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SOC Evaluation of Lithium-ion Battery Based on MFC sensors 4th

SOC Evaluation of Lithium-ion Battery Based on MFC Sensors

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4th International conference on intelligent Systems in Production Engineering and Maintenance , ISPEM 2023

作者： Yan, Lyu Liang, Geng Jie, Gao Faculty of Information Technology Beijing University of Technology Beijing100124 China Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783031450204

The impact of different dimensional parameters of the macro fiber composite (MFC) sensor on its response performance was investigated using finite element simulation software. The frequency domain analysis of performance parameters, such as the electromechanical coupling coefficient, center frequency, and resonant frequency characteristic relationship, was carried out using different simulation models. Based on the simulation results, a MFC sensor suitable for acoustic detection was designed and customized. Then an ultrasonic guided wave detection system for lithium-ion batteries using the optimized MFC sensor was established. And the intrinsic relationship between the state of charge (SOC) and guided wave propagation properties was experimentally investigated. The results showed that the time of flight, signal amplitude, and SOC exhibited an approximately linear distribution, indicating the feasibility and reliability of characterizing the SOC of lithium-ion batteries based on MFC sensors. The mapping relationship between the state of charge and the propagation characteristics provides a foundation for the nondestructive evaluation and quantitative estimation of the state characteristics of lithium-ion batteries. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Lithium-ion batteries

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ADNet: A Neural Network for Accelerometer Signals Denoising

ADNet: A Neural Network for Accelerometer Signals Denoising

引用

International Joint conference on Neural networks (IJCNN)

作者： Zheng, Fengling Li, Wei Ding, Chuntao Cui, Xiaohui Jiangnan Univ Minist Educ Engn Res Ctr Intelligent Technol Healthcare Sch Artif Intelligence & Comp Sci Jiangsu Peoples R China Jiangnan Univ Minist Educ Jiangsu Key Lab Media Design & Software Technol Jiangsu Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Beijing 100044 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Accelerometer signals play a critical role in many fields, for example navigation and vehicle safety. However, uncontrollable factors such as defective equipment and harsh environments make the signals recorded by sensors contain a large amount of noise, which poses a great challenge. Most existing methods are based on traditional signal processing and often face problems of incomplete noise reduction or signal distortion after denoising. In this paper, we propose a data-driven denoising method for accelerometer signals (ADNet) based on the Wave U Net network architecture, incorporating Multi-Head Attention mechanism and Spatial Attention mechanism. This enhances the network's feature extraction capability and accelerates its convergence speed. Meanwhile, the attention mechanism focuses the model's attention on the clean signal's feature information, improving the network's fitting capability to the signal distribution. Therefore, ADNet can achieve more thorough denoising while reducing signal distortion after denoising. Finally, we conduct experiments on Walking speed dataset and field experiment dataset to verify the effectiveness of ADNet. The experimental results show that ADNet outperforms other baseline models in terms of Mean Square Error, Mean Absolute Error, Root Mean Square Error, and Signal-to-Noise Ratio.

关键词： Accelerometer Signals Denoising Convolutional neural network Attention mechanism

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Machine Learning in Motion: Enhancing Human Activity Recognition with Smartphone sensor Data and Performance Metrics 15

Machine Learning in Motion: Enhancing Human Activity Recogni...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kashyap, Shrish Gagandeep Department of Mathematics Chandigarh University Mohali India

ISBN: (纸本)9798350370249

The goal of this undertaking course, is to enhance the efficiency and outcome performance in Human Activity Recognition (HAR) using smartphone sensor data and machine learning (ML) approaches. This field of study is located at the interface between pioneering computational techniques and practical technical implementations. The rising demand for high-performance, robust and user friendly HAR systems is the driver behind this initiative. The importance of such systems is increasing not only in the fields of medicine, training, and cities development but also aided by these areas. This investigation will have a primary goal for appraising many ML models. The models include different types of neural network architectures, including support vector machine (SVM), decision trees (DT), convolutional neural networks (CNN) and long short-term memory (LSTM). In training and validation procedures, the models were given datasets based on information from sensors in smartphones – accelerometers and gyroscopes. Precisely, the goal was to provide a precise identification and value of wide range human behaviors. One of the key components that make this course meaningful is its clear-cut inclination towards practical application in real world situation. To do this, giving priority to the accuracy of models is paramount while simultaneously taking into consideration parameters such as speed of processing and practical aspects when using most smartphones. For HAR tasks, CNN and LSTM achieve better results as opposed to other deep learning approaches. They have amazing ability to harness more or less characteristics of the vibrations, which naturally happen in sensor data. The findings from the studies show that deep learning approaches perform better than traditional ML models. ©2024 IEEE.

关键词： Convolutional neural networks

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FlDbOR: An intelligent Depth-based Opportunistic Routing Protocol for UASNs

FlDbOR: An Intelligent Depth-based Opportunistic Routing Pro...

引用

2023 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2023

作者： Cheng, Yushuai Xie, Lei Chen, Huifang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350316728

Designing the routing protocol for underwater acoustic sensor networks (UASNs) faces with numerous challenges, such as long propagation delay, limited transmission bandwidth, and unreliable transmission. In this paper, an intelligent depth-based opportunistic routing protocol, named as the FlDbOR protocol, is proposed for UASNs. In the proposed routing protocol, a receiver-based mode is adopted to avoid extra overhead of control packets. Moreover, several metrics are used as the inputs of a fuzzy logic system to describe the node state, and the angle information is utilized in order to guarantee the correct direction to the sink node. Finally, the best forwarder is found. Simulation results show that the proposed FlDboR protocol performs well, in terms of the packet delivery ratio, the end-to-end delay, the energy efficiency in the dynamic underwater environment. © 2023 IEEE.

关键词： Fuzzy logic

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Internet-of-Things and Wireless sensor networks as Enablers for Soil Observation in Smallholder Farms

Internet-of-Things and Wireless Sensor Networks as Enablers ...

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2023 International conference on Earth Observation and Geo-Spatial information, ICEOGI 2023

作者： Bayih, Amsale Zelalem De By, Rolf A. Assabie, Yaregal Guarin, Javier Morales Addis Ababa University Computer Science Department Addis Ababa Ethiopia University of Twente Faculty Itc Geo-information Processing Department Netherlands

ISBN: (纸本)9798350396720

Smallholder farms are known to be resource-constrained and remotely located, which makes in-situ data collection challenging. Typically, on-farm data deficiency arises and little support for farming decisions is one cause of poor production. Automated, precise, and affordable data collection and dissemination methods are vital to fill this data gap. The Internet of Things (IoT) and wireless sensor networks (WSNs) are tools fitting the bill, but they require careful design and deployment for local contexts. Our work is an attempt to define an IoT-Wsnsystem as a digital data infrastructure for in-season farm monitoring in rural smallholder farms. Design considerations are identified to enhance system reliability and robustness. A wireless communication protocol that runs reliably in a resource-constrained environment is identified, devices with ease of access, configuration, and maintenance were selected, and an appropriate sampling strategy to address a wide area with minimum resource consumption is designed. LoRa nodes with soil physical property sensors have been distributed among representative farm plots for data collection. And data was sent, autonomously to a back-end system over cellular communication. Our experience reveals the potential of the technology to generate as much required data as needed but with further careful design issues. © 2023 IEEE.

关键词： Wireless sensor networks

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Multi-purpose Tactile Perception Based on Deep Learning in a New Tendon-driven Optical Tactile sensor

Multi-purpose Tactile Perception Based on Deep Learning in a...

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IEEE/RSJ International conference on intelligent Robots and Systems (IROS)

作者： Zhao, Zhou Lu, Zhenyu EPITA Res & Dev Lab LRDE Le Kremlin Bicetre France UWE Bristol Bristol Robot Lab Bristol Avon England

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In this paper, we create a new tendon-connected multi-functional optical tactile sensor, MechTac, for object perception in the field of view (TacTip) and location of touching points in the blind area of vision (TacSide). In a multi-point touch task, the information of the TacSide and the TacTip are overlapped to commonly affect the distribution of papillae pins on the TacTip. Since the effects of TacSide are much less obvious to those affected on the TacTip, a perceiving out-ofview neural network (O2VNet) is created to separate the mixed information with unequal affection. To reduce the dependence of the O2VNet on the grayscale information of the image, we create one new binarized convolutional (BConv) layer in front of the backbone of the O2VNet. The O2VNet can not only achieve real-time temporal sequence prediction (34 ms per image), but also attain the average classification accuracy of 99.06%. The experimental results show that the O2VNet can hold a high classification accuracy even facing the image contrast changes.

关键词： Optical design Neural networks Tactile sensors Optical computing Optical imaging Real-time systems Pins

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Revolutionizing Water Management: Digital Twins and AI for Predictive Maintenance and Sustainability 5th

Revolutionizing Water Management: Digital Twins and AI for P...

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5th International conference on Protection of Historical Constructions-PROHITECH

作者： Syed, Toqeer Ali Naqash, Muhammad Tayyab Siddiqui, Muhammad Shoaib Alqahtany, Saad Said Faizullah, Safiullah Formisano, Antonio Islamic Univ Madinah Fac Comp & Informat Syst Medina Saudi Arabia Islamic Univ Madinah Dept Civil Engn Fac Engn Medina Saudi Arabia Univ Naples Federico II Piazzale Tecchio 80 I-80125 Naples Italy

ISBN: (纸本)9783031873140;9783031873126;9783031873119

The innovative investigation integrates digital and physical water networks to improve their management and predictive capabilities. The used approach applies advanced sensors for detecting leaks and smart meters that monitor water usage, send alerts, usage data and visual information to a digital platform. At the heart of this framework is a digital twin, a virtual replica that continuously reflects the real-time state of the physical network by processing incoming sensor data. A multimodal transformer model is employed to integrate and analyze diverse data streams, including numerical and visual inputs, enabling highly accurate leakage detection, future water usage forecasting, and contextual insights with a prediction accuracy of about 98%. This approach advances water management and provides valuable insights for preserving historical buildings that demonstrates optimize operational efficiency and ensure sustainable water use in smart water networks.

关键词： water networks smart metering historical building leakage detection sensors

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Geotagging in Livestock Farming: AI-Based Technologies, Applications, and Challenges 4th

Geotagging in Livestock Farming: AI-Based Technologies, Appl...

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4th International conference on Emerging Trends and Technologies on intelligent Systems, ETTIS 2024

作者： Verma, Shuchi Sharma, Neelotpal Trivedi, Gaurav Ramjas College Delhi University Delhi India IIT Guwahati Guwahati India

ISBN: (纸本)9789819757022

The geotagging technology has emerged as a transformational tool for livestock management, especially in India, where agricultural technology is changing rapidly. The usage of geotagging technology that has been developed recently may bring changes in all agricultural activities, in livestock management, and in animal health monitoring system. The animal’s size, weight, and growth rate are the significant factors in determining a country’s economy in India. The applications of artificial intelligence (AI) merged with geotagging technology could be considered for discovering the disease surveillance, illegal trade in live animals, and control over livestock movement and overgrazing of pasture. The article discusses the use of geotagging in different animals like beef, pig, sheep, and buffalo industries in India through which cattle, pigs, sheep, and buffaloes can be tracked physically. The aim of this paper is to provide an overview of the potential uses, challenges, and advantages of combining geotagging with geographic information systems (GIS) for livestock management. In addition, this study focuses on the geotagging techniques operated through satellites, for instance, to sensor networks, machine learning and GPS, RFID, Bluetooth, and Wi-Fi items. With wide-range technologies, such as animal behavior sensors, health monitors, and environment data gathering ones like these can help the farmers to make accurate decisions for the productivity and manage resources in a better way. Nevertheless, many obstacles must be solved before geotagging could be very common. It is therefore clear that GPS-based information can facilitate hassle-free management of grazing lands and improve efficiency in livestock production across India as well as in the rest of the world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： information management

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How can Emotions be Classified with ECG sensors, AI techniques and IoT Setup? 2

How can Emotions be Classified with ECG Sensors, AI techniqu...

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2nd International conference on Signal and information processing, IConSIP 2022

作者： Patil, Varsha K. Pawar, Vijaya R AISSMS Institute of Information Technology Department of E&Tc Engineering Pune India Bharati Vidyapeeth's CoE for Women Department of E&Tc Engineering Pune India

ISBN: (纸本)9781728168852

Emotion recognition is a demanding field in the current world. The demands of a dynamically changing world are modifying appliances to incorporate emotion-dependent services. Previously, Intruders at public places, lifts, and entrances could be scanned and selected with the face detection method. However, to incorporate advanced safety, Automatic Emotion Recognition methods are in need for predicting the intentions of intruders. In this connection, we are proposing a system that can automatically identify human emotions with the help of sensors to be used at entrance gates. The system has Real-time hardware sensors which are dealing with various parameters of the 'CASE' Dataset and real-time body parameters like temperature, ECG signals, and heart rate. To make the proposed emotion classification system intelligent, we have used a microcontroller-based system along with Machine learning algorithms like clustering (K means), distance calculation, Linear regression etc. Further proposed system artificial neural networks and Fuzzy classification utilizes to obtain correct results. The proposed system utilizes an ECG sensor, pulse sensor, and temperature sensor. With this article, we are proposing the Internet of Artificial Intelligence Things and a Fuzzy logic-based framework for Emotion Recognition. Our proposed framework is equipped with ECG sensors and temperature sensors. The proposed system has a software requirement of NetBeans IDE. The attractive Graphical interface is created to show the emotion classification of the person who is using sensors of the proposed setup. The novelty of the proposed method is that fuzzy logic, and ANN methods are used with machine learning algorithms, which results in fewer errors in prediction. The measures of errors are given by MSE and RMSE. After emotion classification, analysis and comparison of MSE for No of Actual Emotion Recognized V/s No of Expected Emotion Recognized is performed. A similar emotion recognition setup can be

关键词： Electrocardiograms

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A Comparative Study of Deep Learning Robustness for sensor-based Human Activity Recognition 46

A Comparative Study of Deep Learning Robustness for Sensor-b...

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46th International conference on Telecommunications and Signal processing, TSP 2023

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

ISBN: (纸本)9798350303964

Recognition of human activities has become a highly significant area of research in the current era of the Internet of Things and A.I. technologies. Human activity recognition (HAR) plays a crucial role in monitoring and analyzing human behaviors using various types of sensors. Such sensor-based HAR systems are being used in various industry domains, including healthcare, professional sports, and worker assessment in intelligent factories. The integration of micro electronics mechanical systems sensor technology in smart wearable devices has significantly improved the capabilities of HAR using machine learning and deep learning techniques. However, processing longtime dependent sequence samples with noisy data is still a significant challenge, affecting the classification time and accuracy. This study aimed to analyze noise-tolerant deep learning models for recognizing physical activities from noisy smartphone data. The study analyzed the recognition performances of the five most popular deep learning networks for HAR under different levels of Gaussian noise. The results indicated that the BiGRU network demonstrated outstanding robustness to Gaussian noise of different levels. © 2023 IEEE.

关键词： Smartphones

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