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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30762 条记录，以下是821-830 订阅

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Fault Detection and Classification in Semiconductor Manufacturing for sensor Screening Using Multi-Layer Deep Neural Network 2

Fault Detection and Classification in Semiconductor Manufact...

引用

2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Reddy, S. S. Mohan Rakesh, K. Aluvala, Srinivas Bindu, G. Husseen, Ahmad Srkr Engineering College Department of Ece Andhra Pradesh Bhimavaram India Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The fault detection and classification are a prediction model that employs the sensor equipment data to detect if each wafer is healthy or faulty which is significant to attain a greater yield. However, the manufacturing of semiconductors has difficulties in maintaining high-quality production because of potential defects. Hence, effective fault detection and classification is essential to overcome this issue. In this research, the multi-layer Deep Neural Network (DNN) is proposed for fault detection and classification in semiconductor manufacturing for sensor screening. Initially, the image is acquired from a benchmark dataset of Printed Circuit Board (PCB) dataset. The median filter is employed to eliminate the noise in the acquired image. Then, the feature extracted is established using a Gray Level Co-occurrence Matrix (GLCM). Finally, the multi-layer DNN is used to detect and classify as healthy or faulty in semiconductors effectively. The existing techniques like Convolutional Neural Network-VGG16 (CNN-VGG16), Global Contextual attention augmented You Only Look Once (GCC-YOLO), DL-based Path Aggregation Network (PANet), and improved fully CNN are used to compare with proposed technique. When compared to this existing technique, the multi-layer DNN achieves a better accuracy of 99.89% respectively. © 2024 ieee.

关键词： Printed circuit boards

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Enabling Intelligent Observability Volume Management 17

Enabling Intelligent Observability Volume Management

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Naik, Priyanka Goel, Seep Raichstein, Eran Meth, Kalman Govindarajan, Kavya Chakraborty, Aishwariya Govindarajan, Chander IBM Research Bangalore India IBM Research Haifa Israel

ISBN: (纸本)9798331531195

In the era of centralized clouds, multi-cloud, and edge computing, adaptable observability is essential to efficiently manage the growing volume of metrics data. Observability Volume Manager (OVM) addresses the complexities of dynamic observability data management by offering tailored processing capabilities that ensures efficient and effective observability across diverse infrastructures. This demo highlights OVM's ability to perform dynamic transformations for fine-grained control over observability data collection, while significantly reducing the volume of data collected, processed, and stored. These optimizations drive substantial cost savings and position OVM as an essential solution for cost-efficient observability. © 2025 ieee.

关键词： distributed cloud

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SocialNav-FTI: Field-Theory-Inspired Social-aware Navigation Framework based on Human Behavior and Social Norms

SocialNav-FTI: Field-Theory-Inspired Social-aware Navigation...

引用

2024 international conference on Intelligent Robots and systems

作者： Lu, Siyi Zhong, Ping Ye, Shuqi Chen, Bolei Sheng, Yu Liu, Run Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Univ Chinese Acad Sci Res Ctr Ubiquitous Sensor Networks Beijing 100049 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Social navigation is a key consideration for integrating robots into human environments. Concurrently, it imposes heightened requisites: tasks must not only be executed succesfully without collisions, but also adhere to principles encompassing comprehensibility, courtesy, social compliance, comprehension, foresight, and scenario compliance. In this paper, we present the incorporation of social norms as a guiding framework for robot navigation within social contexts. We adopt field theory to provide a formal elucidation of the social norms, using Physical-Informed Neural Network (PINN) to predict pedestrian movement under the influence of social norms, respectively, and using Reinforcement Learning (RL) for navigation. We use supervised learning to train the pedestrian velocity field prediction model and reinforcement learning to train the navigation policy. We conduct three parts of experiments: (1) analyzing the spatiotemporal characteristics of the velocity field in the walking pedestrians dataset;(2) evaluating the accuracy of the vector field prediction in the pedestrian dataset;(3) using Gazebo simulation and the PEDSIM library to evaluate the improvement of navigation performance under constraints of social norms. Experiments have confirmed that the pedestrian motion data set indeed satisfies the Gaussian divergence theorem and can be described by the concept of field. The performance of navigation strategies incorporating social rules has been improved to a certain extent.

关键词： Reinforcement learning

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2024 ieee 29th international conference on Emerging Technologies and Factory Automation, ETFA 2024

2024 IEEE 29th International Conference on Emerging Technolo...

引用

29th ieee international conference on Emerging Technologies and Factory Automation, ETFA 2024

ISBN: (纸本)9798350361230

The proceedings contain 310 papers. The topics discussed include: evaluation of grasp quality measures in real grasps;trust the robot! enabling flexible collaboration with humans via multi-sensor data integration;towards web platform for cloud-based virtual commissioning of IEC 61499 distributed automation systems;mapping, analysis and simulation on the use of time-sensitive networking in digital substations;impact of compilation optimization levels on execution time variability;a hardware-agnostic approach to supervise heterogeneous autonomous mobile robots;integrating systems of record (SOR) into the asset administration shell (AAS) dataspace;and graph-based change impact visualization for agile cyber-physical production systems engineering.

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Chernobyl-Inspired Optimization for Efficient Wireless sensor Network Routing 5

Chernobyl-Inspired Optimization for Efficient Wireless Senso...

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5th international conference on Emerging systems and Intelligent computing, ESIC 2025

作者： Garima Rani, Amita Kumar, Sanjay Dcrust Computer Science and Engineering Department Murthal Sonepat India Sgt University Computer Science and Engineering Department Gurugram India

ISBN: (纸本)9798331522100

Optimising routing in Wireless sensor Networks (WSNs) is crucial for enhancing their performance, with a focus on energy conservation, reliable data transmission, and network stability. This research presents a unique approach to WSN routing optimization using Chernobyl Disaster Optimization (CDO), a nature-inspired algorithm modeled on the rehabilitation and ecological restoration efforts following the Chernobyl incident. CDO simulates the process of resource redistribution and recovery after a catastrophic event to identify optimal routing paths in WSNs. The study assesses the CDO algorithm's effectiveness against the traditional Gravitational Search Algorithm (GSA) using key performance indicators such as energy usage, jitter, average end-to-end delay (EED), packet loss, and packet delivery fraction (PFD). Simulation results show that the CDO-based routing optimisation method consistently surpasses GSA, delivering improved end-to-end delay, decreased jitter, reduced packet loss, and enhanced energy efficiency. This investigation highlights CDO's potential as a viable alternative to conventional optimisation techniques, offering a more efficient and adaptable solution for WSN routing optimisation, particularly in dynamic, large-scale scenarios. © 2025 ieee.

关键词： Energy efficiency

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Blockchain-based Privacy-preserving Authentication Key Agreement Protocol for Industrial Wireless sensor Networks 28

Blockchain-based Privacy-preserving Authentication Key Agree...

引用

ieee 28th international conference on Parallel and distributed systems (ieee ICPADS)

作者： Su, Xing Xie, Yong Wang, Hongyuan Wang, Hui Qinghai Univ Informat Technol Xining Peoples R China Operat Bur Xining Peoples R China Qinghai Prov Yindajihuang Project Construct Xining Peoples R China

ISBN: (纸本)9781665473156

Industrial wireless sensor networks (IWSNs) are indispensable for Industry 4.0. However, limited to the resource of sensor nodes, designing an effective and secure authentication protocol for real-time data access in IWSNs is full of challenges. In addition, existing protocols fail to balance security and performance for real-time access to IWSNs simultaneously. Thus, we design an efficient authentication key agreement protocol using the physical unclonable function (PUF), elliptic curve cryptography (ECC), and smart contract. We perform formal security analysis under the random oracle model and demonstrate that our protocol meets the basic requirements. Rinkeby, an online Ethereum test network, is used to test the smart contract. Finally, we evaluate computation, communication and storage costs with several similar protocols. The results show that our protocol can provide a higher level of security and the lowest storage cost.

关键词： Authentication Key Agreement (AKA) Elliptic Curve Cryptography (ECC) Blockchain Industrial Wireless sensor Networks (IWSNs)

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

引用

3rd international conference on distributed computing and High Performance computing, DCHPC 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 ieee.

关键词： Forecasting

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Reinforcement Learning Models for Autonomous Decision Making in sensor systems

Reinforcement Learning Models for Autonomous Decision Making...

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2024 international conference on Advances in computing Research on Science Engineering and Technology, ACROSET 2024

作者： Ramu, K. Suman, Sanjay Kumar Rajeswari, U. Sumana, S. Poddar, Hitha Arulananth, T.S. Vmrf Deemed University Paiyanoor Chennai India St. Martin's Engineering College Dept. of Ece Secunderabad India Kalasalingam Academy of Research and Education Department of Business Administration Virudhnagar India Dayananda Sagar Collge of Engineering Dept. of Eee Bengaluru India Easwari Engineering College Dept. of Cse Chennai India Mlr Institute of Technology Dept. of Ece Hyderabad India

ISBN: (纸本)9798350388800

This Smart homes and self-driving vehicles are using sensor networks increasingly in the IoT era. However, handling these systems' massive data sets and letting them make informed judgments is difficult. Reinforcement learning a sort of machine learning may work. This research investigates how sensor systems can make autonomous judgments using reinforcement learning models. It discusses the basic ideas, challenges, and opportunities of this method in numerous fields. The paper also discusses how reinforcement learning makes electronics smarter and more sensitive by learning from and adapting to their environment. The recommended technique combines Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic reinforcement learning. These strategies improve monitoring system decision-making while remaining safe, efficient, and adaptive. The experiments reveal that these strategies increase sensor system decision- making speed and accuracy and learning efficiency. Reinforcement learning is effective in many scenarios, but it excels at safety-sensitive jobs like self-driving automobiles. Despite the potential, resources and safety and dependability remain issues. This article aims to explain reinforcement learning in sensor systems, including its real-world applications, moral difficulties, and future prospects. © 2024 ieee.

关键词： Adversarial machine learning

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Towards an Understanding of distributed Asymmetric Collaborative Visualization on Problem-solving 30

Towards an Understanding of Distributed Asymmetric Collabora...

引用

30th ieee conference Virtual Reality and 3D User Interfaces (ieee VR)

作者： Tong, Wai Xia, Meng Wong, Kam Kwai Bowman, Doug A. Pong, Ting-Chuen Qu, Huamin Yang, Yalong HKUST Hong Kong Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA Virginia Tech Blacksburg VA USA

ISBN: (纸本)9798350348156

This paper provided empirical knowledge of the user experience for using collaborative visualization in a distributed asymmetrical setting through controlled user studies. With the ability to access various computing devices, such as Virtual Reality (VR) head-mounted displays, scenarios emerge when collaborators have to or prefer to use different computing environments in different places. However, we still lack an understanding of using VR in an asymmetric setting for collaborative visualization. To get an initial understanding and better inform the designs for asymmetric systems, we first conducted a formative study with 12 pairs of participants. All participants collaborated in asymmetric (PC-VR) and symmetric settings (PC-PC and VR-VR). We then improved our asymmetric design based on the key findings and observations from the first study. Another ten pairs of participants collaborated with enhanced PC-VR and PC-PC conditions in a follow-up study. We found that a well-designed asymmetric collaboration system could be as effective as a symmetric system. Surprisingly, participants using PC perceived less mental demand and effort in the asymmetric setting (PC-VR) compared to the symmetric setting (PC-PC). We provided fine-grained discussions about the trade-offs between different collaboration settings.

关键词： Human-centered computing Visualization Visualization techniques Human-centered computing Collaborative and social computing Empirical studies in collaborative and social computing

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Strategic Load Balancing: Game Theory Approach for Optimized Resource Allocation in Cloud computing 8

Strategic Load Balancing: Game Theory Approach for Optimized...

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8th international conference on Inventive systems and Control, ICISC 202

作者： Geetha, G. Khare, Anjanay Mahawar, Kartikey Bajaj, Bhavya School of Computing Srmist Ktr Chennai India

ISBN: (纸本)9798350386578

In the field of computing, the importance of efficient load distribution across multiple server environments has become paramount, especially in the context of ensuring high availability and reliability of web services. This study intends to explore the challenges involved in load balancing technology, a critical component used in scaling applications and services across various computing resources. By evenly distributing workloads, load balancers optimize resource utilization, maximize throughput, minimize response time, and avoid overload of any single resource. This study reviews various algorithms and methodologies employed in load balancing, including round-robin, least connections, and IP hash techniques. Furthermore, this study also examines the evolution of load balancing solutions from traditional onpremise appliances to modern cloud-based services, highlighting the shift towards more flexible, scalable, and costefficient models. Through a comprehensive analysis of existing literature and case studies, this study highlights the pivotal role of load balancers in enhancing the performance and reliability of web services, while also addressing the challenges and future prospects in this rapidly advancing field. © 2024 ieee.

关键词： Cloud distributed computing etc Game Theory Load Balancing

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