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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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A Comparative Study of the Performance of Personnel Detection based on Visible and Infrared Images 22

A Comparative Study of the Performance of Personnel Detectio...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Futao Liu Zhenling Li Yuezhong Wan Weida Cao Duanwei Ma Xiaolei Li Peng Yan Maintenance Departments Liaocheng Power Supply Company State Grid Corporation of China China School of Control Science and Engineering Shandong University China Trusted Cloud Computing Lab Shandong TCCA Information Technology Research Institute China

ISBN: (纸本)9781450397810

Personnel security has always been a hot topic of research especially in power systems. Effective detection of people in power scenes has become a high priority. Currently, algorithms for personnel detection have been developed considerably, and they are mainly used for visible images. Since infrared images imaged by thermal radiation can detect living blood creatures easily, they can be used in personnel detection at night. To investigate the effect of visible and infrared images on the performance of personnel detection algorithms in different environments, this thesis uses two object detection algorithms, YOLOv5 and Faster RCNN, to train personnel detection models for visible daytime, visible nighttime, infrared daytime and infrared nighttime datasets respectively. Comparative experiments are conducted to quantitatively and qualitatively analyze the performance advantages and disadvantages of the personnel detection algorithms for visible images and infrared images during the day and night. The experimental results for both models consistently show that the visible images are similar to the infrared images during daylight hours with a good target detection model, the visible image is slightly better in performance. While the infrared image is significantly better than the visible image at night, and the [email protected] can be improved by 0.2. For small human target detection, the infrared image has a significant advantage over the visible image at night, with an average detection size reduction of 48.0% and a minimum detection size reduction of 64.6%.

关键词： Visible images Infrared images Different light conditions YOLOv5 and Faster RCNN Personnel detection

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Multi-Task Driven Multi-Level Dynamical Fusion for Single-Cell Multi-Omics Cell Type Annotation

Multi-Task Driven Multi-Level Dynamical Fusion for Single-Ce...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawei Li Shizhan Chen Zongbo Han Wei Li Jijun Tang Fei Guo College of Intelligence and Computing Tianjin University Tianjin China College of Artificial Intelligence Nankai University Tianjin China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The emergence of single-cell multi-omics sequencing technology has enabled the simultaneous profiling of diverse omics data within individual cells. It offers a more comprehensive perspective on cellular phenotypes and heterogeneity. However, single-cell multi-omics data are inherently high-dimensional and heterogeneous. Due to technical limitations and scarce starting materials, the data are often affected by noise and dropout effects. To address these challenges, we propose a novel multitask driven multi-level dynamical fusion algorithm for single-cell multi-omics cell type annotation, named scMMDyn. Our approach incorporates reconstruction and classification auxiliary tasks to guide the training of trustworthy modules at both the feature and modality levels. It executes dynamical fusion during these stages and finally achieves cross-modality fusion via an attention mechanism. This method effectively mitigates data quality issues through reconstruction tasks and feature-level dynamical fusion while providing interpretability at both feature and modality levels. Experimental results across diverse single-cell multi-omics datasets show that our method surpasses existing approaches in cell type annotation.

关键词： Training Sequential analysis Phenotypes Annotations Heuristic algorithms Data integrity Noise Multitasking Classification algorithms Reliability

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Data-Driven Predictive control for Robust Exoskeleton Locomotion

Data-Driven Predictive Control for Robust Exoskeleton Locomo...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kejun Li Jeeseop Kim Xiaobin Xiong Kaveh Akbari Hamed Yisong Yue Aaron D. Ames Department of Computation and Neural Systems Caltech Pasadena CA USA Department of Mechanical and Civil Engineering Caltech Pasadena CA USA Department of Mechanical Engineering University of Wisconsin-Madison Madison WI USA Department of Mechanical Engineering Virginia Tech VA USA Department of Computing and Mathematical Sciences Caltech Pasadena CA USA Department of Control and Dynamical Systems Caltech Pasadena CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

关键词： Adaptation models Analytical models Perturbation methods Exoskeletons Predictive models Hardware Trajectory Numerical models Predictive control Payloads

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MI3SE: A Multi-User Index-Based Searchable Symmetric Encryption Scheme for Improving Security of Data in Connected Electronic Devices

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IEEE Transactions on Consumer Electronics 2024年

作者： Soleymani, Seyed Ahmad Goudarzi, Shidrokh Anisi, Mohammad Hossein Xiao, Pei Mihaylova, Lyudmila Wang, Wenwu University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom University of West London School of Computing and Engineering LondonW5 5RF United Kingdom University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom GuildfordGU2 7XH United Kingdom GuildfordGU2 7XH United Kingdom

In the Industrial Internet of Things (IIoT), outdoor electronic devices serve crucial roles across sectors, providing vital data for decision-making. However, their exposure to open outdoor environments makes them vulnerable to unauthorized access, physical theft, or compromise, endangering both the device and its data. Ensuring the security of outdoor devices and their data is thus critical. This study addresses data security in outdoor IIoT devices by supporting the encryption of all IIoT-related data in device memory. Accessing and retrieving this data requires operations on encrypted data. Hence, we introduce a Searchable Symmetric Encryption (SSE) scheme called MI3SE, which ensures each device's encryption key is unique and valid for a period based on the device's security sensitivity. Moreover, MI3SE meets key security requirements, including confidentiality, integrity, forward secrecy, and backward secrecy. It is specifically designed to mitigate physical compromise and query pattern analysis through a two-keyword query approach and withstand various attacks, as validated by rigorous security analysis. Comparative evaluations against benchmark schemes underscore the efficacy of MI3SE in terms of both security and performance. Moreover, comprehensive non-mathematical security analysis and simulation experiments affirm the enhanced accuracy and efficacy of MI3SE in securing sensitive data stored in outdoor IIoT devices. © 1975-2011 IEEE.

关键词： Data Security IIoT Outdoor Electronic Devices Searchable Symmetric Encryption (SSE)

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Balancing Human-robot Collaborative Disassembly Line by Using Dingo Optimization Algorithm

Balancing Human-robot Collaborative Disassembly Line by Usin...

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IEEE International Conference on Human-Machine Systems

作者： Xiwang Guo Liangbo Zhou Mengchu Zhou Weitian Wang Jiacun Wang Liang Qi Information and Control Engineering Liaoning Petrochemical University Fushun China Information and Control Liaoning Petrochemical University Fushun China Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA School of Computing Montclair State University Montclair USA Computer Science and Software Eng. Monmouth University West Long Branch NJ USA Computer Intelligent Sci. and Tech. Shandong University of Sci. and Tech. Qingdao China

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Disassembling and recycling scrapped products play important roles in effectively reducing environmental pollution and improving resource sustainability. A multi-product human-robot collaborative disassembly-line-balancing problem (MHDP) arises from doing so and represents an important yet to be solved in the remanufacturing field. This work explores how to use optimization algorithms to schedule tasks between humans and robots to maximize remanufacturing profit in human-robot collaboration contexts. Specifically, we investigate MHDP and develop a mixed integer programming model considering various system constraints. We propose a dingo optimization algorithm that simulates wild dogs’ social hierarchy and chasing processes to solve MHDP. We compare the proposed algorithm with an exact solution finder, i.e., IBM CPLEX and a well-known intelligent optimization method, i.e., Genetic Algorithm to show its competitive performance in terms of solution accuracy and efficiency.

关键词： Integer programming Schedules Pollution Human-machine systems Collaboration Optimization methods Recycling

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Multi-Label Classification with Label Clusters

Research Square

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Research Square 2023年

作者： Gatto, Elaine Cecília Ferrandin, Mauri Cerri, Ricardo Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil

Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once, and local, which divides the problem into many binary problems. In both approaches, learning label correlations is still an open issue. In this paper, we propose a method to cluster the label space and find partitions of disjoint correlated labels called hybrid partition, which can be considered in-between the local and global, combining the benefits of both. Our proposal entails: i) clustering the label space based on label correlations;ii) generating and validating the resulting hybrid partitions;iii) selecting the optimal partitions between them;and iv) evaluating their performances. Since we are also interested in analyzing how hybrid partitions are close to the best possible partitions, we compared our results with the ones obtained using partitions found by an oracle, an exhaustive, and a random search. The oracle and exhaustive strategies generate all possible label partitions for a dataset. While the oracle chooses the best one based on the test set alone, the exhaustive choices are based on validation. Experiments on eight well-known multi-label datasets revealed that both our proposal and the random partitions achieve superior or competitive results compared with the others. This indicates that the conventional global and local approaches may not accurately account for label correlations, requiring significant improvements. In this case, clustering the label space can be a solution. © 2023, CC BY.

关键词： Classification (of information)

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A Nonlinear Observer for a Flexible Robot Arm and its Use in Fault and Collision Detection

A Nonlinear Observer for a Flexible Robot Arm and its Use in...

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IEEE Conference on Decision and control

作者： Claudio Gaz Andrea Cristofaro Pasquale Palumbo Alessandro De Luca Department of Mechanical Engineering Faculty of Science Engineering and Computing Kingston University London UK Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Biotechnology and Biosciences University of Milano Bicocca Milan Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

A nonlinear observer is presented for the estimation of the joint velocities and of the deformation modes and their rates for a two-link flexible robot arm, using only motor encoders and a tip position sensor. The state observer can be used then for trajectory tracking control. By monitoring the mismatch between measured and estimated outputs, we can also detect the occurrence of permanent or intermittent system anomalies such as actuator faults or link collisions.

关键词： Jacobian matrices Actuators Trajectory tracking Observers Arms Robot sensing systems Manipulators

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Research on Heterogeneity Problem of Federated Learning Based on Knowledge Distillation-Translate

Research on Heterogeneity Problem of Federated Learning Base...

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New Energy and Power engineering (ICNEPE), International Conference on

作者： Xunjie Liu Haotian Wang Binghui Guo Yutian Wang Haiwei Gao School of Mathematical Sciences Center for Future Blockchain and Privacy Computing Beihang University Beijing China School of Software Beihang University Beijing China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing China School of Economics Beijing Wuzi University Beijing China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331516086

ISBN: (纸本)9798331516093

With rising computing power, data-driven AI algorithms have become essential in fields like image processing, with significant applications in electricity inspection image detection. However, competition and privacy concerns prevent data sharing between electricity companies, limiting the development of smart inspection technologies. Federated learning offers a solution, enabling companies to collaboratively train a global model without sharing raw data, thus enhancing model performance and data efficiency. When data distribution is inconsistent (non-IID), traditional federated learning faces challenges that affect training efficiency and accuracy. To address this, we propose FedTSD, a federated learning algorithm designed for electricity inspection tasks. By dynamically exchanging teacher-student roles between the global and local models, FedTSD uses a central server to transfer additional knowledge, boosting generalization and reducing overfitting. Experiments on CIFAR-10 and SVHN datasets show that FedTSD outperforms traditional methods, achieving higher accuracy and smoother training, with faster convergence, which is crucial for efficient electricity inspections.

关键词： Training Accuracy Federated learning Electricity Companies Inspection Data models Generators Servers Convergence

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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Computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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