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Occluded human pose estimation based on limb joint augmentation

Neural Computing and Applications 2025年第3期37卷 1241-1253页

作者： Han, Gangtao Song, Chunxiao Wang, Song Wang, Hao Chen, Enqing Wang, Guanghui School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Computer Science Toronto Metropolitan University Toronto Canada

Human pose estimation aims at locating the specific joints of humans from the images or videos. While existing deep learning-based methods have achieved high positioning accuracy, they often struggle with generalization in occlusion scenarios. In this paper, we propose an occluded human pose estimation framework based on limb joint augmentation to enhance the generalization ability of the pose estimation model on the occluded human bodies. Specifically, the occlusion blocks are at first employed to randomly cover the limb joints of the human bodies from the training images, imitating the scene where the objects or other people partially occlude the human body. Trained by the augmented samples, the pose estimation model is encouraged to accurately locate the occluded keypoints based on the visible ones. To further enhance the localization ability of the model, this paper constructs a dynamic structure loss function based on limb graphs to explore the distribution of occluded joints by evaluating the dependence between adjacent joints. Extensive experimental evaluations on two occluded datasets, OCHuman and CrowdPose, demonstrate significant performance improvements without additional computation cost during inference. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Joints (anatomy)

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A Deep-Siamese CNN-based Approach for Identifying Mycosphaerella Coffeeicola Disease in Coffee Foliage 8

A Deep-Siamese CNN-based Approach for Identifying Mycosphaer...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Selvanarayanan, Raveena Rajendran, Surendran Jayaraman, Venkatesh Sathish Kumar, P.J. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Mycosphaerella Coffeeicola (Black Spot Disease) is the most destructive disease in coffee leaves and affects global production. Diseased coffee leaves can adversely affect coffee quality, discolor, distort, or rot the coffee leaves, and impair the coffee beans' cherry, taste, and aroma. The disease can also affect the quality of the coffee beans, leading to lower market value. The management of the Coffee Production department of India controls the disease through a combination of cultural practices, chemical control, and the use of resistant coffee varieties. In the existing work, using single-layer perceptron was trained and validated with 100 epochs to obtain an accuracy of 9 5. 7 1 %. In the piece above of work, an infected leaf is accurately diagnosed using deep learning-based models by employing the Deep-Siamese-based CNN Approach, recognized by a deep Siamese Neural network architecture which is also called a Twin Neural network with high illness recognition precision and simplified hyper-parameters to maximize description capacity while avoiding overfitting concerns, using a network with high sickness detection precision and minimal hyper-parameters. The prototype was tested in the lab on natural plants for known sickness instances, and the results matched the model validation datasets. When compared to several deep-learning models, the suggested structure attained an accuracy of 9 6. 8 9 %. © 2024 IEEE.

关键词： Convolutional neural networks

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Effect of HV Guard in Reducing Electric Field Intensity of Gas Insulated Switchgear Spacer

Effect of HV Guard in Reducing Electric Field Intensity of G...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Fabio, Muhammad R. Hidayat, Syarif Khayam, Umar School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Electrical Power Engineering Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Department of Electrical Power Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

As one of the most important parts of gas insulated switchgear, many technologies have been explored to reduce the electric field strength in gas insulated switchgear spacer. This spacer, despite acting as the secondary insulation in gas insulated switchgear, holds a very important role to hold the high voltage conductor and separate it with the grounded tank. Failure in the spacer could result in overall failure of gas insulated switchgear. One of such accessories is the High Voltage Guard. A bulged conductor acting as the HV Guard is placed nearby the surface of the spacer. This accessory is then tested with finite element simulation for 2 types of 1 phase spacer design, conical design and disk design. Each method is proven, electric field wise, could improve the performance of the spacer. Both electric field in the surface, mainly on the triple junction area, and inside spacer could be suppressed to around 15% to 20% in both conical design and disk design. © 2023 IEEE.

关键词： Gases

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Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

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computer Systems Science & engineering 2023年第1期44卷 205-219页

作者： J.Jean Justus U.Sakthi K.Priyadarshini B.Thiyaneswaran Masoud Alajmi Marwa Obayya Manar Ahmed Hamza Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer *** MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so *** existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task *** the same time,the task scheduling process is yet to be explored in the multi-core *** paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy *** GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks *** performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark *** experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy *** overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

关键词： Task scheduling energy efficiency multi-core systems fitness function makespan

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Elevating Cystic Fibrosis Detection in Lungs using HRCT Images with a Cutting-Edge CNN-Based Approach 2

Elevating Cystic Fibrosis Detection in Lungs using HRCT Imag...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Ganjam, Amrutha Sriya Reddy C, Kishor Kumar Radhika, D. Ganjam, Karthik Singh.t, Monika Akuthota, Keerthi Sai Stanley College of Engineering and Technology for Women abids Department of Computer Science Hyderabad India University of Edinburgh Department School of Informatics MSc Data Science Edinburgh United Kingdom Stanley College of Engineering and Technology for Women Deptartment. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331519056

The creation of an algorithm for recognizing pathological abnormalities in cystic fibrosis is investigated in this paper using the CNN model with a modified psp-net. Currently, Decision Trees, Random Forests, PSP Nets, and Neural Networks are utilized in the diagnosis of cystic fibrosis. Since convolutional neural networks (CNNs) can process complicated picture data rapidly and efficiently, the goal of this study is to use CNNs for the detection of anomalies associated with cystic fibrosis. The method groups distinct annotated images into a simple and efficient structure, runs the set of images through a multiscale CNN procedure, and precisely locates the lung region affected by cystic fibrosis. The result of this paper demonstrated that differences in the training dataset can impact performance, but annotating CT images and categorizing them in terms of similar pathologies can improve the accuracy of the model. The proposed CNN model achieved an Accuracy of 84 %, Precision of 74%, Recall of 79%, F1-Score of 72%, error rate of 16% Which is better when compared with existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in computer Science and Communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham Science Publishers. All rights reserved.

关键词： Electronic health record

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An overview of the spatiotemporal patterns of land displacements in Western Greece 15

An overview of the spatiotemporal patterns of land displacem...

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Earth Resources and Environmental Remote Sensing/GIS Applications XV 2024

作者： Fasoulis, Konstantinos Orfanoudakis, Michalis Hadjidoukas, Panagiotis Pappas, Christoforos Department of Civil Engineering University of Patras Patras265 04 Greece Department of Computer Engineering and Informatics University of Patras Patras265 04 Greece

ISBN: (纸本)9781510681026

Understanding and monitoring geohazards, including not only abrupt phenomena but also gradual land displacements, is of key scientific importance with socioeconomic implications. Satellite remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), allow for detailed estimation of land displacements. Here, we focused on the area of Western Greece, characterized by numerous faults and high seismicity, and we quantified the spatiotemporal patterns of land displacements across the study domain from 2018 to 2022. We used the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique to process Sentinel-1 InSAR data following the Stanford Method for Persistent Scatterer (StaMPS) workflow. Vertical and horizontal land displacements across the study area were estimated and compared with estimates from the European Ground Motion Service (EGMS). The magnitude of these displacements was further scrutinized at the annual and sub-annual time scale by examining the EGMS-derived time series of vertical and horizontal land motion at selected areas with critical infrastructures. The increasing availability of InSAR data, in tandem with advancements and ease in data handling and processing, facilitate the deployment of such remote sensing techniques, and, when combined with observations from ground networks, could assist towards comprehensive monitoring of land processes and infrastructures. © 2024 SPIE.

关键词： Interferometric synthetic aperture radar Deformation Satellites Bridges Data processing Synthetic aperture radar Earthquakes Remote sensing Satellite navigation systems Statistical analysis

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A Low-Cost Real-Time Cyber Physical System for Overcoming Excess Braking Issues In Race Cars 12

A Low-Cost Real-Time Cyber Physical System for Overcoming Ex...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Samaras, V. Tatas, K. Lontos, A. Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus Frederick University Department of Mechanical Engineering Nicosia Cyprus

ISBN: (纸本)9798350321074

This paper presents the development of proof of concept for a low-cost, real-time Cyber Physical System (CPS)/IoT that deals with observed excess braking issues in Formula Student (FS) race cars. The goal is to utilize telemetry aspects through a smart monitoring system under the FS low budget competition restrictions, with low-cost materials, open-source software, Long Range wireless communication (LoRa) and cloud services. The paper also introduces an algorithm for implementing an adaptive sampling rate based on the proximity of monitored parameters to warning levels to minimize unnecessary data transmission. The system prototype was tested under realistic driving conditions, obtaining useful preliminary data that can be used to guide both driving behavior and future vehicle design. The results demonstrate that the proposed approach can be useful for the race team in FS conditions and may be possible to expand it to other similar driving scenarios outside FS. © 2023 IEEE.

关键词： Cyber Physical System

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental Science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Enhancing Connectivity of AODV in Urban Scenarios for VANETs: A QoS-Aware Approach 4

Enhancing Connectivity of AODV in Urban Scenarios for VANETs...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Janet Kumari, J Jesy Thangam, S. Ghosh, Archishman Kolhe, Dhruv Vijay Ghosh, Soumish Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523923

As cities expand, vehicles and congestion become more complex. Efficient vehicle-to-vehicle contact networks are needed for road safety and efficient traffic flow. Thus, Vehicular Ad Hoc Networks are needed to overcome all the issues. The Adhoc On-Demand Distance Vector (AODV) routing protocol is used in VANETs. However, this protocol has many challenges in urban settings due to high vehicle density, rapid topology changes, and non-line-of-sight conditions. This work improves AODV by integrating Quality of Service (QoS) metrics. The prioritizing factors such as overhead and end-to-end delay are used to create a more efficient protocol for urban traffic. The SUMO simulator is used for realistic traffic patterns and NS-2 for network analysis. An evaluation of the protocol against the classical AODV is done and important metrics for real-time communication are focused. The enhanced AODV showed marked improvements. It achieved lower overhead in route finding, an increase in the number of packets sent, and lower end-to-end delay. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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