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International Conference on Electronics Information and Emergency Communication

作者： Lei Cai Kangning Yao Wenbin Xian Chao Gong The 4th System Department North China Institute of Computing Technology Beijing China Computer and Communication Engineering University of Science and Technology Beijing Beijing China

Resource management in edge computing has always been a hot research topic. In the scenario of multiple edge servers and multiple tasks, this paper proposes a task offloading model that optimizes the total cost by weighting energy consumption and latency to reduce energy consumption and latency. Based on the proposed task offloading model, an improved genetic algorithm is proposed to solve the optimal task offloading strategy. Simulation experiments have shown that applying improved genetic algorithms to task offloading models can accelerate convergence speed, find the optimal computing offloading strategy, and improve system performance.

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Optimized Design of a Haptic Unit for Vibrotactile Amplitude Modulation

Optimized Design of a Haptic Unit for Vibrotactile Amplitude...

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IEEE International Conference on Robotics and Biomimetics

作者： Jingchen Huang Yun Fang Weichao Guo Zhijian Qiao Xinjun Sheng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering and the Meta Robotics Institute Shanghai Jiao Tong University Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Communicating information to users is a crucial aspect of human-machine interaction. Vibrotactile feedback encodes information into spatiotemporal vibrations, enabling users to perceive tactile sensations. It offers advantages such as lightweight, wearability, and high stability, with broad applications in sensory substitution, virtual reality, education, and healthcare. However, existing haptic unit designs lack amplitude modulation capabilities, which limits their applications. This paper proposed an optimized design of the haptic unit from the perspective of vibration amplitude modulation. A modified elastic model was developed to describe the propagation and attenuation mechanisms of vibration in the skin. Based on the model, two types of hierarchical architectural design were proposed. The design incorporated various materials arranged in multiple layers to amplify or attenuate the vibration amplitude as it traveled through the structure. An experimental platform was built to evaluate the performance of the optimized design.

关键词： Vibrations Solid modeling Virtual reality Amplitude modulation Robot sensing systems Motors Stability analysis Skin Haptic interfaces Spatiotemporal phenomena

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Evolutionary Reinforcement Learning via Cooperative Coevolution

arXiv

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arXiv 2024年

作者： Hu, Chengpeng Liu, Jialin Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong

Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration. However, the poor scalability of genetic operators limits the efficiency of optimising high-dimensional neural networks. To address this issue, this paper proposes a novel cooperative coevolutionary reinforcement learning (CoERL) algorithm. Inspired by cooperative coevolution, CoERL periodically and adaptively decomposes the policy optimisation problem into multiple subproblems and evolves a population of neural networks for each of the subproblems. Instead of using genetic operators, CoERL directly searches for partial gradients to update the policy. Updating policy with partial gradients maintains consistency between the behaviour spaces of parents and offspring across generations. The experiences collected by the population are then used to improve the entire policy, which enhances the sampling efficiency. Experiments on six benchmark locomotion tasks demonstrate that CoERL outperforms seven state-of-the-art algorithms and baselines. Ablation study verifies the unique contribution of CoERL's core ingredients. Copyright © 2024, The Authors. All rights reserved.

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Simulation Mathematical Modeling of Radar Signals with a Low Probability of Their Unauthorized Reception 3

Simulation Mathematical Modeling of Radar Signals with a Low...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Galkyn, Sergii Khriapkin, Oleksandr Kaliuzhnyi, Mykola Guo, Qiang National Technical University «kharkiv Polytechnic Institute» Department of Computer Science and Intellectual Property Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of System Engineering Kharkov Ukraine Kharkiv National University of Radio Electronics Problem Research Laboratory Kharkiv Ukraine College of Information and Telecommunication Harbin Engineering University Harbin China

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

a technique and software for modeling radar signals with a low probability of their unauthorized reception at the input of the monitoring tool receiver are presented. The technique is illustrated by the formation of a signal model based on the Costas sequence. The methodology and software are designed to develop methods and algorithms: detection, measurement of parameters, recognition of radio emissions of radio electronic equipment, recognition of radio electronic equipment © 2022 IEEE.

关键词： Radar

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Hybrid Variational Autoencoder for Collaborative Filtering 25

Hybrid Variational Autoencoder for Collaborative Filtering

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

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

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A Composite Decomposition Method for Large-Scale Global Optimization

arXiv

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arXiv 2024年

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China The Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Engineering Westlake University Hangzhou310030 China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74048 United States

Cooperative co-evolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of non-additively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups non-separable variables by recursively considering the interactions between each non-separable variable and the formed non-separable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group non-separable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG series designs. © 2024, CC BY.

关键词： Efficiency

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Concurrent Pipeline Rendering Scheme based on GPU multi-queue and partitioning images

Concurrent Pipeline Rendering Scheme based on GPU multi-queu...

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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

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High Performance Distributed Control Law for Large-Scale Linear systems: A Partitioned Distributed Observer Approach

arXiv

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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap, this paper develops a greedy-idea-based large-scale system partition algorithm, which can significantly reduce the dimension of local observers. Then, the partitioned distributed observer for large-scale systems is proposed to overcome the problem that the system dynamics are difficult to estimate due to the coupling between partitions. Furthermore, the two-layer Lyapunov analysis method is adopted and the dynamic transformation lemma of compact errors is proved, which solves the problem of analyzing stability of the error dynamic of the partitioned distributed observer. Finally, it is proved that the distributed control law based on the partitioned distributed observer can also arbitrarily approximate the control performance of the centralized control law, and the dimension of the local observer is greatly reduced compared with the traditional method. The simulation results show that when the similarity between the physical network and the communication network is about 80%, the local observer dimension is greatly reduced by 90% and the relative error between the performance of the distributed control law and that of the centralized control law is less than 1%. © 2024, CC BY.

关键词： Continuous time systems

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Investigating of Classification Algorithms for Heart Disease Risk Prediction

Journal of Intelligent Medicine and Healthcare

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Journal of Intelligent Medicine and Healthcare 2022年第1期1卷 11-31页

作者： Nisha Mary Bilal Khan Abdullah A Asiri Fazal Muhammad Samar Alqhtani Khlood M Mehdar Hanan Talal Halwani Turki Aleyani Khalaf A Alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied Medical Science Najran UniversityKingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Computer Science Department College of Computer Science and Information SystemNajran UniversitySaudi Arabia Anatomy Department Medicine CollegeNajran UniversitySaudi Arabi

Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVMare evaluated using MAE, RAE, precision, recall, f-measure, and *** overall finding indicates that for the dataset taken from UCI, the SVMclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 forMAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for MAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.

关键词： Heart disease classification techniques evaluation measures

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Utilizing Artificial Neural Networks and Multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis

Utilizing Artificial Neural Networks and Multivariate Patien...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav Manish Rai Professor School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India M.Tech Research Scholar MDS University Ajmer Department of Computer Science and Engineering (AI& ML) Manipal University Jaipur India

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, MCV, MCH, MCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle.

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