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Sample records for hybrid approach based

  1. A hybrid agent-based approach for modeling microbiological systems.

    Science.gov (United States)

    Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing

    2008-11-21

    Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.

  2. Mass Optimization of Battery/Supercapacitors Hybrid Systems Based on a Linear Programming Approach

    Science.gov (United States)

    Fleury, Benoit; Labbe, Julien

    2014-08-01

    The objective of this paper is to show that, on a specific launcher-type mission profile, a 40% gain of mass is expected using a battery/supercapacitors active hybridization instead of a single battery solution. This result is based on the use of a linear programming optimization approach to perform the mass optimization of the hybrid power supply solution.

  3. A Hybrid Metaheuristic-Based Approach for the Aerodynamic Optimization of Small Hybrid Wind Turbine Rotors

    DEFF Research Database (Denmark)

    Herbert-Acero, José F.; Martínez-Lauranchet, Jaime; Probst, Oliver

    2014-01-01

    of the sectional blade aerodynamics. The framework considers an innovative nested-hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize...

  4. Control and fault diagnosis based sliding mode observer of a multicellular converter: Hybrid approach

    KAUST Repository

    Benzineb, Omar

    2013-01-01

    In this article, the diagnosis of a three cell converter is developed. The hybrid nature of the system represented by the presence of continuous and discrete dynamics is taken into account in the control design. The idea is based on using a hybrid control and an observer-type sliding mode to generate residuals from the observation errors of the system. The simulation results are presented at the end to illustrate the performance of the proposed approach. © 2013 FEI STU.

  5. A Hybrid Metaheuristic-Based Approach for the Aerodynamic Optimization of Small Hybrid Wind Turbine Rotors

    Directory of Open Access Journals (Sweden)

    José F. Herbert-Acero

    2014-01-01

    Full Text Available This work presents a novel framework for the aerodynamic design and optimization of blades for small horizontal axis wind turbines (WT. The framework is based on a state-of-the-art blade element momentum model, which is complemented with the XFOIL 6.96 software in order to provide an estimate of the sectional blade aerodynamics. The framework considers an innovative nested-hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize the aerodynamic efficiency of small WT (SWT rotors for a wide range of operational conditions. The design variables are (1 the airfoil shape at the different blade span positions and the radial variation of the geometrical variables of (2 chord length, (3 twist angle, and (4 thickness along the blade span. A wind tunnel validation study of optimized rotors based on the NACA 4-digit airfoil series is presented. Based on the experimental data, improvements in terms of the aerodynamic efficiency, the cut-in wind speed, and the amount of material used during the manufacturing process were achieved. Recommendations for the aerodynamic design of SWT rotors are provided based on field experience.

  6. The business case for condition-based maintenance: a hybrid (non-) financial approach

    NARCIS (Netherlands)

    Tiddens, W.W.; Tinga, T.; Braaksma, A.J.J.; Brouwer, O.; Cepin, Marko; Bris, Radim

    2017-01-01

    Although developing business cases is key for evaluating project success, the costs and benefits of condition-based maintenance (CBM) implementations are often not explicitly defined and evaluated. Using the design science methodology, we developed a hybrid business case approach to help managers

  7. A decision support system based on hybrid knowledge approach for nuclear power plant operation

    International Nuclear Information System (INIS)

    Yang, J.O.; Chang, S.H.

    1991-01-01

    This paper describes a diagnostic expert system, HYPOSS (Hybrid Knowledge Based Plant Operation Supporting System), which has been developed to support operators' decision making during the transients of nuclear power plant. HYPOSS adopts the hybrid knowledge approach which combines shallow and deep knowledge to couple the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure: structural, functional, behavioral and heuristic knowledge. Frames and rules are adopted to represent the various knowledge types. Rule-based deduction and abduction are used for shallow and deep knowledge based reasoning respectively. The event-based operational guidelines are provided to the operator according to the diagnosed results

  8. A diagnostic expert system for NPP based on hybrid knowledge approach

    International Nuclear Information System (INIS)

    Yang, Joon On; Chang, Soon Heung

    1989-01-01

    This paper describes a diagnostic expert system, HYPOSS (Hybrid Knowledge Based Plant Operation Supporting System), which has been developed to support operators' decision making during the transients of nuclear power plant. HYPOSS adopts the hybrid knowledge approach which combines shallow and deep knowledge to couple the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure: structural, functional, behavioral and heuristic knowledge. The structural and functional knowledge is represented by three fundamental primitives and five types of functions respectively. The behavioral knowledge is represented using constraints. The inference procedure is based on the human problem solving behavior modeled in HYPOSS. For the validation of HYPOSS, several tests have been performed based on the data produced by a plant simulator. The results of validation studies showed a good applicability of HYPOSS to the anomaly diagnosis of nuclear power plant

  9. Integrated approach for fusion multi-physics coupled analyses based on hybrid CAD and mesh geometries

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, Yuefeng, E-mail: yuefeng.qiu@kit.edu; Lu, Lei; Fischer, Ulrich

    2015-10-15

    Highlights: • Integrated approach for neutronics, thermal and structural analyses was developed. • MCNP5/6, TRIPOLI-4 were coupled with CFX, Fluent and ANSYS Workbench. • A novel meshing approach has been proposed for describing MC geometry. - Abstract: Coupled multi-physics analyses on fusion reactor devices require high-fidelity neutronic models, and flexible, accurate data exchanging between various calculation codes. An integrated coupling approach has been developed to enable the conversion of CAD, mesh, or hybrid geometries for Monte Carlo (MC) codes MCNP5/6, TRIPOLI-4, and translation of nuclear heating data for CFD codes Fluent, CFX and structural mechanical software ANSYS Workbench. The coupling approach has been implemented based on SALOME platform with CAD modeling, mesh generation and data visualization capabilities. A novel meshing approach has been developed for generating suitable meshes for MC geometry descriptions. The coupling approach has been concluded to be reliable and efficient after verification calculations of several application cases.

  10. Advanced control approach for hybrid systems based on solid oxide fuel cells

    International Nuclear Information System (INIS)

    Ferrari, Mario L.

    2015-01-01

    Highlights: • Advanced new control system for SOFC based hybrid plants. • Proportional–Integral approach with feed-forward technology. • Good control of fuel cell temperature. • All critical properties maintained inside safe conditions. - Abstract: This paper shows a new advanced control approach for operations in hybrid systems equipped with solid oxide fuel cell technology. This new tool, which combines feed-forward and standard proportional–integral techniques, controls the system during load changes avoiding failures and stress conditions detrimental to component life. This approach was selected to combine simplicity and good control performance. Moreover, the new approach presented in this paper eliminates the need for mass flow rate meters and other expensive probes, as usually required for a commercial plant. Compared to previous works, better performance is achieved in controlling fuel cell temperature (maximum gradient significantly lower than 3 K/min), reducing the pressure gap between cathode and anode sides (at least a 30% decrease during transient operations), and generating a higher safe margin (at least a 10% increase) for the Steam-to-Carbon Ratio. This new control system was developed and optimized using a hybrid system transient model implemented, validated and tested within previous works. The plant, comprising the coupling of a tubular solid oxide fuel cell stack with a microturbine, is equipped with a bypass valve able to connect the compressor outlet with the turbine inlet duct for rotational speed control. Following model development and tuning activities, several operative conditions were considered to show the new control system increased performance compared to previous tools (the same hybrid system model was used with the new control approach). Special attention was devoted to electrical load steps and ramps considering significant changes in ambient conditions

  11. Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach

    International Nuclear Information System (INIS)

    Monjoly, Stéphanie; André, Maïna; Calif, Rudy; Soubdhan, Ted

    2017-01-01

    This paper introduces a new approach for the forecasting of solar radiation series at 1 h ahead. We investigated on several techniques of multiscale decomposition of clear sky index K_c data such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Decomposition. From these differents methods, we built 11 decomposition components and 1 residu signal presenting different time scales. We performed classic forecasting models based on linear method (Autoregressive process AR) and a non linear method (Neural Network model). The choice of forecasting method is adaptative on the characteristic of each component. Hence, we proposed a modeling process which is built from a hybrid structure according to the defined flowchart. An analysis of predictive performances for solar forecasting from the different multiscale decompositions and forecast models is presented. From multiscale decomposition, the solar forecast accuracy is significantly improved, particularly using the wavelet decomposition method. Moreover, multistep forecasting with the proposed hybrid method resulted in additional improvement. For example, in terms of RMSE error, the obtained forecasting with the classical NN model is about 25.86%, this error decrease to 16.91% with the EMD-Hybrid Model, 14.06% with the EEMD-Hybid model and to 7.86% with the WD-Hybrid Model. - Highlights: • Hourly forecasting of GHI in tropical climate with many cloud formation processes. • Clear sky Index decomposition using three multiscale decomposition methods. • Combination of multiscale decomposition methods with AR-NN models to predict GHI. • Comparison of the proposed hybrid model with the classical models (AR, NN). • Best results using Wavelet-Hybrid model in comparison with classical models.

  12. Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

    Science.gov (United States)

    Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil

    2016-01-01

    Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

  13. Formal verification of dynamic hybrid systems: a NuSMV-based model checking approach

    Directory of Open Access Journals (Sweden)

    Xu Zhi

    2018-01-01

    Full Text Available Software security is an important and challenging research topic in developing dynamic hybrid embedded software systems. Ensuring the correct behavior of these systems is particularly difficult due to the interactions between the continuous subsystem and the discrete subsystem. Currently available security analysis methods for system risks have been limited, as they rely on manual inspections of the individual subsystems under simplifying assumptions. To improve this situation, a new approach is proposed that is based on the symbolic model checking tool NuSMV. A dual PID system is used as an example system, for which the logical part and the computational part of the system are modeled in a unified manner. Constraints are constructed on the controlled object, and a counter-example path is ultimately generated, indicating that the hybrid system can be analyzed by the model checking tool.

  14. Opposition-Based Memetic Algorithm and Hybrid Approach for Sorting Permutations by Reversals.

    Science.gov (United States)

    Soncco-Álvarez, José Luis; Muñoz, Daniel M; Ayala-Rincón, Mauricio

    2018-02-21

    Sorting unsigned permutations by reversals is a difficult problem; indeed, it was proved to be NP-hard by Caprara (1997). Because of its high complexity, many approximation algorithms to compute the minimal reversal distance were proposed until reaching the nowadays best-known theoretical ratio of 1.375. In this article, two memetic algorithms to compute the reversal distance are proposed. The first one uses the technique of opposition-based learning leading to an opposition-based memetic algorithm; the second one improves the previous algorithm by applying the heuristic of two breakpoint elimination leading to a hybrid approach. Several experiments were performed with one-hundred randomly generated permutations, single benchmark permutations, and biological permutations. Results of the experiments showed that the proposed OBMA and Hybrid-OBMA algorithms achieve the best results for practical cases, that is, for permutations of length up to 120. Also, Hybrid-OBMA showed to improve the results of OBMA for permutations greater than or equal to 60. The applicability of our proposed algorithms was checked processing permutations based on biological data, in which case OBMA gave the best average results for all instances.

  15. A Gaussian process regression based hybrid approach for short-term wind speed prediction

    International Nuclear Information System (INIS)

    Zhang, Chi; Wei, Haikun; Zhao, Xin; Liu, Tianhong; Zhang, Kanjian

    2016-01-01

    Highlights: • A novel hybrid approach is proposed for short-term wind speed prediction. • This method combines the parametric AR model with the non-parametric GPR model. • The relative importance of different inputs is considered. • Different types of covariance functions are considered and combined. • It can provide both accurate point forecasts and satisfactory prediction intervals. - Abstract: This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.

  16. Nanotubule and Tour Molecule Based Molecular Electronics: Suggestion for a Hybrid Approach

    Science.gov (United States)

    Srivastava, Deepak; Saini, Subhash (Technical Monitor)

    1998-01-01

    Recent experimental and theoretical attempts and results indicate two distinct broad pathways towards future molecular electronic devices and architectures. The first is the approach via Tour type ladder molecules and their junctions which can be fabricated with solution phase chemical approaches. Second are fullerenes or nanotubules and their junctions which may have better conductance, switching and amplifying characteristics but can not be made through well controlled and defined chemical means. A hybrid approach combining the two pathways to take advantage of the characteristics of both is suggested. Dimension and scale of such devices would be somewhere in between isolated molecule and nanotubule based devices but it maybe possible to use self-assembly towards larger functional and logicalunits.

  17. Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

    Directory of Open Access Journals (Sweden)

    Kaisheng Zhang

    2016-12-01

    Full Text Available Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and base stations (BS. With the hybrid model, the Log Distance Path Loss (LDPL model was used to estimate the pedestrian density from raw network data, and retrieve information with the Gaussian Progress (GP through supervised learning. Temporal-spatial prediction of the pedestrian data was carried out with Machine Learning (ML approaches. Finally, a case study of a real Central Business District (CBD scenario in Shanghai, China using records of millions of cell phone users was conducted. The results showed that the new approach significantly increases the utility and capacity of the mobile network. A more reasonable overcrowding detection and alert system can be developed to improve safety in subway lines and other hotspot landmark areas, such as the Bundle, People’s Square or Disneyland, where a large passenger flow generally exists.

  18. Optimal planning approaches with multiple impulses for rendezvous based on hybrid genetic algorithm and control method

    Directory of Open Access Journals (Sweden)

    JingRui Zhang

    2015-03-01

    Full Text Available In this article, we focus on safe and effective completion of a rendezvous and docking task by looking at planning approaches and control with fuel-optimal rendezvous for a target spacecraft running on a near-circular reference orbit. A variety of existent practical path constraints are considered, including the constraints of field of view, impulses, and passive safety. A rendezvous approach is calculated by using a hybrid genetic algorithm with those constraints. Furthermore, a control method of trajectory tracking is adopted to overcome the external disturbances. Based on Clohessy–Wiltshire equations, we first construct the mathematical model of optimal planning approaches of multiple impulses with path constraints. Second, we introduce the principle of hybrid genetic algorithm with both stronger global searching ability and local searching ability. We additionally explain the application of this algorithm in the problem of trajectory planning. Then, we give three-impulse simulation examples to acquire an optimal rendezvous trajectory with the path constraints presented in this article. The effectiveness and applicability of the tracking control method are verified with the optimal trajectory above as control objective through the numerical simulation.

  19. Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

    Directory of Open Access Journals (Sweden)

    Antonino Laudani

    2015-01-01

    Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

  20. A Hybrid Node Scheduling Approach Based on Energy Efficient Chain Routing for WSN

    Directory of Open Access Journals (Sweden)

    Yimei Kang

    2014-04-01

    Full Text Available Energy efficiency is usually a significant goal in wireless sensor networks (WSNs. In this work, an energy efficient chain (EEC data routing approach is first presented. The coverage and connectivity of WSNs are discussed based on EEC. A hybrid node scheduling approach is then proposed. It includes sleep scheduling for cyclically monitoring regions of interest in time-driven modes and wakeup scheduling for tracking emergency events in event-driven modes. A failure rate is introduced to the sleep scheduling to improve the reliability of the system. A wakeup sensor threshold and a sleep time threshold are introduced in the wakeup scheduling to reduce the consumption of energy to the possible extent. The results of the simulation show that the proposed algorithm can extend the effective lifetime of the network to twice that of PEAS. In addition, the proposed methods are computing efficient because they are very simple to implement.

  1. Heuristic hybrid game approach for fleet condition-based maintenance planning

    International Nuclear Information System (INIS)

    Feng, Qiang; Bi, Xiong; Zhao, Xiujie; Chen, Yiran; Sun, Bo

    2017-01-01

    The condition-based maintenance (CBM) method is commonly used to select appropriate maintenance opportunities according to equipment status over a period of time. The CBM of aircraft fleets is a fleet maintenance planning problem. In this problem, mission requirements, resource constraints, and aircraft statuses are considered to find an optimal strategy set. Given that the maintenance strategies for each aircraft are finite, fleet CBM can be treated as a combinatorial optimization problem. In this study, the process of making a decision on the CBM of military fleets is analyzed. The fleet CBM problem is treated as a two-stage dynamic decision-making problem. Aircraft are divided into dispatch and standby sets; thus, the problem scale is significantly reduced. A heuristic hybrid game (HHG) approach comprising a competition game and a cooperative game is proposed on the basis of heuristic rule. In the dispatch set, a competition game approach is proposed to search for a local optimal strategy matrix. A cooperative game method for the two sets is also proposed to ensure global optimization. Finally, a case study regarding a fleet comprising 20 aircraft is conducted, with the results proving that the approach efficiently generates outcomes that meet the mission risk-oriented schedule requirement. - Highlights: • A new heuristic hybrid game method for fleet condition-based maintenance is proposed. • The problem is simplified by hierarchical solving based on dispatch and standby set. • The local optimal solution is got by competition game algorithm for dispatch set. • The global optimal solution is got by cooperative game algorithm between two sets.

  2. Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

    Directory of Open Access Journals (Sweden)

    Ahmed Hussein

    2018-01-01

    Full Text Available Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

  3. Proposed prediction algorithms based on hybrid approach to deal with anomalies of RFID data in healthcare

    Directory of Open Access Journals (Sweden)

    A. Anny Leema

    2013-07-01

    Full Text Available The RFID technology has penetrated the healthcare sector due to its increased functionality, low cost, high reliability, and easy-to-use capabilities. It is being deployed for various applications and the data captured by RFID readers increase according to timestamp resulting in an enormous volume of data duplication, false positive, and false negative. The dirty data stream generated by the RFID readers is one of the main factors limiting the widespread adoption of RFID technology. In order to provide reliable data to RFID application, it is necessary to clean the collected data and this should be done in an effective manner before they are subjected to warehousing. The existing approaches to deal with anomalies are physical, middleware, and deferred approach. The shortcomings of existing approaches are analyzed and found that robust RFID system can be built by integrating the middleware and deferred approach. Our proposed algorithms based on hybrid approach are tested in the healthcare environment which predicts false positive, false negative, and redundant data. In this paper, healthcare environment is simulated using RFID and the data observed by RFID reader consist of anomalies false positive, false negative, and duplication. Experimental evaluation shows that our cleansing methods remove errors in RFID data more accurately and efficiently. Thus, with the aid of the planned data cleaning technique, we can bring down the healthcare costs, optimize business processes, streamline patient identification processes, and improve patient safety.

  4. Aeroacoustic analysis of the human phonation process based on a hybrid acoustic PIV approach

    Science.gov (United States)

    Lodermeyer, Alexander; Tautz, Matthias; Becker, Stefan; Döllinger, Michael; Birk, Veronika; Kniesburges, Stefan

    2018-01-01

    The detailed analysis of sound generation in human phonation is severely limited as the accessibility to the laryngeal flow region is highly restricted. Consequently, the physical basis of the underlying fluid-structure-acoustic interaction that describes the primary mechanism of sound production is not yet fully understood. Therefore, we propose the implementation of a hybrid acoustic PIV procedure to evaluate aeroacoustic sound generation during voice production within a synthetic larynx model. Focusing on the flow field downstream of synthetic, aerodynamically driven vocal folds, we calculated acoustic source terms based on the velocity fields obtained by time-resolved high-speed PIV applied to the mid-coronal plane. The radiation of these sources into the acoustic far field was numerically simulated and the resulting acoustic pressure was finally compared with experimental microphone measurements. We identified the tonal sound to be generated downstream in a small region close to the vocal folds. The simulation of the sound propagation underestimated the tonal components, whereas the broadband sound was well reproduced. Our results demonstrate the feasibility to locate aeroacoustic sound sources inside a synthetic larynx using a hybrid acoustic PIV approach. Although the technique employs a 2D-limited flow field, it accurately reproduces the basic characteristics of the aeroacoustic field in our larynx model. In future studies, not only the aeroacoustic mechanisms of normal phonation will be assessable, but also the sound generation of voice disorders can be investigated more profoundly.

  5. A novel approach for fire recognition using hybrid features and manifold learning-based classifier

    Science.gov (United States)

    Zhu, Rong; Hu, Xueying; Tang, Jiajun; Hu, Sheng

    2018-03-01

    Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several nonoverlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.

  6. A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings.

    Science.gov (United States)

    Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

    2017-05-18

    The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features' information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components.

  7. A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

    Directory of Open Access Journals (Sweden)

    Jie Liu

    2017-05-01

    Full Text Available The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD. Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components.

  8. A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

    Science.gov (United States)

    Xue, Xiaoming; Zhou, Jianzhong

    2017-01-01

    To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  9. A hybrid agent-based computational economics and optimization approach for supplier selection problem

    Directory of Open Access Journals (Sweden)

    Zahra Pourabdollahi

    2017-12-01

    Full Text Available Supplier evaluation and selection problem is among the most important of logistics decisions that have been addressed extensively in supply chain management. The same logistics decision is also important in freight transportation since it identifies trade relationships between business establishments and determines commodity flows between production and consumption points. The commodity flows are then used as input to freight transportation models to determine cargo movements and their characteristics including mode choice and shipment size. Various approaches have been proposed to explore this latter problem in previous studies. Traditionally, potential suppliers are evaluated and selected using only price/cost as the influential criteria and the state-of-practice methods. This paper introduces a hybrid agent-based computational economics and optimization approach for supplier selection. The proposed model combines an agent-based multi-criteria supplier evaluation approach with a multi-objective optimization model to capture both behavioral and economical aspects of the supplier selection process. The model uses a system of ordered response models to determine importance weights of the different criteria in supplier evaluation from a buyers’ point of view. The estimated weights are then used to calculate a utility for each potential supplier in the market and rank them. The calculated utilities are then entered into a mathematical programming model in which best suppliers are selected by maximizing the total accrued utility for all buyers and minimizing total shipping costs while balancing the capacity of potential suppliers to ensure market clearing mechanisms. The proposed model, herein, was implemented under an operational agent-based supply chain and freight transportation framework for the Chicago Metropolitan Area.

  10. Online energy management strategy of fuel cell hybrid electric vehicles based on data fusion approach

    Science.gov (United States)

    Zhou, Daming; Al-Durra, Ahmed; Gao, Fei; Ravey, Alexandre; Matraji, Imad; Godoy Simões, Marcelo

    2017-10-01

    Energy management strategy plays a key role for Fuel Cell Hybrid Electric Vehicles (FCHEVs), it directly affects the efficiency and performance of energy storages in FCHEVs. For example, by using a suitable energy distribution controller, the fuel cell system can be maintained in a high efficiency region and thus saving hydrogen consumption. In this paper, an energy management strategy for online driving cycles is proposed based on a combination of the parameters from three offline optimized fuzzy logic controllers using data fusion approach. The fuzzy logic controllers are respectively optimized for three typical driving scenarios: highway, suburban and city in offline. To classify patterns of online driving cycles, a Probabilistic Support Vector Machine (PSVM) is used to provide probabilistic classification results. Based on the classification results of the online driving cycle, the parameters of each offline optimized fuzzy logic controllers are then fused using Dempster-Shafer (DS) evidence theory, in order to calculate the final parameters for the online fuzzy logic controller. Three experimental validations using Hardware-In-the-Loop (HIL) platform with different-sized FCHEVs have been performed. Experimental comparison results show that, the proposed PSVM-DS based online controller can achieve a relatively stable operation and a higher efficiency of fuel cell system in real driving cycles.

  11. New MPPT algorithm for PV applications based on hybrid dynamical approach

    KAUST Repository

    Elmetennani, Shahrazed

    2016-10-24

    This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.

  12. New MPPT algorithm for PV applications based on hybrid dynamical approach

    KAUST Repository

    Elmetennani, Shahrazed; Laleg-Kirati, Taous-Meriem; Djemai, M.; Tadjine, M.

    2016-01-01

    This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.

  13. A Hybrid Approach for Reliability Analysis Based on Analytic Hierarchy Process and Bayesian Network

    International Nuclear Information System (INIS)

    Zubair, Muhammad

    2014-01-01

    By using analytic hierarchy process (AHP) and Bayesian Network (BN) the present research signifies the technical and non-technical issues of nuclear accidents. The study exposed that the technical faults was one major reason of these accidents. Keep an eye on other point of view it becomes clearer that human behavior like dishonesty, insufficient training, and selfishness are also play a key role to cause these accidents. In this study, a hybrid approach for reliability analysis based on AHP and BN to increase nuclear power plant (NPP) safety has been developed. By using AHP, best alternative to improve safety, design, operation, and to allocate budget for all technical and non-technical factors related with nuclear safety has been investigated. We use a special structure of BN based on the method AHP. The graphs of the BN and the probabilities associated with nodes are designed to translate the knowledge of experts on the selection of best alternative. The results show that the improvement in regulatory authorities will decrease failure probabilities and increase safety and reliability in industrial area.

  14. Hybrid Approach To Steganography System Based On Quantum Encryption And Chaos Algorithms

    Directory of Open Access Journals (Sweden)

    ZAID A. ABOD

    2018-01-01

    Full Text Available A hybrid scheme for secretly embedding image into a dithered multilevel image is presented. This work inputs both a cover image and secret image, which are scrambling and divided into groups to embedded together based on multiple chaos algorithms (Lorenz map, Henon map and Logistic map respectively. Finally, encrypt the embedded images by using one of the quantum cryptography mechanisms, which is quantum one time pad. The experimental results show that the proposed hybrid system successfully embedded images and combine with the quantum cryptography algorithms and gives high efficiency for secure communication.

  15. Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

    Science.gov (United States)

    Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

    Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

  16. Model-based design approaches for plug-in hybrid vehicle design

    Energy Technology Data Exchange (ETDEWEB)

    Mendes, C.J. [CrossChasm Technologies, Cambridge, ON (Canada); Stevens, M.B.; Fowler, M.W. [Waterloo Univ., ON (Canada). Dept. of Chemical Engineering; Fraser, R.A. [Waterloo Univ., ON (Canada). Dept. of Mechanical Engineering; Wilhelm, E.J. [Paul Scherrer Inst., Villigen (Switzerland). Energy Systems Analysis

    2007-07-01

    A model-based design process for plug-in hybrid vehicles (PHEVs) was presented. The paper discussed steps between the initial design concept and a working vehicle prototype, and focused on an investigation of the software-in-the-loop (SIL), hardware-in-the-loop (HIL), and component-in-the-loop (CIL) design phases. The role and benefits of using simulation were also reviewed. A method for mapping and identifying components was provided along with a hybrid control strategy and component-level control optimization process. The role of simulation in component evaluation, architecture design, and de-bugging procedures was discussed, as well as the role simulation networks can play in speeding deployment times. The simulations focused on work performed on a 2005 Chevrolet Equinox converted to a fuel cell hybrid electric vehicle (FCHEV). Components were aggregated to create a complete virtual vehicle. A simplified vehicle model was implemented onto the on-board vehicle control hardware. Optimization metrics were estimated at 10 alpha values during each control loop iteration. The simulation was then used to tune the control system under a variety of drive cycles and conditions. A CIL technique was used to place a physical hybrid electric vehicle (HEV) component under the control of a real time HEV/PHEV simulation. It was concluded that controllers should have a standardized component description that supports integration into advanced testing procedures. 4 refs., 9 figs.

  17. Combination of Biorthogonal Wavelet Hybrid Kernel OCSVM with Feature Weighted Approach Based on EVA and GRA in Financial Distress Prediction

    Directory of Open Access Journals (Sweden)

    Chao Huang

    2014-01-01

    Full Text Available Financial distress prediction plays an important role in the survival of companies. In this paper, a novel biorthogonal wavelet hybrid kernel function is constructed by combining linear kernel function with biorthogonal wavelet kernel function. Besides, a new feature weighted approach is presented based on economic value added (EVA and grey relational analysis (GRA. Considering the imbalance between financially distressed companies and normal ones, the feature weighted one-class support vector machine based on biorthogonal wavelet hybrid kernel (BWH-FWOCSVM is further put forward for financial distress prediction. The empirical study with real data from the listed companies on Growth Enterprise Market (GEM in China shows that the proposed approach has good performance.

  18. A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

    Directory of Open Access Journals (Sweden)

    Fernando Sánchez Lasheras

    2015-03-01

    Full Text Available Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS technique with the principal component analysis (PCA, dendrograms and classification and regression trees (CARTs. Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.. Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

  19. Evaporator modeling - A hybrid approach

    International Nuclear Information System (INIS)

    Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun

    2009-01-01

    In this paper, a hybrid modeling approach is proposed to model two-phase flow evaporators. The main procedures for hybrid modeling includes: (1) Based on the energy and material balance, and thermodynamic principles to formulate the process fundamental governing equations; (2) Select input/output (I/O) variables responsible to the system performance which can be measured and controlled; (3) Represent those variables existing in the original equations but are not measurable as simple functions of selected I/Os or constants; (4) Obtaining a single equation which can correlate system inputs and outputs; and (5) Identify unknown parameters by linear or nonlinear least-squares methods. The method takes advantages of both physical and empirical modeling approaches and can accurately predict performance in wide operating range and in real-time, which can significantly reduce the computational burden and increase the prediction accuracy. The model is verified with the experimental data taken from a testing system. The testing results show that the proposed model can predict accurately the performance of the real-time operating evaporator with the maximum error of ±8%. The developed models will have wide applications in operational optimization, performance assessment, fault detection and diagnosis

  20. A Hybrid Approach to Protect Palmprint Templates

    Directory of Open Access Journals (Sweden)

    Hailun Liu

    2014-01-01

    Full Text Available Biometric template protection is indispensable to protect personal privacy in large-scale deployment of biometric systems. Accuracy, changeability, and security are three critical requirements for template protection algorithms. However, existing template protection algorithms cannot satisfy all these requirements well. In this paper, we propose a hybrid approach that combines random projection and fuzzy vault to improve the performances at these three points. Heterogeneous space is designed for combining random projection and fuzzy vault properly in the hybrid scheme. New chaff point generation method is also proposed to enhance the security of the heterogeneous vault. Theoretical analyses of proposed hybrid approach in terms of accuracy, changeability, and security are given in this paper. Palmprint database based experimental results well support the theoretical analyses and demonstrate the effectiveness of proposed hybrid approach.

  1. An Approach to Evaluate Stability for Cable-Based Parallel Camera Robots with Hybrid Tension-Stiffness Properties

    Directory of Open Access Journals (Sweden)

    Huiling Wei

    2015-12-01

    Full Text Available This paper focuses on studying the effect of cable tensions and stiffness on the stability of cable-based parallel camera robots. For this purpose, the tension factor and the stiffness factor are defined, and the expression of stability is deduced. A new approach is proposed to calculate the hybrid-stability index with the minimum cable tension and the minimum singular value. Firstly, the kinematic model of a cable-based parallel camera robot is established. Based on the model, the tensions are solved and a tension factor is defined. In order to obtain the tension factor, an optimization of the cable tensions is carried out. Then, an expression of the system's stiffness is deduced and a stiffness factor is defined. Furthermore, an approach to evaluate the stability of the cable-based camera robots with hybrid tension-stiffness properties is presented. Finally, a typical three-degree-of-freedom cable-based parallel camera robot with four cables is studied as a numerical example. The simulation results show that the approach is both reasonable and effective.

  2. A hybrid wavelet transform based short-term wind speed forecasting approach.

    Science.gov (United States)

    Wang, Jujie

    2014-01-01

    It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

  3. A Hybrid Sensor Based Backstepping Control Approach with its Application to Fault-Tolerant Flight Control

    NARCIS (Netherlands)

    Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Falkena, W.

    2013-01-01

    Recently, an incremental type sensor based backstepping (SBB) control approach, based on singular perturbation theory and Tikhonov’s theorem, has been proposed. This Lyapunov function based method uses measurements of control variables and less model knowledge, and it is not susceptible to the model

  4. A hybrid approach to select features and classify diseases based on medical data

    Science.gov (United States)

    AbdelLatif, Hisham; Luo, Jiawei

    2018-03-01

    Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms

  5. A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

    International Nuclear Information System (INIS)

    Kıran, Mustafa Servet; Özceylan, Eren; Gündüz, Mesut; Paksoy, Turan

    2012-01-01

    Highlights: ► PSO and ACO algorithms are hybridized for forecasting energy demands of Turkey. ► Linear and quadratic forms are developed to meet the fluctuations of indicators. ► GDP, population, export and import have significant impacts on energy demand. ► Quadratic form provides better fit solution than linear form. ► Proposed approach gives lower estimation error than ACO and PSO, separately. - Abstract: This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.

  6. Deterministic linear-optics quantum computing based on a hybrid approach

    International Nuclear Information System (INIS)

    Lee, Seung-Woo; Jeong, Hyunseok

    2014-01-01

    We suggest a scheme for all-optical quantum computation using hybrid qubits. It enables one to efficiently perform universal linear-optical gate operations in a simple and near-deterministic way using hybrid entanglement as off-line resources

  7. Deterministic linear-optics quantum computing based on a hybrid approach

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seung-Woo; Jeong, Hyunseok [Center for Macroscopic Quantum Control, Department of Physics and Astronomy, Seoul National University, Seoul, 151-742 (Korea, Republic of)

    2014-12-04

    We suggest a scheme for all-optical quantum computation using hybrid qubits. It enables one to efficiently perform universal linear-optical gate operations in a simple and near-deterministic way using hybrid entanglement as off-line resources.

  8. A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System

    Science.gov (United States)

    Geetha, G.; Safa, M.; Fancy, C.; Saranya, D.

    2018-04-01

    In today’s digital world, it has become an irksome task to find the content of one's liking in an endless variety of content that are being consumed like books, videos, articles, movies, etc. On the other hand there has been an emerging growth among the digital content providers who want to engage as many users on their service as possible for the maximum time. This gave birth to the recommender system comes wherein the content providers recommend users the content according to the users’ taste and liking. In this paper we have proposed a movie recommendation system. A movie recommendation is important in our social life due to its features such as suggesting a set of movies to users based on their interest, or the popularities of the movies. In this paper we are proposing a movie recommendation system that has the ability to recommend movies to a new user as well as the other existing users. It mines movie databases to collect all the important information, such as, popularity and attractiveness, which are required for recommendation. We use content-based and collaborative filtering and also hybrid filtering, which is a combination of the results of these two techniques, to construct a system that provides more precise recommendations concerning movies.

  9. Detection of cardiovascular anomalies: Hybrid systems approach

    KAUST Repository

    Ledezma, Fernando

    2012-06-06

    In this paper, we propose a hybrid interpretation of the cardiovascular system. Based on a model proposed by Simaan et al. (2009), we study the problem of detecting cardiovascular anomalies that can be caused by variations in some physiological parameters, using an observerbased approach. We present the first numerical results obtained. © 2012 IFAC.

  10. Reliable Radiation Hybrid Maps: An Efficient Scalable Clustering-based Approach

    Science.gov (United States)

    The process of mapping markers from radiation hybrid mapping (RHM) experiments is equivalent to the traveling salesman problem and, thereby, has combinatorial complexity. As an additional problem, experiments typically result in some unreliable markers that reduce the overall quality of the map. We ...

  11. A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems

    Directory of Open Access Journals (Sweden)

    Abhishek Bhatia

    2015-03-01

    Full Text Available Distributed constraint satisfaction problems (DisCSPs are among the widely endeavored problems using agent-based simulation. Fernandez et al. formulated sensor and mobile tracking problem as a DisCSP, known as SensorDCSP In this paper, we adopt a customized ERE (environment, reactive rules and entities algorithm for the SensorDCSP, which is otherwise proven as a computationally intractable problem. An amalgamation of the autonomy-oriented computing (AOC-based algorithm (ERE and genetic algorithm (GA provides an early solution of the modeled DisCSP. Incorporation of GA into ERE facilitates auto-tuning of the simulation parameters, thereby leading to an early solution of constraint satisfaction. This study further contributes towards a model, built up in the NetLogo simulation environment, to infer the efficacy of the proposed approach.

  12. Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

    Directory of Open Access Journals (Sweden)

    Arpita Nagpal

    2017-01-01

    Full Text Available Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure uses a firefly optimization algorithm in the local search optimzation phase of the greedy randomized adaptive search procedure (GRASP. Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FFGRASP performs better on seven out of nine datasets.

  13. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  14. Power-Split Hybrid Electric Vehicle Energy Management Based on Improved Logic Threshold Approach

    Directory of Open Access Journals (Sweden)

    Zhumu Fu

    2013-01-01

    Full Text Available We design an improved logic threshold approach of energy management for a power-split HEV assisted by an integrated starter generator (ISG. By combining the efficiency map and the optimum torque curve of internal combustion engine (ICE with the state of charge (SOC of batteries, the improved logic threshold controller manages the ICE within its peak efficiency region at first. Then the electrical power demand is established based on the ICE energy output. On that premise, a variable logic threshold value K is defined to achieve the power distribution between the ISG and the electric motor/generator (EMG. Finally, simulation models for the power-split HEV with improved logic threshold controller are established in ADVISOR. Compared to the equally power-split HEV with the logic threshold controller, when using the improved logic threshold controller, the battery power consumption, the ICE efficiency, the fuel consumption, and the motor driving system efficiency are improved.

  15. Engineering of a novel adjuvant based on lipid-polymer hybrid nanoparticles: A quality-by-design approach.

    Science.gov (United States)

    Rose, Fabrice; Wern, Jeanette Erbo; Ingvarsson, Pall Thor; van de Weert, Marco; Andersen, Peter; Follmann, Frank; Foged, Camilla

    2015-07-28

    The purpose of this study was to design a novel and versatile adjuvant intended for mucosal vaccination based on biodegradable poly(DL-lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) modified with the cationic surfactant dimethyldioctadecylammonium (DDA) bromide and the immunopotentiator trehalose-6,6'-dibehenate (TDB) (CAF01) to tailor humoral and cellular immunity characterized by antibodies and Th1/Th17 responses. Such responses are important for the protection against diseases caused by intracellular bacteria such as Chlamydia trachomatis and Mycobacterium tuberculosis. The hybrid NPs were engineered using an oil-in-water single emulsion method and a quality-by-design approach was adopted to define the optimal operating space (OOS). Four critical process parameters (CPPs) were identified, including the acetone concentration in the water phase, the stabilizer [polyvinylalcohol (PVA)] concentration, the lipid-to-total solid ratio, and the total concentration. The CPPs were linked to critical quality attributes consisting of the particle size, polydispersity index (PDI), zeta-potential, thermotropic phase behavior, yield and stability. A central composite face-centered design was performed followed by multiple linear regression analysis. The size, PDI, enthalpy of the phase transition and yield were successfully modeled, whereas the models for the zeta-potential and the stability were poor. Cryo-transmission electron microscopy revealed that the main structural effect on the nanoparticle architecture is caused by the use of PVA, and two different morphologies were identified: i) A PLGA core coated with one or several concentric lipid bilayers, and ii) a PLGA nanoshell encapsulating lipid membrane structures. The optimal formulation, identified from the OOS, was evaluated in vivo. The hybrid NPs induced antibody and Th1/Th17 immune responses that were similar in quality and magnitude to the response induced by DDA/TDB liposomes, showing that the adjuvant

  16. Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-01-01

    Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

  17. A HYBRID APPROACH FOR RURAL FEEDER DESIGN

    Directory of Open Access Journals (Sweden)

    DAMANJEET KAUR

    2012-08-01

    Full Text Available In this paper, a population based approach for conductor size selection in rural radial distribution system is presented. The proposed hybrid approach implies a particle swarm optimization (PSO approach in combination with mutant property of differential evolution (DE for conductor size selection in radial distribution system. The conductor size for each feeder segment is selected such that the total cost of capital investment and capitalized cost of energy losses is minimized while constraints of voltage at each node and current carrying capacity of conductor is within the limits. The applicability and effectiveness of the proposed method is demonstrated with the help of 32-node test system.

  18. A hybrid approach to protein differential expression in mass spectrometry-based proteomics

    KAUST Repository

    Wang, X.; Anderson, G. A.; Smith, R. D.; Dabney, A. R.

    2012-01-01

    MOTIVATION: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial

  19. Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

    Science.gov (United States)

    Dehghani Soufi, Mahsa; Samad-Soltani, Taha; Shams Vahdati, Samad; Rezaei-Hachesu, Peyman

    2018-06-01

    Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. Control and fault diagnosis based sliding mode observer of a multicellular converter: Hybrid approach

    KAUST Repository

    Benzineb, Omar; Taibi, Fateh; Laleg-Kirati, Taous-Meriem; Boucherit, Mohamed Seghir; Tadjine, Mohamed

    2013-01-01

    control and an observer-type sliding mode to generate residuals from the observation errors of the system. The simulation results are presented at the end to illustrate the performance of the proposed approach. © 2013 FEI STU.

  1. An intelligent hybrid scheme for optimizing parking space: A Tabu metaphor and rough set based approach

    Directory of Open Access Journals (Sweden)

    Soumya Banerjee

    2011-03-01

    Full Text Available Congested roads, high traffic, and parking problems are major concerns for any modern city planning. Congestion of on-street spaces in official neighborhoods may give rise to inappropriate parking areas in office and shopping mall complex during the peak time of official transactions. This paper proposes an intelligent and optimized scheme to solve parking space problem for a small city (e.g., Mauritius using a reactive search technique (named as Tabu Search assisted by rough set. Rough set is being used for the extraction of uncertain rules that exist in the databases of parking situations. The inclusion of rough set theory depicts the accuracy and roughness, which are used to characterize uncertainty of the parking lot. Approximation accuracy is employed to depict accuracy of a rough classification [1] according to different dynamic parking scenarios. And as such, the hybrid metaphor proposed comprising of Tabu Search and rough set could provide substantial research directions for other similar hard optimization problems.

  2. NEBNext Direct: A Novel, Rapid, Hybridization-Based Approach for the Capture and Library Conversion of Genomic Regions of Interest.

    Science.gov (United States)

    Emerman, Amy B; Bowman, Sarah K; Barry, Andrew; Henig, Noa; Patel, Kruti M; Gardner, Andrew F; Hendrickson, Cynthia L

    2017-07-05

    Next-generation sequencing (NGS) is a powerful tool for genomic studies, translational research, and clinical diagnostics that enables the detection of single nucleotide polymorphisms, insertions and deletions, copy number variations, and other genetic variations. Target enrichment technologies improve the efficiency of NGS by only sequencing regions of interest, which reduces sequencing costs while increasing coverage of the selected targets. Here we present NEBNext Direct ® , a hybridization-based, target-enrichment approach that addresses many of the shortcomings of traditional target-enrichment methods. This approach features a simple, 7-hr workflow that uses enzymatic removal of off-target sequences to achieve a high specificity for regions of interest. Additionally, unique molecular identifiers are incorporated for the identification and filtering of PCR duplicates. The same protocol can be used across a wide range of input amounts, input types, and panel sizes, enabling NEBNext Direct to be broadly applicable across a wide variety of research and diagnostic needs. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

  3. A Model-Based Approach for Bridging Virtual and Physical Sensor Nodes in a Hybrid Simulation Framework

    Directory of Open Access Journals (Sweden)

    Mohammad Mozumdar

    2014-06-01

    Full Text Available The Model Based Design (MBD approach is a popular trend to speed up application development of embedded systems, which uses high-level abstractions to capture functional requirements in an executable manner, and which automates implementation code generation. Wireless Sensor Networks (WSNs are an emerging very promising application area for embedded systems. However, there is a lack of tools in this area, which would allow an application developer to model a WSN application by using high level abstractions, simulate it mapped to a multi-node scenario for functional analysis, and finally use the refined model to automatically generate code for different WSN platforms. Motivated by this idea, in this paper we present a hybrid simulation framework that not only follows the MBD approach for WSN application development, but also interconnects a simulated sub-network with a physical sub-network and then allows one to co-simulate them, which is also known as Hardware-In-the-Loop (HIL simulation.

  4. A Hybrid Approach to Teaching Managerial Economics

    Science.gov (United States)

    Metzgar, Matthew

    2014-01-01

    Many institutions in higher education are experimenting with hybrid teaching approaches to undergraduate courses. Online resources may provide a number of advantages to students as compared to in-class approaches. Research regarding the effectiveness of hybrid approaches is mixed and still accumulating. This paper discusses the use of a hybrid…

  5. Hybrid approach for detection of dental caries based on the methods FCM and level sets

    Science.gov (United States)

    Chaabene, Marwa; Ben Ali, Ramzi; Ejbali, Ridha; Zaied, Mourad

    2017-03-01

    This paper presents a new technique for detection of dental caries that is a bacterial disease that destroys the tooth structure. In our approach, we have achieved a new segmentation method that combines the advantages of fuzzy C mean algorithm and level set method. The results obtained by the FCM algorithm will be used by Level sets algorithm to reduce the influence of the noise effect on the working of each of these algorithms, to facilitate level sets manipulation and to lead to more robust segmentation. The sensitivity and specificity confirm the effectiveness of proposed method for caries detection.

  6. A hybrid approach to protein differential expression in mass spectrometry-based proteomics

    KAUST Repository

    Wang, X.

    2012-04-19

    MOTIVATION: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein\\'s associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. RESULTS: We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of \\'presence/absence,\\' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. \\'one-state\\' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate. AVAILABILITY: All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.

  7. Hybrid biasing approaches for global variance reduction

    International Nuclear Information System (INIS)

    Wu, Zeyun; Abdel-Khalik, Hany S.

    2013-01-01

    A new variant of Monte Carlo—deterministic (DT) hybrid variance reduction approach based on Gaussian process theory is presented for accelerating convergence of Monte Carlo simulation and compared with Forward-Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) approach implemented in the SCALE package from Oak Ridge National Laboratory. The new approach, denoted the Gaussian process approach, treats the responses of interest as normally distributed random processes. The Gaussian process approach improves the selection of the weight windows of simulated particles by identifying a subspace that captures the dominant sources of statistical response variations. Like the FW-CADIS approach, the Gaussian process approach utilizes particle importance maps obtained from deterministic adjoint models to derive weight window biasing. In contrast to the FW-CADIS approach, the Gaussian process approach identifies the response correlations (via a covariance matrix) and employs them to reduce the computational overhead required for global variance reduction (GVR) purpose. The effective rank of the covariance matrix identifies the minimum number of uncorrelated pseudo responses, which are employed to bias simulated particles. Numerical experiments, serving as a proof of principle, are presented to compare the Gaussian process and FW-CADIS approaches in terms of the global reduction in standard deviation of the estimated responses. - Highlights: ► Hybrid Monte Carlo Deterministic Method based on Gaussian Process Model is introduced. ► Method employs deterministic model to calculate responses correlations. ► Method employs correlations to bias Monte Carlo transport. ► Method compared to FW-CADIS methodology in SCALE code. ► An order of magnitude speed up is achieved for a PWR core model.

  8. CoFe2O4-TiO2 Hybrid Nanomaterials: Synthesis Approaches Based on the Oil-in-Water Microemulsion Reaction Method

    Directory of Open Access Journals (Sweden)

    Arturo Adrián Rodríguez-Rodríguez

    2017-01-01

    Full Text Available CoFe2O4 nanoparticles decorated and wrapped with TiO2 nanoparticles have been prepared by mixing well-dispersed CoFe2O4 with amorphous TiO2 (impregnation approach and growing amorphous TiO2 over the magnetic core (seed approach, respectively, followed by thermal treatment to achieve TiO2 crystallinity. Synthesis strategies were based on the oil-in-water microemulsion reaction method. Thermally treated nanomaterials were characterized in terms of structure, morphology, and composition, to confirm hybrid nanoparticles formation and relate with the synthesis approaches; textural, optical, and magnetic properties were evaluated. X-ray diffraction revealed coexistence of cubic spinel-type CoFe2O4 and tetragonal anatase TiO2. Electron microscopy images depicted crystalline nanoparticles (sizes below 25 nm, with homogeneous Ti distribution for the hybrid nanoparticles synthesized by seed approach. EDX microanalysis and ICP-AES corroborated established chemical composition. XPS evidenced chemical states, as well as TiO2 predominance over CoFe2O4 surface. According to BET measurements, the hybrid nanoparticles were mesoporous. UV-Vis spectroscopy showed optical response along the UV-visible light region. Magnetic properties suggested the breaking order of magnetic domains due to modification with TiO2, especially for mediated seed approach sample. The properties of the obtained hybrid nanoparticles were different in comparison with its individual components. The results highlight the usefulness of designed microemulsion approaches for the straightforward synthesis of CoFe2O4-TiO2 nanostructured hybrids.

  9. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

    Science.gov (United States)

    Pan, Xiaoyong; Shen, Hong-Bin

    2017-02-28

    RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6

  10. A hybrid approach for global sensitivity analysis

    International Nuclear Information System (INIS)

    Chakraborty, Souvik; Chowdhury, Rajib

    2017-01-01

    Distribution based sensitivity analysis (DSA) computes sensitivity of the input random variables with respect to the change in distribution of output response. Although DSA is widely appreciated as the best tool for sensitivity analysis, the computational issue associated with this method prohibits its use for complex structures involving costly finite element analysis. For addressing this issue, this paper presents a method that couples polynomial correlated function expansion (PCFE) with DSA. PCFE is a fully equivalent operational model which integrates the concepts of analysis of variance decomposition, extended bases and homotopy algorithm. By integrating PCFE into DSA, it is possible to considerably alleviate the computational burden. Three examples are presented to demonstrate the performance of the proposed approach for sensitivity analysis. For all the problems, proposed approach yields excellent results with significantly reduced computational effort. The results obtained, to some extent, indicate that proposed approach can be utilized for sensitivity analysis of large scale structures. - Highlights: • A hybrid approach for global sensitivity analysis is proposed. • Proposed approach integrates PCFE within distribution based sensitivity analysis. • Proposed approach is highly efficient.

  11. Hybrid soft computing approaches research and applications

    CERN Document Server

    Dutta, Paramartha; Chakraborty, Susanta

    2016-01-01

    The book provides a platform for dealing with the flaws and failings of the soft computing paradigm through different manifestations. The different chapters highlight the necessity of the hybrid soft computing methodology in general with emphasis on several application perspectives in particular. Typical examples include (a) Study of Economic Load Dispatch by Various Hybrid Optimization Techniques, (b) An Application of Color Magnetic Resonance Brain Image Segmentation by ParaOptiMUSIG activation Function, (c) Hybrid Rough-PSO Approach in Remote Sensing Imagery Analysis,  (d) A Study and Analysis of Hybrid Intelligent Techniques for Breast Cancer Detection using Breast Thermograms, and (e) Hybridization of 2D-3D Images for Human Face Recognition. The elaborate findings of the chapters enhance the exhibition of the hybrid soft computing paradigm in the field of intelligent computing.

  12. A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm

    International Nuclear Information System (INIS)

    Aghajani, Afshin; Kazemzadeh, Rasool; Ebrahimi, Afshin

    2016-01-01

    Highlights: • Proposing a novel hybrid method for short-term prediction of wind farms with high accuracy. • Investigating the prediction accuracy for proposed method in comparison with other methods. • Investigating the effect of six types of parameters as input data on predictions. • Comparing results for 6 & 4 types of the input parameters – addition of pressure and air humidity. - Abstract: This paper proposes a novel hybrid approach to forecast electric power production in wind farms. Wavelet transform (WT) is employed to filter input data of wind power, while radial basis function (RBF) neural network is utilized for primary prediction. For better predictions the main forecasting engine is comprised of three multilayer perceptron (MLP) neural networks by different learning algorithms of Levenberg–Marquardt (LM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and Bayesian regularization (BR). Meta-heuristic technique Imperialist Competitive Algorithm (ICA) is used to optimize neural networks’ weightings in order to escape from local minima. In the forecast process, the real data of wind farms located in the southern part of Alberta, Canada, are used to train and test the proposed model. The data are a complete set of six meteorological and technical characteristics, including wind speed, wind power, wind direction, temperature, pressure, and air humidity. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. Results of optimizations indicate the superiority of the proposed method over the other mentioned techniques; and, forecasting error is remarkably reduced. For instance, the average normalized root mean square error (NRMSE) and average mean absolute percentage error (MAPE) are respectively 11% and 14% lower for the proposed method in 1-h-ahead forecasts over a 24-h period with six types of input than those for the best of the compared models.

  13. Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

    Science.gov (United States)

    Asaithambi, Sasikumar; Rajappa, Muthaiah

    2018-05-01

    In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

  14. Hybrid perovskites: Approaches towards light-emitting devices

    KAUST Repository

    Alias, Mohd Sharizal

    2016-10-06

    The high optical gain and absorption of organic-inorganic hybrid perovskites have attracted extensive research for photonic device applications. Using the bromide halide as an example, we present key approaches of our work towards realizing efficient perovskites based light-emitters. The approaches involved determination of optical constants for the hybrid perovskites thin films, fabrication of photonic nanostructures in the form of subwavelength grating reflector patterned directly on the hybrid perovskites as light manipulation layer, and enhancing the emission property of the hybrid perovskites by using microcavity structure. Our results provide a platform for realization of hybrid perovskites based light-emitting devices for solid-state lighting and display applications. © 2016 IEEE.

  15. Hybrid perovskites: Approaches towards light-emitting devices

    KAUST Repository

    Alias, Mohd Sharizal; Dursun, Ibrahim; Priante, Davide; Saidaminov, Makhsud I.; Ng, Tien Khee; Bakr, Osman; Ooi, Boon S.

    2016-01-01

    The high optical gain and absorption of organic-inorganic hybrid perovskites have attracted extensive research for photonic device applications. Using the bromide halide as an example, we present key approaches of our work towards realizing efficient perovskites based light-emitters. The approaches involved determination of optical constants for the hybrid perovskites thin films, fabrication of photonic nanostructures in the form of subwavelength grating reflector patterned directly on the hybrid perovskites as light manipulation layer, and enhancing the emission property of the hybrid perovskites by using microcavity structure. Our results provide a platform for realization of hybrid perovskites based light-emitting devices for solid-state lighting and display applications. © 2016 IEEE.

  16. Developing traction control strategy for a plug-in hybrid electric vehicle using innovative optimization based approaches

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, L.; Gu, J.; Dong, Z. [Victoria Univ., BC (Canada). Dept. of Mechanical Engineering

    2010-07-01

    This paper described a traction control system designed for hybrid vehicles with multiple power plants and drive axles. Model-based design tools were used to develop the traction control system and plug-in hybrid vehicle models. Optimization studies were conducted in a finite number of operating states in order to maximize the electrical and mechanical energy conversion efficiency of an extended range electric vehicle. Four global optimization algorithms were then evaluated in relation to their CPU times. The studied algorithms included a genetic algorithm (GA), a particle swarm optimization (PSO) algorithm, a hybrid adaptive metamodel optimization (HAM) and space elimination and unimodal region reduction (SEUMRE) algorithm. A comparative evaluation of the algorithms demonstrated that the PSO algorithm obtained optimal results, while the HAM algorithm used significantly less computational time. Results of the optimization studies were then implemented in a controller model. Results of the study showed that the energy efficiency of the vehicle improved using the developed controller model. 4 refs., 2 tabs., 8 figs.

  17. Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.

  18. Taxonomic Identification of Mediterranean Pines and Their Hybrids Based on the High Resolution Melting (HRM) and trnL Approaches: From Cytoplasmic Inheritance to Timber Tracing

    Science.gov (United States)

    Ganopoulos, Ioannis; Aravanopoulos, Filippos; Madesis, Panagiotis; Pasentsis, Konstantinos; Bosmali, Irene; Ouzounis, Christos; Tsaftaris, Athanasios

    2013-01-01

    Fast and accurate detection of plant species and their hybrids using molecular tools will facilitate the assessment and monitoring of local biodiversity in an era of climate and environmental change. Herein, we evaluate the utility of the plastid trnL marker for species identification applied to Mediterranean pines (Pinus spp.). Our results indicate that trnL is a very sensitive marker for delimiting species biodiversity. Furthermore, High Resolution Melting (HRM) analysis was exploited as a molecular fingerprint for fast and accurate discrimination of Pinus spp. DNA sequence variants. The trnL approach and the HRM analyses were extended to wood samples of two species (Pinus nigra and Pinus sylvestris) with excellent results, congruent to those obtained using leaf tissue. Both analyses demonstrate that hybrids from the P. brutia (maternal parent) × P. halepensis (paternal parent) cross, exhibit the P. halepensis profile, confirming paternal plastid inheritance in Group Halepensis pines. Our study indicates that a single one-step reaction method and DNA marker are sufficient for the identification of Mediterranean pines, their hybrids and the origin of pine wood. Furthermore, our results underline the potential for certain DNA regions to be used as novel biological information markers combined with existing morphological characters and suggest a relatively reliable and open taxonomic system that can link DNA variation to phenotype-based species or hybrid assignment status and direct taxa identification from recalcitrant tissues such as wood samples. PMID:23577179

  19. Hybrid Correlation Energy (HyCE): An Approach Based on Separate Evaluations of Internal and External Components.

    Science.gov (United States)

    Ivanic, Joseph; Schmidt, Michael W

    2018-06-04

    A novel hybrid correlation energy (HyCE) approach is proposed that determines the total correlation energy via distinct computation of its internal and external components. This approach evolved from two related studies. First, rigorous assessment of the accuracies and size extensivities of a number of electron correlation methods, that include perturbation theory (PT2), coupled-cluster (CC), configuration interaction (CI), and coupled electron pair approximation (CEPA), shows that the CEPA(0) variant of the latter and triples-corrected CC methods consistently perform very similarly. These findings were obtained by comparison to near full CI results for four small molecules and by charting recovered correlation energies for six steadily growing chain systems. Second, by generating valence virtual orbitals (VVOs) and utilizing the CEPA(0) method, we were able to partition total correlation energies into internal (or nondynamic) and external (or dynamic) parts for the aforementioned six chain systems and a benchmark test bed of 36 molecules. When using triple-ζ basis sets it was found that per orbital internal correlation energies were appreciably larger than per orbital external energies and that the former showed far more chemical variation than the latter. Additionally, accumulations of external correlation energies were seen to proceed smoothly, and somewhat linearly, as the virtual space is gradually increased. Combination of these two studies led to development of the HyCE approach, whereby the internal and external correlation energies are determined separately by CEPA(0)/VVO and PT2/external calculations, respectively. When applied to the six chain systems and the 36-molecule benchmark test set it was found that HyCE energies followed closely those of triples-corrected CC and CEPA(0) while easily outperforming MP2 and CCSD. The success of the HyCE approach is more notable when considering that its cost is only slightly more than MP2 and significantly cheaper

  20. Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension

    Directory of Open Access Journals (Sweden)

    Ueno Kazuko

    2009-04-01

    Full Text Available Abstract Background Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher et al. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in C. elegans and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze in silico models incorporating the use of model checking approach. Results A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules – Rule I and Rule II – to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from in silico and in vivo experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in

  1. Detection of cardiovascular anomalies: Hybrid systems approach

    KAUST Repository

    Ledezma, Fernando; Laleg-Kirati, Taous-Meriem

    2012-01-01

    In this paper, we propose a hybrid interpretation of the cardiovascular system. Based on a model proposed by Simaan et al. (2009), we study the problem of detecting cardiovascular anomalies that can be caused by variations in some physiological

  2. Stock selection using a hybrid MCDM approach

    Directory of Open Access Journals (Sweden)

    Tea Poklepović

    2014-12-01

    Full Text Available The problem of selecting the right stocks to invest in is of immense interest for investors on both emerging and developed capital markets. Moreover, an investor should take into account all available data regarding stocks on the particular market. This includes fundamental and stock market indicators. The decision making process includes several stocks to invest in and more than one criterion. Therefore, the task of selecting the stocks to invest in can be viewed as a multiple criteria decision making (MCDM problem. Using several MCDM methods often leads to divergent rankings. The goal of this paper is to resolve these possible divergent results obtained from different MCDM methods using a hybrid MCDM approach based on Spearman’s rank correlation coefficient. Five MCDM methods are selected: COPRAS, linear assignment, PROMETHEE, SAW and TOPSIS. The weights for all criteria are obtained by using the AHP method. Data for this study includes information on stock returns and traded volumes from March 2012 to March 2014 for 19 stocks on the Croatian capital market. It also includes the most important fundamental and stock market indicators for selected stocks. Rankings using five selected MCDM methods in the stock selection problem yield divergent results. However, after applying the proposed approach the final hybrid rankings are obtained. The results show that the worse stocks to invest in happen to be the same when the industry is taken into consideration or when not. However, when the industry is taken into account, the best stocks to invest in are slightly different, because some industries are more profitable than the others.

  3. Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach

    International Nuclear Information System (INIS)

    Mayyas, Abdel Ra'ouf; Kumar, Sushil; Pisu, Pierluigi; Rios, Jacqueline; Jethani, Puneet

    2017-01-01

    Highlights: •Vehicle hardware In-the-loop VHiL testing and validation is implemented in vehicle test bed. •Torque at the roller bench test is used to control the torque at wheels to reflect vehicle electrification symptoms. •Electrified powertrain with Equivalent Consumption Minimization Strategy is tested and validated using VHiL. •Fuel economy and power train performance is measured using high precision fuel measurement device. -- Abstract: Hybridization of automotive powertrains by using more than one type of energy converter is considered as an important step towards reducing fuel consumption and air pollutants. Specifically, the development of energy efficient, highly complex, alternative drive-train systems, in which the interactions of different energy converters play an important role, requires new design methods and processes. This paper discusses the inclusion of an alternative hybrid power train into an existing vehicle platform for maximum energy efficiency. The new proposed integrated Vehicle Hardware In-the-loop (VHiL) and Model Based Design (MBD) approach is utilized to evaluate the energy efficiency of electrified powertrain. In VHiL, a complete chassis system becomes an integrated part of the vehicle test bed. A complete conventional Internal Combustion Engine (ICE) powered vehicle is tested in roller bench test for the integration of energy efficient hybrid electric power train modules in closed-loop, real-time, feedback configuration. A model that is a replica of the test vehicle is executed – in real-time- where all hybrid power train modules are included. While the VHiL platform is controlling the signal exchange between the test bed automation software and the vehicle on-board controller, the road load exerted on the driving wheels is manipulated in closed –loop real-time manner in order to reflect all hybrid driving modes including: All Electric Range (AER), Electric Power Assist (EPA) and blended Modes (BM). Upon successful

  4. Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

    Science.gov (United States)

    Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

    2018-02-01

    A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

  5. Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

    International Nuclear Information System (INIS)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

    2014-01-01

    Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

  6. Arts-Based Research: Trojan Horses and Shibboleths. The Liabilities of a Hybrid Research Approach. "What Hath Eisner Wrought?"

    Science.gov (United States)

    Pariser, David

    2009-01-01

    The term "arts-based research" has been debated for some time now. In an article strongly in favor of this approach Bean (2007) identifies three species: "Research on the arts (italics in the original) (art history, visual and cultural studies, media studies etc.)...Research for the arts, refers to research into applied techniques, materials and…

  7. A hybrid multi-criteria decision modeling approach for the best biodiesel blend selection based on ANP-TOPSIS analysis

    Directory of Open Access Journals (Sweden)

    G. Sakthivel

    2015-03-01

    Full Text Available The ever increasing demand and depletion of fossil fuels had an adverse impact on environmental pollution. The selection of appropriate source of biodiesel and proper blending of biodiesel plays a major role in alternate energy production. This paper describes an application of hybrid Multi Criteria Decision Making (MCDM technique for the selection of optimum fuel blend in fish oil biodiesel for the IC engine. The proposed model, Analytical Network Process (ANP is integrated with Technique for Order Performance by Similarity to Ideal Solution (TOPSIS and VlseKriterijumska Optimizacija I Kompromisno Resenje (in Serbian (VIKOR to evaluate the optimum blend. Evaluation of suitable blend is based on the exploratory analysis of the performance, emission and combustion parameters of the single cylinder, constant speed direct injection diesel engine at different load conditions. Here the ANP is used to determine the relative weights of the criteria, whereas TOPSIS and VIKOR are used for obtaining the final ranking of alternative blends. An efficient pair-wise comparison process and ranking of alternatives can be achieved for optimum blend selection through the integration of ANP with TOPSIS and VIKOR. The obtained preference order of the blends for ANP-VIKOR and ANP-TOPSIS are B20 > Diesel > B40 > B60 > B80 > B100 and B20 > B40 > Diesel > B60 > B80 > B100 respectively. Hence by comparing both these methods, B20 is selected as the best blend to operate the internal combustion engines. This paper highlights a new insight into MCDM techniques to evaluate the best fuel blend for the decision makers such as engine manufactures and R& D engineers to meet the fuel economy and emission norms to empower the green revolution.

  8. A hybrid approach for biobjective optimization

    DEFF Research Database (Denmark)

    Stidsen, Thomas Jacob Riis; Andersen, Kim Allan

    2018-01-01

    to singleobjective problems is that no standard multiobjective solvers exist and specialized algorithms need to be programmed from scratch.In this article we will present a hybrid approach, which operates both in decision space and in objective space. The approach enables massive efficient parallelization and can...... be used to a wide variety of biobjective Mixed Integer Programming models. We test the approach on the biobjective extension of the classic traveling salesman problem, on the standard datasets, and determine the full set of nondominated points. This has only been done once before (Florios and Mavrotas...

  9. A hybrid approach to nanoelectronics

    Science.gov (United States)

    Cerofolini, G. F.; Arena, G.; Camalleri, C. M.; Galati, C.; Reina, S.; Renna, L.; Mascolo, D.

    2005-08-01

    The definition of features on the nanometre length scale (NLS) is impossible via conventional lithography, but can be done using extreme ultraviolet, synchrotron-radiation, or electron beam lithography. However, since these techniques are very expensive and still in their infancy, their exploitation in integrated circuit (IC) processing is still highly putative. Geometries on the NLS can however be produced with relative ease using the spacer patterning technique, i.e. transforming vertical features (like film thickness) in the vicinity of a step of a sacrificial layer into horizontal features. The ultimate length that can be produced in this way is controlled by the steepness of the step defining the sacrificial layer, the uniformity of the deposited or grown films, and the anisotropy of its etching. While useful for the preparation of a few devices with special needs, the above trick does not allow by itself the development of a nanotechnology where each layer useful for defining the circuit should be on the NLS and aligned on the underlying geometries with tolerances on the NLS. Setting up such a nanotechnology is a major problem which will involve the IC industry in the post-Roadmap era. Irrespective of the detailed structure of the basic constituents (molecules, supramolecular structures, clusters, etc), ICs with nanoscopic active elements can hardly be prepared without the ability to produce arrays of conductive strips with pitch on the NLS. This work is devoted to describing a scheme (essentially based on the existing microelectronic technology) for their production without the use of advanced lithography and how it can be arranged to host molecular devices.

  10. A hybrid modeling approach for option pricing

    Science.gov (United States)

    Hajizadeh, Ehsan; Seifi, Abbas

    2011-11-01

    The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.

  11. Using hybrid expert system approaches for engineering applications

    Science.gov (United States)

    Allen, R. H.; Boarnet, M. G.; Culbert, C. J.; Savely, R. T.

    1987-01-01

    In this paper, the use of hybrid expert system shells and hybrid (i.e., algorithmic and heuristic) approaches for solving engineering problems is reported. Aspects of various engineering problem domains are reviewed for a number of examples with specific applications made to recently developed prototype expert systems. Based on this prototyping experience, critical evaluations of and comparisons between commercially available tools, and some research tools, in the United States and Australia, and their underlying problem-solving paradigms are made. Characteristics of the implementation tool and the engineering domain are compared and practical software engineering issues are discussed with respect to hybrid tools and approaches. Finally, guidelines are offered with the hope that expert system development will be less time consuming, more effective, and more cost-effective than it has been in the past.

  12. A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach.

    Science.gov (United States)

    Varmazyar, Mohsen; Dehghanbaghi, Maryam; Afkhami, Mehdi

    2016-10-01

    Balanced Scorecard (BSC) is a strategic evaluation tool using both financial and non-financial indicators to determine the business performance of organizations or companies. In this paper, a new integrated approach based on the Balanced Scorecard (BSC) and multi-criteria decision making (MCDM) methods are proposed to evaluate the performance of research centers of research and technology organization (RTO) in Iran. Decision-Making Trial and Evaluation Laboratory (DEMATEL) are employed to reflect the interdependencies among BSC perspectives. Then, Analytic Network Process (ANP) is utilized to weight the indices influencing the considered problem. In the next step, we apply four MCDM methods including Additive Ratio Assessment (ARAS), Complex Proportional Assessment (COPRAS), Multi-Objective Optimization by Ratio Analysis (MOORA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking of alternatives. Finally, the utility interval technique is applied to combine the ranking results of MCDM methods. Weighted utility intervals are computed by constructing a correlation matrix between the ranking methods. A real case is presented to show the efficacy of the proposed approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. A Hybrid Approach on Tourism Demand Forecasting

    Science.gov (United States)

    Nor, M. E.; Nurul, A. I. M.; Rusiman, M. S.

    2018-04-01

    Tourism has become one of the important industries that contributes to the country’s economy. Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. However, it is challenging to produce an accurate forecast since economic data such as the tourism data is affected by social, economic and environmental factors. In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia’s tourism demand. The forecasting performance was assessed by taking the each individual method as a benchmark. The results showed that this hybrid approach outperformed the other two models

  14. Improvement of Dimensional Accuracy of 3-D Printed Parts using an Additive/Subtractive Based Hybrid Prototyping Approach

    Science.gov (United States)

    Amanullah Tomal, A. N. M.; Saleh, Tanveer; Raisuddin Khan, Md.

    2017-11-01

    At present, two important processes, namely CNC machining and rapid prototyping (RP) are being used to create prototypes and functional products. Combining both additive and subtractive processes into a single platform would be advantageous. However, there are two important aspects need to be taken into consideration for this process hybridization. First is the integration of two different control systems for two processes and secondly maximizing workpiece alignment accuracy during the changeover step. Recently we have developed a new hybrid system which incorporates Fused Deposition Modelling (FDM) as RP Process and CNC grinding operation as subtractive manufacturing process into a single setup. Several objects were produced with different layer thickness for example 0.1 mm, 0.15 mm and 0.2 mm. It was observed that pure FDM method is unable to attain desired dimensional accuracy and can be improved by a considerable margin about 66% to 80%, if finishing operation by grinding is carried out. It was also observed layer thickness plays a role on the dimensional accuracy and best accuracy is achieved with the minimum layer thickness (0.1 mm).

  15. Modeling protective anti-tumor immunity via preventative cancer vaccines using a hybrid agent-based and delay differential equation approach.

    Science.gov (United States)

    Kim, Peter S; Lee, Peter P

    2012-01-01

    A next generation approach to cancer envisions developing preventative vaccinations to stimulate a person's immune cells, particularly cytotoxic T lymphocytes (CTLs), to eliminate incipient tumors before clinical detection. The purpose of our study is to quantitatively assess whether such an approach would be feasible, and if so, how many anti-cancer CTLs would have to be primed against tumor antigen to provide significant protection. To understand the relevant dynamics, we develop a two-compartment model of tumor-immune interactions at the tumor site and the draining lymph node. We model interactions at the tumor site using an agent-based model (ABM) and dynamics in the lymph node using a system of delay differential equations (DDEs). We combine the models into a hybrid ABM-DDE system and investigate dynamics over a wide range of parameters, including cell proliferation rates, tumor antigenicity, CTL recruitment times, and initial memory CTL populations. Our results indicate that an anti-cancer memory CTL pool of 3% or less can successfully eradicate a tumor population over a wide range of model parameters, implying that a vaccination approach is feasible. In addition, sensitivity analysis of our model reveals conditions that will result in rapid tumor destruction, oscillation, and polynomial rather than exponential decline in the tumor population due to tumor geometry.

  16. An Object-Based Classification of Mangroves Using a Hybrid Decision Tree—Support Vector Machine Approach

    Directory of Open Access Journals (Sweden)

    Benjamin W. Heumann

    2011-11-01

    Full Text Available Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA, and support vector machine (SVM classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863 for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded.

  17. ICDTag: a prototype for a web-based system for organizing physician-written blog posts using a hybrid taxonomy-folksonomy approach.

    Science.gov (United States)

    Batch, Yamen; Yusof, Maryati Mohd; Noah, Shahrul Azman

    2013-02-27

    Medical blogs have emerged as new media, extending to a wider range of medical audiences, including health professionals and patients to share health-related information. However, extraction of quality health-related information from medical blogs is challenging primarily because these blogs lack systematic methods to organize their posts. Medical blogs can be categorized according to their author into (1) physician-written blogs, (2) nurse-written blogs, and (3) patient-written blogs. This study focuses on how to organize physician-written blog posts that discuss disease-related issues and how to extract quality information from these posts. The goal of this study was to create and implement a prototype for a Web-based system, called ICDTag, based on a hybrid taxonomy-folksonomy approach that follows a combination of a taxonomy classification schemes and user-generated tags to organize physician-written blog posts and extract information from these posts. First, the design specifications for the Web-based system were identified. This system included two modules: (1) a blogging module that was implemented as one or more blogs, and (2) an aggregator module that aggregated posts from different blogs into an aggregator website. We then developed a prototype for this system in which the blogging module included two blogs, the cardiology blog and the gastroenterology blog. To analyze the usage patterns of the prototype, we conducted an experiment with data provided by cardiologists and gastroenterologists. Next, we conducted two evaluation types: (1) an evaluation of the ICDTag blog, in which the browsing functionalities of the blogging module were evaluated from the end-user's perspective using an online questionnaire, and (2) an evaluation of information quality, in which the quality of the content on the aggregator website was assessed from the perspective of medical experts using an emailed questionnaire. Participants of this experiment included 23 cardiologists and

  18. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Directory of Open Access Journals (Sweden)

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

  19. A small animal PET based on GAPDs and charge signal transmission approach for hybrid PET-MR imaging

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Jihoon; Choi, Yong; Hong, Key Jo; Hu, Wei; Jung, Jin Ho; Huh, Yoonsuk [Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, Seoul 121-742 (Korea, Republic of); Kim, Byung-Tae, E-mail: ychoi.image@gmail.com [Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-Dong, Gangnam-Gu, Seoul 135-710 (Korea, Republic of)

    2011-08-15

    Positron emission tomography (PET) employing Geiger-mode avalanche photodiodes (GAPDs) and charge signal transmission approach was developed for small animal imaging. Animal PET contained 16 LYSO and GAPD detector modules that were arranged in a 70 mm diameter ring with an axial field of view of 13 mm. The GAPDs charge output signals were transmitted to a preamplifier located remotely using 300 cm flexible flat cables. The position decoder circuits (PDCs) were used to multiplex the PET signals from 256 to 4 channels. The outputs of the PDCs were digitized and further-processed in the data acquisition unit. The cross-compatibilities of the PET detectors and MRI were assessed outside and inside the MRI. Experimental studies of the developed full ring PET were performed to examine the spatial resolution and sensitivity. Phantom and mouse images were acquired to examine the imaging performance. The mean energy and time resolution of the PET detector were 17.6% and 1.5 ns, respectively. No obvious degradation on PET and MRI was observed during simultaneous PET-MRI data acquisition. The measured spatial resolution and sensitivity at the CFOV were 2.8 mm and 0.7%, respectively. In addition, a 3 mm diameter line source was clearly resolved in the hot-sphere phantom images. The reconstructed transaxial PET images of the mouse brain and tumor displaying the glucose metabolism patterns were imaged well. These results demonstrate GAPD and the charge signal transmission approach can allow the development of high performance small animal PET with improved MR compatibility.

  20. Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Jui-Yu Wu

    2013-01-01

    Full Text Available Stochastic global optimization (SGO algorithms such as the particle swarm optimization (PSO approach have become popular for solving unconstrained global optimization (UGO problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO method and an artificial immune algorithm-based PSO (AIA-PSO method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

  1. A States of Matter Search-Based Approach for Solving the Problem of Intelligent Power Allocation in Plug-in Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Arturo Valdivia-Gonzalez

    2017-01-01

    Full Text Available Recently, many researchers have proved that the electrification of the transport sector is a key for reducing both the emissions of green-house pollutants and the dependence on oil for transportation. As a result, Plug-in Hybrid Electric Vehicles (or PHEVs are receiving never before seen increased attention. Consequently, large-scale penetration of PHEVs into the market is expected to take place in the near future, however, an unattended increase in the PHEVs needs may cause several technical problems which could potentially compromise the stability of power systems. As a result of the growing necessity for addressing such issues, topics related to the optimization of PHEVs’ charging infrastructures have captured the attention of many researchers. Related to this, several state-of-the-art swarm optimization methods (such as the well-known Particle Swarm Optimization (PSO or the recently proposed Gravitational Search Algorithm (GSA approach have been successfully applied in the optimization of the average State of Charge (SoC, which represents one of the most important performance indicators in the context of PHEVs’ intelligent power allocation. Many of these swarm optimization methods, however, are known to be subject to several critical flaws, including premature convergence and a lack of balance between the exploration and exploitation of solutions. Such problems are usually related to the evolutionary operators employed by each of the methods on the exploration and exploitation of new solutions. In this paper, the recently proposed States of Matter Search (SMS swarm optimization method is proposed for maximizing the average State of Charge of PHEVs within a charging station. In our experiments, several different scenarios consisting on different numbers of PHEVs were considered. To test the feasibility of the proposed approach, comparative experiments were performed against other popular PHEVs’ State of Charge maximization approaches

  2. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

    DEFF Research Database (Denmark)

    Pan, Xiaoyong; Shen, Hong Bin

    2017-01-01

    , their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains...... space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can...... be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in i...

  3. Multilayer Approach for Advanced Hybrid Lithium Battery

    KAUST Repository

    Ming, Jun

    2016-06-06

    Conventional intercalated rechargeable batteries have shown their capacity limit, and the development of an alternative battery system with higher capacity is strongly needed for sustainable electrical vehicles and hand-held devices. Herein, we introduce a feasible and scalable multilayer approach to fabricate a promising hybrid lithium battery with superior capacity and multivoltage plateaus. A sulfur-rich electrode (90 wt % S) is covered by a dual layer of graphite/Li4Ti5O12, where the active materials S and Li4Ti5O12 can both take part in redox reactions and thus deliver a high capacity of 572 mAh gcathode -1 (vs the total mass of electrode) or 1866 mAh gs -1 (vs the mass of sulfur) at 0.1C (with the definition of 1C = 1675 mA gs -1). The battery shows unique voltage platforms at 2.35 and 2.1 V, contributed from S, and 1.55 V from Li4Ti5O12. A high rate capability of 566 mAh gcathode -1 at 0.25C and 376 mAh gcathode -1 at 1C with durable cycle ability over 100 cycles can be achieved. Operando Raman and electron microscope analysis confirm that the graphite/Li4Ti5O12 layer slows the dissolution/migration of polysulfides, thereby giving rise to a higher sulfur utilization and a slower capacity decay. This advanced hybrid battery with a multilayer concept for marrying different voltage plateaus from various electrode materials opens a way of providing tunable capacity and multiple voltage platforms for energy device applications. © 2016 American Chemical Society.

  4. A hybrid approach for efficient anomaly detection using metaheuristic methods

    Directory of Open Access Journals (Sweden)

    Tamer F. Ghanem

    2015-07-01

    Full Text Available Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

  5. A facile approach to fabricate flexible all-solid-state supercapacitors based on MnFe2O4/graphene hybrids

    Science.gov (United States)

    Cai, Weihua; Lai, Ting; Dai, Wanlin; Ye, Jianshan

    2014-06-01

    A critical challenge for the construction of flexible electrochemical capacitors is the preparation of flexible electrodes with large specific capacitance and robust mechanical strength. Here, we demonstrate a facile approach to make high performance and flexible electrodes by dropping MnFe2O4/graphene hybrid inks onto flexible graphite sheets (as current collectors and substrates) and drying under an infrared lamp. MnFe2O4/graphene hybrid inks are synthesized by immobilizing the MnFe2O4 microspheres on the graphene nanosheets via a simple solvothermal route. Electrochemical studies show that MnFe2O4/graphene exhibits a high capacitance of 300 F g-1 at a current density of 0.3 A g-1. In addition, the excellent electrochemical performance of a supercapacitor consisting of a sandwich structure of two pieces of MnFe2O4/graphene hybrids modified electrodes separated by polyvinyl alcohol (PVA)-H2SO4 gel electrolyte is further explored. Our studies reveal that the flexible supercapacitor device with 227 μm thickness can achieve a maximum specific capacitance of 120 F g-1 at a current density of 0.1 A g-1 and excellent cycle performance retaining 105% capacitance after 5000 cycles. This research may offer a method for the fabrication of lightweight, stable, flexible and high performance energy storage devices.

  6. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    Energy Technology Data Exchange (ETDEWEB)

    Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2011-02-15

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  7. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F.

    2011-01-01

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  8. Forecasting conditional climate-change using a hybrid approach

    Science.gov (United States)

    Esfahani, Akbar Akbari; Friedel, Michael J.

    2014-01-01

    A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

  9. A hybrid personalized data recommendation approach for geoscience data sharing

    Science.gov (United States)

    WANG, M.; Wang, J.

    2016-12-01

    Recommender systems are effective tools helping Internet users overcome information overloading. The two most widely used recommendation algorithms are collaborating filtering (CF) and content-based filtering (CBF). A number of recommender systems based on those two algorithms were developed for multimedia, online sells, and other domains. Each of the two algorithms has its advantages and shortcomings. Hybrid approaches that combine these two algorithms are better choices in many cases. In geoscience data sharing domain, where the items (datasets) are more informative (in space and time) and domain-specific, no recommender system is specialized for data users. This paper reports a dynamic weighted hybrid recommendation algorithm that combines CF and CBF for geoscience data sharing portal. We first derive users' ratings on items with their historical visiting time by Jenks Natural Break. In the CBF part, we incorporate the space, time, and subject information of geoscience datasets to compute item similarity. Predicted ratings were computed with k-NN method separately using CBF and CF, and then combined with weights. With training dataset we attempted to find the best model describing ideal weights and users' co-rating numbers. A logarithmic function was confirmed to be the best model. The model was then used to tune the weights of CF and CBF on user-item basis with test dataset. Evaluation results show that the dynamic weighted approach outperforms either solo CF or CBF approach in terms of Precision and Recall.

  10. A Hybrid Genetic Algorithm Approach for Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Sydulu Maheswarapu

    2011-08-01

    Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

  11. Bond graph model-based fault diagnosis of hybrid systems

    CERN Document Server

    Borutzky, Wolfgang

    2015-01-01

    This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

  12. A "Hybrid" Approach for Synthesizing Optimal Controllers of Hybrid Systems

    DEFF Research Database (Denmark)

    Zhao, Hengjun; Zhan, Naijun; Kapur, Deepak

    2012-01-01

    to discretization manageable and within bounds. A major advantage of our approach is not only that it avoids errors due to numerical computation, but it also gives a better optimal controller. In order to illustrate our approach, we use the real industrial example of an oil pump provided by the German company HYDAC...

  13. The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

    Science.gov (United States)

    Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

    2014-05-01

    Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the

  14. A Hybrid Approach Based on the Combination of Adaptive Neuro-Fuzzy Inference System and Imperialist Competitive Algorithm: Oil Flow Rate of the Wells Prediction Case Study

    Directory of Open Access Journals (Sweden)

    Shahram Mollaiy Berneti

    2013-04-01

    Full Text Available In this paper, a novel hybrid approach composed of adaptive neuro-fuzzy inference system (ANFIS and imperialist competitive algorithm is proposed. The imperialist competitive algorithm (ICA is used in this methodology to determine the most suitable initial membership functions of the ANFIS. The proposed model combines the global search ability of ICA with local search ability of gradient descent method. To illustrate the suitability and capability of the proposed model, this model is applied to predict oil flow rate of the wells utilizing data set of 31 wells in one of the northern Persian Gulf oil fields of Iran. The data set collected in a three month period for each well from Dec. 2002 to Nov. 2010. For the sake of performance evaluation, the results of the proposed model are compared with the conventional ANFIS model. The results show that the significant improvements are achievable using the proposed model in comparison with the results obtained by conventional ANFIS.

  15. A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Shan Li

    2014-01-01

    Full Text Available With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

  16. Conceptual design of distillation-based hybrid separation processes.

    Science.gov (United States)

    Skiborowski, Mirko; Harwardt, Andreas; Marquardt, Wolfgang

    2013-01-01

    Hybrid separation processes combine different separation principles and constitute a promising design option for the separation of complex mixtures. Particularly, the integration of distillation with other unit operations can significantly improve the separation of close-boiling or azeotropic mixtures. Although the design of single-unit operations is well understood and supported by computational methods, the optimal design of flowsheets of hybrid separation processes is still a challenging task. The large number of operational and design degrees of freedom requires a systematic and optimization-based design approach. To this end, a structured approach, the so-called process synthesis framework, is proposed. This article reviews available computational methods for the conceptual design of distillation-based hybrid processes for the separation of liquid mixtures. Open problems are identified that must be addressed to finally establish a structured process synthesis framework for such processes.

  17. Scalar field dark matter in hybrid approach

    NARCIS (Netherlands)

    Friedrich, Pavel; Prokopec, Tomislav

    2017-01-01

    We develop a hybrid formalism suitable for modeling scalar field dark matter, in which the phase-space distribution associated to the real scalar field is modeled by statistical equal-time two-point functions and gravity is treated by two stochastic gravitational fields in the longitudinal gauge (in

  18. An Adaptive and Hybrid Approach for Revisiting the Visibility Pipeline

    Directory of Open Access Journals (Sweden)

    Ícaro Lins Leitão da Cunha

    2016-04-01

    Full Text Available We revisit the visibility problem, which is traditionally known in Computer Graphics and Vision fields as the process of computing a (potentially visible set of primitives in the computational model of a scene. We propose a hybrid solution that uses a dry structure (in the sense of data reduction, a triangulation of the type J1a, to accelerate the task of searching for visible primitives. We came up with a solution that is useful for real-time, on-line, interactive applications as 3D visualization. In such applications the main goal is to load the minimum amount of primitives from the scene during the rendering stage, as possible. For this purpose, our algorithm executes the culling by using a hybrid paradigm based on viewing-frustum, back-face culling and occlusion models. Results have shown substantial improvement over these traditional approaches if applied separately. This novel approach can be used in devices with no dedicated processors or with low processing power, as cell phones or embedded displays, or to visualize data through the Internet, as in virtual museums applications.

  19. A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

    Directory of Open Access Journals (Sweden)

    Wen-An Yang

    2016-01-01

    Full Text Available Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs like the conventional backpropagation (BP algorithm and support vector machines (SVMs. In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.

  20. A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

    Science.gov (United States)

    Kerczewski, Robert J.; Chomos, Gerald J.; Griner, James H.; Mainger, Steven W.; Martzaklis, Konstantinos S.; Kachmar, Brian A.

    2000-01-01

    Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

  1. Graphene-based hybrid plasmonic modulator

    International Nuclear Information System (INIS)

    Shin, Jin-Soo; Kim, Jin-Soo; Tae Kim, Jin

    2015-01-01

    A graphene-based hybrid plasmonic modulator is designed based on an asymmetric double-electrode plasmonic waveguide structure. The photonic device consists of a monolayer graphene, a thin metal strip, and a thin dielectric layer that is inserted between the grapheme and the metal strip. By electrically tuning the graphene’s refractive index, the propagation loss of the hybrid long-range surface plasmon polariton strip mode in the proposed graphene-based hybrid plasmonic waveguide is switchable, and hence the intensity of the guided modes is modulated. The highest modulation depth is observed at the graphene’s epsilon-near-zero region. The device characteristics are characterized over the entire C-band (1.530–1.565 μm). (paper)

  2. Attention-level transitory response: a novel hybrid BCI approach

    Science.gov (United States)

    Diez, Pablo F.; Garcés Correa, Agustina; Orosco, Lorena; Laciar, Eric; Mut, Vicente

    2015-10-01

    Objective. People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the ‘Midas touch effect’, i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). Approach. Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. Main results. The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min-1 are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. Significance. A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

  3. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  4. A Hybrid Soft Computing Approach for Subset Problems

    Directory of Open Access Journals (Sweden)

    Broderick Crawford

    2013-01-01

    Full Text Available Subset problems (set partitioning, packing, and covering are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S can be partitioned into smaller subsets. All items in S must be contained in one and only one partition. Related problems are set packing (all items must be contained in zero or one partitions and set covering (all items must be contained in at least one partition. Here, we present a hybrid solver based on ant colony optimization (ACO combined with arc consistency for solving this kind of problems. ACO is a swarm intelligence metaheuristic inspired on ants behavior when they search for food. It allows to solve complex combinatorial problems for which traditional mathematical techniques may fail. By other side, in constraint programming, the solving process of Constraint Satisfaction Problems can dramatically reduce the search space by means of arc consistency enforcing constraint consistencies either prior to or during search. Our hybrid approach was tested with set covering and set partitioning dataset benchmarks. It was observed that the performance of ACO had been improved embedding this filtering technique in its constructive phase.

  5. Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter

    Directory of Open Access Journals (Sweden)

    Jing Li

    2016-01-01

    Full Text Available Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF, which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF, which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.

  6. Hybrid x-space: a new approach for MPI reconstruction.

    Science.gov (United States)

    Tateo, A; Iurino, A; Settanni, G; Andrisani, A; Stifanelli, P F; Larizza, P; Mazzia, F; Mininni, R M; Tangaro, S; Bellotti, R

    2016-06-07

    Magnetic particle imaging (MPI) is a new medical imaging technique capable of recovering the distribution of superparamagnetic particles from their measured induced signals. In literature there are two main MPI reconstruction techniques: measurement-based (MB) and x-space (XS). The MB method is expensive because it requires a long calibration procedure as well as a reconstruction phase that can be numerically costly. On the other side, the XS method is simpler than MB but the exact knowledge of the field free point (FFP) motion is essential for its implementation. Our simulation work focuses on the implementation of a new approach for MPI reconstruction: it is called hybrid x-space (HXS), representing a combination of the previous methods. Specifically, our approach is based on XS reconstruction because it requires the knowledge of the FFP position and velocity at each time instant. The difference with respect to the original XS formulation is how the FFP velocity is computed: we estimate it from the experimental measurements of the calibration scans, typical of the MB approach. Moreover, a compressive sensing technique is applied in order to reduce the calibration time, setting a fewer number of sampling positions. Simulations highlight that HXS and XS methods give similar results. Furthermore, an appropriate use of compressive sensing is crucial for obtaining a good balance between time reduction and reconstructed image quality. Our proposal is suitable for open geometry configurations of human size devices, where incidental factors could make the currents, the fields and the FFP trajectory irregular.

  7. Facile approach to prepare Pt decorated SWNT/graphene hybrid catalytic ink

    Energy Technology Data Exchange (ETDEWEB)

    Mayavan, Sundar, E-mail: sundarmayavan@cecri.res.in [Centre for Innovation in Energy Research, CSIR–Central Electrochemical Research Institute, Karaikudi 630006, Tamil Nadu (India); Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 305-701 (Korea, Republic of); Mandalam, Aditya; Balasubramanian, M. [Centre for Innovation in Energy Research, CSIR–Central Electrochemical Research Institute, Karaikudi 630006, Tamil Nadu (India); Sim, Jun-Bo [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 305-701 (Korea, Republic of); Choi, Sung-Min, E-mail: sungmin@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 305-701 (Korea, Republic of)

    2015-07-15

    Highlights: • Pt NPs were in situ synthesized onto CNT–graphene support in aqueous solution. • The as-prepared material was used directly as a catalyst ink without further treatment. • Catalyst ink is active toward methanol oxidation. • This approach realizes both scalable and greener production of hybrid catalysts. - Abstract: Platinum nanoparticles were in situ synthesized onto hybrid support involving graphene and single walled carbon nanotube in aqueous solution. We investigate the reduction of graphene oxide, and platinum nanoparticle functionalization on hybrid support by X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray diffraction, scanning electron microscopy and transmission electron microscopy. The as-prepared platinum on hybrid support was used directly as a catalyst ink without further treatment and is active toward methanol oxidation. This work realizes both scalable and greener production of highly efficient hybrid catalysts, and would be valuable for practical applications of graphene based fuel cell catalysts.

  8. Hybrid attacks on model-based social recommender systems

    Science.gov (United States)

    Yu, Junliang; Gao, Min; Rong, Wenge; Li, Wentao; Xiong, Qingyu; Wen, Junhao

    2017-10-01

    With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.

  9. Infectious disease modeling a hybrid system approach

    CERN Document Server

    Liu, Xinzhi

    2017-01-01

    This volume presents infectious diseases modeled mathematically, taking seasonality and changes in population behavior into account, using a switched and hybrid systems framework. The scope of coverage includes background on mathematical epidemiology, including classical formulations and results; a motivation for seasonal effects and changes in population behavior, an investigation into term-time forced epidemic models with switching parameters, and a detailed account of several different control strategies. The main goal is to study these models theoretically and to establish conditions under which eradication or persistence of the disease is guaranteed. In doing so, the long-term behavior of the models is determined through mathematical techniques from switched systems theory. Numerical simulations are also given to augment and illustrate the theoretical results and to help study the efficacy of the control schemes.

  10. Quality indicators in the mobile industry rankings based on indicators of customer satisfaction with the hybrid approach DEMATEL and ANP appropriate strategy based on gray system

    Directory of Open Access Journals (Sweden)

    Ashouri Fatemeh

    2016-01-01

    Full Text Available The quality of services as a vital element in the strategic competitiveness and commercial success are various methods have been developed to evaluate it. Prioritizing qualitative indicators based on the quality of mobile phone services enables the company gives top priority due to the higher percentage of satisfied customers provide. This study tries to customer satisfaction according to criteria to prioritize mobile operators pay qualitative characteristics. A sample consisted of 450 individuals (46% women, 54% men from IRANCELL operator (Iran in 2015 which selected randomly. Results shows between four considered strategies maximum priorities belong to S1 which is denote to more services beyond customer expectations.

  11. Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach

    Directory of Open Access Journals (Sweden)

    Shailesh Dewangan

    2015-09-01

    Full Text Available Surface integrity remains one of the major areas of concern in electric discharge machining (EDM. During the current study, grey-fuzzy logic-based hybrid optimization technique is utilized to determine the optimal settings of EDM process parameters with an aim to improve surface integrity aspects after EDM of AISI P20 tool steel. The experiment is designed using response surface methodology (RSM considering discharge current (Ip, pulse-on time (Ton, tool-work time (Tw and tool-lift time (Tup as process parameters. Various surface integrity characteristics such as white layer thickness (WLT, surface crack density (SCD and surface roughness (SR are considered during the current research work. Grey relational analysis (GRA combined with fuzzy-logic is used to determine grey fuzzy reasoning grade (GFRG. The optimal solution based on this analysis is found to be Ip = 1 A, Ton = 10 μs, Tw = 0.2 s, and Tup = 0.0 s. Analysis of variance (ANOVA results clearly indicate that Ton is the most contributing parameter followed by Ip, for multiple performance characteristics of surface integrity.

  12. A Hybrid Approach to the Optimization of Multiechelon Systems

    Directory of Open Access Journals (Sweden)

    Paweł Sitek

    2015-01-01

    Full Text Available In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP and constraint logic programming (CLP are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in the ECLiPSe system using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets.

  13. A novel Monte Carlo approach to hybrid local volatility models

    NARCIS (Netherlands)

    A.W. van der Stoep (Anton); L.A. Grzelak (Lech Aleksander); C.W. Oosterlee (Cornelis)

    2017-01-01

    textabstractWe present in a Monte Carlo simulation framework, a novel approach for the evaluation of hybrid local volatility [Risk, 1994, 7, 18–20], [Int. J. Theor. Appl. Finance, 1998, 1, 61–110] models. In particular, we consider the stochastic local volatility model—see e.g. Lipton et al. [Quant.

  14. A hybrid approach to designing inbound-resupply strategies

    NARCIS (Netherlands)

    Dullaert, Wout; Vernimmen, Bert; Raa, Birger; Witlox, Frank

    A new hybrid approach was developed to determine the optimal inbound-resupply strategy when suppliers ship goods to receivers. The optimal reorder level was calculated on the basis of a simulation of the distribution of demand and the lead time of the various sourcing alternatives. An evolutionary

  15. A hybrid generative-discriminative approach to speaker diarization

    NARCIS (Netherlands)

    Noulas, A.K.; van Kasteren, T.; Kröse, B.J.A.

    2008-01-01

    In this paper we present a sound probabilistic approach to speaker diarization. We use a hybrid framework where a distribution over the number of speakers at each point of a multimodal stream is estimated with a discriminative model. The output of this process is used as input in a generative model

  16. HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

    Science.gov (United States)

    Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

    2017-12-01

    For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2014-01-01

    Full Text Available Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP, the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR, artificial neural network (ANN, multivariate adaptive regression splines (MARS, and support vector regression (SVR techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models.

  18. Solving University Scheduling Problem Using Hybrid Approach

    Directory of Open Access Journals (Sweden)

    Aftab Ahmed Shaikh

    2011-10-01

    Full Text Available In universities scheduling curriculum activity is an essential job. Primarily, scheduling is a distribution of limited resources under interrelated constraints. The set of hard constraints demand the highest priority and should not to be violated at any cost, while the maximum soft constraints satisfaction mounts the quality scale of solution. In this research paper, a novel bisected approach is introduced that is comprisesd of GA (Genetic Algorithm as well as Backtracking Recursive Search. The employed technique deals with both hard and soft constraints successively. The first phase decisively is focused over elimination of all the hard constraints bounded violations and eventually produces partial solution for subsequent step. The second phase is supposed to draw the best possible solution on the search space. Promising results are obtained by implementation on the real dataset. The key points of the research approach are to get assurance of hard constraints removal from the dataset and minimizing computational time for GA by initializing pre-processed set of chromosomes.

  19. Hybrid methodological approach to context-dependent speech recognition

    Directory of Open Access Journals (Sweden)

    Dragiša Mišković

    2017-01-01

    Full Text Available Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

  20. A Hybrid Analysis Approach to Improve Financial Distress Forecasting: Empirical Evidence from Iran

    Directory of Open Access Journals (Sweden)

    Shakiba Khademolqorani

    2015-01-01

    Full Text Available Bankruptcy prediction is an important problem facing financial decision support for stakeholders of firms, including auditors, managers, shareholders, debt-holders, and potential investors, as well as academic researchers. Popular discourse on financial distress forecasting focuses on developing the discrete models to improve the prediction. The aim of this paper is to develop a novel hybrid financial distress model based on combining various statistical and machine learning methods. Then multiple attribute decision making method is exploited to choose the optimized model from the implemented ones. Proposed approaches have also been applied in Iranian companies that performed previous models and it can be consolidated with the help of the hybrid approach.

  1. A hybrid ensemble learning approach to star-galaxy classification

    Science.gov (United States)

    Kim, Edward J.; Brunner, Robert J.; Carrasco Kind, Matias

    2015-10-01

    There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a more robust star-galaxy classification. To demonstrate this hybrid, ensemble approach, we combine a purely morphological classifier, a supervised machine learning method based on random forest, an unsupervised machine learning method based on self-organizing maps, and a hierarchical Bayesian template-fitting method. Using data from the CFHTLenS survey (Canada-France-Hawaii Telescope Lensing Survey), we consider different scenarios: when a high-quality training set is available with spectroscopic labels from DEEP2 (Deep Extragalactic Evolutionary Probe Phase 2 ), SDSS (Sloan Digital Sky Survey), VIPERS (VIMOS Public Extragalactic Redshift Survey), and VVDS (VIMOS VLT Deep Survey), and when the demographics of sources in a low-quality training set do not match the demographics of objects in the test data set. We demonstrate that our Bayesian combination technique improves the overall performance over any individual classification method in these scenarios. Thus, strategies that combine the predictions of different classifiers may prove to be optimal in currently ongoing and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope.

  2. Agricultural Tractor Selection: A Hybrid and Multi-Attribute Approach

    Directory of Open Access Journals (Sweden)

    Jorge L. García-Alcaraz

    2016-02-01

    Full Text Available Usually, agricultural tractor investments are assessed using traditional economic techniques that only involve financial attributes, resulting in reductionist evaluations. However, tractors have qualitative and quantitative attributes that must be simultaneously integrated into the evaluation process. This article reports a hybrid and multi-attribute approach to assessing a set of agricultural tractors based on AHP-TOPSIS. To identify the attributes in the model, a survey including eighteen attributes was given to agricultural machinery salesmen and farmers for determining their importance. The list of attributes was presented to a decision group for a case of study, and their importance was estimated using AHP and integrated into the TOPSIS technique. In this case, one tractor was selected from a set of six alternatives, integrating six attributes in the model: initial cost, annual maintenance cost, liters of diesel per hour, safety of the operator, maintainability and after-sale customer service offered by the supplier. Based on the results obtained, the model can be considered easy to apply and to have good acceptance among farmers and salesmen, as there are no special software requirements for the application.

  3. Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

    Directory of Open Access Journals (Sweden)

    Dehua Zheng

    2017-12-01

    Full Text Available The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs and distribution system operators (DSOs in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT, genetic algorithm (GA and artificial neural network (ANN is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

  4. Hybrid simulation of reactor kinetics in CANDU reactors using a modal approach

    International Nuclear Information System (INIS)

    Monaghan, B.M.; McDonnell, F.N.; Hinds, H.W.T.; m.

    1980-01-01

    A hybrid computer model for simulating the behaviour of large CANDU (Canada Deuterium Uranium) reactor cores is presented. The main dynamic variables are expressed in terms of weighted sums of a base set of spatial natural-mode functions with time-varying co-efficients. This technique, known as the modal or synthesis approach, permits good three-dimensional representation of reactor dynamics and is well suited to hybrid simulation. The hybrid model provides improved man-machine interaction and real-time capability. The model was used in two applications. The first studies the transient that follows a loss of primary coolant and reactor shutdown; the second is a simulation of the dynamics of xenon, a fission product which has a high absorption cross-section for neutrons and thus has an important effect on reactor behaviour. Comparison of the results of the hybrid computer simulation with those of an all-digital one is good, within 1% to 2%

  5. A hybrid system approach to airspeed, angle of attack and sideslip estimation in Unmanned Aerial Vehicles

    KAUST Repository

    Shaqura, Mohammad; Claudel, Christian

    2015-01-01

    , low power autopilots in real-time. The computational method is based on a hybrid decomposition of the modes of operation of the UAV. A Bayesian approach is considered for estimation, in which the estimated airspeed, angle of attack and sideslip

  6. Hybrid employment recommendation algorithm based on Spark

    Science.gov (United States)

    Li, Zuoquan; Lin, Yubei; Zhang, Xingming

    2017-08-01

    Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

  7. Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

    International Nuclear Information System (INIS)

    Mohanta, Dusmanta Kumar; Sadhu, Pradip Kumar; Chakrabarti, R.

    2007-01-01

    This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power

  8. A hybrid clustering approach to recognition of protein families in 114 microbial genomes

    Directory of Open Access Journals (Sweden)

    Gogarten J Peter

    2004-04-01

    Full Text Available Abstract Background Grouping proteins into sequence-based clusters is a fundamental step in many bioinformatic analyses (e.g., homology-based prediction of structure or function. Standard clustering methods such as single-linkage clustering capture a history of cluster topologies as a function of threshold, but in practice their usefulness is limited because unrelated sequences join clusters before biologically meaningful families are fully constituted, e.g. as the result of matches to so-called promiscuous domains. Use of the Markov Cluster algorithm avoids this non-specificity, but does not preserve topological or threshold information about protein families. Results We describe a hybrid approach to sequence-based clustering of proteins that combines the advantages of standard and Markov clustering. We have implemented this hybrid approach over a relational database environment, and describe its application to clustering a large subset of PDB, and to 328577 proteins from 114 fully sequenced microbial genomes. To demonstrate utility with difficult problems, we show that hybrid clustering allows us to constitute the paralogous family of ATP synthase F1 rotary motor subunits into a single, biologically interpretable hierarchical grouping that was not accessible using either single-linkage or Markov clustering alone. We describe validation of this method by hybrid clustering of PDB and mapping SCOP families and domains onto the resulting clusters. Conclusion Hybrid (Markov followed by single-linkage clustering combines the advantages of the Markov Cluster algorithm (avoidance of non-specific clusters resulting from matches to promiscuous domains and single-linkage clustering (preservation of topological information as a function of threshold. Within the individual Markov clusters, single-linkage clustering is a more-precise instrument, discerning sub-clusters of biological relevance. Our hybrid approach thus provides a computationally efficient

  9. Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

    Science.gov (United States)

    Chiadamrong, N.; Piyathanavong, V.

    2017-12-01

    Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

  10. Restraining approach for the spurious kinematic modes in hybrid equilibrium element

    Science.gov (United States)

    Parrinello, F.

    2013-10-01

    The present paper proposes a rigorous approach for the elimination of spurious kinematic modes in hybrid equilibrium elements, for three well known mesh patches. The approach is based on the identification of the dependent equations in the set of inter-element and boundary equilibrium equations of the sides involved in the spurious kinematic mode. Then the kinematic variables related to the dependent equations are reciprocally constrained and, by application of master slave elimination method, the set of inter-element equilibrium equations is reduced to full rank. The elastic solutions produced by means of the proposed approach verify the homogeneous, the inter-element and the boundary equilibrium equations. Hybrid stress formulation is developed in a rigorous mathematical setting. The results of linear elastic analysis obtained by the proposed approach and by classical displacement based method are compared for some structural examples.

  11. HYBRID EDUCATION: THE ESTIMATION IN THE CATEGORIES OF INFORMATION-AXIOLOGICAL APPROACH

    Directory of Open Access Journals (Sweden)

    A. S. Kizilova

    2018-01-01

    Full Text Available Introduction: a hybrid assessment of reality is a new information-axiological level of communication between people. The term "hybrid (hybrid training" has been used as a result of the integration of digital and communication technologies in the form of online courses.Materials and methods: the main Russian forms of education are analyzed. The evaluation of the forms of education in the categories of the information-axiological approach is made on the basis of the following idea: everything is interchangeable, since everything has value. The mixing principles and models used in the process of hybrid formation are considered. Due to the fact that any mixed training requires planning, the analysis of the project and the target group, content analysis and financial analysis in this process is carried out.Results: specific educational methods are studied at the Bauman MSTU, subject to a hybrid assessment in the categories of the information-axiological approach. The analysis showed that the above explanation of the term "hybrid formation" is extremely narrow and one-sided. In particular, the search for information on volunteer education and the search for a socially-based education was conducted not only in the Bauman MSTU, but in Russia as a whole. However, the result was the portals of international student organizations with their own projects. Another example of a different interpretation of the "hybrid education" may be the assumption of quite axiologically new duties.Discussion and Conclusions: hybrid education is not limited to any temporal and territorial framework. It can manifest itself not only in the Internet-sphere, but also in the most diverse spheres of everyday life, with the interaction of various people and entire societies.

  12. Hybrid-Based Dense Stereo Matching

    Science.gov (United States)

    Chuang, T. Y.; Ting, H. W.; Jaw, J. J.

    2016-06-01

    Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

  13. Optimal energy management for a flywheel-based hybrid vehicle

    NARCIS (Netherlands)

    Berkel, van K.; Hofman, T.; Vroemen, B.G.; Steinbuch, M.

    2011-01-01

    This paper presents the modeling and design of an optimal Energy Management Strategy (EMS) for a flywheel-based hybrid vehicle, that does not use any electrical motor/generator, or a battery, for its hybrid functionalities. The hybrid drive train consists of only low-cost components, such as a

  14. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

    Science.gov (United States)

    Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

    2016-09-01

    This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

  15. A hybrid approach to simulate multiple photon scattering in X-ray imaging

    International Nuclear Information System (INIS)

    Freud, N.; Letang, J.-M.; Babot, D.

    2005-01-01

    A hybrid simulation approach is proposed to compute the contribution of scattered radiation in X- or γ-ray imaging. This approach takes advantage of the complementarity between the deterministic and probabilistic simulation methods. The proposed hybrid method consists of two stages. Firstly, a set of scattering events occurring in the inspected object is determined by means of classical Monte Carlo simulation. Secondly, this set of scattering events is used as a starting point to compute the energy imparted to the detector, with a deterministic algorithm based on a 'forced detection' scheme. For each scattering event, the probability for the scattered photon to reach each pixel of the detector is calculated using well-known physical models (form factor and incoherent scattering function approximations, in the case of Rayleigh and Compton scattering respectively). The results of the proposed hybrid approach are compared to those obtained with the Monte Carlo method alone (Geant4 code) and found to be in excellent agreement. The convergence of the results when the number of scattering events increases is studied. The proposed hybrid approach makes it possible to simulate the contribution of each type (Compton or Rayleigh) and order of scattering, separately or together, with a single PC, within reasonable computation times (from minutes to hours, depending on the number of pixels of the detector). This constitutes a substantial benefit, compared to classical simulation methods (Monte Carlo or deterministic approaches), which usually requires a parallel computing architecture to obtain comparable results

  16. A hybrid approach to simulate multiple photon scattering in X-ray imaging

    Energy Technology Data Exchange (ETDEWEB)

    Freud, N. [CNDRI, Laboratory of Nondestructive Testing using Ionizing Radiations, INSA-Lyon Scientific and Technical University, Bat. Antoine de Saint-Exupery, 20, avenue Albert Einstein, 69621 Villeurbanne Cedex (France)]. E-mail: nicolas.freud@insa-lyon.fr; Letang, J.-M. [CNDRI, Laboratory of Nondestructive Testing using Ionizing Radiations, INSA-Lyon Scientific and Technical University, Bat. Antoine de Saint-Exupery, 20, avenue Albert Einstein, 69621 Villeurbanne Cedex (France); Babot, D. [CNDRI, Laboratory of Nondestructive Testing using Ionizing Radiations, INSA-Lyon Scientific and Technical University, Bat. Antoine de Saint-Exupery, 20, avenue Albert Einstein, 69621 Villeurbanne Cedex (France)

    2005-01-01

    A hybrid simulation approach is proposed to compute the contribution of scattered radiation in X- or {gamma}-ray imaging. This approach takes advantage of the complementarity between the deterministic and probabilistic simulation methods. The proposed hybrid method consists of two stages. Firstly, a set of scattering events occurring in the inspected object is determined by means of classical Monte Carlo simulation. Secondly, this set of scattering events is used as a starting point to compute the energy imparted to the detector, with a deterministic algorithm based on a 'forced detection' scheme. For each scattering event, the probability for the scattered photon to reach each pixel of the detector is calculated using well-known physical models (form factor and incoherent scattering function approximations, in the case of Rayleigh and Compton scattering respectively). The results of the proposed hybrid approach are compared to those obtained with the Monte Carlo method alone (Geant4 code) and found to be in excellent agreement. The convergence of the results when the number of scattering events increases is studied. The proposed hybrid approach makes it possible to simulate the contribution of each type (Compton or Rayleigh) and order of scattering, separately or together, with a single PC, within reasonable computation times (from minutes to hours, depending on the number of pixels of the detector). This constitutes a substantial benefit, compared to classical simulation methods (Monte Carlo or deterministic approaches), which usually requires a parallel computing architecture to obtain comparable results.

  17. Silicon based quantum dot hybrid qubits

    Science.gov (United States)

    Kim, Dohun

    2015-03-01

    The charge and spin degrees of freedom of an electron constitute natural bases for constructing quantum two level systems, or qubits, in semiconductor quantum dots. The quantum dot charge qubit offers a simple architecture and high-speed operation, but generally suffers from fast dephasing due to strong coupling of the environment to the electron's charge. On the other hand, quantum dot spin qubits have demonstrated long coherence times, but their manipulation is often slower than desired for important future applications. This talk will present experimental progress of a `hybrid' qubit, formed by three electrons in a Si/SiGe double quantum dot, which combines desirable characteristics (speed and coherence) in the past found separately in qubits based on either charge or spin degrees of freedom. Using resonant microwaves, we first discuss qubit operations near the `sweet spot' for charge qubit operation. Along with fast (>GHz) manipulation rates for any rotation axis on the Bloch sphere, we implement two independent tomographic characterization schemes in the charge qubit regime: traditional quantum process tomography (QPT) and gate set tomography (GST). We also present resonant qubit operations of the hybrid qubit performed on the same device, DC pulsed gate operations of which were recently demonstrated. We demonstrate three-axis control and the implementation of dynamic decoupling pulse sequences. Performing QPT on the hybrid qubit, we show that AC gating yields π rotation process fidelities higher than 93% for X-axis and 96% for Z-axis rotations, which demonstrates efficient quantum control of semiconductor qubits using resonant microwaves. We discuss a path forward for achieving fidelities better than the threshold for quantum error correction using surface codes. This work was supported in part by ARO (W911NF-12-0607), NSF (PHY-1104660), DOE (DE-FG02-03ER46028), and by the Laboratory Directed Research and Development program at Sandia National Laboratories

  18. A novel hybridization approach for detection of citrus viroids.

    Science.gov (United States)

    Murcia, N; Serra, P; Olmos, A; Duran-Vila, N

    2009-04-01

    Citrus plants are natural hosts of several viroid species all belonging to the family Pospiviroidae. Previous attempts to detect viroids from field-grown species and cultivars yielded erratic results unless analyses were performed using Etrog citron a secondary bio-amplification host. To overcome the use of Etrog citron a number of RT-PCR approaches have been proposed with different degrees of success. Here we report the suitability of an easy to handle northern hybridization protocol for viroid detection of samples collected from field-grown citrus species and cultivars. The protocol involves: (i) Nucleic acid preparations from bark tissue samples collected from field-grown trees regardless of the growing season and storage conditions; (ii) Separation in 5% PAGE or 1% agarose, blotting to membrane and fixing; (iii) Hybridization with viroid-specific DIG-labelled probes and detection with anti-DIG-alkaline phosphatase conjugate and autoradiography with the CSPD substrate. The method has been tested with viroid-infected trees of sweet orange, lemon, mandarin, grapefruit, sour orange, Swingle citrumello, Tahiti lime and Mexican lime. This novel hybridization approach is extremely sensitive, easy to handle and shortens the time needed for reliable viroid indexing tests. The suitability of PCR generated DIG-labelled probes and the sensitivity achieved when the samples are separated and blotted from non-denaturing gels are discussed.

  19. Hybrid Engine Powered City Car: Fuzzy Controlled Approach

    Science.gov (United States)

    Rahman, Ataur; Mohiuddin, AKM; Hawlader, MNA; Ihsan, Sany

    2017-03-01

    This study describes a fuzzy controlled hybrid engine powered car. The car is powered by the lithium ion battery capacity of 1000 Wh is charged by the 50 cc hybrid engine and power regenerative mode. The engine is operated with lean mixture at 3000 rpm to charge the battery. The regenerative mode that connects with the engine generates electrical power of 500-600 W for the deceleration of car from 90 km/h to 20 km/h. The regenerated electrical power has been used to power the air-conditioning system and to meet the other electrical power. The battery power only used to propel the car. The regenerative power also found charging the battery for longer operation about 40 minutes and more. The design flexibility of this vehicle starts with whole-vehicle integration based on radical light weighting, drag reduction, and accessory efficiency. The energy efficient hybrid engine cut carbon dioxide (CO2) and nitrogen oxides (N2O) emission about 70-80% as the loads on the crankshaft such as cam-follower and its associated rotating components are replaced by electromagnetic systems, and the flywheel, alternator and starter motor are replaced by a motor generator. The vehicle was tested and found that it was able to travel 70 km/litre with the power of hybrid engine.

  20. A Hybrid Approach for Thread Recommendation in MOOC Forums

    OpenAIRE

    Ahmad. A. Kardan; Amir Narimani; Foozhan Ataiefard

    2017-01-01

    Recommender Systems have been developed to provide contents and services compatible to users based on their behaviors and interests. Due to information overload in online discussion forums and users diverse interests, recommending relative topics and threads is considered to be helpful for improving the ease of forum usage. In order to lead learners to find relevant information in educational forums, recommendations are even more needed. We present a hybrid thread recommender system for MOOC ...

  1. Interactive Digital Storytelling: Towards a Hybrid Conceptual Approach

    OpenAIRE

    Spierling, Ulrike

    2005-01-01

    1 Introduction In this contribution, Interactive Digital Storytelling is viewed as a hybrid form of game design and cinematic storytelling for the understanding and making of future learning and entertainment applications. The paper shall present formal design models that provide a conceptual bridge between both traditional linear narrative techniques as well as agent-based emergent conversations with virtual characters. In summary, a theoretical classification of thinking models for authors ...

  2. Tough hybrid ceramic-based material with high strength

    International Nuclear Information System (INIS)

    Guo, Shuqi; Kagawa, Yutaka; Nishimura, Toshiyuki

    2012-01-01

    This study describes a tough and strong hybrid ceramic material consisting of platelet-like zirconium compounds and metal. A mixture of boron carbide and excess zirconium powder was heated to 1900 °C using a liquid-phase reaction sintering technique to produce a platelet-like ZrB 2 -based hybrid ceramic bonded by a thin zirconium layer. The platelet-like ZrB 2 grains were randomly present in the as-sintered hybrid ceramic. Relative to non-hybrid ceramics, the fracture toughness and flexural strength of the hybrid ceramic increased by approximately 2-fold.

  3. Approaches to Low Fuel Regression Rate in Hybrid Rocket Engines

    Directory of Open Access Journals (Sweden)

    Dario Pastrone

    2012-01-01

    Full Text Available Hybrid rocket engines are promising propulsion systems which present appealing features such as safety, low cost, and environmental friendliness. On the other hand, certain issues hamper the development hoped for. The present paper discusses approaches addressing improvements to one of the most important among these issues: low fuel regression rate. To highlight the consequence of such an issue and to better understand the concepts proposed, fundamentals are summarized. Two approaches are presented (multiport grain and high mixture ratio which aim at reducing negative effects without enhancing regression rate. Furthermore, fuel material changes and nonconventional geometries of grain and/or injector are presented as methods to increase fuel regression rate. Although most of these approaches are still at the laboratory or concept scale, many of them are promising.

  4. A hybrid approach for minimizing makespan in permutation flowshop scheduling

    DEFF Research Database (Denmark)

    Govindan, Kannan; Balasundaram, R.; Baskar, N.

    2017-01-01

    This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan (total completion time). To solve scheduling problems, a combination of Decision Tree (DT) and Scatter Search (SS) algorithms are used. Initially, the DT is used to generate a seed...... solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification...

  5. Hybrid closure of atrial septal defect: A modified approach

    Directory of Open Access Journals (Sweden)

    Kshitij Sheth

    2015-01-01

    Full Text Available A 3.5-year-old girl underwent transcatheter closure of patent ductus arteriosus in early infancy during which time her secundum atrial septal defect (ASD was left alone. When she came for elective closure of ASD, she was found to have bilaterally blocked femoral veins. The defect was successfully closed with an Amplatzer septal occluder (ASO; St. Jude Medical, Plymouth, MN, USA using a hybrid approach via a sub-mammary mini-thoracotomy incision without using cardiopulmonary bypass. At the end of 1-year follow-up, the child is asymptomatic with device in a stable position without any residual shunt.

  6. Rule-based energy management strategies for hybrid vehicles

    NARCIS (Netherlands)

    Hofman, T.; Druten, van R.M.; Serrarens, A.F.A.; Steinbuch, M.

    2007-01-01

    Int. J. of Electric and Hybrid Vehicles (IJEHV), The highest control layer of a (hybrid) vehicular drive train is termed the Energy Management Strategy (EMS). In this paper an overview of different control methods is given and a new rule-based EMS is introduced based on the combination of Rule-Based

  7. Yield performance and stability of CMS-based triticale hybrids.

    Science.gov (United States)

    Mühleisen, Jonathan; Piepho, Hans-Peter; Maurer, Hans Peter; Reif, Jochen Christoph

    2015-02-01

    CMS-based triticale hybrids showed only marginal midparent heterosis for grain yield and lower dynamic yield stability compared to inbred lines. Hybrids of triticale (×Triticosecale Wittmack) are expected to possess outstanding yield performance and increased dynamic yield stability. The objectives of the present study were to (1) examine the optimum choice of the biometrical model to compare yield stability of hybrids versus lines, (2) investigate whether hybrids exhibit a more pronounced grain yield performance and yield stability, and (3) study optimal strategies to predict yield stability of hybrids. Thirteen female and seven male parental lines and their 91 factorial hybrids as well as 30 commercial lines were evaluated for grain yield in up to 20 environments. Hybrids were produced using a cytoplasmic male sterility (CMS)-inducing cytoplasm that originated from Triticumtimopheevii Zhuk. We found that the choice of the biometrical model can cause contrasting results and concluded that a group-by-environment interaction term should be added to the model when estimating stability variance of hybrids and lines. midparent heterosis for grain yield was on average 3 % with a range from -15.0 to 11.5 %. No hybrid outperformed the best inbred line. Hybrids had, on average, lower dynamic yield stability compared to the inbred lines. Grain yield performance of hybrids could be predicted based on midparent values and general combining ability (GCA)-predicted values. In contrast, stability variance of hybrids could be predicted only based on GCA-predicted values. We speculated that negative effects of the used CMS cytoplasm might be the reason for the low performance and yield stability of the hybrids. For this purpose a detailed study on the reasons for the drawback of the currently existing CMS system in triticale is urgently required comprising also the search of potentially alternative hybridization systems.

  8. A DYNAMIC APPROACH TO IMPROVING THE EXECUTION OF DEVELOPMENT STRATEGIES FOR DATE-RELATED INDUSTRIES IN IRAN, BASED ON HYBRID GRA AND ISM METHODS

    Directory of Open Access Journals (Sweden)

    S. Ghane

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This paper presents a hybrid Grey Relational Analysis (GRA and Interpretive Structural Modelling (ISM methodology for appropriate dynamic analysis, in order to improve the execution of development strategies for date-related industries in Iran. The proposed framework consists of four main phases: (1 establishment of strategies; (2 GRA analysis; (3 binary comparison to determine hierarchy levels using the ISM method; and (4 dynamic analysis. The analysis reveals that the strategic priorities to improve Iran’s date industries can be arranged on three levels. The first level consists of the by-products of date packing and processing. On the second level, three tactics are determined: developing ready-for-use date products, deriving date fruit products, and fermentation of products. The third level is to blend dates with other foods and palm products (excluding dates.

    AFRIKAANSE OPSOMMING: Hierdie artikel hou ’n hibirede GRA en ISM metodologie voor vir dinamiese analise om ont-wikkelingstrategieë vir die dadelindustrie in Iran te verbeter. Die voorgestelde raamwerk bestaan uit vier fases: (1 vasstelling van die strategieë; (2 GRA analise; (3 binêre vergelyking om die hierargievlakke te bepaal deur die ISM metode te gebruik; en (4 dinamiese analise. Die analise toon dat die strategiese prioriteite om die dadelindustrie in Iran te verbeter in drie vlakke gerangskik kan word. Die eerste vlak behels die neweprodukte van dadelverpakking en -prosessering. Op die tweede vlak word drie taktieke ontwikkel: die ontwikkeling van gereed-vir-gebruik dadelprodukte, dadelvrugteprodukte en die fermentasie van produkte. Die derde vlak behels die versny van dadels met ander voedsel- en palmprodukte (uitgesonderd dadels.

  9. Hybrid Modeling Method for a DEP Based Particle Manipulation

    Directory of Open Access Journals (Sweden)

    Mohamad Sawan

    2013-01-01

    Full Text Available In this paper, a new modeling approach for Dielectrophoresis (DEP based particle manipulation is presented. The proposed method fulfills missing links in finite element modeling between the multiphysic simulation and the biological behavior. This technique is amongst the first steps to develop a more complex platform covering several types of manipulations such as magnetophoresis and optics. The modeling approach is based on a hybrid interface using both ANSYS and MATLAB to link the propagation of the electrical field in the micro-channel to the particle motion. ANSYS is used to simulate the electrical propagation while MATLAB interprets the results to calculate cell displacement and send the new information to ANSYS for another turn. The beta version of the proposed technique takes into account particle shape, weight and its electrical properties. First obtained results are coherent with experimental results.

  10. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

    International Nuclear Information System (INIS)

    Pousinho, H.M.I.; Mendes, V.M.F.; Catalao, J.P.S.

    2011-01-01

    The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

  11. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

    Energy Technology Data Exchange (ETDEWEB)

    Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal); Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)

    2011-01-15

    The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches. (author)

  12. Development of bio-hybrid material based on Salmonella ...

    African Journals Online (AJOL)

    The immobilization of a whole microbial cell is an important process used in nanotechnology of biosensors and other related fields, especially the development of bio-hybrid materials based on live organisms and inorganic compounds. Here, we described an essay to develop a bio-hybrid material based on Salmonella ...

  13. A bottom-up approach for the synthesis of highly ordered fullerene-intercalated graphene hybrids

    Directory of Open Access Journals (Sweden)

    Dimitrios eGournis

    2015-02-01

    Full Text Available Much of the research effort on graphene focuses on its use as a building block for the development of new hybrid nanostructures with well-defined dimensions and properties suitable for applications such as gas storage, heterogeneous catalysis, gas/liquid separations, nanosensing and biomedicine. Towards this aim, here we describe a new bottom-up approach, which combines self-assembly with the Langmuir Schaefer deposition technique to synthesize graphene-based layered hybrid materials hosting fullerene molecules within the interlayer space. Our film preparation consists in a bottom-up layer-by-layer process that proceeds via the formation of a hybrid organo-graphene oxide Langmuir film. The structure and composition of these hybrid fullerene-containing thin multilayers deposited on hydrophobic substrates were characterized by a combination of X-ray diffraction, Raman and X-ray photoelectron spectroscopies, atomic force microscopy and conductivity measurements. The latter revealed that the presence of C60 within the interlayer spacing leads to an increase in electrical conductivity of the hybrid material as compared to the organo-graphene matrix alone.

  14. Mixed H∞ and passive control for linear switched systems via hybrid control approach

    Science.gov (United States)

    Zheng, Qunxian; Ling, Youzhu; Wei, Lisheng; Zhang, Hongbin

    2018-03-01

    This paper investigates the mixed H∞ and passive control problem for linear switched systems based on a hybrid control strategy. To solve this problem, first, a new performance index is proposed. This performance index can be viewed as the mixed weighted H∞ and passivity performance. Then, the hybrid controllers are used to stabilise the switched systems. The hybrid controllers consist of dynamic output-feedback controllers for every subsystem and state updating controllers at the switching instant. The design of state updating controllers not only depends on the pre-switching subsystem and the post-switching subsystem, but also depends on the measurable output signal. The hybrid controllers proposed in this paper can include some existing ones as special cases. Combine the multiple Lyapunov functions approach with the average dwell time technique, new sufficient conditions are obtained. Under the new conditions, the closed-loop linear switched systems are globally uniformly asymptotically stable with a mixed H∞ and passivity performance index. Moreover, the desired hybrid controllers can be constructed by solving a set of linear matrix inequalities. Finally, a numerical example and a practical example are given.

  15. Hybrid method based on embedded coupled simulation of vortex particles in grid based solution

    Science.gov (United States)

    Kornev, Nikolai

    2017-09-01

    The paper presents a novel hybrid approach developed to improve the resolution of concentrated vortices in computational fluid mechanics. The method is based on combination of a grid based and the grid free computational vortex (CVM) methods. The large scale flow structures are simulated on the grid whereas the concentrated structures are modeled using CVM. Due to this combination the advantages of both methods are strengthened whereas the disadvantages are diminished. The procedure of the separation of small concentrated vortices from the large scale ones is based on LES filtering idea. The flow dynamics is governed by two coupled transport equations taking two-way interaction between large and fine structures into account. The fine structures are mapped back to the grid if their size grows due to diffusion. Algorithmic aspects of the hybrid method are discussed. Advantages of the new approach are illustrated on some simple two dimensional canonical flows containing concentrated vortices.

  16. Genetic algorithm and neural network hybrid approach for job-shop scheduling

    OpenAIRE

    Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

    1998-01-01

    Copyright @ 1998 ACTA Press This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and ...

  17. 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.

  18. Neuro-genetic hybrid approach for the solution of non-convex economic dispatch problem

    International Nuclear Information System (INIS)

    Malik, T.N.; Asar, A.U.

    2009-01-01

    ED (Economic Dispatch) is non-convex constrained optimization problem, and is used for both on line and offline studies in power system operation. Conventionally, it is solved as convex problem using optimization techniques by approximating generator input/output characteristic. Curves of monotonically increasing nature thus resulting in an inaccurate dispatch. The GA (Genetic Algorithm) has been used for the solution of this problem owing to its inherent ability to address the convex and non-convex problems equally. This approach brings the solution to the global minimum region of search space in a short time and then takes longer time to converge to near optimal results. GA based hybrid approaches are used to fine tune the near optimal results produced by GA. This paper proposes NGH (Neuro Genetic Hybrid) approach to solve the economic dispatch with valve point effect. The proposed approach combines the GA with the ANN (Artificial Neural Network) using SI (Swarm Intelligence) learning rule. The GA acts as a global optimizer and the neural network fine tunes the GA results to the desired targets. Three machines standard test system has been tested for validation of the approach. Comparing the results with GA and NGH model based on back-propagation learning, the proposed approach gives contrast improvements showing the promise of the approach. (author)

  19. Rapid Deployment of Optimal Control for Building HVAC Systems using Innovative Software Tools and a Hybrid Heuristic/Model Based Control Approach

    Science.gov (United States)

    2017-03-21

    Monitoring and Verification Protocol LCDR Lieutenant Commander ME Mechanical Engineering MPC Model Predictive Control: a model-based system...Activity PE Professional Engineer PG&E Pacific Gas & Electric : a California utility company PI Proportional + Integral: a common software element...INTRODUCTION The Department of Defense (DoD) spends approximately $4 billion per year on facility energy consumption to power and fuel over 500

  20. Microemulsion based hybrid biofuels using glycerol monooleate

    International Nuclear Information System (INIS)

    Bora, Plaban; Konwar, Lakhya Jyoti; Deka, Dhanapati

    2016-01-01

    Highlights: • Fuel quality of GMO based MHBFs. • Effect of externally added monoglyceride surfactant (GMO) on fuel characteristics of MHBF. • Structural and dynamic behaviors of GMO based MHBFs. • Can offer strong candidature for future biofuel industry. - Abstract: The present investigation aims to highlighten the effect of monoglyceride surfactant (GMO) on structure and dynamic behavior and other fuel characteristics of microemulsion based hybrid biofuels (MHBFs). Fuel quality of MHBFs formulated using purified GMO (>90%), which was prepared by esterification of glycerol, was investigated in the study. Phase behaviors, droplet size distribution, number of droplets present in the system, average droplet size and average length of surface active agents were studied as a part of structural investigations of the GMO based MHBFs. Diffusion coefficient, energy barrier to droplet coalescence and rate of coalescence of droplets were also investigated for the formulated MHBFs. The number of droplets, length of surface active agent and the diffusion co-efficient were in the ranges of 1.87 × 10"2"1–5.66 × 10"2"1/m"3, 0.92–1.07 nm and 1.00 × 10"−"1"1–1.79 × 10"−"1"1 m"2/s, respectively. The rate of droplet coalescence was obtained in the range 2.77 × 10"−"4–8.78 × 10"−"4 times the collision factor. MHBFs incorporating the glycerol derived bio-based nonionic surfactant GMO exhibited viscosity of 4.12 mm"2/s (at 40 °C), gross calorific value (GCV) of 39.17 MJ/kg and pour point of −7 °C.

  1. Problem and Project Based Learning in Hybrid Spaces

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Davidsen, Jacob; Hodgson, Vivien

    2016-01-01

    There is a need within networked learning to understand and conceptualise the interplay between digital and physical spaces or what we could term hybrid spaces. Therefore, we discuss a recent study of students from two different programmes who are engaged in long-term, group-based problem...... and project based learning. Based on interviews, workshops and observations of students’ actual group practices in open, shared and flexible spaces in Aalborg University (AAU), we identify and discuss how students incorporate networked and digital technologies into their group work and into the study places...... they create for themselves. We describe how in one of the programmes ‘nomadic’ groups of students used different technologies and spaces for ‘placemaking’. We then show how their experience and approach to collaborative work differs to that of the more static or ‘artisan’ groups of students in the other...

  2. Hybrid silica luminescent materials based on lanthanide-containing lyotropic liquid crystal with polarized emission

    Energy Technology Data Exchange (ETDEWEB)

    Selivanova, N.M., E-mail: natsel@mail.ru [Kazan National Research Technological University, 68 Karl Marx Str., Kazan 420015 (Russian Federation); Vandyukov, A.E.; Gubaidullin, A.T. [A.E. Arbuzov Institute of Organic and Physical Chemistry of the Kazan Scientific Center of the Russian Academy of Sciences, 8 Acad. Arbuzov Str., Kazan 420088 (Russian Federation); Galyametdinov, Y.G. [Kazan National Research Technological University, 68 Karl Marx Str., Kazan 420015 (Russian Federation)

    2014-11-14

    This paper represents the template method for synthesis of hybrid silica films based on Ln-containing lyotropic liquid crystal and characterized by efficient luminescence. Luminescence films were prepared in situ by the sol–gel processes. Lyotropic liquid crystal (LLC) mesophases C{sub 12}H{sub 25}O(CH{sub 2}CH{sub 2}O){sub 10}H/Ln(NO{sub 3}){sub 3}·6H{sub 2}O/H{sub 2}O containing Ln (III) ions (Dy, Tb, Eu) were used as template. Polarized optical microscopy, X-ray powder diffraction, and FT-IR-spectroscopy were used for characterization of liquid crystal mesophases and hybrid films. The morphology of composite films was studied by the atomic force microscopy method (AFM). The optical properties of the resulting materials were evaluated. It was found that hybrid silica films demonstrate significant increase of their lifetime in comparison with an LLC system. New effects of linearly polarized emission revealed for Ln-containing hybrid silica films. Polarization in lanthanide-containing hybrid composites indicates that silica precursor causes orientation of emitting ions. - Highlights: • We suggest a new simple approach for creating luminescence hybrid silica films. • Ln-containing hybrid silica films demonstrate yellow, green and red emissions. • Tb(III)-containing hybrid film have a high lifetime. • We report effects of linearly polarized emission in hybrid film.

  3. A hybrid data compression approach for online backup service

    Science.gov (United States)

    Wang, Hua; Zhou, Ke; Qin, MingKang

    2009-08-01

    With the popularity of Saas (Software as a service), backup service has becoming a hot topic of storage application. Due to the numerous backup users, how to reduce the massive data load is a key problem for system designer. Data compression provides a good solution. Traditional data compression application used to adopt a single method, which has limitations in some respects. For example data stream compression can only realize intra-file compression, de-duplication is used to eliminate inter-file redundant data, compression efficiency cannot meet the need of backup service software. This paper proposes a novel hybrid compression approach, which includes two levels: global compression and block compression. The former can eliminate redundant inter-file copies across different users, the latter adopts data stream compression technology to realize intra-file de-duplication. Several compressing algorithms were adopted to measure the compression ratio and CPU time. Adaptability using different algorithm in certain situation is also analyzed. The performance analysis shows that great improvement is made through the hybrid compression policy.

  4. A hybrid approach to parameter identification of linear delay differential equations involving multiple delays

    Science.gov (United States)

    Marzban, Hamid Reza

    2018-05-01

    In this paper, we are concerned with the parameter identification of linear time-invariant systems containing multiple delays. The approach is based upon a hybrid of block-pulse functions and Legendre's polynomials. The convergence of the proposed procedure is established and an upper error bound with respect to the L2-norm associated with the hybrid functions is derived. The problem under consideration is first transformed into a system of algebraic equations. The least squares technique is then employed for identification of the desired parameters. Several multi-delay systems of varying complexity are investigated to evaluate the performance and capability of the proposed approximation method. It is shown that the proposed approach is also applicable to a class of nonlinear multi-delay systems. It is demonstrated that the suggested procedure provides accurate results for the desired parameters.

  5. A Study on a Hybrid Approach for Diagnosing Faults in Nuclear Power Plant

    International Nuclear Information System (INIS)

    Yang, M.; Zhang, Z.J.; Peng, M.J.; Yan, S.Y.; Wang, H.; Ouyang, J.

    2006-01-01

    Proper and rapid identification of malfunctions is of premier importance for the safe operation of Nuclear Power Plants (NPP). Many monitoring or/and diagnosis methodologies based on artificial and computational intelligence have been proposed to aid operator to understand system problems, perform trouble-shooting action and reduce human error under serious pressure. However, because no single method is adequate to handle all requirements for diagnostic system, hybrid approaches where different methods work in conjunction to solve parts of the problem interest researchers greatly. In this study, Multilevel Flow Models (MFM) and Artificial Neural Network (ANN) are proposed and employed to develop a fault diagnosis system with the intention of improving the success rate of recognition on the one hand, and improving the understandability of diagnostic process and results on the other hand. Several simulation cases were conducted for evaluating the performance of the proposed diagnosis system. The simulation results validated the effectiveness of the proposed hybrid approach. (authors)

  6. Three-Dimensional Printing Based Hybrid Manufacturing of Microfluidic Devices.

    Science.gov (United States)

    Alapan, Yunus; Hasan, Muhammad Noman; Shen, Richang; Gurkan, Umut A

    2015-05-01

    Microfluidic platforms offer revolutionary and practical solutions to challenging problems in biology and medicine. Even though traditional micro/nanofabrication technologies expedited the emergence of the microfluidics field, recent advances in advanced additive manufacturing hold significant potential for single-step, stand-alone microfluidic device fabrication. One such technology, which holds a significant promise for next generation microsystem fabrication is three-dimensional (3D) printing. Presently, building 3D printed stand-alone microfluidic devices with fully embedded microchannels for applications in biology and medicine has the following challenges: (i) limitations in achievable design complexity, (ii) need for a wider variety of transparent materials, (iii) limited z-resolution, (iv) absence of extremely smooth surface finish, and (v) limitations in precision fabrication of hollow and void sections with extremely high surface area to volume ratio. We developed a new way to fabricate stand-alone microfluidic devices with integrated manifolds and embedded microchannels by utilizing a 3D printing and laser micromachined lamination based hybrid manufacturing approach. In this new fabrication method, we exploit the minimized fabrication steps enabled by 3D printing, and reduced assembly complexities facilitated by laser micromachined lamination method. The new hybrid fabrication method enables key features for advanced microfluidic system architecture: (i) increased design complexity in 3D, (ii) improved control over microflow behavior in all three directions and in multiple layers, (iii) transverse multilayer flow and precisely integrated flow distribution, and (iv) enhanced transparency for high resolution imaging and analysis. Hybrid manufacturing approaches hold great potential in advancing microfluidic device fabrication in terms of standardization, fast production, and user-independent manufacturing.

  7. Three hybridization models based on local search scheme for job shop scheduling problem

    Science.gov (United States)

    Balbi Fraga, Tatiana

    2015-05-01

    This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.

  8. Dry Port Location Problem: A Hybrid Multi-Criteria Approach

    Directory of Open Access Journals (Sweden)

    BENTALEB Fatimazahra

    2016-03-01

    Full Text Available Choosing a location for a dry port is a problem which becomes more essential and crucial. This study deals with the problem of locating dry ports. On this matter, a model combining multi-criteria (MACBETH and mono-criteria (BARYCENTER methods to find a solution to dry port location problem has been proposed. In the first phase, a systematic literature review was carried out on dry port location problem and then a methodological classification was presented for this research. In the second phase, a hybrid multi-criteria approach was developed in order to determine the best dry port location taking different criteria into account. A Computational practice and a qualitative analysis from a case study in the Moroccan context have been provided. The results show that the optimal location is very convenient with the geographical region and the government policies.

  9. New MPPT algorithm based on hybrid dynamical theory

    KAUST Repository

    Elmetennani, Shahrazed

    2014-11-01

    This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.

  10. New MPPT algorithm based on hybrid dynamical theory

    KAUST Repository

    Elmetennani, Shahrazed; Laleg-Kirati, Taous-Meriem; Benmansour, K.; Boucherit, M. S.; Tadjine, M.

    2014-01-01

    This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.

  11. Design, analysis and modeling of a novel hybrid powertrain system based on hybridized automated manual transmission

    Science.gov (United States)

    Wu, Guang; Dong, Zuomin

    2017-09-01

    Hybrid electric vehicles are widely accepted as a promising short to mid-term technical solution due to noticeably improved efficiency and lower emissions at competitive costs. In recent years, various hybrid powertrain systems were proposed and implemented based on different types of conventional transmission. Power-split system, including Toyota Hybrid System and Ford Hybrid System, are well-known examples. However, their relatively low torque capacity, and the drive of alternative and more advanced designs encouraged other innovative hybrid system designs. In this work, a new type of hybrid powertrain system based hybridized automated manual transmission (HAMT) is proposed. By using the concept of torque gap filler (TGF), this new hybrid powertrain type has the potential to overcome issue of torque gap during gearshift. The HAMT design (patent pending) is described in details, from gear layout and design of gear ratios (EV mode and HEV mode) to torque paths at different gears. As an analytical tool, mutli-body model of vehicle equipped with this HAMT was built to analyze powertrain dynamics at various steady and transient modes. A gearshift was decomposed and analyzed based basic modes. Furthermore, a Simulink-SimDriveline hybrid vehicle model was built for the new transmission, driveline and vehicle modular. Control strategy has also been built to harmonically coordinate different powertrain components to realize TGF function. A vehicle launch simulation test has been completed under 30% of accelerator pedal position to reveal details during gearshift. Simulation results showed that this HAMT can eliminate most torque gap that has been persistent issue of traditional AMT, improving both drivability and performance. This work demonstrated a new type of transmission that features high torque capacity, high efficiency and improved drivability.

  12. Bioinspired Nanocellulose Based Hybrid Materials With Novel Interfacial Properties

    Science.gov (United States)

    Keten, Sinan

    This talk will overview a simulation-based approach to enhancing the mechanical properties of nanocomposites by utilizing cellulose - the most abundant and renewable structural biopolymer found on our planet. Cellulose nanocrystals (CNCs) exhibit outstanding mechanical properties exceeding that of Kevlar, serving as reinforcing domains in nature's toughest hierarchical nanocomposites such as wood. Yet, weak interfaces at the surfaces of CNCs have so far made it impossible to scale these inherent properties to macroscopic systems. In this work, I will discuss how surface functionalization of CNCs influences their properties in their self-assembled films and nanocomposites with engineered polymer matrices . Specifically, the role of ion exchange based surface modifications and polymer conjugation will be discussed, where atomistic and coarse-grained simulations will reveal new insights into how superior mechanical properties can potentially be attained by hybrid constructs.

  13. Dynamic Garment Simulation based on Hybrid Bounding Volume Hierarchy

    Directory of Open Access Journals (Sweden)

    Zhu Dongyong

    2016-12-01

    Full Text Available In order to solve the computing speed and efficiency problem of existing dynamic clothing simulation, this paper presents a dynamic garment simulation based on a hybrid bounding volume hierarchy. It firstly uses MCASG graph theory to do the primary segmentation for a given three-dimensional human body model. And then it applies K-means cluster to do the secondary segmentation to collect the human body’s upper arms, lower arms, upper legs, lower legs, trunk, hip and woman’s chest as the elementary units of dynamic clothing simulation. According to different shapes of these elementary units, it chooses the closest and most efficient hybrid bounding box to specify these units, such as cylinder bounding box and elliptic cylinder bounding box. During the process of constructing these bounding boxes, it uses the least squares method and slices of the human body to get the related parameters. This approach makes it possible to use the least amount of bounding boxes to create close collision detection regions for the appearance of the human body. A spring-mass model based on a triangular mesh of the clothing model is finally constructed for dynamic simulation. The simulation result shows the feasibility and superiority of the method described.

  14. Transaction based approach

    Science.gov (United States)

    Hunka, Frantisek; Matula, Jiri

    2017-07-01

    Transaction based approach is utilized in some methodologies in business process modeling. Essential parts of these transactions are human beings. The notion of agent or actor role is usually used for them. The paper on a particular example describes possibilities of Design Engineering Methodology for Organizations (DEMO) and Resource-Event-Agent (REA) methodology. Whereas the DEMO methodology can be regarded as a generic methodology having its foundation in the theory of Enterprise Ontology the REA methodology is regarded as the domain specific methodology and has its origin in accountancy systems. The results of these approaches is that the DEMO methodology captures everything that happens in the reality with a good empirical evidence whereas the REA methodology captures only changes connected with economic events. Economic events represent either change of the property rights to economic resource or consumption or production of economic resources. This results from the essence of economic events and their connection to economic resources.

  15. A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI

    Directory of Open Access Journals (Sweden)

    Piotr Stawicki

    2017-04-01

    Full Text Available Steady state visual evoked potentials (SSVEPs-based Brain-Computer interfaces (BCIs, as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system with 32 participants. Although the highest information transfer rates (ITRs were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively. In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control, and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

  16. A hybrid modelling approach to simulating foot-and-mouth disease outbreaks in Australian livestock

    Directory of Open Access Journals (Sweden)

    Richard A Bradhurst

    2015-03-01

    Full Text Available Foot-and-mouth disease (FMD is a highly contagious and economically important viral disease of cloven-hoofed animals. Australia's freedom from FMD underpins a valuable trade in live animals and animal products. An outbreak of FMD would result in the loss of export markets and cause severe disruption to domestic markets. The prevention of, and contingency planning for, FMD are of key importance to government, industry, producers and the community. The spread and control of FMD is complex and dynamic due to a highly contagious multi-host pathogen operating in a heterogeneous environment across multiple jurisdictions. Epidemiological modelling is increasingly being recognized as a valuable tool for investigating the spread of disease under different conditions and the effectiveness of control strategies. Models of infectious disease can be broadly classified as: population-based models that are formulated from the top-down and employ population-level relationships to describe individual-level behaviour, individual-based models that are formulated from the bottom-up and aggregate individual-level behaviour to reveal population-level relationships, or hybrid models which combine the two approaches into a single model.The Australian Animal Disease Spread (AADIS hybrid model employs a deterministic equation-based model (EBM to model within-herd spread of FMD, and a stochastic, spatially-explicit agent-based model (ABM to model between-herd spread and control. The EBM provides concise and computationally efficient predictions of herd prevalence and clinical signs over time. The ABM captures the complex, stochastic and heterogeneous environment in which an FMD epidemic operates. The AADIS event-driven hybrid EBM/ABM architecture is a flexible, efficient and extensible framework for modelling the spread and control of disease in livestock on a national scale. We present an overview of the AADIS hybrid approach and a description of the model

  17. A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

    Science.gov (United States)

    Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

    2016-01-01

    Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

  18. An energy management approach of hybrid vehicles using traffic preview information for energy saving

    International Nuclear Information System (INIS)

    Zheng, Chunhua; Xu, Guoqing; Xu, Kun; Pan, Zhongming; Liang, Quan

    2015-01-01

    Highlights: • Energy management approach of hybrid vehicles using traffic preview information. • Vehicle velocity profile and fuel consumption are optimized at the same time. • It is proved that a further energy saving is achieved by the proposed approach. • The proposed approach is useful especially for autonomous hybrid vehicles. - Abstract: The traffic preview information is very helpful for hybrid vehicles when distributing the power requirement of the vehicle to power sources and when determining the next driving route of the vehicle. In this research, an energy management approach for hybrid vehicles is proposed, which optimizes the vehicle velocity profile while minimizing the fuel consumption with the help of the traffic preview information, so that a further energy saving for hybrid vehicles can be achieved. The Pontryagin’s Minimum Principle (PMP) is adopted on the proposed approach. A fuel cell hybrid vehicle (FCHV) is selected as an example, and the proposed energy management approach is applied to the FCHV in a computer simulation environment for the offline and online cases respectively. Simulation results show that the fuel economy of the FCHV is improved by the proposed energy management approach compared to a benchmark case where the driving cycle is fixed and only the hybrid power split (allocation) ratio is optimized. The proposed energy management approach is useful especially for the autonomous hybrid vehicles.

  19. The hybrid thermography approach applied to architectural structures

    Science.gov (United States)

    Sfarra, S.; Ambrosini, D.; Paoletti, D.; Nardi, I.; Pasqualoni, G.

    2017-07-01

    This work contains an overview of infrared thermography (IRT) method and its applications relating to the investigation of architectural structures. In this method, the passive approach is usually used in civil engineering, since it provides a panoramic view of the thermal anomalies to be interpreted also thanks to the use of photographs focused on the region of interest (ROI). The active approach, is more suitable for laboratory or indoor inspections, as well as for objects having a small size. The external stress to be applied is thermal, coming from non-natural apparatus such as lamps or hot / cold air jets. In addition, the latter permits to obtain quantitative information related to defects not detectable to the naked eyes. Very recently, the hybrid thermography (HIRT) approach has been introduced to the attention of the scientific panorama. It can be applied when the radiation coming from the sun, directly arrives (i.e., possibly without the shadow cast effect) on a surface exposed to the air. A large number of thermograms must be collected and a post-processing analysis is subsequently applied via advanced algorithms. Therefore, an appraisal of the defect depth can be obtained passing through the calculation of the combined thermal diffusivity of the materials above the defect. The approach is validated herein by working, in a first stage, on a mosaic sample having known defects while, in a second stage, on a Church built in L'Aquila (Italy) and covered with a particular masonry structure called apparecchio aquilano. The results obtained appear promising.

  20. Agent-based power sharing scheme for active hybrid power sources

    Science.gov (United States)

    Jiang, Zhenhua

    The active hybridization technique provides an effective approach to combining the best properties of a heterogeneous set of power sources to achieve higher energy density, power density and fuel efficiency. Active hybrid power sources can be used to power hybrid electric vehicles with selected combinations of internal combustion engines, fuel cells, batteries, and/or supercapacitors. They can be deployed in all-electric ships to build a distributed electric power system. They can also be used in a bulk power system to construct an autonomous distributed energy system. An important aspect in designing an active hybrid power source is to find a suitable control strategy that can manage the active power sharing and take advantage of the inherent scalability and robustness benefits of the hybrid system. This paper presents an agent-based power sharing scheme for active hybrid power sources. To demonstrate the effectiveness of the proposed agent-based power sharing scheme, simulation studies are performed for a hybrid power source that can be used in a solar car as the main propulsion power module. Simulation results clearly indicate that the agent-based control framework is effective to coordinate the various energy sources and manage the power/voltage profiles.

  1. Hybrid Power Management-Based Vehicle Architecture

    Science.gov (United States)

    Eichenberg, Dennis J.

    2011-01-01

    Hybrid Power Management (HPM) is the integration of diverse, state-of-the-art power devices in an optimal configuration for space and terrestrial applications (s ee figure). The appropriate application and control of the various power devices significantly improves overall system performance and efficiency. The basic vehicle architecture consists of a primary power source, and possibly other power sources, that provides all power to a common energy storage system that is used to power the drive motors and vehicle accessory systems. This architecture also provides power as an emergency power system. Each component is independent, permitting it to be optimized for its intended purpose. The key element of HPM is the energy storage system. All generated power is sent to the energy storage system, and all loads derive their power from that system. This can significantly reduce the power requirement of the primary power source, while increasing the vehicle reliability. Ultracapacitors are ideal for an HPM-based energy storage system due to their exceptionally long cycle life, high reliability, high efficiency, high power density, and excellent low-temperature performance. Multiple power sources and multiple loads are easily incorporated into an HPM-based vehicle. A gas turbine is a good primary power source because of its high efficiency, high power density, long life, high reliability, and ability to operate on a wide range of fuels. An HPM controller maintains optimal control over each vehicle component. This flexible operating system can be applied to all vehicles to considerably improve vehicle efficiency, reliability, safety, security, and performance. The HPM-based vehicle architecture has many advantages over conventional vehicle architectures. Ultracapacitors have a much longer cycle life than batteries, which greatly improves system reliability, reduces life-of-system costs, and reduces environmental impact as ultracapacitors will probably never need to be

  2. Engineering of small interfering RNA-loaded lipidoid-poly(DL-lactic-co-glycolic acid) hybrid nanoparticles for highly efficient and safe gene silencing: A quality by design-based approach.

    Science.gov (United States)

    Thanki, Kaushik; Zeng, Xianghui; Justesen, Sarah; Tejlmann, Sarah; Falkenberg, Emily; Van Driessche, Elize; Mørck Nielsen, Hanne; Franzyk, Henrik; Foged, Camilla

    2017-11-01

    Safety and efficacy of therapeutics based on RNA interference, e.g., small interfering RNA (siRNA), are dependent on the optimal engineering of the delivery technology, which is used for intracellular delivery of siRNA to the cytosol of target cells. We investigated the hypothesis that commonly used and poorly tolerated cationic lipids might be replaced with more efficacious and safe lipidoids as the lipid component of siRNA-loaded lipid-polymer hybrid nanoparticles (LPNs) for achieving more efficient gene silencing at lower and safer doses. However, formulation design of such a complex formulation is highly challenging due to a strong interplay between several contributing factors. Hence, critical formulation variables, i.e. the lipidoid content and siRNA:lipidoid ratio, were initially identified, followed by a systematic quality-by-design approach to define the optimal operating space (OOS), eventually resulting in the identification of a robust, highly efficacious and safe formulation. A 17-run design of experiment with an I-optimal approach was performed to systematically assess the effect of selected variables on critical quality attributes (CQAs), i.e. physicochemical properties (hydrodynamic size, zeta potential, siRNA encapsulation/loading) and the biological performance (in vitro gene silencing and cell viability). Model fitting of the obtained data to construct predictive models revealed non-linear relationships for all CQAs, which can be readily overlooked in one-factor-at-a-time optimization approaches. The response surface methodology further enabled the identification of an OOS that met the desired quality target product profile. The optimized lipidoid-modified LPNs revealed more than 50-fold higher in vitro gene silencing at well-tolerated doses and approx. a twofold increase in siRNA loading as compared to reference LPNs modified with the commonly used cationic lipid dioleyltrimethylammonium propane (DOTAP). Thus, lipidoid-modified LPNs show highly

  3. A Hybrid ACO Approach to the Matrix Bandwidth Minimization Problem

    Science.gov (United States)

    Pintea, Camelia-M.; Crişan, Gloria-Cerasela; Chira, Camelia

    The evolution of the human society raises more and more difficult endeavors. For some of the real-life problems, the computing time-restriction enhances their complexity. The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous permutation of the rows and the columns of a square matrix in order to keep its nonzero entries close to the main diagonal. The MBMP is a highly investigated {NP}-complete problem, as it has broad applications in industry, logistics, artificial intelligence or information recovery. This paper describes a new attempt to use the Ant Colony Optimization framework in tackling MBMP. The introduced model is based on the hybridization of the Ant Colony System technique with new local search mechanisms. Computational experiments confirm a good performance of the proposed algorithm for the considered set of MBMP instances.

  4. Hybrid Quantum-Classical Approach to Quantum Optimal Control.

    Science.gov (United States)

    Li, Jun; Yang, Xiaodong; Peng, Xinhua; Sun, Chang-Pu

    2017-04-14

    A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving answers from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a seven-qubit nuclear magnetic resonance system, on which we have succeeded in optimizing state preparation without involving classical computation of the large Hilbert space evolution.

  5. Hybrid modelling framework by using mathematics-based and information-based methods

    International Nuclear Information System (INIS)

    Ghaboussi, J; Kim, J; Elnashai, A

    2010-01-01

    Mathematics-based computational mechanics involves idealization in going from the observed behaviour of a system into mathematical equations representing the underlying mechanics of that behaviour. Idealization may lead mathematical models that exclude certain aspects of the complex behaviour that may be significant. An alternative approach is data-centric modelling that constitutes a fundamental shift from mathematical equations to data that contain the required information about the underlying mechanics. However, purely data-centric methods often fail for infrequent events and large state changes. In this article, a new hybrid modelling framework is proposed to improve accuracy in simulation of real-world systems. In the hybrid framework, a mathematical model is complemented by information-based components. The role of informational components is to model aspects which the mathematical model leaves out. The missing aspects are extracted and identified through Autoprogressive Algorithms. The proposed hybrid modelling framework has a wide range of potential applications for natural and engineered systems. The potential of the hybrid methodology is illustrated through modelling highly pinched hysteretic behaviour of beam-to-column connections in steel frames.

  6. Hybrid Fundamental Solution Based Finite Element Method: Theory and Applications

    Directory of Open Access Journals (Sweden)

    Changyong Cao

    2015-01-01

    Full Text Available An overview on the development of hybrid fundamental solution based finite element method (HFS-FEM and its application in engineering problems is presented in this paper. The framework and formulations of HFS-FEM for potential problem, plane elasticity, three-dimensional elasticity, thermoelasticity, anisotropic elasticity, and plane piezoelectricity are presented. In this method, two independent assumed fields (intraelement filed and auxiliary frame field are employed. The formulations for all cases are derived from the modified variational functionals and the fundamental solutions to a given problem. Generation of elemental stiffness equations from the modified variational principle is also described. Typical numerical examples are given to demonstrate the validity and performance of the HFS-FEM. Finally, a brief summary of the approach is provided and future trends in this field are identified.

  7. A frequency domain approach to analyzing passive battery-ultracapacitor hybrids supplying periodic pulsed current loads

    International Nuclear Information System (INIS)

    Kuperman, Alon; Aharon, Ilan; Kara, Avi; Malki, Shalev

    2011-01-01

    Highlights: → Passive battery-ultracapacitor hybrids are examined. → Frequency domain analysis is employed. → The ultracapacitor branch operates as a low-pass filter for the battery. → The battery supplies the average load demand. → Design requirements are discussed. - Abstract: A Fourier-based analysis of passive battery-ultracapacitor hybrid sources is introduced in the manuscript. The approach is first introduced for a general load, and then is followed by a study for a case of periodic pulsed current load. It is shown that the ultracapacitor branch is perceived by the battery as a low-pass filter, which absorbs the majority of the high frequency harmonic current and letting the battery to supply the average load demand in addition to the small part of dynamic current. Design requirements influence on the ultracapacitor capacitance and internal resistance choice are quantitatively discussed. The theory is enforced by simulation and experimental results, showing an excellent agreement.

  8. A hybrid approach to solving the problem of design of nuclear fuel cells

    International Nuclear Information System (INIS)

    Montes T, J. L.; Perusquia del C, R.; Ortiz S, J. J.; Castillo, A.

    2015-09-01

    An approach to solving the problem of fuel cell design for BWR power reactor is presented. For this purpose the hybridization of a method based in heuristic knowledge rules called S15 and the advantages of a meta-heuristic method is proposed. The synergy of potentialities of both techniques allows finding solutions of more quality. The quality of each solution is obtained through a multi-objective function formed from the main cell parameters that are provided or obtained during the simulation with the CASMO-4 code. To evaluate this alternative of solution nuclear fuel cells of reference of nuclear power plant of Laguna Verde were used. The results show that in a systematic way the results improve when both methods are coupled. As a result of the hybridization process of the mentioned techniques an improvement is achieved in a range of 2% with regard to the achieved results in an independent way by the S15 method. (Author)

  9. A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study.

    Science.gov (United States)

    Goh, Yang Miang; Askar Ali, Mohamed Jawad

    2016-08-01

    One of the key challenges in improving construction safety and health is the management of safety behavior. From a system point of view, workers work unsafely due to system level issues such as poor safety culture, excessive production pressure, inadequate allocation of resources and time and lack of training. These systemic issues should be eradicated or minimized during planning. However, there is a lack of detailed planning tools to help managers assess the impact of their upstream decisions on worker safety behavior. Even though simulation had been used in construction planning, the review conducted in this study showed that construction safety management research had not been exploiting the potential of simulation techniques. Thus, a hybrid simulation framework is proposed to facilitate integration of safety management considerations into construction activity simulation. The hybrid framework consists of discrete event simulation (DES) as the core, but heterogeneous, interactive and intelligent (able to make decisions) agents replace traditional entities and resources. In addition, some of the cognitive processes and physiological aspects of agents are captured using system dynamics (SD) approach. The combination of DES, agent-based simulation (ABS) and SD allows a more "natural" representation of the complex dynamics in construction activities. The proposed hybrid framework was demonstrated using a hypothetical case study. In addition, due to the lack of application of factorial experiment approach in safety management simulation, the case study demonstrated sensitivity analysis and factorial experiment to guide future research. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

    Science.gov (United States)

    Koeppen, W. C.; Pilger, E.; Wright, R.

    2011-07-01

    We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

  11. Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach.

    Directory of Open Access Journals (Sweden)

    Kristen Feher

    Full Text Available Heterosis, the greater vigor of hybrids compared to their parents, has been exploited in maize breeding for more than 100 years to produce ever better performing elite hybrids of increased yield. Despite extensive research, the underlying mechanisms shaping the extent of heterosis are not well understood, rendering the process of selecting an optimal set of parental lines tedious. This study is based on a dataset consisting of 112 metabolite levels in young roots of four parental maize inbred lines and their corresponding twelve hybrids, along with the roots' biomass as a heterotic trait. Because the parental biomass is a poor predictor for hybrid biomass, we established a model framework to deduce the biomass of the hybrid from metabolite profiles of its parental lines. In the proposed framework, the hybrid metabolite levels are expressed relative to the parental levels by incorporating the standard concept of additivity/dominance, which we name the Combined Relative Level (CRL. Our modeling strategy includes a feature selection step on the parental levels which are demonstrated to be predictive of CRL across many hybrid metabolites. We demonstrate that these selected parental metabolites are further predictive of hybrid biomass. Our approach directly employs the diallel structure in a multivariate fashion, whereby we attempt to not only predict macroscopic phenotype (biomass, but also molecular phenotype (metabolite profiles. Therefore, our study provides the first steps for further investigations of the genetic determinants to metabolism and, ultimately, growth. Finally, our success on the small-scale experiments implies a valid strategy for large-scale experiments, where parental metabolite profiles may be used together with profiles of selected hybrids as a training set to predict biomass of all possible hybrids.

  12. Hybrid generative-discriminative approach to age-invariant face recognition

    Science.gov (United States)

    Sajid, Muhammad; Shafique, Tamoor

    2018-03-01

    Age-invariant face recognition is still a challenging research problem due to the complex aging process involving types of facial tissues, skin, fat, muscles, and bones. Most of the related studies that have addressed the aging problem are focused on generative representation (aging simulation) or discriminative representation (feature-based approaches). Designing an appropriate hybrid approach taking into account both the generative and discriminative representations for age-invariant face recognition remains an open problem. We perform a hybrid matching to achieve robustness to aging variations. This approach automatically segments the eyes, nose-bridge, and mouth regions, which are relatively less sensitive to aging variations compared with the rest of the facial regions that are age-sensitive. The aging variations of age-sensitive facial parts are compensated using a demographic-aware generative model based on a bridged denoising autoencoder. The age-insensitive facial parts are represented by pixel average vector-based local binary patterns. Deep convolutional neural networks are used to extract relative features of age-sensitive and age-insensitive facial parts. Finally, the feature vectors of age-sensitive and age-insensitive facial parts are fused to achieve the recognition results. Extensive experimental results on morphological face database II (MORPH II), face and gesture recognition network (FG-NET), and Verification Subset of cross-age celebrity dataset (CACD-VS) demonstrate the effectiveness of the proposed method for age-invariant face recognition well.

  13. A diagnostic expert system for the nuclear power plant b ased on the hybrid knowledge approach

    International Nuclear Information System (INIS)

    Yang, J.O.; Chang, S.H.

    1989-01-01

    A diagnostic expert system, the hybrid knowledge based plant operation supporting system (HYPOSS), which has been developed to support operators' decisionmaking during the transients of the nuclear power plant, is described. HYPOSS adopts the hybrid knowledge approach, which combines both shallow and deep knowledge to take advantage of the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure. They are structural, functional, behavioral, and heuristic knowledge. The structural and functional knowledge is represented by three fundamental primitives and five types of functions, respectively. The behavioral knowledge is represented using constraints. The inference procedure is based on the human problem-solving behavior modeled in HYPOSS. The event-based operational guidelines are provided to the operator according to the diagnosed results. If the exact anomalies cannot be identified while some of the critical safety functions are challenged, the function-based operational guidelines are provided to the operator. For the validation of HYPOSS, several tests have been performed based on the data produced by a plant simulator. The results of validation studies show good applicability of HYPOSS to the anomaly diagnosis of nuclear power plant

  14. Mobile phone use while driving: a hybrid modeling approach.

    Science.gov (United States)

    Márquez, Luis; Cantillo, Víctor; Arellana, Julián

    2015-05-01

    The analysis of the effects that mobile phone use produces while driving is a topic of great interest for the scientific community. There is consensus that using a mobile phone while driving increases the risk of exposure to traffic accidents. The purpose of this research is to evaluate the drivers' behavior when they decide whether or not to use a mobile phone while driving. For that, a hybrid modeling approach that integrates a choice model with the latent variable "risk perception" was used. It was found that workers and individuals with the highest education level are more prone to use a mobile phone while driving than others. Also, "risk perception" is higher among individuals who have been previously fined and people who have been in an accident or almost been in an accident. It was also found that the tendency to use mobile phones while driving increases when the traffic speed reduces, but it decreases when the fine increases. Even though the urgency of the phone call is the most important explanatory variable in the choice model, the cost of the fine is an important attribute in order to control mobile phone use while driving. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

    Science.gov (United States)

    Oh, Yoo Rhee; Kim, Hong Kook

    In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

  16. Performance analysis of switching based hybrid FSO/RF transmission

    KAUST Repository

    Usman, Muneer

    2014-09-01

    Hybrid free space optical (FSO)/ radio frequency (RF) systems have emerged as a promising solution for high data rate wireless back haul.We present and analyze a switching based transmission scheme for hybrid FSO/RF system. Specifically, either FSO or RF link will be active at a certain time instance, with FSO link enjoying a higher priority. Analytical expressions have been obtained for the outage probability, average bit error rate and ergodic capacity for the resulting system. Numerical examples are presented to compare the performance of the hybrid scheme with FSO only scenario.

  17. Performance analysis of switching based hybrid FSO/RF transmission

    KAUST Repository

    Usman, Muneer; Yang, Hongchuan; Alouini, Mohamed-Slim

    2014-01-01

    Hybrid free space optical (FSO)/ radio frequency (RF) systems have emerged as a promising solution for high data rate wireless back haul.We present and analyze a switching based transmission scheme for hybrid FSO/RF system. Specifically, either FSO or RF link will be active at a certain time instance, with FSO link enjoying a higher priority. Analytical expressions have been obtained for the outage probability, average bit error rate and ergodic capacity for the resulting system. Numerical examples are presented to compare the performance of the hybrid scheme with FSO only scenario.

  18. Hybrid Air Quality Modeling Approach for use in the Hear-road Exposures to Urban air pollutant Study(NEXUS)

    Science.gov (United States)

    The paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associa...

  19. Graphene-based hybrid for enantioselective sensing applications.

    Science.gov (United States)

    Zor, Erhan; Morales-Narváez, Eden; Alpaydin, Sabri; Bingol, Haluk; Ersoz, Mustafa; Merkoçi, Arben

    2017-01-15

    Chirality is a major field of research of chemical biology and is essential in pharmacology. Accordingly, approaches for distinguishing between different chiral forms of a compound are of great interest. We report on an efficient and generic enantioselective sensor that is achieved by coupling reduced graphene oxide with γ-cyclodextrin (rGO/γ-CD). The enantioselective sensing capability of the resulting structure was operated in both electrical and optical mode for of tryptophan enantiomers (D-/L-Trp). In this sense, voltammetric and photoluminescence measurements were conducted and the experimental results were compared to molecular docking method. We gain insight into the occurring recognition mechanism with selectivity toward D- and L-Trp as shown in voltammetric, photoluminescence and molecular docking responses. As an enantioselective solid phase on an electrochemical transducer, thanks to the different dimensional interaction of enantiomers with hybrid material, a discrepancy occurs in the Gibbs free energy leading to a difference in oxidation peak potential as observed in electrochemical measurements. The optical sensing principle is based on the energy transfer phenomenon that occurs between photoexcited D-/L-Trp enantiomers and rGO/γ-CD giving rise to an enantioselective photoluminescence quenching due to the tendency of chiral enantiomers to form complexes with γ-CD in different molecular orientations as demonstrated by molecular docking studies. The approach, which is the first demonstration of applicability of molecular docking to show both enantioselective electrochemical and photoluminescence quenching capabilities of a graphene-related hybrid material, is truly new and may have broad interest in combination of experimental and computational methods for enantiosensing of chiral molecules. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. A Hybrid Approach to Spatial Multiplexing in Multiuser MIMO Downlinks

    Directory of Open Access Journals (Sweden)

    Spencer Quentin H

    2004-01-01

    Full Text Available In the downlink of a multiuser multiple-input multiple-output (MIMO communication system, simultaneous transmission to several users requires joint optimization of the transmitted signals. Allowing all users to have multiple antennas adds an additional degree of complexity to the problem. In this paper, we examine the case where a single base station transmits to multiple users using linear processing (beamforming at each of the antenna arrays. We propose generalizations of several previous iterative algorithms for multiuser transmit beamforming that allow multiple antennas and multiple data streams for each user, and that take into account imperfect channel estimates at the transmitter. We then present a new hybrid algorithm that is based on coordinated transmit-receive beamforming, and combines the strengths of nonorthogonal iterative solutions with zero-forcing solutions. The problem of distributing power among the subchannels is solved by using standard bit-loading algorithms combined with the subchannel gains resulting from the zero-forcing solution. The result is a significant performance improvement over equal power distribution. At the same time, the number of iterations required to compute the final solution is reduced.

  1. Engineering of small interfering RNA-loaded lipidoid-poly(DL-lactic-co-glycolic acid) hybrid nanoparticles for highly efficient and safe gene silencing: A quality by design-based approach

    DEFF Research Database (Denmark)

    Thanki, Kaushik; Zeng, Xianghui; Justesen, Sarah

    2017-01-01

    used and poorly tolerated cationic lipids might be replaced with more efficacious and safe lipidoids as the lipid component of siRNA-loaded lipid-polymer hybrid nanoparticles (LPNs) for achieving more efficient gene silencing at lower and safer doses. However, formulation design of such a complex...... formulation is highly challenging due to a strong interplay between several contributing factors. Hence, critical formulation variables, i.e. the lipidoid content and siRNA:lipidoid ratio, were initially identified, followed by a systematic quality-by-design approach to define the optimal operating space (OOS......), eventually resulting in the identification of a robust, highly efficacious and safe formulation. A 17-run design of experiment with an I-optimal approach was performed to systematically assess the effect of selected variables on critical quality attributes (CQAs), i.e. physicochemical properties...

  2. An integrated optimization approach for a hybrid energy system in electric vehicles

    International Nuclear Information System (INIS)

    Hung, Yi-Hsuan; Wu, Chien-Hsun

    2012-01-01

    Highlights: ► Second-order control-oriented dynamics for a battery/supercapacitor EV is modeled. ► Multiple for-loop programming and global searchwith constraints are main design principles of integrated optimization algorithm (IOA). ► Optimal hybridization is derived based on maximizing energy storage capacity. ► Optimal energy management in three EV operation modes is searched based on minimizing total consumed power. ► Simulation results prove that 6+% of total energy is saved by the IOA method. -- Abstract: This paper develops a simple but innovative integrated optimization approach (IOA) for deriving the best solutions of component sizing and control strategies of a hybrid energy system (HES) which consists of a lithium battery and a supercapacitor module. To implement IOA, a multiple for-loop structure with a preset cost function is needed to globally calculate the best hybridization and energy management of the HES. For system hybridization, the optimal size ratio is evaluated by maximizing the HES energy stored capacity at various costs. For energy management, the optimal power distribution combined with a three-mode rule-based strategy is searched to minimize the total consumed energy. Combining above two for-loop structures and giving a time-dependent test scenario, the IOA is derived by minimizing the accumulated HES power. Simulation results show that 6% of the total HES energy can be saved in the IOA case compared with the original system in two driving cycles: ECE and UDDS, and two vehicle weights, respectively. It proves that the IOA effectively derives the maximum energy storage capacity and the minimum energy consumption of the HES at the same time. Experimental verification will be carried out in the near future.

  3. An Approach for Designing Thermal Management Systems for Electric and Hybrid Vehicle Battery Packs

    International Nuclear Information System (INIS)

    Pesaran, Ahmad A.; Keyser, Matthew; Burch, Steve

    1999-01-01

    If battery packs for electric vehicles (EVs) and hybrid electric vehicles (HEVs) are to operate effectively in all climates, thermal management of the packs is essential. In this paper, we will review a systematic approach for designing and evaluating battery pack thermal management systems. A thermal management system using air as the heat transfer medium is less complicated than a system using liquid cooling/heating. Generally, for parallel HEVs, an air thermal management system is adequate, whereas for EVs and series HEVs, liquid-based systems may be required for optimum thermal performance. Further information on battery thermal management can be found on the Web site www.ctts.nrel.gov/BTM

  4. Towards a comprehensive theory for He II: I. A zero-temperature hybrid approach

    International Nuclear Information System (INIS)

    Ghassib, H.B.; Khudeir, A.M.

    1982-09-01

    A simple hybrid approach based on a gauge theory as well as a Hartree formalism, is presented for He II at zero temperature. Although this is intended to be merely a first step in an all-embracing theory, it already resolves quite neatly several old inconsistencies and corrects a few errors. As an illustration of its feasibility, a crude but instructive calculation is performed for the static structure factor of the system at low momentum transfers. A number of planned extensions and generalizations are outlined. (author)

  5. Fuzzy hybrid MCDM approach for selection of wind turbine service technicians

    Directory of Open Access Journals (Sweden)

    Goutam Kumar Bose

    2016-01-01

    Full Text Available This research paper is aimed to present a fuzzy Hybrid Multi-criteria decision making (MCDM methodology for selecting employees. The present study aspires to present the hybrid approach of Fuzzy multiple MCDM techniques with tactical viewpoint to support the recruitment process of wind turbine service technicians. The methodology is based on the application of Fuzzy ARAS (Additive Ratio Assessment and Fuzzy MOORA (Multi-Objective Optimization on basis of Ratio Analysis which are integrated through group decision making (GDM method in the model for selection of wind turbine service technicians’ ranking. Here a group of experts from different fields of expertise are engaged to finalize the decision. Series of tests are conducted regarding physical fitness, technical written test, practical test along with general interview and medical examination to facilitate the final selection using the above techniques. In contrast to single decision making approaches, the proposed group decision making model efficiently supports the wind turbine service technicians ranking process. The effectiveness of the proposed approach manifest from the case study of service technicians required for the maintenance department of wind power plant using Fuzzy ARAS and Fuzzy MOORA. This set of potential technicians is evaluated based on five main criteria.

  6. Multi-agent system-based event-triggered hybrid control scheme for energy internet

    DEFF Research Database (Denmark)

    Dou, Chunxia; Yue, Dong; Han, Qing Long

    2017-01-01

    This paper is concerned with an event-triggered hybrid control for the energy Internet based on a multi-agent system approach with which renewable energy resources can be fully utilized to meet load demand with high security and well dynamical quality. In the design of control, a multi-agent system...

  7. A Hybrid Approach to Processing Big Data Graphs on Memory-Restricted Systems

    KAUST Repository

    Harshvardhan,

    2015-05-01

    With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques that can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM and uses a paging-like technique to load sub graphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16, 000+ cores.

  8. Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems.

    Science.gov (United States)

    Dresselhaus, Thomas; Yang, Jack; Kumbhar, Sadhana; Waller, Mark P

    2013-04-09

    Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.

  9. Multi-level and hybrid modelling approaches for systems biology.

    Science.gov (United States)

    Bardini, R; Politano, G; Benso, A; Di Carlo, S

    2017-01-01

    During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

  10. A hybrid bio-jetting approach for directly engineering living cells

    International Nuclear Information System (INIS)

    Kwok, Albert; Irvine, Scott; Arumuganathar, Sumathy; Jayasinghe, Suwan N; McEwan, Jean R

    2008-01-01

    This paper reports developments on a hybrid cell-engineering protocol coupling both bio-electrosprays and aerodynamically assisted bio-jets for process-handling living cells. The current work demonstrates the ability to couple these two cell-jetting protocols for handling a wide range of cells for deposition. The post-treated cells are assessed for their viability by way of flow cytometry, which illustrates a significant population of viable cells post-treatment in comparison to those controls. This work is the first example of coupling these two protocols for the process handling of living cells. The hybrid protocol demonstrates the achievement of stable cone jetting of a cellular suspension in the single-needle configuration which was previously unachieved with single-needle bio-electrosprays. Furthermore the living cells explored in these investigations expressed GFP, thus demonstrating the ability to couple gene therapy with this hybrid protocol. Hence, this approach could one day be explored for building biologically viable tissues incorporating a therapeutic payload for combating a range of cellular/tissue-based pathologies

  11. Quinoline-Based Hybrid Compounds with Antimalarial Activity

    Directory of Open Access Journals (Sweden)

    Xhamla Nqoro

    2017-12-01

    Full Text Available The application of quinoline-based compounds for the treatment of malaria infections is hampered by drug resistance. Drug resistance has led to the combination of quinolines with other classes of antimalarials resulting in enhanced therapeutic outcomes. However, the combination of antimalarials is limited by drug-drug interactions. In order to overcome the aforementioned factors, several researchers have reported hybrid compounds prepared by reacting quinoline-based compounds with other compounds via selected functionalities. This review will focus on the currently reported quinoline-based hybrid compounds and their preclinical studies.

  12. Behavioral based safety approaches

    International Nuclear Information System (INIS)

    Maria Michael Raj, I.

    2009-01-01

    Approach towards the establishment of positive safety culture at Heavy Water Plant, Tuticorin includes the adoption of several important methodologies focused on human behavior and culminates with achievement of Total Safety Culture where Quality and Productivity are integrated with Safety

  13. When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System

    KAUST Repository

    Liu, Xiao

    2017-03-21

    Privacy risks of recommender systems have caused increasing attention. Users’ private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users’ private data. Existing approaches focus either on keeping users’ private data protected during recommendation computation or on preventing the inference of any single user’s data from the recommendation result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.

  14. When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System

    KAUST Repository

    Liu, Xiao; Liu, An; Zhang, Xiangliang; Li, Zhixu; Liu, Guanfeng; Zhao, Lei; Zhou, Xiaofang

    2017-01-01

    result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP

  15. Light programmable organic transistor memory device based on hybrid dielectric

    Science.gov (United States)

    Ren, Xiaochen; Chan, Paddy K. L.

    2013-09-01

    We have fabricated the transistor memory devices based on SiO2 and polystyrene (PS) hybrid dielectric. The trap states densities with different semiconductors have been investigated and a maximum 160V memory window between programming and erasing is realized. For DNTT based transistor, the trapped electron density is limited by the number of mobile electrons in semiconductor. The charge transport mechanism is verified by light induced Vth shift effect. Furthermore, in order to meet the low operating power requirement of portable electronic devices, we fabricated the organic memory transistor based on AlOx/self-assembly monolayer (SAM)/PS hybrid dielectric, the effective capacitance of hybrid dielectric is 210 nF cm-2 and the transistor can reach saturation state at -3V gate bias. The memory window in transfer I-V curve is around 1V under +/-5V programming and erasing bias.

  16. Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

    Directory of Open Access Journals (Sweden)

    Yuliang Su

    2015-04-01

    Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.

  17. A hybrid life cycle and multi-criteria decision analysis approach for identifying sustainable development strategies of Beijing's taxi fleet

    International Nuclear Information System (INIS)

    Cai, Yanpeng; Applegate, Scott; Yue, Wencong; Cai, Jianying; Wang, Xuan; Liu, Gengyuan; Li, Chunhui

    2017-01-01

    To identify and evaluate sustainable strategies of taxi fleet in Beijing in terms of economic, policy, and environmental implications, a hybrid approach was developed through incorporating multi-criteria decision analysis (MCDA) methods within a general life-cycle analysis (LCA) framework. The approach can (a) help comprehensive evaluate environmental impacts of multiple types of vehicles, (b) facilitate analysis of environmental, economic and policy features of such vehicles, and (c) identify desirable taxi fleet development strategies for the city. The developed approach represented an improvement of the decision-making capability for taxi implementation based on multiple available technologies and their performance that can be specifically tailored to Beijing. The results demonstrated that the proposed approach could comprehensively reflect multiple implications of strategies for the taxi fleet in Beijing to reduce air pollution in the city. The results also indicated that the electric vehicle powered with the year 2020 electricity projections would be the ideal solution, outranking the other alternatives. The conventional vehicle ranked the lowest among the alternatives. The plug-in hybrid vehicle powered by 2020 electricity projects ranked the third, followed by the plug-in hybrid vehicle ranking the fourth, and the hybrid vehicle ranking the fifth. - Highlights: • An hybrid approach was proposed for evaluating sustainable strategies of Beijing's taxi fleet. • This approach was based on the combination of multi-criteria decision analysis methods and life-cycle assessment. • Environmental, economic and policy performances of multiple strategies were compared. • Detailed responses of taxi drivers and local residents were interviewed. • The electric vehicle would be the ideal solution for Beijing Taxi fleet.

  18. A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Jian Xiong

    2012-01-01

    Full Text Available This paper addresses multiobjective flexible job-shop scheduling problem (FJSP with three simultaneously considered objectives: minimizing makespan, minimizing total workload, and minimizing maximal workload. A hybrid multiobjective evolutionary approach (H-MOEA is developed to solve the problem. According to the characteristic of FJSP, a modified crowding distance measure is introduced to maintain the diversity of individuals. In the proposed H-MOEA, well-designed chromosome representation and genetic operators are developed for FJSP. Moreover, a local search procedure based on critical path theory is incorporated in H-MOEA to improve the convergence ability of the algorithm. Experiment results on several well-known benchmark instances demonstrate the efficiency and stability of the proposed algorithm. The comparison with other recently published approaches validates that H-MOEA can obtain Pareto-optimal solutions with better quality and/or diversity.

  19. Hybridization success is largely limited to homoploid Prunus hybrids: a multidisciplinary approach

    Czech Academy of Sciences Publication Activity Database

    Macková, L.; Vít, Petr; Ďurišová, Ľ.; Eliáš, P. Jr.; Urfus, T.

    2017-01-01

    Roč. 303, č. 4 (2017), s. 481-495 ISSN 0378-2697 Institutional support: RVO:67985939 Keywords : absolute genome size * interspecific hybridization * embryology Subject RIV: EF - Botanics OBOR OECD: Plant sciences, botany Impact factor: 1.239, year: 2016

  20. A promising hybrid approach to SPECT attenuation correction

    International Nuclear Information System (INIS)

    Lewis, N.H.; Faber, T.L.; Corbett, J.R.; Stokely, E.M.

    1984-01-01

    Most methods for attenuation compensation in SPECT either rely on the assumption of uniform attenuation, or use slow iteration to achieve accuracy. However, hybrid methods that combine iteration with simple multiplicative correction can accommodate nonuniform attenuation, and such methods converge faster than other iterative techniques. The authors evaluated two such methods, which differ in use of a damping factor to control convergence. Both uniform and nonuniform attenuation were modeled, using simulated and phantom data for a rotating gamma camera. For simulations done with 360 0 data and the correct attenuation map, activity levels were reconstructed to within 5% of the correct values after one iteration. Using 180 0 data, reconstructed levels in regions representing lesion and background were within 5% of the correct values in three iterations; however, further iterations were needed to eliminate the characteristic streak artifacts. The damping factor had little effect on 360 0 reconstruction, but was needed for convergence with 180 0 data. For both cold- and hot-lesion models, image contrast was better from the hybrid methods than from the simpler geometric-mean corrector. Results from the hybrid methods were comparable to those obtained using the conjugate-gradient iterative method, but required 50-100% less reconstruction time. The relative speed of the hybrid methods, and their accuracy in reconstructing photon activity in the presence of nonuniform attenuation, make them promising tools for quantitative SPECT reconstruction

  1. Using a Hybrid Approach for a Leadership Cohort Program

    Science.gov (United States)

    Norman, Maxine A.

    2013-01-01

    Because information technology continues to change rapidly, Extension is challenged with learning and using technology appropriately. We assert Extension cannot shy away from the challenges but must embrace technology because audiences and external forces demand it. A hybrid, or blended, format of a leadership cohort program was offered to public…

  2. Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

    Directory of Open Access Journals (Sweden)

    Maytham S. Ahmed

    2016-09-01

    Full Text Available Demand response (DR program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA-based artificial neural network (ANN to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM, are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy by ANN can be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period.

  3. Entity recognition in the biomedical domain using a hybrid approach.

    Science.gov (United States)

    Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

    2017-11-09

    This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

  4. An Odometry-free Approach for Simultaneous Localization and Online Hybrid Map Building

    Directory of Open Access Journals (Sweden)

    Wei Hong Chin

    2016-11-01

    Full Text Available In this paper, a new approach is proposed for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM which comprises two main components: 1 Steady state genetic algorithm (SSGA for self-localization and occupancy grid map building; 2 Bayesian Adaptive Resonance Associative Memory (ARAM for online topological map building. The model of the explored environment is formed as a hybrid representation, both topological and grid-based, and it is incrementally constructed during the exploration process. During occupancy map building, robot estimated self-position is updated by SSGA. At the same time, robot estimated self position is transmit to Bayesian ARAM for topological map building and localization. The effectiveness of our proposed approach is validated by a number of standardized benchmark datasets and real experimental results carried on mobile robot. Benchmark datasets are used to verify the proposed method capable of generating topological map in different environment conditions. Real robot experiment is to verify the proposed method can be implemented in real world.

  5. A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran

    International Nuclear Information System (INIS)

    Mostafavi, Elham Sadat; Ramiyani, Sara Saeidi; Sarvar, Rahim; Moud, Hashem Izadi; Mousavi, Seyyed Mohammad

    2013-01-01

    This paper presents an innovative hybrid approach for the estimation of the solar global radiation. New prediction equations were developed for the global radiation using an integrated search method of genetic programming (GP) and simulated annealing (SA), called GP/SA. The solar radiation was formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years in two cities of Iran were used to develop GP/SA-based models. Separate models were established for each city. The generalization of the models was verified using a separate testing database. A sensitivity analysis was conducted to investigate the contribution of the parameters affecting the solar radiation. The derived models make accurate predictions of the solar global radiation and notably outperform the existing models. -- Highlights: ► A hybrid approach is presented for the estimation of the solar global radiation. ► The proposed method integrates the capabilities of GP and SA. ► Several climatological and meteorological parameters are included in the analysis. ► The GP/SA models make accurate predictions of the solar global radiation.

  6. A tunable hybrid metamaterial absorber based on vanadium oxide films

    International Nuclear Information System (INIS)

    Wen Qiye; Zhang Huaiwu; Yang Qinghui; Long Yang; Jing Yulan; Lin Yuan; Chen Zhi; Zhang Peixin

    2012-01-01

    A tunable hybrid metamaterial absorber (MA) in the microwave band was designed, fabricated and characterized. The hybrid MA was realized by incorporating a VO 2 film into the conventional resonant MA. By thermally triggering the insulator-metal phase transition of the VO 2 film, the impedance match condition was broken and a deep amplitude modulation of about 63.3% to the electromagnetic wave absorption was achieved. A moderate blue-shift of the resonance frequency was observed which is promising for practical applications. This VO 2 -based MA exhibits many advantages such as strong tunability, frequency agility, simple fabrication and ease of scaling to the terahertz band. (paper)

  7. MAS Based Event-Triggered Hybrid Control for Smart Microgrids

    DEFF Research Database (Denmark)

    Dou, Chunxia; Liu, Bin; Guerrero, Josep M.

    2013-01-01

    This paper is focused on an advanced control for autonomous microgrids. In order to improve the performance regarding security and stability, a hierarchical decentralized coordinated control scheme is proposed based on multi-agents structure. Moreover, corresponding to the multi-mode and the hybrid...... haracteristics of microgrids, an event-triggered hybrid control, including three kinds of switching controls, is designed to intelligently reconstruct operation mode when the security stability assessment indexes or the constraint conditions are violated. The validity of proposed control scheme is demonstrated...

  8. Solving SAT Problem Based on Hybrid Differential Evolution Algorithm

    Science.gov (United States)

    Liu, Kunqi; Zhang, Jingmin; Liu, Gang; Kang, Lishan

    Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybrid differential evolution algorithm is proposed to solve the Satisfiability problem. It makes full use of strong local search capacity of hill-climbing algorithm and strong global search capability of differential evolution algorithm, which makes up their disadvantages, improves the efficiency of algorithm and avoids the stagnation phenomenon. The experiment results show that the hybrid algorithm is efficient in solving SAT problem.

  9. Route-Based Control of Hybrid Electric Vehicles: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Gonder, J. D.

    2008-01-01

    Today's hybrid electric vehicle controls cannot always provide maximum fuel savings over all drive cycles. Route-based controls could improve HEV fuel efficiency by 2%-4% and help save nearly 6.5 million gallons of fuel annually.

  10. Development of bio-hybrid material based on Salmonella ...

    African Journals Online (AJOL)

    Teodoro

    2016-07-13

    Jul 13, 2016 ... Full Length Research Paper. Development of bio-hybrid material based on. Salmonella Typhimurium and layered double hydroxides. Slah Hidouri .... the LDH with co-precipitation synthesis method was successfully done according the study given by Hidouri et al. (2011), Abdelkader et al. (2011), Hidouri et ...

  11. Silane-based hybrid materials for biomedical applications

    NARCIS (Netherlands)

    Kros, A.; Jansen, J.A.; Holder, S.J.; Nolte, R.J.M.; Sommerdijk, N.A.J.M.

    2002-01-01

    In this paper, the preparation of different hybrid silane materials is presented and their possible use in biomedical applications is discussed. The first example describes the development of biocompatible coatings based on sol-gel silicates, which can be used as a protective coating for implantable

  12. Two new inorganic-organic hybrid materials based on inorganic ...

    Indian Academy of Sciences (India)

    fields such as catalysis, pharmacology, medicine, nan- otechnology, and molecular ... such POM-based hybrid materials: (a) organic ligands graft onto POMs directly; .... average value of 6.028, close to the ideal value of 6 for MoVI. The bond ...

  13. DNA hybridization sensor based on pentacene thin film transistor.

    Science.gov (United States)

    Kim, Jung-Min; Jha, Sandeep Kumar; Chand, Rohit; Lee, Dong-Hoon; Kim, Yong-Sang

    2011-01-15

    A DNA hybridization sensor using pentacene thin film transistors (TFTs) is an excellent candidate for disposable sensor applications due to their low-cost fabrication process and fast detection. We fabricated pentacene TFTs on glass substrate for the sensing of DNA hybridization. The ss-DNA (polyA/polyT) or ds-DNA (polyA/polyT hybrid) were immobilized directly on the surface of the pentacene, producing a dramatic change in the electrical properties of the devices. The electrical characteristics of devices were studied as a function of DNA immobilization, single-stranded vs. double-stranded DNA, DNA length and concentration. The TFT device was further tested for detection of λ-phage genomic DNA using probe hybridization. Based on these results, we propose that a "label-free" detection technique for DNA hybridization is possible through direct measurement of electrical properties of DNA-immobilized pentacene TFTs. Copyright © 2010 Elsevier B.V. All rights reserved.

  14. Multifunctional Graphene-based Hybrid Nanomaterials for Electrochemical Energy Storage.

    Science.gov (United States)

    Gupta, Sanju

    Intense research in renewable energy is stimulated by global demand of electric energy. Electrochemical energy storage and conversion systems namely, supercapacitors and batteries, represent the most efficient and environmentally benign technologies. Moreover, controlled nanoscaled architectures and surface chemistry of electrochemical electrodes is enabling emergent next-generation efficient devices approaching theoretical limit of energy and power densities. This talk will present our recent activities to advance design, development and deployment of composition, morphology and microstructure controlled two- and three-dimensional graphene-based hybrids architectures. They are chemically and molecularly bridged with carbon nanotubes, conducting polymers, transition metal oxides and mesoproprous silicon wrapped with graphene nanosheets as engineered electrodes for supercapacitor cathodes and battery anodes. They showed significant enhancement in terms of gravimetric specific capacitance, interfacial capacitance, charging-discharging rate and cyclability. We will also present fundamental physical-chemical interfacial processes (ion transfer kinetics and diffusion), imaging electroactive sites, and topography at electrode/electrolyte interface governing underlying electrochemical mechanisms via scanning electrochemical microscopy. KY NSF EPSCoR.

  15. A Viola-Jones based hybrid face detection framework

    Science.gov (United States)

    Murphy, Thomas M.; Broussard, Randy; Schultz, Robert; Rakvic, Ryan; Ngo, Hau

    2013-12-01

    Improvements in face detection performance would benefit many applications. The OpenCV library implements a standard solution, the Viola-Jones detector, with a statistically boosted rejection cascade of binary classifiers. Empirical evidence has shown that Viola-Jones underdetects in some instances. This research shows that a truncated cascade augmented by a neural network could recover these undetected faces. A hybrid framework is constructed, with a truncated Viola-Jones cascade followed by an artificial neural network, used to refine the face decision. Optimally, a truncation stage that captured all faces and allowed the neural network to remove the false alarms is selected. A feedforward backpropagation network with one hidden layer is trained to discriminate faces based upon the thresholding (detection) values of intermediate stages of the full rejection cascade. A clustering algorithm is used as a precursor to the neural network, to group significant overlappings. Evaluated on the CMU/VASC Image Database, comparison with an unmodified OpenCV approach shows: (1) a 37% increase in detection rates if constrained by the requirement of no increase in false alarms, (2) a 48% increase in detection rates if some additional false alarms are tolerated, and (3) an 82% reduction in false alarms with no reduction in detection rates. These results demonstrate improved face detection and could address the need for such improvement in various applications.

  16. Hybrid Genetic Algorithm Optimization for Case Based Reasoning Systems

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2008-01-01

    The success of a CBR system largely depen ds on an effective retrieval of useful prior case for the problem. Nearest neighbor and induction are the main CBR retrieval algorithms. Each of them can be more suitable in different situations. Integrated the two retrieval algorithms can catch the advantages of both of them. But, they still have some limitations facing the induction retrieval algorithm when dealing with a noisy data, a large number of irrelevant features, and different types of data. This research utilizes a hybrid approach using genetic algorithms (GAs) to case-based induction retrieval of the integrated nearest neighbor - induction algorithm in an attempt to overcome these limitations and increase the overall classification accuracy. GAs can be used to optimize the search space of all the possible subsets of the features set. It can deal with the irrelevant and noisy features while still achieving a significant improvement of the retrieval accuracy. Therefore, the proposed CBR-GA introduces an effective general purpose retrieval algorithm that can improve the performance of CBR systems. It can be applied in many application areas. CBR-GA has proven its success when applied for different problems in real-life

  17. Hybrid network defense model based on fuzzy evaluation.

    Science.gov (United States)

    Cho, Ying-Chiang; Pan, Jen-Yi

    2014-01-01

    With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

  18. Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

    Science.gov (United States)

    Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

    2018-04-01

    A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

  19. A Model Predictive Control Approach for Fuel Economy Improvement of a Series Hydraulic Hybrid Vehicle

    Directory of Open Access Journals (Sweden)

    Tri-Vien Vu

    2014-10-01

    Full Text Available This study applied a model predictive control (MPC framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV. The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID controllers are used for cruising control.

  20. Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

    Science.gov (United States)

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

  1. A Hybrid Heuristic Optimization Approach for Leak Detection in Pipe Networks Using Ordinal Optimization Approach and the Symbiotic Organism Search

    Directory of Open Access Journals (Sweden)

    Chao-Chih Lin

    2017-10-01

    Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.

  2. A hybrid approach to automatic de-identification of psychiatric notes.

    Science.gov (United States)

    Lee, Hee-Jin; Wu, Yonghui; Zhang, Yaoyun; Xu, Jun; Xu, Hua; Roberts, Kirk

    2017-11-01

    De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research. This paper presents a natural language processing system for automatic de-identification of psychiatric notes, which was designed to participate in the 2016 CEGS N-GRID shared task Track 1. The system has a hybrid structure that combines machine leaning techniques and rule-based approaches. The rule-based components exploit the structure of the psychiatric notes as well as characteristic surface patterns of PHI mentions. The machine learning components utilize supervised learning with rich features. In addition, the system performance was boosted with integration of additional data to the training set through domain adaptation. The hybrid system showed overall micro-averaged F-score 90.74 on the test set, second-best among all the participants of the CEGS N-GRID task. Copyright © 2017. Published by Elsevier Inc.

  3. Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

    DEFF Research Database (Denmark)

    Wang, Yong; Cai, Zixing; Zhou, Yuren

    2009-01-01

    A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

  4. A Hybrid Harmony Search Algorithm Approach for Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Mimoun YOUNES

    2012-08-01

    Full Text Available Optimal Power Flow (OPF is one of the main functions of Power system operation. It determines the optimal settings of generating units, bus voltage, transformer tap and shunt elements in Power System with the objective of minimizing total production costs or losses while the system is operating within its security limits. The aim of this paper is to propose a novel methodology (BCGAs-HSA that solves OPF including both active and reactive power dispatch It is based on combining the binary-coded genetic algorithm (BCGAs and the harmony search algorithm (HSA to determine the optimal global solution. This method was tested on the modified IEEE 30 bus test system. The results obtained by this method are compared with those obtained with BCGAs or HSA separately. The results show that the BCGAs-HSA approach can converge to the optimum solution with accuracy compared to those reported recently in the literature.

  5. Novel hybrid materials based on the vanadium oxide nanobelts

    Energy Technology Data Exchange (ETDEWEB)

    Zabrodina, G.S., E-mail: kudgs@mail.ru [G.A. Razuvaev Institute of Organometallic Chemistry of Russian Academy of Sciences, Nizhny Novgorod 603950 (Russian Federation); Lobachevsky State University, Nizhny Novgorod 603950 (Russian Federation); Makarov, S.G.; Kremlev, K.V. [G.A. Razuvaev Institute of Organometallic Chemistry of Russian Academy of Sciences, Nizhny Novgorod 603950 (Russian Federation); Lobachevsky State University, Nizhny Novgorod 603950 (Russian Federation); Yunin, P.A.; Gusev, S.A. [Institute for Physics of Microstructures Russian Academy of Sciences, Nizhny Novgorod 603087 (Russian Federation); Kaverin, B.S.; Kaverina, L.B. [G.A. Razuvaev Institute of Organometallic Chemistry of Russian Academy of Sciences, Nizhny Novgorod 603950 (Russian Federation); Ketkov, S.Yu. [G.A. Razuvaev Institute of Organometallic Chemistry of Russian Academy of Sciences, Nizhny Novgorod 603950 (Russian Federation); Lobachevsky State University, Nizhny Novgorod 603950 (Russian Federation)

    2016-04-15

    Graphical abstract: - Highlights: • Flat and curved vanadium oxide nanobelts have been synthesized. • Hybrid material was prepared via decoration of flexible nanobelts with zinc phthalocyanine. • Investigations of the thermal stability, morphologies and structures were carried out. - Abstract: Novel hybrid materials based on zinc phthalocyanine and nanostructured vanadium oxides have attracted extensive attention for the development of academic research and innovative industrial applications such as flexible electronics, optical sensors and heterogeneous catalysts. Vanadium oxides nanobelts were synthesized via a hydrothermal treatment V{sub 2}O{sub 5}·nH{sub 2}O gel with surfactants (TBAB, CTAB) used as structure-directing agents, where CTAB – cetyltrimethylammonium bromide, TBAB – tetrabutylammonium bromide. Hybrid materials were prepared decoration of (CTA){sub 0.33}V{sub 2}O{sub 5} flexible nanobelts with cationic zinc phthalocyanine by the ion-exchange route. Investigations of the thermal stability, morphologies and structures of the (CTA){sub 0.33}V{sub 2}O{sub 5}, (TBA){sub 0.16}V{sub 2}O{sub 5} nanobelts and zinc phthalocyanine exchange product were carried out. The hybrid materials based on the nanostructured vanadium oxide and zinc phthalocyanine were tested as photocatalysts for oxidation of citronellol and 2-mercaptoethanol by dioxygen.

  6. Investigation of Υ Dor - δ Sct hybrid stars based on high precission space photometry and complementary ground based spectroscopy

    International Nuclear Information System (INIS)

    Hareter, M.

    2013-01-01

    Stellar pulsation carries information on the physical condition within the star. While pressure modes (p modes) probe the outer layers of a star, gravity modes (g modes) penetrate deep into the radiative zone and thus carry valuable information on the physical conditions there. gamma Dor stars are stars that pulsate in such modes, apart from white dwarfs and slowly pulsating B (SPB) stars. Therefore, these stars are important test benches for stellar evolution and pulsation theory. delta Sct - gamma Dor hybrids are stars that pulsate like gamma Dor stars with g modes but also with p modes as the delta Sct stars do. This makes them even more suited for asteroseismology. The CoRoT long runs offer a great opportunity to analyse a large sample of stars observed homogeneously, uninterrupted and long time base of about 150 days, which is practically unachievable with ground based observation. Since space missions avoid the scintillation caused by the Earth's atmosphere, they allow to detect stellar oscillations on a sub-millimagnitude level even for stars as faint as 15th magnitude. The photometric data is supplemented by AAOmega classification spectroscopy, allowing to determine effective tem- peratures and surface gravity. With these data a statistical approach was adopted to describe the pulsation behaviour gamma Dor and delta Sct - gamma Dor hybrid stars. A temperature - period relation was found for gamma Dor and delta Sct stars, but none for delta Sct - gamma Dor hybrid stars, when considering their strongest g mode or p mode, respectively. The instability domain of hybrid stars is equal to that of delta Sct stars and is not con- fined to the overlapping region of the delta Sct and gamma Dor IS in the Hertzsprung- Russell diagram. Hybrid stars behave differently in the g mode regime than gamma Dor stars, which poses a serious question on how to define properly a delta Sct - gamma Dor hybrid. The convective flux blocking mechanism is supposed to work for stars

  7. Chromosomal regions involved in hybrid performance and heterosis : their AFLP-based identification and practical use in prediction models

    NARCIS (Netherlands)

    Vuylsteke, M.; Kuiper, M.; Stam, P.

    2000-01-01

    In this paper, a novel approach towards the prediction of hybrid performance and heterosis is presented. Here, we describe an approach based on: (i) the assessment of associations between AFLPÒ22 AFLPÒ is a registered trademark of Keygene N.V. ,33 The methylation AFLPÒ method is subject to a patent

  8. An hybrid and non-modern approach to urban studies

    Directory of Open Access Journals (Sweden)

    Marc Grau i Solés

    2012-03-01

    Full Text Available This article draws upon the so-called Forat de la Vergonya urban controversy and the urban transformation process of a neighborhood in Barcelona: el Casc Antic. Drawing on inputs from Actor-Network Theory (ANT, the city is explored as a multiple urban assemblage. Besides, we analyze the dichotomous nature of the modern notion of politics. Especially, the role of object-subject dichotomy is explored. Through the analysis of citizen participation opportunities we propose a new hybrid notion of citizen participation and urban policy.

  9. Active diagnosis of hybrid systems - A model predictive approach

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Ravn, Anders P.; Izadi-Zamanabadi, Roozbeh

    2009-01-01

    A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and fault...... can be used as a test signal for sanity check at the commissioning or for detection of faults hidden by regulatory actions of the controller. The method is tested on the two tank benchmark example. ©2009 IEEE....

  10. Analysis of Hybrid-Integrated High-Speed Electro-Absorption Modulated Lasers Based on EM/Circuit Co-simulation

    DEFF Research Database (Denmark)

    Johansen, Tom Keinicke; Krozer, Viktor; Kazmierski, C.

    2009-01-01

    An improved electromagnetic simulation (EM) based approach has been developed for optimization of the electrical to optical (E/O) transmission properties of integrated electro-absorption modulated lasers (EMLs) aiming at 100 Gbit/s Ethernet applications. Our approach allows for an accurate analysis...... of the EML performance in a hybrid microstrip assembly. The established EM-based approach provides a design methodology for the future hybrid integration of the EML with its driving electronics....

  11. Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method

    Directory of Open Access Journals (Sweden)

    Majid Shadman Roodposhti

    2016-09-01

    Full Text Available Assessing Landslide Susceptibility Mapping (LSM contributes to reducing the risk of living with landslides. Handling the vagueness associated with LSM is a challenging task. Here we show the application of hybrid GIS-based LSM. The hybrid approach embraces fuzzy membership functions (FMFs in combination with Shannon entropy, a well-known information theory-based method. Nine landslide-related criteria, along with an inventory of landslides containing 108 recent and historic landslide points, are used to prepare a susceptibility map. A random split into training (≈70% and testing (≈30% samples are used for training and validation of the LSM model. The study area—Izeh—is located in the Khuzestan province of Iran, a highly susceptible landslide zone. The performance of the hybrid method is evaluated using receiver operating characteristics (ROC curves in combination with area under the curve (AUC. The performance of the proposed hybrid method with AUC of 0.934 is superior to multi-criteria evaluation approaches using a subjective scheme in this research in comparison with a previous study using the same dataset through extended fuzzy multi-criteria evaluation with AUC value of 0.894, and was built on the basis of decision makers’ evaluation in the same study area.

  12. A Formal Approach to User Interface Design using Hybrid System Theory, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Optimal Synthesis Inc.(OSI) proposes to develop an aiding tool for user interface design that is based on mathematical formalism of hybrid system theory. The...

  13. Extending a Hybrid Tag-Based Recommender System with Personalization

    DEFF Research Database (Denmark)

    Durao, Frederico; Dolog, Peter

    2010-01-01

    extension for a hybrid tag-based recommender system, which suggests similar Web pages based on the similarity of their tags. The semantic extension aims at discovering tag relations which are not considered in basic syntax similarity. With the goal of generating more semantically grounded recommendations......, the proposal extends a hybrid tag-based recommender system with a semantic factor, which looks for tag relations in different semantic sources. In order to evaluate the benefits acquired with the semantic extension, we have compared the new findings with results from a previous experiment involving 38 people......Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. This paper proposes a semantic...

  14. Graphene-based transparent electrodes for hybrid solar cells

    Directory of Open Access Journals (Sweden)

    Pengfei eLi

    2014-11-01

    Full Text Available The graphene-based transparent and conductive films were demonstrated to be cost-effective electrodes working in organic-inorganic hybrid Schottky solar cells. Large area graphene films were produced by chemical vapor deposition (CVD on copper foils and transferred onto glass as transparent electrodes. The hybrid solar cell devices consist of solution processed poly (3, 4-ethlenedioxythiophene: poly (styrenesulfonate (PEDOT: PSS which is sandwiched between silicon wafer and graphene electrode. The solar cells based on graphene electrodes, especially those doped with HNO3, has comparable performance to the reference devices using commercial indium tin oxide (ITO. Our work suggests that graphene-based transparent electrode is a promising candidate to replace ITO.

  15. The Importance of Being Hybrid for Spatial Epidemic Models:A Multi-Scale Approach

    Directory of Open Access Journals (Sweden)

    Arnaud Banos

    2015-11-01

    Full Text Available This work addresses the spread of a disease within an urban system, definedas a network of interconnected cities. The first step consists of comparing two differentapproaches: a macroscopic one, based on a system of coupled Ordinary DifferentialEquations (ODE Susceptible-Infected-Recovered (SIR systems exploiting populations onnodes and flows on edges (so-called metapopulational model, and a hybrid one, couplingODE SIR systems on nodes and agents traveling on edges. Under homogeneous conditions(mean field approximation, this comparison leads to similar results on the outputs on whichwe focus (the maximum intensity of the epidemic, its duration and the time of the epidemicpeak. However, when it comes to setting up epidemic control strategies, results rapidlydiverge between the two approaches, and it appears that the full macroscopic model is notcompletely adapted to these questions. In this paper, we focus on some control strategies,which are quarantine, avoidance and risk culture, to explore the differences, advantages anddisadvantages of the two models and discuss the importance of being hybrid when modelingand simulating epidemic spread at the level of a whole urban system.

  16. A Hybrid Architecture for Vision-Based Obstacle Avoidance

    Directory of Open Access Journals (Sweden)

    Mehmet Serdar Güzel

    2013-01-01

    Full Text Available This paper proposes a new obstacle avoidance method using a single monocular vision camera as the only sensor which is called as Hybrid Architecture. This architecture integrates a high performance appearance-based obstacle detection method into an optical flow-based navigation system. The hybrid architecture was designed and implemented to run both methods simultaneously and is able to combine the results of each method using a novel arbitration mechanism. The proposed strategy successfully fused two different vision-based obstacle avoidance methods using this arbitration mechanism in order to permit a safer obstacle avoidance system. Accordingly, to establish the adequacy of the design of the obstacle avoidance system, a series of experiments were conducted. The results demonstrate the characteristics of the proposed architecture, and the results prove that its performance is somewhat better than the conventional optical flow-based architecture. Especially, the robot employing Hybrid Architecture avoids lateral obstacles in a more smooth and robust manner than when using the conventional optical flow-based technique.

  17. Numerical methodologies for investigation of moderate-velocity flow using a hybrid computational fluid dynamics - molecular dynamics simulation approach

    International Nuclear Information System (INIS)

    Ko, Soon Heum; Kim, Na Yong; Nikitopoulos, Dimitris E.; Moldovan, Dorel; Jha, Shantenu

    2014-01-01

    Numerical approaches are presented to minimize the statistical errors inherently present due to finite sampling and the presence of thermal fluctuations in the molecular region of a hybrid computational fluid dynamics (CFD) - molecular dynamics (MD) flow solution. Near the fluid-solid interface the hybrid CFD-MD simulation approach provides a more accurate solution, especially in the presence of significant molecular-level phenomena, than the traditional continuum-based simulation techniques. It also involves less computational cost than the pure particle-based MD. Despite these advantages the hybrid CFD-MD methodology has been applied mostly in flow studies at high velocities, mainly because of the higher statistical errors associated with low velocities. As an alternative to the costly increase of the size of the MD region to decrease statistical errors, we investigate a few numerical approaches that reduce sampling noise of the solution at moderate-velocities. These methods are based on sampling of multiple simulation replicas and linear regression of multiple spatial/temporal samples. We discuss the advantages and disadvantages of each technique in the perspective of solution accuracy and computational cost.

  18. Environmentally friendly hybrid coatings for corrosion protection: silane based pre-treatments and nanostructured waterborne coatings

    OpenAIRE

    Fedel, Michele

    2009-01-01

    This thesis considers a nanotechnology approach based on the production of metals pre-treatments and organic coatings (a complete protection system at all) designed from the nanoscale. The final aim is to develop protection systems with improved corrosion protection properties and a low environmental impact. In particular, multifunctional silane hybrid molecules were used to design sol-gel pre-treatments for metals and to modify the inner structure of UV curable waterborne organic coatings...

  19. Plasma heating and hot ion sustaining in mirror based hybrids

    International Nuclear Information System (INIS)

    Moiseenko, V. E.; Ågren, O.

    2012-01-01

    Possibilities of plasma heating and sloshing ion sustaining in mirror based hybrids are briefly reviewed. Sloshing ions, i.e. energetic ions with a velocity distribution concentrated to a certain pitch-angle, play an important role in plasma confinement and generation of fusion neutrons in mirror machines. Neutral beam injection (NBI) is first discussed as a method to generate sloshing ions. Numerical results of NBI modeling for a stellarator-mirror hybrid are analyzed. The sloshing ions could alternatively be sustained by RF heating. Fast wave heating schemes, i.e. magnetic beach, minority and second harmonic heating, are addressed and their similarities and differences are described. Characteristic features of wave propagation in mirror hybrid devices including both fundamental harmonic minority and second harmonic heating are examined. Minority heating is efficient for a wide range of minority concentration and plasma densities; it allows one to place the antenna aside from the hot ion location. A simple-design strap antenna suitable for this has good performance. However, this scenario is appropriate only for light minority ions. The second harmonic heating can be applied for the heavy ion component. Arrangements are similar for minority and second harmonic heating. The efficiency of second harmonic heating is influenced by a weaker wave damping than for minority heating. Numerical calculations show that in a hybrid reactor scaled mirror machine the deuterium sloshing ions could be heated within the minority heating scheme, while the tritium ions could be sustained by second harmonic heating.

  20. Hybrid empirical--theoretical approach to modeling uranium adsorption

    International Nuclear Information System (INIS)

    Hull, Larry C.; Grossman, Christopher; Fjeld, Robert A.; Coates, John T.; Elzerman, Alan W.

    2004-01-01

    An estimated 330 metric tons of U are buried in the radioactive waste Subsurface Disposal Area (SDA) at the Idaho National Engineering and Environmental Laboratory (INEEL). An assessment of U transport parameters is being performed to decrease the uncertainty in risk and dose predictions derived from computer simulations of U fate and transport to the underlying Snake River Plain Aquifer. Uranium adsorption isotherms were measured for 14 sediment samples collected from sedimentary interbeds underlying the SDA. The adsorption data were fit with a Freundlich isotherm. The Freundlich n parameter is statistically identical for all 14 sediment samples and the Freundlich K f parameter is correlated to sediment surface area (r 2 =0.80). These findings suggest an efficient approach to material characterization and implementation of a spatially variable reactive transport model that requires only the measurement of sediment surface area. To expand the potential applicability of the measured isotherms, a model is derived from the empirical observations by incorporating concepts from surface complexation theory to account for the effects of solution chemistry. The resulting model is then used to predict the range of adsorption conditions to be expected in the vadose zone at the SDA based on the range in measured pore water chemistry. Adsorption in the deep vadose zone is predicted to be stronger than in near-surface sediments because the total dissolved carbonate decreases with depth

  1. Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

    Directory of Open Access Journals (Sweden)

    Hao Zhu

    2017-04-01

    Full Text Available Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO, probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.

  2. Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

    Institute of Scientific and Technical Information of China (English)

    Zhu Hao; Tian Hui; Cai Guobiao

    2017-01-01

    Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO) method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncer-tainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO), probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM) used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.

  3. A hybrid approach for probabilistic forecasting of electricity price

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Wang, Yelei

    2014-01-01

    to the nonstationarities involved in market clearing prices (MCPs), it is rather difficult to accurately predict MCPs in advance. The challenge is getting intensified as more and more renewable energy and other new technologies emerged in smart grids. Therefore transformation from traditional point forecasts...... electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market.......The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due...

  4. A Hybrid Recommender System Based on User-Recommender Interaction

    OpenAIRE

    Zhang, Heng-Ru; Min, Fan; He, Xu; Xu, Yuan-Yuan

    2015-01-01

    Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction...

  5. Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

    Directory of Open Access Journals (Sweden)

    Bogdan Gliwa

    2011-01-01

    Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

  6. A hybrid deterministic-probabilistic approach to model the mechanical response of helically arranged hierarchical strands

    Science.gov (United States)

    Fraldi, M.; Perrella, G.; Ciervo, M.; Bosia, F.; Pugno, N. M.

    2017-09-01

    Very recently, a Weibull-based probabilistic strategy has been successfully applied to bundles of wires to determine their overall stress-strain behaviour, also capturing previously unpredicted nonlinear and post-elastic features of hierarchical strands. This approach is based on the so-called "Equal Load Sharing (ELS)" hypothesis by virtue of which, when a wire breaks, the load acting on the strand is homogeneously redistributed among the surviving wires. Despite the overall effectiveness of the method, some discrepancies between theoretical predictions and in silico Finite Element-based simulations or experimental findings might arise when more complex structures are analysed, e.g. helically arranged bundles. To overcome these limitations, an enhanced hybrid approach is proposed in which the probability of rupture is combined with a deterministic mechanical model of a strand constituted by helically-arranged and hierarchically-organized wires. The analytical model is validated comparing its predictions with both Finite Element simulations and experimental tests. The results show that generalized stress-strain responses - incorporating tension/torsion coupling - are naturally found and, once one or more elements break, the competition between geometry and mechanics of the strand microstructure, i.e. the different cross sections and helical angles of the wires in the different hierarchical levels of the strand, determines the no longer homogeneous stress redistribution among the surviving wires whose fate is hence governed by a "Hierarchical Load Sharing" criterion.

  7. Electrochemical DNA Hybridization Sensors Based on Conducting Polymers

    Science.gov (United States)

    Rahman, Md. Mahbubur; Li, Xiao-Bo; Lopa, Nasrin Siraj; Ahn, Sang Jung; Lee, Jae-Joon

    2015-01-01

    Conducting polymers (CPs) are a group of polymeric materials that have attracted considerable attention because of their unique electronic, chemical, and biochemical properties. This is reflected in their use in a wide range of potential applications, including light-emitting diodes, anti-static coating, electrochromic materials, solar cells, chemical sensors, biosensors, and drug-release systems. Electrochemical DNA sensors based on CPs can be used in numerous areas related to human health. This review summarizes the recent progress made in the development and use of CP-based electrochemical DNA hybridization sensors. We discuss the distinct properties of CPs with respect to their use in the immobilization of probe DNA on electrode surfaces, and we describe the immobilization techniques used for developing DNA hybridization sensors together with the various transduction methods employed. In the concluding part of this review, we present some of the challenges faced in the use of CP-based DNA hybridization sensors, as well as a future perspective. PMID:25664436

  8. Biocomposites and hybrid biomaterials based on calcium orthophosphates

    Science.gov (United States)

    Dorozhkin, Sergey V.

    2011-01-01

    The state-of-the-art of biocomposites and hybrid biomaterials based on calcium orthophosphates that are suitable for biomedical applications is presented in this review. Since these types of biomaterials offer many significant and exciting possibilities for hard tissue regeneration, this subject belongs to a rapidly expanding area of biomedical research. Through successful combinations of the desired properties of matrix materials with those of fillers (in such systems, calcium orthophosphates might play either role), innovative bone graft biomaterials can be designed. Various types of biocomposites and hybrid biomaterials based on calcium orthophosphates, either those already in use or being investigated for biomedical applications, are extensively discussed. Many different formulations, in terms of the material constituents, fabrication technologies, structural and bioactive properties as well as both in vitro and in vivo characteristics, have already been proposed. Among the others, the nanostructurally controlled biocomposites, those containing nanodimensional compounds, biomimetically fabricated formulations with collagen, chitin and/or gelatin as well as various functionally graded structures seem to be the most promising candidates for clinical applications. The specific advantages of using biocomposites and hybrid biomaterials based on calcium orthophosphates in the selected applications are highlighted. As the way from the laboratory to the hospital is a long one, and the prospective biomedical candidates have to meet many different necessities, this review also examines the critical issues and scientific challenges that require further research and development. PMID:23507726

  9. Electrochemical DNA Hybridization Sensors Based on Conducting Polymers

    Directory of Open Access Journals (Sweden)

    Md. Mahbubur Rahman

    2015-02-01

    Full Text Available Conducting polymers (CPs are a group of polymeric materials that have attracted considerable attention because of their unique electronic, chemical, and biochemical properties. This is reflected in their use in a wide range of potential applications, including light-emitting diodes, anti-static coating, electrochromic materials, solar cells, chemical sensors, biosensors, and drug-release systems. Electrochemical DNA sensors based on CPs can be used in numerous areas related to human health. This review summarizes the recent progress made in the development and use of CP-based electrochemical DNA hybridization sensors. We discuss the distinct properties of CPs with respect to their use in the immobilization of probe DNA on electrode surfaces, and we describe the immobilization techniques used for developing DNA hybridization sensors together with the various transduction methods employed. In the concluding part of this review, we present some of the challenges faced in the use of CP-based DNA hybridization sensors, as well as a future perspective.

  10. Thermal modeling of a hydraulic hybrid vehicle transmission based on thermodynamic analysis

    International Nuclear Information System (INIS)

    Kwon, Hyukjoon; Sprengel, Michael; Ivantysynova, Monika

    2016-01-01

    Hybrid vehicles have become a popular alternative to conventional powertrain architectures by offering improved fuel efficiency along with a range of environmental benefits. Hydraulic Hybrid Vehicles (HHV) offer one approach to hybridization with many benefits over competing technologies. Among these benefits are lower component costs, more environmentally friendly construction materials, and the ability to recover a greater quantity of energy during regenerative braking which make HHVs partially well suited to urban environments. In order to further the knowledge base regarding HHVs, this paper explores the thermodynamic characteristics of such a system. A system model is detailed for both the hydraulic and thermal components of a closed circuit hydraulic hybrid transmission following the FTP-72 driving cycle. Among the new techniques proposed in this paper is a novel method for capturing rapid thermal transients. This paper concludes by comparing the results of this model with experimental data gathered on a Hardware-in-the-Loop (HIL) transmission dynamometer possessing the same architecture, components, and driving cycle used within the simulation model. This approach can be used for several applications such as thermal stability analysis of HHVs, optimal thermal management, and analysis of the system's thermodynamic efficiency. - Highlights: • Thermal modeling for HHVs is introduced. • A model for the hydraulic and thermal system is developed for HHVs. • A novel method for capturing rapid thermal transients is proposed. • The thermodynamic system diagram of a series HHV is predicted.

  11. Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach

    Directory of Open Access Journals (Sweden)

    Charles R. Lane

    2014-12-01

    Full Text Available Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2 for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA. We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85 for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.

  12. Numerical Solution of Piecewise Constant Delay Systems Based on a Hybrid Framework

    Directory of Open Access Journals (Sweden)

    H. R. Marzban

    2016-01-01

    Full Text Available An efficient numerical scheme for solving delay differential equations with a piecewise constant delay function is developed in this paper. The proposed approach is based on a hybrid of block-pulse functions and Taylor’s polynomials. The operational matrix of delay corresponding to the proposed hybrid functions is introduced. The sparsity of this matrix significantly reduces the computation time and memory requirement. The operational matrices of integration, delay, and product are employed to transform the problem under consideration into a system of algebraic equations. It is shown that the developed approach is also applicable to a special class of nonlinear piecewise constant delay differential equations. Several numerical experiments are examined to verify the validity and applicability of the presented technique.

  13. A hybrid approach to fault diagnosis of roller bearings under variable speed conditions

    Science.gov (United States)

    Wang, Yanxue; Yang, Lin; Xiang, Jiawei; Yang, Jianwei; He, Shuilong

    2017-12-01

    Rolling element bearings are one of the main elements in rotating machines, whose failure may lead to a fatal breakdown and significant economic losses. Conventional vibration-based diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speeds. This constraint limits the bearing diagnosis to the industrial application significantly. A hybrid approach to fault diagnosis of roller bearings under variable speed conditions is proposed in this work, based on computed order tracking (COT) and variational mode decomposition (VMD)-based time frequency representation (VTFR). COT is utilized to resample the non-stationary vibration signal in the angular domain, while VMD is used to decompose the resampled signal into a number of band-limited intrinsic mode functions (BLIMFs). A VTFR is then constructed based on the estimated instantaneous frequency and instantaneous amplitude of each BLIMF. Moreover, the Gini index and time-frequency kurtosis are both proposed to quantitatively measure the sparsity and concentration measurement of time-frequency representation, respectively. The effectiveness of the VTFR for extracting nonlinear components has been verified by a bat signal. Results of this numerical simulation also show the sparsity and concentration of the VTFR are better than those of short-time Fourier transform, continuous wavelet transform, Hilbert-Huang transform and Wigner-Ville distribution techniques. Several experimental results have further demonstrated that the proposed method can well detect bearing faults under variable speed conditions.

  14. A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES

    Directory of Open Access Journals (Sweden)

    M. Seidi

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT:Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. Remanufacturing is an industrial process that makes used products reusable. One of the important aspects in both reverse logistics and remanufacturing is the pricing of returned and remanufactured products (called cores. In this paper, we focus on pricing the cores and remanufactured cores. First we present a mathematical model for this purpose. Since this model does not satisfy our requirements, we propose a simulation optimisation approach. This approach consists of a hybrid genetic algorithm based on a neural network employed as the fitness function. We use automata learning theory to obtain the learning rate required for training the neural network. Numerical results demonstrate that the optimal value of the acquisition price of cores and price of remanufactured cores is obtained by this approach.

    AFRIKAANSE OPSOMMING: Volhoubaarheid het ‘n belangrike saak geword in die meeste ekonomieë, wat verskeie maatskappye genoop het om produkherwinning en omgekeerde logistiek te onder oë te neem. Hervervaardiging is ‘n industriële proses wat gebruikte produkte weer bruikbaar maak. Een van die belangrike aspekte in beide omgekeerde logistiek en hervervaardiging is die prysbepaling van herwinne en hervervaardigde produkte. Hierdie artikel fokus op die prysbepalingsaspekte by wyse van ‘n wiskundige model.

  15. Monitoring of beer fermentation based on hybrid electronic tongue.

    Science.gov (United States)

    Kutyła-Olesiuk, Anna; Zaborowski, Michał; Prokaryn, Piotr; Ciosek, Patrycja

    2012-10-01

    Monitoring of biotechnological processes, including fermentation is extremely important because of the rapidly occurring changes in the composition of the samples during the production. In the case of beer, the analysis of physicochemical parameters allows for the determination of the stage of fermentation process and the control of its possible perturbations. As a tool to control the beer production process a sensor array can be used, composed of potentiometric and voltammetric sensors (so-called hybrid Electronic Tongue, h-ET). The aim of this study is to apply electronic tongue system to distinguish samples obtained during alcoholic fermentation. The samples originate from batch of homemade beer fermentation and from two stages of the process: fermentation reaction and maturation of beer. The applied sensor array consists of 10 miniaturized ion-selective electrodes (potentiometric ET) and silicon based 3-electrode voltammetric transducers (voltammetric ET). The obtained results were processed using Partial Least Squares (PLS) and Partial Least Squares-Discriminant Analysis (PLS-DA). For potentiometric data, voltammetric data, and combined potentiometric and voltammetric data, comparison of the classification ability was conducted based on Root Mean Squared Error (RMSE), sensitivity, specificity, and coefficient F calculation. It is shown, that in the contrast to the separately used techniques, the developed hybrid system allowed for a better characterization of the beer samples. Data fusion in hybrid ET enables to obtain better results both in qualitative analysis (RMSE, specificity, sensitivity) and in quantitative analysis (RMSE, R(2), a, b). Copyright © 2012 Elsevier B.V. All rights reserved.

  16. A Low Cost, Hybrid Approach to Data Mining, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed effort will combine a low cost physical modeling approach with inductive, data-centered modeling in an aerosopace relevant context to demonstrate...

  17. Ultrafast Dynamic Pressure Sensors Based on Graphene Hybrid Structure.

    Science.gov (United States)

    Liu, Shanbiao; Wu, Xing; Zhang, Dongdong; Guo, Congwei; Wang, Peng; Hu, Weida; Li, Xinming; Zhou, Xiaofeng; Xu, Hejun; Luo, Chen; Zhang, Jian; Chu, Junhao

    2017-07-19

    Mechanical flexible electronic skin has been focused on sensing various physical parameters, such as pressure and temperature. The studies of material design and array-accessible devices are the building blocks of strain sensors for subtle pressure sensing. Here, we report a new and facile preparation of a graphene hybrid structure with an ultrafast dynamic pressure response. Graphene oxide nanosheets are used as a surfactant to prevent graphene restacking in aqueous solution. This graphene hybrid structure exhibits a frequency-independent pressure resistive sensing property. Exceeding natural skin, such pressure sensors, can provide transient responses from static up to 10 000 Hz dynamic frequencies. Integrated by the controlling system, the array-accessible sensors can manipulate a robot arm and self-rectify the temperature of a heating blanket. This may pave a path toward the future application of graphene-based wearable electronics.

  18. Nanocomposite-Based Bulk Heterojunction Hybrid Solar Cells

    Directory of Open Access Journals (Sweden)

    Bich Phuong Nguyen

    2014-01-01

    Full Text Available Photovoltaic devices based on nanocomposites composed of conjugated polymers and inorganic nanocrystals show promise for the fabrication of low-cost third-generation thin film photovoltaics. In theory, hybrid solar cells can combine the advantages of the two classes of materials to potentially provide high power conversion efficiencies of up to 10%; however, certain limitations on the current within a hybrid solar cell must be overcome. Current limitations arise from incompatibilities among the various intradevice interfaces and the uncontrolled aggregation of nanocrystals during the step in which the nanocrystals are mixed into the polymer matrix. Both effects can lead to charge transfer and transport inefficiencies. This paper highlights potential strategies for resolving these obstacles and presents an outlook on the future directions of this field.

  19. A new approach to flow simulation using hybrid models

    Science.gov (United States)

    Solgi, Abazar; Zarei, Heidar; Nourani, Vahid; Bahmani, Ramin

    2017-11-01

    The necessity of flow prediction in rivers, for proper management of water resource, and the need for determining the inflow to the dam reservoir, designing efficient flood warning systems and so forth, have always led water researchers to think about models with high-speed response and low error. In the recent years, the development of Artificial Neural Networks and Wavelet theory and using the combination of models help researchers to estimate the river flow better and better. In this study, daily and monthly scales were used for simulating the flow of Gamasiyab River, Nahavand, Iran. The first simulation was done using two types of ANN and ANFIS models. Then, using wavelet theory and decomposing input signals of the used parameters, sub-signals were obtained and were fed into the ANN and ANFIS to obtain hybrid models of WANN and WANFIS. In this study, in addition to the parameters of precipitation and flow, parameters of temperature and evaporation were used to analyze their effects on the simulation. The results showed that using wavelet transform improved the performance of the models in both monthly and daily scale. However, it had a better effect on the monthly scale and the WANFIS was the best model.

  20. Hybrid Enhanced Epidermal SpaceSuit Design Approaches

    Science.gov (United States)

    Jessup, Joseph M.

    A Space suit that does not rely on gas pressurization is a multi-faceted problem that requires major stability controls to be incorporated during design and construction. The concept of Hybrid Epidermal Enhancement space suit integrates evolved human anthropomorphic and physiological adaptations into its functionality, using commercially available bio-medical technologies to address shortcomings of conventional gas pressure suits, and the impracticalities of MCP suits. The prototype HEE Space Suit explored integumentary homeostasis, thermal control and mobility using advanced bio-medical materials technology and construction concepts. The goal was a space suit that functions as an enhanced, multi-functional bio-mimic of the human epidermal layer that works in attunement with the wearer rather than as a separate system. In addressing human physiological requirements for design and construction of the HEE suit, testing regimes were devised and integrated into the prototype which was then subject to a series of detailed tests using both anatomical reproduction methods and human subject.

  1. DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

    Science.gov (United States)

    Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin; Choo, Kim-Kwang Raymond

    2016-01-01

    To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).

  2. DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

    Directory of Open Access Journals (Sweden)

    Firdaus Afifi

    Full Text Available To deal with the large number of malicious mobile applications (e.g. mobile malware, a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS and particle swarm optimization (PSO. Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE and ant colony optimization (ANFIS-ACO.

  3. DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

    Science.gov (United States)

    Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin

    2016-01-01

    To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). PMID:27611312

  4. Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Zahid Farid

    2016-01-01

    Full Text Available In indoor environments, WiFi (RSS based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs. This model exploits machine learning, in particular Artificial Natural Network (ANN techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

  5. Microarray-based whole-genome hybridization as a tool for determining procaryotic species relatedness

    Energy Technology Data Exchange (ETDEWEB)

    Wu, L.; Liu, X.; Fields, M.W.; Thompson, D.K.; Bagwell, C.E.; Tiedje, J. M.; Hazen, T.C.; Zhou, J.

    2008-01-15

    The definition and delineation of microbial species are of great importance and challenge due to the extent of evolution and diversity. Whole-genome DNA-DNA hybridization is the cornerstone for defining procaryotic species relatedness, but obtaining pairwise DNA-DNA reassociation values for a comprehensive phylogenetic analysis of procaryotes is tedious and time consuming. A previously described microarray format containing whole-genomic DNA (the community genome array or CGA) was rigorously evaluated as a high-throughput alternative to the traditional DNA-DNA reassociation approach for delineating procaryotic species relationships. DNA similarities for multiple bacterial strains obtained with the CGA-based hybridization were comparable to those obtained with various traditional whole-genome hybridization methods (r=0.87, P<0.01). Significant linear relationships were also observed between the CGA-based genome similarities and those derived from small subunit (SSU) rRNA gene sequences (r=0.79, P<0.0001), gyrB sequences (r=0.95, P<0.0001) or REP- and BOX-PCR fingerprinting profiles (r=0.82, P<0.0001). The CGA hybridization-revealed species relationships in several representative genera, including Pseudomonas, Azoarcus and Shewanella, were largely congruent with previous classifications based on various conventional whole-genome DNA-DNA reassociation, SSU rRNA and/or gyrB analyses. These results suggest that CGA-based DNA-DNA hybridization could serve as a powerful, high-throughput format for determining species relatedness among microorganisms.

  6. Ring-Opening Polymerization of N-Carboxyanhydrides for Preparation of Polypeptides and Polypeptide-Based Hybrid Materials with Various Molecular Architectures

    KAUST Repository

    Pahovnik, David; Hadjichristidis, Nikolaos

    2015-01-01

    Different synthetic approaches utilizing ring-opening polymerization of N-carboxyanhydrides for preparation of polypeptide and polypeptide-based hybrid materials with various molecular architectures are described. An overview of polymerization

  7. Molecular and morphological approaches for species delimitation and hybridization investigations of two Cichla species

    Directory of Open Access Journals (Sweden)

    Andrea A. F. Mourão

    Full Text Available ABSTRACT The hybridization is a widely-discussed issue in several studies with fish species. For some authors, hybridization may be related with diversification and speciation of several groups, or also with the extinction of populations or species. Difficulties to differentiate species and hybrids may be a problem to correctly apply a management of wild species, because hybrid lineages, especially the advanced ones, may resemble the parental species. The genus Cichla Bloch & Schneider, 1801 constitutes an interesting experimental model, considering that hybridization and taxonomic uncertainties hinder a correct identification. Considering these problems, in this study, we developed genetic methodologies and applied meristic and morphometric approaches in wild samples in order to identify species and for test a possible hybridization between Cichla kelberi Kullander & Ferreira, 2006 and Cichla piquiti Kullander & Ferreira, 2006. For this, C. kelberi, C. piquiti and potential hybrid ( carijó individuals were collected in Paraná and Tietê rivers (SP, Brazil. For meristic and morphometric methods, the individuals were analyzed using the statistical software Pcord 5:31, while for molecular methods, primers for PCR-multiplex were designed and enzyme for PCR-RFLP were selected, under the species-specific nucleotide. All results indicated that the carijó is not an interspecific hybrid, because it presented identical genetic pattern and morphology closed to C. piquiti. Thus, we propose that carijó is a C. piquiti morphotype. In addition, this study promotes a new molecular tool that could be used in future research, monitoring and management programs of the genus Cichla.

  8. An event driven hybrid identity management approach to privacy enhanced e-health.

    Science.gov (United States)

    Sánchez-Guerrero, Rosa; Almenárez, Florina; Díaz-Sánchez, Daniel; Marín, Andrés; Arias, Patricia; Sanvido, Fabio

    2012-01-01

    Credential-based authorization offers interesting advantages for ubiquitous scenarios involving limited devices such as sensors and personal mobile equipment: the verification can be done locally; it offers a more reduced computational cost than its competitors for issuing, storing, and verification; and it naturally supports rights delegation. The main drawback is the revocation of rights. Revocation requires handling potentially large revocation lists, or using protocols to check the revocation status, bringing extra communication costs not acceptable for sensors and other limited devices. Moreover, the effective revocation consent--considered as a privacy rule in sensitive scenarios--has not been fully addressed. This paper proposes an event-based mechanism empowering a new concept, the sleepyhead credentials, which allows to substitute time constraints and explicit revocation by activating and deactivating authorization rights according to events. Our approach is to integrate this concept in IdM systems in a hybrid model supporting delegation, which can be an interesting alternative for scenarios where revocation of consent and user privacy are critical. The delegation includes a SAML compliant protocol, which we have validated through a proof-of-concept implementation. This article also explains the mathematical model describing the event-based model and offers estimations of the overhead introduced by the system. The paper focus on health care scenarios, where we show the flexibility of the proposed event-based user consent revocation mechanism.

  9. A Generalized Hybrid Multiscale Modeling Approach for Flow and Reactive Transport in Porous Media

    Science.gov (United States)

    Yang, X.; Meng, X.; Tang, Y. H.; Guo, Z.; Karniadakis, G. E.

    2017-12-01

    Using emerging understanding of biological and environmental processes at fundamental scales to advance predictions of the larger system behavior requires the development of multiscale approaches, and there is strong interest in coupling models at different scales together in a hybrid multiscale simulation framework. A limited number of hybrid multiscale simulation methods have been developed for subsurface applications, mostly using application-specific approaches for model coupling. The proposed generalized hybrid multiscale approach is designed with minimal intrusiveness to the at-scale simulators (pre-selected) and provides a set of lightweight C++ scripts to manage a complex multiscale workflow utilizing a concurrent coupling approach. The workflow includes at-scale simulators (using the lattice-Boltzmann method, LBM, at the pore and Darcy scale, respectively), scripts for boundary treatment (coupling and kriging), and a multiscale universal interface (MUI) for data exchange. The current study aims to apply the generalized hybrid multiscale modeling approach to couple pore- and Darcy-scale models for flow and mixing-controlled reaction with precipitation/dissolution in heterogeneous porous media. The model domain is packed heterogeneously that the mixing front geometry is more complex and not known a priori. To address those challenges, the generalized hybrid multiscale modeling approach is further developed to 1) adaptively define the locations of pore-scale subdomains, 2) provide a suite of physical boundary coupling schemes and 3) consider the dynamic change of the pore structures due to mineral precipitation/dissolution. The results are validated and evaluated by comparing with single-scale simulations in terms of velocities, reactive concentrations and computing cost.

  10. Bioenergy II. Biomass Valorisation by a Hybrid Thermochemical Fractionation Approach

    Energy Technology Data Exchange (ETDEWEB)

    De Wild, P.J.; Den Uil, H.; Reith, J.H. [ECN Biomass, Coal and Environmental Research, Petten (Netherlands); Lunshof, A.; Hendriks, C.; Van Eck, E. [Radboud University, Nijmegen (Netherlands); Heeres, E. [University of Groningen, Groningen (Netherlands)

    2009-11-15

    The need for green renewable sources is adamant because of the adverse effects of the increasing use of fossil fuels on our society. Biomass has been considered as a very attractive candidate for green energy carriers, chemicals and materials. The development of cheap and efficient fractionation technology to separate biomass into its main constituents is highly desirable. It enables treatment of each constituent separately, using dedicated conversion technologies to get specific target chemicals. The synergistic combination of aquathermolysis (hot pressurised water treatment) and pyrolysis (thermal degradation in the absence of oxygen) is a promising thermolysis option, integrating fractionation of biomass with production of valuable chemicals. Batch aquathermolysis in an autoclave and subsequent pyrolysis using bubbling fluidised bed reactor technology with beech, poplar, spruce and straw indicate the potential of this hybrid concept to valorise lignocellulosic biomass. Hemicellulose-derived furfural was obtained in yields that ranged from 2 wt% for spruce to 8 wt% for straw. Hydroxymethylfurfural from hemicellulose was obtained in yields from 0.3 wt% for poplar to 3 wt% for spruce. Pyrolysis of the aquathermolised biomass types resulted in 8 wt% (straw) to 11 wt% (spruce) of cellulose-derived levoglucosan. Next to the furfurals and levoglucosan, appreciable amounts of acetic acid were obtained as well from the aquathermolysis step, ranging from 1 wt% for spruce to 5 wt% for straw. To elucidate relations between the chemical changes occurring in the biomass during the integrated process and type and amount of the chemical products formed, a 13C-solid state NMR study has been conducted. Main conclusions are that aquathermolysis results in hemicellulose degradation to lower molecular weight components. Lignin ether bonds are broken, but apart from that, lignin is hardly affected by the aquathermolysis. Cellulose is also retained, although it seems to become more

  11. Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

    Science.gov (United States)

    Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

    2007-04-01

    One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

  12. A Hybrid Computational Intelligence Approach Combining Genetic Programming And Heuristic Classification for Pap-Smear Diagnosis

    DEFF Research Database (Denmark)

    Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan

    2001-01-01

    The paper suggests the combined use of different computational intelligence (CI) techniques in a hybrid scheme, as an effective approach to medical diagnosis. Getting to know the advantages and disadvantages of each computational intelligence technique in the recent years, the time has come...

  13. Students' Game Performance Improvements during a Hybrid Sport Education-Step-Game-Approach Volleyball Unit

    Science.gov (United States)

    Araújo, Rui; Mesquita, Isabel; Hastie, Peter; Pereira, Cristiana

    2016-01-01

    The purpose of this study was to examine a hybrid combination of sport education and the step-game-approach (SGA) on students' gameplay performance in volleyball, taking into account their sex and skill-level. Seventeen seventh-grade students (seven girls, 10 boys, average age 11.8) participated in a 25-lesson volleyball season, in which the…

  14. A hybrid approach to decision making and information fusion: Combining humans and artificial agents

    NARCIS (Netherlands)

    Groen, Frans C.A.; Pavlin, Gregor; Winterboer, Andi; Evers, Vanessa

    This paper argues that hybrid human–agent systems can support powerful solutions to relevant problems such as Environmental Crisis management. However, it shows that such solutions require comprehensive approaches covering different aspects of data processing, model construction and the usage. In

  15. TwitterNEED: a hybrid approach for named entity extraction and disambiguation for tweets

    NARCIS (Netherlands)

    Habib, Mena Badieh; van Keulen, Maurice

    Twitter is a rich source of continuously and instantly updated information. Shortness and informality of tweets are challenges for Natural Language Processing tasks. In this paper, we present TwitterNEED, a hybrid approach for Named Entity Extraction and Named Entity Disambiguation for tweets. We

  16. A Hybrid Approach to Processing Big Data Graphs on Memory-Restricted Systems

    KAUST Repository

    Harshvardhan,; West, Brandon; Fidel, Adam; Amato, Nancy M.; Rauchwerger, Lawrence

    2015-01-01

    that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM

  17. Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

    Science.gov (United States)

    Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

    2017-01-01

    Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99

  18. A hybrid linked data approach to support asset management

    NARCIS (Netherlands)

    Luiten, G.T.; Bohms, H.M.; O'Keeffe, A.; Nederveen, S. van; Bakker, J.; Wikstrom, L.

    2016-01-01

    This paper evaluates experiences with applying a linked data approach for coping with the many challenges for information management in asset management from the perspective of National Road Authorities (NRAs). As influential players, NRAs are often the initiators of innovation in the civil

  19. Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2013-01-01

    Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

  20. A Hybrid approach for aeroacoustic analysis of the engine exhaust system

    OpenAIRE

    Sathyanarayana, Y; Munjal, ML

    2000-01-01

    This paper presents a new hybrid approach for prediction of noise radiation from engine exhaust systems. It couples the time domain analysis of the engine and the frequency domain analysis of the muffler, and has the advantages of both. In this approach, cylinder/cavity is analyzed in the time domain to calculate the exhaust mass flux history at the exhaust valve by means of the method of characteristics, avoiding the tedious procedure of interpolation at every mesh point and solving a number...

  1. Unilateral robotic hybrid mini-maze: a novel experimental approach.

    Science.gov (United States)

    Moslemi, Mohammad; Rawashdeh, Badi; Meyer, Mark; Nguyen, Duy; Poston, Robert; Gharagozloo, Farid

    2016-03-01

    A complete Cox maze IV procedure is difficult to accomplish using current endoscopic and minimally invasive techniques. These techniques are hampered by inability to adequately dissect the posterior structures of the heart and place all necessary lesions. We present a novel approach, using robotic technology, that achieves placement of all the lesions of the complete maze procedure. In three cadaveric human models, the technical feasibility of using robotic instruments through the right chest to dissect the posterior structures of the heart and place all Cox maze lesions was performed. The entire posterior aspect of the heart was dissected in the cadaveric model facilitating successful placement of all Cox maze IV lesions with robotic assistance through minimally invasive incisions. The robotic Cox maze IV procedure through the novel right thoracic approach is feasible. This obviates the need for sternotomy and avoids the associated morbidity of the conventional Cox-maze procedure. Copyright © 2015 John Wiley & Sons, Ltd.

  2. The evolution of green jobs in Scotland: A hybrid approach

    International Nuclear Information System (INIS)

    Connolly, Kevin; Allan, Grant J; McIntyre, Stuart G

    2016-01-01

    In support of its ambitious target to reduce CO_2 emissions the Scottish Government is aiming to have the equivalent of 100% of Scottish electricity consumption generated from renewable sources by 2020. This is, at least in part, motivated by an expectation of subsequent employment growth in low carbon and renewable energy technologies; however there is no official data source to track employment in these areas. This has led to a variety of definitions, methodologies and alternative estimates being produced. Building on a recent study (Bishop and Brand, 2013) we develop a “hybrid” approach which combines the detail of “bottom-up” surveys with “top-down” trend data to produce estimates on employment in Low Carbon Environmental Goods and Services (LCEGS). We demonstrate this methodology to produce estimates for such employment in Scotland between 2004 and 2012. Our approach shows how survey and official sources can combine to produce a more timely measure of employment in LCEGS activities, assisting policymakers in tracking, consistently, developments. Applying our approach, we find that over this period employment in LCEGS in Scotland grew, but that this was more volatile than aggregate employment, and in particular that employment in this sector was particularly badly hit during the great recession. - Highlights: • A “hybrid” approach estimates green jobs from bottom-up detail and top-down data. • Illustrative results show the evolution of such jobs in Scotland from 2004 to 2012. • Method provides policymakers a timely measure of the jobs success of energy policy.

  3. A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

    Science.gov (United States)

    Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein

    2018-03-01

    As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.

  4. Hybrid approaches to nanometer-scale patterning: Exploiting tailored intermolecular interactions

    International Nuclear Information System (INIS)

    Mullen, Thomas J.; Srinivasan, Charan; Shuster, Mitchell J.; Horn, Mark W.; Andrews, Anne M.; Weiss, Paul S.

    2008-01-01

    In this perspective, we explore hybrid approaches to nanometer-scale patterning, where the precision of molecular self-assembly is combined with the sophistication and fidelity of lithography. Two areas - improving existing lithographic techniques through self-assembly and fabricating chemically patterned surfaces - will be discussed in terms of their advantages, limitations, applications, and future outlook. The creation of such chemical patterns enables new capabilities, including the assembly of biospecific surfaces to be recognized by, and to capture analytes from, complex mixtures. Finally, we speculate on the potential impact and upcoming challenges of these hybrid strategies.

  5. A hybrid approach on the management of crop pests | Onuodu ...

    African Journals Online (AJOL)

    This algorithm has been implemented using JAVA and MATLAB. The new method has been applied on agro-based datasets of soybean and yeast for forming clusters that could help farmers in the management of crop pests. The model developed could be beneficial to Nigerian farmers and the Agro-based industries, ...

  6. A hybrid modelling approach to develop scenarios for China's carbon dioxide emissions to 2050

    International Nuclear Information System (INIS)

    Gambhir, Ajay; Schulz, Niels; Napp, Tamaryn; Tong, Danlu; Munuera, Luis; Faist, Mark; Riahi, Keywan

    2013-01-01

    This paper describes a hybrid modelling approach to assess the future development of China's energy system, for both a “hypothetical counterfactual baseline” (HCB) scenario and low carbon (“abatement”) scenarios. The approach combines a technology-rich integrated assessment model (MESSAGE) of China's energy system with a set of sector-specific, bottom-up, energy demand models for the transport, buildings and industrial sectors developed by the Grantham Institute for Climate Change at Imperial College London. By exploring technology-specific solutions in all major sectors of the Chinese economy, we find that a combination of measures, underpinned by low-carbon power options based on a mix of renewables, nuclear and carbon capture and storage, would fundamentally transform the Chinese energy system, when combined with increasing electrification of demand-side sectors. Energy efficiency options in these demand sectors are also important. - Highlights: • Combining energy supply and demand models reveals low-carbon technology choices across China's economy. • China could reduce its CO 2 emissions to close to 3 Gt in 2050, costing around 2% of GDP. • Decarbonising the power sector underpins the energy system transformation. • Electrification of industrial processes, building heating and transport is required. • Energy efficiency across the demand side is also important

  7. A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

    Science.gov (United States)

    Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

    2015-06-01

    The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

  8. Developing hybrid approaches to predict pKa values of ionizable groups

    Science.gov (United States)

    Witham, Shawn; Talley, Kemper; Wang, Lin; Zhang, Zhe; Sarkar, Subhra; Gao, Daquan; Yang, Wei

    2011-01-01

    Accurate predictions of pKa values of titratable groups require taking into account all relevant processes associated with the ionization/deionization. Frequently, however, the ionization does not involve significant structural changes and the dominating effects are purely electrostatic in origin allowing accurate predictions to be made based on the electrostatic energy difference between ionized and neutral forms alone using a static structure. On another hand, if the change of the charge state is accompanied by a structural reorganization of the target protein, then the relevant conformational changes have to be taken into account in the pKa calculations. Here we report a hybrid approach that first predicts the titratable groups, which ionization is expected to cause conformational changes, termed “problematic” residues, then applies a special protocol on them, while the rest of the pKa’s are predicted with rigid backbone approach as implemented in multi-conformation continuum electrostatics (MCCE) method. The backbone representative conformations for “problematic” groups are generated with either molecular dynamics simulations with charged and uncharged amino acid or with ab-initio local segment modeling. The corresponding ensembles are then used to calculate the pKa of the “problematic” residues and then the results are averaged. PMID:21744395

  9. A hybrid stochastic approach for self-location of wireless sensors in indoor environments.

    Science.gov (United States)

    Lloret, Jaime; Tomas, Jesus; Garcia, Miguel; Canovas, Alejandro

    2009-01-01

    Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided.

  10. A Hybrid Stochastic Approach for Self-Location of Wireless Sensors in Indoor Environments

    Directory of Open Access Journals (Sweden)

    Alejandro Canovas

    2009-05-01

    Full Text Available Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided.

  11. Non-covalently functionalized carbon nanostructures for synthesizing carbon-based hybrid nanomaterials.

    Science.gov (United States)

    Li, Haiqing; Song, Sing I; Song, Ga Young; Kim, Il

    2014-02-01

    Carbon nanostructures (CNSs) such as carbon nanotubes, graphene sheets, and nanodiamonds provide an important type of substrate for constructing a variety of hybrid nanomaterials. However, their intrinsic chemistry-inert surfaces make it indispensable to pre-functionalize them prior to immobilizing additional components onto their surfaces. Currently developed strategies for functionalizing CNSs include covalent and non-covalent approaches. Conventional covalent treatments often damage the structure integrity of carbon surfaces and adversely affect their physical properties. In contrast, the non-covalent approach offers a non-destructive way to modify CNSs with desired functional surfaces, while reserving their intrinsic properties. Thus far, a number of surface modifiers including aromatic compounds, small-molecular surfactants, amphiphilic polymers, and biomacromolecules have been developed to non-covalently functionalize CNS surfaces. Mediated by these surface modifiers, various functional components such as organic species and inorganic nanoparticles were further decorated onto their surfaces, resulting in versatile carbon-based hybrid nanomaterials with broad applications in chemical engineering and biomedical areas. In this review, the recent advances in the generation of such hybrid nanostructures based on non-covalently functionalized CNSs will be reviewed.

  12. Innovative molecular approach to the identification of Colossoma macropomum and its hybrids

    Directory of Open Access Journals (Sweden)

    Fátima Gomes

    2012-06-01

    Full Text Available Tambaqui (Colossoma macropomum is the fish species most commonly raised in the Brazilian fish farms. The species is highly adaptable to captive conditions, and is both fast-growing and relatively fecund. In recent years, artificial breeding has produced hybrids with Characiform species, known as "Tambacu" and "Tambatinga". Identifying hybrids is a difficult process, given their morphological similarities with the parent species. This study presents an innovative molecular approach to the identification of hybrids based primarily on Multiplex PCR of a nuclear gene (α-Tropomyosin, which was tested on 93 specimens obtained from fish farms in northern Brazil. The sequencing of a 505-bp fragment of the Control Region (CR permitted the identification of the maternal lineage of the specimen, all of which corresponded to C. macropomum. Unexpectedly, only two CR haplotype were found in 93 samples, a very low genetic diversity for the pisciculture of Tambaqui. Multiplex PCR identified 42 hybrids, in contrast with 23 identified by the supplier on the basis of external morphology. This innovative tool has considerable potential for the development of the Brazilian aquaculture, given the possibility of the systematic identification of the genetic traits of both fry-producing stocks, and the fry and juveniles raised in farms.O Tambaqui (Colossoma macropomum é a espécie de peixe mais comumente cultivada em pisciculturas no Brasil. A espécie é altamente adaptada às condições de cativeiro, apresentando rápido crescimento e alta fecundidade. Nos últimos anos tem ocorrido o cruzamento artificial entre espécies de Characiformes, produzindo os híbridos "Tambacu" e "Tambatinga". A identificação de híbridos é uma tarefa difícil, em virtude da grande similaridade morfológica entre as espécies parentais. O presente estudo apresenta uma abordagem molecular inovadora para identificação de híbridos com base em PCR Multiplex de um gene nuclear (

  13. A hybrid, coupled approach for modeling charged fluids from the nano to the mesoscale

    Science.gov (United States)

    Cheung, James; Frischknecht, Amalie L.; Perego, Mauro; Bochev, Pavel

    2017-11-01

    We develop and demonstrate a new, hybrid simulation approach for charged fluids, which combines the accuracy of the nonlocal, classical density functional theory (cDFT) with the efficiency of the Poisson-Nernst-Planck (PNP) equations. The approach is motivated by the fact that the more accurate description of the physics in the cDFT model is required only near the charged surfaces, while away from these regions the PNP equations provide an acceptable representation of the ionic system. We formulate the hybrid approach in two stages. The first stage defines a coupled hybrid model in which the PNP and cDFT equations act independently on two overlapping domains, subject to suitable interface coupling conditions. At the second stage we apply the principles of the alternating Schwarz method to the hybrid model by using the interface conditions to define the appropriate boundary conditions and volume constraints exchanged between the PNP and the cDFT subdomains. Numerical examples with two representative examples of ionic systems demonstrate the numerical properties of the method and its potential to reduce the computational cost of a full cDFT calculation, while retaining the accuracy of the latter near the charged surfaces.

  14. Numerical schemes for the hybrid modeling approach of gas-particle turbulent flows

    International Nuclear Information System (INIS)

    Dorogan, K.

    2012-01-01

    Hybrid Moments/PDF methods have shown to be well suitable for the description of poly-dispersed turbulent two-phase flows in non-equilibrium which are encountered in some industrial situations involving chemical reactions, combustion or sprays. They allow to obtain a fine enough physical description of the poly-dispersity, non-linear source terms and convection phenomena. However, their approximations are noised with the statistical error, which in several situations may be a source of a bias. An alternative hybrid Moments-Moments/PDF approach examined in this work consists in coupling the Moments and the PDF descriptions, within the description of the dispersed phase itself. This hybrid method could reduce the statistical error and remove the bias. However, such a coupling is not straightforward in practice and requires the development of accurate and stable numerical schemes. The approaches introduced in this work rely on the combined use of the up-winding and relaxation-type techniques. They allow to obtain stable unsteady approximations for a system of partial differential equations containing non-smooth external data which are provided by the PDF part of the model. A comparison of the results obtained using the present method with those of the 'classical' hybrid approach is presented in terms of the numerical errors for a case of a co-current gas-particle wall jet. (author)

  15. 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.

  16. Tri-generation based hybrid power plant scheduling for renewable resources rich area with energy storage

    International Nuclear Information System (INIS)

    Pazheri, F.R.

    2015-01-01

    Highlights: • Involves scheduling of the tri-generation based hybrid power plant. • Utilization of renewable energy through energy storage is discussed. • Benefits of the proposed model are illustrated. • Energy efficient and environmental friendly dispatch is analyzed. • Modeled scheduling problem is applicable to any fuel enriched area. - Abstract: Solving power system scheduling is crucial to ensure smooth operations of the electric power industry. Effective utilization of available conventional and renewable energy sources (RES) by tri-generation and with the aid of energy storage facilities (ESF) can ensure clean and energy efficient power generation. Such power generation can play an important role in countries, like Saudi Arabia, where abundant fossil fuels (FF) and renewable energy sources (RES) are available. Hence, effective modeling of such hybrid power systems scheduling is essential in such countries based on the available fuel resources. The intent of this paper is to present a simple model for tri-generation based hybrid power system scheduling for energy resources rich area in presence of ESF, to ensure optimum fuel utilization and minimum pollutant emissions while meeting the power demand. This research points an effective operation strategy which ensure a clean and energy efficient power scheduling by exploiting available energy resources effectively. Hence, it has an important role in current and future power generation. In order to illustrate the benefits of the presented approach a clean and energy efficient hybrid power supply scheme for King Saud University (KSU), Saudi Arabia, is proposed and analyzed here. Results show that the proposed approach is very suitable for KSU since adequate solar power is available during its peak demand periods

  17. Design of time interval generator based on hybrid counting method

    Energy Technology Data Exchange (ETDEWEB)

    Yao, Yuan [State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Wang, Zhaoqi [State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Lu, Houbing [State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Hefei Electronic Engineering Institute, Hefei 230037 (China); Chen, Lian [State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Jin, Ge, E-mail: goldjin@ustc.edu.cn [State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, Anhui 230026 (China)

    2016-10-01

    Time Interval Generators (TIGs) are frequently used for the characterizations or timing operations of instruments in particle physics experiments. Though some “off-the-shelf” TIGs can be employed, the necessity of a custom test system or control system makes the TIGs, being implemented in a programmable device desirable. Nowadays, the feasibility of using Field Programmable Gate Arrays (FPGAs) to implement particle physics instrumentation has been validated in the design of Time-to-Digital Converters (TDCs) for precise time measurement. The FPGA-TDC technique is based on the architectures of Tapped Delay Line (TDL), whose delay cells are down to few tens of picosecond. In this case, FPGA-based TIGs with high delay step are preferable allowing the implementation of customized particle physics instrumentations and other utilities on the same FPGA device. A hybrid counting method for designing TIGs with both high resolution and wide range is presented in this paper. The combination of two different counting methods realizing an integratable TIG is described in detail. A specially designed multiplexer for tap selection is emphatically introduced. The special structure of the multiplexer is devised for minimizing the different additional delays caused by the unpredictable routings from different taps to the output. A Kintex-7 FPGA is used for the hybrid counting-based implementation of a TIG, providing a resolution up to 11 ps and an interval range up to 8 s.

  18. Design of time interval generator based on hybrid counting method

    International Nuclear Information System (INIS)

    Yao, Yuan; Wang, Zhaoqi; Lu, Houbing; Chen, Lian; Jin, Ge

    2016-01-01

    Time Interval Generators (TIGs) are frequently used for the characterizations or timing operations of instruments in particle physics experiments. Though some “off-the-shelf” TIGs can be employed, the necessity of a custom test system or control system makes the TIGs, being implemented in a programmable device desirable. Nowadays, the feasibility of using Field Programmable Gate Arrays (FPGAs) to implement particle physics instrumentation has been validated in the design of Time-to-Digital Converters (TDCs) for precise time measurement. The FPGA-TDC technique is based on the architectures of Tapped Delay Line (TDL), whose delay cells are down to few tens of picosecond. In this case, FPGA-based TIGs with high delay step are preferable allowing the implementation of customized particle physics instrumentations and other utilities on the same FPGA device. A hybrid counting method for designing TIGs with both high resolution and wide range is presented in this paper. The combination of two different counting methods realizing an integratable TIG is described in detail. A specially designed multiplexer for tap selection is emphatically introduced. The special structure of the multiplexer is devised for minimizing the different additional delays caused by the unpredictable routings from different taps to the output. A Kintex-7 FPGA is used for the hybrid counting-based implementation of a TIG, providing a resolution up to 11 ps and an interval range up to 8 s.

  19. A Probability-Based Hybrid User Model for Recommendation System

    Directory of Open Access Journals (Sweden)

    Jia Hao

    2016-01-01

    Full Text Available With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

  20. An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

    OpenAIRE

    Carreras Pérez, Marc; Yuh, Junku; Batlle i Grabulosa, Joan

    2001-01-01

    Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

  1. A hybrid approach to device integration on a genetic analysis platform

    International Nuclear Information System (INIS)

    Brennan, Des; Justice, John; Aherne, Margaret; Galvin, Paul; Jary, Dorothee; Kurg, Ants; Berik, Evgeny; Macek, Milan

    2012-01-01

    Point-of-care (POC) systems require significant component integration to implement biochemical protocols associated with molecular diagnostic assays. Hybrid platforms where discrete components are combined in a single platform are a suitable approach to integration, where combining multiple device fabrication steps on a single substrate is not possible due to incompatible or costly fabrication steps. We integrate three devices each with a specific system functionality: (i) a silicon electro-wetting-on-dielectric (EWOD) device to move and mix sample and reagent droplets in an oil phase, (ii) a polymer microfluidic chip containing channels and reservoirs and (iii) an aqueous phase glass microarray for fluorescence microarray hybridization detection. The EWOD device offers the possibility of fully integrating on-chip sample preparation using nanolitre sample and reagent volumes. A key challenge is sample transfer from the oil phase EWOD device to the aqueous phase microarray for hybridization detection. The EWOD device, waveguide performance and functionality are maintained during the integration process. An on-chip biochemical protocol for arrayed primer extension (APEX) was implemented for single nucleotide polymorphism (SNiP) analysis. The prepared sample is aspirated from the EWOD oil phase to the aqueous phase microarray for hybridization. A bench-top instrumentation system was also developed around the integrated platform to drive the EWOD electrodes, implement APEX sample heating and image the microarray after hybridization. (paper)

  2. Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

    Directory of Open Access Journals (Sweden)

    Oliveira Rui

    2010-09-01

    Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

  3. A hybridized K-means clustering approach for high dimensional ...

    African Journals Online (AJOL)

    International Journal of Engineering, Science and Technology ... Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays ... Recently cluster analysis is a popularly used data analysis method in number of areas.

  4. A Hybrid Feature Selection Approach for Arabic Documents Classification

    NARCIS (Netherlands)

    Habib, Mena Badieh; Sarhan, Ahmed A. E.; Salem, Abdel-Badeeh M.; Fayed, Zaki T.; Gharib, Tarek F.

    Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge number of features. Feature selection tries to

  5. Event-triggered hybrid control based on multi-Agent systems for Microgrids

    DEFF Research Database (Denmark)

    Dou, Chun-xia; Liu, Bin; Guerrero, Josep M.

    2014-01-01

    This paper is focused on a multi-agent system based event-triggered hybrid control for intelligently restructuring the operating mode of an microgrid (MG) to ensure the energy supply with high security, stability and cost effectiveness. Due to the microgrid is composed of different types...... of distributed energy resources, thus it is typical hybrid dynamic network. Considering the complex hybrid behaviors, a hierarchical decentralized coordinated control scheme is firstly constructed based on multi-agent sys-tem, then, the hybrid model of the microgrid is built by using differential hybrid Petri...

  6. A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs.

    Science.gov (United States)

    Cameron, Delroy; Sheth, Amit P; Jaykumar, Nishita; Thirunarayan, Krishnaprasad; Anand, Gaurish; Smith, Gary A

    2014-12-01

    While contemporary semantic search systems offer to improve classical keyword-based search, they are not always adequate for complex domain specific information needs. The domain of prescription drug abuse, for example, requires knowledge of both ontological concepts and "intelligible constructs" not typically modeled in ontologies. These intelligible constructs convey essential information that include notions of intensity, frequency, interval, dosage and sentiments, which could be important to the holistic needs of the information seeker. In this paper, we present a hybrid approach to domain specific information retrieval that integrates ontology-driven query interpretation with synonym-based query expansion and domain specific rules, to facilitate search in social media on prescription drug abuse. Our framework is based on a context-free grammar (CFG) that defines the query language of constructs interpretable by the search system. The grammar provides two levels of semantic interpretation: 1) a top-level CFG that facilitates retrieval of diverse textual patterns, which belong to broad templates and 2) a low-level CFG that enables interpretation of specific expressions belonging to such textual patterns. These low-level expressions occur as concepts from four different categories of data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and sentiments), 3) concepts in lexicons with only partial ontology representation, called lexico-ontology concepts (such as side effects and routes of administration (ROA)), and 4) domain specific expressions (such as date, time, interval, frequency and dosage) derived solely through rules. Our approach is embodied in a novel Semantic Web platform called PREDOSE, which provides search support for complex domain specific information needs in prescription drug abuse epidemiology. When applied to a corpus of over 1 million drug abuse-related web forum posts, our search framework proved effective in retrieving

  7. SPR based hybrid electro-optic biosensor for β-lactam antibiotics determination in water

    Science.gov (United States)

    Galatus, Ramona; Feier, Bogdan; Cristea, Cecilia; Cennamo, Nunzio; Zeni, Luigi

    2017-09-01

    The present work aims to provide a hybrid platform capable of complementary and sensitive detection of β-lactam antibiotics, ampicillin in particular. The use of an aptamer specific to ampicillin assures good selectivity and sensitivity for the detection of ampicillin from different matrice. This new approach is dedicated for a portable, remote sensing platform based on low-cost, small size and low-power consumption solution. The simple experimental hybrid platform integrates the results from the D-shape surface plasmon resonance plastic optical fiber (SPR-POF) and from the electrochemical (bio)sensor, for the analysis of ampicillin, delivering sensitive and reliable results. The SPR-POF already used in many previous applications is embedded in a new experimental setup with fluorescent fibers emitters, for broadband wavelength analysis, low-power consumption and low-heating capabilities of the sensing platform.

  8. Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Ameli

    2012-01-01

    Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

  9. NBI - plasma vaporization hybrid approach in bladder cancer endoscopic management.

    Science.gov (United States)

    Stănescu, F; Geavlete, B; Georgescu, D; Jecu, M; Moldoveanu, C; Adou, L; Bulai, C; Ene, C; Geavlete, P

    2014-06-15

    A prospective study was performed aiming to evaluate the surgical efficacy, perioperative safety profile, diagnostic accuracy and medium term results of a multi-modal approach consisting in narrow band imaging (NBI) cystoscopy and bipolar plasma vaporization (BPV) when compared to the standard protocol represented by white light cystoscopy (WLC) and transurethral resection of bladder tumors (TURBT). A total of 260 patients with apparently at least one bladder tumor over 3 cm were included in the trial. In the first group, 130 patients underwent conventional and NBI cystoscopy followed by BPV, while in a similar number of cases of the second arm, classical WLC and TURBT were applied. In all non-muscle invasive bladder tumors' (NMIBT) pathologically confirmed cases, standard monopolar Re-TUR was performed at 4-6 weeks after the initial intervention, followed by one year' BCG immunotherapy. The follow-up protocol included abdominal ultrasound, urinary cytology and WLC, performed every 3 months for a period of 2 years. The obturator nerve stimulation, bladder wall perforation, mean hemoglobin level drop, postoperative bleeding, catheterization period and hospital stay were significantly reduced for the plasma vaporization technique by comparison to conventional resection. Concerning tumoral detection, the present data confirmed the NBI superiority when compared to standard WLC regardless of tumor stage (95.3% vs. 65.1% for CIS, 93.3% vs. 82.2% for pTa, 97.4% vs. 94% for pT1, 95% vs. 84.2% overall). During standard Re-TUR the overall (6.3% versus 17.4%) and primary site (3.6% versus 12.8%) residual tumors' rates were significantly lower for the NBI-BPV group. The 1 (7.2% versus 18.3%) and 2 (11.5% versus 25.8%) years' recurrence rates were substantially lower for the combined approach. NBI cystoscopy significantly improved diagnostic accuracy, while bipolar technology showed a higher surgical efficiency, lower morbidity and faster postoperative recovery. The combined

  10. An Approach to Management of Health Care and Medical Diagnosis Using of a Hybrid Disease Diagnosis System

    Directory of Open Access Journals (Sweden)

    Hodjat Hamidi

    2017-02-01

    Full Text Available Introduction: In order to simplify the information exchange within the medical diagnosis process, a collaborative software agent’s framework is presented. The purpose of the framework is to allow the automated information exchange between different medicine specialists. Methods: This study presented architecture of a hybrid disease diagnosis system. The architecture employed a learning algorithm and used soft computing to build a medical knowledge base. These machine intelligences are combined in a complementary approach to overcome the weakness of each other. To evaluate the hybrid learning algorithm and compare it with other methods, 699 samples were used in each experiment, where 60% was for training, 20% was for cross validation, and 20% for testing. Results: The results were obtained from the experiments on the breast cancer dataset. Different methods of soft computing system were merged to create diagnostic software functionality. As it is shown in the structure, the system has the ability to learn and collect knowledge that can be used in the detection of new images. Currently, the system is at the design stage. The system is to evaluate the performance of hybrid learning algorithm. The preliminary results showed a better performance of this system than other methods. However, the results can be tested with hybrid system on larger data sets to improve hybrid learning algorithm. Conclusion: The purpose of this paper was to simplify the diagnosis process of a patient by splitting the medical domain concepts (e.g., causes, effects, symptoms, tests in human body systems (e.g., respiratory, cardiovascular, though maintaining the holistic perspective through the links between common concepts.

  11. A hybrid firefly algorithm and pattern search technique for SSSC based power oscillation damping controller design

    Directory of Open Access Journals (Sweden)

    Srikanta Mahapatra

    2014-12-01

    Full Text Available In this paper, a novel hybrid Firefly Algorithm and Pattern Search (h-FAPS technique is proposed for a Static Synchronous Series Compensator (SSSC-based power oscillation damping controller design. The proposed h-FAPS technique takes the advantage of global search capability of FA and local search facility of PS. In order to tackle the drawback of using the remote signal that may impact reliability of the controller, a modified signal equivalent to the remote speed deviation signal is constructed from the local measurements. The performances of the proposed controllers are evaluated in SMIB and multi-machine power system subjected to various transient disturbances. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with some recently published approaches such as Differential Evolution (DE and Particle Swarm Optimization (PSO. It is observed that the proposed approach yield superior damping performance compared to some recently reported approaches.

  12. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  13. A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features

    Directory of Open Access Journals (Sweden)

    Chen Li

    2018-03-01

    Full Text Available To increase prediction accuracy of dissolved oxygen (DO in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP. Then, the trend term is predicted using grey model (GM because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD, EEMD-BP and MEC-BP models based on the mean absolute error (MAE, mean absolute percentage error (MAPE, mean square error (MSE and root mean square error (RMSE. Our hybrid model is proven to be an effective approach to predict aquaculture DO.

  14. Hybrid modeling approach to improve the forecasting capability for the gaseous radionuclide in a nuclear site

    International Nuclear Information System (INIS)

    Jeong, Hyojoon; Hwang, Wontae; Kim, Eunhan; Han, Moonhee

    2012-01-01

    Highlights: ► This study is to improve the reliability of air dispersion modeling. ► Tracer experiments assumed gaseous radionuclides were conducted at a nuclear site. ► The performance of a hybrid modeling combined ISC with ANFIS was investigated.. ► Hybrid modeling approach shows better performance rather than a single ISC model. - Abstract: Predicted air concentrations of radioactive materials are important for an environmental impact assessment for the public health. In this study, the performance of a hybrid modeling combined with the industrial source complex (ISC) model and an adaptive neuro-fuzzy inference system (ANFIS) for predicting tracer concentrations was investigated. Tracer dispersion experiments were performed to produce the field data assuming the accidental release of radioactive material. ANFIS was trained in order that the outputs of the ISC model are similar to the measured data. Judging from the higher correlation coefficients between the measured and the calculated ones, the hybrid modeling approach could be an appropriate technique for an improvement of the modeling capability to predict the air concentrations for radioactive materials.

  15. Hybrid perturbation methods based on statistical time series models

    Science.gov (United States)

    San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

    2016-04-01

    In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

  16. Graphene-Based Materials for Lithium-Ion Hybrid Supercapacitors.

    Science.gov (United States)

    Ma, Yanfeng; Chang, Huicong; Zhang, Miao; Chen, Yongsheng

    2015-09-23

    Lithium-ion hybrid supercapacitors (LIHSs), also called Li-ion capacitors, have attracted much attention due to the combination of the rapid charge-discharge and long cycle life of supercapacitors and the high energy-storage capacity of lithium-ion batteries. Thus, LIHSs are expected to become the ultimate power source for hybrid and all-electric vehicles in the near future. As an electrode material, graphene has many advantages, including high surface area and porous structure, high electric conductivity, and high chemical and thermal stability, etc. Compared with other electrode materials, such as activated carbon, graphite, and metal oxides, graphene-based materials with 3D open frameworks show higher effective specific surface area, better control of channels, and higher conductivity, which make them better candidates for LIHS applications. Here, the latest advances in electrode materials for LIHSs are briefly summarized, with an emphasis on graphene-based electrode materials (including 3D graphene networks) for LIHS applications. An outlook is also presented to highlight some future directions. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. A low-cost hybrid drivetrain concept based on compressed air energy storage

    International Nuclear Information System (INIS)

    Brown, T.L.; Atluri, V.P.; Schmiedeler, J.P.

    2014-01-01

    Highlights: • A new pneumatic hybrid concept is introduced. • A proof-of-concept prototype system is built and tested. • The experimental system has a round-trip efficiency of just under 10%. • A thermodynamics model is used to predict the performance of modified designs. • An efficiency of nearly 50% is possible with reasonable design changes. - Abstract: This paper introduces a new low-cost hybrid drivetrain concept based on compressed air energy storage. In contrast to most contemporary approaches to pneumatic hybridization, which require modification to the primary power plant, this concept is based on a stand-alone pneumatic system that could be readily integrated with existing vehicles. The pneumatic system consists of an air tank and a compressor–expander that is coupled to the rest of the drivetrain via an infinitely variable transmission. Rather than incorporating more expensive technologies such as variable valve timing or a variable compression ratio compressor, a fixed valve system consisting of a rotary valve and passive check valves is optimized to operate efficiently over a range of tank pressures. The feasibility of this approach is established by thermodynamic modeling and the construction of a proof-of-concept prototype, which is also used to fine tune model parameters. While the proof-of-concept system shows a round trip efficiency of just under 10%, modeling shows that a round trip efficiency of 26% is possible with a revised design. If waste heat from the engine is used to maintain an elevated tank temperature, efficiencies of nearly 50% may be possible, indicating that the concept could be effective for practical hybridization of passenger vehicles

  18. Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

    Directory of Open Access Journals (Sweden)

    Chengdong Li

    2018-01-01

    Full Text Available To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity. In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN, the generalized radial basis function neural network (GRBFNN, the extreme learning machine (ELM, and the support vector regressor (SVR are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption

  19. A fuzzy hybrid approach for project manager selection

    Directory of Open Access Journals (Sweden)

    Ahmad Jafarnejad Chaghooshi

    2016-09-01

    Full Text Available Suitable project manager has a significant impact on successful accomplishment of the project. Managers should possess such skills in order to effectively cope with the competition. In this respect, selecting managers based on their skills can lead to a competitive advantage towards the achievement of organizational goals. selection of the suitable project manager can be viewed as a multi-criteria decision making (MCDM problem and an extensive evaluation of criteria, such as Technical skills, experience skills, Personal qualities and the related criteria must be considered in the selection process of project manager. The fuzzy set theory and MCDM methods appears as an essential tools to provide a decision framework that incorporates imprecise judgments and multi criteria nature of project manager selection process inherent in this process. This paper proposes the joint use of the Fuzzy DEMATEL (FDEMATEL and Fuzzy VIKOR methods for the decision-making process of selecting the most suitable managers for projects. First, with the opinions of the senior managers based on project management competency model (ICB-IPMA, all the criteria required for the selection are gathered. Then the FDEMATEL method is used to prioritize the importance of various criteria and FVIKOR used to rank the alternatives in a preferred order to select the best project managers from a number of alternatives. Next, a real case study used to illustrate the process of the proposed method. Finally, some conclusions are discussed at the end of this study.

  20. Hybrid Approach of Aortic Diseases: Zone 1 Delivery and Volumetric Analysis on the Descending Aorta

    Directory of Open Access Journals (Sweden)

    José Augusto Duncan

    Full Text Available Abstract Introduction: Conventional techniques of surgical correction of arch and descending aortic diseases remains as high-risk procedures. Endovascular treatments of abdominal and descending thoracic aorta have lower surgical risk. Evolution of both techniques - open debranching of the arch and endovascular approach of the descending aorta - may extend a less invasive endovascular treatment for a more extensive disease with necessity of proximal landing zone in the arch. Objective: To evaluate descending thoracic aortic remodeling by means of volumetric analysis after hybrid approach of aortic arch debranching and stenting the descending aorta. Methods: Retrospective review of seven consecutive patients treated between September 2014 and August 2016 for diseases of proximal descending aorta (aneurysms and dissections by hybrid approach to deliver the endograft at zone 1. Computed tomography angiography were analyzed using a specific software to calculate descending thoracic aorta volumes pre- and postoperatively. Results: Follow-up was done in 100% of patients with a median time of 321 days (range, 41-625 days. No deaths or permanent neurological complications were observed. There were no endoleaks or stent migrations. Freedom from reintervention was 100% at 300 days and 66% at 600 days. Median volume reduction was of 45.5 cm3, representing a median volume shrinkage by 9.3%. Conclusion: Hybrid approach of arch and descending thoracic aorta diseases is feasible and leads to a favorable aortic remodeling with significant volume reduction.

  1. Does the acceptance of hybrid learning affect learning approaches in France?

    Science.gov (United States)

    Marco, Lionel Di; Venot, Alain; Gillois, Pierre

    2017-01-01

    Acceptance of a learning technology affects students' intention to use that technology, but the influence of the acceptance of a learning technology on learning approaches has not been investigated in the literature. A deep learning approach is important in the field of health, where links must be created between skills, knowledge, and habits. Our hypothesis was that acceptance of a hybrid learning model would affect students' way of learning. We analysed these concepts, and their correlations, in the context of a flipped classroom method using a local learning management system. In a sample of all students within a single year of study in the midwifery program (n= 38), we used 3 validated scales to evaluate these concepts (the Study Process Questionnaire, My Intellectual Work Tools, and the Hybrid E-Learning Acceptance Model: Learner Perceptions). Our sample had a positive acceptance of the learning model, but a neutral intention to use it. Students reported that they were distractible during distance learning. They presented a better mean score for the deep approach than for the superficial approach (Paffected by acceptance of a hybrid learning model, due to the flexibility of the tool. However, we identified problems in the students' time utilization, which explains their neutral intention to use the system.

  2. A hybrid fuzzy MCDM approach to maintenance Quality Function Deployment

    Directory of Open Access Journals (Sweden)

    Davy George Valavi

    2015-01-01

    Full Text Available Maintenance Quality Function Deployment (MQFD is a model, which enhances the synergic power of Quality Function Deployment (QFD and Total Productive Maintenance (TPM. One of the crucial and important steps during the implementation of MQFD is the determination of the importance or weightages of the critical factors (CF and sub factors (SF. The CFs and SFs have to be compared precisely for the successful implementation of MQFD. The crisp pair-wise comparison in the conventional Analytical Hierarchy Process (AHP may be insufficient to determine the degree of weightage of CFs and SFs where vagueness and uncetainties are associated. In this paper, a modification of AHP based MQFD by incorporating fuzzy operations is proposed, which can improve the accuracy of determination of the weightages. A case study showing the applicability of this method is illustrated in this paper.

  3. A Hybrid Approach to Proving Memory Reference Monotonicity

    KAUST Repository

    Oancea, Cosmin E.

    2013-01-01

    Array references indexed by non-linear expressions or subscript arrays represent a major obstacle to compiler analysis and to automatic parallelization. Most previous proposed solutions either enhance the static analysis repertoire to recognize more patterns, to infer array-value properties, and to refine the mathematical support, or apply expensive run time analysis of memory reference traces to disambiguate these accesses. This paper presents an automated solution based on static construction of access summaries, in which the reference non-linearity problem can be solved for a large number of reference patterns by extracting arbitrarily-shaped predicates that can (in)validate the reference monotonicity property and thus (dis)prove loop independence. Experiments on six benchmarks show that our general technique for dynamic validation of the monotonicity property can cover a large class of codes, incurs minimal run-time overhead and obtains good speedups. © 2013 Springer-Verlag.

  4. Linear electro-optical behavior of hybrid nanocomposites based on silicon carbide nanocrystals and polymer matrices

    Science.gov (United States)

    Bouclé, J.; Kassiba, A.; Makowska-Janusik, M.; Herlin-Boime, N.; Reynaud, C.; Desert, A.; Emery, J.; Bulou, A.; Sanetra, J.; Pud, A. A.; Kodjikian, S.

    2006-11-01

    An electro-optical activity has been recently reported for hybrid nanocomposite thin films where inorganic silicon carbide nanocrystals (ncSiC) are incorporated into polymer matrices. The role of the interface SiC polymer is suggested as the origin of the observed second order nonlinear optical susceptibility in the hybrid materials based on poly-(methylmethacrylate) (PMMA) or poly-( N -vinylcarbazole) matrices. In this work, we report an analysis of the electro-optical response of this hybrid system as a function of the ncSiC content and surface state in order to precise the interface effect in the observed phenomenon. Two specific ncSiC samples with similar morphology and different surface states are incorporated in the PMMA matrix. The effective Pockels parameters of the corresponding hybrid nanocomposites have been estimated up to 7.59±0.74pm/V ( 1wt.% of ncSiC in the matrix). The interfacial region ncSiC polymer is found to play the main role in the observed effect. Particularly, the electronic defects on the ncSiC nanocrystal surface modify the interfacial electrical interactions between the two components. The results are interpreted and discussed on the basis of the strong influence of these active centers in the interfacial region at the nanoscale, which are found to monitor the local hyperpolarizabilities and the macroscopic nonlinear optical susceptibilities. This approach allows us to complete the description and understanding of the electro-optical response in the hybrid SiC /polymer systems.

  5. A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-08-25

    This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.

  6. Form-based Approaches vs. Task-Based Approaches

    Directory of Open Access Journals (Sweden)

    Zahra Talebi

    2015-07-01

    Full Text Available This study aimed at investigating whether task-based approaches bear any superiority to that of more traditional ones evident in presentation-practice- and production phase .to fulfill the purpose of the study, the participants within the age range of 11-19, took part in the study. Following a pretest, treatment, and a posttest, the obtained data was analyzed using analysis of covariance (ANCOVA to examine the effects of the variables. The results of the analysis showed that participants in the PPP group did significantly better in the grammar recognition of the posttest than that of the task group. However, their counterparts in the task group gained better scores in the writing section of the test .this research study provided evidence in support of task proponents' claim in the merit of task-based activity in raising learners' implicit knowledge claiming to play the primary role in spontaneous speech.

  7. OPTIMISATION OF BUFFER SIZE FOR ENHANCING QOS OF VIDEO TRAFFIC USING CROSS LAYERED HYBRID TRANSPORT LAYER PROTOCOL APPROACH

    Directory of Open Access Journals (Sweden)

    S. Matilda

    2011-03-01

    Full Text Available Video streaming is gaining importance, with the wide popularity of multimedia rich applications in the Internet. Video streams are delay sensitive and require seamless flow for continuous visualization. Properly designed buffers offer a solution to queuing delay. The diagonally opposite QoS metrics associated with video traffic poses an optimization problem, in the design of buffers. This paper is a continuation of our previous work [1] and deals with the design of buffers. It aims at finding the optimum buffer size for enhancing QoS offered to video traffic. Network-centric QoS provisioning approach, along with hybrid transport layer protocol approach is adopted, to arrive at an optimum size which is independent of RTT. In this combinational approach, buffers of routers and end devices are designed to satisfy the various QoS parameters at the transport layer. OPNET Modeler is used to simulate environments for testing the design. Based on the results of simulation it is evident that the hybrid transport layer protocol approach is best suited for transmitting video traffic as it supports the economical design.

  8. Skull defect reconstruction based on a new hybrid level set.

    Science.gov (United States)

    Zhang, Ziqun; Zhang, Ran; Song, Zhijian

    2014-01-01

    Skull defect reconstruction is an important aspect of surgical repair. Historically, a skull defect prosthesis was created by the mirroring technique, surface fitting, or formed templates. These methods are not based on the anatomy of the individual patient's skull, and therefore, the prosthesis cannot precisely correct the defect. This study presented a new hybrid level set model, taking into account both the global optimization region information and the local accuracy edge information, while avoiding re-initialization during the evolution of the level set function. Based on the new method, a skull defect was reconstructed, and the skull prosthesis was produced by rapid prototyping technology. This resulted in a skull defect prosthesis that well matched the skull defect with excellent individual adaptation.

  9. A hybrid approach to predict the relationship between tablet tensile strength and compaction pressure using analytical powder compression.

    Science.gov (United States)

    Persson, Ann-Sofie; Alderborn, Göran

    2018-04-01

    The objective was to present a hybrid approach to predict the strength-pressure relationship (SPR) of tablets using common compression parameters and a single measurement of tablet tensile strength. Experimental SPR were derived for six pharmaceutical powders with brittle and ductile properties and compared to predicted SPR based on a three-stage approach. The prediction was based on the Kawakita b -1 parameter and the in-die Heckel yield stress, an estimate of maximal tensile strength, and a parameter proportionality factor α. Three values of α were used to investigate the influence of the parameter on the SPR. The experimental SPR could satisfactorily be described by the three stage model, however for sodium bicarbonate the tensile strength plateau could not be observed experimentally. The shape of the predicted SPR was to a minor extent influenced by the Kawakita b -1 but the width of the linear region was highly influenced by α. An increased α increased the width of the linear region and thus also the maximal predicted tablet tensile strength. Furthermore, the correspondence between experimental and predicted SPR was influenced by the α value and satisfactory predictions were in general obtained for α = 4.1 indicating the predictive potential of the hybrid approach. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  10. A hybrid approach for short-term forecasting of wind speed.

    Science.gov (United States)

    Tatinati, Sivanagaraja; Veluvolu, Kalyana C

    2013-01-01

    We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.

  11. Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Karbaschian

    2014-05-01

    Full Text Available The main advantage of hybrid powertrains is based on the efficient transfer of power and torque from power sources to the powertrain as well as recapturing of reversible energies without effecting the vehicle performance. The benefits of hybrid hydraulic powertrains can be better utilized with an appropriate power management. In this paper, different types of power management algorithms like off-line and on-line methods are briefly reviewed and classified. Finally, the algorithms are evaluated and compared. Therefore, different related criteria are evaluated and applied.

  12. Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

    Science.gov (United States)

    Wallace, Byron C; Noel-Storr, Anna; Marshall, Iain J; Cohen, Aaron M; Smalheiser, Neil R; Thomas, James

    2017-11-01

    Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone. Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  13. PV-wind hybrid system performance. A new approach and a case study

    Energy Technology Data Exchange (ETDEWEB)

    Arribas, Luis; Cano, Luis; Cruz, Ignacio [Departamento de Energias Renovables, CIEMAT, Avda. Complutense 22, 28040 Madrid (Spain); Mata, Montserrat; Llobet, Ermen [Ecotecnia, Roc Boronat 78, 08005 Barcelona (Spain)

    2010-01-15

    Until now, there is no internationally accepted guideline for the measurement, data exchange and analysis of PV-Wind Hybrid Systems. As there is a need for such a tool, so as to overcome the barrier that the lack of confidence due to the absence of reliability means for the development of the market of Hybrid Systems, an effort has been made to suggest one tool for PV-Wind Hybrid Systems. The suggested guidelines presented in this work are based on the existing guidelines for PV Systems, as a PV-Wind Hybrid system can be roughly thought of as a PV System to which wind generation has been added. So, the guidelines for PV Systems are valid for the PV-Wind System, and only the part referred to wind generation should be included. This has been the process followed in this work. The proposed method is applied to a case study, the CICLOPS Project, a 5 kW PV, 7.5 kW Wind Hybrid system installed at the Isolated Wind Systems Test Site that CIEMAT owns in CEDER (Soria, Spain). This system has been fully monitored through a year and the results of the monitoring activity, characterizing the long-term performance of the system are shown in this work. (author)

  14. PV-wind hybrid system performance. A new approach and a case study

    International Nuclear Information System (INIS)

    Arribas, Luis; Cano, Luis; Cruz, Ignacio; Mata, Montserrat; Llobet, Ermen

    2010-01-01

    Until now, there is no internationally accepted guideline for the measurement, data exchange and analysis of PV-Wind Hybrid Systems. As there is a need for such a tool, so as to overcome the barrier that the lack of confidence due to the absence of reliability means for the development of the market of Hybrid Systems, an effort has been made to suggest one tool for PV-Wind Hybrid Systems. The suggested guidelines presented in this work are based on the existing guidelines for PV Systems, as a PV-Wind Hybrid system can be roughly thought of as a PV System to which wind generation has been added. So, the guidelines for PV Systems are valid for the PV-Wind System, and only the part referred to wind generation should be included. This has been the process followed in this work. The proposed method is applied to a case study, the CICLOPS Project, a 5 kW PV, 7.5 kW Wind Hybrid system installed at the Isolated Wind Systems Test Site that CIEMAT owns in CEDER (Soria, Spain). This system has been fully monitored through a year and the results of the monitoring activity, characterizing the long-term performance of the system are shown in this work. (author)

  15. A hybrid computational-experimental approach for automated crystal structure solution

    Science.gov (United States)

    Meredig, Bryce; Wolverton, C.

    2013-02-01

    Crystal structure solution from diffraction experiments is one of the most fundamental tasks in materials science, chemistry, physics and geology. Unfortunately, numerous factors render this process labour intensive and error prone. Experimental conditions, such as high pressure or structural metastability, often complicate characterization. Furthermore, many materials of great modern interest, such as batteries and hydrogen storage media, contain light elements such as Li and H that only weakly scatter X-rays. Finally, structural refinements generally require significant human input and intuition, as they rely on good initial guesses for the target structure. To address these many challenges, we demonstrate a new hybrid approach, first-principles-assisted structure solution (FPASS), which combines experimental diffraction data, statistical symmetry information and first-principles-based algorithmic optimization to automatically solve crystal structures. We demonstrate the broad utility of FPASS to clarify four important crystal structure debates: the hydrogen storage candidates MgNH and NH3BH3; Li2O2, relevant to Li-air batteries; and high-pressure silane, SiH4.

  16. A hybrid system approach to airspeed, angle of attack and sideslip estimation in Unmanned Aerial Vehicles

    KAUST Repository

    Shaqura, Mohammad

    2015-06-01

    Fixed wing Unmanned Aerial Vehicles (UAVs) are an increasingly common sensing platform, owing to their key advantages: speed, endurance and ability to explore remote areas. While these platforms are highly efficient, they cannot easily be equipped with air data sensors commonly found on their larger scale manned counterparts. Indeed, such sensors are bulky, expensive and severely reduce the payload capability of the UAVs. In consequence, UAV controllers (humans or autopilots) have little information on the actual mode of operation of the wing (normal, stalled, spin) which can cause catastrophic losses of control when flying in turbulent weather conditions. In this article, we propose a real-time air parameter estimation scheme that can run on commercial, low power autopilots in real-time. The computational method is based on a hybrid decomposition of the modes of operation of the UAV. A Bayesian approach is considered for estimation, in which the estimated airspeed, angle of attack and sideslip are described statistically. An implementation on a UAV is presented, and the performance and computational efficiency of this method are validated using hardware in the loop (HIL) simulation and experimental flight data and compared with classical Extended Kalman Filter estimation. Our benchmark tests shows that this method is faster than EKF by up to two orders of magnitude. © 2015 IEEE.

  17. Hybrid Vibration Control under Broadband Excitation and Variable Temperature Using Viscoelastic Neutralizer and Adaptive Feedforward Approach

    Directory of Open Access Journals (Sweden)

    João C. O. Marra

    2016-01-01

    Full Text Available Vibratory phenomena have always surrounded human life. The need for more knowledge and domain of such phenomena increases more and more, especially in the modern society where the human-machine integration becomes closer day after day. In that context, this work deals with the development and practical implementation of a hybrid (passive-active/adaptive vibration control system over a metallic beam excited by a broadband signal and under variable temperature, between 5 and 35°C. Since temperature variations affect directly and considerably the performance of the passive control system, composed of a viscoelastic dynamic vibration neutralizer (also called a viscoelastic dynamic vibration absorber, the associative strategy of using an active-adaptive vibration control system (based on a feedforward approach with the use of the FXLMS algorithm working together with the passive one has shown to be a good option to compensate the neutralizer loss of performance and generally maintain the extended overall level of vibration control. As an additional gain, the association of both vibration control systems (passive and active-adaptive has improved the attenuation of vibration levels. Some key steps matured over years of research on this experimental setup are presented in this paper.

  18. A hybrid MCDM approach for ranking suppliers by considering ethical factors

    OpenAIRE

    Azadfallah, Mohammad

    2016-01-01

    One of the negative effects of cooperating with un-ethically behaving suppliers is that it may devastate the companies' credibility among employees, customers and the public. In this paper, a hybrid Multiple Criteria Decision Making (MCDM) approach (Disjunctive-WPM method) is proposed to resolve this limitation. The proposed methods consist of the following steps: 1. drop unethical solutions and 2. rank the remaining solutions. Therefore, the aim of t...

  19. Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

    OpenAIRE

    Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

    2015-01-01

    Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

  20. A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

    Science.gov (United States)

    Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

    2018-01-01

    This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

  1. Analysis and design of permanent magnet biased magnetic bearing based on hybrid factor

    Directory of Open Access Journals (Sweden)

    Jinji Sun

    2016-03-01

    Full Text Available In this article, hybrid factor is proposed for hybrid magnetic bearing. The hybrid factor is defined as the ratio of the force produced by the permanent magnet and the forces produced by the permanent magnet and current in hybrid magnetic bearing. It is deduced from a certain radial hybrid magnetic bearing using its important parameters such as the current stiffness and displacement stiffness at first and then the dynamic model of magnetically suspended rotor system is established. The relationship between structural parameters and control system parameters is analyzed based on the hybrid factor. Some influencing factors of hybrid factor in hybrid magnetic bearing, such as the size of the permanent magnet, length of air gap, and area of the stator poles, are analyzed in this article. It can be concluded that larger hybrid factor can be caused by the smaller power loss according to the definition of hybrid factor mentioned above. Meanwhile, the hybrid factor has a maximum value, which is related to control system parameters such as proportional factor expect for structural parameters. Finally, the design steps of parameters of hybrid magnetic bearing can be concluded.

  2. Cryptic Species Due to Hybridization: A Combined Approach to Describe a New Species (Carex: Cyperaceae).

    Science.gov (United States)

    Maguilla, Enrique; Escudero, Marcial

    2016-01-01

    Disappearance of diagnostic morphological characters due to hybridization is considered to be one of the causes of the complex taxonomy of the species-rich (ca. 2000 described species) genus Carex (Cyperaceae). Carex furva s.l. belongs to section Glareosae. It is an endemic species from the high mountains of the Iberian Peninsula (Spain and Portugal). Previous studies suggested the existence of two different, cryptic taxa within C. furva s.l. Intermediate morphologies found in the southern Iberian Peninsula precluded the description of a new taxa. We aimed to determine whether C. furva s.l. should be split into two different species based on the combination of morphological and molecular data. We sampled ten populations across its full range and performed a morphological study based on measurements on herbarium specimens and silica-dried inflorescences. Both morphological and phylogenetic data support the existence of two different species within C. furva s.l. Nevertheless, intermediate morphologies and sterile specimens were found in one of the southern populations (Sierra Nevada) of C. furva s.l., suggesting the presence of hybrid populations in areas where both supposed species coexist. Hybridization between these two putative species has blurred morphological and genetic limits among them in this hybrid zone. We have proved the utility of combining molecular and morphological data to discover a new cryptic species in a scenario of hybridization. We now recognize a new species, C. lucennoiberica, endemic to the Iberian Peninsula (Sierra Nevada, Central system and Cantabrian Mountains). On the other hand, C. furva s.s. is distributed only in Sierra Nevada, where it may be threatened by hybridization with C. lucennoiberica. The restricted distribution of both species and their specific habitat requirements are the main limiting factors for their conservation.

  3. A hybrid algorithm for instant optimization of beam weights in anatomy-based intensity modulated radiotherapy: a performance evaluation study

    International Nuclear Information System (INIS)

    Vaitheeswaran, Ranganathan; Sathiya Narayanan, V.K.; Bhangle, Janhavi R.; Nirhali, Amit; Kumar, Namita; Basu, Sumit; Maiya, Vikram

    2011-01-01

    The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) (∼ 2% - 5% improvement) and Homogeneity Index (HI) (∼ 4% - 10% improvement) as compared to GEM and FSA algorithms (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are

  4. A Novel Combined Hybrid Approach to Enable Revascularisation of a Trauma-Induced Subclavian Artery Injury

    Directory of Open Access Journals (Sweden)

    C.N. Sabbagh

    Full Text Available : Introduction: This case highlights the complexity of upper limb revascularization after a subclavian artery traumatic injury and strengthens the role of a hybrid/multi-disciplinary approach to such injuries. Report: A 45-year-old male patient presented with an acute right upper limb following a traumatic injury to the right subclavian artery due to a motor vehicle accident (MVA. Associated injuries included an unstable cervical spine injury, a large open right clavicular injury, and a brain injury, which limited the potential revascularisation options available. The arm was revascularised using a hybrid endovascular/open surgical approach, namely embolization of the proximal subclavian artery (just distal to vertebral artery and a right common femoral artery to distal axillary artery bypass using prosthetic material. Discussion: Blunt injuries to the subclavian artery are often high impact, complex and associated with multiple injuries to surrounding structures, which limit the role of standard procedures used in the elective setting. This case highlights the role of multidisciplinary team involvement, using a hybrid approach and a novel distal inflow site to restore upper limb perfusion. Keywords: Upper limb, Ischemia, Trauma, Revascularization

  5. Locating and classifying defects using an hybrid data base

    Energy Technology Data Exchange (ETDEWEB)

    Luna-Aviles, A; Diaz Pineda, A [Tecnologico de Estudios Superiores de Coacalco. Av. 16 de Septiembre 54, Col. Cabecera Municipal. C.P. 55700 (Mexico); Hernandez-Gomez, L H; Urriolagoitia-Calderon, G; Urriolagoitia-Sosa, G [Instituto Politecnico Nacional. ESIME-SEPI. Unidad Profesional ' Adolfo Lopez Mateos' Edificio 5, 30 Piso, Colonia Lindavista. Gustavo A. Madero. 07738 Mexico D.F. (Mexico); Durodola, J F [School of Technology, Oxford Brookes University, Headington Campus, Gipsy Lane, Oxford OX3 0BP (United Kingdom); Beltran Fernandez, J A, E-mail: alelunaav@hotmail.com, E-mail: luishector56@hotmail.com, E-mail: jdurodola@brookes.ac.uk

    2011-07-19

    A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200x20x5 mm. One half of them were plain and the other half has a notch (3 mm x 4 mm) which is close to the defect area (19 mm x 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

  6. Locating and classifying defects using an hybrid data base

    Science.gov (United States)

    Luna-Avilés, A.; Hernández-Gómez, L. H.; Durodola, J. F.; Urriolagoitia-Calderón, G.; Urriolagoitia-Sosa, G.; Beltrán Fernández, J. A.; Díaz Pineda, A.

    2011-07-01

    A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200×20×5 mm. One half of them were plain and the other half has a notch (3 mm × 4 mm) which is close to the defect area (19 mm × 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

  7. Locating and classifying defects using an hybrid data base

    International Nuclear Information System (INIS)

    Luna-Aviles, A; Diaz Pineda, A; Hernandez-Gomez, L H; Urriolagoitia-Calderon, G; Urriolagoitia-Sosa, G; Durodola, J F; Beltran Fernandez, J A

    2011-01-01

    A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200x20x5 mm. One half of them were plain and the other half has a notch (3 mm x 4 mm) which is close to the defect area (19 mm x 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

  8. Hybrid wind–photovoltaic–diesel–battery system sizing tool development using empirical approach, life-cycle cost and performance analysis: A case study in Scotland

    International Nuclear Information System (INIS)

    Gan, Leong Kit; Shek, Jonathan K.H.; Mueller, Markus A.

    2015-01-01

    Highlights: • Methods of sizing a hybrid wind–photovoltaic–diesel–battery system is described. • The hybrid system components are modelled using empirical data. • Twenty years lifecycle cost of the hybrid system is considered. • The trade-offs between battery storage capacity and diesel fuel usage is studied. • A hybrid system sizing tool has been developed as a graphical user interface (GUI). - Abstract: The concept of off-grid hybrid wind energy system is financially attractive and more reliable than stand-alone power systems since it is based on more than one electricity generation source. One of the most expensive components in a stand-alone wind-power system is the energy storage system as very often it is oversized to increase system autonomy. In this work, we consider a hybrid system which consists of wind turbines, photovoltaic panels, diesel generator and battery storage. One of the main challenges experienced by project managers is the sizing of components for different sites. This challenge is due to the variability of the renewable energy resource and the load demand for different sites. This paper introduces a sizing model that has been developed and implemented as a graphical user interface, which predicts the optimum configuration of a hybrid system. In particular, this paper focuses on seeking the optimal size of the batteries and the diesel generator usage. Both of these components are seen to be trade-offs from each other. The model simulates real time operation of the hybrid system, using the annual measured hourly wind speed and solar irradiation. The benefit of using time series approach is that it reflects a more realistic situation; here, the peaks and troughs of the renewable energy resource are a central part of the sizing model. Finally, load sensitivity and hybrid system performance analysis are demonstrated.

  9. Optimizing Thermal-Elastic Properties of C/C–SiC Composites Using a Hybrid Approach and PSO Algorithm

    Science.gov (United States)

    Xu, Yingjie; Gao, Tian

    2016-01-01

    Carbon fiber-reinforced multi-layered pyrocarbon–silicon carbide matrix (C/C–SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C–SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C–SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method. PMID:28773343

  10. A hybrid joint based controller for an upper extremity exoskeleton

    Science.gov (United States)

    Mohd Khairuddin, Ismail; Taha, Zahari; Majeed, Anwar P. P. Abdul; Hakeem Deboucha, Abdel; Azraai Mohd Razman, Mohd; Aziz Jaafar, Abdul; Mohamed, Zulkifli

    2016-02-01

    This paper presents the modelling and control of a two degree of freedom upper extremity exoskeleton. The Euler-Lagrange formulation was used in deriving the dynamic modelling of both the human upper limb as well as the exoskeleton that consists of the upper arm and the forearm. The human model is based on anthropometrical measurements of the upper limb. The proportional-derivative (PD) computed torque control (CTC) architecture is employed in this study to investigate its efficacy performing joint-space control objectives specifically in rehabilitating the elbow and shoulder joints along the sagittal plane. An active force control (AFC) algorithm is also incorporated into the PD-CTC to investigate the effectiveness of this hybrid system in compensating disturbances. It was found that the AFC- PD-CTC performs well against the disturbances introduced into the system whilst achieving acceptable trajectory tracking as compared to the conventional PD-CTC control architecture.

  11. A hybrid joint based controller for an upper extremity exoskeleton

    International Nuclear Information System (INIS)

    Khairuddin, Ismail Mohd; Taha, Zahari; Majeed, Anwar P.P. Abdul; Deboucha, Abdel Hakeem; Razman, Mohd Azraai Mohd; Jaafar, Abdul Aziz; Mohamed, Zulkifli

    2016-01-01

    This paper presents the modelling and control of a two degree of freedom upper extremity exoskeleton. The Euler-Lagrange formulation was used in deriving the dynamic modelling of both the human upper limb as well as the exoskeleton that consists of the upper arm and the forearm. The human model is based on anthropometrical measurements of the upper limb. The proportional-derivative (PD) computed torque control (CTC) architecture is employed in this study to investigate its efficacy performing joint-space control objectives specifically in rehabilitating the elbow and shoulder joints along the sagittal plane. An active force control (AFC) algorithm is also incorporated into the PD-CTC to investigate the effectiveness of this hybrid system in compensating disturbances. It was found that the AFC- PD-CTC performs well against the disturbances introduced into the system whilst achieving acceptable trajectory tracking as compared to the conventional PD-CTC control architecture. (paper)

  12. A New Hybrid Bathroom System Based on Energy Saving Concept

    Directory of Open Access Journals (Sweden)

    Cui Bo-wen

    2016-01-01

    Full Text Available Based on the characteristics of hot water supply in bathroom, this article proposes a new hybrid energy hot water supply system. The programmable logic controller(PLC as the master controller was adopted in this system, which could automatically detect and storage main thermal physical of the system, such as temperature, water level, solar radiation intensity, power consumption and so on. The active thermal utilization technology of solar energy, air-source heat pump technology, solar energy intensive natural ventilation technology and low temperature hot water floor radiant heating technology were organically integrated in this system, which has the advantages of energy conservation and environment protection, high automation, safe and reliable operation, etc. At the same time, it can make good use of electric power cost between on-peak and off-peak, and promote the optimal allocation of power resources and reduce the cost, which can achieve the goal of intelligent control and energy saving.

  13. Efficient Vaccine Distribution Based on a Hybrid Compartmental Model.

    Directory of Open Access Journals (Sweden)

    Zhiwen Yu

    Full Text Available To effectively and efficiently reduce the morbidity and mortality that may be caused by outbreaks of emerging infectious diseases, it is very important for public health agencies to make informed decisions for controlling the spread of the disease. Such decisions must incorporate various kinds of intervention strategies, such as vaccinations, school closures and border restrictions. Recently, researchers have paid increased attention to searching for effective vaccine distribution strategies for reducing the effects of pandemic outbreaks when resources are limited. Most of the existing research work has been focused on how to design an effective age-structured epidemic model and to select a suitable vaccine distribution strategy to prevent the propagation of an infectious virus. Models that evaluate age structure effects are common, but models that additionally evaluate geographical effects are less common. In this paper, we propose a new SEIR (susceptible-exposed-infectious šC recovered model, named the hybrid SEIR-V model (HSEIR-V, which considers not only the dynamics of infection prevalence in several age-specific host populations, but also seeks to characterize the dynamics by which a virus spreads in various geographic districts. Several vaccination strategies such as different kinds of vaccine coverage, different vaccine releasing times and different vaccine deployment methods are incorporated into the HSEIR-V compartmental model. We also design four hybrid vaccination distribution strategies (based on population size, contact pattern matrix, infection rate and infectious risk for controlling the spread of viral infections. Based on data from the 2009-2010 H1N1 influenza epidemic, we evaluate the effectiveness of our proposed HSEIR-V model and study the effects of different types of human behaviour in responding to epidemics.

  14. Approach to Hybrid Energy Storage Systems Dimensioning for Urban Electric Buses Regarding Efficiency and Battery Aging

    Directory of Open Access Journals (Sweden)

    Jorge Nájera

    2017-10-01

    Full Text Available This paper focuses on Hybrid Energy Storage Systems (HESS, consisting of a combination of batteries and Electric Double Layer Capacitors (EDLC, for electric urban busses. The aim of the paper is to develop a methodology to determine the hybridization percentage that allows the electric bus to work with the highest efficiency while reducing battery aging, depending on the chosen topology, control strategy, and driving cycle. Three power electronic topologies are qualitatively analyzed based on different criteria, with the topology selected as the favorite being analyzed in detail. The whole system under study is comprised of the following elements: a battery pack (LiFePO4 batteries, an EDLC pack, up to two DC-DC converters (depending on the topology, and an equivalent load, which behaves as an electric bus drive (including motion resistances and inertia. Mathematical models for the battery, EDLCs, DC-DC converter, and the vehicle itself are developed for this analysis. The methodology presented in this work, as the main scientific contribution, considers performance variation (energy efficiency and battery aging and hybridization percentage (ratio between batteries and EDLCs, defined in terms of mass, using a power load profile based on standard driving cycles. The results state that there is a hybridization percentage that increases energy efficiency and reduces battery aging, maximizing the economic benefits of the vehicle, for every combination of topology, type of storage device, control strategy, and driving cycle.

  15. A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting

    International Nuclear Information System (INIS)

    Azimi, R.; Ghayekhloo, M.; Ghofrani, M.

    2016-01-01

    Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar

  16. Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

    Directory of Open Access Journals (Sweden)

    Miraemiliana Murat

    2017-09-01

    Full Text Available Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM, Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD, Histogram of Oriented Gradients (HOG, Hu invariant moments (Hu and Zernike moments (ZM. Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN, random forest (RF, support vector machine (SVM, k-nearest neighbour (k-NN, linear discriminant analysis (LDA and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM. In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS and Pearson’s coefficient correlation (PCC. The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia

  17. Motivation, students' needs and learning outcomes: a hybrid game-based app for enhanced language learning.

    Science.gov (United States)

    Berns, Anke; Isla-Montes, José-Luis; Palomo-Duarte, Manuel; Dodero, Juan-Manuel

    2016-01-01

    In the context of European Higher Education students face an increasing focus on independent, individual learning-at the expense of face-to-face interaction. Hence learners are, all too often, not provided with enough opportunities to negotiate in the target language. The current case study aims to address this reality by going beyond conventional approaches to provide students with a hybrid game-based app, combining individual and collaborative learning opportunities. The 4-week study was carried out with 104 German language students (A1.2 CEFR) who had previously been enrolled in a first-semester A1.1 level course at a Spanish university. The VocabTrainerA1 app-designed specifically for this study-harnesses the synergy of combining individual learning tasks and a collaborative murder mystery game in a hybrid level-based architecture. By doing so, the app provides learners with opportunities to apply their language skills to real-life-like communication. The purpose of the study was twofold: on one hand we aimed to measure learner motivation, perceived usefulness and added value of hybrid game-based apps; on the other, we sought to determine their impact on language learning. To this end, we conducted focus group interviews and an anonymous Technology Acceptance Model survey (TAM). In addition, students took a pre-test and a post-test. Scores from both tests were compared with the results obtained in first-semester conventional writing tasks, with a view to measure learning outcomes. The study provides qualitative and quantitative data supporting our initial hypotheses. Our findings suggest that hybrid game-based apps like VocabTrainerA1-which seamlessly combine individual and collaborative learning tasks-motivate learners, stimulate perceived usefulness and added value, and better meet the language learning needs of today's digital natives. In terms of acceptance, outcomes and sustainability, the data indicate that hybrid game-based apps significantly improve

  18. A Hybrid Approach to Cognitive Engineering: Supporting Development of a Revolutionary Warfighter-Centered Command and Control System

    National Research Council Canada - National Science Library

    Ockerman, Jennifer; McKneely, Jennifer A; Koterba, Nathan

    2005-01-01

    ...) for the requirements analysis and design of revolutionary command and control systems and domains. This hybrid approach uses knowledge elicitation and representation techniques from several current cognitive engineering methodologies...

  19. A novel approach identifying hybrid sterility QTL on the autosomes of Drosophila simulans and D. mauritiana.

    Science.gov (United States)

    Dickman, Christopher T D; Moehring, Amanda J

    2013-01-01

    When species interbreed, the hybrid offspring that are produced are often sterile. If only one hybrid sex is sterile, it is almost always the heterogametic (XY or ZW) sex. Taking this trend into account, the predominant model used to explain the genetic basis of F1 sterility involves a deleterious interaction between recessive sex-linked loci from one species and dominant autosomal loci from the other species. This model is difficult to evaluate, however, as only a handful of loci influencing interspecies hybrid sterility have been identified, and their autosomal genetic interactors have remained elusive. One hindrance to their identification has been the overwhelming effect of the sex chromosome in mapping studies, which could 'mask' the ability to accurately map autosomal factors. Here, we use a novel approach employing attached-X chromosomes to create reciprocal backcross interspecies hybrid males that have a non-recombinant sex chromosome and recombinant autosomes. The heritable variation in phenotype is thus solely caused by differences in the autosomes, thereby allowing us to accurately identify the number and location of autosomal sterility loci. In one direction of backcross, all males were sterile, indicating that sterility could be entirely induced by the sex chromosome complement in these males. In the other direction, we identified nine quantitative trait loci that account for a surprisingly large amount (56%) of the autosome-induced phenotypic variance in sterility, with a large contribution of autosome-autosome epistatic interactions. These loci are capable of acting dominantly, and thus could contribute to F1 hybrid sterility.

  20. A novel approach identifying hybrid sterility QTL on the autosomes of Drosophila simulans and D. mauritiana.

    Directory of Open Access Journals (Sweden)

    Christopher T D Dickman

    Full Text Available When species interbreed, the hybrid offspring that are produced are often sterile. If only one hybrid sex is sterile, it is almost always the heterogametic (XY or ZW sex. Taking this trend into account, the predominant model used to explain the genetic basis of F1 sterility involves a deleterious interaction between recessive sex-linked loci from one species and dominant autosomal loci from the other species. This model is difficult to evaluate, however, as only a handful of loci influencing interspecies hybrid sterility have been identified, and their autosomal genetic interactors have remained elusive. One hindrance to their identification has been the overwhelming effect of the sex chromosome in mapping studies, which could 'mask' the ability to accurately map autosomal factors. Here, we use a novel approach employing attached-X chromosomes to create reciprocal backcross interspecies hybrid males that have a non-recombinant sex chromosome and recombinant autosomes. The heritable variation in phenotype is thus solely caused by differences in the autosomes, thereby allowing us to accurately identify the number and location of autosomal sterility loci. In one direction of backcross, all males were sterile, indicating that sterility could be entirely induced by the sex chromosome complement in these males. In the other direction, we identified nine quantitative trait loci that account for a surprisingly large amount (56% of the autosome-induced phenotypic variance in sterility, with a large contribution of autosome-autosome epistatic interactions. These loci are capable of acting dominantly, and thus could contribute to F1 hybrid sterility.

  1. Electrochemical supercapacitors based on novel hybrid materials made of carbon nanotubes and polyoxometalates

    Energy Technology Data Exchange (ETDEWEB)

    Cuentas-Gallegos, Ana Karina; Martinez-Rosales, Rosa; Rincon, Marina E. [Centro de Investigacion en Energia, Universidad Nacional Autonoma de Mexico, Priv. Xochicalco s/n, Col. Centro, C.P. 62580 Temixco, Mor (Mexico); Baibarac, Mihaela; Gomez-Romero, Pedro [Instituto de Ciencia de Materiales de Barcelona, CSIC, Campus de la UAB, 08193 Bellaterra (Spain)

    2007-08-15

    We have characterized symmetric solid-state supercapacitors in swagelok cells using film electrodes made of novel hybrid materials based on multiwalled carbon nanotubes (CNT) and phosphomolybdate polyanion (Cs-PMo12) with PVA as binder. These hybrid materials were carried out by Cs-PMo12 adhesion onto previously functionalized CNT, in order to disperse both components at a molecular level and use Cs-PMo12 as energy density enhancer in supercapacitor cells. Our results show high capacitance values (up to 285 F/g at I = 200 mA/g) due to the contribution of Cs-PMo12, which was revealed on the higher energy density values compared to pure CNT electrodes. Additionally, good stability was observed during 500 charge-discharge cycles for most hybrid electrodes. These preliminary results show a new approach to enhance energy density of double layer supercapacitor cells through the introduction of Cs-PMo12, whereas from a material science point of view these materials are innovative, and open the way to search for diverse applications aside from supercapacitors (sensors, catalysts, photovoltaic cells, etc.). (author)

  2. Hybrid Structural Reliability Analysis under Multisource Uncertainties Based on Universal Grey Numbers

    Directory of Open Access Journals (Sweden)

    Xingfa Yang

    2018-01-01

    Full Text Available Nondeterministic parameters of certain distribution are employed to model structural uncertainties, which are usually assumed as stochastic factors. However, model parameters may not be precisely represented due to some factors in engineering practices, such as lack of sufficient data, data with fuzziness, and unknown-but-bounded conditions. To this end, interval and fuzzy parameters are implemented and an efficient approach to structural reliability analysis with random-interval-fuzzy hybrid parameters is proposed in this study. Fuzzy parameters are first converted to equivalent random ones based on the equal entropy principle. 3σ criterion is then employed to transform the equivalent random and the original random parameters to interval variables. In doing this, the hybrid reliability problem is transformed into the one only with interval variables, in other words, nonprobabilistic reliability analysis problem. Nevertheless, the problem of interval extension existed in interval arithmetic, especially for the nonlinear systems. Therefore, universal grey mathematics, which can tackle the issue of interval extension, is employed to solve the nonprobabilistic reliability analysis problem. The results show that the proposed method can obtain more conservative results of the hybrid structural reliability.

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

    Science.gov (United States)

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

    2018-03-01

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

  4. Alternative policy impacts on US GHG emissions and energy security: A hybrid modeling approach

    International Nuclear Information System (INIS)

    Sarica, Kemal; Tyner, Wallace E.

    2013-01-01

    This study addresses the possible impacts of energy and climate policies, namely corporate average fleet efficiency (CAFE) standard, renewable fuel standard (RFS) and clean energy standard (CES), and an economy wide equivalent carbon tax on GHG emissions in the US to the year 2045. Bottom–up and top–down modeling approaches find widespread use in energy economic modeling and policy analysis, in which they differ mainly with respect to the emphasis placed on technology of the energy system and/or the comprehensiveness of endogenous market adjustments. For this study, we use a hybrid energy modeling approach, MARKAL–Macro, that combines the characteristics of two divergent approaches, in order to investigate and quantify the cost of climate policies for the US and an equivalent carbon tax. The approach incorporates Macro-economic feedbacks through a single sector neoclassical growth model while maintaining sectoral and technological detail of the bottom–up optimization framework with endogenous aggregated energy demand. Our analysis is done for two important objectives of the US energy policy: GHG reduction and increased energy security. Our results suggest that the emission tax achieves results quite similar to the CES policy but very different results in the transportation sector. The CAFE standard and RFS are more expensive than a carbon tax for emission reductions. However, the CAFE standard and RFS are much more efficient at achieving crude oil import reductions. The GDP losses are 2.0% and 1.2% relative to the base case for the policy case and carbon tax. That difference may be perceived as being small given the increased energy security gained from the CAFE and RFS policy measures and the uncertainty inherent in this type of analysis. - Highlights: • Evaluates US impacts of three energy/climate policies and a carbon tax (CT) • Analysis done with bottom–up MARKAL model coupled with a macro model • Electricity clean energy standard very close to

  5. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation

    International Nuclear Information System (INIS)

    Mohammadi, Kasra; Shamshirband, Shahaboddin; Tong, Chong Wen; Arif, Muhammad; Petković, Dalibor; Ch, Sudheer

    2015-01-01

    Highlights: • Horizontal global solar radiation (HGSR) is predicted based on a new hybrid approach. • Support Vector Machines and Wavelet Transform algorithm (SVM–WT) are combined. • Different sets of meteorological elements are used to predict HGSR. • The precision of SVM–WT is assessed thoroughly against ANN, GP and ARMA. • SVM–WT would be an appealing approach to predict HGSR and outperforms others. - Abstract: In this paper, a new hybrid approach by combining the Support Vector Machine (SVM) with Wavelet Transform (WT) algorithm is developed to predict horizontal global solar radiation. The predictions are conducted on both daily and monthly mean scales for an Iranian coastal city. The proposed SVM–WT method is compared against other existing techniques to demonstrate its efficiency and viability. Three different sets of parameters are served as inputs to establish three models. The results indicate that the model using relative sunshine duration, difference between air temperatures, relative humidity, average temperature and extraterrestrial solar radiation as inputs shows higher performance than other models. The statistical analysis demonstrates that SVM–WT approach enjoys very good performance and outperforms other approaches. For the best SVM–WT model, the obtained statistical indicators of mean absolute percentage error, mean absolute bias error, root mean square error, relative root mean square error and coefficient of determination for daily estimation are 6.9996%, 0.8405 MJ/m 2 , 1.4245 MJ/m 2 , 7.9467% and 0.9086, respectively. Also, for monthly mean estimation the values are 3.2601%, 0.5104 MJ/m 2 , 0.6618 MJ/m 2 , 3.6935% and 0.9742, respectively. Based upon relative percentage error, for the best SVM–WT model, 88.70% of daily predictions fall within the acceptable range of −10% to +10%

  6. A Hybrid Change Detection Approach for Damage Detection and Recovery Monitoring

    Science.gov (United States)

    de Alwis Pitts, Dilkushi; Wieland, Marc; Wang, Shifeng; So, Emily; Pittore, Massimiliano

    2014-05-01

    Following a disaster, change detection via pre- and post-event very high resolution remote sensing images is an essential technique for damage assessment and recovery monitoring over large areas in complex urban environments. Most assessments to date focus on detection, destruction and recovery of man-made objects that facilitate shelter and accessibility, such as buildings, roads, bridges, etc., as indicators for assessment and better decision making. Moreover, many current change-detection mechanisms do not use all the data and knowledge which are often available for the pre-disaster state. Recognizing the continuous rather than dichotomous character of the data-rich/data-poor distinction permits the incorporation of ancillary data and existing knowledge into the processing flow. Such incorporation could improve the reliability of the results and thereby enhance the usability of robust methods for disaster management. This study proposes an application-specific and robust change detection method from multi-temporal very high resolution multi-spectral satellite images. This hybrid indicator-specific method uses readily available pre-disaster GIS data and integrates existing knowledge into the processing flow to optimize the change detection while offering the possibility to target specific types of changes to man-made objects. The indicator-specific information of the GIS objects is used as a series of masks to treat the GIS objects with similar characteristics similarly for better accuracy. The proposed approach is based on a fusion of a multi-index change detection method based on gradient, texture and edge similarity filters. The change detection index is flexible for disaster cases in which the pre-disaster and post-disaster images are not of the same resolution. The proposed automated method is evaluated with QuickBird and Ikonos datasets for abrupt changes soon after disaster. The method could also be extended in a semi-automated way for monitoring

  7. Multi-Agent System based Event-Triggered Hybrid Controls for High-Security Hybrid Energy Generation Systems

    DEFF Research Database (Denmark)

    Dou, Chun-Xia; Yue, Dong; Guerrero, Josep M.

    2017-01-01

    This paper proposes multi-agent system based event- triggered hybrid controls for guaranteeing energy supply of a hybrid energy generation system with high security. First, a mul-ti-agent system is constituted by an upper-level central coordi-nated control agent combined with several lower......-level unit agents. Each lower-level unit agent is responsible for dealing with internal switching control and distributed dynamic regula-tion for its unit system. The upper-level agent implements coor-dinated switching control to guarantee the power supply of over-all system with high security. The internal...

  8. A Hybrid Column Generation approach for an Industrial Waste Collection Routing Problem

    DEFF Research Database (Denmark)

    Hauge, Kristian; Larsen, Jesper; Lusby, Richard Martin

    2014-01-01

    , while empty containers must be returned to the depot to await further assignments. Unlike, the traditional ROROR problem, where vehicles may transport one skip container at a time regardless of whether it is full or not, we consider cases in which a vehicle can transport up to eight containers, at most...... two of which can be full. We propose a Generalized Set Partitioning formulation of the problem and describe a hybrid column generation procedure to solve it. A fast Tabu Search heuristic is used to generate new columns. The proposed methodology is tested on nine data sets, four of which are actual......, real-world problem instances. Results indicate that the hybrid column generation outperforms a purely heuristic approach in terms of both running time and solution quality. High quality solutions to problems containing up to 100 orders can be solved in approximately 15 minutes....

  9. A hybrid least squares support vector machines and GMDH approach for river flow forecasting

    Science.gov (United States)

    Samsudin, R.; Saad, P.; Shabri, A.

    2010-06-01

    This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

  10. Non-adaptive and adaptive hybrid approaches for enhancing water quality management

    Science.gov (United States)

    Kalwij, Ineke M.; Peralta, Richard C.

    2008-09-01

    SummaryUsing optimization to help solve groundwater management problems cost-effectively is becoming increasingly important. Hybrid optimization approaches, that combine two or more optimization algorithms, will become valuable and common tools for addressing complex nonlinear hydrologic problems. Hybrid heuristic optimizers have capabilities far beyond those of a simple genetic algorithm (SGA), and are continuously improving. SGAs having only parent selection, crossover, and mutation are inefficient and rarely used for optimizing contaminant transport management. Even an advanced genetic algorithm (AGA) that includes elitism (to emphasize using the best strategies as parents) and healing (to help assure optimal strategy feasibility) is undesirably inefficient. Much more efficient than an AGA is the presented hybrid (AGCT), which adds comprehensive tabu search (TS) features to an AGA. TS mechanisms (TS probability, tabu list size, search coarseness and solution space size, and a TS threshold value) force the optimizer to search portions of the solution space that yield superior pumping strategies, and to avoid reproducing similar or inferior strategies. An AGCT characteristic is that TS control parameters are unchanging during optimization. However, TS parameter values that are ideal for optimization commencement can be undesirable when nearing assumed global optimality. The second presented hybrid, termed global converger (GC), is significantly better than the AGCT. GC includes AGCT plus feedback-driven auto-adaptive control that dynamically changes TS parameters during run-time. Before comparing AGCT and GC, we empirically derived scaled dimensionless TS control parameter guidelines by evaluating 50 sets of parameter values for a hypothetical optimization problem. For the hypothetical area, AGCT optimized both well locations and pumping rates. The parameters are useful starting values because using trial-and-error to identify an ideal combination of control

  11. A new hybrid BCI paradigm based on P300 and SSVEP.

    Science.gov (United States)

    Wang, Minjue; Daly, Ian; Allison, Brendan Z; Jin, Jing; Zhang, Yu; Chen, Lanlan; Wang, Xingyu

    2015-04-15

    P300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain-computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored. The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength. The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm. A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification. The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. A hybrid approach for integrated healthcare cooperative purchasing and supply chain configuration.

    Science.gov (United States)

    Rego, Nazaré; Claro, João; Pinho de Sousa, Jorge

    2014-12-01

    This paper presents an innovative and flexible approach for recommending the number, size and composition of purchasing groups, for a set of hospitals willing to cooperate, while minimising their shared supply chain costs. This approach makes the financial impact of the various cooperation alternatives transparent to the group and the individual participants, opening way to a negotiation process concerning the allocation of the cooperation costs and gains. The approach was developed around a hybrid Variable Neighbourhood Search (VNS)/Tabu Search metaheuristic, resulting in a flexible tool that can be applied to purchasing groups with different characteristics, namely different operative and market circumstances, and to supply chains with different topologies and atypical cost characteristics. Preliminary computational results show the potential of the approach in solving a broad range of problems.

  13. Physics-based hybrid method for multiscale transport in porous media

    Science.gov (United States)

    Yousefzadeh, Mehrdad; Battiato, Ilenia

    2017-09-01

    Despite advancements in the development of multiscale models for flow and reactive transport in porous media, the accurate, efficient and physics-based coupling of multiple scales in hybrid models remains a major theoretical and computational challenge. Improving the predictivity of macroscale predictions by means of multiscale algorithms relative to classical at-scale models is the primary motivation for the development of multiscale simulators. Yet, very few are the quantitative studies that explicitly address the predictive capability of multiscale coupling algorithms as it is still generally not possible to have a priori estimates of the errors that are present when complex flow processes are modeled. We develop a nonintrusive pore-/continuum-scale hybrid model whose coupling error is bounded by the upscaling error, i.e. we build a predictive tightly coupled multiscale scheme. This is accomplished by slightly enlarging the subdomain where continuum-scale equations are locally invalid and analytically defining physics-based coupling conditions at the interfaces separating the two computational sub-domains, while enforcing state variable and flux continuity. The proposed multiscale coupling approach retains the advantages of domain decomposition approaches, including the use of existing solvers for each subdomain, while it gains flexibility in the choice of the numerical discretization method and maintains the coupling errors bounded by the upscaling error. We implement the coupling in finite volumes and test the proposed method by modeling flow and transport through a reactive channel and past an array of heterogeneously reactive cylinders.

  14. Hybrid Integrated Silicon Microfluidic Platform for Fluorescence Based Biodetection

    Directory of Open Access Journals (Sweden)

    André Darveau

    2007-09-01

    Full Text Available The desideratum to develop a fully integrated Lab-on-a-chip device capable ofrapid specimen detection for high throughput in-situ biomedical diagnoses and Point-of-Care testing applications has called for the integration of some of the novel technologiessuch as the microfluidics, microphotonics, immunoproteomics and Micro ElectroMechanical Systems (MEMS. In the present work, a silicon based microfluidic device hasbeen developed for carrying out fluorescence based immunoassay. By hybrid attachment ofthe microfluidic device with a Spectrometer-on-chip, the feasibility of synthesizing anintegrated Lab-on-a-chip type device for fluorescence based biosensing has beendemonstrated. Biodetection using the microfluidic device has been carried out usingantigen sheep IgG and Alexafluor-647 tagged antibody particles and the experimentalresults prove that silicon is a compatible material for the present application given thevarious advantages it offers such as cost-effectiveness, ease of bulk microfabrication,superior surface affinity to biomolecules, ease of disposability of the device etc., and is thussuitable for fabricating Lab-on-a-chip type devices.

  15. All solid-state V2O5-based flexible hybrid fiber supercapacitors

    Science.gov (United States)

    Li, Huan; He, Jin; Cao, Xin; Kang, Liping; He, Xuexia; Xu, Hua; Shi, Feng; Jiang, Ruibin; Lei, Zhibin; Liu, Zong-Huai

    2017-12-01

    Vanadium pentoxide/single-walled carbon nanotube (V2O5-SWCNT) hybrid fibers with good electrochemical performance and flexibility are firstly prepared by using wet-spinning method. V2O5 nanobelt suspension is obtained by mixing V2O5 bulk, 30% H2O2, H2O and followed by hydrothermally treating at 190 °C for 15 h. SWCNT suspension is suspended into V2O5 nanobelt suspension under vigorous stirring, the V2O5-SWCNT homogenous suspension is obtained. It is injected into a coagulation bath composed of 5 wt % CaCl2 ethanol-water solution using syringe pump, V2O5-SWCNT hybrid fibers are prepared by washing with deionized water and drying at room temperature. Reduced graphene oxide (RGO)-SWCNT hybrid fibers are also prepared by the similar wet-spinning approach and followed by reducing GO-SWCNT hybrid fibers in an aqueous solution of hydriodic acid. All solid-state asymmetric V2O5/SWCNT//RGO/SWCNT fiber supercapacitors are assembled with V2O5-SWCNT fiber as positive electrode and RGO-SWCNT fiber as negative electrode by using PVA-H3PO4 as gel electrolyte. The assembled device not only shows maximum volumetric energy density of 1.95 mW h cm-3 at a volumetric power density of 7.5 mW cm-3, superior rate performance and cycling stability, but also exhibits remarkable flexibility to tolerate long-term and repeated bending. This work will open a new application filed of V2O5-based fibers in wearable energy storage devices.

  16. Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

    Science.gov (United States)

    Chen, Yangzhou; Guo, Yuqi; Wang, Ying

    2017-03-29

    In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

  17. A hybrid technique for private location-based queries with database protection

    KAUST Repository

    Ghinita, Gabriel

    2009-01-01

    Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. To protect user privacy, it is important not to disclose exact user coordinates to un-trusted entities that provide location-based services. Currently, there are two main approaches to protect the location privacy of users: (i) hiding locations inside cloaking regions (CRs) and (ii) encrypting location data using private information retrieval (PIR) protocols. Previous work focused on finding good trade-offs between privacy and performance of user protection techniques, but disregarded the important issue of protecting the POI dataset D. For instance, location cloaking requires large-sized CRs, leading to excessive disclosure of POIs (O(|D|) in the worst case). PIR, on the other hand, reduces this bound to , but at the expense of high processing and communication overhead. We propose a hybrid, two-step approach to private location-based queries, which provides protection for both the users and the database. In the first step, user locations are generalized to coarse-grained CRs which provide strong privacy. Next, a PIR protocol is applied with respect to the obtained query CR. To protect excessive disclosure of POI locations, we devise a cryptographic protocol that privately evaluates whether a point is enclosed inside a rectangular region. We also introduce an algorithm to efficiently support PIR on dynamic POI sub-sets. Our method discloses O(1) POI, orders of magnitude fewer than CR- or PIR-based techniques. Experimental results show that the hybrid approach is scalable in practice, and clearly outperforms the pure-PIR approach in terms of computational and communication overhead. © 2009 Springer Berlin Heidelberg.

  18. Relative efficiency of hydrogen technologies for the hydrogen economy : a fuzzy AHP/DEA hybrid model approach

    International Nuclear Information System (INIS)

    Lee, S.

    2009-01-01

    As a provider of national energy security, the Korean Institute of Energy Research is seeking to establish a long term strategic technology roadmap for a hydrogen-based economy. This paper addressed 5 criteria regarding the strategy, notably economic impact, commercial potential, inner capacity, technical spinoff, and development cost. The fuzzy AHP and DEA hybrid model were used in a two-stage multi-criteria decision making approach to evaluate the relative efficiency of hydrogen technologies for the hydrogen economy. The fuzzy analytic hierarchy process reflects the uncertainty of human thoughts with interval values instead of clear-cut numbers. It therefore allocates the relative importance of 4 criteria, notably economic impact, commercial potential, inner capacity and technical spin-off. The relative efficiency of hydrogen technologies for the hydrogen economy can be measured via data envelopment analysis. It was concluded that the scientific decision making approach can be used effectively to allocate research and development resources and activities

  19. Relative efficiency of hydrogen technologies for the hydrogen economy : a fuzzy AHP/DEA hybrid model approach

    Energy Technology Data Exchange (ETDEWEB)

    Lee, S. [Korea Inst. of Energy Research, Daejeon (Korea, Republic of). Energy Policy Research Division; Mogi, G. [Tokyo Univ., (Japan). Dept. of Technology Management for Innovation, Graduate School of Engineering; Kim, J. [Korea Inst. of Energy Research, Daejeon (Korea, Republic of)

    2009-07-01

    As a provider of national energy security, the Korean Institute of Energy Research is seeking to establish a long term strategic technology roadmap for a hydrogen-based economy. This paper addressed 5 criteria regarding the strategy, notably economic impact, commercial potential, inner capacity, technical spinoff, and development cost. The fuzzy AHP and DEA hybrid model were used in a two-stage multi-criteria decision making approach to evaluate the relative efficiency of hydrogen technologies for the hydrogen economy. The fuzzy analytic hierarchy process reflects the uncertainty of human thoughts with interval values instead of clear-cut numbers. It therefore allocates the relative importance of 4 criteria, notably economic impact, commercial potential, inner capacity and technical spin-off. The relative efficiency of hydrogen technologies for the hydrogen economy can be measured via data envelopment analysis. It was concluded that the scientific decision making approach can be used effectively to allocate research and development resources and activities.

  20. Hybrid light transport model based bioluminescence tomography reconstruction for early gastric cancer detection

    Science.gov (United States)

    Chen, Xueli; Liang, Jimin; Hu, Hao; Qu, Xiaochao; Yang, Defu; Chen, Duofang; Zhu, Shouping; Tian, Jie

    2012-03-01

    Gastric cancer is the second cause of cancer-related death in the world, and it remains difficult to cure because it has been in late-stage once that is found. Early gastric cancer detection becomes an effective approach to decrease the gastric cancer mortality. Bioluminescence tomography (BLT) has been applied to detect early liver cancer and prostate cancer metastasis. However, the gastric cancer commonly originates from the gastric mucosa and grows outwards. The bioluminescent light will pass through a non-scattering region constructed by gastric pouch when it transports in tissues. Thus, the current BLT reconstruction algorithms based on the approximation model of radiative transfer equation are not optimal to handle this problem. To address the gastric cancer specific problem, this paper presents a novel reconstruction algorithm that uses a hybrid light transport model to describe the bioluminescent light propagation in tissues. The radiosity theory integrated with the diffusion equation to form the hybrid light transport model is utilized to describe light propagation in the non-scattering region. After the finite element discretization, the hybrid light transport model is converted into a minimization problem which fuses an l1 norm based regularization term to reveal the sparsity of bioluminescent source distribution. The performance of the reconstruction algorithm is first demonstrated with a digital mouse based simulation with the reconstruction error less than 1mm. An in situ gastric cancer-bearing nude mouse based experiment is then conducted. The primary result reveals the ability of the novel BLT reconstruction algorithm in early gastric cancer detection.

  1. Towards a hybrid strong/weak coupling approach to jet quenching

    CERN Document Server

    Casalderrey-Solana, Jorge; Milhano, José Guilherme; Pablos, Daniel; Rajagopal, Krishna

    2014-01-01

    We explore a novel hybrid model containing both strong and weak coupling physics for high energy jets traversing a deconfined medium. This model is based on supplementing a perturbative DGLAP shower with strongly coupled energy loss rate. We embed this system into a realistic hydrodynamic evolution of hot QCD plasma. We confront our results with LHC data, obtaining good agreement for jet RAARAA, dijet imbalance AJAJ and fragmentation functions.

  2. Solution Approach to Automatic Generation Control Problem Using Hybridized Gravitational Search Algorithm Optimized PID and FOPID Controllers

    Directory of Open Access Journals (Sweden)

    DAHIYA, P.

    2015-05-01

    Full Text Available This paper presents the application of hybrid opposition based disruption operator in gravitational search algorithm (DOGSA to solve automatic generation control (AGC problem of four area hydro-thermal-gas interconnected power system. The proposed DOGSA approach combines the advantages of opposition based learning which enhances the speed of convergence and disruption operator which has the ability to further explore and exploit the search space of standard gravitational search algorithm (GSA. The addition of these two concepts to GSA increases its flexibility for solving the complex optimization problems. This paper addresses the design and performance analysis of DOGSA based proportional integral derivative (PID and fractional order proportional integral derivative (FOPID controllers for automatic generation control problem. The proposed approaches are demonstrated by comparing the results with the standard GSA, opposition learning based GSA (OGSA and disruption based GSA (DGSA. The sensitivity analysis is also carried out to study the robustness of DOGSA tuned controllers in order to accommodate variations in operating load conditions, tie-line synchronizing coefficient, time constants of governor and turbine. Further, the approaches are extended to a more realistic power system model by considering the physical constraints such as thermal turbine generation rate constraint, speed governor dead band and time delay.

  3. Hybrid crystals of cuprates and iron-based superconductors

    Science.gov (United States)

    Xia, Dai; Cong-Cong, Le; Xian-Xin, Wu; Jiang-Ping, Hu

    2016-07-01

    We propose two possible new compounds, Ba2CuO2Fe2As2 and K2CuO2Fe2Se2, which hybridize the building blocks of two high temperature superconductors, cuprates and iron-based superconductors. These compounds consist of square CuO2 layers and antifluorite-type Fe2 X 2 (X = As, Se) layers separated by Ba/K. The calculations of binding energies and phonon spectra indicate that they are dynamically stable, which ensures that they may be experimentally synthesized. The Fermi surfaces and electronic structures of the two compounds inherit the characteristics of both cuprates and iron-based superconductors. These compounds can be superconductors with intriguing physical properties to help to determine the pairing mechanisms of high T c superconductivity. Project supported by the National Basic Research Program of China (Grant No. 2015CB921300), the National Natural Science Foundation of China (Grant Nos. 1190020 and 11334012), and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB07000000).

  4. A templated approach for multi-physics modeling of hybrid energy systems in Modelica

    Energy Technology Data Exchange (ETDEWEB)

    Greenwood, Michael Scott [ORNL; Cetiner, Sacit M. [ORNL; Harrison, Thomas J. [ORNL; Fugate, David [Oak Ridge National Laboratory (ORNL)

    2018-01-01

    A prototypical hybrid energy system (HES) couples a primary thermal power generator (i.e., nuclear power plant) with one or more additional subsystems beyond the traditional balance of plant electricity generation system. The definition and architecture of an HES can be adapted based on the needs and opportunities of a given local market. For example, locations in need of potable water may be best served by coupling a desalination plant to the HES. A location near an oil refinery may have a need for emission-free hydrogen production. The flexible, multidomain capabilities of Modelica are being used to investigate the dynamics (e.g., thermal hydraulics and electrical generation/consumption) of such a hybrid system. This paper examines the simulation infrastructure created to enable the coupling of multiphysics subsystem models for HES studies. A demonstration of a tightly coupled nuclear hybrid energy system implemented using the Modelica based infrastructure is presented for two representative cases. An appendix is also included providing a step-by-step procedure for using the template-based infrastructure.

  5. Energy level alignment at hybridized organic-metal interfaces from a GW projection approach

    Science.gov (United States)

    Chen, Yifeng; Tamblyn, Isaac; Quek, Su Ying

    Energy level alignments at organic-metal interfaces are of profound importance in numerous (opto)electronic applications. Standard density functional theory (DFT) calculations generally give incorrect energy level alignments and missing long-range polarization effects. Previous efforts to address this problem using the many-electron GW method have focused on physisorbed systems where hybridization effects are insignificant. Here, we use state-of-the-art GW methods to predict the level alignment at the amine-Au interface, where molecular levels do hybridize with metallic states. This non-trivial hybridization implies that DFT result is a poor approximation to the quasiparticle states. However, we find that the self-energy operator is approximately diagonal in the molecular basis, allowing us to use a projection approach to predict the level alignments. Our results indicate that the metallic substrate reduces the HOMO-LUMO gap by 3.5 4.0 eV, depending on the molecular coverage/presence of Au adatoms. Our GW results are further compared with those of a simple image charge model that describes the level alignment in physisorbed systems. Syq and YC acknowledge Grant NRF-NRFF2013-07 and the medium-sized centre program from the National Research Foundation, Singapore.

  6. Photo-Ionization of Noble Gases: A Demonstration of Hybrid Coupled Channels Approach

    Directory of Open Access Journals (Sweden)

    Vinay Pramod Majety

    2015-01-01

    Full Text Available We present here an application of the recently developed hybrid coupled channels approach to study photo-ionization of noble gas atoms: Neon and Argon. We first compute multi-photon ionization rates and cross-sections for these inert gas atoms with our approach and compare them with reliable data available from R-matrix Floquet theory. The good agreement between coupled channels and R-matrix Floquet theory show that our method treats multi-electron systems on par with the well established R-matrix theory. We then apply the time dependent surface flux (tSURFF method with our approach to compute total and angle resolved photo-electron spectra from Argon with linearly and circularly polarized 12 nm wavelength laser fields, a typical wavelength available from Free Electron Lasers (FELs.

  7. Materiality in a Practice-Based Approach

    Science.gov (United States)

    Svabo, Connie

    2009-01-01

    Purpose: The paper aims to provide an overview of the vocabulary for materiality which is used by practice-based approaches to organizational knowing. Design/methodology/approach: The overview is theoretically generated and is based on the anthology Knowing in Organizations: A Practice-based Approach edited by Nicolini, Gherardi and Yanow. The…

  8. Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach

    Science.gov (United States)

    Domino, Stefan P.

    2018-04-01

    A hybrid, design-order sliding mesh algorithm, which uses a control volume finite element method (CVFEM), in conjunction with a discontinuous Galerkin (DG) approach at non-conformal interfaces, is outlined in the context of a low-Mach fluid dynamics equation set. This novel hybrid DG approach is also demonstrated to be compatible with a classic edge-based vertex centered (EBVC) scheme. For the CVFEM, element polynomial, P, promotion is used to extend the low-order P = 1 CVFEM method to higher-order, i.e., P = 2. An equal-order low-Mach pressure-stabilized methodology, with emphasis on the non-conformal interface boundary condition, is presented. A fully implicit matrix solver approach that accounts for the full stencil connectivity across the non-conformal interface is employed. A complete suite of formal verification studies using the method of manufactured solutions (MMS) is performed to verify the order of accuracy of the underlying methodology. The chosen suite of analytical verification cases range from a simple steady diffusion system to a traveling viscous vortex across mixed-order non-conformal interfaces. Results from all verification studies demonstrate either second- or third-order spatial accuracy and, for transient solutions, second-order temporal accuracy. Significant accuracy gains in manufactured solution error norms are noted even with modest promotion of the underlying polynomial order. The paper also demonstrates the CVFEM/DG methodology on two production-like simulation cases that include an inner block subjected to solid rotation, i.e., each of the simulations include a sliding mesh, non-conformal interface. The first production case presented is a turbulent flow past a high-rate-of-rotation cube (Re, 4000; RPM, 3600) on like and mixed-order polynomial interfaces. The final simulation case is a full-scale Vestas V27 225 kW wind turbine (tower and nacelle omitted) in which a hybrid topology, low-order mesh is used. Both production simulations

  9. Template-based fabrication of nanowire-nanotube hybrid arrays

    International Nuclear Information System (INIS)

    Ye Zuxin; Liu Haidong; Schultz, Isabel; Wu Wenhao; Naugle, D G; Lyuksyutov, I

    2008-01-01

    The fabrication and structure characterization of ordered nanowire-nanotube hybrid arrays embedded in porous anodic aluminum oxide (AAO) membranes are reported. Arrays of TiO 2 nanotubes were first deposited into the pores of AAO membranes by a sol-gel technique. Co nanowires were then electrochemically deposited into the TiO 2 nanotubes to form the nanowire-nanotube hybrid arrays. Scanning electron microscopy and transmission electron microscopy measurements showed a high nanowire filling factor and a clean interface between the Co nanowire and the TiO 2 nanotube. Application of these hybrids to the fabrication of ordered nanowire arrays with highly controllable geometric parameters is discussed

  10. AEROSTATIC AND AERODYNAMIC MODULES OF A HYBRID BUOYANT AIRCRAFT: AN ANALYTICAL APPROACH

    Directory of Open Access Journals (Sweden)

    Anwar Ul Haque

    2015-05-01

    Full Text Available An analytical approach is essential for the estimation of the requirements of aerodynamic and aerostatic lift for a hybrid buoyant aircraft. Such aircrafts have two different modules to balance the weight of aircraft; aerostatic module and aerodynamic module. Both these modules are to be treated separately for estimation of the mass budget of propulsion systems and required power. In the present work, existing relationships of aircraft and airship are reviewed for its further application for these modules. Limitations of such relationships are also disussed and it is precieved that it will provide a strating point for better understanding of design anatomy of such aircraft.

  11. "Elephant trunk" and endovascular stentgrafting : a hybrid approach to the treatment of extensive thoracic aortic aneurysm

    OpenAIRE

    Holubec, Tomás; Raupach, Jan; Dominik, Jan; Vojácek, Jan

    2013-01-01

    hybrid approach to elephant trunk technique for treatment of thoracic aortic aneurysms combines a conventional surgical and endovascular therapy. Compared to surgery alone, there is a presumption that mortality and morbidity is reduced. We present a case report of a 42-year-old man with a giant aneurysm of the entire thoracic aorta, significant aortic and tricuspid regurgitation and ventricular septum defect. The patient underwent multiple consecutive operations and interventions having, am...

  12. Hybrid welding of carbon-fiber reinforced epoxy based composites

    NARCIS (Netherlands)

    Lionetto, Francesca; De Nicolas Morillas, M.; Pappadà, Silvio; Buccoliero, Giuseppe; Fernandez Villegas, I.; Maffezzoli, Alfonso

    2018-01-01

    The approach for joining thermosetting matrix composites (TSCs) proposed in this study is based on the use of a low melting co-cured thermoplastic film, added as a last ply in the stacking sequence of the composite laminate. During curing, the thermoplastic film partially penetrates in the first

  13. Preliminary Study on Hybrid Computational Phantom for Radiation Dosimetry Based on Subdivision Surface

    International Nuclear Information System (INIS)

    Jeong, Jong Hwi; Choi, Sang Hyoun; Cho, Sung Koo; Kim, Chan Hyeong

    2007-01-01

    The anthropomorphic computational phantoms are classified into two groups. One group is the stylized phantoms, or MIRD phantoms, which are based on mathematical representations of the anatomical structures. The shapes and positions of the organs and tissues in these phantoms can be adjusted by changing the coefficients of the equations in use. The other group is the voxel phantoms, which are based on tomographic images of a real person such as CT, MR and serially sectioned color slice images from a cadaver. Obviously, the voxel phantoms represent the anatomical structures of a human body much more realistically than the stylized phantoms. A realistic representation of anatomical structure is very important for an accurate calculation of radiation dose in the human body. Consequently, the ICRP recently has decided to use the voxel phantoms for the forthcoming update of the dose conversion coefficients. However, the voxel phantoms also have some limitations: (1) The topology and dimensions of the organs and tissues in a voxel model are extremely difficult to change, and (2) The thin organs, such as oral mucosa and skin, cannot be realistically modeled unless the voxel resolution is prohibitively high. Recently, a new approach has been implemented by several investigators. The investigators converted their voxel phantoms to hybrid computational phantoms based on NURBS (Non-Uniform Rational B-Splines) surface, which is smooth and deformable. It is claimed that these new phantoms have the flexibility of the stylized phantom along with the realistic representations of the anatomical structures. The topology and dimensions of the anatomical structures can be easily changed as necessary. Thin organs can be modeled without affecting computational speed or memory requirement. The hybrid phantoms can be also used for 4-D Monte Carlo simulations. In this preliminary study, the external shape of a voxel phantom (i.e., skin), HDRK-Man, was converted to a hybrid computational

  14. A new hybrid observer based rotor imbalance vibration control via passive autobalancer and active bearing actuation

    Science.gov (United States)

    Jung, DaeYi; DeSmidt, Hans

    2018-02-01

    Many researchers have explored the use of active bearings, such as non-contact Active Magnetic Bearings (AMB), to control imbalance vibration in rotor systems. Meanwhile, the advantages of a passive Auto-balancer device (ABD) eliminating the imbalance effect of rotor without using other active means have been recently studied. This paper develops a new hybrid imbalance vibration control approach for an ABD-rotor system supported by a normal passive bearing in augmented with an AMB to enhance the balancing and vibration isolation capabilities. Essentially, an ABD consists of several freely moving eccentric balancing masses mounted on the rotor, which, at supercritical operating speeds, act to cancel the rotor's imbalance at steady-state. However, due to the inherent nonlinearity of the ABD, the potential for other, non-synchronous limit-cycle behavior exists resulting in increased rotor vibration. To address this, the algorithm of proposed hybrid control is designed to guarantee globally asymptotic stability of the synchronous balanced condition. This algorithm also incorporates with a "Luenberger-like" observer that continuously estimates the states of a balancer ball circulating around within ABD. In particular, it is shown that the balanced equilibrium can be made globally attractive under the hybrid control strategy, and that the control power levels of AMB are significantly reduced via the addition of the ABD because the control is designed such that it is only switched on for the abnormal operation of ABD and will be disengaged otherwise. Moreover, unlike other imbalance vibration control applications based upon ABD such as rotor speed regulator [21,22], this approach enables the controller to achieve the desirable performance without altering rotor speed once the rotor initially reaches the target speed. These applications are relevant to limited power applications such as in satellite reaction wheels, flywheel energy storage batteries or CD-ROM application.

  15. Modeling level change in Lake Urmia using hybrid artificial intelligence approaches

    Science.gov (United States)

    Esbati, M.; Ahmadieh Khanesar, M.; Shahzadi, Ali

    2017-06-01

    The investigation of water level fluctuations in lakes for protecting them regarding the importance of these water complexes in national and regional scales has found a special place among countries in recent years. The importance of the prediction of water level balance in Lake Urmia is necessary due to several-meter fluctuations in the last decade which help the prevention from possible future losses. For this purpose, in this paper, the performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the lake water level balance has been studied. In addition, for the training of the adaptive neuro-fuzzy inference system, particle swarm optimization (PSO) and hybrid backpropagation-recursive least square method algorithm have been used. Moreover, a hybrid method based on particle swarm optimization and recursive least square (PSO-RLS) training algorithm for the training of ANFIS structure is introduced. In order to have a more fare comparison, hybrid particle swarm optimization and gradient descent are also applied. The models have been trained, tested, and validated based on lake level data between 1991 and 2014. For performance evaluation, a comparison is made between these methods. Numerical results obtained show that the proposed methods with a reasonable error have a good performance in water level balance prediction. It is also clear that with continuing the current trend, Lake Urmia will experience more drop in the water level balance in the upcoming years.

  16. Heterogeneous Catalysis of Polyoxometalate Based Organic–Inorganic Hybrids

    Directory of Open Access Journals (Sweden)

    Yuanhang Ren

    2015-03-01

    Full Text Available Organic–inorganic hybrid polyoxometalate (POM compounds are a subset of materials with unique structures and physical/chemical properties. The combination of metal-organic coordination complexes with classical POMs not only provides a powerful way to gain multifarious new compounds but also affords a new method to modify and functionalize POMs. In parallel with the many reports on the synthesis and structure of new hybrid POM compounds, the application of these compounds for heterogeneous catalysis has also attracted considerable attention. The hybrid POM compounds show noteworthy catalytic performance in acid, oxidation, and even in asymmetric catalytic reactions. This review summarizes the design and synthesis of organic–inorganic hybrid POM compounds and particularly highlights their recent progress in heterogeneous catalysis.

  17. Li-Ion, Ultra-capacitor Based Hybrid Energy Module

    National Research Council Canada - National Science Library

    Daboussi, Zaher; Paryani, Anil; Khalil, Gus; Catherino, Henry; Gargies, Sonya

    2007-01-01

    Ultra-capacitors in multi kilo-farad ranges are now starting to be considered as alternatives or complimentary to batteries for products ranging from toys to hybrid vehicles as well as for space applications...

  18. Li-Ion, Ultra-capacitor Based Hybrid Energy Module

    National Research Council Canada - National Science Library

    Daboussi, Zaher; Paryani, Anil; Khalil, Gus; Catherino, Henry; Gargies, Sonya

    2007-01-01

    .... To determine the optimum utilization of ultra-capacitors in applications where high power density and high energy density are required, an optimized Li-Ion/Ultra-capacitor Hybrid Energy Module (HEM...

  19. Disturbance observer-based adaptive sliding mode hybrid projective ...

    Indian Academy of Sciences (India)

    Ayub Khan

    2018-04-21

    Apr 21, 2018 ... systems, the effect of external disturbances, energy fluc- tuation and other such ...... When we apply the above proposed strategy to achieve hybrid projective ..... ory and intelligent control (Springer, 2016) pp. 681–. 697.

  20. Three-dimensional hybrid networks based on aspartic acid

    Indian Academy of Sciences (India)

    WINTEC

    Keywords. Aspartic acid; hybrid compounds; nickel aspartate; lead aspartate; achiral frameworks. ..... and coordinated to water molecules as well as car- .... (b) Dan M 2004 J. Mol. Struct. ... Sheldrick G M 1994 SADABS: Siemens area detector.

  1. A Hybrid Power Management (HPM) Based Vehicle Architecture

    Science.gov (United States)

    Eichenberg, Dennis J.

    2011-01-01

    Society desires vehicles with reduced fuel consumption and reduced emissions. This presents a challenge and an opportunity for industry and the government. The NASA John H. Glenn Research Center (GRC) has developed a Hybrid Power Management (HPM) based vehicle architecture for space and terrestrial vehicles. GRC's Electrical and Electromagnetics Branch of the Avionics and Electrical Systems Division initiated the HPM Program for the GRC Technology Transfer and Partnership Office. HPM is the innovative integration of diverse, state-of-the-art power devices in an optimal configuration for space and terrestrial applications. The appropriate application and control of the various power devices significantly improves overall system performance and efficiency. The basic vehicle architecture consists of a primary power source, and possibly other power sources, providing all power to a common energy storage system, which is used to power the drive motors and vehicle accessory systems, as well as provide power as an emergency power system. Each component is independent, permitting it to be optimized for its intended purpose. This flexible vehicle architecture can be applied to all vehicles to considerably improve system efficiency, reliability, safety, security, and performance. This unique vehicle architecture has the potential to alleviate global energy concerns, improve the environment, stimulate the economy, and enable new missions.

  2. Oxindole based oxadiazole hybrid analogs: Novel α-glucosidase inhibitors.

    Science.gov (United States)

    Taha, Muhammad; Imran, Syahrul; Rahim, Fazal; Wadood, Abdul; Khan, Khalid Mohammed

    2018-02-01

    Inhibition of α-glucosidase is an effective strategy for controlling post-prandial hyperglycemia in diabetic patients. Beside these α-glucosidase inhibitors has been also used as anti-obesity and anti-viral drugs. Keeping in view the greater importance of α-glucosidase inhibitors here in this study we are presenting oxindole based oxadiazoles hybrid analogs (1-20) synthesis, characterized by different spectroscopic techniques including 1 H NMR and EI-MS and their α-glucosidase inhibitory activity. All compounds were found potent inhibitors for the enzyme with IC 50 values ranging between 1.25 ± 0.05 and 268.36 ± 4.22 µM when compared with the standard drug acarbose having IC 50 value 895.09 ± 2.04 µM. Our study identifies novel series of potent α-glucosidase inhibitors and further investigation on this may led to the lead compounds. A structure activity relationship has been established for all compounds. The interactions of the active compounds and enzyme active site were established with the help of molecular docking studies. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Flat microwave photonic filter based on hybrid of two filters

    International Nuclear Information System (INIS)

    Qi, Chunhui; Pei, Li; Ning, Tigang; Li, Jing; Gao, Song

    2010-01-01

    A new microwave photonic filter (MPF) hybrid of two filters that can realize both multiple taps and a flat bandpass or bandstop response is presented. Based on the phase character of a Mach–Zehnder modulator (MZM), a two taps finite impulse response (FIR) filter is obtained as the first part. The second part is obtained by taking full advantage of the wavelength selectivity of the fiber Bragg grating (FBG) and the gain of a erbium-doped fiber (EDF). Combining the two filters, the flat bandpass or bandstop response is realized by changing the coupler's factor k, the reflectivity of FBG1 R 1 or the gain of the EDF g. Optimizing the system parameters, a flat bandpass response with amplitude depth of more than 45 dB is obtained at k = 0.5, R 1 = 0.33, g = 10, and a flat bandstop response is also obtained at k = 0.4, R 1 = 0.5, g = 2. In addition, the free-spectral range (FSR) can be controlled by changing the length of the EDF and the length difference between two MZMs. The method is proved feasible by some experiments. Such a method offers realistic solutions to support future radio-frequency (RF) optical communication systems

  4. Hybrid Video Coding Based on Bidimensional Matching Pursuit

    Directory of Open Access Journals (Sweden)

    Lorenzo Granai

    2004-12-01

    Full Text Available Hybrid video coding combines together two stages: first, motion estimation and compensation predict each frame from the neighboring frames, then the prediction error is coded, reducing the correlation in the spatial domain. In this work, we focus on the latter stage, presenting a scheme that profits from some of the features introduced by the standard H.264/AVC for motion estimation and replaces the transform in the spatial domain. The prediction error is so coded using the matching pursuit algorithm which decomposes the signal over an appositely designed bidimensional, anisotropic, redundant dictionary. Comparisons are made among the proposed technique, H.264, and a DCT-based coding scheme. Moreover, we introduce fast techniques for atom selection, which exploit the spatial localization of the atoms. An adaptive coding scheme aimed at optimizing the resource allocation is also presented, together with a rate-distortion study for the matching pursuit algorithm. Results show that the proposed scheme outperforms the standard DCT, especially at very low bit rates.

  5. A novel hybrid approach with multidimensional-like effects for compressible flow computations

    Science.gov (United States)

    Kalita, Paragmoni; Dass, Anoop K.

    2017-07-01

    A multidimensional scheme achieves good resolution of strong and weak shocks irrespective of whether the discontinuities are aligned with or inclined to the grid. However, these schemes are computationally expensive. This paper achieves similar effects by hybridizing two schemes, namely, AUSM and DRLLF and coupling them through a novel shock switch that operates - unlike existing switches - on the gradient of the Mach number across the cell-interface. The schemes that are hybridized have contrasting properties. The AUSM scheme captures grid-aligned (and strong) shocks crisply but it is not so good for non-grid-aligned weaker shocks, whereas the DRLLF scheme achieves sharp resolution of non-grid-aligned weaker shocks, but is not as good for grid-aligned strong shocks. It is our experience that if conventional shock switches based on variables like density, pressure or Mach number are used to combine the schemes, the desired effect of crisp resolution of grid-aligned and non-grid-aligned discontinuities are not obtained. To circumvent this problem we design a shock switch based - for the first time - on the gradient of the cell-interface Mach number with very impressive results. Thus the strategy of hybridizing two carefully selected schemes together with the innovative design of the shock switch that couples them, affords a method that produces the effects of a multidimensional scheme with a lower computational cost. It is further seen that hybridization of the AUSM scheme with the recently developed DRLLFV scheme using the present shock switch gives another scheme that provides crisp resolution for both shocks and boundary layers. Merits of the scheme are established through a carefully selected set of numerical experiments.

  6. Hybridization and sequencing of nucleic acids using base pair mismatches

    Science.gov (United States)

    Fodor, Stephen P. A.; Lipshutz, Robert J.; Huang, Xiaohua

    2001-01-01

    Devices and techniques for hybridization of nucleic acids and for determining the sequence of nucleic acids. Arrays of nucleic acids are formed by techniques, preferably high resolution, light-directed techniques. Positions of hybridization of a target nucleic acid are determined by, e.g., epifluorescence microscopy. Devices and techniques are proposed to determine the sequence of a target nucleic acid more efficiently and more quickly through such synthesis and detection techniques.

  7. FPGA implementation of a hybrid on-line process monitoring in PC based real-time systems

    Directory of Open Access Journals (Sweden)

    Jovanović Bojan

    2011-01-01

    Full Text Available This paper presents one way of FPGA implementation of hybrid (hardware-software based on-line process monitoring in Real-Time systems (RTS. The reasons for RTS monitoring are presented at the beginning. The summary of different RTS monitoring approaches along with its advantages and drawbacks are also exposed. Finally, monitoring module is described in details. Also, FPGA implementation results and some useful monitoring system applications are mentioned.

  8. A simplified computational fluid-dynamic approach to the oxidizer injector design in hybrid rockets

    Science.gov (United States)

    Di Martino, Giuseppe D.; Malgieri, Paolo; Carmicino, Carmine; Savino, Raffaele

    2016-12-01

    Fuel regression rate in hybrid rockets is non-negligibly affected by the oxidizer injection pattern. In this paper a simplified computational approach developed in an attempt to optimize the oxidizer injector design is discussed. Numerical simulations of the thermo-fluid-dynamic field in a hybrid rocket are carried out, with a commercial solver, to investigate into several injection configurations with the aim of increasing the fuel regression rate and minimizing the consumption unevenness, but still favoring the establishment of flow recirculation at the motor head end, which is generated with an axial nozzle injector and has been demonstrated to promote combustion stability, and both larger efficiency and regression rate. All the computations have been performed on the configuration of a lab-scale hybrid rocket motor available at the propulsion laboratory of the University of Naples with typical operating conditions. After a preliminary comparison between the two baseline limiting cases of an axial subsonic nozzle injector and a uniform injection through the prechamber, a parametric analysis has been carried out by varying the oxidizer jet flow divergence angle, as well as the grain port diameter and the oxidizer mass flux to study the effect of the flow divergence on heat transfer distribution over the fuel surface. Some experimental firing test data are presented, and, under the hypothesis that fuel regression rate and surface heat flux are proportional, the measured fuel consumption axial profiles are compared with the predicted surface heat flux showing fairly good agreement, which allowed validating the employed design approach. Finally an optimized injector design is proposed.

  9. Hybrid reactor based on combined cavitation and ozonation: from concept to practical reality.

    Science.gov (United States)

    Gogate, P R; Mededovic-Thagard, S; McGuire, D; Chapas, G; Blackmon, J; Cathey, R

    2014-03-01

    The present work gives an in depth discussion related to the development of a hybrid advanced oxidation reactor, which can be effectively used for the treatment of various types of water. The reactor is based on the principle of intensifying degradation/disinfection using a combination of hydrodynamic cavitation, acoustic cavitation, ozone injection and electrochemical oxidation/precipitation. Theoretical studies have been presented to highlight the uniform distribution of the cavitational activity and enhanced generation of hydroxyl radicals in the cavitation zone, as well as higher turbulence in the main reactor zone. The combination of these different oxidation technologies have been shown to result in enhanced water treatment ability, which can be attributed to the enhanced generation of hydroxyl radicals, enhanced contact of ozone and contaminants, and the elimination of mass transfer resistances during electrochemical oxidation/precipitation. Compared to the use of individual approaches, the hybrid reactor is expected to intensify the treatment process by 5-20 times, depending on the application in question, which can be confirmed based on the literature illustrations. Also, the use of Ozonix® has been successfully proven while processing recycled fluids at commercial sites on over 750 oil and natural gas wells during hydraulic operations around the United States. The superiority of the hybrid process over conventional chemical treatments in terms of bacteria and scale reduction as well as increased water flowability and better chemical compatibility, which is a key requirement for oil and gas applications, has been established. Copyright © 2013 Elsevier B.V. All rights reserved.

  10. A Trichosporonales genome tree based on 27 haploid and three evolutionarily conserved 'natural' hybrid genomes.

    Science.gov (United States)

    Takashima, Masako; Sriswasdi, Sira; Manabe, Ri-Ichiroh; Ohkuma, Moriya; Sugita, Takashi; Iwasaki, Wataru

    2018-01-01

    To construct a backbone tree consisting of basidiomycetous yeasts, draft genome sequences from 25 species of Trichosporonales (Tremellomycetes, Basidiomycota) were generated. In addition to the hybrid genomes of Trichosporon coremiiforme and Trichosporon ovoides that we described previously, we identified an interspecies hybrid genome in Cutaneotrichosporon mucoides (formerly Trichosporon mucoides). This hybrid genome had a gene retention rate of ~55%, and its closest haploid relative was Cutaneotrichosporon dermatis. After constructing the C. mucoides subgenomes, we generated a phylogenetic tree using genome data from the 27 haploid species and the subgenome data from the three hybrid genome species. It was a high-quality tree with 100% bootstrap support for all of the branches. The genome-based tree provided superior resolution compared with previous multi-gene analyses. Although our backbone tree does not include all Trichosporonales genera (e.g. Cryptotrichosporon), it will be valuable for future analyses of genome data. Interest in interspecies hybrid fungal genomes has recently increased because they may provide a basis for new technologies. The three Trichosporonales hybrid genomes described in this study are different from well-characterized hybrid genomes (e.g. those of Saccharomyces pastorianus and Saccharomyces bayanus) because these hybridization events probably occurred in the distant evolutionary past. Hence, they will be useful for studying genome stability following hybridization and speciation events. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  11. A hybrid filtering approach for storage optimization in main-memory cloud database

    Directory of Open Access Journals (Sweden)

    Ghada M. Afify

    2015-11-01

    Full Text Available Enterprises and cloud service providers face dramatic increase in the amount of data stored in private and public clouds. Thus, data storage costs are growing hastily because they use only one single high-performance storage tier for storing all cloud data. There’s considerable potential to reduce cloud costs by classifying data into active (hot and inactive (cold. In the main-memory databases research, recent works focus on approaches to identify hot/cold data. Most of these approaches track tuple accesses to identify hot/cold tuples. In contrast, we introduce a novel Hybrid Filtering Approach (HFA that tracks both tuples and columns accesses in main-memory databases. Our objective is to enhance the performance in terms of three dimensions: storage space, query elapsed time and CPU time. In order to validate the effectiveness of our approach, we realized its concrete implementation on Hekaton, a SQL’s server memory-optimized engine using the well-known TPC-H benchmark. Experimental results show that the proposed HFA outperforms Hekaton approach in respect of all performance dimensions. In specific, HFA reduces the storage space by average of 44–96%, reduces the query elapsed time by average of 25–93% and reduces the CPU time by average of 31–97% compared to the traditional database approach.

  12. Hybrid discrete PSO and OPF approach for optimization of biomass fueled micro-scale energy system

    International Nuclear Information System (INIS)

    Gómez-González, M.; López, A.; Jurado, F.

    2013-01-01

    Highlights: ► Method to determine the optimal location and size of biomass power plants. ► The proposed approach is a hybrid of PSO algorithm and optimal power flow. ► Comparison among the proposed algorithm and other methods. ► Computational costs are enough lower than that required for exhaustive search. - Abstract: This paper addresses generation of electricity in the specific aspect of finding the best location and sizing of biomass fueled gas micro-turbine power plants, taking into account the variables involved in the problem, such as the local distribution of biomass resources, biomass transportation and extraction costs, operation and maintenance costs, power losses costs, network operation costs, and technical constraints. In this paper a hybrid method is introduced employing discrete particle swarm optimization and optimal power flow. The approach can be applied to search the best sites and capacities to connect biomass fueled gas micro-turbine power systems in a distribution network among a large number of potential combinations and considering the technical constraints of the network. A fair comparison among the proposed algorithm and other methods is performed.

  13. Output Tracking Control of Switched Hybrid Systems: A Fliess Functional Expansion Approach

    Directory of Open Access Journals (Sweden)

    Fenghua He

    2013-01-01

    Full Text Available The output tracking problem is investigated for a nonlinear affine system with multiple modes of continuous control inputs. We convert the family of nonlinear affine systems under consideration into a switched hybrid system by introducing a multiple-valued logic variable. The Fliess functional expansion is adopted to express the input and output relationship of the switched hybrid system. The optimal switching control is determined for a multiple-step output tracking performance index. The proposed approach is applied to a multitarget tracking problem for a flight vehicle aiming for one real target with several decoys flying around it in the terminal guidance course. These decoys appear as apparent targets and have to be distinguished with the approaching of the flight vehicle. The guidance problem of one flight vehicle versus multiple apparent targets should be considered if no large miss distance might be caused due to the limitation of the flight vehicle maneuverability. The target orientation at each time interval is determined. Simulation results show the effectiveness of the proposed method.

  14. Hybrid Multicriteria Group Decision Making Method for Information System Project Selection Based on Intuitionistic Fuzzy Theory

    Directory of Open Access Journals (Sweden)

    Jian Guo

    2013-01-01

    Full Text Available Information system (IS project selection is of critical importance to every organization in dynamic competing environment. The aim of this paper is to develop a hybrid multicriteria group decision making approach based on intuitionistic fuzzy theory for IS project selection. The decision makers’ assessment information can be expressed in the form of real numbers, interval-valued numbers, linguistic variables, and intuitionistic fuzzy numbers (IFNs. All these evaluation pieces of information can be transformed to the form of IFNs. Intuitionistic fuzzy weighted averaging (IFWA operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate IS project in group decision making environment. Finally, a numerical example for information system projects selection is given to illustrate application of hybrid multi-criteria group decision making (MCGDM method based on intuitionistic fuzzy theory and TOPSIS method.

  15. Hybrid macro-micro fluidics system for a chip-based biosensor

    Science.gov (United States)

    Tamanaha, C. R.; Whitman, L. J.; Colton, R. J.

    2002-03-01

    We describe the engineering of a hybrid fluidics platform for a chip-based biosensor system that combines high-performance microfluidics components with powerful, yet compact, millimeter-scale pump and valve actuators. The microfluidics system includes channels, valveless diffuser-based pumps, and pinch-valves that are cast into a poly(dimethylsiloxane) (PDMS) membrane and packaged along with the sensor chip into a palm-sized plastic cartridge. The microfluidics are driven by pump and valve actuators contained in an external unit (with a volume ~30 cm3) that interfaces kinematically with the PDMS microelements on the cartridge. The pump actuator is a simple-lever, flexure-hinge displacement amplifier that increases the motion of a piezoelectric stack. The valve actuators are an array of cantilevers operated by shape memory alloy wires. All components can be fabricated without the need for complex lithography or micromachining, and can be used with fluids containing micron-sized particulates. Prototypes have been modeled and tested to ensure the delivery of microliter volumes of fluid and the even dispersion of reagents over the chip sensing elements. With this hybrid approach to the fluidics system, the biochemical assay benefits from the many advantages of microfluidics yet we avoid the complexity and unknown reliability of immature microactuator technologies.

  16. Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet

    Directory of Open Access Journals (Sweden)

    Lu Ke

    2017-01-01

    Full Text Available High-frequency components are often discarded for data denoising when applying pure wavelet multiscale or empirical mode decomposition (EMD based approaches. Instead, they may raise the problem of energy leakage in vibration signals. Hybrid EMD and wavelet packet (EMD-WP is proposed to denoise Global Positioning System- (GPS- based structure monitoring data. First, field observables are decomposed into a collection of intrinsic mode functions (IMFs with different characteristics. Second, high-frequency IMFs are denoised using the wavelet packet; then the monitoring data are reconstructed using the denoised IMFs together with the remaining low-frequency IMFs. Our algorithm is demonstrated on a synthetic displacement response of a 3-story frame excited by El Centro earthquake along with a set of Gaussian random white noises on different levels added. We find that the hybrid method can effectively weaken the multipath effect with low frequency and can potentially extract vibration feature. However, false modals may still exist by the rest of the noise contained in the high-frequency IMFs and when the frequency of the noise is located in the same band as that of effective vibration. Finally, real GPS observables are implemented to evaluate the efficiency of EMD-WP method in mitigating low-frequency multipath.

  17. A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles

    DEFF Research Database (Denmark)

    Sousa, Tiago; Vale, Zita; Carvalho, Joao Paulo

    2014-01-01

    The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed...... to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated...... annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA...

  18. Thermo-optic characteristics of hybrid polymer/silica microstructured optical fiber: An analytical approach

    Science.gov (United States)

    Sharma, Dinesh Kumar; Sharma, Anurag; Tripathi, Saurabh Mani

    2018-04-01

    Microstructured optical fibers (MOFs) allow a variety of advanced materials to be infiltrated in their air-voids for obtaining the increased fiber functionality, and offering a new versatile platform for developing the compact sensors devices. We aim to investigate the thermal characteristics of high-index core triangular hybrid polymer/silica MOFs with circular air-voids infused with polymer by using the analytical field model [1]. We demonstrate that infiltration of air-voids with polymer, e.g., polydimethylsiloxane (PDMS) can facilitate to tune the fundamental modal properties of MOF such as effective index of the mode, near and the far-field profiles, effective mode area and the numerical aperture over the temperature ranging from 0 °C to 100 °C, for different values of relative air-void ratios. The evolution of the mode shape for a given temperature has been investigated in transition from near-field to far-field regime. We have studied the thermal dependence of splice losses between hybrid MOF and the standard step-index single-mode optical fiber in combination with Fresnel losses. For enhancing the evanescent field interactions, we have evaluated fraction of power associated with fundamental mode of hybrid MOF. We have compared the accuracy of our results with those based on full-vector finite-difference (FD) method, as available in the literature.

  19. A hierarchical approach of hybrid image classification for land use and land cover mapping

    Directory of Open Access Journals (Sweden)

    Rahdari Vahid

    2018-01-01

    Full Text Available Remote sensing data analysis can provide thematic maps describing land-use and land-cover (LULC in a short period. Using proper image classification method in an area, is important to overcome the possible limitations of satellite imageries for producing land-use and land-cover maps. In the present study, a hierarchical hybrid image classification method was used to produce LULC maps using Landsat Thematic mapper TM for the year of 1998 and operational land imager OLI for the year of 2016. Images were classified using the proposed hybrid image classification method, vegetation cover crown percentage map from normalized difference vegetation index, Fisher supervised classification and object-based image classification methods. Accuracy assessment results showed that the hybrid classification method produced maps with total accuracy up to 84 percent with kappa statistic value 0.81. Results of this study showed that the proposed classification method worked better with OLI sensor than with TM. Although OLI has a higher radiometric resolution than TM, the produced LULC map using TM is almost accurate like OLI, which is because of LULC definitions and image classification methods used.

  20. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

    Science.gov (United States)

    Moeeni, Hamid; Bonakdari, Hossein; Ebtehaj, Isa

    2017-03-01

    Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA-GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years' worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA-GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA-ANN models. The results indicate that the SARIMA-GEP model ( R 2=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA-ANN ( R 2=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA-GEP over the SARIMA-ANN model.

  1. Automation of column-based radiochemical separations. A comparison of fluidic, robotic, and hybrid architectures

    Energy Technology Data Exchange (ETDEWEB)

    Grate, J.W.; O' Hara, M.J.; Farawila, A.F.; Ozanich, R.M.; Owsley, S.L. [Pacific Northwest National Laboratory, Richland, WA (United States)

    2011-07-01

    Two automated systems have been developed to perform column-based radiochemical separation procedures. These new systems are compared with past fluidic column separation architectures, with emphasis on using disposable components so that no sample contacts any surface that any other sample has contacted, and setting up samples and columns in parallel for subsequent automated processing. In the first new approach, a general purpose liquid handling robot has been modified and programmed to perform anion exchange separations using 2 mL bed columns in 6 mL plastic disposable column bodies. In the second new approach, a fluidic system has been developed to deliver clean reagents through disposable manual valves to six disposable columns, with a mechanized fraction collector that positions one of four rows of six vials below the columns. The samples are delivered to each column via a manual 3-port disposable valve from disposable syringes. This second approach, a hybrid of fluidic and mechanized components, is a simpler more efficient approach for performing anion exchange procedures for the recovery and purification of plutonium from samples. The automation architectures described can also be adapted to column-based extraction chromatography separations. (orig.)

  2. A Practical Approach to Improve Optical Channel Utilization Period for Hybrid FSO/RF Systems

    Directory of Open Access Journals (Sweden)

    Ahmet Akbulut

    2014-01-01

    Full Text Available In hybrid FSO/RF systems, mostly a hard switching mechanism is preferred in case of the FSO signal level falls below to the predefined threshold. In this work, a computationally simple approach is proposed to increase the utilization of the FSO channels bandwidth advantage. For the channel, clear air conditions have been supposed with the atmospheric turbulence. In this approach, FSO bit rate is adaptively changed to achieve desired BER performance. An IM/DD modulation, OOK (NRZ format has been used to show the benefit of the proposed method. Furthermore, to be more realistic with respect to the atmospheric turbulence variations within a day, some experimental observations have been followed up.

  3. A hybrid biomechanical intensity based deformable image registration of lung 4DCT

    International Nuclear Information System (INIS)

    Samavati, Navid; Velec, Michael; Brock, Kristy

    2015-01-01

    Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm.A hybrid DIR algorithm is proposed based on, a biomechanical model–based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used.Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone.The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5

  4. Hybrid approach to structure modeling of the histamine H3 receptor: Multi-level assessment as a tool for model verification.

    Directory of Open Access Journals (Sweden)

    Jakub Jończyk

    Full Text Available The crucial role of G-protein coupled receptors and the significant achievements associated with a better understanding of the spatial structure of known receptors in this family encouraged us to undertake a study on the histamine H3 receptor, whose crystal structure is still unresolved. The latest literature data and availability of different software enabled us to build homology models of higher accuracy than previously published ones. The new models are expected to be closer to crystal structures; and therefore, they are much more helpful in the design of potential ligands. In this article, we describe the generation of homology models with the use of diverse tools and a hybrid assessment. Our study incorporates a hybrid assessment connecting knowledge-based scoring algorithms with a two-step ligand-based docking procedure. Knowledge-based scoring employs probability theory for global energy minimum determination based on information about native amino acid conformation from a dataset of experimentally determined protein structures. For a two-step docking procedure two programs were applied: GOLD was used in the first step and Glide in the second. Hybrid approaches offer advantages by combining various theoretical methods in one modeling algorithm. The biggest advantage of hybrid methods is their intrinsic ability to self-update and self-refine when additional structural data are acquired. Moreover, the diversity of computational methods and structural data used in hybrid approaches for structure prediction limit inaccuracies resulting from theoretical approximations or fuzziness of experimental data. The results of docking to the new H3 receptor model allowed us to analyze ligand-receptor interactions for reference compounds.

  5. Hybrid Materials Based on Magnetic Layered Double Hydroxides: A Molecular Perspective.

    Science.gov (United States)

    Abellán, Gonzalo; Martí-Gastaldo, Carlos; Ribera, Antonio; Coronado, Eugenio

    2015-06-16

    dielectric strength, to produce two-dimensional nanosheets with atomic thickness that can be used as macromolecular building blocks in the assembly of nanocomposites. We describe how these advantageous properties turn LDHs into excellent vehicles for the preparation of multifunctional materials with increasing levels of complexity. For clarity, the reader will first find a succinct description of the most relevant aspects controlling the magnetism of LDHs followed by their use in the preparation of magnetic hybrids from a molecular perspective. This includes the intercalation anionic species of increasing nuclearity like paramagnetic mononuclear complexes, stimulus-responsive molecular guests, one- and two-dimensional coordination polymers, or even preassembled 2D networks. This approach allows us to evolve from "dual-function" materials with coexistence, for example, of magnetism and superconductivity, to smart materials in which the magnetic or structural properties of the LDH layers can be tuned by applying an external stimulus like light or temperature. We will conclude with a brief look into the promising features offered by magnetic nanocomposites based on LDHs and our views on the most promising directions to be pursued in this context.

  6. DIAGNOSIS WINDOWS PROBLEMS BASED ON HYBRID INTELLIGENCE SYSTEMS

    Directory of Open Access Journals (Sweden)

    SAFWAN O. HASOON

    2013-10-01

    Full Text Available This paper describes the artificial intelligence technologies by integrating Radial Basis Function networks with expert systems to construct a robust hybrid system. The purpose of building the hybrid system is to give recommendations to repair the operating system (Windows problems and troubleshoot the problems that can be repaired. The neural network has unique characteristics which it can complete the uncompleted data, the expert system can't deal with data that is incomplete, but using the neural network individually has some disadvantages which it can't give explanations and recommendations to the problems. The expert system has the ability to explain and give recommendations by using the rules and the human expert in some conditions. Therefore, we have combined the two technologies. The paper will explain the integration methods between the two technologies and which method is suitable to be used in the proposed hybrid system.

  7. Contrast improvement of continuous wave diffuse optical tomography reconstruction by hybrid approach using least square and genetic algorithm

    Science.gov (United States)

    Patra, Rusha; Dutta, Pranab K.

    2015-07-01

    Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10-3, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.

  8. Passive hybrid force-position control for tele-operation based on real-time simulation of a virtual mechanism

    International Nuclear Information System (INIS)

    Joly, L.; Andriot, C.

    1995-01-01

    Hybrid force-position control aims at controlling position and force in separate directions. It is particularly useful to perform certain robotic tasks. In tele-operation context, passivity is important because it ensures stability when the system interacts with any passive environment. In this paper, we propose an original approach to hybrid force-position control of a force reflecting tele-robot system. It is based on real-time simulation of a virtual mechanism corresponding to the task. the resulting control law is passive. Experiments on a 6 degrees of freedom tele-operation system consisting in following a bent pipe under several control modes validate the approach. (authors). 12 refs., 6 figs

  9. Hybride magnetic nanostructure based on amino acids functionalized polypyrrole

    Energy Technology Data Exchange (ETDEWEB)

    Nan, Alexandrina, E-mail: alexandrina.nan@itim-cj.ro; Bunge, Alexander; Turcu, Rodica [National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat, 400293 Cluj-Napoca (Romania)

    2015-12-23

    Conducting polypyrrole is especially promising for many commercial applications because of its unique optical, electric, thermal and mechanical properties. We report the synthesis and characterization of novel pyrrole functionalized monomers and core-shell hybrid nanostructures, consisting of a conjugated polymer layer (amino acids functionalized pyrrole copolymers) and a magnetic nanoparticle core. For functionalization of the pyrrole monomer we used several amino acids: tryptophan, leucine, phenylalanine, serine and tyrosine. These amino acids were linked via different types of hydrophobic linkers to the nitrogen atom of the pyrrole monomer. The magnetic core-shell hybrid nanostructures are characterized by various methods such as FTIR spectroscopy, transmission electron microscopy (TEM) and magnetic measurements.

  10. An Introduction to the Hybrid Approach of Neural Networks and the Linear Regression Model : An Illustration in the Hedonic Pricing Model of Building Costs

    OpenAIRE

    浅野, 美代子; マーコ, ユー K.W.

    2007-01-01

    This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...

  11. New approaches to the development of hybrid nanocomposites: from structural materials to high-tech applications

    International Nuclear Information System (INIS)

    Gerasin, V A; Antipov, Evgenii M; Karbushev, V V; Kulichikhin, Valerii G; Karpacheva, Galina P; Talroze, Raisa V; Kudryavtsev, Y V

    2013-01-01

    Current challenges in the development of various polymer nanocomposites and in the study of their properties are considered. Results of studying hybrid structural (polymer–layered silicates, polymer–nanodiamonds) and functional (based on conducting or liquid-crystalline polymers) nanomaterials are presented. Methods of modification of nanoparticles and their dispersion in a polymer matrix, and the role of interactions between a polymer matrix and fillers, as well as of nanoparticle morphology realized in the course of processing, are discussed. The bibliography includes 453 references.

  12. Near infrared photodetector based on polymer and indium nitride nanorod organic/inorganic hybrids

    International Nuclear Information System (INIS)

    Lai, Wei-Jung; Li, Shao-Sian; Lin, Chih-Cheng; Kuo, Chun-Chiang; Chen, Chun-Wei; Chen, Kuei-Hsien; Chen, Li-Chyong

    2010-01-01

    We propose a nanostructured near infrared photodetector based on indium nitride (InN) nanorod/poly(3-hexylthiophene) hybrids. The current-voltage characteristic of the hybrid device demonstrates the typical p-n heterojunction diode behavior, consisting of p-type polymer and n-type InN nanorods. The device shows a photoresponse range of 900-1260 nm under various reverse biases. An external quantum efficiency of 3.4% at 900 nm operated at -10 V reverse bias was obtained, which is comparable with devices based on lead sulfide and lead selenide hybrid systems.

  13. Hybrid Macro-Micro Fluidics System for a Chip-Based Biosensor

    National Research Council Canada - National Science Library

    Tamanaha, C. R; Whitman, L. J; Colton, R.J

    2002-01-01

    We describe the engineering of a hybrid fluidics platform for a chip-based biosensor system that combines high-performance microfluidics components with powerful, yet compact, millimeter-scale pump and valve actuators...

  14. An approach for identification of unknown viruses using sequencing-by-hybridization.

    Science.gov (United States)

    Katoski, Sarah E; Meyer, Hermann; Ibrahim, Sofi

    2015-09-01

    Accurate identification of biological threat agents, especially RNA viruses, in clinical or environmental samples can be challenging because the concentration of viral genomic material in a given sample is usually low, viral genomic RNA is liable to degradation, and RNA viruses are extremely diverse. A two-tiered approach was used for initial identification, then full genomic characterization of 199 RNA viruses belonging to virus families Arenaviridae, Bunyaviridae, Filoviridae, Flaviviridae, and Togaviridae. A Sequencing-by-hybridization (SBH) microarray was used to tentatively identify a viral pathogen then, the identity is confirmed by guided next-generation sequencing (NGS). After optimization and evaluation of the SBH and NGS methodologies with various virus species and strains, the approach was used to test the ability to identify viruses in blinded samples. The SBH correctly identified two Ebola viruses in the blinded samples within 24 hr, and by using guided amplicon sequencing with 454 GS FLX, the identities of the viruses in both samples were confirmed. SBH provides at relatively low-cost screening of biological samples against a panel of viral pathogens that can be custom-designed on a microarray. Once the identity of virus is deduced from the highest hybridization signal on the SBH microarray, guided (amplicon) NGS sequencing can be used not only to confirm the identity of the virus but also to provide further information about the strain or isolate, including a potential genetic manipulation. This approach can be useful in situations where natural or deliberate biological threat incidents might occur and a rapid response is required. © 2015 Wiley Periodicals, Inc.

  15. Organic Gelators as Growth Control Agents for Stable and Reproducible Hybrid Perovskite-Based Solar Cells

    KAUST Repository

    Masi, Sofia

    2017-03-03

    Low-molecular-weight organic gelators are widely used to influence the solidification of polymers, with applications ranging from packaging items, food containers to organic electronic devices, including organic photovoltaics. Here, this concept is extended to hybrid halide perovskite-based materials. In situ time-resolved grazing incidence wide-angle X-ray scattering measurements performed during spin coating reveal that organic gelators beneficially influence the nucleation and growth of the perovskite precursor phase. This can be exploited for the fabrication of planar n-i-p heterojunction devices with MAPbI3 (MA = CH3NH3+) that display a performance that not only is enhanced by ≈25% compared to solar cells where the active layer is produced without the use of a gelator but that also features a higher stability to moisture and a reduced hysteresis. Most importantly, the presented approach is straightforward and simple, and it provides a general method to render the film formation of hybrid perovskites more reliable and robust, analogous to the control that is afforded by these additives in the processing of commodity “plastics.”

  16. A Hybrid Distance-Based Ideal-Seeking Consensus Ranking Model

    Directory of Open Access Journals (Sweden)

    Madjid Tavana

    2007-01-01

    Full Text Available Ordinal consensus ranking problems have received much attention in the management science literature. A problem arises in situations where a group of k decision makers (DMs is asked to rank order n alternatives. The question is how to combine the DM rankings into one consensus ranking. Several different approaches have been suggested to aggregate DM responses into a compromise or consensus ranking; however, the similarity of consensus rankings generated by the different algorithms is largely unknown. In this paper, we propose a new hybrid distance-based ideal-seeking consensus ranking model (DCM. The proposed hybrid model combines parts of the two commonly used consensus ranking techniques of Beck and Lin (1983 and Cook and Kress (1985 into an intuitive and computationally simple model. We illustrate our method and then run a Monte Carlo simulation across a range of k and n to compare the similarity of the consensus rankings generated by our method with the best-known method of Borda and Kendall (Kendall 1962 and the two methods proposed by Beck and Lin (1983 and Cook and Kress (1985. DCM and Beck and Lin's method yielded the most similar consensus rankings, whereas the Cook-Kress method and the Borda-Kendall method yielded the least similar consensus rankings.

  17. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-01

    Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

  18. A Hybrid Readout Solution for GaN-Based Detectors Using CMOS Technology

    Directory of Open Access Journals (Sweden)

    Preethi Padmanabhan

    2018-02-01

    Full Text Available Gallium nitride (GaN and its alloys are becoming preferred materials for ultraviolet (UV detectors due to their wide bandgap and tailorable out-of-band cutoff from 3.4 eV to 6.2 eV. GaN based avalanche photodiodes (APDs are particularly suitable for their high photon sensitivity and quantum efficiency in the UV region and for their inherent insensitivity to visible wavelengths. Challenges exist however for practical utilization. With growing interests in such photodetectors, hybrid readout solutions are becoming prevalent with CMOS technology being adopted for its maturity, scalability, and reliability. In this paper, we describe our approach to combine GaN APDs with a CMOS readout circuit, comprising of a linear array of 1 × 8 capacitive transimpedance amplifiers (CTIAs, implemented in a 0.35 µm high voltage CMOS technology. Further, we present a simple, yet sustainable circuit technique to allow operation of APDs under high reverse biases, up to ≈80 V with verified measurement results. The readout offers a conversion gain of 0.43 µV/e−, obtaining avalanche gains up to 103. Several parameters of the CTIA are discussed followed by a perspective on possible hybridization, exploiting the advantages of a 3D-stacked technology.

  19. A Hybrid Readout Solution for GaN-Based Detectors Using CMOS Technology †

    Science.gov (United States)

    Hancock, Bruce; Nikzad, Shouleh; Bell, L. Douglas; Kroep, Kees; Charbon, Edoardo

    2018-01-01

    Gallium nitride (GaN) and its alloys are becoming preferred materials for ultraviolet (UV) detectors due to their wide bandgap and tailorable out-of-band cutoff from 3.4 eV to 6.2 eV. GaN based avalanche photodiodes (APDs) are particularly suitable for their high photon sensitivity and quantum efficiency in the UV region and for their inherent insensitivity to visible wavelengths. Challenges exist however for practical utilization. With growing interests in such photodetectors, hybrid readout solutions are becoming prevalent with CMOS technology being adopted for its maturity, scalability, and reliability. In this paper, we describe our approach to combine GaN APDs with a CMOS readout circuit, comprising of a linear array of 1 × 8 capacitive transimpedance amplifiers (CTIAs), implemented in a 0.35 µm high voltage CMOS technology. Further, we present a simple, yet sustainable circuit technique to allow operation of APDs under high reverse biases, up to ≈80 V with verified measurement results. The readout offers a conversion gain of 0.43 µV/e−, obtaining avalanche gains up to 103. Several parameters of the CTIA are discussed followed by a perspective on possible hybridization, exploiting the advantages of a 3D-stacked technology. PMID:29401655

  20. A Hybrid Readout Solution for GaN-Based Detectors Using CMOS Technology.

    Science.gov (United States)

    Padmanabhan, Preethi; Hancock, Bruce; Nikzad, Shouleh; Bell, L Douglas; Kroep, Kees; Charbon, Edoardo

    2018-02-03

    Gallium nitride (GaN) and its alloys are becoming preferred materials for ultraviolet (UV) detectors due to their wide bandgap and tailorable out-of-band cutoff from 3.4 eV to 6.2 eV. GaN based avalanche photodiodes (APDs) are particularly suitable for their high photon sensitivity and quantum efficiency in the UV region and for their inherent insensitivity to visible wavelengths. Challenges exist however for practical utilization. With growing interests in such photodetectors, hybrid readout solutions are becoming prevalent with CMOS technology being adopted for its maturity, scalability, and reliability. In this paper, we describe our approach to combine GaN APDs with a CMOS readout circuit, comprising of a linear array of 1 × 8 capacitive transimpedance amplifiers (CTIAs), implemented in a 0.35 µm high voltage CMOS technology. Further, we present a simple, yet sustainable circuit technique to allow operation of APDs under high reverse biases, up to ≈80 V with verified measurement results. The readout offers a conversion gain of 0.43 µV/e - , obtaining avalanche gains up to 10³. Several parameters of the CTIA are discussed followed by a perspective on possible hybridization, exploiting the advantages of a 3D-stacked technology.

  1. A Hybrid Scheme Based on Pipelining and Multitasking in Mobile Application Processors for Advanced Video Coding

    Directory of Open Access Journals (Sweden)

    Muhammad Asif

    2015-01-01

    Full Text Available One of the key requirements for mobile devices is to provide high-performance computing at lower power consumption. The processors used in these devices provide specific hardware resources to handle computationally intensive video processing and interactive graphical applications. Moreover, processors designed for low-power applications may introduce limitations on the availability and usage of resources, which present additional challenges to the system designers. Owing to the specific design of the JZ47x series of mobile application processors, a hybrid software-hardware implementation scheme for H.264/AVC encoder is proposed in this work. The proposed scheme distributes the encoding tasks among hardware and software modules. A series of optimization techniques are developed to speed up the memory access and data transferring among memories. Moreover, an efficient data reusage design is proposed for the deblock filter video processing unit to reduce the memory accesses. Furthermore, fine grained macroblock (MB level parallelism is effectively exploited and a pipelined approach is proposed for efficient utilization of hardware processing cores. Finally, based on parallelism in the proposed design, encoding tasks are distributed between two processing cores. Experiments show that the hybrid encoder is 12 times faster than a highly optimized sequential encoder due to proposed techniques.

  2. Springback prediction in sheet metal forming process based on the hybrid SA

    International Nuclear Information System (INIS)

    Guo Yuqin; Jiang Hong; Wang Xiaochun; Li Fuzhu

    2005-01-01

    In terms of the intensive similarity between the sheet metal forming-springback process and that of the annealing of metals, it is suggested that the simulation of the sheet metal forming process is performed with the Nonlinear FEM and the springback prediction is implemented by solving the large-scale combinational optimum problem established on the base of the energy descending and balancing in deformed part. The BFGS-SA hybrid SA approach is proposed to solve this problem and improve the computing efficiency of the traditional SA and its capability of obtaining the global optimum solution. At the same time, the correlative annealing strategies for the SA algorithm are determined in here. By comparing the calculation results of sample part with those of experiment measurement at the specified sections, the rationality of the schedule of springback prediction used and the validity of the BFGS-SA algorithm proposed are verified

  3. A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

    Directory of Open Access Journals (Sweden)

    Qingyang Zhang

    2015-02-01

    Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

  4. Modular approach for conversion to the ion-hybrid wave and α gyroresonance

    International Nuclear Information System (INIS)

    Kaufman, A.N.; Morehead, J.J.; Brizard, A.J.; Tracy, E.R.

    1997-01-01

    Linear conversion of an incoming magnetosonic wave (a.k.a. fast or compressional wave) to an ion-hybrid wave can be considered as a 3-step process in ray phase space. This is demonstrated by casting the cold-fluid model into the Friedland-Kaufman normal form for linear mode conversion. First, the incoming magnetosonic ray (MSR) converts a fraction of its action to an intermediate ion-hybrid ray (IHR), with the transmitted ray proceeding through the conversion layer. The IHR propagates in k-space to a second conversion point, where it converts in turn a fraction of its action into a reflected MSR, with the remainder of the its action constituting the converted IHR. The modular approach gives exact agreement with the more standard Budden formulation for the transmission, reflection and conversion coefficients, but has the important advantage of exposing the intermediate IHR. The existence of the intermediate IHR has important physical consequences as it can resonate with α particles. We estimate the time-integrated damping coefficient between the two conversions and show that ∫γdt is of order -100, thus the IH wave is completely annihilated between conversions and transfers its energy to the α close-quote s. This suggests that proposals to use the IH mode for current drive or DT heating are likely to fail in the presence of fusion α close-quote s. copyright 1997 American Institute of Physics

  5. Electrochemical biosensing based on polypyrrole/titania nanotube hybrid

    Energy Technology Data Exchange (ETDEWEB)

    Xie, Yibing, E-mail: ybxie@seu.edu.cn; Zhao, Ye

    2013-12-01

    The glucose oxidase (GOD) modified polypyrrole/titania nanotube enzyme electrode is fabricated for electrochemical biosensing application. The titania nanotube array is grown directly on a titanium substrate through an anodic oxidation process. A thin film of polypyrrole is coated onto titania nanotube array to form polypyrrole/titania nanotube hybrid through a normal pulse voltammetry process. GOD-polypyrrole/titania nanotube enzyme electrode is prepared by the covalent immobilization of GOD onto polypyrrole/titania nanotube hybrid via the cross-linker of glutaraldehyde. The morphology and microstructure of nanotube electrodes are characterized by field emission scanning electron microscopy and Fourier transform infrared analysis. The biosensing properties of this nanotube enzyme electrode have been investigated by means of cyclic voltammetry and chronoamperometry. The hydrophilic polypyrrole/titania nanotube hybrid provides highly accessible nanochannels for GOD encapsulation, presenting good enzymatic affinity. As-formed GOD-polypyrrole/titania nanotube enzyme electrode well conducts bioelectrocatalytic oxidation of glucose, exhibiting a good biosensing performance with a high sensitivity, low detection limit and wide linear detection range. - Graphical abstract: The schematic diagram presents the fabrication of glucose oxidase modified polypyrrole/titania (GOD-PPy/TiO{sub 2}) nanotube enzyme electrode for biosensing application. - Highlights: • Hydrophilic polypyrrole/titania nanotube hybrid is well used as biosensing substrate. • Polypyrrole promotes GOD immobilization on titania nanotubes via glutaraldehyde. • GOD-polypyrrole/titania enzyme electrode shows good bioelectrocatalytic reactivity.

  6. Electrochemical biosensing based on polypyrrole/titania nanotube hybrid

    International Nuclear Information System (INIS)

    Xie, Yibing; Zhao, Ye

    2013-01-01

    The glucose oxidase (GOD) modified polypyrrole/titania nanotube enzyme electrode is fabricated for electrochemical biosensing application. The titania nanotube array is grown directly on a titanium substrate through an anodic oxidation process. A thin film of polypyrrole is coated onto titania nanotube array to form polypyrrole/titania nanotube hybrid through a normal pulse voltammetry process. GOD-polypyrrole/titania nanotube enzyme electrode is prepared by the covalent immobilization of GOD onto polypyrrole/titania nanotube hybrid via the cross-linker of glutaraldehyde. The morphology and microstructure of nanotube electrodes are characterized by field emission scanning electron microscopy and Fourier transform infrared analysis. The biosensing properties of this nanotube enzyme electrode have been investigated by means of cyclic voltammetry and chronoamperometry. The hydrophilic polypyrrole/titania nanotube hybrid provides highly accessible nanochannels for GOD encapsulation, presenting good enzymatic affinity. As-formed GOD-polypyrrole/titania nanotube enzyme electrode well conducts bioelectrocatalytic oxidation of glucose, exhibiting a good biosensing performance with a high sensitivity, low detection limit and wide linear detection range. - Graphical abstract: The schematic diagram presents the fabrication of glucose oxidase modified polypyrrole/titania (GOD-PPy/TiO 2 ) nanotube enzyme electrode for biosensing application. - Highlights: • Hydrophilic polypyrrole/titania nanotube hybrid is well used as biosensing substrate. • Polypyrrole promotes GOD immobilization on titania nanotubes via glutaraldehyde. • GOD-polypyrrole/titania enzyme electrode shows good bioelectrocatalytic reactivity

  7. Model-based health monitoring of hybrid systems

    CERN Document Server

    Wang, Danwei; Low, Chang Boon; Arogeti, Shai

    2013-01-01

    Offers in-depth comprehensive study on health monitoring for hybrid systems Includes new concepts, such as GARR, mode tracking and multiple failure prognosis Contains many examples, making the developed techniques easily understandable and accessible Introduces state-of-the-art algorithms and methodologies from experienced researchers

  8. Hybrid structures based on montmorillonite/modified starch intercalate

    Czech Academy of Sciences Publication Activity Database

    Duchek, P.; Špírková, Milena

    2010-01-01

    Roč. 104, č. 9 (2010), s. 885 ISSN 0009-2770. [International Conference on Polysaccharides-Glycoscience /6./. 29.09.2010-1.10.2010, Praha] R&D Projects: GA AV ČR(CZ) IAAX08240901 Institutional research plan: CEZ:AV0Z40500505 Keywords : organic/inorganic hybrides * montmorillonite Subject RIV: JI - Composite Materials

  9. Microcantilver-based DNA hybridization sensors for Salmonella identification

    Directory of Open Access Journals (Sweden)

    Carlo Ricciardi

    2012-02-01

    Full Text Available The detection of pathogenic microorganisms in foods remains a challenging since the safety of foodstuffs has to be ensured by the food producing companies. Conventional methods for the detection and identification of bacteria mainly rely on specific microbiological and biochemical identification. Biomolecular methods, are commonly used as a support for traditional techniques, thanks to their high sensitivity, specificity and not excessive costs. However, new methods like biosensors for example, can be an exciting alternative to the more traditional tecniques for the detection of pathogens in food. In this study we report Salmonella enterica serotype Enteritidis DNA detection through a novel class of label-free biosensors: microcantilevers (MCs. In general, MCs can operate as a microbalance and is used to detect the mass of the entities anchored to the cantilever surface using the decrease in the resonant frequency. We use DNA hybridization as model reaction system and for this reason, specific single stranded probe DNA of the pathogen and three different DNA targets (single-stranded complementary DNA, PCR product and serial dilutions of DNA extracted from S. Enteritidis strains were applied. Two protocols were reported in order to allow the probe immobilization on cantilever surface: i MC surface was functionalized with 3-aminopropyltriethoxysilane and glutaraldehyde and an amino-modified DNA probe was used; ii gold-coated sensors and thiolated DNA probes were used in order to generate a covalent bonding (Th-Au. For the first one, measures after hybridization with the PCR product showed related frequency shift 10 times higher than hybridization with complementary probe and detectable signals were obtained at the concentrations of 103 and 106 cfu/mL after hybridization with bacterial DNA. There are currently optimizations of the second protocol, where preliminary results have shown to be more uniform and therefore more precise within each of the

  10. Nanostructured ZnO-based biosensor: DNA immobilization and hybridization

    Directory of Open Access Journals (Sweden)

    Ahmed Mishaal Mohammed

    2017-09-01

    Full Text Available An electrochemical DNA biosensor was successfully fabricated by using (3-aminopropyl triethoxysilane (APTES with zinc oxide (ZnO nanorods synthesized using microwave-assisted chemical bath deposition method on thermally oxidized SiO2 thin films. The structural quality and morphology of the ZnO nanorods were determined by employing scanning electron microscopy (SEM and X-ray diffraction (XRD, which show a hexagonal wurtzite structure with a preferred orientation along the (101 direction. The surface of the SiO2 thin films was chemically modified with ZnO. Label-free detection DNA immobilization and hybridization were performed using potassium hexacyanoferrate with cyclic voltammetry (CV measurements. The capacitance, permittivity, and conductivity profiles of the fabricated sensor clearly indicate DNA immobilization and hybridization. Results show that the capacitance values of bare, ZnO- modified surface immobilization, and target DNA hybridization were 46×10−12F, 47×10−8F, 27μF, and 17μF, respectively, at 1Hz. The permittivity measurement increased from 3.94×103 to 251×103 and 165×103 at the frequency range of approximately 200 to 1Hz for bare and DNA immobilization and hybridization, respectively. The measured conductivity values for the bare, ZnO, immobilized, and hybridization device were 2.4×10−9, 10×10−8, 1.6×10−7, and 1.3×10−7Scm−1, respectively. Keywords: Zinc oxide, Biosensor, Capacitance, Permittivity, Conductivity

  11. Methods for Reachability-based Hybrid Controller Design

    Science.gov (United States)

    2012-05-10

    approaches for airport runways ( Teo and Tomlin, 2003). The results of the reachability calculations were validated in extensive simulations as well as...UAV flight experiments (Jang and Tomlin, 2005; Teo , 2005). While the focus of these previous applications lies largely in safety verification, the work...B([15, 0],a0)× [−π,π])\\ V,∀qi ∈ Q, where a0 = 30m is the protected radius (chosen based upon published data of the wingspan of a Boeing KC -135

  12. Multiway study of hybridization in nanoscale semiconductor labeled DNA based on fluorescence resonance energy transfer

    DEFF Research Database (Denmark)

    Gholami, Somayeh; Kompany Zare, Mohsen

    2013-01-01

    donor-QD acceptor) upon hybridization with a label free target was monitored by two-dimensional photoluminescence excitation spectroscopy (2D-PLE). Detection of a target oligonucleotide strand, using sandwiched nanoassembly in a separation-free format, was performed with the appearance of a new feature...... and model based analysis of 2D-PLE data was implemented by means of PAR-AFAC and hard trilinear decomposition (HTD), allowing to fit a proper model for FRET-based sandwich DNA hybridization systems. This study is the first successful application of a multiway chemometric technique to consider FRET based DNA...... hybridization in sandwiched nanoassemblies. A multi-equilibria model was properly fitted to the data and confirmed there is a competition between ternary and binary complex formation. Equilibrium constants of DNA hybridization in sandwiched nanoassemblies were estimated for the first time. Equilibrium constants...

  13. An Event-based Approach to Hybrid Systems Diagnosability

    Data.gov (United States)

    National Aeronautics and Space Administration — Diagnosability is an important issue in the design of diagnostic systems, because it helps identify whether sufficient information is available to distinguish all...

  14. Numerical Prediction of Combustion-induced Noise using a hybrid LES/CAA approach

    Science.gov (United States)

    Ihme, Matthias; Pitsch, Heinz; Kaltenbacher, Manfred

    2006-11-01

    Noise generation in technical devices is an increasingly important problem. Jet engines in particular produce sound levels that not only are a nuisance but may also impair hearing. The noise emitted by such engines is generated by different sources such as jet exhaust, fans or turbines, and combustion. Whereas the former acoustic mechanisms are reasonably well understood, combustion-generated noise is not. A methodology for the prediction of combustion-generated noise is developed. In this hybrid approach unsteady acoustic source terms are obtained from an LES and the propagation of pressure perturbations are obtained using acoustic analogies. Lighthill's acoustic analogy and a non-linear wave equation, accounting for variable speed of sound, have been employed. Both models are applied to an open diffusion flame. The effects on the far field pressure and directivity due to the variation of speed of sound are analyzed. Results for the sound pressure level will be compared with experimental data.

  15. Optimum design of brake friction material using hybrid entropy-GRA approach

    Directory of Open Access Journals (Sweden)

    Kumar Naresh

    2016-01-01

    Full Text Available The effect of Kevlar and natural fibres on the performance of brake friction materials was evaluated. Four friction material specimens were developed by varying the proportion of Kevlar and natural fibres. Two developed composite contained 5-10 wt.% of Kevlar fibre while in the other two the Kevlar fibre was replaced with same amount of natural fibre. SAE J661 protocol was used for the assessment of the tribological properties on a Chase testing machine. Result shows that the specimens containing Kevlar fibres shows higher friction and wear performance, whereas Kevlar replacement with natural fibre resulted in improved fade, recovery and friction fluctuations. Further hybrid entropy-GRA (grey relation analysis approach was applied to select the optimal friction materials using various performance defining attributes (PDA including friction, wear, fade, recovery, friction fluctuations and cost. The friction materials with 10 wt% of natural fibre exhibited the best overall quality.

  16. A Hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

    Energy Technology Data Exchange (ETDEWEB)

    Morton, April M [ORNL; Nagle, Nicholas N [ORNL; Piburn, Jesse O [ORNL; Stewart, Robert N [ORNL; McManamay, Ryan A [ORNL

    2017-01-01

    As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.

  17. Preparation, characterisation, engine performance and emission characteristics of coconut oil based hybrid fuels

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Pranil J.; Singh, Anirudh [Division of Physics, School of Engineering and Physics, Faculty of Science, Technology and Environment, University of the South Pacific, 325 Fletcher Road, Suva (Fiji); Khurma, Jagjit [Division of Chemistry, School of Biological, Chemical and Environmental Sciences, Faculty of Science, Technology and Environment, University of the South Pacific, Suva (Fiji)

    2010-09-15

    In this study, hybrid fuels consisting of coconut oil, aqueous ethanol and a surfactant (butan-1-ol) were prepared and tested as a fuel in a direct injection diesel engine. After determining fuel properties such as the density, viscosity and gross calorific values of these fuels, they were used to run a diesel engine. The engine performance and exhaust emissions were investigated and compared with that of diesel. The experimental results show that the efficiency of the hybrid fuels is comparable to that of diesel. As the viscosity of the hybrid fuels decreased and approached that of diesel, the efficiency increased progressively towards that of diesel. The exhaust emissions were lower than those for diesel, except carbon monoxide emissions, which increased. Hence, it is concluded that these hybrid fuels can be used successfully as an alternative fuel in diesel engines without any modifications. Their completely renewable nature ensures that they are environmentally friendly with regard to their emissions characteristics. (author)

  18. Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

    Science.gov (United States)

    Abdolmaleki, Azizeh; Ghasemi, Jahan B

    2017-01-01

    Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Evaluation of wind power generation potential using a three hybrid approach for households in Ardebil Province, Iran

    International Nuclear Information System (INIS)

    Qolipour, Mojtaba; Mostafaeipour, Ali; Shamshirband, Shahaboddin; Alavi, Omid; Goudarzi, Hossein; Petković, Dalibor

    2016-01-01

    Highlights: • Technical–economic feasibility of small wind turbines for six areas in Ardabil province of Iran was investigated. • Hybrid approach of Data Envelopment Analysis, Balanced Scorecard, and Game Theory was analyzed. • HOMER software was used for economic evaluation. • Technical–economic feasibility was studied using wind speed data during 2008–2014. • The areas of Airport, Nir, Namin, BilaSavar, Firozabad and Ardabil were ranked from first to last, respectively. - Abstract: The objective of the present paper is to conduct a thorough technical–economic evaluation for the construction of small wind turbines in six areas within Ardabil province of Iran using the Hybrid Optimization of Multiple Energy Resources software, and also to rank these areas by a hybrid approach composed of Data Envelopment Analysis, Balanced Scorecard, and Game Theory methodologies. Higher accuracy of the proposed hybrid approach and its ability to properly detect the relationships between the decision-making components make it preferable over the simple Data Envelopment Analysis method. Technical–economic feasibility study is conducted by analyzing wind speed data for period from 2008 to 2014 using Hybrid Optimization of Multiple Energy Resources software. In the next step, the type of equipment used in the design, benefit, costs, total net costs, depreciation and amount of generated electricity are obtained separately for each location. The results show that; Airport, Nir, Namin, Bilasavar, Firozabad and Ardabil were rank first to last respectively.

  20. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

    KAUST Repository

    McCabe, Matthew

    2017-12-06

    With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory ‘predictor’ variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association