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A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

computers, Materials & Continua 2024年第12期81卷 3561-3582页

作者： Jianyong Wang Mingliang Gao Qilei Li Hyunbum Kim Gwanggil Jeon School of Electrical and Electronic Engineering Shandong University of TechnologyZibo255000China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK Department of Embedded Systems Engineering Incheon National UniversityIncheon22012Republic of Korea

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public *** counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and *** approaches vary in terms of supervision techniques,network architectures,and model ***,most crowd counting methods rely on fully supervised learning,which has proven to be ***,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often *** a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical *** paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision *** summarize and critically analyze the key methods in these two categories,highlighting their strengths and ***,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark *** believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

关键词： Crowd counting density estimation convolutional neural network(CNN) un/semi-supervised learning

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Short-term load forecasting of mixed industrial users in distribution networks using DARTS-LSTM method

Short-term load forecasting of mixed industrial users in dis...

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2024 International Symposium on New Energy Technologies and Power systems, NETPS 2024

作者： Shi, Wenyu Yang, Dechang Dehghaniang, Payman College of Information and Electrical Engineering China Agricultural University Beijing100083 China Department of Electrical and Computer Engineering George Washington University Washington United States

Load forecasting has perennially stood as a fundamental pillar within the power industry, with precision in prognostication constituting a paramount necessity for critical functions including grid management, optimization of energy storage, and response to demand fluctuations. In the present study, we introduce a comprehensive global modeling prediction framework aimed at forecasting large-scale power demand within time series data, alongside the development of a novel short-term power load forecasting model. By integrating all time series data pertaining to the same regression task and employing a unified univariate prediction function across the entire time series dataset, we efficaciously forecast large-scale heterogeneous loads aggregated at various levels within distribution networks. The proposed framework leverages the cutting-edge DARTS (Differentiable Architecture Search) deep learning framework for implementation. Through comprehensive experimentation on real-world data, our model framework demonstrates superior performance in terms of overall prediction accuracy, scalability, and robustness in handling missing and non-stationary time series data. © Published under licence by IOP Publishing Ltd.

关键词： Demand response

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural Computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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Guest Editorial Special Issue on Self-Powered Sensors and Wearable Electronic systems

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第4期3卷 118-119页

作者： Nag, Anindya Ozioko, Oliver Kim, Woo Soo Andrews, Joseph Technische Universität Dresden Faculty of Electrical and Computer Engineering Dresden01062 Germany Dresden01069 Germany University of Derby Department of Electrical and Electronic Engineering DerbyDE22 1GB United Kingdom Simon Fraser University School of Mechatronic Systems Engineering BurnabyBCV5A 1S6 Canada University of Wisconsin-Madison Department of Mechanical Engineering and Department of Electrical and Computer Engineering MadisonWI53706 United States

Wearable sensing has recently been highly preferred due to its quick and accurate measurement of physiological parameters. These sensors have been devised using various polymers [1], [2] and nanomaterials [3], [4] suited for the chosen application. With the exponential growth of wearable electronics [5], [6], [7], there is a need to broaden their capabilities in terms of functionality and availability. Commercializing these wearable electronics needs further encouragement to use these sensors as point-of-care devices. Self-powered sensors [8], [9] are one of the growing aspects in the sector of wearable sensing. With the growing requirement for energy usage, self-powered sensing systems need to be developed to generate and harvest energy ubiquitously [10], [11]. This Special Issue highlights some of the published papers that work on using smart textiles and self-powered devices for efficient and sustainable sensing applications. © 2022 IEEE.

关键词： Wearable sensors

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Joint Access Selection and Computation Offloading in LEO Ubiquitous Edge Computing Networks: An Alternating DRL-Based Approach

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1870-1886页

作者： Yang, Junyi Zhang, Yuanjun Xiao, Zhenyu Han, Zhu Beihang University School of Electronic and Information Engineering Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the increase in users' service diversity and demand for quality of experience (QoE), the utilization of low earth orbit (LEO) satellite networks for assisted or independent offloading of computation tasks has become a promising trend. However, due to the high mobility and uneven resource distribution of LEO satellites, it is difficult to meet the requirements of low delay and low overhead by using traditional optimization algorithms or common reinforcement learning (RL) algorithms. Therefore, in this paper, we consider a scenario in which terrestrial users offload delay-sensitive (DS) computation tasks to a LEO satellite network in order to study the joint access selection and computation offloading problem. First, we analyze the characteristics of the scenario and the computation tasks, and establish a generic mathematical model. Then, based on the block descent coordinate (BCD) principle, we propose a novel algorithm of alternating Dueling DQN (ADDQN) for the joint decision-making problem, where access selection and computation offloading are performed with corresponding independent agent respectively. Comprehensive simulations show that compared with other benchmark algorithms, the proposed method not only has better convergence, but also can maximize the number of successfully completed sub-tasks and the optimization objective value meanwhile reducing the unnecessary access handovers. © 2024 IEEE.

关键词： Satellites Computational modeling Decision making Low earth orbit satellites Handover Benchmark testing Mathematical models Quality of experience Optimization Convergence

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Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

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Intelligent Automation & Soft Computing 2023年第4期36卷 299-312页

作者： Abdullah A.Asiri Bilal Khan Fazal Muhammad Shams ur Rahman Hassan A.Alshamrani Khalaf A.Alshamrani Muhammad Irfan Fawaz F.Alqhtani Department of Radiological Sciences College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Computer Science City University of Science and Information TechnologyPeshawarPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science Engineering University of Engineering and TechnologyMardan23200Pakistan Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause *** technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of ***(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic *** diseases that cause death need to be identiﬁed through such techniques and technologies to overcome the mortality *** brain tumor is one of the most common causes of *** have already proposed various models for the classiﬁcation and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classiﬁcation process with improved efﬁ***,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving *** the Kaggle dataset,these strategies are tested using classiﬁcation accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation *** results show that SVM outperforms other algorithms,with 95.3%accuracy.

关键词： MRI images brain tumor machine learning-based classiﬁcation

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain systems

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computer Modeling in engineering & Sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Learning Media Advancement Using Virtual Reality Technology for Heavy Equipment Safety in Indonesian Vocational Schools

Learning Media Advancement Using Virtual Reality Technology ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Al Mubarok, Shofiyul Anam Nugroho, Supeno Mardi Susiki Muqtadiroh, Feby Artwodini Sumpeno, Surya Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798350350821

Mining Geology is one of the study programs provided by Indonesian Vocational Schools that explores surface mining. Mining is a work in hazardous environments that poses numerous risks. The use of heavy equipment is a frequent cause of mining accidents. Therefore, enhancing vocational students' understanding of heavy equipment safety is essential. However, having heavy equipment for learning its safety is costly and complex maintenance, demanding substantial financial investment from schools. Moreover, many vocational schools still use traditional learning media that cannot provide an immersive experience in learning. Thus, this study developed an immersive and engaging virtual reality (VR)-based learning media for vocational students learning heavy equipment safety. The validity and feasibility of the learning media contents were evaluated by experts. The result concluded that the contents presented are valid and have good feasibility. Moreover, the learning media was tested in the learning process and the usability was assessed by vocational students. The result concluded that the learning media demonstrates significant potential to improve the educational experience in vocational schools, leading to better learning outcomes and increased student engagement. © 2024 IEEE.

关键词： Virtual environments

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Learning hyperspectral images from RGB images via a coarse-to-fine CNN

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Science China(information Sciences) 2022年第5期65卷 51-64页

作者： Shaohui MEI Yunhao GENG Junhui HOU Qian DU School of Electronics and Information Northwestern Polytechnical University Department of Computer Science City University of Hong Kong Department of Electrical and Computer Engineering Mississippi State University

Hyperspectral remote sensing is well-known for its extraordinary spectral distinguishability to discriminate different materials. However, the cost of hyperspectral image(HSI) acquisition is much higher compared to traditional RGB imaging. In addition, spatial and temporal resolutions are sacrificed to obtain very high spectral resolution owing to the limitations of sensor technologies. Therefore, in this paper, HSIs are reconstructed using easily acquired RGB images and a convolutional neural network(CNN). As a result,high spatial and temporal resolution RGB images can be inherited to HSIs. Specifically, a two-stage CNN,referred to as the spectral super-resolution network(SSR-Net), is designed to learn the transformation model between RGB images and HSIs from training data, including a band prediction network(BP-Net) to estimate hyperspectral bands from RGB images and a refinement network(RF-Net) to further reduce spectral distortion in the band prediction step. As a result, the learned joint features in the proposed SSR-Net can directly predict HSIs from their corresponding scenes in RGB images without prior knowledge. Experimental results obtained on several benchmark datasets demonstrate that the proposed SSR-Net outperforms several state-of-the-art methods by ensuring higher quality in HSI reconstruction, and significantly improves the performance of traditional RGB images in classification.

关键词： hyperspectral reconstruction convolutional neural network deep learning

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Improved Multi-Path Routing for QoS on MANET

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computer systems Science & engineering 2023年第6期45卷 2521-2536页

作者： M.Vargheese Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A Mobile Ad hoc NETwork(MANET)is a self-configuring network that is not reliant on *** paper introduces a new multipath routing method based on the Multi-Hop Routing(MHR)*** is the consecutive selection of suitable relay nodes to send information across nodes that are not within direct range of each *** to ensure good MHR leads to several negative consequences,ultimately causing unsuccessful data transmission in a *** research work consists of three *** first to attempt to propose an efficient MHR protocol is the design of Priority Based Dynamic Routing(PBDR)to adapt to the dynamic MANET environment by reducing Node Link Failures(NLF)in the *** is achieved by dynamically considering a node’s mobility parameters like relative velocity and link duration,which enable the next-hop *** method works more efficiently than the traditional *** the second stage is the Improved Multi-Path Dynamic Routing(IMPDR).The enhancement is mainly focused on further improving the Quality of Service(QoS)in MANETs by introducing a QoS timer at every node to help in the QoS routing of *** QoS is the most vital metric that assesses a protocol,its dynamic estimation has improved network performance *** method uses distance,linkability,trust,and QoS as the four parameters for the next-hop *** is compared against traditional routing *** Network Simulator-2(NS2)is used to conduct a simulation analysis of the protocols under *** proposed tests are assessed for the Packet Delivery Ratio(PDR),Packet Loss Rate(PLR),End-to-End Delay(EED),and Network Throughput(NT).

关键词： Multi-path routing quality of service node-link failure packet delivery ratio

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