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ISHD:Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

computer Modeling in engineering & Sciences 2023年第10期137卷 509-526页

作者： Wu Song Yayuan Tang Wenxue Tan Sheng Ren School of Computer and Electrical Engineering Hunan University of Arts and SciencesChangde415000China School of Information Engineering Hunan University of Science and TechnologyYongzhou425119China School of Computer Science and Engineering Central South UniversityChangsha410083China

In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.

关键词： Deep learning object detection video surveillance of exam room smart examination environment

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Vision-Based Navigation for a Small-Sized Drone: A Simple AI Strategy at IMAV'22

Vision-Based Navigation for a Small-Sized Drone: A Simple AI...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Farooq, Adil Nain Zukhraf, Syeda Zillay Shafi, Sadman Kamal, Mohsin Chumuang, Narumol University Of Cyprus Department Of Electrical And Computer Engineering Cyprus Penn State University Department Of Electrical Engineering United States National University Of Sciences And Technology School Of Electrical Engineering And Computer Science Pakistan Muban Chombueng Rajabhat University Department Of Digital Media Technology Thailand

ISBN: (纸本)9798350370096

Autonomous drone racing competitions serve as a testing ground for enhancing the perceptual, planning, and control aspects of micro unmanned aerial vehicles (MAVs). This study thoroughly outlines the strategy, methodology, and technical development of a lightweight collision avoidance module for small drones, employed during our participation in the IMAV 2022 Nanocopter AI Challenge held at TU Delft, The Nether-lands. Tailored for nano-sized drones, this competition presented challenges related to agile maneuvers, obstacle avoidance, and navigation in a dynamic indoor arena. We provide detailed insights into our obstacle avoidance algorithm, employing an AI computer vision approach and showcasing its robust performance in dynamic environments. The accuracy of the proposed strategy was verified through simulation testing, highlighting the effectiveness of our approach in a simulated Webots environment across diverse parameter settings. The shared insights aim to contribute significantly to the ongoing evolution of autonomous drone navigation. Moreover, the simulation of our proposed strategy can be seen on the YouTube video link: https://***/HVZPAfyf57M. © 2024 IEEE.

关键词： Drones

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HoLens:A visual analytics design for higher-order movement modeling and visualization

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Computational Visual media 2024年第6期10卷 1079-1100页

作者： Zezheng Feng Fang Zhu Hongjun Wang Jianing Hao Shuang-Hua Yang Wei Zeng Huamin Qu Department of Computer Science and Engineering The Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Thrust of Computational Media and Arts(CMA) The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511458China Department of Computer Science University of ReadingBerkshire RG66AHUK Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement *** methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the ***,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban *** overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban *** mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral ***,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state *** real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using *** also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.

关键词： data visualization movement modeling state sequence visualization movement visualization urban visual analytics

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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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Handwritten Digital Image Recognition based on Fusion of Multiple Machine Vision Algorithms 6

Handwritten Digital Image Recognition based on Fusion of Mul...

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6th International Conference on Information Technologies and electrical engineering, ICITEE 2023

作者： Lin, Bingrong Yang, Zhi Yang, Minsheng Xie, Xiaole Wang, Xiong Liu, Huiting Lan, Yang College of Computer and Electrical Engineering Huan University of Arts and Science China College of Computer and Electrical Engineering Huan University of Arts and Science Key Laboratory of Distributed Electric Propulsion Vehicle Control Technology China

ISBN: (纸本)9798400708299

Handwritten digit recognition technology is increasingly used in a wide range of fields, but the recognition rate still has room for further improvement. Currently, there are a large number of studies focusing on the use of activation functions to improve the handwritten digit recognition rate, compared with ReLU, Sigmod and other activation functions, this paper chooses to take the Mish activation function to optimise the handwritten digit recognition algorithm, using Pytorch machine learning framework, designed a deep convolutional neural network model based on the MNIST dataset to carry out digit recognition, and to improve the accuracy of model recognition through the learning of image features for digit classification to improve the model recognition accuracy. The experimental results show that the Mish activation function can increase the accuracy of handwritten digit recognition to 99%. Keywords: Handwritten digit recognition, Deep convolutional neural network, Mish activation function, MNIST Dataset, Pytorch © 2023 ACM.

关键词： Chemical activation

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Double Mutational Salp Swarm Algorithm:From Optimal Performance Design to Analysis

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Journal of Bionic engineering 2023年第1期20卷 184-211页

作者： Chao Lin Pengjun Wang Xuehua Zhao Huiling Chen College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou325035China School of Digital Media Shenzhen Institute of Information TechnologyShenzhen518172China

The Salp Swarm Algorithm(SSA)is a population-based Meta-heuristic Algorithm(MA)that simulates the behavior of a group of salps foraging in the *** the basic SSA has stable exploration capability and convergence speed,it still can fall into local optimum when solving complex optimization problems,which may be due to low utilization of population information and unbalanced exploration-to-exploitation ***,this study proposes a Double Mutation Salp Swarm Algorithm(DMSSA).In this study,a Cuckoo Mutation Strategy(CMS)and an Adaptive DE Mutation Strategy(ADMS)are introduced into the structure of the original *** former mutation strategy is summarized as three basic operations:judgment,shuffling,and *** purpose is to fully consider the information among search agents and use the differences between different search agents to participate in the update of positions,making the optimization process both diverse in exploration and minor in *** latter strategy employs three basic operations:selection,mutation,and *** the follower part,some individuals do not blindly adopt the original follow ***,the global optimal position and differences are considered,and the variation factor is adjusted adaptively,allowing the new algorithm to balance exploration,exploitation,and convergence *** evaluate the performance of DMSSA,comparisons are made with numerous algorithms on 30 IEEE CEC2014 benchmark *** statistical results confirm the better performance and significant difference of DMSSA in solving benchmark function ***,the applicability and scalability of DMSSA to optimization problems with constraints are further confirmed in three experiments on classical engineering design optimization *** source code of the proposed algorithm will be available at:https://***/ncjsq/Double-Mutational-Salp-Swarm-Algorithm.

关键词： Salp swarm algorithm Meta-heuristic algorithm Global optimization-Exploration Exploitation Bionic

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Investigating Haptic Co-creation with Reinforcement Learning 14th

Investigating Haptic Co-creation with Reinforcement Learnin...

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14th International Conference on Human Haptic Sensing and Touch Enabled computer Applications, EuroHaptics 2024

作者： Regimbal, Juliette Cooperstock, Jeremy R. Department of Electrical and Computer Engineering McGill University Montréal Canada Centre for Interdisciplinary Research in Music Media and Technology McGill University Montréal Canada

ISBN: (纸本)9783031700606

Co-creative computer agents can be used to support designers they work alongside. However, this approach to co-creativity has not been studied in haptics. In this paper, we investigate this topic by applying an interactive reinforcement learning technique developed for sound synthesis to a two degree-of-freedom force-feedback design space. We developed a prototype authoring tool to study the interaction between designers and this type of co-creative agent. As these agents are trained on the designer’s feedback in real-time, this method is flexible and could be applied to other hardware and software configurations. Five participants were recruited to use the tool in two artistic tasks and were interviewed about their experiences. An analysis of the participants’ comments and actions indicated that co-creation in haptics can help designers explore new effects if they are sufficiently able to control the extent and direction of exploration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

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Journal of Bionic engineering 2023年第3期20卷 1296-1332页

作者： Chao Lin Pengjun Wang Ali Asghar Heidari Xuehua Zhao Huiling Chen College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035ZhejiangChina College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou325035China School of Digital Media Shenzhen Institute of Information TechnologyShenzhen518172China

The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

关键词： Salp swarm algorithm Swarm intelligence Global optimization Exploration Exploitation

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Hierarchical Transfer Multi-task Learning Approach for Scene Classification 27th

Hierarchical Transfer Multi-task Learning Approach for Scen...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khoshkangini, Reza Tajgardan, Mohsen Jamali, Mahtab Ljungqvist, Martin Georg Mihailescu, Radu-Casian Davidsson, Paul Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Axis Communications AB Lund Sweden

ISBN: (纸本)9783031781063

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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An Intelligent Diagnostic Framework Based on Digital Twins and Partial Transfer Learning: Methodology and Industrial Application

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 1-13页

作者： Azari, Mehdi Saman Ricci, Liana Santini, Stefania Flammini, Francesco Linnaeus University Department of Computer Science and Media Technology Växjo352 52 Sweden GE Power Sweden Steam Power Unit Norrköping602 23 Sweden University of Naples Federico II Department of Electrical Engineering and Information Technology Naples80125 Italy Mälardalen University School of Innovation Design and Engineering Eskilstuna632 17 Sweden Linnaeus University Department of Computer Science and Media Technology Växjö352 52 Sweden

In Industry 4.0, efficient fault diagnosis is crucial for predictive maintenance but is often hindered by significant domain shifts between training and testing domains and lack of training datasets, limiting the effectiveness of machine learning in practice. Transfer learning has been used to address these challenges by utilizing knowledge from similar source domains. However, the scarcity of faulty data from real machines and the difficulty of obtaining labeled datasets from lab machines as source domain pose significant obstacles. This paper presents a novel diagnostic framework that integrates digital twins and transfer learning to overcome these limitations. Digital twins generate training datasets, while a model update strategy based on parameter sensitivity analysis improves adaptability. The framework also incorporates a partial transfer diagnostic model with a double attention mechanism to handle data distribution discrepancies and label inconsistencies between digital twins and real machines. Validated on an industrial rotating machine case study using real data, the proposed approach improves diagnostic accuracy by over 10% compared to state-of-the-art methods. © 2023 IEEE.

关键词： Predictive maintenance

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