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

共 1546 条记录，以下是1041-1050 订阅

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Smartphone–controlled industrial robots: Design and user performance evaluation

Smartphone–controlled industrial robots: Design and user pe...

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international Symposium on Computational Intelligence and Informatics

作者： Adrienn Deák Zoltán Szántó Áron Fehér Lőrinc Márton Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania

ISBN: (纸本)9798350398830

this work presents a solution for distant control of industrial robots using mobile devices. the developed application can monitor the motion of the robot based on video and sensor information, it can send commands and control scripts to the robot, and it ensures the jogging–like manual control of the robot. In addition to the aforementioned functionalities, the application can analyze the teleoperation performances of the user. A set of performance metrics were introduced to rate user performances. they are useful to evaluate and educate remote manual robot control techniques. Experimental measurements are also presented to show the applicability of the developed remote control application and user performance evaluation method.

关键词： Service robots Robot control Human factors Robot sensing systems Manipulators software software reliability

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IoT Based Street Light Controller and Monitoring System 8

IoT Based Street Light Controller and Monitoring System

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8th ieee international conference on Adaptive Science and Technology, ICAST 2021

作者： Lartsey, Perry Etornam Ayitey, Douglas Tetteh Acakpovi, Amevi Arthur, Ronald Esare University of Ghana Computer Engineering Department Accra Ghana Accra Technical University Electrical/Electronic Engineering Department Accra Ghana Regional Maritime University Electrical/Electronic Engineering Department Accra Ghana

ISBN: (纸本)9781665427173

Street lighting has become an essential commodity for human life as it provides lighting in cities, towns and most especially on roads to reduce risk of accidents at night. though necessary, streetlight systems not properly managed can result in risk of accidents, excessive loss of energy, financial losses and dissatisfaction of customers. this paper proposes a smart system to manage streetlight operation, relying on sensors and internet of things technologies. the proposed smart streetlight system consisted of hardware and software design. While the hardware makes use of sensors, Arduino board and GPS devices to build an integrated system capable of reporting the state of the street light through available wireless network, the software aspect consisted of an online application, capable of remote monitoring and control. the smart streetlight was conceived, built and tested. Results showed an effective operation, leading to reduced energy consumption of the streetlight, subsequent reduction of carbon footprint, effective detection of fault, effective location of fault owing to GPS devices and reduced downtime. the system is further expandable to cover a whole town/nation streetlight system. © 2021 ieee.

关键词： Energy efficiency

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Adaptive threshold for Unknown Traffic Identification

Adaptive Threshold for Unknown Traffic Identification

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ieee international conference on Cloud Computing and Intelligence systems (CCIS)

作者： Pengcheng Wang Jianfeng Guan Zhuang Han School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665456579

Network traffic classification has become an important foundation of network security. However, as the types of protocols and applications of the network continue to increase, unknown network traffic is also emerging. In the face of unknown network threats, how to identify unknown network threats in a complex network environment to make corresponding preparations in advance has become extremely important. Aiming at the influence of unknown traffic on classification accuracy in the prediction process, this paper proposes an Adaptive threshold for Unknown Traffic Identification (AT-UTI) algorithm using particle swarm optimization algorithm to optimize the search of the set threshold, to reduce the impact of unknown traffic on the accuracy of the model. We evaluated our model achieving an accuracy of 93.27%. Our results demonstrate the effectiveness of AT-UTI in unknown traffic identification.

关键词： Adaptation models Protocols Telecommunication traffic Predictive models Network security Prediction algorithms Real-time systems

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Dueling DQN-Rollout for Collision Avoidance Path Planning with Vehicle Speed Location 3

Dueling DQN-Rollout for Collision Avoidance Path Planning wi...

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3rd international Symposium on computer Technology and Information Science, ISCTIS 2023

作者： Nian, Gujiayin Xiao, Jingzhong Zhou, Xuchuan School of Computer Science and Engineering Southwest Minzu University Chengdu China

ISBN: (纸本)9798350325386

the rapid progress of artificial intelligence has led to significant advancements in the field of autonomous driving, yet effective collision avoidance path planning remains a challenging task. In response, deep reinforcement learning offers an efficient and modern alternative to traditional navigation strategies. this paper proposes a novel approach that incorporates vehicle speed location into the deep reinforcement learning process, utilizing the Dueling DQN-Rollout framework to consider both the distance of the road and obstacles ahead. the agent interacts with the environment to learn a policy, with a reward function that accounts for deviations from the intended path and collisions with obstacles. the training process focuses on imparting human-like driving skills to the autonomous vehicle. By employing the rollout algorithm, the rough Q-value is optimized to reduce training costs. Experimental results demonstrate that this approach can successfully plan a collision-free path for autonomous driving from origin to destination on a simulation platform. © 2023 ieee.

关键词： Reinforcement learning

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A Lightweight Extraction Method for Human Skeletal Joint Points of Wind Tunnel Pilots Based on GST

A Lightweight Extraction Method for Human Skeletal Joint Poi...

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international conference on Vision, Image and Signal Processing (ICVISP)

作者： Wei Zhang Wentao Gong Ying Duan Zhisheng Gao School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Wind tunnel skydiving training, as an essential means of skydiving and aviation training, relies on efficient posture capture to ensure training effectiveness. However, due to factors such as complex wind tunnel backgrounds,pilot body occlusion, small joint targets, and high model complexity, the real-time extraction accuracy of the current model needs to be improved. To this end, we propose a lightweight method based on Gst to extract the critical skeleton points of wind tunnel pilots. this method redesigns the lightweight keypoint extraction model YOLOv8nPose-Gst, an improved version of YOLOv8nPose aimed at improving wind tunnel pilot attitude detection accuracy and *** introducing the CoordAtt attention mechanism,the model is better at focusing on small keypoint targets, and combined with the HeadGst convolutional head design, it effectively reduces computational complexity while maintaining high detection accuracy. In addition, this article adopts the NmPIoU loss function to accelerate training and further improve model performance. Our method achieved significant results across multiple metrics on the wind tunnel pilot dataset. Compared with the baseline model YOLOv8nPose, map@50, and map@50-95, the values reached 0.801 and 0.564, 2.2 percentage points and 3.3 percentage points higher than the original model. Meanwhile, the model complexity GFLOPs is 7.8, a decrease of 1.4; the number of parameters has also decreased from 3.289 to 2.932.

关键词： Training Adaptation models Accuracy Wind tunnels Computational modeling Data models Skeleton Real-time systems Computational complexity Context modeling

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Energy simulation scenario to social housing building: combining heat pump and renewable energy system

Energy simulation scenario to social housing building: combi...

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international Multidisciplinary conference on computer and Energy Science (SpliTech)

作者： Andrea Vallati Costanza Vittoria Fiorini Francesco Muzi Miriam di Matteo Dept. of Astronautical Electrical and Energy Engineering Sapienza University Rome Italy

Control and improvement of both the energy and environmental quality of buildings is responsible for almost 40% of the emissions caused by energy processes. 84% of Italian buildings are residential, making redevelopment approaches necessary for reducing emissions and consumption. RESHeat (Renewable Energy System for Residential Building Heating and Electricity Production) has been realized to heat and cool residential buildings with renewable energy. the RESHeat system consists primarily of a heat pump, photovoltaic/thermal modules, and two thermal energy storage units. One of the novelties of the RESHeat system is the use of waste thermal energy from the PV cooling system to ensure constant energy levels in the cold sink of the heat pump (COP over 5). Furthermore, PV-T is coupled with the system to maximize the amount of renewable energy produced. the study focuses on a social house building from the 1980s that is used as a demonstration site for the RESHeat system. It consists of a three-story building with 13 different-sized flats. the dynamic simulation was performed using TRNSYS software to compare the RESHeat system to the current situation, having two scenarios: A: Actual Heating, B: RESHeat System. By retrofitting existing systems, an efficient heating system and air conditioning system can be provided; these implementations can also significantly reduce overall consumption and CO2 emissions. the results show that the heat pump's yearly average COP is XX due to the energy storage unit. RESHeat system covers building heating needs through a combination of renewable energy sources and energy storage.

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Function Clustering to Optimize Resource Utilization on Container Platform

Function Clustering to Optimize Resource Utilization on Cont...

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international conference on Parallel and Distributed systems (ICPADS)

作者： Chao-Yu Lee Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan The Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan

In recent years, container technology has gained significant attention in the software industry, with many businesses opting for its elasticity, cost-effectiveness, and ease of implementation. the most common way to ensure a seamless user experience is to keep a substantial number of containers active throughout the day, which causes resource over-provision. Conversely, closing the container right after handling the requests can reduce memory consumption but generate a cold start whenever the request arrives. Cold start occurrence and resource usage is a trade-off and presents a significant challenge on the container *** address this challenge, we observe that serving consecutive requests with the same container can notably decrease the number of cold starts. We propose TAC, a Temporal Adjacency Function Clustering algorithm, to meet the challenge. TAC selects the functions with time adjacency requests into a cluster from the historical data. TAC packs functions serving time adjacency requests into a cluster to reduce cold starts and enable efficient resource utilization. the experiment result shows that TAC reduces 8% cold start occurrences and 53% memory usage with the real-world traces compared to the state-of-the-art methods, e.g., Defuse [1] and Hybrid histogram policy [2].

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Electronic Medical Record Text Classification Based on Dual Attention Mechanism 8th

Electronic Medical Record Text Classification Based on Dual ...

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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： Wang, Hua Bai, Xue-Feng Cui, Xiu-Tao Li, Long Zheng, Ye-Ping School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Shanghai Key Laboratory of Power Station Automation Technology Shanghai200444 China DewertOKIN Technology Group Co. Ltd. JiaXing314000 China Bewatec Medical Devices Co. Ltd. JiaXing314000 China JiaXing Second Hospital JiaXing314000 China

ISBN: (纸本)9789819601875

With the digitization of medical information and the rapid growth of medical big data, electronic medical records (EMR) have become an indispensable part of the medical field. the secondary application of electronic medical records is focused on assisting medical diagnosis to improve the accuracy of clinical diagnosis. In this study, we propose a method based on dual attention mechanisms to identify key information in free-text electronic medical records. Firstly, we introduce a gated attention AG module to selectively adjust the attention weights of different positions in the input sequence, enabling the model to better focus on key information. Additionally, we incorporate the SE-Res module with residual units to enhance the representation capability of key text features and improve classification performance. We adopt a Transformer-based model and fine-tune it using the pre-trained BERT model to adapt to our classification task. Experimental results on the publicly available dataset of CCKS2018 electronic medical records demonstrate that the proposed method achieves an accuracy of 0.9812, recall of 0.9793, F1-score of 0.9836, confusion matrix, and average accuracy of 0.9811, effectively enhancing the effectiveness of text classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion

Cross-modal Search Method of Technology Video based on Adver...

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ieee international conference on Cloud Computing and Intelligence systems (CCIS)

作者： Xiangbin Liu Junping Du Meiyu Liang Ang Li Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing

ISBN: (纸本)9781665456579

Technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solved. To address the above problems, this paper proposes a novel Feature Fusion based Adversarial Cross-modal Retrieval method (FFACR) to achieve text-to-video matching, ranking and searching. the proposed method uses the framework of adversarial learning to construct a video multimodal feature fusion network and a feature mapping network as generator, a modality discrimination network as discriminator. Generator and discriminator are trained alternately based on adversarial learning, so that the data obtained by the feature mapping network is semantically consistent with the original data and the modal features are eliminated. Experimental results demonstrate that the proposed method performs better in text-to-video search on the self-built datasets of technology videos.

关键词： Cloud computing Semantics Search problems Adversarial machine learning Generators Intelligent systems Videos

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Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

Unsupervised Semantic Representation Learning of Scientific ...

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ieee international conference on Cloud Computing and Intelligence systems (CCIS)

作者： Hongrui Gao Yawen Li Meiyu Liang Zeli Guan Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing School of Economics and Management Beijing University of Posts and Telecommunications Beijing

ISBN: (纸本)9781665456579

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features makes the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning.

关键词： Representation learning Cloud computing Semantics Supervised learning computer architecture Benchmark testing Graph neural networks

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