Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management ***,they are generally developed in a supervised manner which requires a considerable ...
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Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management ***,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data *** response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as *** importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training *** large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed *** results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are ***,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are *** this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed *** method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.
computerscience domain has got massive developments in the past decade, especially in the field of Big Data and Artificial Intelligence. Many industries are adopting these technologies to improve their products and s...
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The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, da...
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Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme ...
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Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enabl...
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Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.
SDN, short for Software Defined Networking, represents a network architecture controlled by software applications. Distributed Denial-of-Service (DDoS) attacks create a challenge to these SDN environments and lead to ...
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With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being *** using such information provides critical and convenient services,we are gradually exposing ...
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With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being *** using such information provides critical and convenient services,we are gradually exposing every piece of our behavior and *** are aware of the privacy risks and have been working on preserving privacy while sensing human *** survey reviews existing studies on privacy-preserving human activity *** first introduce the sensors and captured private information related to human *** then propose a taxonomy to structure the methods for preserving private information from two aspects:individual and collaborative activity *** each of the two aspects,the methods are classified into three levels:signal,algorithm,and ***,we discuss the open challenges and provide future directions.
Accurate skin tumor identification and categorization are crucial for quick diagnosis and treatment in dermatological care. In our research, we developed a reliable model that successfully categorizes a wide range of ...
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With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates a...
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The creation of a Real-Time Task Manager with a user-friendly interface and effective system monitoring is presented in this work utilising Python. The Psutil library was used to retrieve comprehensive system informat...
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