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7th International Symposium on Autonomous Systems, ISAS 2024

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

ISBN: (纸本)9798350363173

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. © 2024 IEEE.

关键词： Controllers

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Machine Learning-Aided Data Analysis in Single-Protein Conductance Measurement with Electron Tunneling Probes

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Chinese Journal of Chemistry 2024年第1期42卷 67-72页

作者： Yuxin Yang Tao Jiang Ye Tian Biaofeng Zeng Longhua Tang State Key Laboratoryof Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang University HangzhouZhejiang 310027China Nanhu Brain-computer Interface Institute HangzhouZhejiang 311100China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical EngineeringZhejiang University HangzhouZhejiang 310027China

The electrical tunneling sensors have excellent potential in the next generation of single-molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution *** tunneling signals that have been measured at a high sampling rate may provide detailed molecular *** the extraordinarily large amount of data that has been gathered,it is still difficult to correlate signal transformations with molecular processes,which creates great obstacles for signal *** learning is an effective tool for data analysis that is currently gaining more *** has demonstrated promising results when used to analyze data from single-molecule electrical *** order to extract meaningful information from raw measurement data,we have combined intelligent machine learning with tunneling electrical *** the purpose of analyzing tunneling electrical signals,we investigated the clustering approach,which is a classic algorithm in machine learning.A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model ***,customized statistical algorithms were *** has been proven to efficiently gather molecular information and enhance the effectiveness of data analysis.

关键词： Tunneling sensor Single-molecule measurement Machine learning Single-protein conductance Molecular electrochemistry Nanotechnology Molecular electronics

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Partially Degenerate Multi-objective Test Problems 12th

Partially Degenerate Multi-objective Test Problems

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12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Pang, Lie Meng Nan, Yang 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

ISBN: (纸本)9783031272493

Degenerate multi-objective test problems are included in test suites to evaluate EMO algorithms on a wide variety of test problems. However, it was pointed out in some studies that the frequently-used degenerate DTLZ5, DTLZ6 and WFG3 test problems do not have degenerate Pareto fronts. Their Pareto fronts are different from the originally intended degenerate shapes. Actually, they are partially degenerate test problems. Modified formulations of DTLZ5 and DTLZ6 were proposed to remove the non-degenerate parts of their Pareto fronts. However, the original formulations of DTLZ5, DTLZ6 and WFG3 continue to be used as degenerate test problems in many studies whereas they are not degenerate test problems. One issue in their use as degenerate test problems is that reference point sets for IGD calculation are sampled from the originally intended degenerate Pareto fronts whereas they are not the true Pareto fronts. Nevertheless, the original DTLZ5, DTLZ6 and WFG3 formulations are useful for performance evaluation of EMO algorithms since their Pareto front shapes are similar to some real-world problems and much more complicated than other test problems. That is, their use helps us to evaluate the performance of EMO algorithms on a wide variety of test problems including realistic and challenging test problems. In this paper, we clearly demonstrate the usefulness of the original DTLZ5, DTLZ6 and WFG3 formulations. Then, after pointing out the difficulty in their use in computational experiments, we explain how we can obtain reliable experimental results on those test problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Effects of External Archives on the Performance of Multi-Objective Evolutionary Algorithms on Real-World Problems

Effects of External Archives on the Performance of Multi-Obj...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

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

ISBN: (纸本)9798350314588

External archives have attracted more and more attention in the evolutionary multi-objective optimization (EMO) community. This is because a solution set selected from an external archive is usually better than the final population of an EMO algorithm. Whereas the effects of subset selection from external archives have already been investigated on artificial test problems, its effects on real-world problems have not been examined. In this paper, we examine the effects of subset selection from external archives for ten EMO algorithms on two real-world problem suites. Experimental results show that the performance improvement by subset selection is large for most algorithms and many problems but small for a few algorithms and a few problems (i.e., algorithm dependent and problem dependent). © 2023 IEEE.

关键词： Multiobjective optimization

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Improving the Efficiency of R2HCA-EMOA 11th

Improving the Efficiency of R2HCA-EMOA

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

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

ISBN: (纸本)9783030720612

R2HCA-EMOA is a recently proposed hypervolume-based evolutionary multi-objective optimization (EMO) algorithm. It uses an R2 indicator variant to approximate the hypervolume contribution of each solution. Meanwhile, it uses a utility tensor structure to facilitate the calculation of the R2 indicator variant. This makes it very efficient for solving many-objective optimization problems. Compared with HypE, another hypervolume-based EMO algorithm designed for many-objective problems, R2HCA-EMOA runs faster and at the same time achieves better performance. Thus, R2HCA-EMOA is more attractive for practical use. In this paper, we further improve the efficiency of R2HCA-EMOA without sacrificing its performance. We propose two strategies for the efficiency improvement. One is to simplify the environmental selection, and the other is to change the number of direction vectors depending on the state of evolution. Numerical experiments clearly show that the efficiency of R2HCA-EMOA is significantly improved without deteriorating its performance. © 2021, Springer Nature Switzerland AG.

关键词： Efficiency

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Distance-Based Subset Selection With a Knee Point for Multi-Objective Optimization 13

Distance-Based Subset Selection With a Knee Point for Multi-...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

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

ISBN: (纸本)9798350373332

Recently, the use of an unbounded external archive in the design of evolutionary multi-objective optimization (EMO) algorithms has received increasing attention. An important component in the use of an unbounded external archive is its subset selection method used to select a pre-specified number of solutions from the archive. One of the most commonly used subset selection method is distance-based subset selection (DSS) since it is very easy to calculate and can easily scale to many-objective optimization. However, the DSS's selection mechanism usually lead to a small hypervolume value for the selected subset even when the optimal subset with respect to the minimum distance among the selected solutions is obtained. In this paper, we propose a modification to the DSS algorithm by selecting a knee point as the first solution. The knee point is often a solution of interest to decision makers in the absence of preference information. Experimental results show that the inclusion of a knee point generally has a positive effect on improving the hypervolume value of the selected subset. © 2024 IEEE.

关键词： Optimization algorithms

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Pathogen stress heightens sensorimotor dimensions in the human collective semantic space

Communications psychology

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Communications psychology 2025年第1期3卷 2页

作者： Ze Fu Huimin Chen Zhan Liu Maosong Sun Zhiyuan Liu Yanchao Bi State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing 100875 China. School of Journalism and Communication Tsinghua University Beijing 100084 China. Department of Computer Science and Technology Tsinghua University Beijing 100084 China. Department of Computer Science and Technology Tsinghua University Beijing 100084 China. liuzy@***. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing 100875 China. ybi@***. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health Peking University Beijing 100871 China. ybi@***. IDG/McGovern Institute for Brain Research Peking University Beijing 100871 China. ybi@***. Institute for Artificial Intelligence Peking University Beijing 100871 China. ybi@***. Chinese Institute for Brain Research Beijing 102206 China. ybi@***.

Infectious diseases have been major causes of death throughout human history and are assumed to broadly affect human psychology. However, whether and how conceptual processing, an internal world model central to various cognitive processes, adapts to such salient stress variables remains largely unknown. To address this, we conducted three studies examining the relationship between pathogen severity and semantic space, probed through the main neurocognitive semantic dimensions revealed by large-scale text analyses: one cross-cultural study (across 43 countries) and two historical studies (over the past 100 years). Across all three studies, we observed that increasing pathogen severity was associated with an enhancement of the sensory-motor dimension in the collective semantic space. These patterns remained robust after controlling for the effects of sociocultural variables, including economic wealth and societal norms of tightness. These results highlight the universal dynamic mechanisms of collective semantics, such that pathogen stress potentially drives sensorially oriented semantic processing.

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An Improved Fuzzy Classifier-Based Evolutionary Algorithm for Expensive Multiobjective Optimization Problems with Complicated Pareto Sets 12th

An Improved Fuzzy Classifier-Based Evolutionary Algorithm fo...

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12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Zhang, Jinyuan He, Linjun 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

ISBN: (纸本)9783031272493

Various surrogate-based multiobjective evolutionary algori-thms (MOEAs) have been proposed to solve expensive multiobjective optimization problems (MOPs). However, these algorithms are usually examined on test suites with unrealistically simple Pareto sets (e.g., ZDT and DTLZ test suites). Real-world MOPs usually have complicated Pareto sets, such as a vehicle dynamic design problem and a power plant design optimization problem. Such MOPs are challenging to construct reliable surrogates for surrogate-based MOEAs. Constructed surrogates with low accuracy are likely to make incorrect predictions and even mislead the search direction. In this paper, we propose an improved fuzzy classifier-based MOEA by leveraging the accuracy information of the classifier. The proposed algorithm is compared with five state-of-the-art algorithms on two well-known test suites with complicated Pareto sets and four real-world problems. Experimental results demonstrate the effectiveness of the proposed algorithm in solving realistic MOPs with complicated Pareto sets when only a limited number of function evaluations are available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Effects of Initialization Methods on the Performance of Surrogate-Based Multiobjective Evolutionary Algorithms

Effects of Initialization Methods on the Performance of Surr...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Zhang, Jinyuan Ishibuchi, Hisao He, Linjun Nan, Yang Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665430654

Initialization plays a crucial role in surrogate-based multiobjective evolutionary algorithms (MOEAs) when tackling computationally expensive multiobjective optimization problems. During the initialization process, solutions are generated to train surrogate models. Consequently, the accuracy of these surrogate models depends on the quality of the initial solutions, which in turn directly impacts the performance of surrogate-based MOEAs. Despite the widespread use of Latin hypercube sampling as an initialization method in surrogate-based MOEAs, there is a lack of comprehensive research examining the effectiveness of different initialization methods. Additionally, the impact of the number of initial solutions on the performance of surrogate-based MOEAs remains largely unexplored. This paper aims to bridge these research gaps by comparing the usefulness of two commonly employed initialization methods (i.e., random sampling and Latin hypercube sampling) in surrogate-based MOEAs. Furthermore, it investigates how varying the number of initial solutions influences the performance of surrogate-based MOEAs. © 2023 IEEE.

关键词： initialization Multiobjective evolutionary algorithms surrogate models

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Population Sizing of Evolutionary Large-Scale Multiobjective Optimization 11th

Population Sizing of Evolutionary Large-Scale Multiobjective...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： He, Cheng Cheng, Ran Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) are emerging and widely existed in real-world applications, which involve a large number of decision variables and multiple conflicting objectives. Evolutionary algorithms (EAs) are naturally suitable for multiobjective optimization due to their population-based property, allowing the search of optima simultaneously. Nevertheless, LSMOPs are challenging for conventional EAs, mainly due to the huge volume of search space in LSMOPs. Thus, it is important to explore the impact of the population sizing on the performance of conventional multiobjective EAs (MOEAs) in solving LSMOPs. In this work, we compare several representative MOEAs with different settings of population sizes on some transformer ratio error estimation (TREE) problems in the power system. These test cases are defined on combinations of three population sizes, three TREE problems, and five MOEAs. Our results indicate that the performances of conventional MOEAs with different population sizes in solving LSMOPs are different. The impact of population sizing is most significant for differential evolution based and particle swarm based MOEAs. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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