The Optical Fiber Composite Overhead Conductor (OPPC) cable is a special type of power optical cable that combines overhead conductor and communication capabilities. It has advantages such as strong wind resistance, g...
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Wind and solar energy inverter-based resources (IBRs) has been employed extensively with the aim of carbon neutrality. Power systems also make extensive use of power electronic converters, leading to interactions betw...
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In the Offline Finding Network(OFN), offline Bluetooth tags broadcast to the surrounding area, the finder devices receiving the broadcast signal and upload location information to the IoT(Internet of Things) cloud ser...
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In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback ...
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High-precision spindles are a vital study topic because of the growing demand for high-speed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of...
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The existing facility layout problem (FLP) only considers the layout of processing facilities. However, in the current scenario of industrial logistics, there are not only working facilities, but also a lot of transpo...
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Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow spec...
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Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols ***,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal ***,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network *** this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection *** consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is *** addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal *** paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data *** experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.
The fusion of large language models and robotic systems has introduced a transformative paradigm in human–robot interaction,offering unparalleled capabilities in natural language understanding and task *** review pap...
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The fusion of large language models and robotic systems has introduced a transformative paradigm in human–robot interaction,offering unparalleled capabilities in natural language understanding and task *** review paper offers a comprehensive analysis of this nascent but rapidly evolving domain,spotlighting the recent advances of Large Language Models(LLMs)in enhancing their structures and performances,particularly in terms of multimodal input handling,high-level reasoning,and plan ***,it probes the current methodologies that integrate LLMs into robotic systems for complex task completion,from traditional probabilistic models to the utilization of value functions and metrics for optimal *** these advancements,the paper also reveals the formidable challenges that confront the field,such as contextual understanding,data privacy and ethical *** our best knowledge,this is the first study to comprehensively analyze the advances and considerations of LLMs in Human–Robot Interaction(HRI)based on recent progress,which provides potential avenues for further research.
As the complexity of space exploration missions augments,how to enhance the overall performance of communication,ranging or other functions has become a challengeable *** the integration of communication and ranging,w...
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As the complexity of space exploration missions augments,how to enhance the overall performance of communication,ranging or other functions has become a challengeable *** the integration of communication and ranging,we present a bit-level composite signal for simultaneous ranging and *** this composite method,through a specially designed mapping scheme using low-weight codewords,the information sequence is converted to a sparse sequence which is then superimposed on the ranging *** ranging,the correlation characteristics of the ranging code component can be maintained to calculate the transmitter-receiver *** communications,the sparse sequence can be extracted without interference by eliminating the ranging code *** results show that the proposed composite signal can support communication and ranging simultaneously with limited sacrifice of ranging performance,and the performance loss of ranging can be controlled and minimized by lowering the density of information sequences using different sparsification encoding methods.
This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional **...
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This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional *** work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in *** results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.
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