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Research on an optical fiber sensor for real-time monitoring of gas pressure inside lithium batteries 3

Research on an optical fiber sensor for real-time monitoring...

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3rd International conference on Electronic information Engineering and Data processing, EIEDP 2024

作者： Shi, Xuefeng Tong, Xinglin Zhang, Cui Huang, Wenzhong Zeng, Fanchao Li, Chengfei Wang, Yuhan Chen, Long School of Information Engineering Wuhan University of Technology Hubei Wuhan430070 China National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Hubei Wuhan430070 China

ISBN: (纸本)9781510680531

Thermal runaway of lithium batteries is a matter of great concern to relevant scholars. The gas pressure during the charge and discharge control process of lithium batteries is directly related to the performance of lithium batteries. Relevant scholars have been pursuing a method that can safely monitor the internal gas pressure of lithium batteries. Based on the characteristics of optical fiber sensors such as small size, anti-electromagnetic interference, and intrinsic safety, this paper designs and prepares an optical fiber gas sensor that can be used to measure the internal gas of lithium batteries. The measurement range of the sensor is 0-240kPa, and the resolution of the sensor is 0.5kPa, the sensor's accuracy reaches 5kPa, which can be used as a feasible detection method for gas generated during the working process of lithium batteries. © 2024 SPIE.

关键词： Finite element method

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A recommendation algorithm based on local principle component analysis in mobile wireless sensor networks

A recommendation algorithm based on local principle componen...

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2022 IEEE Asia-Pacific conference on Image processing, Electronics and Computers, IPEC 2022

作者： Lu, Shiwei Zhejiang Business Technology Institute Ningbo315012 China

ISBN: (纸本)9781665409025

Modern technological advances have created low-cost, low-power, multi-functional micro sensing devices, which can form a network of thousands of sensors distributed in a wide area After data collection, processing and analysis, sensor networks can obtain information at any time and anywhere, so as to become a part of intelligent environment In a wide range of applications, sensor networks have innovated the sensing function This is because of its reliability, accuracy, flexibility, cost performance, and ease of use The data detected and collected by intelligent sensors may involve machine failures, earthquakes, floods, and signs of terrorist attacks The functions of sensor networks are: collecting information, processing information, monitoring the environment, which can be used for military or civil. sensor network is a comprehensive technology based on wireless communication, digital electronics, MEMS and so on According to the high dimensionality and sparsity of rating matrix in traditional collaborative filtering recommendation system, a new recommendation model based on Local Principle Component Analysis is proposed in mobile wireless sensor networks to make dimension reduction for different subject genre respectively, and remainsthe real interested users in one specific subject of the web pages. © 2022 IEEE.

关键词： Wireless sensor networks

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Modeling and Optimization Methods for Charging Route Planning and sensor Battery Capacity Minimization for Wireless Rechargeable sensor networks

Modeling and Optimization Methods for Charging Route Plannin...

引用

2023 International conference on Automation Control, Algorithm, and intelligent Bionics, ACAIB 2023

作者： Zhao, Wenhao Xiang, Suji Shu, Ziqing Chen, Zeliang Zhang, Jing College of Mechanical Engineering University of South China Hunan Hengyang421001 China School of Economics Management and Law University of South China Hunan Hengyang421001 China College of Chemistry and Chemical Engineering University of South China Hunan Hengyang421001 China

ISBN: (纸本)9781510667662

The wireless rechargeable sensor network mainly consists of a data processing unit, a sensor (data acquisition unit), and a mobile charger (energy supply unit). For the wireless rechargeable sensor network to work properly, the mobile charger needs to charge the sensors periodically through the route given by the data processing unit, but the mobile charger incurs unnecessary energy loss on the charging journey. This paper discusses the problem of route planning and the minimum capacity of sensor batteries for single versus multiple mobile chargers. First, the TSP and MTSP problems of minimum energy consumption of mobile chargers over the distance are solved by modeling them with Monte Carlo optimization simulated annealing algorithm, and improved genetic algorithm, respectively. Then, the problem is further transformed into a linear programming and multi-objective optimization model to solve for the minimum capacity of the battery. In addition, computational experiments using the LKH algorithm and CA learning methods are conducted in this paper to propose optimization scenarios for the route planning model. © 2023 SPIE.

关键词： Multiobjective optimization

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A Hybrid Residual CNN with Channel Attention Mechanism for Continuous User Identification Using Wearable Motion sensors 47

A Hybrid Residual CNN with Channel Attention Mechanism for C...

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47th International conference on Telecommunications and Signal processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350365597

Recognizing individuals continuously using wearable devices has several applications in personalized services and security. This paper presents a hybrid deep network architecture called SE-ResBiLSTM that integrates a squeeze-and-excitation (SE) mechanism with residual bidirectional LSTM layers. The goal is to enhance the accuracy of person identification using data from wearable motion sensors. The SE blocks facilitate channel-wise feature refinement, highlighting helpful information. Residual connections enable the training of deeper LSTM architectures. The proposed method's accuracy in identifying individuals across increasing periods between training and testing is evaluated using the SP-SW HAR dataset, which contains activity data collected from wristwatch sensors. By leveraging representation learning and exhibiting resilience against sensor noise and temporal fluctuations, SE-ResBiLSTM improves recognition accuracy compared to regular and convolutional LSTM networks. According to the trial data, SE-ResBiLSTM achieved a maximum identification accuracy of 98.71 %. The experimental results demonstrate that this approach significantly boosts the performance of user identification using wristwatch sensors. © 2024 IEEE.

关键词： Wearable sensors

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intelligent sensor networks in Industry 5.0. Generalized Concept of Creating Digital Platforms for Managing Complex Systems Based on a Regularizing Bayesian Approach 25

Intelligent Sensor Networks in Industry 5.0. Generalized Con...

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25th International conference on Soft Computing and Measurements, SCM 2022

作者： Prokopchina, Svetlana V. Financial University under the Government of the Russian Federation Moscow Russia

ISBN: (纸本)9781665496698

Within the framework of the Industry 4.0 concept, intensive development of the processes of intellectualization of sensory systems is envisaged. Among the most important specific properties of real measuring processes in complex systems is, first of all, their implementation in conditions of considerable uncertainty. The uncertainty is caused by a priori incompleteness, inaccuracy, vagueness of information about a complex measuring object and its functioning environment, which does not allow us to build an adequate model of the object before conducting a measuring experiment, identify and formalize the influencing factors of the external environment and develop effective algorithms for the functioning of information and measurement *** article proposes an approach to the intellectualization of measurement systems in conditions of uncertainty by creating intelligent sensor networks based on Bayesian intelligent technologies (BITS) and means of their implementation. Typical modules of such networks are considered, which are integrated sets of various sensors and intelligent measurement information processing *** sensor sets may include both physically implemented measuring devices and virtual sensors for measuring non-quantitative or integral characteristics. The results of the work of the networks are comprehensive assessments of the state of complex objects and recommendations for ensuring their sustainable functioning. An important part of such systems is the built-in means of a complete metrological justification of all the solutions obtained. The systems have a hierarchical architecture corresponding to the levels of management of complex objects, which has the possibility of self-development based on newly received information. This is achieved thanks to models and measurement scales with dynamic constraints, on which all algorithms used in these networks are built. © 2022 IEEE.

关键词： sensor networks

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Machine Learning Based Smart Helmet for Air Quality and Hazardous Event Detection in the Mining Industry

Machine Learning Based Smart Helmet for Air Quality and Haza...

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2023 International conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Devi, D. Prasanna, N. Ram, R.A. Prateek Mohamed, N. Niyaz Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India

ISBN: (纸本)9798350300857

Ensuring the safety of underground miners is of utmost importance in the mining industry, given the devastating consequences of mining accidents. To address this concern, this research study has developed an intelligent headgear equipped with an array of sensors to continuously monitor the working conditions of miners. This advanced device is engineered to identify critical parameters such as temperature, humidity, concentrations of harmful gases, and vibrations. It is equipped with dedicated sensors for each of these parameters, and it promptly transmits real-time data to a centralized control centre. The control centre can analyse this information and take immediate actions, which may include worker evacuation or improvements to working conditions. The proposed system utilizes an Arduino microcontroller for processing the data from these sensors and then transmits it via a Wi-Fi module. The proposed model has utilized Cloud-based loT and machine learning algorithms to enhance the accuracy of the sensor readings. Specifically, an Artificial Neural Network (ANN) algorithm is employed to train a dataset of sensor information stored in CS V format. This innovative system has the potential to significantly elevate safety standards and minimize the risk of mining accidents. © 2023 IEEE.

关键词： Neural networks

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Semantic sensor Network Ontology for Forest Fire Management Decision Support

Semantic Sensor Network Ontology for Forest Fire Management ...

引用

2025 International conference on intelligent Control, Computing and Communications, IC3 2025

作者： Kirubakaran, S. Hussein, Ramy R. Ramakrishna, Thanneeru Venkata Duraipandi Reddy, Gurram Vijendar Sandeep, C.S. Cmr College of Engineering & Technology Department of Cse Telangana Hyderabad India College of technical engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Information Technology Pulloor Tamil Nadu Kariapatti626115 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Jawaharlal College of Engineering and Technology Department of Ece Kerala Palakkad India

ISBN: (纸本)9798331527495

Forests are essential resources for each country, since they are essential to maintaining environmental equilibrium. The negative effects of deforestation, especially those caused by forest fires, highlight the need of efficient management and monitoring. When determining the risk of a fire, giving warnings, and assigning resources for emergency management, fire weather indices are essential measures. Utilising sensor networks-Semantic sensor networks (SSN) in particular-improves the ability to gather data on a range of meteorological factors, including temperature, relative humidity, and wind speed. processing data streams produced by these sensors poses difficulties for estimating fire weather indicators. This methodology guarantees the system's flexibility in responding to changing circumstances, facilitating reasoning and preserving the open world's generic semantics as established by all participating ontologies. The suggested fire management system contributes to effective forest fire prevention and control by functioning in accordance with the current circumstances © 2025 IEEE.

关键词： Risk management

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Harnessing Deep Learning for Activity Recognition in Seniors' Daily Routines with Wearable sensors 47

Harnessing Deep Learning for Activity Recognition in Seniors...

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47th International conference on Telecommunications and Signal processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Department of Applied Statistics Faculty of Applied Science Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Wearable sensors are increasingly popular for monitoring the daily activities of older individuals, sparking significant interest in human activity recognition (HAR). Accurate and timely identification of these activities is crucial for providing personalized care and timely interventions to maintain the well-being of elderly individuals. However, existing HAR methods often need support with complex activity patterns, limited labeled data, and the need for real-time performance. To address these challenges, this study proposes a novel deep-learning approach called ResBiGRU for precisely identifying daily activities in older adults using wearable sensors. The ResBiGRU architecture combines residual connections with BiGRU networks to capture short-term and long-term dependencies in sensor data effectively. We also propose a data augmentation strategy to improve generalization performance. Evaluation of the HAR70+ dataset, consisting of activities performed by 18 elderly individuals, demonstrates superior performance of our ResBiGRU model with an accuracy of 97.39% and an F1-score of 97.74%. Furthermore, our model supports real-time inference, making it suitable for practical applications. Implementing the ResBiGRU approach could significantly enhance monitoring and care for older adults by accurately identifying their daily activities. This research advances HAR methods tailored for elderly individuals, opening new avenues for personalized healthcare and assisted living technologies. © 2024 IEEE.

关键词： Wearable sensors

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Fault Warning Model of Machining Center Multi-sensor Data Based on Skip-Graph Convolutional networks 2

Fault Warning Model of Machining Center Multi-Sensor Data Ba...

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2nd International conference on Machine Learning, Cloud Computing, and intelligent Mining, MLCCIM 2023

作者： Huang, Jiewen Yang, Ying Yang, Lei School of Computer Electronics and Information Guangxi University Nanning China Guangxi Academy of Sciences Nanning China

ISBN: (纸本)9798350328530

The high efficiency continuous operation of automatic processing center is very important for enterprises to control production cost and improve profitability. The key to achieve this goal is to reduce the sudden shutdown of machining center. In order to accurately warn the sudden faults in the machining center, we proposed a fault warning method based on multi-sensor data fusion. This method constructs multi-sensor data into a graph, designs an improved graph convolution network to find out valuable features from the fused data, and effectively utilizes those relationship features between multiple sensors. An early warning accuracy of up to 96% was achieved on the data set collected from the production shop. It can help machining center maintenance personnel to carry out preventive maintenance, reduce the risk of sudden shutdown of traditional maintenance based on the situation and waste the remaining life of parts maintained regularly, and effectively improve production efficiency. Compared with basic graph convolutional network method and graph attention network method, compared with the other two methods, our method performs better. © 2023 IEEE.

关键词： Convolution

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Multi-Classification of Ultra-Wideband Non-Line-of-Sight Ranging Signals using Deep Learning 5

Multi-Classification of Ultra-Wideband Non-Line-of-Sight Ran...

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5th International conference on intelligent Control, Measurement and Signal processing, ICMSP 2023

作者： Dong, Mengyao Liu, Yiming Zhang, Yifang School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350336030

Ultra-wideband (UWB) is an emerging technology with numerous applications, such as indoor positioning, object tracking, and wireless sensor networks. However, non-line-of-sight (NLOS) scenarios can cause significant performance degradation. In this paper, we propose a deep learning model specifically designed for accurately identifying various types NLOS UWB ranging signals for better error mitigation. We extract 13 feature parameters from low-cost UWB sensors to characterize channel quality. Our proposed deep learning model consists of a convolutional neural network module, a long short-term memory module, and a fully connected module for extracting spatial and time-domain features. Through field experiments, we demonstrate that our model achieves high accuracy and F1 score of 0.98 and 0.97, respectively, indicating its potential for accurately identifying different types of NLOS cases in UWB ranging. © 2023 IEEE.

关键词： Long short-term memory

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