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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis

Procedia computer science 2023年 225卷 4930-4942页

作者： Joanna Kołodziejczyk Yuliia Tarasenko Piotr Sulikowski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw 04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment.

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Chance Constrained Routing and Charging Problem of Electric Vehicles

Chance Constrained Routing and Charging Problem of Electric ...

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Chinese Automation Congress (CAC)

作者： Shibo Chen Canqi Yao Haoyu Miao Zhicheng Li Zaiyue Yang Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Shenzhen Polytechnic Internet of Things Research Institute Shenzhen China

Different from traditional vehicles, the routing problem of electric vehicles (EVs) is closely coupled with their charging. Hence, operating EV requires a joint optimization of both routing and charging. Moreover, the travel time and energy consumption uncertainties also bring challenges to the EV operation in practice. In this paper, we study the routing and charging problem of EVs under the chance constrained optimization framework, where uncertainties are tackled with probabilistic constraints. An iterative algorithm is proposed, which transforms the original challenging mixed-integer nonlinear program into the iteration of two linear programs. The finite convergence of this algorithm is also proved. Numerical results show that our proposed algorithm can achieve near-optimal solutions in rather short computation times.

关键词： Energy consumption Uncertainty Automation Transforms Routing Electric vehicles Probabilistic logic

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A Dexterity and Exploration Enhanced Quadruped Robot With a Multi-Functional Retractable Variable Stiffness Manipulator

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IEEE/ASME Transactions on Mechatronics 2025年

作者： Wang, Xuchen Liang, Yiying Wang, Shengzhi Shu, Jing Lai, Wenkai Huang, Zhanpeng Zhu, Puchen An, Jiajun Zhou, Jianshu Ma, Xin Southern University of Science and Technology Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Institute for Robotics Shenzhen 518055 China National University of Singapore Department of Mechanical Engineering 119077 Singapore Tsinghua University Division of Advanced Manufacturing Shenzhen International Graduate School Shenzhen 518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong University of California Department of Mechanical Engineering Berkeley 94720 CA United States

Navigating complex environments—ranging from confined spaces to open areas blocked by obstacles or out of reach of traditional robotic arms—remains a major challenge for conventional robotic systems. These challenges include limited dexterity, adaptability, and accessibility. To address these issues, this article proposed a dexterity and exploration enhanced quadruped robot (DEEQR), which was designed with a quadruped base and a multifunctional retractable variable stiffness manipulator (MRVSM). The MRVSM features a compact active-passive composite continuum mechanism with a 12.8 mm diameter, a high-stiffness telescopic mechanism, and a 2.5 mm diameter working channel that supports interchangeable tools such as a 2-DOF grasper or other miniaturized tools. This design enables precise manipulation and exploration in narrow and obstructed environments. Experiments demonstrated that DEEQR can traverse uneven terrain, navigate confined gaps, and perform complex tasks such as a bomb disposal operation inside a backpack with a 5 cm diameter entrance. The robot also successfully explored and delivered water in blocked spaces beyond the direct reach of the quadruped base. These results highlight DEEQR's strong potential for anti-terrorism applications and search and rescue missions in challenging environments. © 1996-2012 IEEE.

关键词： Continuum robot quadruped robot telescopic mechanism

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Fuzzy Approach for the Temporary Logistics Hubs’ Selection Planning in Disaster Region

Fuzzy Approach for the Temporary Logistics Hubs’ Selection ...

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International IEEE Conference on intelligent systems, IS

作者： Gia Sirbiladze Janusz Kacprzyk Jose Antonio Lozano Bezhan Ghvaberidze Bidzina Midodashvili Bidzina Matsaberidze Department of Computer Sciences Iv. Javakhishvili Tbilsi State University Tbilisi Georgia Polish Academy of Sciences Intelligent Systems Laboratory Systems Research Institute Warsawa Poland Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain

ISBN: (纸本)9781665492768

Two-stage fuzzy methodology for the optimal planning of selection of temporary logistics hubs (TLHs) is developed. At the first stage, a q-rung orthopair fuzzy TOPSIS approach is developed to form and present expert knowledge about opening temporary logistics hubs. Fuzzy TOPSIS-based approach allows to determine the order of opening of TLHs and to provide post-disaster decision-making. At the second stage, by help of the built fuzzy TOPSIS aggregation a new objective function is given. By use of the built criterion TLHs’ total identification level of the order of establishment can be maximized, and together with the criterion concerning minimization of the selected TLHs’ quantity, poses a multi-objective facility location set covering problem. The offered approach is demonstrated by the example of temporary logistics y. The analysis of the results obtained revealed the importance of taking into account the opinions of multiple decision-makers for ensuring coordination in service delivery in disaster region.

关键词： Decision making Programming Numerical simulation Minimization Linear programming Numerical models Planning

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redPATH:Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer

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Genomics, Proteomics & Bioinformatics 2021年第2期19卷 292-305页

作者： Kaikun Xie Zehua Liu Ning Chen Ting Chen Institute for Artificial Intelligence State Key Lab of Intelligent Technology and SystemsDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Tsinghua-Fuzhou Institute of Digital Technology Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Center for Computational and Integrative Biology Massachusetts General HospitalHarvard Medical SchoolBostonMA 02114USA Department of Molecular Biology Massachusetts General HospitalHarvard Medical SchoolBostonMA 02114USA Broad Institute of Massachusetts Institute of Technology and Harvard CambridgeMA 02142USA

The recent advancement of single-cell RNA sequencing(scRNA-seq)technologies facilitates the study of cell lineages in developmental processes and *** this study,we developed a computational method,called redPATH,to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path ***,we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological *** validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets,as well as other single-cell transcriptome *** particular,we identified a stem cell-like subpopulation in malignant glioma *** cells express known proliferative markers,such as GFAP,ATP1A2,IGFBPL1,and ALDOC,and remain silenced for quiescent markers such as ***,we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor *** conclusion,redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental *** is available at https://***/tinglabs/redPATH.

关键词： Single-cell pseudotime reconstruction Consensus Hamiltonian path Cell differentiation Cell proliferation Cell development and diseases

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Extracting Semantic Knowledge from GANs with Unsupervised Learning

arXiv

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arXiv 2022年

作者： Xu, Jianjin Zhang, Zhaoxiang Hu, Xiaolin The School of Mathematics and Computer Science Panzhihua University The Department of Computer Science and Technology Tsinghua University China The Insitute of Automation Chinese Academy of Sciences The Centre for Artificial Intelligence and Robotics Hong Kong Innovation and Science Insitute Chinese Academy of Sciences Hong Kong The Department of Computer Science and Technology Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems THU-Bosch JCML Center THBI IDG/McGovern Institute for Brain Research BNRist Tsinghua University Beijing China Beijing China

Recently, unsupervised learning has made impressive progress on various tasks. Despite the dominance of discriminative models, increasing attention is drawn to representations learned by generative models and in particular, Generative Adversarial Networks (GANs). Previous works on the interpretation of GANs reveal that GANs encode semantics in feature maps in a linearly separable form. In this work, we further find that GAN’s features can be well clustered with the linear separability assumption. We propose a novel clustering algorithm, named KLiSH, which leverages the linear separability to cluster GAN’s features. KLiSH succeeds in extracting fine-grained semantics of GANs trained on datasets of various objects, e.g., car, portrait, animals, and so on. With KLiSH, we can sample images from GANs along with their segmentation masks and synthesize paired image-segmentation datasets. Using the synthesized datasets, we enable two downstream applications. First, we train semantic segmentation networks on these datasets and test them on real images, realizing unsupervised semantic segmentation. Second, we train image-to-image translation networks on the synthesized datasets, enabling semantic-conditional image synthesis without human annotations. © 2022, CC BY-SA.

关键词： Unsupervised learning

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Theoretical Model Construction of Deformation-Force for Soft Grippers Part I: Co-rotational Modeling and Force Control for Design Optimization

arXiv

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arXiv 2023年

作者： Dong, Huixu Guo, Haotian Yang, Sihao Qiu, Chen Dai, Jiansheng Chen, I-Ming Zhejiang University Hangzhou310058 China Robotics Research Center Nanyang Technological University Singapore639798 Singapore Maider Medical Industry Equipment Co. Itd 317607 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems SUSTech Institute of Robotics Southern University of Science and Technology Shenzhen518055 China Centre for Robotics Research Department of Engineering King’s College London Strand LondonWC2R 2LS United Kingdom

Compliant grippers, owing to adaptivity and safety, have attracted considerable attention for unstructured grasping in real applications, such as industrial or logistic scenarios. However, accurately modeling the bidirectional relationship between shape deformation and contact force for such grippers, the Fin-Ray grippers as an example, remains stagnant to date. To address this research gap, this article devises, presents, and experimentally validates a universal bidirectional force-displacement mathematical model for compliant grippers based on the co-rotational concept, which endows such grippers with an intrinsic force sensing capability and offers a better insight into the design optimization. In Part I of the article, we introduce the fundamental theory of the co-rotational approach, where arbitrary large deformation of beam elements can be modeled. Its intrinsic principle allowing taking materials with varying stiffness, various connection types and key design parameters into consideration with few assumptions. Further, the force-displacement relationship is numerically derived, providing accurate displacement estimations of the gripper under external forces with minor computational loads. The performance of the proposed method is experimentally verified through comparison with Finite Element Analysis (FEA) in simulation, obtaining a fair degree of accuracy (6%), and design optimization of Fin-Ray grippers is systematically investigated. Part II of this article demonstrating the force sensing capabilities and the effects of representative co-rotational modeling parameters on model accuracy is released in Arxiv1 Copyright © 2023, The Authors. All rights reserved.

关键词： Grippers

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Beyond Intuition, a Framework for Applying GPs to Real-World Data

arXiv

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arXiv 2023年

作者： Tazi, Kenza Lin, Jihao Andreas Viljoen, Ross Gardner, Alex John, St Ge, Hong Turner, Richard E. Department of Engineering University of Cambridge Cambridge United Kingdom British Antarctic Survey Cambridge United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science Aalto University Espoo Finland

Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and limited guidelines on how to apply GPs beyond simple low-dimensional datasets. We propose a framework to identify the suitability of GPs to a given problem and how to set up a robust and well-specified GP model. The guidelines formalise the decisions of experienced GP practitioners, with an emphasis on kernel design and options for computational scalability. The framework is then applied to a case study of glacier elevation change yielding more accurate results at test time. © 2023, CC BY.

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos 38

Retrospective for the Dynamic Sensorium Competition for pred...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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Wheel-Terrain Contact Geometry Estimation using the Aside-Wheel RGB-D Sensor over Soft Terrain

Wheel-Terrain Contact Geometry Estimation using the Aside-Wh...

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robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Pengxuan Zhana Chen Yao Longteng Hu Feng Xue Peichen Wang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China SUSTech Guang-dong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin of Institute of Technology (HIT) Harbin China College of Engineering University of Michigan Ann Arbor Michigan USA School of Electronics Harbin Institute of Technology Harbin China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Wheeled mobile robots (WMRs) have exhibited notable competitiveness in various applications, including geo-graphic and planetary exploration. Nonetheless, when traversing on soft terrains, their wheels might encounter sinkage and slippage, leading to mission failure. Consequently, real-time estimation of contact parameters such as sinkage and slip ratio holds significant importance for rover operations. In this paper, we introduce a novel algorithm that employs only one aside-wheel RGB-D camera for the simultaneous detection of wheel sinkage and slip ratio on deformable terrains in real-time. We estimate sinkage through the depth module by processing point cloud data, and we estimate slip ratio through the color module with a sequence of image processing steps. Utilizing only one single camera reduces errors arising from sensor signal alignment and enhances detection accuracy. With a single-wheel testbed, we simulate wheel motion under different slip ratios and carry out numerous experiments to validate the effectiveness of the proposed algorithm. Our algorithm attains a detection frequency of up to 20Hz and exhibits outstanding precision. The relative sinkage error remains below 4%, while the relative slippage error is confined within 0.8%.

关键词： Accuracy Image color analysis Robot vision systems Wheels Estimation Cameras Real-time systems

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