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research on pedestrian detection based on multi-level fine-grained YOLOX algorithm

International Journal of Intelligent Computing and Cybernetics 2023年第2期16卷 295-313页

作者： Hong Wang Yong Xie Shasha Tian Lu Zheng Xiaojie Dong Yu Zhu Department of Computer Science South-Central Minzu UniversityWuhanChina Hubei Provincial Engineering Research Center of Agricultural Blockchain and Intelligent Management WuhanChina Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises WuhanChina

Purpose-The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian *** study proposes a multi-level fine-grained YOLOX pedestrian detection ***/methodology/approach-First,to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion,this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer ***,for the CSPLayer of the PAFPN,a weight gain-based normalization-based attention module(NAM)is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of ***,the authors experimentally determined the optimal values for the confidence loss ***-The experimental results show that,compared with the original YOLOX algorithm,the AP of the improved algorithm increased by 2.90%,the Recall increased by 3.57%,and F1 increased by 2%on the pedestrian *** limitations/implications-The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target ***/value-The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.

关键词： Pedestrian detection Multi-scale feature fusion Small object Occluded pedestrians

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WindVibraTransformer: A Foundational Model for Precise and Robust Wind Turbine Condition Monitoring via Vibration Signals 23

WindVibraTransformer: A Foundational Model for Precise and R...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Wakayama, Takuya Inoue, Taiki Ogata, Jun Iida, Makoto Ogawa, Tetsuji Waseda University Department of Communications and Computer Engineering Japan Japan Research Center for Advanced Science and Technology The University of Tokyo Japan

ISBN: (纸本)9798350374889

We aimed to develop WindVibraTransformer, a foundational model based on Transformers applied to vibration spectrograms, to derive precise and robust feature representations for wind turbine condition monitoring. In anomaly detection for wind turbine equipment, it is common to employ inlier modeling, where models are built solely on data from normal operation, with deviations classified as anomalies. Acquiring data for wind turbines, especially from the specific turbine being monitored, is challenging, often resulting in limited availability. However, this limitation can increase susceptibility to false alarms due to environmental variations. Thus, in this study, we explored the use of a vision Transformer trained with self-supervised learning to predict masked spectra conditioned on unmasked ones from vibration spectrograms, serving as the precise and robust feature extractor. Since this model aims to capture relationships within the context of the surroundings rather than solely the overview of the spectrogram, the resulting feature representations are expected to be complex yet less directly influenced by environmental variations. The complexity of these feature representations is addressed by constructing the normal state model using highly expressive normalizing flows. Experimental comparisons conducted on anomaly detection using real wind turbine vibration signals demonstrated that our approach enables highly reliable detection compared to existing unsupervised auto-encoder-based and supervised wind turbine classifier-based feature extraction methods. © 2024 IEEE.

关键词： Spectrographs

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Blockchain-based privacy and security preserving in electronic health: a systematic review

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Multimedia Tools and Applications 2023年第18期82卷 28493-28519页

作者： Kiania, Kianoush Jameii, Seyed Mahdi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

In today’s world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Smart contract

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AAD-LLM: Adaptive Anomaly Detection Using Large Language Models

AAD-LLM: Adaptive Anomaly Detection Using Large Language Mod...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Russell-Gilbert, Alicia Sommers, Alexander Thompson, Andrew Cummins, Logan Mittal, Sudip Rahimi, Shahram Seale, Maria Jaboure, Joseph Arnold, Thomas Church, Joshua Mississippi State University Computer Science & Engineering United States Engineer Research and Development Center Department of Defence United States

ISBN: (纸本)9798350362480

For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically, traditional PdM approaches are not transferable or multimodal. This work examines the use of Large Language Models (LLMs) for anomaly detection in complex and dynamic manufacturing systems. The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications. The research also seeks to enable more collaborative decision-making between the model and plant operators by allowing for the enriching of input series data with semantics. Additionally, the research aims to address the issue of concept drift in dynamic industrial settings by integrating an adaptability mechanism. The literature review examines the latest developments in LLM time series tasks alongside associated adaptive anomaly detection methods to establish a robust theoretical framework for the proposed architecture. This paper presents a novel model framework (AAD-LLM) that doesn't require any training or finetuning on the dataset it is applied to and is multimodal. Results suggest that anomaly detection can be converted into a "language"task to deliver effective, context-aware detection in data-constrained industrial applications. This work, therefore, contributes significantly to advancements in anomaly detection methodologies. © 2024 IEEE.

关键词： Predictive maintenance

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Adaptive Indoor People-Counting System Based on Edge AI Computing

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第1期8卷 255-263页

作者： Yen, Mao-Hsu Lin, Bor-Shyh Kuo, Yu-Lun Lee, I-Jung Lin, Bor-Shing National Taiwan Ocean University Department of Computer Science and Engineering Keelung City202301 Taiwan National Yang Ming Chiao Tung University Institute of Imaging and Biomedical Photonics Tainan71150 Taiwan Chi Mei Medical Center Department of Medical Research Tainan71004 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237303 Taiwan

Since the outbreak of COVID-19 in 2019, people counting in confined spaces has become essential for controlling the flow of people and reducing viral spread. Many people-counting systems use a fisheye lens to achieve a wide viewing range. However, the images captured with a fisheye lens can have different sizes and deformations due to different camera heights, which consequently affects the accuracy of people counting. In this study, a Raspberry Pi 4 development board attached to a fisheye lens was used to construct an edge computing platform for people counting by using artificial intelligence. A modified You Only Look Once (YOLO) model, which has three output layers and a spatial pyramid pooling module, is proposed to recognize people in images captured at various heights. For efficiently running this deep learning model on the proposed edge computing platform, a TensorFlow Lite framework was used to reduce the model size. The final experimental results indicated that the mean average error and mean squared error of the modified tiny version of the YOLO v4 model were 0.249 and 0.292, respectively, and the detection speed of the platform was 2 frames per second. The results indicate that the proposed edge computing platform can efficiently and accurately count the number of people. Because the proposed system uses edge computing, it can effectively solve personal privacy and transmission bandwidth problems. © 2017 IEEE.

关键词： Cameras

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Improving the Resource Allocation in IoT Systems Based on the Integration of Reinforcement Learning and Rule-Based Approaches in Multi-agent Systems 8

Improving the Resource Allocation in IoT Systems Based on th...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Yarahmadi, Hossein Navidi, Hamidreza Challenger, Moharram University of Antwerp Department of Computer Science Belgium Islamic Azad University Department of Computer Engineering Borujerd Branch Borujerd Iran Shahed University Department of Mathematics and Computer Science Tehran Iran University of Antwerp Flanders Make Strategic Research Center Department of Computer Science Flanders Belgium

ISBN: (纸本)9798350374674

In recent years, the spread of Multi-agent Systems (MAS) and Reinforcement Learning (RL) as a subdomain of Machine Learning (ML) has affected many fields, such as Cyber-Physical Systems (CPS) and the Internet of Things (IoT). There are many Multi-agent Reinforcement Learning (MARL) applications in CPS and IoT, including energy management, smart transportation systems, job scheduling, etc. Due to the fact that the RL and MARL are slow processes, it is necessary to improve and accelerate them. In this study, we review some applications of MAS in the CPS and IoT. To improve the decision-making in MAS, we apply a combination of learning and reasoning (rule-based). We tested the performance of our proposed method in smart homes for 4 distinct tasks, including Face Detection and Window Blind Control, Finger Touch and Gate Control, Weather Check and Thermostat Control, and Temperature Check and Start Fan to assess its efficacy. We applied our method to the job scheduling problem using two parameters: process unit and bandwidth. The results reveal that the proposed approach performed better than the bankruptcy methods based on these criteria. © 2024 IEEE.

关键词： Multi agent systems

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Multivariate Data Augmentation for Predictive Maintenance using Diffusion

Multivariate Data Augmentation for Predictive Maintenance us...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Thompson, Andrew Sommers, Alexander Russell-Gilbert, Alicia Cummins, Logan Mittal, Sudip Rahimi, Shahram Seale, Maria Jaboure, Joseph Arnold, Thomas Church, Joshua Mississippi State University Computer Science & Engineering United States Engineer Research and Development Center Department of Defence United States

ISBN: (纸本)9798350362480

Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to detect system faults, improving predictive maintenance efficiency. Typically there is a lack of fault data to train these models, due to organizations working to keep fault occurrences and down time to a minimum. For newly installed systems, no fault data exists since they have yet to fail. By using diffusion models for synthetic data generation, the complex training datasets for these predictive models can be supplemented with high level synthetic fault data to improve their performance in anomaly detection. By learning the relationship between healthy and faulty data in similar systems, a diffusion model can attempt to apply that relationship to healthy data of a newly installed system that has no fault data. The diffusion model would then be able to generate useful fault data for the new system, and enable predictive models to be trained for predictive maintenance. The following paper demonstrates a system for generating useful, multivariate synthetic data for predictive maintenance, and how it can be applied to systems that have yet to fail. © 2024 IEEE.

关键词： Predictive maintenance

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ATM-NeRF: Accelerating Training for NeRF Rendering on Mobile Devices via Geometric Regularization

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IEEE Transactions on Multimedia 2025年 27卷 3279-3293页

作者： Chen, Yang Zhang, Lin Zhao, Shengjie Zhou, Yicong Tongji University School of Software Engineering Shanghai201804 China Ministry of Education Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Shanghai201804 China University of Macau Department of Computer and Information Science 999078 China

Recently, an increasing number of researchers have been dedicated to transferring the impressive novel view synthesis capability of Neural Radiance Fields (NeRF) to resource-constrained mobile devices. One common solution is to pre-train NeRF and bake it into textured meshes which are well supported by mobile graphics hardware. However, the training process of existing methods often requires several hours even with multiple high-end NVIDIA V100 GPUs. The underlying reason is that these schemes mainly rely on photometric rendering loss, neglecting the geometric relationship between the pre-trained NeRF and the baked results. Standing on this point, we present ATM-NeRF (Accelerating Training for Mobile rendering based on NeRF), which is the first to apply effective geometric regularization constraints during both the pre-training and the baking training stages for faster convergence. Specifically, in the initial NeRF pre-training stage, we enforce consistency of the multi-resolution density grids representing the scene geometry to mitigate the shape-radiance ambiguity problem to some extent, achieving a coarse mesh with smoothness. In the second stage, we utilize the positions and geometric features of 3D points projected from the pre-trained posed depths to provide geometric supervision for joint refinement of geometry and appearance of the coarse mesh. As a result, our ATM-NeRF achieves comparable rendering quality to MobileNeRF with a training speed that is about 30× ∼ 70× faster while maintaining finer structure details of the exported mesh. Our source code and the demo video have been released at https://***/ATM-NeRF/. © 1999-2012 IEEE.

关键词： Mesh generation

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IMGTP: A Unified Framework for Improving and Measuring the Generalizability of Trajectory Prediction Models

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Luyao Zhou, Zikang Wang, Jianping Li, Yung-Hui Jan, Nien-Yi Lin, Yi-Rong Lin, Yen-Cheng National Engineering Research Center of Educational Big Data and the Faculty of Artificial Intelligence in Education Central China Normal University China Department of Computer Science City University of Hong Kong and City University of Hong Kong Shenzhen Research Institute China AI Research Center of Hon Hai Research Institute China

Accurately predicting nearby agents' future trajectories is fundamental for ensuring the safety and efficiency of autonomous driving. However, existing learning-based trajectory prediction models struggle with poor generalization to out-of-distribution (OOD) scenes, potentially leading to failed predictions in real-world applications. To tackle this challenge, we introduce IMGTP, a unified framework for systematically evaluating, improving, and measuring the generalization performance of trajectory prediction models in OOD scenes. IMGTP consists of three primary components: the Argoverse-Shift dataset, the FrenÃ©t+ strategy, and an approach for OOD measurement. First, the Argoverse-Shift is a cross-domain dataset that highlights the significant differences in data distributions across domains, serving as a testing platform for models' generalization ability. Second, the FrenÃ©t+ strategy leverages map centerlines and the FrenÃ©t Frame to enhance the generalizability of baseline models by mitigating the domain gap resulting from road geometry variations. Third, we provide a comprehensive evaluation methodology for OOD measurement, quantifying the range of data that can be effectively handled by prediction models. This measurement approach employs the accuracy rate of the correctly predicted samples and their corresponding mean distance as well as standard deviation to the training set center as indicators of the model's generalization ability. The mean distance also serves as a valid criterion for OOD detection and uncertainty quantification. Experimental results demonstrate that our IMGTP framework enhances the generalization capability of mainstream trajectory prediction models by substantially improving their prediction performance in unseen domains. This advancement contributes to developing more robust and safer prediction models for autonomous driving in the real world. The code can be found at https://***/XIAOYEJIAYOU/Frenet-Strategy. IEEE

关键词： Autonomous vehicles

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Challenges and future directions of secure federated learning:a survey

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Frontiers of computer science 2022年第5期16卷 173-180页

作者： Kaiyue ZHANG Xuan SONG Chenhan ZHANG Shui YU Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Faculty of Engineering and Information Technology University of Technology SydneySydney 2007Australia SUSTech-UTokyo Joint Research Center on Super Smart City Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big *** is an algorithm that does not collect users’raw data,but aggregates model parameters from each client and therefore protects user’s ***,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning *** addition,some recent studies have shown that attackers can recover information merely from ***,there is still lots of room to improve the current federated learning *** this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated *** open issues and existing solutions in federated learning are *** also point out the future research directions of federated learning.

关键词： federated learning privacy protection security

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