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Comprehensive electrical models for a wireless sensor network device

Heliyon 2024年第23期10卷 e40415页

作者： León Ávila, Bernardo Yaser García Vázquez, Carlos Alberto Pérez Baluja, Osmel Cotfas, Daniel Tudor Cotfas, Petru Adrian Transilvania University of Brașov Department of Electronics and Computers Faculty of Electrical Engineering and Computer Science Brasov 500036 Romania University of Sancti Spiritus “José Martí Pérez” Department of Computer Engineering Faculty of Business and Technical Sciences Comandante Manuel Fajardo s/n Olivos 1 Sancti Spíritus 60100 Cuba Technological University of Havana Department of Automation and Computing Faculty of Automation and Biomedical Engineering Street 114 No. 11901 between Ciclovía and Rotonda Havana 19390 Cuba

This paper proposes a novel circuit model of a Wireless Sensor Network (WSN) device. The model is designed to accurately represent the behavior of a WSN device operating in a duty cycle, capturing the essential characteristics of its components, including the microcontroller, sensor, transceiver, and power supply. The proposed model incorporates static and dynamic power consumption aspects, reflecting the energy usage patterns during various operational states such as sensing, processing, and communication. The model's accuracy is validated through a comparative analysis with a LTspice® implementation and a real mote implemented for this study. Simulation results closely match the empirical data, demonstrating the model's effectiveness in predicting the mote's behavior. This approach provides a valuable tool for optimizing power consumption and extending the lifetime of WSN deployments within a simulation environment, offering significant insights for designers and researchers in this field. © 2024 The Authors

关键词： Electric model SPICE model Wireless sensor network node

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Numerical Analysis of Internal Defect Detection in Aluminum Alloy Pipes Aims to Provid Data Support for AI Models

Numerical Analysis of Internal Defect Detection in Aluminum ...

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Advanced Information, Mechanical engineering, Robotics and automation (AIMERA), IEEE International Conference on

作者： Pengshihan Bai Shang Wang Jianqiao Long Kexin Zheng Jing Ma Nandu Li Steve Bruce School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing China School of Automotive Engineering Beijing Polytechnic Beijing China School of Computing Newcastle University UK Dezhou Natural Resources Bureau Shandong China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China Department of Research & Development SEH Automation Pte Ltd Singapore

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

The online effective detection of internal defects in metals has long faced many challenges. The rapid development of Artificial Intelligence (AI) technology has provided possibilities for improving defect detection techniques. However, AI algorithms rely heavily on large amounts of data, which are difficult to obtain through traditional experimental methods. To address this issue, this study proposes a method for the internal defect detection of aluminum alloy pipes and provides data support for AI models and algorithms. We established a "pinch roller-pressure roller-aluminum alloy pipes" model and obtained the variation rules of Mises stress, constraint reaction force caused by the change of internal defect position through numerical calculation. The results show that the position change of the internal defect has a clear functional relationship with the constraint reaction force of roller-A. When the defect is located near the lower part of roller-B, the constraint reaction force of roller-A reaches the maximum value (978.8 N). This study provides empirical evidence for establishing an online defectdetection AI models. It clarifies the evolution pattern of constraint forces related to crack location. These findings provide data support for developing efficient and accurate defect online detection systems based on AI to address the limitations of traditional methods.

关键词： Force Metals Production Aluminum alloys Data models Numerical models Artificial intelligence Stress Monitoring Defect detection

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Human Activity Mining in Multi-Occupancy Contexts Based on Nearby Interaction Under a Fuzzy Approach

SSRN

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SSRN 2023年

作者： Polo-Rodríguez, Aurora Cavallo, Filippo Nugent, Chris Medina-Quero, Javier Department of Computer Science University of Jaén Jaén23071 Spain Department of Industrial Engineering University of Florence Florence50121 Italy School of Computing Ulster University Northern Ireland BelfastBT48 7JL United Kingdom Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain

Multioccupation encompasses real-life environments in which people interact in the same common space. Recognising activities in this context for each inhabitant has been challenging and complex. This work presents a fuzzy knowledge-based system for mining human activities in multi-occupancy contexts based on nearby interaction based on the Ultra-wideband. First, interest zone spatial location is modelled using a straightforward fuzzy logic approach, enabling discriminating short-term event interactions. Second, linguistic protoforms use fuzzy rules to describe long-term events for mining human activities in a multi-occupancy context. A data set with multimodal sensors has been collected and labelled to exhibit the application of the approach. The results show an encouraging performance (0.9 precision) in the discrimination of multiple occupations. © 2023, The Authors. All rights reserved.

关键词： Ultra-wideband (UWB)

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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IEEE International Conference on Robotics and Biomimetics

作者： Tian Xiang Qiwei Meng Ji Zhang Beibei Zhang Wei Song Anhuan Xie Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong HKSAR China Research Center for Intelligent Computing Software Research Institute of Intelligent Computing Zhejiang Laboratory Hangzhou China Electrical and Computer Engineering Dalhousie University Halifax Canada

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model’s partitioning strategy. It also serves as an important metric influencing the model’s suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future.

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Atmospheror: Towards an Olfactory Interactive System for Enhancing Social Presence and Interaction in Synchronous Online Classes

Atmospheror: Towards an Olfactory Interactive System for Enh...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in computing Systems, CHI EA 2023

作者： Lu, Qi Zhang, Yuewei Zhang, Yuxin Ma, Shao-En Zhang, Yunfan Qin, Yuou Gao, Peizhong Zhang, Qiwei Xu, Yingqing The Future Laboratory Academy of Arts & Design Tsinghua University Beijing China The Future Laboratory Industiral Engineering Tsinghua University Beijing China The Future Laboratory School of Software Tsinghua University Beijing China The Future Laboratory Tsinghua University Beijing China The Future Laboratory Department of Automation Tsinghua University Beijing China College of Design and Innovation Tongji University Shanghai China Creative Computing Institute University of the Arts London London United Kingdom

ISBN: (纸本)9781450394222

Synchronous online classes have become increasingly common in recent years. However, students and instructors often face challenges when giving and receiving reactions and feedback. Existing solutions include audio and visual cues, which often act as distractions. A potential alternative is olfactory cues. This paper proposes Atmospheror, an interactive system that supports interaction and feedback between students and instructors through an ambient olfactory display. We performed a pilot study with two instructors and 13 students to evaluate the effectiveness of Atmospheror and its design and implementation, applying both quantitative and qualitative methods. Preliminary results indicate that Atmospheror can promote students' concentration and increase interactivity in online classes. User interviews provided suggestions for future enhancements of the selection and treatment of odor, hardware implementation, and interface system mechanisms. © 2023 Owner/Author.

关键词： Students

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

arXiv

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arXiv 2024年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 2024, CC BY.

关键词： Generative adversarial networks

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Interference Factors and Compensation Methods when Using Infrared Thermography for Temperature Measurement: A Review

arXiv

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arXiv 2025年

作者： Pan, Dong Mo, Tan Jiang, Zhaohui Duan, Yuxia Maldague, Xavier Gui, Weihua State Key Laboratory of Precision Manufacturing for Extreme Service Performance the School of Automation Central South University Changsha410083 China School of Automation Central South University Changsha410083 China School of Physics Central South University Changsha410083 China Department of Electrical and Computing Engineering Université Laval QuébecQCG1V 0B1 Canada

Infrared thermography (IRT) is a widely used temperature measurement technology, but it faces the problem of measurement errors under interference factors. This paper attempts to summarize the common interference factors and temperature compensation methods when applying IRT. According to the source of factors affecting the infrared temperature measurement accuracy, the interference factors are divided into three categories: factors from the external environment, factors from the measured object, and factors from the infrared thermal imager itself. At the same time, the existing compensation methods are classified into three categories: Mechanism Modeling-based Compensation method (MMC), Data-Driven Compensation method (DDC), and Mechanism and Data jointly driven Compensation method (MDC). Furthermore, we discuss the problems existing in the temperature compensation methods and future research directions, aiming to provide some references for researchers in academia and industry when using IRT technology for temperature measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (temperature measurement)

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Accurate Anomaly Detection Leveraging Knowledge-enhanced GAT

Accurate Anomaly Detection Leveraging Knowledge-enhanced GAT

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IEEE International Conference on Web Services (ICWS)

作者： Yi Li Zhangbing Zhou Shuiguang Deng Xiao Sun Xiao Xue Sami Yangui Walid Gaaloul School of Information Engineering China University of Geosciences (Beijing) Beijing China Computer Science Department TELECOM SudParis Evry France College of Computer Science and Technology Zhejiang University Hangzhou China Department of Automation Tsinghua University Beijing China College of Intelligence and Computing Tianjin University Tianjin China University of Toulouse Toulouse France

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Anomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection.

关键词： Knowledge engineering Accuracy Web services Time series analysis Predictive models Graph neural networks Data models Internet of Things Forecasting Anomaly detection

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Training of Professionals on PV Systems in Bulgaria

Training of Professionals on PV Systems in Bulgaria

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Energy Efficiency and Agricultural engineering (EE&AE), International Conference on

作者： Boris I. Evstatiev Nikolay Valov Tsvetelina Mladenova Vladislav Hinkov Nicolay Mihailov Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Systems and Technologies University of Ruse “Angel Kanchev” Ruse Bulgaria Computing and Information Services Center University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

The article presents the experience of a team from Ruse University “Angel Kanchev” (RUAK) in organizing and conducting training for installers of photovoltaic systems. These trainings are a response to the rapidly increased growth of photovoltaic (PV) plants in the country in recent years. Taking into account the qualification frameworks of the National Agency for Training and Education, a curriculum, theoretical part, and practical exercises have been developed. The offered trainings are met with interest by the business, and their results are positive.

关键词： Photovoltaic systems Renewable energy sources Companies Market research Problem-solving Qualifications

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