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4th International conference on Electronic and Electrical Engineering and intelligent System, ICE3IS 2024

作者： Rosyadi, Mas'uda Isnaeni Bambang, S. Putranto, Lesnanto Multa Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350378368

Protection against overvoltage during lightning causes damage to components and sensors in electronic devices, including the Structural Health Monitoring System (SHMS) used in bridges and tunnels in Indonesia, causing one of the functions not to work. The SHMS is equipped with a lightning rod, but its protection in the DC circuit still needs to be improved. Protection of DC networks need to be done to ensure SHMS performance. TVS diode and MOV series are proposed as protection equipment that cuts excess voltage to lower transient waves. The reliability of the protection system can be met by testing the RS485 module connected to the sensor. Implementing the protection system can work better when compared to the RS485 module not connected to the protection system. © 2024 IEEE.

关键词： Overvoltage protection

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Spatial or Temporal Signal Considered for Gait Recognition based on Optic-fiber sensor

Spatial or Temporal Signal Considered for Gait Recognition b...

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2023 IEEE International conference on Robotics and Biomimetics, ROBIO 2023

作者： Jia, Hanze Ma, Hongtao Cheng, Yang Ju, Linhang Feng, Yanggang Zhang, Wuxiang Beihang University School of Mechanical Engineering and Automation China Intelligent Robot Co. Ltd China Beihang University Beijing Advanced Innovation Centre for Biomedical Engineering China

ISBN: (纸本)9798350325706

Gait recognition plays a significant role in the realm of wearable robotics, particularly in facilitating the development of sensors for gait analysis in rehabilitation. In this study, we proposed a novel optic-based sensor for gait recognition and employed four neural network models to extract both temporal and spatial information. Through a comparative analysis of these neural network models across the gait cycle, we investigate the implications of different sensor placement locations and the contributions made by diverse types of information in the context of gait analysis. Through all the models, RNN performed the highest accuracy when the sensor was fixed on the thigh or the calf, and temporal convolutional capsule network(TCCN) performed better when the sensors were fixed both on the thigh and the calf. The results suggested that temporal information could be mainly considered during gait cycle when a single sensor was fixed, and the combination of both temporal and spatial information is critical for accurate gait recognition for multiple sensors placement. This work can provide a new idea for the chosen of recognition models with different sensor placement patterns. © 2023 IEEE.

关键词： Abnormal gait recognition Neural networks optic-based sensor design spatial information temporal information

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A Hybrid, Distributed Condition Monitoring System using MEMS Microphones, Artificial Neural networks, and Cloud Computing 16

A Hybrid, Distributed Condition Monitoring System using MEMS...

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16th CIRP conference on intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Dorka, Frithjof Lucke, Dominik Richards, Grant P. Hochschule Reutlingen ESB Business School Alteburgstraße 150 Reutlingen72762 Germany Purdue University 401 N. Grant Street West LafayetteIN47907 United States Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 12 Stuttgart70569 Germany

Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance. However, the cost of traditional sensors, data acquisition systems, and the required information technology expert-knowledge challenge the industry. This paper presents a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The system's feasibility is validated using a testbed for reciprocating linear-motion axes. © 2023 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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PhD Forum Abstract: Multi-View Service Provisioning in Cloud-Edge-End networks with Hierarchical Resources 23

PhD Forum Abstract: Multi-View Service Provisioning in Cloud...

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23rd ACM/IEEE International conference on information processing in sensor networks, IPSN 2024

作者： Zou, Haodong Hong Kong Baptist University Hong Kong and BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798350362015

With the surge of end devices and intelligent services, computing resource has begun to migrate from the cloud to end devices to meet the growing demand of users, forming a hierarchical resource distribution pattern in cloud-edge-end networks. Existing research work focuses on using edge computing technique to build a cloud-edge collaborative service offloading and task scheduling method to reduce service delay or energy consumption. However, different user groups and application scenarios may have different preferences for service quality requirements even for the same kind of service. For example, video analysis in autonomous driving focuses more on delay while video analysis in surveillance focuses more on accuracy. Meanwhile, heterogeneous cloud-edge-end devices have significant differences in the amount of resources, which poses great challenges for efficient provisioning of services. To solve this problem, we intend to propose multi-view service provisioning method in cloud-edge-end networks with hierarchically distributed resources. Firstly, we design a mapping scheme between service quality and heterogeneous resource occupation to estimate the amount of resources required for a given requirement. Secondly, we utilize model compression methods to customize powerful large models into smaller and lighter one according to the requirements of tasks. Thirdly, as resources are distributed hierarchically in cloud-edge-end networks, efficient service placement should be carried out with the goal of achieving diverse needs. The effectiveness of the proposed method is demonstrated through numerical simulations compared to state-of-the-art baselines and experiments on an implemented prototype system. © 2024 IEEE.

关键词： Numerical methods

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Special Issue on Computational Intelligence for Perception and Decision-Making of Autonomous Systems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第6期6卷 1287-1289页

作者： Tang, Yang Yen, Gary Kurths, Juergen East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ 130 Meilong Rd Shanghai Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Humboldt Univ Inst Phys D-12489 Berlin Germany

The papers in this special section focus on computational intelligence for perception and decision making of autonomous systems. Due to powerful capabilities in environmental perception, real-time computing, and intelligent decision-making, autonomous systems have demonstrated their great potential to efficiently accomplish a variety of complex tasks that humans cannot. Hence, autonomous systems are able to facilitate the development of almost every walk of life and have attracted increasing attention from both academia and industry. However, given high dimensional, heterogeneous, unstructured, and unpredictable data sampled from different modalities of sensors, autonomous systems with conventional algorithms may fail to acquire the accurate information related to the environment, and make the appropriate decision to complete assigned tasks. Notice that recent advanced computational intelligence algorithms including deep neural networks and evolutionary algorithms have the unique ability to efficiently extract useful information from the multi-source heterogeneous data, and thus have been successfully applied in the fields of computer vision, natural language processing, and so on. Therefore, it is promising to have a thorough and tight integration between computational intelligence and autonomous systems by upgrading advanced and innovative computational intelligence algorithms to ensure high-level environmental perception and decision-making of autonomous systems.

关键词： Special issues and sections Artificial intelligence Computational intelligence Decision making Autonomous systems sensor systems Real-time systems Neural networks Natural language processing intelligent sensors Evolutionary computation

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SRS: Scalable and Robust Snapshot Algorithm for Wireless sensor networks 3

SRS: Scalable and Robust Snapshot Algorithm for Wireless Sen...

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3rd IEEE International conference on intelligent Technologies, CONIT 2023

作者： Mishra, Raghvendra Sharma, Teek Parval National Institute of Technology Department of Computer Science HP Hamirpur India

ISBN: (纸本)9798350338607

Global state determination of distributed systems gives information that is useful in applications of checkpointing, distributed garbage collection, deadlock detection, distributed algorithm termination detection, event boundary detection, distributed debugging, etc. Determination of the global state in a wireless sensor network needs a different approach than of traditional distributed networks due to unique properties of WSN like a constraint on resources, energy constraints, remote hazardous environments where sensors are deployed, network topology that is dynamic in nature, etc. In this paper, we propose a distributed snapshot algorithm that is scalable, robust, and provides efficient communication among WSN nodes which is based on tree architecture. We designed an algorithm in such a way that it can easily be scaled to any number of nodes without affecting the performance of the underlying system and it can effectively handle packet loss so that the communication cost of the whole system is maintained as minimal. We performed simulations in OMNET++ and did the analysis of results based on the number of messages exchanged, response time, and performance in packet loss situations. © 2023 IEEE.

关键词： Fault tolerance

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Systematic Study on Improving the All-Weather Monitoring Accuracy of Horse Walking Rooms Using Signal processing Technology

Systematic Study on Improving the All-Weather Monitoring Acc...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Sun, Zhuo Yan, Xin Wuhan Business University Wuhan430056 China

ISBN: (纸本)9798331504205

This study proposes an all-weather monitoring system for horse walking rooms based on signal processing technology. The fast Fourier transform (FFT) algorithm is used to optimize the monitoring accuracy and achieve efficient and accurate real-time monitoring. The system consists of a sensor array, a signal processing module, and a data analysis module, which can monitor key parameters such as temperature, humidity, and motion status. First, the system collects the original signals of multiple sensors, suppresses noise through a filtering algorithm, and extracts effective information;then, the FFT algorithm is used to perform frequency domain analysis on the signal, which improves the efficiency and accuracy of extracting key signal features. The simulation test results show that the monitoring error of the optimized system is reduced to 0.85% under typical conditions, which is significantly better than the 1.8% of the traditional method. In high-noise and complex environments, the system signal interference reduction rate is 45%, and the robustness is improved by about 30%. The experiment verifies the monitoring accuracy and stability of the system under all-weather conditions, providing strong support for the intelligent management of horse walking rooms. © 2025 IEEE.

关键词： Fast Fourier transforms

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Dual-functional Internet of Meta-material Things networks: Integrated Sensing and Communication

Dual-functional Internet of Meta-material Things Networks: I...

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IEEE conference on Global Communications (IEEE GLOBECOM) - intelligent Communications for Shared Prosperity

作者： Xu, Zhiquan Liu, Xu Zhang, Hongliang Song, Lingyang Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Peking Univ Sch Elect Beijing Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798350310900

The Internet of Meta-material Things (Meta-IoT) has significant potential for future IoT applications due to its ability to function without a battery and its ease of maintenance. Specially, the environmental information can be obtained by analyzing the frequency response of Meta-IoT sensor from the reflected signals. Through the reflection, these Meta-IoT sensors can also provide additional paths for improving communication performance while performing sensing. In this paper, we design a dual-functional Meta-IoT network for integrated sensing and communication (ISAC), i.e., the Meta-IoT sensors can sense environmental conditions and facilitate communication simultaneously. However, ensuring the quality of both sensing and communication for multiple users is not trivial since the impact of the sensor structure on sensing and communication at the same time. Moreover, a customized beamforming scheme is required to balance the performance of two functions. To address these challenges, we optimize the sensor structure and the transmit beamforming. Simulation results show that the communication performance can be significantly improved with the proposed scheme while the sensing performance is guaranteed.

关键词： Integrated sensing and communication MetaIoT meta-material sensor

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

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2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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A Graph-Based Approach for Missing sensor Data Imputation

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IEEE sensors JOURNAL 2021年第20期21卷 23133-23144页

作者： Jiang, Xiao Tian, Zean Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

The Internet of Things (IoT) oriented intelligent services require high-quality sensor data delivery in the wireless sensor networks (WSNs). However, either due to the sensor malfunctions and commutation errors or simply due to the expensive overhead for making full data forwarding, data corruption and loss is relatively common in WSNs, which adversely affects the data quality and the further decisions taking from data. Motivated by the emerging field of graph signal processing (GSP), we propose to impute the missing values in wireless sensor networks based on the topological information carried in the product graph. The proposed solution captures the joint time-space dependencies among the sensor data through a spatiotemporal (ST) graph, which is a time-vertex graph constructed by taking a strong product of a temporal graph and a spatial sensor network graph. Then, the sensory data are mapped onto the vertices of the ST graph and the spatial-temporal nature of sensor data can be further characterized by the notion of smoothness used in GSP. Moreover, instead of imputing with a given spatial graph, we propose a graph learning-based imputation framework to infer underlying space dependencies between the sensors and thus enhance the data imputation performances. Finally, we validate the proposed recovery method using real-world sensor network datasets. The results demonstrate the superior performance of our proposed graph-based method in sensor data imputation, especially when massive sensor data are lost.

关键词： sensors Spatiotemporal phenomena Wireless sensor networks sensor phenomena and characterization intelligent sensors Internet of Things Data models Graph signal processing missing data recovery product graph wireless sensor networks

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