were limited to problems of a scientific and situation neutral nature. The attempts to move beyond these problems to a full simulation of man's intelligence has faltered and slowed it progress, largely because of the inability of Artificial Intelligence to deal with human characteristic, such as feelings, goals, and desires. This dissertation takes the position that an impasse has resulted because Artificial Intelligence has never been properly defined as a science: its objects and methods have never been identified. The following study undertakes to provide such a definition, i.e., the required ground for Artificial Intelligence. The procedure and methods employed in this study are based on Heidegger's philosophy and techniques of analysis as developed in Being and Time. Results of this study show that both the discipline of Artificial Intelligence and the concerns of Heidegger in Being and Time have the same object; fundamental ontology. The application of Heidegger's conclusions concerning fundamental ontology unites the various aspects of Artificial Intelligence and provides the articulation which shows the parts of this discipline and how they are related.

  11. 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.

  12. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

    Science.gov (United States)

    Lin, Lan; Jin, Cong; Fu, Zhenrong; Zhang, Baiwen; Bin, Guangyu; Wu, Shuicai

    2016-03-01

    Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.

  13. 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.

  14. 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.

  15. Analysis and discussion of different methods of artificial ice-high specimen preparation

    Institute of Scientific and Technical Information of China (English)

    ShuJuan Zhang; Wei Ma; ZhiZhong Sun; HaiMin Du

    2014-01-01

    Because ice-high foundation soil is widely distributed in permafrost regions, the correct preparation of ice-high specimens is of critical interest in engineering design for foundation stability. Past research has shown that the uniaxial compression strength of ice-high frozen soils changes as the ice or total water content increases;the differences of different methods of specimen preparation are analyzed here and the advantages and disadvantages of them are presented. It is confirmed that the role of crushed ice is significantly different from that of naturally frozen ice in frozen soils, and the size and amount of crushed ice will influence the strength and deformation mechanism of frozen soils. Therefore, it is strongly recommended that when a ice-high specimen is artificially prepared, the ice should be frozen through natural means and not be replaced with crushed ice.

  16. 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.

  17. Statistical material parameters identification based on artificial neural networks for stochastic computations

    Science.gov (United States)

    Novák, Drahomír; Lehký, David

    2017-07-01

    A general methodology to obtain statistical material model parameters is presented. The procedure is based on the coupling of a stochastic simulation and an artificial neural network. The identification parameters play the role of basic random variables with a scatter reflecting the physical range of possible values. The efficient small-sample simulation method Latin Hypercube Sampling is used for the stochastic preparation of the training set utilized in training the neural network. Once the network has been trained, it represents an approximation consequently utilized in a following way: To provide the best possible set of model parameters for the given experimental data. The paper focuses the attention on the statistical inverse analysis of material model parameters where statistical moments (usually means and standard deviations) of input parameters have to be identified based on experimental data. A hierarchical statistical parameters database within the framework of reliability software is presented. The efficiency of the approach is verifiedusing numerical example of fracture-mechanical parameters determination of fiber reinforced and plain concretes.

  18. Artificial Neural Network Based Model for Forecasting of Inflation in India

    Directory of Open Access Journals (Sweden)

    Gour Sundar Mitra Thakur

    2016-03-01

    Full Text Available Inflation can be attributed to both microeconomic and macroeconomic factors which influence the stability of the economy of any nation. With the raising of recession at the end of the year 2008, world communities started paying much contemplation on inflation and put enormous hard work to predict it accurately. Prediction of inflation is not a simple task. Moreover, the behavior of inflation is so complex and uncertain that both economists and statisticians have been striving to model and forecast inflation in an accurate way. As a result, many researchers have proposed inflation forecasting models based on different methods; however the accuracy is always being a major constraint. In this paper, we have analyzed the historical monthly economic data of India between January 2000 and December 2012 and constructed an inflation forecasting model based on feed forward back propagation neural network. Initially some critical factors that can considerably influence the inflation of India have been identified, then an efficient artificial neural network (ANN model has been proposed to forecast the inflation. Accuracy of the model is proved to be satisfactory when compared with the forecasting of some well-known agencies.

  19. A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

    Science.gov (United States)

    Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.

    1992-01-01

    In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

  20. Comparison of the MURA and an improved single-receptor (SIRA) trajectory source apportionment (TSA) method using artificial sources

    Science.gov (United States)

    Lee, Stephanie; Ashbaugh, Lowell

    Two trajectory source apportionment methods were tested using an artificially generated data set to determine their ability to detect the known sources. The forward-looking step from the multi-receptor trajectory analysis (MURA) method was added to the conditional probability (CP)method of Ashbaugh et al. [1985. A residence time probability analysis of sulfur concentrations at Grand Canyon National Park. Atmospheric Environment 19(8), 1263-1270] to develop the single-receptor forward CP (SIRA) method. The multi-receptor (MURA) and the SIRA methods were tested with three simulations using artificially generated sources. The ability of the methods to detect the sources was quantified for each simulation. The first simulation showed that the SIRA method is an improvement over the original CP method. The MURA trajectory method proved to be superior at identifying sources for the simulation located in the west and comparable to the SIRA method for the two simulations located in the east.

  1. [Methods of extending the resource of fiber ionite artificial soils in space greenhouses].

    Science.gov (United States)

    Krivobok, A S; Berkovich, Iu A; Il'in, V K; Chuvil'skaia, N A; Shcherbakova, V A

    2012-01-01

    The vegetable cultivation technology developed in view of long-term autonomous missions is based on root nutrition provided by fiber artificial soils (AS) containing ion-exchange resins. Useful life of ASs is limited by two factors which are nutrients depletion in ion-exchanger and clogging of the AS threshold space by roots remnants. Purpose of the investigation is to try out hydrolysis and ensuing microbial decomposition of roots remnants as a way to extend the resource of used fiber ionite AS. This principle of doing away with the roots remnants recovers almost completely the maximal water-absorbing capacity of AS BIONA-V3 so that it can be used again for crops cultivation.

  2. An artificial immune approach for optical image based vision inspection

    Institute of Scientific and Technical Information of China (English)

    Hong Zheng(郑宏); Nanfeng Xiao(肖南风); Jinhui Lan(蓝金辉)

    2003-01-01

    This paper presents a novel approach of visual inspection for texture surface defects. The approach usesartificial immune theory in learning the detection of texture defects. In this paper, texture defects areregards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholdsused for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from thenatural immune system is employed to learn antibodies. Experimental results on textile image inspectionare presented to illustrate the merit and feasibility of the proposed method.

  3. 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

    2016-11-17

    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.

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

    OpenAIRE

    Dong Yumin; Zhao Li

    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...

  5. Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian - River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple-variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

  6. EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    F. Arce

    2017-09-01

    Full Text Available Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

  7. Efficient Lane Detection Based on Artificial Neural Networks

    Science.gov (United States)

    Arce, F.; Zamora, E.; Hernández, G.; Sossa, H.

    2017-09-01

    Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

  8. Comparing and Validating Methods of Reading Instruction Using Behavioural and Neural Findings in an Artificial Orthography

    Science.gov (United States)

    2017-01-01

    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. PMID:28425742

  9. 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.

  10. Risk analysis of dam based on artificial bee colony algorithm with fuzzy c-means clustering

    Energy Technology Data Exchange (ETDEWEB)

    Li, Haojin; Li, Junjie; Kang, Fei

    2011-05-15

    Risk analysis is a method which has been incorporated into infrastructure engineering. Fuzzy c-means clustering (FCM) is a simple and fast method utilized most of the time, but it can induce errors as it is sensitive to initialization. The aim of this paper was to propose a new method for risk analysis using an artificial bee colony algorithm (ABC) with FCM. This new technique is first explained and then applied on three experiments. Results demonstrated that the combination of artificial bee colony algorithm fuzzy c-means clustering (ABCFCM) is overcoming the FCM issue since it is not initialization sensitive and experiments showed that this algorithm is more accurate and than FCM. This paper provides a new tool for risk analysis which can be used for risk prioritizing and reinforcing dangerous dams in a more scientific way.

  11. 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.

  12. 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...

  13. Artificially Augmented Training for Anomaly-based Network Intrusion Detection Systems

    Directory of Open Access Journals (Sweden)

    Chockalingam Karuppanchetty

    2015-09-01

    Full Text Available Attacks on web servers are becoming increasingly prevalent; the resulting social and economic impact of successful attacks is also exacerbated by our dependency on web-based applications. There are many existing attack detection and prevention schemes, which must be carefully configured to ensure their efficacy. In this paper, we present a study challenges that arise in training network payload anomaly detection schemes that utilize collected network traffic for tuning and configuration. The advantage of anomaly-based intrusion detection is in its potential for detecting zero day attacks. These types of schemes, however, require extensive training to properly model the normal characteristics of the system being protected. Usually, training is done through the use of real data collected by monitoring the activity of the system. In practice, network operators or administrators may run into cases where they have limited availability of such data. This issue can arise due to the system being newly deployed (or heavily modified or due to the content or behavior that leads to normal characterization having been changed. We show that artificially generated packet payloads can be used to effectively augment the training and tuning. We evaluate the method using real network traffic collected at a server site; We illustrate the problem at first (use of highly variable and unsuitable training data resulting in high false positives of 3.6∼10%, then show improvements using the augmented training method (false positives as low as 0.2%. We also measure the impact on network performance, and present a lookup based optimization that can be used to improve latency and throughput.

  14. Artificial frozen orbit control scheme based on J2 perturbation

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Since the inclination of frozen orbit with non-rotation of the perigee that occurs due to J2 perturbation must be equal to the critical inclination, this regulation has restricted the application of frozen orbit a lot. In this paper, we propose two control strategies to eliminate the secular growth of the argument of the perigee for orbits that are not at the critical inclination. One control strategy is using transverse continuous low-thrust, and the other is using both the transverse and the radial continuous low-thrusts. Fuel optimization in the second control strategy is addressed to make sure that the fuel consumption is the minimum. Both strategies have no effect on other orbital parameters’ secular motion. It is proved that the strategy with transverse control could save more energy than the one with radial control. Simulations show that the second control strategy could save 54.6% and 86% of energy, respectively, compared with the two methods presented in the references.

  15. 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.

  16. 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.

  17. Studies of zeolite-based artificial photosynthetic systems

    Science.gov (United States)

    Zhang, Haoyu

    Two ruthenium polypyridyl compounds of structural formula [(bpy) 2RuL]2+ (RuL) and [(bpy)2RuLDQ]4+ (RuLDQ) (where bpy = bipyridine, L = trans-1,2-bis-4-(4'-methyl)-2,2'-bipyridyl) ethane, LDQ = 1-[4-(4'-methyl)-2,2'-bipyridyl)]-2-[4-(4'-N,N'-tetramethylene-2,2'-bipyridinium)] ethene) were synthesized and purified. From pH titrations, it was found that the Ru complex was a stronger base (pKa* = 6) in the excited state than in the ground state (pKa = 4). Photolysis of the RuL complex in solutions at pH 7 and 12 led to formation of species with increased emission quantum yields, ˜55 nm blue-shift of the emission maximum to 625 nm and disappearance of the absorption band at 330 nm, the latter arising from the olefinic bond of the L ligand. Photoproducts formed at neutral pH have been analyzed. It was found that the major product was a dimer of RuL, dimerizing around the double bond. Photoreactions did not occur in the dark or in the aprotic solvent acetonitrile. We proposed that a Ru(III) radical intermediate was formed by photoinduced excited-state electron and proton transfer, which initiated the dimerization. The radical intermediate also underwent photochemical degradative reductions. Below pH 4, the emission quenching was proposed to arise via protonation of the monoprotonated RuLH + followed by electron transfer to the viologen-type moiety created by protonation. The products of photodegradation at pH > 12 were different from those of pH 7, but the mechanism of the degradation at pH > 12 was not elucidated. RuLDQ was stable under visible irradiation. We examined nanocrystalline zeolite as a host for light absorbing sensitizers (electron donors) and electron acceptors. Nanocrystalline zeolite Y (NanoY) with uniform particle size, pure phase was prepared. NanoY was obtained by periodically removing nanocrystals from the mother liquor and recycling the unused reagents. The nanoparicles were characterized by XRD and TEM. Optically clear colloidal solutions of Nano

  18. A METHOD TO IMPROVE RELIABILITY OF GEARBOX FAULT DETECTION WITH ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    P.V. Srihari

    2010-12-01

    Full Text Available Fault diagnosis of gearboxes plays an important role in increasing the availability of machinery in condition monitoring. An effort has been made in this work to develop an artificial neural networks (ANN based fault detection system to increase reliability. Two prominent fault conditions in gears, worn-out and broken teeth, are simulated and five feature parameters are extracted based on vibration signals which are used as input features to the ANN based fault detection system developed in MATLAB, a three layered feed forward network using a back propagation algorithm. This ANN system has been trained with 30 sets of data and tested with 10 sets of data. The learning rate and number of hidden layer neurons are varied individually and the optimal training parameters are found based on the number of epochs. Among the five different learning rates used the 0.15 is deduced to be optimal one and at that learning rate the number of hidden layer neurons of 9 was the optimal one out of the three values considered. Then keeping the training parameters fixed, the number of hidden layers is varied by comparing the performance of the networks and results show the two and three hidden layers have the best detection accuracy.

  19. Automatic event detection based on artificial neural networks

    Science.gov (United States)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

    The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to

  20. The effects of artificial substrates on freshwater pond productivity and water quality and the implications for periphyton-based aquaculture

    NARCIS (Netherlands)

    Azim, M.E.; Wahab, M.A.; Verdegem, M.C.J.; Dam, van A.A.; Rooij, van J.M.; Beveridge, M.C.M.

    2002-01-01

    As a first step in assessing the viability of periphyton-based fish production in South Asian pond aquaculture systems, the effects of artificial substrates on development of periphyton and on water quality were evaluated. Earthen ponds (10 x 7.5 m) were provided with an artificial substrate constru

  1. Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model

    OpenAIRE

    Roldán Blay, Carlos; Escrivá-Escrivá, Guillermo; Álvarez Bel, Carlos María; Roldán Porta, Carlos; Rodriguez-Garcia, Javier

    2013-01-01

    This paper presents the upgrading of a method for predicting short-term building energy consumption that was previously developed by the authors (EUs method). The upgrade uses a time temperature curve (TTC) forecast model. The EUs method involves the use of artificial neural networks (ANNs) for predicting each independent process end-uses (EUs). End-uses consume energy with a specific behaviour in function of certain external variables. The EUs method obtains the total consumption by the ad...

  2. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2011-04-01

    Full Text Available This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The experiments demonstrate that: 1 the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2 the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization. Therefore, our approach is effective and rapid.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. A NOVEL ARTIFICIAL HYDROCARBON NETWORKS BASED SPACE VECTOR PULSE WIDTH MODULATION CONTROLLER FOR INDUCTION MOTORS

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2014-01-01

    Full Text Available Most of machine-operated industrial processes implement electric machinery as their work sources, implying the necessary improvement of control techniques and power electronics drivers. Many years have passed since the control conflicts related to induction motors have been overcome through torque-flux control techniques so their advantages over direct current motors have made them to be the most common electric actuator found behind industrial automation. In fact, induction motors can be easily operated using a Direct Torque Control (DTC. Since, it is based on a hysteresis control of the torque and flux errors, its performance is characterized by a quick reaching of the set point, but also a high ripple on both torque and flux. In order to enhance that technique, this study introduces a novel hybrid fuzzy controller with artificial hydrocarbon networks (FMC that is used in a Space Vector Pulse Width Modulation (SVPWM technique, so-called FMC-SVPWM-DTC. In fact, this study describes the proposal and its design method. Experimental results over a velocity-torque cascade topology proved that the proposed FMC-SVPWM-DTC responses highly effective almost suppressing rippling in torque and flux. It also performed a faster speed response than in a conventional DTC. In that sense, the proposed FMC-SVPWM-DTC can be used an alternative approach for controlling induction motors.

  9. NOISE IDENTIFICATION FOR HYDRAULIC AXIAL PISTON PUMP BASED ON ARTIFICIAL NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The noise identification model of the neural networks is established for the 63SCY14-1B hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks. The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks. Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.

  10. Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Chelgani, S. Chehreh; Jorjani, E.; Mesroghli, Sh.; Bagherieh, A.H. [Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Poonak, Hesarak Tehran (Iran); Hower, James C. [Center for Applied Energy Research, University of Kentucky, 2540 Research Park Drive, Lexington, KY 40511 (United States)

    2008-01-15

    The effects of proximate and ultimate analysis, maceral content, and coal rank (R{sub max}) for a wide range of Kentucky coal samples from calorific value of 4320 to 14960 (BTU/lb) (10.05 to 34.80 MJ/kg) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that the relationship between (a) Moisture, ash, volatile matter, and total sulfur; (b) ln (total sulfur), hydrogen, ash, ln ((oxygen + nitrogen)/carbon) and moisture; (c) ln (exinite), semifusinite, micrinite, macrinite, resinite, and R{sub max} input sets with HGI in linear condition can achieve the correlation coefficients (R{sup 2}) of 0.77, 0.75, and 0.81, respectively. The ANN, which adequately recognized the characteristics of the coal samples, can predict HGI with correlation coefficients of 0.89, 0.89 and 0.95 respectively in testing process. It was determined that ln (exinite), semifusinite, micrinite, macrinite, resinite, and R{sub max} can be used as the best predictor for the estimation of HGI on multivariable regression (R{sup 2} = 0.81) and also artificial neural network methods (R{sup 2} = 0.95). The ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the hardgrove grindability index prediction. (author)

  11. Optimal control of end-port glass tank furnace regenerator temperature based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    CHEN Xi; ZHAO Guo-zhu

    2005-01-01

    In the paper, an artificial neural network (ANN) method is put forward to optimize melting temperature control, which reveals the nonlinear relationships of tank melting temperature disturbances with secondary wind flow and fuel pressure, implements dynamic feed-forward complementation and dynamic correctional ratio between air and fuel in the main control system. The application to Anhui Fuyang Glass Factory improved the control character of the melting temperature greatly.

  12. Controlling the pKa of the bacteriorhodopsin Schiff base by use of artificial retinal analogues.

    OpenAIRE

    1986-01-01

    Artificial bacteriorhodopsin pigments based on synthetic retinal analogues carrying an electron-withdrawing CF3 substituent group were prepared. The effects of CF3 on the spectra, photocycles, and Schiff base pKa values of the pigments were analyzed. A reduction of 5 units in the pKa of the Schiff base is observed when the CF3 substituent is located at the C-13 polyene position, in the vicinity of the protonated Schiff base nitrogen. The results lead to the unambiguous characterization of the...

  13. RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

    Science.gov (United States)

    Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

    2016-02-01

    Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

  14. Comparison effect of artificial tooth type and cyclic loading on the bond strength to auto-polymerized acrylic denture base resins

    Directory of Open Access Journals (Sweden)

    Fateme Nematollahi

    2013-05-01

    Full Text Available   Background and Aims: Failure of bonding between artificial teeth and denture base material is a considerable problem for patients who wear dentures. According to the different impact of artificial teeth and different information about resistance force of mastication and also with deficiency in researchs, this study was designed to compare the bond strength of composite and acrylic artificial teeth to auto-polymerized denture base resins with and without cyclic loading.   Materials and Methods: In this experimental and in vitro study, an acrylic resin auto-polymerized (Rapid Repair, Dentsply and four artificial teeth (Acrylic Marjan new, Composite Glamour teeth and Ivoclar acrylic and composite teeth were used. Therefore, 8 groups of 10 specimens each were evaluated. All specimens were thermocycled for 5000 cycles, in water baths between 5 and 55 ◦ C. Half the specimens in each group were treated with cyclic loading at 50N for 14, 400 cycles at 1.2 Hz. The shear bond strengths were measured using a Universal Testing Machine. Data were analyzed using Two-way ANOVA test.   Results: Statistical analysis demonstrated no significant effect of cyclic loading on the shear bond strength, but the type of artificial tooth affected the shear bond strength (P=0.006. Also, the interaction between Cyclic loading and the type of artificial tooth showed no significant difference (P=0.98. Tukey test showed that acrylic teeth (Ivoclar had statistically higher bond strength values than that of other teeth (PGlamour=0.02, (PComposite ivoclar=0.01 and (PMarjan new=0.02.   Conclusion: Within the limitation of this study, the predominant type of fracture in all groups was cohesive, therefore the bond strength was adequate in all teeth and the type of artificial tooth may influence the bond strength of denture teeth to denture base resin. Cyclic loading had no significant effect on the bond strength of denture teeth to the auto-polymerized acrylic resin.

  15. 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.

  16. 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.

  17. 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.

  18. Artificial immune system based on adaptive clonal selection for feature selection and parameters optimisation of support vector machines

    Science.gov (United States)

    Sadat Hashemipour, Maryam; Soleimani, Seyed Ali

    2016-01-01

    Artificial immune system (AIS) algorithm based on clonal selection method can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure along with the feature selection significantly impacts on the classification accuracy rate. In this study, AIS based on Adaptive Clonal Selection (AISACS) algorithm has been used to optimise the SVM parameters and feature subset selection without degrading the SVM classification accuracy. Several public datasets of University of California Irvine machine learning (UCI) repository are employed to calculate the classification accuracy rate in order to evaluate the AISACS approach then it was compared with grid search algorithm and Genetic Algorithm (GA) approach. The experimental results show that the feature reduction rate and running time of the AISACS approach are better than the GA approach.

  19. Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Abdelsalam, Ibrahim; Wang, Huai

    2017-01-01

    methods suffer from shortcomings such as, low estimation accuracy, extra hardware, and increased cost. Therefore, development of new condition monitoring methodologies that are based on advanced software algorithms could be the way out of the aforementioned challenges and shortcomings. In this paper......, a proposed software condition monitoring methodology based on Artificial Neural Network (ANN) algorithm is presented. Matlab software is used to train and generate the proposed ANN. The proposed methodology estimates the capacitance of the DC-link capacitor in a three phase front-end diode bridge AC....../DC/AC converter. The estimation is based on the usage of single phase output current and dc-link voltage ripple. The impact of training data type, source and amount are also investigated for estimation accuracy analysis. Experimental validation of the proposed method is also conducted....

  20. Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

    OpenAIRE

    Khalil, Khaled M.; Abdel-Aziz, M.; Nazmy, Taymour T.; Salem, Abdel-Badeeh M.

    2010-01-01

    Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, a...

  1. Artificial neural network based on SQUIDs: demonstration of network training and operation

    Science.gov (United States)

    Chiarello, F.; Carelli, P.; Castellano, M. G.; Torrioli, G.

    2013-12-01

    We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on.

  2. Can artificial neural networks provide an "expert's" view of medical students performances on computer based simulations?

    OpenAIRE

    Stevens, R. H.; K. Najafi

    1992-01-01

    Artificial neural networks were trained to recognize the test selection patterns of students' successful solutions to seven immunology computer based simulations. When new student's test selections were presented to the trained neural network, their problem solutions were correctly classified as successful or non-successful > 90% of the time. Examination of the neural networks output weights after each test selection revealed a progressive increase for the relevant problem suggesting that a s...

  3. Early-age concrete strength estimation based on piezoelectric sensor using artificial neural network

    Science.gov (United States)

    Kim, Junkyeong; Kim, Ju-Won; Park, Seunghee

    2014-04-01

    Recently, novel methods to estimate the strength of concrete have been reported based on numerous NDT methods. Especially, electro-mechanical impedance technique using piezoelectric sensors are studied to estimate the strength of concrete. However, the previous research works could not provide the general information about the early-age strength important to manage the quality of concrete and/or the construction process. In order to estimate the early-age strength of concrete, the electro-mechanical impedance method and the artificial neural network(ANN) is utilized in this study. The electro-mechanical impedance varies with the mechanical properties of host structures. Because the strength development is most influential factor among the change of mechanical properties at early-age of curing, it is possible to estimate the strength of concrete by analyzing the change of E/M impedance. The strength of concrete is a complex function of several factors like mix proportion, temperature, elasticity, etc. Because of this, it is hard to mathematically derive equations about strength of concrete. The ANN can provide the solution about early-age strength of concrete without mathematical equations. To verify the proposed approach, a series of experimental studies are conducted. The impedance signals are measured using embedded piezoelectric sensors during curing process and the resonant frequency of impedance is extracted as a strength feature. The strength of concrete is calculated by regression of strength development curve obtained by destructive test. Then ANN model is established by trained using experimental results. Finally the ANN model is verified using impedance data of other sensors.

  4. 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.

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

    Science.gov (United States)

    Del Rosario Martinez-Blanco, Ma; Ornelas-Vargas, Gerardo; Castañeda-Miranda, Celina Lizeth; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, Jose M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

    2016-11-01

    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 (6)LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation.

  6. Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

    Institute of Scientific and Technical Information of China (English)

    Mostafa Lashkarbolooki; Ali Zeinolabedini Hezave; Mahdi Bayat

    2017-01-01

    Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in dif-ferent industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network (ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal con-ductivities for normal and aromatic hydrocarbons are collected from different previously published literature. During the modeling stage, to discriminate different substances, critical temperature (Tc), critical pressure (Pc) and acentric factor (ω) are utilized as the network inputs besides the temperature. During the examination, ef-fects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correla-tions and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neu-ral network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent (AARD), mean square error (MSE), and correlation coefficient (R2) of lower than 0.2%, 1.05 × 10?7 and 0.9994, respectively.

  7. Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

    Science.gov (United States)

    Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

    2016-01-01

    According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

  8. 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

    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...... 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...

  9. 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...

  10. A POSTERIORI ERROR ESTIMATES OF A NON-CONFORMING FINITE ELEMENT METHOD FOR PROBLEMS WITH ARTIFICIAL BOUNDARY CONDITIONS

    Institute of Scientific and Technical Information of China (English)

    Xianmin Xu; Zhiping Li

    2009-01-01

    An a posteriori error estimator is obtained for a nonconforming finite element approx-imation of a linear elliptic problem, which is derived from a corresponding unbounded domain problem by applying a nonlocal approximate artificial boundary condition. Our method can be easily extended to obtain a class of a posteriori error estimators for various conforming and nonconforming finite element approximations of problems with different artificial boundary conditions. The reliability and efficiency of our a posteriori error esti-mator are rigorously proved and axe verified by numerical examples.

  11. 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 pan...

  12. Artificial Neural Networks Based War Scene Classification using Invariant Moments and GLCM Features: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Daniel Madan Raja S,

    2011-02-01

    Full Text Available In this paper we are trying to classify a war scene from the natural scene. For this purpose two set of image categories are taken viz., opencountry & war tank. By using Invariant Moments and Gray LevelCo-occurrence Matrix (GLCM, features are extracted from the images. The extracted features are trained and tested with Artificial Neural Networks (ANN using feed forward back propagation algorithm. The comparative results are proving efficiency of Artificial Neural Networks towards war scene classification problems by using Gray Level Co-occurrence Matrix (GLCM feature extraction method. It can be concluded that the proposed work significantly and directly contributes to scene classification and its new applications. The complete work is experimented in Matlab 7.6.0 using real world dataset.

  13. 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.

  14. 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.

  15. Transformer Protection by Using FL Based Artificial Intelligent Buchholz Relay against Incipient Faults

    Directory of Open Access Journals (Sweden)

    SOUMYADEEP SAMONTO

    2016-03-01

    Full Text Available Switchgear and Protection are the two vital terminology of Electrical power system. Normally the components of any switchgear needs better protection schemes to be set for a composite power system. Many explorers worked on artificial intelligent breaker but an indulgence of fuzzy theory is nevertheless very absent in case of buchholz relay. Here in this paper discussion has been drawn in favor of the Artificial Intelligent Buchholz (AIB relay where inputs are level of transformer oil and rate of oil rising due to over current. To fit with the transformer tank it is needed to measure level of transformer oil and the rate at which volume increasing. The constructional feature of a rate of rise pressure relay is taken into account in this work along with the working principle of a buchholz relay. The change in the inputs will give a crisp output to change the contacts state from normally closed to normally open by tripping via alarm circuit indeed like the basic buchholz relay does. The entire concept has been developed under MATLAB environment using Mamdani based Fuzzy Inference System. Experimental output data validates the implementation of Transformer Protection by Using Fuzzy Logic Based Artificial Intelligent Buchholz Relay.

  16. 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.

  17. Artificial immune system based neural networks for solving multi-objective programming problems

    Directory of Open Access Journals (Sweden)

    Waiel F. Abd El-Wahed

    2010-12-01

    Full Text Available In this paper, a hybrid artificial intelligent approach based on the clonal selection principle of artificial immune system (AIS and neural networks is proposed to solve multi-objective programming problems. Due to the sensitivity to the initial values of initial population of antibodies (Ab’s, neural networks is used to initialize the boundary of the antibodies for AIS to guarantee that all the initial population of Ab’s is feasible. The proposed approach uses dominance principle and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the “not so good” antibodies. A secondary (or external population that stores the nondominated solutions found along the search process is used. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the Pareto front.

  18. Confined State and Electronic Transport in an Artificial Graphene-Based Tunnel Junction

    Institute of Scientific and Technical Information of China (English)

    袁建辉; 张建军; 曾奇军; 张俊佩; 成泽

    2011-01-01

    Artificial graphene structures embedded in semiconductors could open novel routes for studies of electron interactions in 1ow-dimensional systems. We propose a way to manipulate the transport properties of massless Dirac fermions in an artificial graphene-based tunnel junction. Velocity-modulation control of electron wave propagation in the different regions can be regarded as velocity barriers. Transmission probability of electron is affected profoundly by this velocity barrier. We find that there is no confinement for Dirac electron as the velocity ratio ζ is less than 1, but when the velocity ratio is larger than 1 the confined state appears in the continuum band. These localized Dirac electrons may lead to the decreasing of transmission probability.

  19. 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.

  20. Design and simulation of a cable-pulley-based transmission for artificial ankle joints

    Science.gov (United States)

    Liu, Huaxin; Ceccarelli, Marco; Huang, Qiang

    2016-06-01

    In this paper, a mechanical transmission based on cable pulley is proposed for human-like actuation in the artificial ankle joints of human-scale. The anatomy articular characteristics of the human ankle is discussed for proper biomimetic inspiration in designing an accurate, efficient, and robust motion control of artificial ankle joint devices. The design procedure is presented through the inclusion of conceptual considerations and design details for an interactive solution of the transmission system. A mechanical design is elaborated for the ankle joint angular with pitch motion. A multi-body dynamic simulation model is elaborated accordingly and evaluated numerically in the ADAMS environment. Results of the numerical simulations are discussed to evaluate the dynamic performance of the proposed design solution and to investigate the feasibility of the proposed design in future applications for humanoid robots.

  1. Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    R. S.M.N. Malar

    2009-01-01

    Full Text Available Continuous Stirred Tank Reactor (CSTR is one of the common reactors in chemical plant. Problem statement: Developing a model incorporating the nonlinear dynamics of the system warrants lot of computation. An efficient control of the product concentration can be achieved only through accurate model. Approach: In this study, attempts were made to alleviate the above mentioned problem using “Artificial Intelligence” (AI techniques. One of the AI techniques namely Artificial Neural Networks (ANN was used to model the CSTR incorporating its non-linear characteristics. Two nonlinear models based control strategies namely internal model control and direct inverse control were designed using the neural networks and applied to the control of isothermal CSTR. Results: The simulation results for the above control schemes with set point tracking were presented. Conclusion: Results indicated that neural networks can learn accurate models and give good non-linear control when model equations are not known.

  2. 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.

  3. 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...

  4. 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...

  5. 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.

  6. Novel blood sampling method of an artificial endocrine pancreas via the cardiopulmonary bypass circuit.

    Science.gov (United States)

    Kawahito, Shinji; Higuchi, Seiichi; Mita, Naoji; Kitagawa, Tetsuya; Kitahata, Hiroshi

    2013-12-01

    We tried to perform continuous blood glucose monitoring during cardiovascular surgery involving cardiopulmonary bypass using an artificial endocrine pancreas (STG-22 or -55; Nikkiso, Tokyo, Japan); however, we often encountered problems during these procedures because insufficient blood was obtained for monitoring. Thus, we started performing the blood sampling via the venous side of the cardiopulmonary bypass circuit. As a result, continuous blood glucose monitoring using an artificial endocrine pancreas was proven to be stable and reliable during cardiovascular surgery involving cardiopulmonary bypass.

  7. Designing novel copolymers of donor-acceptor polymers using an artificial intelligence method

    Science.gov (United States)

    Kapoor, Vinita; Bakhshi, A. K.

    2013-08-01

    Using the ab initio Hartree-Fock crystal orbital results of three donor-acceptor polymers, PFUCO ([A]x), PSIFCO ([B]x) and PSIFCH ([C]x), the electronic properties of their novel quasi-one-dimensional copolymers (AmBn)x and (AmCn)x were investigated using an artificial intelligence technique, the genetic algorithm, in combination with negative factor counting and inverse iteration method. The repeat units in PFUCO consist of bifuran bridged by electron accepting groups Y (>Cdbnd O); while in PSIFCO and PSIFCH, the repeat units consist of bicyclopentadifluorosilole bridged by electron accepting groups Y (Y is >Cdbnd O in PSIFCO, and >Cdbnd CH2 in PSIFCH). The trends in the electronic properties of the copolymers (AmBn)x and (AmCn)x as a function of block sizes m and n, and arrangement of units (periodic and random) in the copolymer chain are also discussed. The results obtained are important guidelines for molecular designing of copolymers with tailor-made conduction properties.

  8. 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.

  9. Repassivation behavior of titanium in artificial saliva investigated with a photon rupture method

    Energy Technology Data Exchange (ETDEWEB)

    Sakairi, Masatoshi, E-mail: msakairi@eng.hokudai.ac.j [Faculty of Engineering, Hokkaido University, Kita-13, Nishi-8, Kita-ku, Sapporo, 080-8628 (Japan); Kinjyo, Misaki [Graduate School of Engineering, Hokkaido University, Kita-13, Nishi-8, Kita-ku, Sapporo, 080-8628 (Japan); Kikuchi, Tatsuya [Faculty of Engineering, Hokkaido University, Kita-13, Nishi-8, Kita-ku, Sapporo, 080-8628 (Japan)

    2011-01-15

    Repassivation kinetics of titanium were investigated in artificial saliva by the photon rupture method, PRM, combined with anodizing. PRM uses focused pulsed Nd-YAG laser beam irradiation to remove oxide film and metal. The effects of the applied potential and F{sup -} ions on the repassivation kinetics of titanium were examined electrochemically. All conditions in this study use specimens that were repassivated within about 100 ms after the laser beam irradiation. The current transients after the laser beam irradiation showed a rapid increase, followed by a decrease with a slope of the log i vs. log t plots of about -1.5. The measured slope is steeper than what would be expected with the high field oxide film formation theory. This result suggests that the oxidation of titanium takes place through a combination of electrochemical and chemical reactions. More NaF results in a higher peak current density and amount of charge after laser beam irradiation. The influence of F{sup -} ions on the repassivation kinetics may be explained by localized pH changes caused by dissolution of titanium immediately after the laser beam irradiation.

  10. 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...

  11. Calculation method of ship collision force on bridge using artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Wei FAN; Wan-cheng YUAN; Qi-wu FAN

    2008-01-01

    Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software.

  12. Dynamic rupture and crushing of an extruded tube using artificial neural network (ANN) approximation method

    Institute of Scientific and Technical Information of China (English)

    Javad Marzbanrad; Behrooz Mashadi; Amir Afkar; Mostafa Pahlavani

    2016-01-01

    A numerical study of the crushing of thin-walled circular aluminum tubes has been carried out to investigate the crashworthiness behaviors under axial impact loading. These kinds of tubes are usually used in automobile and train structures to absorb the impact energy. Previous researches show that thin-walled circular tube has the highest energy absorption under axial impact amongst different structures. In this work, the crushing between two rigid flat plates and the tube rupture by 4 and 6 blades cutting tools is modeled with the help of ductile failure criterion using the numerical method. The tube material is aluminum EN AW-7108 T6 and its length and diameter are 300 mm and 50 mm, respectively. Using the artificial neural network (ANN), the most important surfaces of energy absorption parameters, including the maximum displacement of the striker, the maximum axial force, the specific energy absorption and the crushing force efficiency in terms of impact velocity and tube thickness are obtained and compared to each other. The analyses show that the tube rupture by the 6 blades cutting tool has more energy absorption in comparison with others. Furthermore, the results demonstrate that tube cutting with the help of multi-blades cutting tools is more stable, controllable and predictable than tube folding.

  13. Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines.

    Science.gov (United States)

    Pérez-Magariño, S; Ortega-Heras, M; González-San José, M L; Boger, Z

    2004-04-19

    Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.

  14. 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.

  15. 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.

  16. Statistic Approach versus Artificial Intelligence for Rainfall Prediction Based on Data Series

    Directory of Open Access Journals (Sweden)

    Indrabayu

    2013-04-01

    Full Text Available This paper proposed a new idea in comparing two common predictors i.e. the statistic method and artificial intelligence (AI for rainfall prediction using empirical data series. The statistic method uses Auto- Regressive Integrated Moving (ARIMA and Adaptive Splines Threshold Autoregressive (ASTAR, most favorable statistic tools, while in the AI, combination of Genetic Algorithm-Neural Network (GA-NN is chosen. The results show that ASTAR gives best prediction compare to others, in term of root mean square (RMSE and following trend between prediction and actual.

  17. Artificial Blood

    Directory of Open Access Journals (Sweden)

    Umit Yasar

    2012-04-01

    Full Text Available The problems and additional cost factor involved in collecting and storing human blood, as well as the pending worldwide shortages are the main driving forces in the development of blood substitutes. Studies on artificial blood basically aim to develop oxygen carrying compounds, produce stem cell-based erythrocyte cells in vitro and, implement the functions and movements of natural hemoglobin molecules found in erythrocyte cells through artificial erythrocyte cells. Consequently, major areas of research in artificial blood studies are haemoglobin-based oxygen carriers, perfluorochemicals, respirocytes and stem cells. Even though these artificial erythrocyte cells do not qualify as perfect red blood cell substitutes yet, they have many potential clinical and non clinical uses. Studies are being carried out on the elimination of side effects of blood substitutes and extensive clinical trials are being conducted to test their safety and efficacy. Artificial blood substitutes could only be used clinically for patient management following clinical trials and approvals, and will be the ultimate global solution to the problems associated with donor scarcity, blood collection and transfusion-mediated diseases. [Archives Medical Review Journal 2012; 21(2.000: 95-108

  18. Proton exchange membrane fuel cells modeling based on artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Yudong Tian; Xinjian Zhu; Guangyi Cao

    2005-01-01

    To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

  19. Targeting DNA base pair mismatch with artificial nucleobases. Advances and perspectives in triple helix strategy.

    Science.gov (United States)

    Malnuit, Vincent; Duca, Maria; Benhida, Rachid

    2011-01-21

    This review, divided into three sections, describes the contribution of the chemists' community to the development and application of triple helix strategy by using artificial nucleic acids, particularly for the recognition of DNA sequences incorporating base pair inversions. Firstly, the development of nucleobases that recognise CG inversion is surveyed followed secondly by specific recognition of TA inverted base pair. Finally, we point out in the last section recent perspectives and applications, driven from knowledge in nucleic acids interactions, in the growing field of nanotechnology and supramolecular chemistry at the border area of physics, chemistry and molecular biology.

  20. Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

    CERN Document Server

    Khalil, Khaled M; Nazmy, Taymour T; Salem, Abdel-Badeeh M

    2010-01-01

    Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, and intensity-based learning) for crisis response operations. In addition, the proposed response model is applied on the spread of pandemic influenza in Egypt as a case study.

  1. Microstructural Analysis of Ti-Based Shape Memory Alloys Following the Electrochemical Corrosion in Artificial Saliva

    Science.gov (United States)

    Baciu, C.; Baciu, E. R.; Cimpoeșu, R.; Levente, C. G.; Bosinceanu, D. G.; Baciu, M.; Bejinariu, C.

    2017-06-01

    The investigations carried out aimed to highlight the structural modifications occurred in the Ti-based shape memory alloys subject to electrocorrosion in Afnor artificial saliva. The behavior to corrosion was highlighted by fast electrochemical tests, mainly by dynamic potentiometry. From the microstructural analysis we noticed that the specimens of the two Ti-based shape memory alloys show traces of “pitting” corrosion on their surface of diverse sizes, a fact that will raise issues in terms of cytotoxicity due to the corrosion products released.

  2. 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

  3. Predicting the Grouting Ability of Sandy Soils by Artificial Neural Networks Based On Experimental Tests

    Directory of Open Access Journals (Sweden)

    Mahmoud Hassanlourad

    2014-12-01

    Full Text Available In this paper, the grouting ability of sandy soils is investigated by artificial neural networks based on the results of chemical grout injection tests. In order to evaluate the soil grouting potential, experimental samples were prepared and then injected. The sand samples with three different particle sizes (medium, fine, and silty and three relative densities (%30, %50, and %90 were injected with the sodium silicate grout with three different concentrations (water to sodium silicate ratio of 0.33, 1, and 2. A multi-layer Perceptron type of the artificial neural network was trained and tested using the results of 138 experimental tests. The multi-layer Perceptron included one input layer, two hidden layers and one output layer. The input parameters consisted of initial relative densities of grouted samples, the average size of particles (D50, the ratio of the grout water to sodium silicate and the grout pressure. The output parameter was the grout injection radius. The results of the experimental tests showed that the radius of grout injection is a complicated function of the mentioned parameters. In addition, the results of the trained artificial neural network showed to be reasonably consistent with the experimental results.

  4. 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.

  5. Research of Obstacle Avoidance Algorithm for Robot Based on Artificial Potential Field%基于人工势场法的机器人避障算法

    Institute of Scientific and Technical Information of China (English)

    叶彬强; 王一

    2012-01-01

    提出了基于人工势场的机器人轨迹规划算法,根据人工势场法原理,将机器人轨迹规划的人工势场算法分为3层进行设计。为了检测避障程序能否正常运行,对其进行了仿真分析。结果证实,基于人工势场法的机器人轨迹规划算法具备较精确的移动轨迹。%The robot intelligent obstacle avoidance emerges and develops because of the rapid development of artificial intelligence and high-performance requirements of high-technology. This paper has put forward and researched the path planning method for mobile robot based on artificial potential field according to the elements of artificial potential field. The path planning method for mobile robot would be divided into three parts to design. At the same time, in order to detect whether the path planning program runs correctly, some emulators are carried. As a result, the path planning method mobile robot based on artificial potential field had a better exact locomotive

  6. Island rescue PCR: a rapid and efficient method for isolating transcribed sequences from yeast artificial chromosomes and cosmids.

    Science.gov (United States)

    Valdes, J M; Tagle, D A; Collins, F S

    1994-06-07

    The identification of transcripts from large genomic regions cloned in yeast artificial chromosomes (YACs) or cosmids continues to be a critical and often rate-limiting step in positional cloning of human disease genes. We have developed a PCR-based method for rapid and efficient generation of probes from YACs or cosmids that can be used for cDNA library screening. The method, which we call island rescue PCR (IRP), is based upon the observation that the 5' ends of many genes are associated with (G+C)-rich regions called CpG islands. In IRP, the YAC of interest is digested with a restriction enzyme that recognizes sequences of high CpG content, and vectorette linkers are ligated to the cleaved ends. The PCR is used to amplify the region extending from the cleaved restriction enzyme site to the nearest SINE (Alu) repeat. In many cases this product contains sequences from the 5' end of the associated gene. cDNA clones isolated with these products are then verified by mapping them back to the original YAC. The method allows rapid screening of > 500 kb of human genomic insert in one experiment, is tolerant of contaminating yeast sequences, and can also be applied to cosmid pools. In a control experiment, the method was able to identify cDNA clones for the neurofibromatosis type 1 (NF1) gene using a probe generated from a YAC in the region. Application of IRP has yielded nine other genes from YACs isolated from chromosome locations 4p16.3 and 17q21.

  7. 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.

  8. The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange

    CERN Document Server

    Ahangar, Reza Gharoie; Pournaghshband, Hassan

    2010-01-01

    In this paper, researchers estimated the stock price of activated companies in Tehran (Iran) stock exchange. It is used Linear Regression and Artificial Neural Network methods and compared these two methods. In Artificial Neural Network, of General Regression Neural Network method (GRNN) for architecture is used. In this paper, first, researchers considered 10 macro economic variables and 30 financial variables and then they obtained seven final variables including 3 macro economic variables and 4 financial variables to estimate the stock price using Independent components Analysis (ICA). So, we presented an equation for two methods and compared their results which shown that artificial neural network method is more efficient than linear regression method.

  9. 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.

  10. 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

  11. Implementing an Agent Based Artificial Stock Market Model in JADE – An Illustration

    Directory of Open Access Journals (Sweden)

    PN Kumar

    2013-06-01

    Full Text Available Agent-based approach to economic and financial analysis is a suitable research methodolgy for developing and understanding the complex patterns and phenomena that are observed in economic systems. In agent-based financial market models, prices can be endogenously formed by the system itself as the result of interaction of market participants. By using agents for the study, heterogeneous, boundedly rational, and adaptive behaviour of market participants can be analysed and its impact assessed. The collective behaviour of such groups is determined by the interaction of individual behaviours distributed across the group. This being the scenario prevailing in stock markets, agent based models are suitable for the study. Through this paper, we have attempted to illustrate a detailed implementation of multi agents in an artificial stock market invoking the agent-based methodology on Java Agent Development (JADE environment, a platform to develop multi-agent systems. The Extended Glosten and Milgrom Model, an agent based artificial stock market model, has been chosen to depict the multi-agent environment model in JADE.

  12. Transplantation of three-dimensional artificial human vascular tissues fabricated using an extracellular matrix nanofilm-based cell-accumulation technique.

    Science.gov (United States)

    Asano, Yoshiya; Shimoda, Hiroshi; Okano, Daisuke; Matsusaki, Michiya; Akashi, Mitsuru

    2017-04-01

    We have established a novel three-dimensional (3D) tissue-constructing technique, referred to as the 'cell-accumulation method', which is based on the self-assembly of cultured human cells. In this technique, cells are coated with fibronectin and gelatin to construct extracellular matrix (ECM) nanofilms and cultured to form multi-layers in vitro. By using this method, we have successfully fabricated artificial tissues with vascular networks constructed by co-cultivation of human umbilical vein-derived vascular endothelial cells between multi-layers of normal human dermal fibroblasts. In this study, to assess these engineered vascular tissues as therapeutic implants, we transplanted the 3D human tissues with microvascular networks, fabricated based on the cell-accumulation method, onto the back skin of nude mice. After the transplantation, we found vascular networks with perfusion of blood in the transplanted graft. At the boundary between host and implanted tissue, connectivity between murine and human vessels was found. Transmission electron microscopy of the implanted artificial vascular tubules demonstrated the ultrastructural features of blood capillaries. Moreover, maturation of the vascular tissues after transplantation was shown by the presence of pericyte-like cells and abundant collagen fibrils in the ECM surrounding the vasculature. These results demonstrated that artificial human vascular tissues constructed by our method were engrafted and matured in animal skin. In addition, the implanted artificial human vascular networks were connected with the host circulatory system by anastomosis. This method is an attractive technique for engineering prevascularized artificial tissues for transplantation. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  13. An independent evaluation of a new method for automated interpretation of lung scintigrams using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Holst, H.; Jaerund, A.; Evander, E.; Taegil, K.; Edenbrandt, L. [Dept. of Clinical Physiology, Lund Univ. (Sweden); Maare, K. [Dept. of Radiology, Linkoeping Univ. (Sweden); Aastroem, K. [Dept. of Mathematics, Lund Inst. of Technology (Sweden); Ohlsson, M. [Dept. of Theoretical Physics, Lund Univ. (Sweden)

    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

  14. An independent evaluation of a new method for automated interpretation of lung scintigrams using artificial neural networks.

    Science.gov (United States)

    Holst, H; Måre, K; Järund, A; Aström, K; Evander, E; Tägil, K; Ohlsson, M; Edenbrandt, L

    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

  15. Using Custom Fiber Bragg Grating-Based Sensors to Monitor Artificial Landslides

    Science.gov (United States)

    Zhang, Qinghua; Wang, Yuan; Sun, Yangyang; Gao, Lei; Zhang, Zhenglin; Zhang, Wenyuan; Zhao, Pengchong; Yue, Yin

    2016-01-01

    Four custom fiber Bragg grating (FBG)-based sensors are developed to monitor an artificial landslide located in Nanjing, China. The sensors are composed of a rod and two FBGs. Based on the strength of the rods, two sensors are referred to as “hard sensors” (Sensor 1 and Sensor 2), the other two are referred to as “soft sensors” (Sensor 3 and Sensor 4). The two FBGs are fixed on each sensor rod at distances of 50 cm and 100 cm from the top of the rod (an upper FBG and a lower FBG). In the experiment presented in this paper, the sensors are installed on a slope on which an artificial landslide is generated through both machine-based and manual excavation. The fiber sensing system consists of the four custom FBG-based sensors, optical fiber, a static fiber grating demodulation instrument (SM125), and a PC with the necessary software. Experimental data was collected in the presence of an artificial landslide, and the results show that the lower FBGs are more sensitive than the upper FBGs for all four of the custom sensors. It was also found that Sensor 2 and Sensor 4 are more capable of monitoring small-scale landslides than Sensor 1 and Sensor 3, and this is mainly due to their placement location with respect to the landslide. The stronger rods used in the hard sensors make them more adaptable to the harsh environments of large landslides. Thus, hard sensors should be fixed near the landslide, while soft sensors should be placed farther away from the landslide. In addition, a clear tendency of strain variation can be detected by the soft sensors, which can be used to predict landslides and raise a hazard alarm. PMID:27598163

  16. Using Custom Fiber Bragg Grating-Based Sensors to Monitor Artificial Landslides.

    Science.gov (United States)

    Zhang, Qinghua; Wang, Yuan; Sun, Yangyang; Gao, Lei; Zhang, Zhenglin; Zhang, Wenyuan; Zhao, Pengchong; Yue, Yin

    2016-09-02

    Four custom fiber Bragg grating (FBG)-based sensors are developed to monitor an artificial landslide located in Nanjing, China. The sensors are composed of a rod and two FBGs. Based on the strength of the rods, two sensors are referred to as "hard sensors" (Sensor 1 and Sensor 2), the other two are referred to as "soft sensors" (Sensor 3 and Sensor 4). The two FBGs are fixed on each sensor rod at distances of 50 cm and 100 cm from the top of the rod (an upper FBG and a lower FBG). In the experiment presented in this paper, the sensors are installed on a slope on which an artificial landslide is generated through both machine-based and manual excavation. The fiber sensing system consists of the four custom FBG-based sensors, optical fiber, a static fiber grating demodulation instrument (SM125), and a PC with the necessary software. Experimental data was collected in the presence of an artificial landslide, and the results show that the lower FBGs are more sensitive than the upper FBGs for all four of the custom sensors. It was also found that Sensor 2 and Sensor 4 are more capable of monitoring small-scale landslides than Sensor 1 and Sensor 3, and this is mainly due to their placement location with respect to the landslide. The stronger rods used in the hard sensors make them more adaptable to the harsh environments of large landslides. Thus, hard sensors should be fixed near the landslide, while soft sensors should be placed farther away from the landslide. In addition, a clear tendency of strain variation can be detected by the soft sensors, which can be used to predict landslides and raise a hazard alarm.

  17. 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.

  18. The Application of the Acoustic Method to the Exploration of Urban Active Faults and a Comparison with the Artificial Earthquake, and Radar Methods

    Institute of Scientific and Technical Information of China (English)

    Zhao Genmo; Wang Dahong; Ren Feng; Chen Yukun

    2004-01-01

    The depth of upper fault point is the key data for ascertaining the active age of a buried fault on a plain. The difference of depth obtained from same fault may be dozens to several hundred meters when using different geophysical methods. It can result in the absolutely opposite conclusions when judging fault activity. Because of a lack of an artificial earthquake source with wide band and high central-frequency, many kinds of methods have to be used together.The higher the frequency of the artificial earthquake wave, electromagnetic wave and sonic wave, the higher the resolution. However the attenuation is also very fast and the exploration depth is very shallow. The reverse is also true. The frequency of artificial seismic waves is in the tens of Hz. Its exploration depth is big and the resolution is poor. The frequency of radar electromagnetic waves is about a million Hz, indicating that the resolving power is better, but the exploration depth is very shallow.However, the acoustic frequency is thousands of Hz, its resolving power is better than that of the artificial earthquake method and the exploration depth is larger than that of the radar method. So it is suitable for extra-shallow exploration in the thick deposit strata of the Quaternary. The preliminary results detected using the high frequency acoustic method in extra-shallow layers indicates that previous inferences about some fault activity in the easternpart of the North China plain may need to be greatly corrected.

  19. Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

    Directory of Open Access Journals (Sweden)

    Topál Dániel

    2016-02-01

    Full Text Available Time series often contain breakpoints of different origin, i.e. breakpoints, caused by (i shifts in trend, (ii other changes in trend and/or, (iii changes in variance. In the present study, artificially generated time series with white and red noise structures are analyzed using three recently developed breakpoint detection methods. The time series are modified so that the exact “locations” of the artificial breakpoints are prescribed, making it possible to evaluate the methods exactly. Hence, the study provides a deeper insight into the behaviour of the three different breakpoint detection methods. Utilizing this experience can help solving breakpoint detection problems in real-life data sets, as is demonstrated with two examples taken from the fields of paleoclimate research and petrology.

  20. Simultaneous Determination of Iron and Manganese in Water Using Artificial Neural Network Catalytic Spectrophotometric Method

    Institute of Scientific and Technical Information of China (English)

    JI Hongwei; XU Yan; LI Shuang; XIN Huizhen; CAO Hengxia

    2012-01-01

    A new analytical method using Back-Propagation(BP)artificial neural network and kinetic spectrophotometry for simultaneous determination of iron and magnesium in tap water,the Yellow River water and seawater is established.By conditional experiments,the optimum analytical conditions and parameters are obtained.Levenberg-Marquart(L-M)algorithm is used for calculation in BP neural network.The topological structure of three-layer BP ANN network architecture is chosen as 15-16-2(nodes).The initial value of gradient coefficientμ is fixed at 0.001 and the increase factor and reduction factor ofμ take the default values of the system.The data are processed by computers with our own programs written in MATLAB 7.0.The relative standard deviation of the calculated results for iron and manganese is 2.30% and 2.67% respectively.The results of standard addition method show that for the tap water,the recoveries of iron and manganese are in the ranges of 98.0%-104.3% and 96.5%-104.5%,and the RSD is in the range of 0.23%-0.98%;for the Yellow River water(Lijin district of Shandong Province),the recoveries of iron and manganese are in the ranges of 96.0%-101.0% and 98.7%-104.2%,and the RSD is in the range of 0.13%-2.52%;for the seawater in Qingdao offshore,the recoveries of iron and manganese are in the ranges of 95.3%-104.8% and 95.3%-104.7%,and the RSD is in the range of 0.14%-2.66%.It is found that 21 common cations and anions do not interfere with the determination of iron and manganese under the optimum experimental conditions.This method exhibits good reproducibility and high accuracy in the determination of iron and manganese and can be used for the simultaneous determination of iron and manganese in tap water and natural water.By using the established ANN-catalytic spectrophotometric method,the iron and manganese concentrations of the surface seawater at 11 sites in Qingdao offshore are determined and the level distribution maps of

  1. Comparison of Tensile Strength of Four Kind of Acrylic Artificial Teeth to Acrylic Denture Base In Vitro

    Directory of Open Access Journals (Sweden)

    Rosthamkhani F

    2012-01-01

    Full Text Available Introduction: Acrylic teeth properties in complete denture prostheses are important in prosthesis survival. In this regard, tooth tensile bond strength with denture base is very important. The purpose of this study was to compare tensile bond strength of three kind of artificial teeth to denture base manufactured in Iran with a kind manufactured in Italy. Materials & Methods: In this experimental in vitro study, four kind of artificial teeth (Italian Ivoclar, Yaghoot, Herasit plus and Acradent were used. Nine anterior and premolar teeth were selected and were prepared as Cylinders with 4mm diameter. Through flasking, toothbase was bonded to heat-cured acryl. Samples were thermocycled for 2500times (5˚C-55˚C and then were put in plastic pipes and under stretch to fracture by Zwick Z250 instrument. The data were analysed by One-Way ANOVA and Tukey test.Results: Mean tensile bond strength was 260N for Ivoclar, 195N for Herasit, 124N for yaghoot and 54N for Acradent. Adhesive fracture percentage was 100% for Acradent, 60% for Ivoclar, 50% for yaghoot and 35.7% for Herasit. The rest was for cohesive fracture.Conclusion: The highest tensile bond strength was seen with Ivoclar followed by Herasit, Yaghoot and Acradent respectively.

  2. Comparison of efficiency between two artificial insemination methods using frozen-thawed semen in domestic cat (Felis catus): artificial insemination in domestic cats.

    Science.gov (United States)

    Villaverde, Ana Izabel Silva Balbin; Melo, Cely Marini; Martin, Ian; Ferreira, Tatiana Henriques; Papa, Frederico Ozanam; Taconeli, Cesar Augusto; Lopes, Maria Denise

    2009-09-01

    The aim of this study was to compare the efficiency of the intravaginal (IVAI) vs. intrauterine artificial insemination (IUAI) using frozen-thawed sperm in the domestic cat. Semen was collected from two tom cats using an artificial vagina and samples were assessed for motility (computer-assisted sperm analysis (CASA)), sperm morphology and plasma membrane integrity. After dilution with TRIS/OEP/YOLK (4% of glycerol), sperm samples were loaded into 0.25 mL straws (25 x 10(6)motile sperm/straw), incubated at 5 degrees C for 20 min and cryopreserved over liquid nitrogen (LN(2)) vapor for 15 min and then immersed in LN(2). For each AI, four straws from the same male were thawed (12s at 46 degrees C) and centrifuged at 250 x g for 8 min to pellet the sperm. The supernatant was discarded and sperm pellet resuspended with the remaining liquid, approximately 100 microL, and analyzed as described above. Queens were treated with a single im injection of 100 IU eCG to induce ovarian follicular development. Final oocyte maturation and ovulation was induced with 100 IU hCG given im at 82-84 h after eCG administration. Thirty hours after hCG administration, females were inseminated either intrauterine (n=8 queens) or intravaginally (n=8 queens), using thawed sperm from a single male. Although a pronounced decrease in sperm motility, acrosome and plasma membrane integrity was observed in sperm samples from both cats, a pregnancy rate of 75% was achieved when using the intrauterine AI method compared with 0% pregnancy when inseminated intravaginally.

  3. 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)

  4. 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.

  5. Method of Measurement of Capacitance and Dielectric Loss Factor Using Artificial Neural Networks

    OpenAIRE

    Roj Jerzy; Cichy Adam

    2015-01-01

    A novel method of dielectric loss factor measuring has been described. It is based on a quasi-balanced method for the capacitance measurement. These AC circuits allow to measure only one component of the impedance. However, after analyzing a quasi-balanced circuit's processing equation, it is possible to derive a novel method of dielectric loss factor measuring. Dielectric loss factor can be calculated after detuning the circuit from its quasi-equilibrium state. There are two possible ways of...

  6. Optimization of Spherical Roller Bearing Design Using Artificial Bee Colony Algorithm and Grid Search Method

    Science.gov (United States)

    Tiwari, Rajiv; Waghole, Vikas

    2015-07-01

    Bearing standards impose restrictions on the internal geometry of spherical roller bearings. Geometrical and strength constraints conditions have been formulated for the optimization of bearing design. The long fatigue life is one of the most important criteria in the optimum design of bearing. The life is directly proportional to the dynamic capacity; hence, the objective function has been chosen as the maximization of dynamic capacity. The effect of speed and static loads acting on the bearing are also taken into account. Design variables for the bearing include five geometrical parameters: the roller diameter, the roller length, the bearing pitch diameter, the number of rollers, and the contact angle. There are a few design constraint parameters which are also included in the optimization, the bounds of which are obtained by initial runs of the optimization. The optimization program is made to run for different values of these design constraint parameters and a range of the parameters is obtained for which the objective function has a higher value. The artificial bee colony algorithm (ABCA) has been used to solve the constrained optimized problem and the optimum design is compared with the one obtained from the grid search method (GSM), both operating independently. Both the ABCA and the GSM have been finally combined together to reach the global optimum point. A constraint violation study has also been carried out to give priority to the constraint having greater possibility of violations. Optimized bearing designs show a better performance parameter with those specified in bearing catalogs. The sensitivity analysis of bearing parameters has also been carried out to see the effect of manufacturing tolerance on the objective function.

  7. Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults

    Energy Technology Data Exchange (ETDEWEB)

    Reifman, J.; Wei, T.Y.C.; Vitela, J.E. [Argonne National Lab., IL (United States); Applequist, C. A. [Commonwealth Research Corp., Chicago, IL (United States); Chasensky, T.M. [Commonwealth Edison Co., Chicago, IL (United States)

    1996-03-01

    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.

  8. Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

    Science.gov (United States)

    Li, Hong; Ding, Xue

    2017-03-01

    This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

  9. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    Science.gov (United States)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  10. 基于人工蜂群算法的电网故障诊断%Fault Diagnosis of Power Network Based on Artificial Bee Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    韦晓广; 陈奎

    2012-01-01

    In order to solve 0-1 programming problem in fault diagnosis of power network, the paper proposed optimization methods of artificial bee colony algorithm from aspects of algebra and geometry. The simulation results show that the artificial bee colony algorithm is feasible and reasonable, and the overall performance is significantly superior to traditional genetic algorithms; artificial bee colony algorithm based on geometric has better stability and search capabilities than the algorithm based on algebraic, and is more suitable for occasions with high stability and accuracy requirements. Fault diagnosis of power network, artificial bee colony algorithm, algebra method,%针对电网故障诊断中的0-1规划问题,从代数和几何角度优化了人工蜂群算法.仿真结果表明,人工蜂群算法具有可行性和合理性,并且综合性能显著优于传统的遗传算法 ;在两种人工蜂群算法中,基于几何思想的人工蜂群算法具有更好的稳定性和搜索能力,更加适用于对稳定性和精准度要求很高的场合.

  11. Entropy-based artificial viscosity stabilization for non-equilibrium Grey Radiation-Hydrodynamics

    Energy Technology Data Exchange (ETDEWEB)

    Delchini, Marc O., E-mail: delchinm@email.tamu.edu; Ragusa, Jean C., E-mail: jean.ragusa@tamu.edu; Morel, Jim, E-mail: jim.morel@tamu.edu

    2015-09-01

    The entropy viscosity method is extended to the non-equilibrium Grey Radiation-Hydrodynamic equations. The method employs a viscous regularization to stabilize the numerical solution. The artificial viscosity coefficient is modulated by the entropy production and peaks at shock locations. The added dissipative terms are consistent with the entropy minimum principle. A new functional form of the entropy residual, suitable for the Radiation-Hydrodynamic equations, is derived. We demonstrate that the viscous regularization preserves the equilibrium diffusion limit. The equations are discretized with a standard Continuous Galerkin Finite Element Method and a fully implicit temporal integrator within the MOOSE multiphysics framework. The method of manufactured solutions is employed to demonstrate second-order accuracy in both the equilibrium diffusion and streaming limits. Several typical 1-D radiation-hydrodynamic test cases with shocks (from Mach 1.05 to Mach 50) are presented to establish the ability of the technique to capture and resolve shocks.

  12. Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Han

    2014-01-01

    Full Text Available A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA, originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision.

  13. Parameters identification for photovoltaic module based on an improved artificial fish swarm algorithm.

    Science.gov (United States)

    Han, Wei; Wang, Hong-Hua; Chen, Ling

    2014-01-01

    A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision.

  14. Artificial Intelligence based Solver for Governing Model of Radioactivity Cooling, Self-gravitating Clouds and Clusters of Galaxies

    Directory of Open Access Journals (Sweden)

    Junaid Ali Khan

    2013-06-01

    Full Text Available In this study, a reliable alternate platform is developed based on artificial neural network optimized with soft computing technique for a non-linear singular system that can model complex physical phenomenas of the nature like radioactivity cooling, self-gravitating clouds and clusters of galaxies. The trial solution is mathematically represented by feed-forward neural network. A cost function is defined in an unsupervised manner that is optimized by a probabilistic meta-heuristic global search technique based on annealing in metallurgy. The results of the designed scheme are evaluated by comparing with the desired response of the system. The applicability, stability and reliability of the proposed method is validated by Monte Carlo simulations.

  15. The operative diagnostics to adaptation heart to physical load on the base of artificial neuron networks

    Directory of Open Access Journals (Sweden)

    Timoshchenko E.V.

    2010-03-01

    Full Text Available In article are considered questions of the revealing the breach of the warmhearted rhythm beside athlete and athlete during physical load. The estimation of the condition heart was defined as of electrocardiographically of the examination. Mathematical model is designed for interpreting electrocardiogram data on base artificial neuron networks. It is created software, which allows to conduct the diagnostics of the heart diseases in the field of discovery different arrhythmias. Introduction result called on work can render the practical help at determination of the breaches of the warmhearted rhythm.

  16. Enhancing Wireless Sensor Network Security using Artificial Neural Network based Trust Model

    Directory of Open Access Journals (Sweden)

    Adwan Yasin

    2016-09-01

    Full Text Available Wireless sensor network (WSN is widely used in environmental conditions where the systems depend on sensing and monitoring approach. Water pollution monitoring system depends on a network of wireless sensing nodes which communicate together depending on a specific topological order. The nodes distributed in a harsh environment to detect the polluted zones within the WSN range based on the sensed data. WSN exposes several malicious attacks as a consequence of its presence in such open environment, so additional techniques are needed alongside with the existing cryptography approach. In this paper an enhanced trust model based on the use of radial base artificial neural network (RBANN is presented to predict the future behavior of each node based on its weighted direct and indirect behaviors, in order to provide a comprehensive trust model that helps to detect and eliminate malicious nodes within the WSN. The proposed model considered the limited power, storage and processing capabilities of the system.

  17. 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.

  18. Task Scheduling Based On Load Balancing Using Artificial Bee Colony In Cloud Computing Environment

    Directory of Open Access Journals (Sweden)

    Fatemeh Rastkhadiv

    2016-12-01

    Full Text Available Cloud computing is a development of distributed computing, parallel computing and grid computing. The aim of cloud computing is providing dynamic leasing of server capabilities as scalable, virtualized services to end users. Resource management as an important issue of cloud computing.Load balancing is a problem of resource management. Recently, Cloud schedulers based on bio-inspired and metaheuristic techniques have been proposed. A good task scheduler should adapt its scheduling strategy to the dynamic environment. Load balancing of nonpreemptive independent tasks on virtual machines is an important aspect of task scheduling in clouds.In this paper, wehave proposeda new cloud scheduler based on load balancing using by Artificial Bee Colonyalgorithm, one of the most popular bio-inspired technique. Artificial bee colony algorithm is an optimization metaheuristic algorithm based on a particular intelligent behavior of honey bee swarms. Our scheduler is designed to achieve well balanced load across virtual machines for maximizing the throughput and deliver to theminimum makespan. The experimental results simulatedusing Cloudsim shows its effectiveness to optimize load balancing and task scheduling compared with both of FCFS and ACO. Our proposed approach allows for more agile task handling while reducing task completion time. Our proposed approach reduces makespan and degree of imbalance.It distributes tasks and makes load balancing between virtual machines. Consequently the proposed algorithm increases performance and resource efficiency.

  19. A New Nonlinear Compound Forecasting Method Based on ANN

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper the compound-forecasting method is discussed. The compound-forecasting method is one of the hotspots in the current predication. Firstly, the compound-forecasting method is introduced and various existing compound-forecasting methods arediscussed. Secondly, the Artificial Neural Network (ANN) is brought in compound-prediction research and a nonlinear compound-prediction model based on ANN is presented. Finally, inorder to avoid irregular weight, a new method is presented which uses principal component analyses to increase the availability of compound-forecasting information. Higherforecasting precision is achieved in practice.

  20. Assessment of acid neutralizing capacity in cola-based drinks and energy beverages by artificial saliva.

    Science.gov (United States)

    Felix, Kariny Ramos; Bressan, Maria Claudia; Kanis, Luiz Alberto; de Oliveira, Marcelo Tomas

    2013-07-01

    To assess the pH of regular and light cola-based drinks and energy drinks, and examine the acid neutralizing capacity by the addition of artificial saliva. Ten packages of each product purchased locally at different stores were evaluated. The pH was measured by a pH-electrode calibrated in standard buffer solutions. To assess the pH neutralizing capacity, 1 ml/min of artificial saliva was added until a pH of 5.5 set as a cutoff point was reached in the tested solution. The data were subjected to analysis of variance (ANOVA), at the 0.05 significance level. The surveyed beverages had an initial acidic pH, ranging between 2.3 and 3.4. The average amount of saliva required to raise the pH of the cola-based drinks above 5.5 ranged between 6.0 and 6.8 ml. Energy drinks used volumes between 11.3 and 12.5 ml; however, it was not possible to achieve a pH of 5.5. According to the methodology used, it was concluded that: (1) All beverages analyzed showed an initial acidic pH. (2) There was no statistical difference between the initial pH level and acid neutralization by the addition of artificial saliva in both regular and light drinks. It was not possible to reach the appropriate pH, set as the cutoff point, for the energy drinks.

  1. 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-design met......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...

  2. 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.

  3. An optimized control of ventilation in coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    付华; 邵良杉

    2002-01-01

    According to the nonlinear and time-dependent features of the ventilation systems for coal mines, a neural network method is applied to control the ventilator for coal mines in real time. The technical processes of coal mine ventilation system are introduced, and the principle of controlling a ventilation fan is also explained in detail. The artificial neutral network method is used to calculate the wind quantity needed by work spots in coal mine on the basis of the data collected by the system, including ventilation conditions, environmental temperatures, humidity, coal dust and the contents of all kinds of poisonous and harmful gases. Then the speed of ventilation fan is controlled according to the required wind which is determined by an overall integration of data. A neural network method is presented for overall optimized solution or the genetic algorithm of simulated annealing.

  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. 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.

  6. 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.

  7. 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

  8. 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.

  9. Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

    Directory of Open Access Journals (Sweden)

    Pouraria Hassan

    2016-01-01

    Full Text Available In this study, artificial neural networks (ANNs have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D, the ratio of the cold outlet diameter to the tube diameter(d/D, inlet pressure(P, and cold mass fraction (Y on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y. The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

  10. 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.

  11. Artificial Neural Network based Diagnostic Model For Causes of Success and Failures

    CERN Document Server

    Kaur, Bikrampal

    2010-01-01

    In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited to the problems that are well-understood and known solution(s). On the other hand the ANNs have learning by example and processing capabilities similar to that of a human brain. ANN has been followed due to its inherent advantage over conversion algorithmic like approaches and having capabilities, training and human like intuitive decision making capabilities. Therefore, this ANN based approach is likely to help researchers and organizations to reach a better solution to the problem of managing the human resource. The stud...

  12. Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kapur, G.S.; Sastry, M.I.S.; Jaiswal, A.K.; Sarpal, A.S

    2004-03-17

    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 {sup 1}H and {sup 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 {sup 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 {sup 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.

  13. 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.

  14. 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...

  15. 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.

  16. A New Unsupervised Pre-processing Algorithm Based on Artificial Immune System for ERP Assessment in a P300-based GKT

    Directory of Open Access Journals (Sweden)

    S. Shojaeilangari

    2012-09-01

    Full Text Available In recent years, an increasing number of researches have been focused on bio-inspired algorithms to solve the elaborate engineering problems. Artificial Immune System (AIS is an artificial intelligence technique which has potential of solving problems in various fields. The immune system, due to self-regulating nature, has been an inspiration source of unsupervised learning methods for pattern recognition task. The purpose of this study is to apply the AIS to pre-process the lie-detection dataset to promote the recognition of guilty and innocent subjects. A new Unsupervised AIS (UAIS was proposed in this study as a pre-processing method before classification. Then, we applied three different classifiers on pre-processed data for Event Related Potential (ERP assessment in a P300-based Guilty Knowledge Test (GKT. Experiment results showed that UAIS is a successful pre-processing method which is able to improve the classification rate. In our experiments, we observed that the classification accuracies for three different classifiers: K-Nearest Neighbourhood (KNN, Support Vector Machine (SVM and Linear Discriminant Analysis (LDA were increased after applying UAIS pre-processing. Using of scattering criterion to assessment the features before and after pre-processing proved that our proposed method was able to perform data mapping from a primary feature space to a new area where the data separability was improved significantly.

  17. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

    OpenAIRE

    2016-01-01

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment w...

  18. Static and Dynamic Mechanics Analysis on Artificial Hip Joints with Different Interface Designs by the Finite Element Method

    Institute of Scientific and Technical Information of China (English)

    Hai-bo Jiang

    2007-01-01

    Four different structural models of artificial joints were developed and the finite element method (FEM) was employed to investigate their mechanical characteristics under static and dynamic conditions. The materials used in the FEM calculation were ultra-high molecular weight polyethylene (UHMWPE), 316L stainless steel, CoCrMo alloy and Ti6A14V alloy. The stress distribution, strain, and elastic deformation under static and dynamic conditions were obtained. Analysis and comparison of the calculation results of different models were conducted. It is shown that with the same parameters the model of a metallic femur head covered with an artificial cartilage layer is more similar to the structure of the natural human joint and its mechanical characteristics are the best of the four models.

  19. ARTIFICIAL INTELLIGENCE METHODS FOR DECISION MAKING AND PREDICTING THE BEHAVIOR OF DYNAMICAL SYSTEMS

    Directory of Open Access Journals (Sweden)

    Kymratova A. M.

    2014-11-01

    Full Text Available This article proposes a modification and training the Cellular Automaton predictive model. The author presents a modified system of models and methods for time series prediction with memory based on the theory of fuzzy sets and linear cellular automata

  20. 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

  1. Entropy-based benchmarking methods

    OpenAIRE

    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 preservation method of Causey and Trager (1981) may violate this principle, while its requirements are explicitly taken into account in the pro-posed entropy-based benchmarking methods. Our illustrati...

  2. 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.

  3. 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.

  4. 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.

  5. A novel method for semen collection and artificial insemination in large parrots (Psittaciformes).

    Science.gov (United States)

    Lierz, Michael; Reinschmidt, Matthias; Müller, Heiner; Wink, Michael; Neumann, Daniel

    2013-01-01

    The paper described a novel technique for semen collection in large psittacines (patent pending), a procedure which was not routinely possible before. For the first time, a large set of semen samples is now available for analysis as well as for artificial insemination. Semen samples of more than 100 psittacine taxa were collected and analysed; data demonstrate large differences in the spermatological parameters between families, indicating an ecological relationship with breeding behaviour (polygamous versus monogamous birds). Using semen samples for artificial insemination resulted in the production of offspring in various families, such as Macaws and Cockatoos, for the first time ever. The present technique represents a breakthrough in species conservation programs and will enable future research into the ecology and environmental factors influencing endangered species.

  6. Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting

    Indian Academy of Sciences (India)

    J C Fu; M H Hsu; Y Duann

    2016-02-01

    Flood is the worst weather-related hazard in Taiwan because of steep terrain and storm. The tropical storm often results in disastrous flash flood. To provide reliable forecast of water stages in rivers is indispensable for proper actions in the emergency response during flood. The river hydraulic model based on dynamic wave theory using an implicit finite-difference method is developed with river roughness updating for flash flood forecast. The artificial neural network (ANN) is employed to update the roughness of rivers in accordance with the observed river stages at each time-step of the flood routing process. Several typhoon events at Tamsui River are utilized to evaluate the accuracy of flood forecasting. The results present the adaptive n-values of roughness for river hydraulic model that can provide a better flow state for subsequent forecasting at significant locations and longitudinal profiles along rivers.

  7. A QSAR Study of Some Cyclobutenediones as CCR1 Antagonists by Artificial Neural Networks Based on Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    E Arkan

    2011-12-01

    Full Text Available Background and the purpose of the study: A quantitative structure activity relationship (QSAR model based on artificial neural networks (ANN was developed to study the activities of 29 derivatives of 3-amino-4-(2-(2-(4-benzylpiperazin-1-yl-2-oxoethoxy phenylamino cyclobutenedione as C-C chemokine receptor type 1(CCR1 inhibitors. Methods: A feed-forward ANN with error back-propagation learning algorithm was used for model building which was achieved by optimizing initial learning rate, learning momentum, epoch and the number of hidden neurons. Results: Good results were obtained with a Root Mean Square Error (RMSE and correlation coefficients (R2 of 0.189 and 0.906 for the training and 0.103 and 0.932 prediction sets, respectively. Conclusion: The results reflect a nonlinear relationship between the Principal components obtained from calculated molecular descriptors and the inhibitory activities of the investigated molecules.

  8. [Polymeric materials for biomedical purposes obtained by radiation methods. V. hybrid artificial pancreas].

    Science.gov (United States)

    Burczak, K; Rosiak, J

    1994-01-01

    The authors present a review of works done on the elaboration of a hybrid-type artificial pancreas. The article discusses construction designs, applied polymeric materials as well as biocompatibility problems of polymeric membranes that encapsulate the Langerhans islets. On example of hydrogel membranes prepared by radiation crosslinking of poly(vinyl alcohol) (PVA) the dependence of the diffusion coefficients of glucose, insulin and immunoglobulin G on the crosslinking degree of hydrogels has been shown.

  9. Event detection challenges, methods, and applications in natural and artificial systems

    OpenAIRE

    Kerman, Mitchell C.; Jiang, Wei; Blumberg, Alan F.; Buttrey, Samuel E.

    2009-01-01

    A system is a combination of elements whose collaborative actions produce results generally not attainable by the elements acting alone, and an event is a significant occurrence or large-scale activity that is unusual relative to normal patterns of behavior. Event detection, or the process of identifying the occurrence of an event, within both natural and artificial (or man-made) systems has long been a topic of research, and a variety of techniques have been developed to address event detec...

  10. 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.

  11. Estimation of resistance pear cultivars to Erwinia amylovora using artificial immature pear fruits method

    OpenAIRE

    Gavrilović, Veljko; Stanisavljević, Rade; Stošić, Stefan; Stevanović, Miloš; Aleksić, Goran; Stajić, Milica; Dolovac, Nenad

    2014-01-01

    Susceptibility of different pear cultivars to Erwinia amaylovora by artificial inoculated immature pear fruits are shown in this article. According obtained results significant differences among cultivars are confirmed and they could be divided in four groups. Most susceptibly cultivars were Santa Marija. Second group includes Williams, Morettini, Carmen, Hardenpont. As most resistant shown to be Magness, Turandot and two local varietyies Karamanka, as well as another unknown local cultivar. ...

  12. Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

    Science.gov (United States)

    Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

    2016-09-01

    The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

  13. 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.

  14. 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

    2016-01-01

    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. PMID:27399696

  15. Automatic system for radar echoes filtering based on textural features and artificial intelligence

    Science.gov (United States)

    Hedir, Mehdia; Haddad, Boualem

    2016-11-01

    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.

  16. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2016-07-01

    Full Text Available 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.

  17. 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.

  18. Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network

    Institute of Scientific and Technical Information of China (English)

    LIN Qi-quan; PENG Da-shu; ZHU Yuan-zhi

    2005-01-01

    An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.

  19. Artificial neural network based fault identification scheme implementation for a three-phase induction motor.

    Science.gov (United States)

    Kolla, Sri R; Altman, Shawn D

    2007-04-01

    This paper presents results from the implementation and testing of a PC based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three-phase voltages and currents from a 1/3 HP squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure using the JavaNNS program. The trained network is placed in a LabVIEW based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor off. The complete system is successfully tested in real time by creating different faults on the motor.

  20. 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

    2013-01-01

    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. PMID:23367113