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International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery

作者： Min Cai Xiaobo Lu Xuehui Wu Yifei Feng School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (纸本)9781509040940

In order to discover forest fire as early as possible, forest fire detection should focus on the smoke in early fire. This paper focuses on three key issues: motion segmentation, feature extraction and classifier design. Background subtraction based on visual background extractor (Vibe) is chosen to divide suspected smoke area when taking into account the accuracy and time consumption. And then do some corresponding morphological processing. Later on, various static and dynamic characteristics of smoke were extracted and different tests were done based on different feature combinations in forest fire smoke detection system. Lots of smoke detecting system only think about static features which will result in a certain degree of misjudgment. Analyzing the false positive rate and recognition rate of these experiments' results, the combination of movement direction, high-frequency energy based on wavelet transformation and compactness is selected to constitute the final recognition vectors. In addition, the continuity which is not mentioned in other researches won't be ignored in this paper. The final experimental results showed that the accuracy rate of this method for smoke detection could reach 92.7%.

关键词： Smoke detection Vibe Feature extraction Support vector machine

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Fixed-time consensus of multi-agent systems over undirected networks

Fixed-time consensus of multi-agent systems over undirected ...

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第35届中国控制会议

作者： Qingling Wang Yuanda Wang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (纸本)9781509009107

This paper investigates the distributed fixed-time consensus problem of networked agents under the connected interaction topology,featuring an upper bound of the convergence time and being independent of initial *** achieve fixed-time consensus,a new class of distributed nonlinear consensus protocols are constructed for first-order dynamic agents with or without external *** studies indicate that under the proposed protocols,all states of the agents will converge to a common value in fixed ***,the effectiveness of the theoretical results is illustrated by simulation results.

关键词： fixed-time consensus distributed nonlinear protocols convergence time

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Two-layer Fuzzy Kernel Regression for Human Emotional Intention Understanding

Two-layer Fuzzy Kernel Regression for Human Emotional Intent...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Luefeng Chen Mengtian Zhou Min Wu Jinhua She Kaoru Hirota School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China School of Engineering Tokyo University of Technology Tokyo Japan Tokyo Institute of Technology Beijing Institute of Technology Yokohama Japan

A two-layer fuzzy kernel regression (TLFKR) model is proposed for understanding human emotional intention in human-robot interaction, where TLFKR model consists of two layers, including fuzzy c-means (FCM) with kernel ridge regression (Kernel 1) for information analysis layer, fuzzy support vector regressions (FSVR) (Kernel 2) for intention understanding layer. TLFKR model represents the weight impact for each emotional information and aims to improve smooth human-robot interaction by endowing robot with human emotional intention understanding capability. Experimental Results show that the proposal obtains an intention understanding accuracy of 65.67%/68.33%/80.67% with the clusters number c=2/3/6 (according to different genders/ages/nationalities), which are 7.34%/7.18%/8.67% and 18.67%/21.33%/33.67% higher than that of TLFSVR and SVR, respectively. Additionally, preliminary application experiments are performed in the developing emotional social robot system, where two mobile robots and volunteers experience a scenario of "drinking at a bar", and social robots are able to express basic emotions and understand human order intention.

关键词： Kernel Robots Training Support vector machines Proposals Information analysis Bars

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Speech emotion recognition based on deep belief networks and wavelet packet cepstral coefficients

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International Journal of Simulation: systems, Science and Technology 2016年第28期17卷 28.1-28.5页

作者： Huang, Yongming Wu, Ao Zhang, Guobao Li, Yue School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China

A wavelet packet based adaptive filter-bank construction combined with Deep Belief Network(DBN) feature learning method is proposed for speech signal processing in this paper. On this basis, a set of acoustic features are extracted for speech emotion recognition, namely Coiflet Wavelet Packet Cepstral Coefficients (CWPCC). CWPCC extends the conventional Mel-Frequency Cepstral Coefficients (MFCC) by adapting the filter-bank structure according to the decision task. And Deep Belief Networks (DBNs) are artificial neural networks having more than one hidden layer, which are first pre-trained layer by layer and then fine-tuned using back propagation algorithm. The well-trained deep neural networks are capable of modeling complex and non-linear features of input training data and can better predict the probability distribution over classification labels. Speech emotion recognition system is constructed with the feature set, DBNs feature learning structure and Support Vector Machine as classifier. Experimental results on Berlin emotional speech database show that the Coiflet Wavelet Packet is more suitable in speech emotion recognition than other acoustics features and proposed DBNs feature learning structure combined with CWPCC improve emotion recognition performance over the conventional emotion recognition method. © 2016, UK Simulation Society. All rights reserved.

关键词： Filter banks

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Pedestrian Detection via Contour Fragments

Pedestrian Detection via Contour Fragments

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第35届中国控制会议

作者： Dandi Chen Siyu Xia Yuan Zhou Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Computer Science and Engineering Department University of Connecticut

ISBN: (纸本)9781509009107

Pedestrian detection has been the key point of several hot research fields,including intelligent surveillance,driving assistance,intelligent robot,*** to pedestrian appearance,multi-pose,angle of view,environment and other factors,the existing pedestrian detection algorithms still have some unsolved technical *** this paper,we focus on contour *** on traditional feature descriptors and feature selection methods,we proposed a novel oriented Chamfer distance(OCD) feature to describe pedestrian *** improves anti-interference ability of image noise using Bag of Words(BOW)model and image multi-scale ***,we conducted experimental comparisons using scale-invariant feature transform(SIFT),histogram of oriented gradient(HOG),speeded up robust feature(SURF) and OCD feature in feature extraction and parameter optimization,in Boosting and support vector machine(SVM) classification *** results demonstrate that the proposed OCD feature has great discriminant ability of pedestrian contours.

关键词： Pedestrian detection Contour feature Oriented Chamfer distance feature Support vector machine Boosting

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On stability of sampled-data extended state observer for networked systems

On stability of sampled-data extended state observer for net...

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Asian control Conference

作者： Yuan Huang Junzheng Wang Dawei Shi Jian Xue State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China Harvard John A. Paulson School of Engineering & Applied Sciences Harvard University Cambridge MA USA

ISBN: (纸本)9781509015740;9781509015733

The problem of sampled-data state observation is considered in this work. A nonlinear continuous-time system with internal uncertainty and external disturbance is introduced, for which the sampled-data observation problem based on an extended state observer is formulated. We first introduce the framework of the observed system, in which the output measurement is sampled and transmitted to extended state observer periodically. Then based on this framework, the observation performance of the sampled-data extended state observer (SD-ESO) is investigated. For the observer considered, the uniformly ultimately bounded property has been guaranteed. Furthermore, a co-design method for the sample interval and high gain parameter of SD-ESO is provided to achieve the desired observation performance. The obtained theoretic results are validated through the numerical simulations.

关键词： Observers Uncertainty Networked control systems Convergence Distributed databases Gold Australia

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Data-Based Robust control for Unknown Nonlinear systems 35

Data-Based Robust Control for Unknown Nonlinear Systems

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第35届中国控制会议

作者： YANG Xiong LIU Derong WEI Qinglai School of Electrical Engineering and Automation Tianjin University School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781509009107

In this paper, we develop a data-based robust control method for a class of unknown nonlinear systems with input constraints. First, we transform the robust control problem into a constrained optimal control problem by introducing a value function for the nominal system. Then, under the framework of integral reinforcement learning, we construct an actor-critic architecture to approximately solve the constrained optimal control problem. Based on the present architecture, we only require system data rather than the availability of full/partial prior knowledge of system dynamics. In addition, by using Lyapunov's direct method, we prove that the obtained approximate optimal control can guarantee the unknown nonlinear system to be uniformly ultimately bounded. Finally, we provide an example to illustrate the effectiveness of the developed method.

关键词： Constrained input Integral reinforcement learning Nonlinear systems Optimal control Robust control

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An algorithm for assembly job shop scheduling problem

An algorithm for assembly job shop scheduling problem

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2016 2nd International Conference on Electronics,Network and Computer engineering(ICENCE 2016)

作者： Xiao-Qin Wan Hong-Sen Yan School of Automation Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University

ISBN: (纸本)9781510830073

The problem of assembly job shop scheduling(AJSS) is studied with the objective of minimizing the weighted sum of earliness and tardiness penalties. An insertion search algorithm is proposed to solve the problem. The operations of job with tardiness or earliness are shifted left or right to optimize the sequence. Simulation results validate the effectiveness of the proposed model and algorithm.

关键词： Assembly job shop tardiness earliness

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Towards quality advancement of underwater machine vision with generative adversarial networks

arXiv

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arXiv 2017年

作者： Chen, Xingyu Yu, Junzhi Kong, Shihan Wu, Zhengxing Fang, Xi Wen, Li University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China

Underwater machine vision has attracted significant attention, but its low quality has prevented it from a wide range of applications. Although many different algorithms have been developed to solve this problem, real-time adaptive methods are frequently deficient. In this paper, based on filtering and the use of generative adversarial networks (GANs), two approaches are proposed for the aforementioned issue, i.e., a filteringbased restoration scheme (FRS) and a GAN-based restoration scheme (GAN-RS). Distinct from previous methods, FRS restores underwater images in the Fourier domain, which is composed of a parameter search, filtering, and enhancement. Aiming to further improve the image quality, GAN-RS can adaptively restore underwater machine vision in real time without the need for pretreatment. In particular, information in the Lab color space and the dark channel is developed as loss functions, namely, underwater index loss and dark channel prior loss, respectively. More specifically, learning from the underwater index, the discriminator is equipped with a carefully crafted underwater branch to predict the underwater probability of an image. A multi-stage loss strategy is then developed to guarantee the effective training of GANs. Through extensive comparisons on the image quality and applications, the superiority of the proposed approaches is confirmed. Consequently, the GAN-RS is considerably faster and achieves a state-of-the-art performance in terms of the color correction, contrast stretch, dehazing, and feature restoration of various underwater scenes. The source code will be made available. Copyright © 2017, The Authors. All rights reserved.

关键词： Image reconstruction

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Distributed Consensus Tracking control for Multiple Simple-Pendulum Network systems 35

Distributed Consensus Tracking Control for Multiple Simple-P...

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第35届中国控制会议

作者： JIANG Yulian WANG Shenquan LI Yuanchun LIU Derong College of Electrical and Electronic Engineering Changchun University of Technology The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509009107

This paper investigates the consensus tracking problem for networked control systems consisting of multiple simplependulums driven by corresponding DC motors.A distributed adaptive consensus control algorithm is developed to guarantee the state tracking for each simple-pendulum with nonlinear *** dynamic model of networked simple-pendulums driven by DC motors is obtained first,and the mathematical description of consensus tracking control problem for multi-agent system is ***,fully distributed consensus tracking protocol without any global information is designed by using adaptive control technology to adjust the coupling weights between neighboring ***,simulation results demonstrate the effectiveness of the proposed consensus tracking control algorithm.

关键词： consensus tracking simple-pendulum network system adaptive control nonlinear dynamics multi-agent system

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