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International Electric Machines & Drives Conference

作者： Andre A. Silva Ali M. Bazzi Shalabh Gupta Laboratory of Intelligent Networks and Knowledge-Perception Systems (LINKS) ooth Engineering Center for Advanced Technology (BECAT) University of Connecticut Storrs CT USA Advanced Power Electronics and Electric Drives Lab (APEDL) Center for Clean Energy Engineering University of Connecticut Storrs CT USA

This paper applies machine learning techniques to fault diagnosis in electric motor drives. As faults in motor drives can cause safety hazards in applications such as electric traction, propulsion, aircraft, and others, it is desired to diagnose the fault, i.e., detect it and isolate it, in order to recover or engage safe-mode operation. Machine learning methods are divided into three main steps: 1) Feature extraction using Principal Component Analysis (PCA);2) Classification using the k-Nearest Neighbor (k-NN) or Probabilistic Neural Network (PNN) methods;and 3) Classifier performance evaluation using the Cross-Validation (CV) method. While electric machine, inverter, and sensor faults are introduced, the supervised learning algorithms are applied to four case studies where two fault modes occur in a current sensor, and two occur in the speed encoder. Classification accuracy, i.e., the ability to diagnose a fault, for all four cases is shown to exceed 98%. The paper also investigates a load profile used in automotive driving cycles which produces richer dynamic responses that are more interesting from an application perspective. The final goal is to implement these algorithms in real-time such that fault diagnosis can be used to isolate drive faults, especially sensor faults, and operate in a safe mode.

关键词： PCA PNN CV

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Evaluation of fall detection classification approaches

Evaluation of fall detection classification approaches

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International Conference on intelligent and advanced systems, ICIAS

作者： Hamideh Kerdegari Khairulmizam Samsudin Abdul Rahman Ramli Saeid Mokaram Computer Systems Laboratory Department of Computer and Communication Systems Universiti Putra Malaysia Serdang Malaysia Intelligent Systems and Robotics Laboratory Institute of Advanced Technology Universiti Putra Malaysia Serdang Malaysia

As we grow old, our desire for being independence does not decrease while our health needs to be monitored more frequently. Accidents such as falling can be a serious problem for the elderly. An accurate automatic fall detection system can help elderly people be safe in every situation. In this paper a waist worn fall detection system has been proposed. A tri-axial accelerometer (ADXL345) was used to capture the movement signals of human body and detect events such as walking and falling to a reasonable degree of accuracy. A set of laboratory-based falls and activities of daily living (ADL) were performed by healthy volunteers with different physical characteristics. This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. The aim of this paper is to investigate the performance of different classification algorithms for a set of recorded acceleration data. The algorithms are Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR. The acceleration data with a total data of 6962 instances and 29 attributes were used to evaluate the performance of the different classification algorithm. Results show that the Multilayer Perceptron algorithm is the best option among other mentioned algorithms, due to its high accuracy in fall detection.

关键词： Acceleration Feature extraction Accuracy Support vector machines Senior citizens Accelerometers Classification algorithms

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Mobile robots communication and control framework for USARSim

Mobile robots communication and control framework for USARSi...

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International Conference on intelligent and advanced systems, ICIAS

作者： Saeid Mokaram Khairulmizam Samsudin Abdul Rahman Ramli Hamideh Kerdegari Computer Systems Laboratory Department of Computer and Communication Systems Universiti Putra Malaysia Serdang Malaysia Intelligent Systems and Robotics Laboratory Institute of Advanced Technology Universiti Putra Malaysia Serdang Malaysia

In recent years there have been intensive efforts in robotics researches from the earliest stages of education. Subject of this paper is a powerful mobile robot communication and control framework for USARSim simulator that can be used both for research and education. Mobile Robots Communication and Control Framework (MCCF) is developed in order to offer faster and easier communication process with the USARSim server within Matlab that differentiates it from most existing basic open source control interfaces. Most notably, it takes the advantages of easy integration with other analysis and control methods that have been provided in Matlab tool-boxes. MCCF enables communication and control of a wide range of robots platforms including but not limited to wheeled-robots, legged-robots, submarine robots and aerial robots. In this paper we describe its general architecture, features and examples of utilization for researchers who are interested in mobile robot simulations for education and research.

关键词： Robot sensing systems MATLAB Mobile robots Servers Java

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New stability criteria for linear systems with time-varying interval delay

New stability criteria for linear systems with time-varying ...

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2012 UKACC International Conference on Control, CONTROL 2012

作者： Sun, Jian Chen, Jie Liu, G.P. School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex Systems Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China Faculty of Advanced Technology University of Glamorgan Pontypridd CF37 1DL United Kingdom CTGT Center Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9781467315609

This paper is concerned with the problem of stability of systems with time-varying delay in a given interval. A novel Lyapunov-Krasovskii functional is proposed to obtain new stability conditions. Some triple integral terms are introduced in the Lyapunov-Krasovskii functional and the information on the lower bound on the delay are sufficiently used. New delay-dependent stability criteria are derived using integral inequalities and formulated in terms of linear matrix inequality (LMI). Comparing numerical examples show that the proposed criteria yield a larger upper bound on the delay for a given lower bound on the delay than existing results. © 2012 IEEE.

关键词： Stability criteria

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Leader-follower synchronization for complex dynamical networks via sampled-data control

Leader-follower synchronization for complex dynamical networ...

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31st Chinese Control Conference, CCC 2012

作者： Wangli, He Feng, Qian Han, Qing-Long Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China Centre for Intelligent and Networked Systems Central Queensland University Rockhampton QLD 4702 Australia School of Information and Communication Technology Central Queensland University Rockhampton QLD 4702 Australia

ISBN: (纸本)9789881563811

This paper is concerned with leader-follower synchronization of complex dynamical networks with sampled-data control. By sampling the signal from the leader at some discrete time instants and using a zero-order hold function, synchronization is achieved between the network and a desired orbit, known as the leader. By applying Lyapunov functional approach and the property of the network topology matrix, a delay-dependent criterion is derived. It is shown that synchronization of N coupled dynamical systems with a leader can be recast into the stability of N decoupled systems, in which eigenvalues of the network topology matrix are involved. Finally, a chaotic neural network is used to illustrate the effectiveness of the proposed method. © 2012 Chinese Assoc of Automati.

关键词： Synchronization

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intelligent anti-phishing framework using multiple classifiers combination

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Journal of Computational Information systems 2012年第17期8卷 7267-7281页

作者： Zhuang, Weiwei Jiang, Qingshan Cognitive Science Department Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen 361005 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China

In the phishing website detection field, improving the detection accuracy and efficiency is an important research topic. On the basis of existing research, the authors propose an intelligent anti-phishing framework using multiple classifiers combination. In particular, the method first parse and analyze the webpage content and extract 7 different types of features such as title, keywords and link text information to represent the webpage. Incremental bagging method is introduced to update the training sets and improved feature selection algorithm cuts the "redundant" features on the imbalanced dataset. 7 feature classifiers are then built based on these different feature representations. Finally multiple classifiers combination algorithm is used to combine the prediction results from each individual classifier for phishing website detection. Empirical studies on large and real daily phishing websites collected from Kingsoft Internet Security Lab demonstrate that our proposed model outperforms other commonly used anti-phishing methods and tools in phishing website detection. © 2012 Binary Information Press.

关键词： Feature Selection

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Leader-follower synchronization for complex dynamical networks via sampled-data control

Leader-follower synchronization for complex dynamical networ...

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Chinese Control Conference

作者： Wangli He Feng Qian Qing-Long Han The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781467325813

关键词： Synchronization Sampled-data control Complex networks

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New stability criteria for linear systems with time-varying interval delay

New stability criteria for linear systems with time-varying ...

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UKACC International Conference on Control (CONTROL)

作者： Jian Sun Jie Chen G.P. Liu School of Automation Key Laboratory of Complex Systems Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing China CTGT Center Harbin Institute of Technology Harbin China Faculty of Advanced Technology University of Glamorgan Pontypridd UK

关键词： Delay Stability criteria Time varying systems Linear matrix inequalities Numerical stability Linear systems

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An AIS data Visualization Model for Assessing Maritime Traffic Situation and its Applications

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Procedia Engineering 2012年 29卷 365-369页

作者： Pan Jiacai Jiang Qingshan Hu Jinxing Shao Zheping Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen 361005 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Navigation Institute Jimei University Xiamen 361021 China

Correct appraisal of the marine traffic situation is important for safe shipping. However, the traditional maritime traffic survey and analysis are not good ready for easily indicating the dangerous shipping area. We propose a novel visualization model to appraise the maritime traffic situation based on Ship's Automatic Identification System. The rate of ship turn, speed acceleration and ship encounter are incorporated into a new index to indicate the relative dangerous shipping area. Our new model is applied to analyze the maritime traffic situations of Xiamen Bay and Meizhou Wan and proved to be practical and useful for maritime traffic decision-making and management of marine authorities and mariners.

关键词： Maritime Traffic Situation Ship AIS Data Visualization Rate of Turn Speed Acceleration Ship Encounter

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A highly interpretable fuzzy rule base using ordinal structure for obstacle avoidance of mobile robot

A highly interpretable fuzzy rule base using ordinal structu...

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作者： Samsudin, Khairulmizam Ahmad, Faisul Arif Mashohor, Syamsiah Intelligent System and Robotics Laboratory Institute of Advanced Technology Universiti Putra Malaysia 43400 Serdang Selangor Malaysia Department of Computer and Communication Systems Faculty of Engineering Universiti Putra Malaysia 43400 Serdang Selangor Malaysia

Conventional fuzzy logic controller is applicable when there are only two fuzzy inputs with usually one output. Complexity increases when there are more than one inputs and outputs making the system unrealizable. The ordinal structure model of fuzzy reasoning has an advantage of managing high-dimensional problem with multiple input and output variables ensuring the interpretability of the rule set. This is achieved by giving an associated weight to each rule in the defuzzification process. In this work, a methodology to design an ordinal fuzzy logic controller with application for obstacle avoidance of Khepera mobile robot is presented. The implementation will show that ordinal structure fuzzy is easier to design with highly interpretable rules compared to conventional fuzzy controller. In order to achieve high accuracy, a specially tailored Genetic Algorithm (GA) approach for reinforcement learning has been proposed to optimize the ordinal structure fuzzy controller. Simulation results demonstrated improved obstacle avoidance performance in comparison with conventional fuzzy controllers. Comparison of direct and incremental GA for optimization of the controller is also presented. © 2010 Elsevier B.V. All rights reserved.

关键词： Genetic algorithms

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