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IEEE Transactions on Haptics: Editorial

IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

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Optimizing Gene Expression Analysis Using Clustering Algorithms 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Bikku, Thulasi Ramu, J. Sekhar, J. Chandra Pratap, V. Krishna Pujari, Jeevana Jyothi Computer Science and Engineering Amrita School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Gene expression analysis plays a crucial role in understanding biological processes and diseases. However, the high-dimensional nature of gene expression data poses challenges for its analysis and interpretation. Clustering algorithms have been widely used for identifying patterns in gene expression data, but their effectiveness is limited by the choice of algorithm and parameters used. In this study, we propose an approach for optimizing gene expression analysis using clustering algorithms. Initially, a range of clustering techniques were assessed, encompassing K-means, hierarchical clustering, and DBSCAN. This assessment was conducted using gene expression information sourced from a publicly accessible database. Subsequently, various techniques for feature selection were employed. These techniques encompassed principal component analysis (PCA) as well as correlation-based feature selection (CFS). Their objective was twofold: to decrease the data's dimensionality and to enhance the effectiveness of the clustering process. From the study, we came to know that the combination of K-means clustering with PCA feature selection outperformed other methods in terms of clustering accuracy and stability. We also identified a set of genes that were consistently associated with different clusters, providing insights into the underlying biological processes. Overall, our study demonstrates the importance of careful algorithm and parameter selection for gene expression analysis and highlights the potential of clustering algorithms for identifying meaningful patterns in gene expression data. The suggested methodology entails merging DBSCAN with K-means clustering, which could be extended to diverse high-dimensional datasets for the enhancement of their analysis and comprehension. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Principal component analysis

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Submodular Participatory Budgeting 1

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18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024

作者： Yuan, Jing Tang, Shaojie Department of Computer Science and Engineering University of North Texas Denton United States Department of Management Science and Systems School of Management University at Buffalo Buffalo United States

ISBN: (数字)9789819777983

ISBN: (纸本)9789819777976

Participatory budgeting refers to the practice of allocating public resources by collecting and aggregating individual preferences. Most existing studies in this field often assume an additive utility function, where each individual holds a private utility for each candidate project, and the total utility of a set of funded projects is simply the sum of the utilities of all projects. We argue that this assumption does not always hold in reality. For example, building two playgrounds in the same neighborhood does not necessarily lead to twice the utility of building a single playground. To address this, we extend the existing study by proposing a submodular participatory budgeting problem, assuming that the utility function of each individual is a monotone and submodular function over funded projects. We propose and examine three preference elicitation methods and analyze their performances in terms of distortion. Notably, if the utility function is additive, our aggregation rule designed for threshold approval votes achieves a better distortion than the state-of-the-art approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Budget control

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Demonstration Exam as a Tool to Assess Professional Competencies Level of IT- Specialists 4

Demonstration Exam as a Tool to Assess Professional Competen...

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4th International Conference on Technology Enhanced Learning in Higher Education, TELE 2024

作者： Galkin, Alexander Alexeev, Vladimir Institute of Computer Science Lipetsk State Technical University Lipetsk Russia Lipetsk State Technical University Department of Automated Control Systems Lipetsk Russia

ISBN: (纸本)9798350353532

The paper presents an approach to assess the level of competencies mastery in the field of information technology when studying specialized courses. This approach, which is also called the demonstration exam, consists of complex interaction between educational organizations and employer partners in the development of examination tasks, assessment criteria and conducting procedures. The interaction of educational organizations with partner employers makes it possible to guarantee that students, if they successfully pass the demonstration exam, acquire relevant and in-demand professional competencies in the labor market in the field of information technology. © 2024 IEEE.

关键词： Demonstrations

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Vibrating Particles System Algorithm for Solving Classification Problems

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computer systems science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Electrical Motor Fault Detection System using AI's Random Forest Classifier Technique 6

Electrical Motor Fault Detection System using AI's Random Fo...

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6th IEEE International Conference on Advanced systems and Emergent Technologies, IC_ASET 2023

作者： Yaqub, Raziq Ali, Hassan Bin Abd Wahab, Mohd Helmy Alabama A&m University Departmnet of Electrical Engineering and Computer Science HuntsvilleAL United States Qatar Department of Sustainable Systems Engineering Doha Qatar Departmnet of Computer Engineering Johor Malaysia

ISBN: (纸本)9798350321029

Induction motors are very commonly used to create traction for electric vehicles. In electric vehicles and other rotating machine systems, faults develop during regular operation, which, if unaddressed, can accelerate the damage to machines. Therefore, early detection and prediction of faults hold the key importance in predictive and preventive maintenance. This work focuses on collecting sounds and vibrations data of an electric motor, which are the critical indicators of fault initiation in electric vehicle systems. We use artificial intelligence (AI) to predict and classify the types of faults associated with different vibration frequencies and sound levels. We explore random forest classifier technique for faults prediction and derive promising results for different fault conditions. Results show that we correctly predicted and classified different types of manually induced faults associated with different vibration frequencies and sound levels with a 92% accuracy and 86 to 91 % precision level. © 2023 IEEE.

关键词： Fault detection

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Geometry Meets Vectors: Approximation Algorithms for Multidimensional Packing 42

Geometry Meets Vectors: Approximation Algorithms for Multidi...

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42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2022

作者： Khan, Arindam Sharma, Eklavya Sreenivas, K.V.N. Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Industrial & Enterprise Systems Engineering University of Illinois at Urbana-Champaign IL United States

ISBN: (纸本)9783959772617

We study the generalized multidimensional bin packing problem (GVBP) that generalizes both geometric packing and vector packing. Here, we are given n rectangular items where the ith item has width w(i), height h(i), and d nonnegative weights v1(i), v2(i),..., vd(i). Our goal is to get an axis-parallel non-overlapping packing of the items into square bins so that for all j ∈ [d], the sum of the jth weight of items in each bin is at most 1. This is a natural problem arising in logistics, resource allocation, and scheduling. Despite being well-studied in practice, approximation algorithms for this problem have rarely been explored. We first obtain two simple algorithms for GVBP having asymptotic approximation ratios 6(d+ 1) and 3(1 + ln(d + 1) + Ε). We then extend the Round-and-Approx (R&A) framework [3, 6] to wider classes of algorithms, and show how it can be adapted to GVBP. Using more sophisticated techniques, we obtain better approximation algorithms for GVBP, and we get further improvement by combining them with the R&A framework. This gives us an asymptotic approximation ratio of 2(1 + ln((d + 4)/2)) + Ε for GVBP, which improves to 2.919 + Ε for the special case of d = 1. We obtain further improvement when the items are allowed to be rotated. We also present algorithms for a generalization of GVBP where the items are high dimensional cuboids. © Arindam Khan, Eklavya Sharma, and K. V. N. Sreenivas;licensed under Creative Commons License CC-BY 4.0.

关键词： Approximation algorithms

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Internet of Things (IoT) based Model for Water Management System

Internet of Things (IoT) based Model for Water Management Sy...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Haque, Md. Alimul Ahmad, Sultan Eljialy, A.E.M. Uddin, Mohammed Yousuf Sonal, Deepa Veer Kunwar Singh University Department of Computer Science Ara India College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Department of Computer Science Alkharj11942 Saudi Arabia Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali140413 India College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Department of Information Systems Alkharj11942 Saudi Arabia Patna Women's College Patna University Department of Computer Science Patna India

ISBN: (纸本)9798350347050

A significant decline in water quality assurance in the supply system is a factor related to public health. Clean water demand has increased over the last decade, and supply in India is uncertain. Water pressure is now the world's most significant obstacle. Water management can then take place, and the most important factor is how the services available worldwide are handled. One of the main concerns around sustainable globalization is water contamination. The traditional water quality analysis methods need a lot of time and effort. The standard must be tracked in real-time to ensure the safe delivery of drinking water. This paper presents the design and implementation of an affordable framework for real-time water quality monitoring and filtering in IoT. The proposed model helps collect, distribute, and analyze the data on water quality in real time. This allows the framework to respond automatically to changes in system performance. Data and analysis have been given to support the experiment of the proposed model. The model also provides a consumer interface to demonstrate the performance indicators such as lower-top and acceptable levels with infographics and meters. © 2023 IEEE.

关键词： pH sensors

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Shortest Job First with Gateway-based Resource Management Strategy for Fog Enabled Cloud Computing 4

Shortest Job First with Gateway-based Resource Management St...

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4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Anbazhagan, K. Kumar, Rotash Thilagavathy, R. Anuradha, D. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Government Engineering College Department of Electronics and Communication Engineering Rajasthan Jhalawar India College of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350393354

In recent years, fog computing has extended cloud services to the network edge, lowering network latency and congestion and overcoming cloud computing restrictions. Many hurdles, however, must be overcome before the benefits of the fog-IoT paradigm for low-latency applications can be completely realized. The most challenging difficulty in IoT applications is developing efficient resource management strategies that maximize throughput while minimizing delay. IoT and fog computing, rather than simply utilizing cloud computing technology, have brought intriguing new data management solutions. Fog computing makes real-time analytics applications conceivable. After combining fog computing technologies with Internet of Things (IoT) applications, the system can reply in milliseconds. Existing fog resource managers focus on a subset of fog resource management parameters such as system response time, network bandwidth, energy consumption, and latency. No Fog resource management takes multiple factors into account at the same time for decision-making, which will become increasingly crucial in intelligent homes. Gateway-based Fog Computing pioneered the introduction of the shortest job (SJFGFC) to address these difficulties. The technique is validated in an IoT-based imaginative house automation scenario before being evaluated in a small-scale bright home experiment using the iFogSim toolbox and empirical models. © 2024 IEEE.

关键词： Fog computing

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