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arXiv 2024年

作者： Maric, Bruno Zoric, Filip Petric, Frano Orsag, Matko The LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

Programming by demonstration (PbD) is a simple and efficient way to program robots without explicit robot programming. PbD enables unskilled operators to easily demonstrate and guide different robots to execute task. In this paper we present comparison of demonstration methods with comprehensive user study. Each participant had to demonstrate drawing simple pattern with human demonstration using virtual marker and kinesthetic teaching with robot manipulator. To evaluate differences between demonstration methods, we conducted user study with 24 participants which filled out NASA raw task load index (rTLX) and system usability scale (SUS). We also evaluated similarity of the executed trajectories to measure difference between demonstrated and ideal trajectory. We concluded study with finding that human demonstration using a virtual marker is on average 8 times faster, superior in terms of quality and imposes 2 times less overall workload than kinesthetic teaching. © 2024, CC BY.

关键词： Demonstrations

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Adaptive Neuro Fuzzy Inference Systems Based Maximum Power Point Tracking for a Photovoltaic System Connected to a Grid 3rd

Adaptive Neuro Fuzzy Inference Systems Based Maximum Power P...

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3rd International Conference on control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Mohammed, Karam Khairullah Buyamin, Salinda Mekhilef, Saad Rosmin, Norzanah Shamsudin, Mohamad Amir Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia

ISBN: (纸本)9789811939228

This paper presented an adaptive neuro fuzzy inference system (ANFIS) for photovoltaic (PV) connected to the grid system for fast and precise maximum power point tracking (MPPT) in various weather conditions. Due to the high cost of sensors and the inaccuracy of irradiance readings, this study suggested an ANFIS approach for tracking maximum power without irradiance and voltage sensors. Instead, it relied on an equation to estimate the irradiance value. The generated power by PV is converted to the grid via a voltage source converter (VSC). MATLAB/SIMULINK has been used to simulate the proposed controller. As per simulation results, the proposed method was shown to be effective in all of the tested cases. A comparison with particle swarm optimization algorithm (PSO) and Perturb and Observe (P&O) has been provided to assess the efficacy of the proposed method. The results demonstrated the efficacy of the proposed technique by providing a rapid and precise tracking in 0.02 s with high efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy systems

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REQUIRED NUMBER OF ITERATIONS FOR SPARSE SIGNAL RECOVERY VIA ORTHOGONAL LEAST SQUARES

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Journal of Computational Mathematics 2023年第1期41卷 1-17页

作者： Haifeng Li Jing Zhang Jinming Wen Dongfang Li Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control College of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China College of Information Science and Technology Jinan UniversityGuangzhou 510632China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise *** least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery *** this paper,we investigate the number of iterations required for recovering x with the OLS *** show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.

关键词： Sparse signal recovery Orthogonal least squares(OLS) Restricted isometry property(RIP)

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Adaptive Global-local Fusion Network Based Deep Unsupervised Hashing for Remote Sensing Image Retrieval

Adaptive Global-local Fusion Network Based Deep Unsupervised...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zekun Xu Yipeng Zhou Quan Z. Sheng Chao Li Tongtong Lou Weipeng Jing College of Computer and Control Engineering Northeast Forestry University Harbin China School of Computing Macquarie University Sydney Australia

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Unsupervised hashing methods have gained wide-spread popularity for remote sensing (RS) image retrieval due to their high efficiency. Existing methods heavily rely on similarity matrix generated by pre-trained models as supervised signals. However, such pre-trained models obtained from natural images fail to comprehensively extract features in RS images, yielding unreliable similarity relationships. Considering complex features in RS images, we propose a novel adaptive global-local fusion network based deep unsupervised hashing (AFDUH) method. The advantages of AFDUH lie in the use of large kernel convolution and bi-level routing attention mechanism for learning local and global features simultaneously. AFDUH fuses these features under different resolutions in an interactive fashion to deduce high-quality similarity matrix based on contrastive learning. Besides, we embed a sample selection strategy in AFDUH, which can filter out unreliable supervised signals to further improve retrieval accuracy. Extensive experiments on two RS datasets demonstrate that AFDUH outperforms the state-of-the-art baselines.

关键词： Adaptation models Adaptive systems Accuracy Image retrieval Feature extraction Transformers Routing

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Design of Personalization Warehouse Management Platform Based on SaaS Model 25

Design of Personalization Warehouse Management Platform Base...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Guo, Qi Sun, Hongbo Ji, Wen Yantai University School of Computer and Control Engineering Shandong Yantai264005 China Institute of Computing Technology Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9781665405270

SaaS is widely used in the fields of operation management, business process outsourcing, data analysis, and information security. However, with the improvement of the degree of information, the generalized SaaS platform is unable to satisfy the requirements of enterprise personalization. SaaS products face great challenges in personalized customization technology due to the application of multi-tenant architecture. The challenges include multi-tenant customization in SaaS model, data isolation during customization, and mapping of multi-tenant virtual warehouse locations to actual locations. Therefore, we decompose personalization technology into metadata driver, cloud data placement and mapping mechanism for research. We decompose the personalized technologies into metadata drive, cloud data placement, and mapping mechanism. In order to solve the problem that traditional personalization customization is unable to be applied in the SaaS field, we propose a multi-tenant personalized warehousing mode architecture, designed a personalization warehouse management platform based on SaaS model, and realized SaaS-based data isolation, interface customization, rapid positioning, and on-demand customization services. Experimental results show that the proposed multi-tenant personalized warehouse model can achieve data isolation and customization, and reflect the advantages of highly automated warehousing, shared storage resources, and on-demand customization in terms of warehouse management, data security, and user experience. © 2022 IEEE.

关键词： Metadata

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Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods 9

Classification of Attention Performance Post-Longitudinal tD...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Indian Institute of Technology Mandi Applied Cognitive Science Laboratory Mandi India Indian Institute of Technology Mandi School of Computing And Electrical Engineering Mandi India Institute of Nuclear Medicine And Allied Sciences Cognitive Control And Machine Learning Group Delhi India

ISBN: (纸本)9798350394474

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention. © 2024 IEEE.

关键词： Electroencephalography

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Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks 2

Residual Network and Embedding Usage: New Tricks of Node Cla...

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2nd International Conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2021

作者： Chi, Huixuan Wang, Yuying Hao, Qinfen Xia, Hong Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks. In the literature, however, most tricks or techniques are either briefly mentioned as implementation details or only visible in source code. In this paper, we first summarize some existing effective tricks used in GCNs mini-batch training. Based on this, two novel tricks named GCN_res Framework and Embedding Usage are proposed by leveraging residual network and pre-trained embedding to improve baseline’s test accuracy in different datasets. Experiments on Open Graph Benchmark (OGB) show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%∼2.84%. We open source our implementation at https://***/ytchx1999/PyGOGB-Tricks. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Convolution

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Diversity and Freshness-Aware Regret Minimizing Set Queries 29th

Diversity and Freshness-Aware Regret Minimizing Set Queries

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29th International computing and Combinatorics Conference, COCOON 2023

作者： Guo, Hongjie Li, Jianzhong Shen, Fangyao Gao, Hong Faculty of Computing Harbin Institute of Technology Heilongjiang Harbin China School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783031491924

Multi-criteria decision-making often involves selecting a small representative set from a database. A recently proposed method is the regret minimization set (RMS) queries. It aims to rectify the shortcomings of needing a utility function in top-k queries and the overly large result size of skyline queries. However, the existing definition of RMS only ensures one result under any utility function, and do not consider the diversity and freshness of the returned results. In this paper, we define a strong regret set, which guarantees the utility value error of k data points under any utility function. Given this new definition, we propose two problems, namely the Minimum Size problem and the Max-sum Diversity and Freshness problem. Both proposed problems have been proven to be NP-hard. Correspondingly, we devise approximation algorithms for them, and analyze algorithms’ time complexities and the approximation ratios of the solutions obtained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Approximation algorithms

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Dynamic Formation Tracking of Multi-agent Systems with Bounded Unknown Leader Input 5th

Dynamic Formation Tracking of Multi-agent Systems with Bou...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Su, Piaoyi Dong, Xiwang Zhao, Qin Li, Qingdong Ren, Zhang School of Automation Science and Electronic Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Beijing Institute of Control and Electronics Technology Beijing100038 China

ISBN: (纸本)9789811939976

This literature investigate the dynamic formation tracking problem of multiple Lagrange systems with linear nonautonomous leader over directed topology. In order to estimate the leader system, a distributed control scheme is proposed firstly by constructing adaptive distributed observer. Secondly, this paper design a nonlinear function to decrease the influence from the leader’s bounded unknown input. Then, the dynamic formation tracking problem of multiple Euler-Lagrange systems with nonlinear uncertainties and disturbances is solved by distributed tracking algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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ROCFL: A Robust Clustered Federated Learning Framework towards Heterogeneous Data

ROCFL: A Robust Clustered Federated Learning Framework towar...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Zihan Guan, Zhitao Yuan, Shuai An, Ningyu Liang, Xiao North China Electric Power University School of Control and Computer Engineering Beijing China Canada State Grid Smart Grid Research Institute State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350357677

As a decentralized machine learning approach, federated learning has become a key solution for numerous applications. However, one primary challenge lies in the data heterogeneity issue when developing effective federated learning algorithms. While many existing frameworks address the concern of non-independent and identically distributed data(non-IID), they have not considered heterogeneity of data quality. Therefore, we propose ROCFL, a robust clustered federated learning method in this study, which can amplify the disparity in weight allocation between models trained on different quality data. We first develop an optimal clustering matching mechanism that groups clients with similar data distributions. This allows the optimal clustering model to be derived without specifying a predetermined number of clusters. Next, we introduce a personalized weight allocation strategy, which assigns a weight benchmark to each cluster based on its cluster importance index. This strategy mitigates the negative impacts of low-quality data during model aggregation. Finally, we design a federated aggregation strategy grounded in a sampling approach. It not only ensures unbiased sampling but also significantly reduces both computational and communication overheads. Experiments have been carried out and the results demonstrate that higher accuracy and robustness can be achieved by ROCFL in scenarios wherein data distribution and quality heterogeneity coexist, in comparison with the benchmarks. © 2023 IEEE.

关键词： Learning algorithms

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