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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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Recognizing Human-object Interactions for a Home Assistive Robot 8

Recognizing Human-object Interactions for a Home Assistive R...

引用

8th international conference on Automation, Control and Robotics engineering, CACRE 2023

作者： Wang, Liangliang Huo, Guanglei Chen, Xinwei Huang, Chengxi Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot - Xiaozhi II verify its robustness. © 2023 ieee.

关键词： Human robot interaction

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DeepASD Framework: A Deep Learning-Assisted Automatic Sarcasm Detection in Facial Emotions 8

DeepASD Framework: A Deep Learning-Assisted Automatic Sarcas...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Jose, Jiby Mariya Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Valavoor P.O. Kerala Kottayam District686635 India

ISBN: (纸本)9798350396638

the vibrant human-machine research provides space for assessing sentiments in facial emotions. Capturing apt sarcasm-related emotions, especially in online meetings or stress interviews, is a challenging aspect. the purpose of this research is to apply deep learning algorithms to effectively assess the sarcasm in human facial emotions in an automatic fashion using the proposed Deep Learning-Assisted Automatic Sarcasm Detection (DeepASD) framework. Our framework trains facial sarcasm-related emotions from internet sources and applies deep learning algorithms to perform visual sarcasm detections. the proposed framework processes algorithms on edge-enabled compute nodes, including GPU-based machines. We evaluated the DeepASD framework using various deep learning algorithms such as EfficientNet, XceptionNet, InceptionNet, ResNet, DenseNet, ConvNext, MobileNet, and their variants;and, we observed that Mobilenetv3 achieved a better learning accuracy of 96.44 percent and energy consumption of 7959 Joules using minimal trainable/non-trainable parameters while detecting sarcasm in facial emotions. Our work will be beneficial for online interviewers, business enthusiasts, or future robotic machine developers for accomplishing accurate decisions considering sarcasm in facial emotions. © 2023 ieee.

关键词： Edge computing

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Respiratory Sound Data Augmentation Method based on Respiratory Physiology 8

Respiratory Sound Data Augmentation Method based on Respirat...

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8th ieee/ACIS international conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Komoto, Shunsuke Matsunaga, Kazuto Hirano, Tsunahiko Doi, Keiko Matsunaga, Kazuto Nishifuji, Seiji Nakashima, Shota Yamaguchi University Department of Electrical and Electronic Engineering Ube Japan Yamaguchi University Department of Respiratory Medicine and Infectious Disease Ube Japan

ISBN: (纸本)9798350373615

the number of patients with respiratory diseases, e.g., chronic obstructive pulmonary disease (COPD) and interstitial pneumonia, is increasing. these diseases make it difficult for patients to be aware of their own symptoms because the initial symptoms are unlikely to appear. thus, there is increasing demand for systems that gather respiratory information continuously to realize early disease detection with high sensitivity. Machine learning techniques have been proposed to detect abnormal respiratory sounds, and these detection techniques utilize data augmentation methods to improve accuracy. However, their effectiveness has not been investigated. therefore, in this paper, we examine the respiratory sound data augmentation method with high identification accuracy targeting the detection of abnormal respiratory sound. In addition, we propose effective data augmentation methods for respiratory sound based on respiratory physiology. the effectiveness of the data augmentation was verified experimentally by distinguishing between expiratory and inspiratory sounds from the respiratory sounds of 16 subjects. the experimental results demonstrate the effectiveness of data augmentation in respiratory sounds obtained from the neck region. In addition, the identification accuracy increased by 2.5 points compared to conventional augmentation methods, thereby confirming the efficacy of the proposed method in terms of improving accuracy. © 2023 ieee.

关键词： Machine learning

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Simulation of the Health Monitoring and Disease Warning System on the Pig Herd with a Cloud-based GAMA Platform 8

Simulation of the Health Monitoring and Disease Warning Syst...

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8th ieee/ACIS international conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Nguyen, Xuan-Truong Pham, Linh Manh Bui, Quang Hung VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798350373615

More and more Internet of things devices are being used in precision farming to measure parameters related to animal growth. Sensors from precision feeding systems are used to collect and analyze data on feed intake of group-housed pigs helping farmers to make valuable decisions that help in pig growth. Besides, African Swine Fever (ASF) is a disease occurring in pigs that kills millions of them. the outbreak comes from the lack of management by localities, connection between farms, and the ability to improve productivity. It is a fact that effects of disease on animals can be observed early through data of voluntary feed intake which can be used to detect dangerous outbreaks of ASF over a wide graphical area in real time. this will contribute to reducing economic losses due to epidemics and improving the quality of animal products. In this paper, we first introduce a cloud-based simulation of the precision feeding system to quantify the cumulative feed intake of growing pigs, thanks to which it is possible to detect early signs of disease in the daily decrease in food intake. Secondly, output statistical data on the disease of pig herds from the first simulation are used for the second simulation of ASF transmission in Hanoi, which is an essential tool for policymakers to inform decision-making people and help them understand better the impact of the disease outbreaks. through these simulations on GAMA platform, we wish to introduce a cost-effective research model on disease transmission in pig herds using cloud-based simulation and illustration instead of investing heavily in expensive actual equipment. © 2023 ieee.

关键词： Losses

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NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices for Indoor Positioning 8

NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices f...

引用

8th international conference on computer and Communication systems, ICCCS 2023

作者： Wu, Tong Li, Han Gao, Bo Xiong, Ke Fan, Pingyi Ben Letaief, Khaled Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Advanced Network Technologies Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665456128

Ultra-wideband (UWB) is a popular technology for indoor positioning. However, non-line-of-sight (NLOS) propagation caused by human occlusion dramatically affects the accuracy of UWB-based indoor positioning. Most of the existing works focused on mitigating NLOS errors in the scenarios with building structure occlusion, but the NLOS mitigation under human occlusion has not been well addressed. To this end, this paper aims to mitigate the NLOS impact of human occlusion, and then proposes a method of NLOS mitigation to reduce NLOS ranging errors under human occlusion with low-cost UWB devices (i.e., NM-LUD). It takes a set of signal features extracted from low-cost devices as input, calculates their weights and mitigates the ranging error based on the features' weights. Experiment results on the two data sets collected by LinkTrack S and UML1 UWB devices show that the method reduces ranging error from 0.9009 m to 0.1435 m (about by 84.07%) and from 0.7466 m to 0.0792 m (about by 89.39%) respectively, which greatly mitigates the influence of NLOS caused by human occlusion. © 2023 ieee.

关键词： Costs

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Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge Computing 8

Deep Unified Model for Face Recognition based on Convolution...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Senthilkumar, V. Saranya, P. Rani, B Kezia Srinivasan, P. Kuchipudi, Ramu Walid, Md. Abul Ala Kumaraguru College of Technology Department of Cse Tamilnadu Coimbatore India Dr. N.G.P. Institute of Technology Department of Ece Tamilnadu Coimbatore India Vasavi College of Engineering Department of Information Technology Telangana Hyderabad India Sona College of Technology Department of Ece Tamilnadu Salem India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Bangladesh

ISBN: (纸本)9798350396638

CCTV, communication, and alarm systems use face recognition technologies. Face detection in photos is a popular topic in science for practical reasons and because it challenges computer-generated vision systems. the variety of shooting situations (position, lighting, hairdo, emotion, backdrop, etc.) and face traits requires versatility. Deep learning-based image identification methods beat machine learning methods in efficiency and information processing. Modern computer systems have major authentication issues. Internet-connected smart devices are producing more data every day. A new model is needed to handle its vast data output. Deep learning and edge computing process vast volumes of data with high precision. Many trust facial recognition systems. SIFT and accelerated robust features are used in traditional facial recognition algorithms (SURF). this paper presents a convolutional neural network-based face identification and recognition solution that outperforms established methods. Tagged photographs of people taken in the outdoors teach the face-recognition algorithm (LFW). the suggested system had 99.1% accuracy on test data. © 2023 ieee.

关键词： Face recognition

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Hybrid Decision Based Multi-Agent Deep Reinforcement learning for Task Offloading in Collaborative Edge-Cloud Computing 24

Hybrid Decision Based Multi-Agent Deep Reinforcement learnin...

引用

24th ieee international conference on High Performance Computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Chen, Juan Wu, Zongling Wu, Lei Xia, Yunni Wang, Yang Xiong, Ling Shi, Canghong School of Computer and Software Engineering Xihua University Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350319934

To support computing-intensive and latency-sensitive Internet-of-things (IoT) applications, we establish a three-layer collaborative edge-cloud computing network, in which multiple IoT devices, edge servers and cloud servers are allocated at IoT layer, edge layer and cloud layer, ***, it is still challenging to satisfy the service requests with inevitable resource competition among task offloading. To solve the joint computation offloading and resource allocation problem, we formulate it as a multi-agent Markov decision process (MAMDP), where action space of each agent contains discrete-continuous hybrid decision. Firstly, we relax the discrete action to a continuous set by developing a probabilistic method. then, by taking advantage of a continuous action based centralized training and distributed execution strategy, a cooperative multi-agent deep reinforcement learning (MADRL) based framework with each IoT device acts as an agent is established. the objective function is to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of edge/cloud servers. the simulation results demonstrate the advantageous of the proposed MADRL over the three state-of-the-art DRL based agents in reducing the system cost. © 2022 ieee.

关键词： Reinforcement learning

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A Structure-Based Method for Prediction of Protein-Protein Interaction Sites through Combining Local and Global Features 8th

A Structure-Based Method for Prediction of Protein-Protein...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Hongyang Zhao, Yanpeng Liu, Ziyi He, Song Bo, Xiaochen Zan, Peng School of Mechatronics Engineering and Automation Shanghai Key Laboratory of Power Station Automation Technology Shanghai University Shanghai200444 China Department of Biotechnology Institute of Health Service and Transfusion Medicine Beijing100850 China

ISBN: (纸本)9789819601875

the identification of protein-protein interaction(PPI) sites is critical for drug discovery and analysis of protein function. So far, numerous computational methods based on artificial intelligence have been developed to accelerate the identification of PPI sites. However, those methods learned only sequential features or local structural features, leading to low predictive performance. therefore, it is urgently necessary to develop a new method to improve the performance of predicting PPI sites. Herein, a new structure-based method for predicting the protein-protein interaction sites through combing the local and global encoder, SLGPPIS, was therefore proposed. Specifically, SLGPPIS utilizes the local and global encoder to extract the residue feature respectively and concatenating them to predict the PPI sites. Compared with other existing methods, SLGPPIS exhibited superior performance on prediction of PPI sites. Moreover, a visual interpretative experiment was conducted to systematically investigate the prediction process of SLGPPIS. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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An Optimized H6-Type Single-Phase PV Inverter with Bi-Directional Quasi-Diode Clamping 8

An Optimized H6-Type Single-Phase PV Inverter with Bi-Direct...

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8th international conference on Technology and Energy Management, ICTEM 2023

作者： Farahani, Mohammad Shamsi-Nejad, Mohammad-Ali University of Birjand Faculty of Engineering Birjand Iran

ISBN: (纸本)9781665452854

Transformerless inverters (TLIs) have become increasingly popular in single-phase distributed photovoltaic power generation systems due to the superiorities of their high efficiency, slighter volume, moreover lower cost than the one with the transformer. However, in the absence of a transformer, high-frequency common-mode voltage (CMV) brings about hazardous ground leakage current flow. To reduce the leakage current, plenty of TLI structures have been published in the works, and among them, H5, H6, and Heric topologies are the three topology's very popular mainstays. the third has the highest efficiency but the worst CM behavior owning to the switches' junction capacitances. Consequently, the CM leakage current may exceed the recommended limit. In this paper, an optimized HERIC inverter with a quasi-diode clamp structure, namely OHERIC-BQDC topology, is suggested, in that a bi-directional clamping based upon MOSFETs, but like a diode clamping without dead time, is added on the basic HERIC inverter to eradicate the common-mode voltage fluctuation in freewheeling periods. A Matlab/Simulink pattern is constructed to investigate the feasibility of the suggested approach. In addition, PSIM's software package is used to confirm the effectiveness of the suggested HERIC-based inverter. © 2023 ieee.

关键词： Diodes

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Protection of the Blinding Area in Active Distribution Network by Multi-Function Relays 8

Protection of the Blinding Area in Active Distribution Netwo...

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8th international conference on Technology and Energy Management, ICTEM 2023

作者： Zareein, Mohammad Farkhani, Jalal Sahebkar K.N. Toosi University Department of Electrical Engineering Tehran Iran Aalborg University Department of Energy Aalborg Denmark

ISBN: (纸本)9781665452854

the overcurrent relay (OCR) is the major device to protect the distribution network feeder. the OCR issues increase with distributed generation (DG) embedded in active distribution networks (ADN). In addition, high impedance fault (HIF) has been challenging to properly detect the fault in the power systems. the blinding areas lead to a delay or non-tripping in the OCR. the typical method to cover the blinding area is the increasing sensitivity of relay settings. Changes in the settings of the protective relays may cause challenges to protect ADN. Most of the modern protection relays are digital multifunction. the relays can activate multiple protections, simultaneously. this paper presents a simple and economical solution to protect the fault in blinding areas by the OCR and thermal overload relay. the blind possibility points are determined in a case study by changing the fault impedances and DG presence. the case study is based on a real distribution network that includes two CHP generators with 4 MW capacity. the simulations are carried out on a realistic case study using DIg SILENT software. the results show the proposed method can cover the blinding zones with the thermal overload and OCRs. © 2023 ieee.

关键词： Relay protection

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