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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Nengneng Peng Rui Zhang Haihua Zeng Fei Wang Kai Li Yuanqing Li Xiaobin Zhuang School of Automation Science and Engineering South China University of Technology Guangzhou Key Laboratory of Brain Computer Interaction and Applications Guangzhou China

ISBN: (纸本)9781457702198

In this paper, we propose an intelligent nursing bed system which is controlled by a hybrid brain-computer interface (BCI) involving steady-state visual evoked potential (SSVEP) and P300. Specifically, the hybrid BCI includes an asynchronous brain switch based on SSVEP and P300, and a P300-based BCI. The brain switch is used to turn on/off the control system of the electric nursing bed through idle/control state detection, whereas the P300-based BCI is for operating the nursing bed. At the beginning, the user may focus on one group of flashing buttons in the graphic user interface (GUI) of the brain switch, which can simultaneously evoke SSVEP and P300, to switch on the control system. Here, the combination of SSVEP and P300 is used for improving the performance of the brain switch. Next, the user can control the nursing bed using the P300-based BCI. The GUI of the P300-based BCI includes 10 flashing buttons, which correspond to 10 functional operations, namely, left-side up, left-side down, back up, back down, bedpan open, bedpan close, legs up, legs down, right-side up, and right-side down. For instance, he/she can focus on the flashing button “back up” in the GUI of the P300-based BCI to activate the corresponding control such that the nursing bed is adjusted up. Eight healthy subjects participated in our experiment, and obtained an average accuracy of 93.75% and an average false positive rate (FPR) of 0.15 event/min. The effectiveness of our system was thus demonstrated.

关键词： Medical services Switches Graphical user interfaces Electroencephalography Support vector machines Legged locomotion

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Modeling and controller design of vertical take-off and landing turbojet powered aircraft

Modeling and controller design of vertical take-off and land...

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International Symposium on Autonomous Systems (ISAS)

作者： Tianyu Ouyang Yankai Yin Fei Kang Jiachen Zhang Yu Tian Xiaoquan Yan Wenchuan Kuang Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Tianjin Fulibo Intelligent Technology Co. Ltd Tianjin China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Aiming at the problems such as high altitude flight difficulty and poor wind resistance of rotary-wing UAV in disaster rescue, this paper proposes a vertical take-off and landing turbojet powered aircraft. The vehicle uses fuel as its power source, and four turbojet engines with vector propulsion systems provide thrust. Aiming at the difficult problem of controller design caused by complex nonlinear characteristics of aircraft, a T-S modeling method of aircraft is presented. Firstly, a dynamic model of the aircraft is established by analyzing the relationship between the force and rotation of the aircraft. On this basis, the T-S fuzzy model of aircraft is established by using sector nonlinear method. The model can decompose the complex nonlinear system into several simple linear subsystems, which is more convenient for controller design and analysis. To verify the validity of the T-S model, a fuzzy LQR controller is designed. Simulation results show that the designed controller can realize fast stabilization of the system. The structure design of the aircraft and T-S fuzzy control are of great practical significance for disaster relief in the complex plateau environment.

关键词： Resistance Analytical models Atmospheric modeling Disasters Vectors Aircraft manufacture Aircraft

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Parallel Exploration via Negatively Correlated Search

arXiv

引用

arXiv 2019年

作者： Yang, Peng Yang, Qi Tang, Ke Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Effective exploration is a key to successful search. The recently proposed Negatively Correlated Search (NCS) tries to achieve this by parallel exploration, where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited simultaneously. Various applications have verified the advantages of such novel search behaviors. Nevertheless, the mathematical understandings are still lacking as the previous NCS was mostly devised by intuition. In this paper, a more principled NCS is presented, explaining that the parallel exploration is equivalent to the explicit maximization of both the population diversity and the population solution qualities, and can be optimally obtained by partially gradient descending both models with respect to each search process. For empirical assessments, the reinforcement learning tasks that largely demand exploration ability is considered. The new NCS is applied to the popular reinforcement learning problems, i.e., playing Atari games, to directly train a deep convolution network with 1.7 million connection weights in the environments with uncertain and delayed rewards. Empirical results show that the significant advantages of NCS over the compared state-of-the-art methods can be highly owed to the effective parallel exploration ability. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Online Trip Planning for Public Bike Systems

Online Trip Planning for Public Bike Systems

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jiawei Mu Jianhui Zhang Tianyu Zhang Bei Zhao Wanqing Zhang Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

Public Bike System (PBS) not only provides convenient travel service but also alleviates the last-mile problem. With the increasing awareness of environmental protection and green commuting, people prefer to use the public bike as transportation for short-distance travel. However, the explosion of users in PBS leads to new congestion problems. To relieve the pressure of PBS, there are many types of research on system prediction, operation, and trip planning. However, there is few work focusing on the online trip planning problem. To study the case, we propose an Online Matching Trip Planning algorithm (OMTP), and we prove the theoretical lower bound of OMTP is 1 - 1/e. And then, we consider the short-term conflicts among users and design an Online Group Trip Planning algorithm (OGTP). We design two kinds of experiments- Generated Data Based and Real Data Based. In the generated data based experiment, we reveal the impact of different parameters with the generated trip data. In the real data based experiment, we validate our proposed algorithms with the real trip data set in New York City. The results show that OMTP and OGTP save time per trip on average.

关键词： Simulation Urban areas Green products Transportation Prediction algorithms Sensor systems Planning

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A novel CNet-assisted evolutionary level repairer and its applications to super mario bros

arXiv

引用

arXiv 2020年

作者： Shu, Tianye Wang, Ziqi Liu, Jialin Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Applying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patterns may be generated due to the violation of constraints for level design. A traditional way of repairing the defective levels is programming specific rule-based repairers to patch the flaw. However, programming these constraints is sometimes complex and not straightforward. An autonomous level repairer which is capable of learning the constraints is needed. In this paper, we propose a novel approach, CNet, to learn the probability distribution of tiles giving its surrounding tiles on a set of real levels, and then detect the illegal tiles in generated new levels. Then, an evolutionary repairer is designed to search for optimal replacement schemes equipped with a novel search space being constructed with the help of CNet and a novel heuristic function. The proposed approaches are proved to be effective in our case study of repairing GAN-generated and artificially destroyed levels of Super Mario Bros. game. Our CNet-assisted evolutionary repairer can also be easily applied to other games of which the levels can be represented by a matrix of objects or tiles. Copyright © 2020, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Decomposition-based multi-objective evolutionary algorithm design under two algorithm frameworks

arXiv

引用

arXiv 2020年

作者： Pang, Lie Meng Ishibuchi, Hisao Shang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed. The existing EMO algorithms are mainly developed based on the final population framework. In the final population framework, the final population of an EMO algorithm is presented to the decision maker. Thus, it is required that the final population produced by an EMO algorithm is a good solution set. Recently, the use of solution selection framework was suggested for the design of EMO algorithms. This framework has an unbounded external archive to store all the examined solutions. A pre-specified number of solutions are selected from the archive as the final solutions presented to the decision maker. When the solution selection framework is used, EMO algorithms can be designed in a more flexible manner since the final population is not necessarily to be a good solution set. In this paper, we examine the design of MOEA/D under these two frameworks. We use an offline genetic algorithm-based hyper-heuristic method to find the optimal configuration of MOEA/D in each framework. The DTLZ and WFG test suites and their minus versions are used in our experiments. The experimental results suggest the possibility that a more flexible, robust and high-performance MOEA/D algorithm can be obtained when the solution selection framework is used. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic methods

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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

引用

computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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Hypervolume-optimal μ-distributions on line/plane-based pareto fronts in three dimensions

arXiv

引用

arXiv 2021年

作者： Shang, Ke Ishibuchi, Hisao Chen, Weiyu Nan, Yang Liao, Weiduo Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Hypervolume is widely used in the evolutionary multi-objective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with μ solutions on a Pareto front, a larger hypervolume means a better solution set. Investigating the distribution of the solution set with the largest hypervolume is an important topic in EMO, which is the so-called hypervolume optimal μ-distribution. Theoretical results have shown that the μ solutions are uniformly distributed on a linear Pareto front in two dimensions. However, the μ solutions are not always uniformly distributed on a single-line Pareto front in three dimensions. They are only uniform when the single-line Pareto front has one constant objective. In this paper, we further investigate the hypervolume optimal μ-distribution in three dimensions. We consider the line- and plane-based Pareto fronts. For the line-based Pareto fronts, we extend the single-line Pareto front to two-line and three-line Pareto fronts, where each line has one constant objective. For the plane-based Pareto fronts, the linear triangular and inverted triangular Pareto fronts are considered. First, we show that the μ solutions are not always uniformly distributed on the line-based Pareto fronts. The uniformity depends on how the lines are combined. Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization. It is locally optimal with respect to a (μ + 1) selection scheme. Our results can help researchers in the community to better understand and utilize the hypervolume indicator. © 2021, CC BY.

关键词： Multiobjective optimization

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Niching diversity estimation for multi-modal multi-objective optimization

arXiv

引用

arXiv 2021年

作者： Peng, Yiming Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Niching is an important and widely used technique in evolutionary multi-objective optimization. Its applications mainly focus on maintaining diversity and avoiding early convergence to local optimum. Recently, a special class of multi-objective optimization problems, namely, multi-modal multi-objective optimization problems (MMOPs), started to receive increasing attention. In MMOPs, a solution in the objective space may have multiple inverse images in the decision space, which are termed as equivalent solutions. Since equivalent solutions are overlapping (i.e., occupying the same position) in the objective space, standard diversity estimators such as crowding distance are likely to select one of them and discard the others, which may cause diversity loss in the decision space. In this study, a general niching mechanism is proposed to make standard diversity estimators more efficient when handling MMOPs. In our experiments, we integrate our proposed niching diversity estimation method into SPEA2 and NSGA-II and evaluate their performance on several MMOPs. Experimental results show that the proposed niching mechanism notably enhances the performance of SPEA2 and NSGA-II on various MMOPs. Copyright © 2021, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm

arXiv

引用

arXiv 2020年

—A multi-modal multi-objective optimization problem is a special kind of multi-objective optimization problem with multiple Pareto subsets. In this paper, we propose an efficient multi-modal multi-objective optimization algorithm based on the widely used MOEA/D algorithm. In our proposed algorithm, each weight vector has its own sub-population. With a clearing mechanism and a greedy removal strategy, our proposed algorithm can effectively preserve equivalent Pareto optimal solutions (i.e., different Pareto optimal solutions with same objective values). Experimental results show that our proposed algorithm can effectively preserve the diversity of solutions in the decision space when handling large-scale multi-modal multi-objective optimization problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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