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作者： Sripan, Rungaroon Suksawat, Bandit Technical Pedagogic Research and Development Program Graduate School King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Teacher Training in Mechanical Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9781424474530

This paper proposes the students' learning assessment by using fuzzy logic. The framework of practical learning system for computer discipline is also presented to explain a conceptual design of an intelligent tutorial system. The proposed framework composes of six components including interface module, domain knowledge, inference engine, student module, mentor module and pedagogical module. The inference engine performed students' group classification form on-line pre-test examination before starting the practical worksheet application. Two input parameters consisting of percentage of score and time were established as inputs for membership functions of fuzzy logic system. The twenty-five fuzzy rules were created for the proposed method by experts. The defuzzification of output membership functions, including good, fair and improve were performed by using the centroid method. In this paper, 26 students were tested in order to compare the students' learning performance, assessed by fuzzy logic and t-score method. The results revealed that the proposed method was a flexible process to classify students' learning group based on the objectives of subject and the real time performance comparing with t-score method. ©ICROS.

关键词： Membership functions

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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering University of Brasilia Brasilia D.F Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F Brazil

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide suboptimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems.

关键词： Mathematics Field programmable gate arrays Table lookup

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Evaluation of film adhesion to substrates by means of surface acoustic wave dispersion

Evaluation of film adhesion to substrates by means of surfac...

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作者： Du, Jikai Tittmann, Bernhard R. Ju, Hyeong Sick Department of Engineering Science and Mechanics Pennsylvania State University University Park PA 16802 United States Graduate Program in Acoustics Pennsylvania State University University Park PA 16802 United States Mechanical Engineering Department South Dakota State University Box 2219 SCEH 236 Brookings SD 57007-0294 United States

For thin film structures acoustically classified as slow-on-fast systems, modeling and evaluation of their interfacial condition are known to be very complex and difficult due to dispersion and multi-mode excitation of acoustic waves. This paper presents a quantitative model and a reliable measurement procedure established for adhesion evaluation of such film structures. An effective interface model employing a virtual intermediate layer is utilized for the dispersion prediction of the surface acoustic wave, which is affected by various interfacial conditions. Through acoustic microscopy experiments, this model presents a potential method to classify the bonding condition. Comparisons with a destructive scratch test and an acoustic imaging verify the failure mode of the film structure. © 2010 Elsevier B.V. All rights reserved.

关键词： Adhesion

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Multivariable nonlinear boiler power plant identification through neural networks and Particle Swarm Optimization approaches

Multivariable nonlinear boiler power plant identification th...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Fábio A. Guerra Helon V. H. Ayala André E. Lazzaretti Marcio R. Sans Leandro S. Coelho Cesar A. Tacla Eletroelectronics Department (DPEE) Electrical System Division (DVSE) Centro Politécnico da UFPR LACTEC-Institute of Technology for Development Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program (LAS/PPGEPS) Pontifical Catholic University of Paraná Curitiba Parana Brazil Graduate Program in Eletrical Engineering and Industrial Informatics (CPGEI) Federal University of Technology Parana Curitiba Parana Brazil

The identification of nonlinear systems with artificial neural networks models has been successfully used in many applications. Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models for nonlinear systems is vital in many fields of engineering. The Radial Basis Function Neural Network (RBF-NN) is a powerful approach for nonlinear identification and can be improved using Particle Swarm Optimization (PSO) approaches. This paper presents a multivariable nonlinear system identification using RBF-NN combined with standard PSO and Constriction Factor PSO (CFPSO) approaches in order to determine the RBF-NN parameters. RBF-NN is considered to be a good choice for black-box modeling problems due to its rapid learning capacity and, therefore, has been applied successfully to nonlinear time series modeling and nonlinear identification. On the other hand, PSO was inspired by the choreography of bird flocks and fish schools and can be seen as a distributed behavior algorithm that performs multidimensional search. Furthermore, promising simulation results from performance analysis of the proposed RBF-NN with PSO training approaches are presented and discussed in this paper showing promising results.

关键词： Artificial neural networks Boilers Neurons Modeling Power generation Optimization Nonlinear systems

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The contribution of bilateral working memory and parietal activation in audiovisual voluntary spatial and temporal attention

The contribution of bilateral working memory and parietal ac...

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2010 IEEE/ICME International Conference on Complex Medical engineering, CME2010

作者： Li, Chunlin Wu, Jinglong Han, Hongbin Chui, Dehua Department of Energy Systems Engineering Division of Industrial Innovation Sciences Graduate School of Natural Science and Technology Japan Department of Mechanical Engineering Faculty of Engineering Okayama University Japan 3-1-1 Tsushima-naka Okayama Japan International WIC Institute Beijing University of Technology China Radiology Peking University Third Hospital Beijing China Neuroscience Research Institute Third Hospital of Peking University China

ISBN: (纸本)9781424468430

Visual cue in the top-down attention mechanism was investigated that it could effectively improve the target cognition reaction quality. Recent brain studies showed that the right dorsolateral prefrontal cortex (rDLPFC) played an important role to keep the task-relevant information and task rule during tasks. In this study, we focused on the neural network of attention and the function of rDLPFC, whether it modulates the attention during visual and auditory divided spatial and temporal attention. We used a top-down attention paradigm in which a visual cue directs the attention of participants to both visual and auditory target stimulus in a spatial (attention was directed to unilateral target distinctly) in VAS (visual auditory spatial) task and a temporal (attention was directed to a time point alternatively although humans are difficult to pay attention to a time point clearly) in VAT (visual auditory temporal) attention task. A non-informative cue task was also carried out in VAN (visual auditory neutral) task in order to observe the activation of working memory. Behavioral data showed no significant difference between VAS, VAT and VAN by twos. The parietal and frontal cortices were activated in both the VAS and VAT attention tasks. Furthermore, in the VAS and VAT attention task, it used a bilateral frontal-parietal network to modulate the cognition of visual and auditory target. But inthe VAT attention task, right parietal cortex was not activ ated as well as the VAS task. The activation of DLPFC shows st ronger in the spatial attention task than the temporal task, and in a non-informative cue task, the activation of DLPFC is the lo west in the three tasks. © 2010 IEEE.

关键词： Chemical activation

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Fault-tolerant control of trains based on hybrid redundancy

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ICIC Express Letters 2010年第6 A期4卷 2139-2146页

作者： Takagi, Taro Takahashi, Masanori Graduate School of Industrial Engineering Tokai University 9-1-1 Toroku Kumamoto 862-8652 Japan Department of Mechanical Systems Engineering Tokai University 9-1-1 Toroku Kumamoto 862-8652 Japan

This paper presents a fault-tolerant control system (FTCS) for electric trains with actuator failures. The proposed FTCS is based on a hybrid of static and dynamic redundancies, and so has the two control modes: the static redundancy mode and the dynamic one. Therefore, in both the two modes, a unity high-gain feedback controlleris introduced to attain the stabilization and the asymptotic tracking. Furthermore, in this paper, to select an appropriate mode, a switching logic is designed with information about the control performance. Thus, the logic-based switching can avoid a fatal and inhibitory accident caused by the failure even if it is unknown. This paper also shows several numerical simulations to confirm the effectiveness of the FTCS. ICIC International © 2010 ISSN 1881-803X.

关键词： Fault tolerance

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A two-dimensional expectation-perception analysis for RFID benefits in supply chain management

A two-dimensional expectation-perception analysis for RFID b...

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International Conference on Computers & industrial engineering (CIE)

作者： Pao-Tiao Chuang J. Wesley Barnes Department of Asia-Pacific Industrial and Business Management National University of Kaohsiung Kaohsiung Taiwan Graduate Program in Operations Research and Industrial Engineering University of Texas Austin Austin TX USA

This paper explores two important themes in the implementation of RFID in supply chain management: (1) analyzing differences between actual (perceived) and potential (expected) key benefits to see if there are benefit shortfalls for the key performances; and (2) performing a two-dimensional expectation-perception analysis (EPA) for the purpose of identifying competitive niche and strategically allocating and adjusting the company's resources. Empirical data were collected through surveys of executives of selected Taiwan based companies who had experienced RFID adoption in the supply chain practices. Finally, managerial implications and suggestions were provided for companies and industries that may be considering the adoption of RFID in SCM.

关键词： Radiofrequency identification Companies Supply chains Real time systems Materials handling Supply chain management

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Clonal Selection Algorithm with Oppositional Approach Applied to Trajectory Planning of a Robotic Manipulator

Clonal Selection Algorithm with Oppositional Approach Applie...

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Brazilian Symposium on Neural Networks

作者： Daniel C. Jeronymo Yuri C. C. Borges Leandro dos S. Coelho Department of Electrical Engineering Federal University of Paraná (UFPR) Curitiba Brazil Telecommunications and Control Department Polytechnique School University of São Paulo Sao Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

A complex optimization problem in robotics is the optimum movement between certain physical configurations while minimizing certain criteria. The fundamental objective in movement planning for robot arms is to minimize movement time, total distance, joint torque, avoiding obstacles and the robot arm itself as an obstacle. This paper presents a study on clonal selection algorithm (CSA) and CSA with oppositional approach to optimize the 2D point to point movement of a 3-links robot arm. Simulations with MATLAB of the problem were implemented with both techniques to minimize an objective function depending on the mentioned criteria. Results of both methods are presented, showing that the CSA with oppositional approach presented better results than the classical CSA in terms of convergence and response quality.

关键词： Manipulators Joints Planning Torque Trajectory Optimization

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Forecasting electricity prices using a RBF neural network With GARCH errors

Forecasting electricity prices using a RBF neural network Wi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Andre Alves Portela Santos Leandro dos Santos Coelho Carlos Eduardo Klein Department of Statistics Universidad Carlos III de Madrid Spain Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil CNPq National Council for Scientific and Technological Development Brazil

In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose a novel estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide hour-ahead point and direction-of-change forecasts of the Spanish electricity pool prices.

关键词： Load modeling Artificial neural networks Predictive models

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Frontiers in neuromedical engineering

Frontiers in neuromedical engineering

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IEEE/ICME International Conference on Complex Medical engineering, CME

作者： Jinglong Wu Jiajia Yang Chunlin Li Tianyi Yan Haibo Wang Xiujun Li International WIC Institute Beijing University of Technology China Department of Mechanical Engineering and Faculty of Engineering Okayama University Okayama Japan Department of Energy Systems Engineering Division of Industrial Innovation Sciences Graduate School of Natural Science and Technology Okayama University Japan

In this article, we introduced some topics relevant to neuromedical engineering and used several measurement methods. In the future, we will continue to develop new technologies and search for new ideas for diagnosing... 详细信息

关键词： Nervous system

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