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AIR-PV: a benchmark dataset for photovoltaic panel extraction in optical remote sensing imagery

science China(information sciences) 2023年第4期66卷 122-123页

作者： Zhiyuan YAN Peijin WANG Feng XU Xian SUN Wenhui DIAO Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory for Information Science of Electromagnetic Waves(MoE) Fudan University

The energy crisis has been a hot topic of global *** devices, a typical new energy source, have progressed rapidly and become among the main sources of power generation in the world [1]. Most photovoltaic fields are often constructed in large areas, making it difficult to monitor photovoltaic panel situations. Combining remote sensing(RS) and deep learning [2],

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Towards explainable traffic flow prediction with large language models

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Communications in Transportation Research 2024年第1期4卷 474-490页

作者： Xusen Guo Qiming Zhang Junyue Jiang Mingxing Peng Meixin Zhu Hao Frank Yang Intelligent Transportation Thrust System HubThe Hong Kong University of Science and Technology(Guangzhou)Guangzhou511400China Department of Civil and System Engineering Johns Hopkins UniversityBaltimore21218USA Guangdong Provincial Key Lab of Integrated Communication Sensing and Computation for Ubiquitous Internet of ThingsGuangzhou511400China

Traffic forecasting is crucial for intelligent transportation *** has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic ***,recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted *** both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning *** tackle these challenges,we propose a traffic flow prediction model based on large language models(LLMs)to generate explainable traffic predictions,named *** transferring multi-modal traffic data into natural language descriptions,xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic *** LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow ***,xTP-LLM shows competitive accuracy compared with deep learning baselines,while providing an intuitive and reliable explanation for *** study contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation.

关键词： Traffic flow predictionLarge language models Spatial-temporal prediction Explainability

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Multitarget gas alarm system for high-performance and anti-interference chemical warfare agents simulants detection

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science China(Technological sciences) 2025年第3期68卷 349-358页

作者： Yubin YUAN Qiang WU Menglong JIAO Haiyang WU Weihua LIU Xuming WANG Xiangrui BU Gong LI Chuanyu HAN Long HU Xin LI Xiaoli WANG Shool of Microelectronics School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’an710049China The Key Lab of Micro-nano Electronics and System Integration of Xi’an City Xi’an710049China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou310024China School of Automation and Information Engineering Xi’an University of TechnologyXi’an710048China Key Laboratory for Physical Electronics and Devices of the Ministry of Education Xi’an Jiaotong UniversityXi’an710049China School of Science Xi’an Jiaotong UniversityXi’an710049China

Chemical warfare agents(CWAs)can cause significant harm to health and even death in a very short time even when inhaled in small amounts(~100 mg min/m^(3)).Detection equipment based on conventional techniques,including gas chromatography-mass spectrometry,Raman spectroscopy,fluorescence spectroscopy,and ion mobility spectrometry,have the disadvantages of high cost,long detection time,and high power ***,portable,and convenient equipment for target CWAs detection under interference is a *** this study,we propose an alarm system comprising a multitype micro sensor array and a field-programmable gate array-based readout circuit for CWAs simulants *** common volatile organic compound,temperature,and water vapor interference,the proposed multitype sensor-based detection system realizes an alarm function in less than 20 s with thresholds of 1 ppm dimethyl methylphosphonate(DMMP),4.6 ppm 2,2'-dichlorodiethyl ether(DCE),and 20 ppm SO_(2).Furthermore,the proposed detection system was evaluated outside the laboratory and passed the wilderness test,demonstrating its reliability in the *** believe that the proposed alarm system will contribute to the development of intelligent,portable,low-cost,and anti-interference CWAs detection technology.

关键词： alarm system CWAs simulants surface acoustic wave sensor mixed gas anti-interference

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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Measurement-based Path Loss Prediction and Analysis in Water Scenarios

Measurement-based Path Loss Prediction and Analysis in Water...

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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Li, Hanpeng Chen, Xiaomin Li, Hangang Ma, Jian Ye, Xuchao Song, Lixin Zhang, Taotao Zhong, Weizhi Zhu, Qiuming Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Aviation Key Laboratory of Science and Technology On Aero Electromechanical System Integration Nanjing211106 China State Key Lab. of Complex Electromagnetic Environment Effects On Electronics and Information System Luoyang471003 China

ISBN: (纸本)9798350383317

Unmanned aerial vehicles (UAVs) have enabled the unmanned management of water areas. Path loss (PL) serves as a critical factor that affects the UAV communication quality. In this work, a PL model for UAV channel in water scenarios is proposed, aiming to explore the multidimensional relationship between transceiver distance, carrier frequency, UAV height, and PL by constructing an Artificial Neural Network (ANN). Moreover, channel measurements are conducted in a river area, and the obtained data are analyzed using the proposed ANN model as well as the close-in (CI) model. The analysis results demonstrates that the PL increases initially and then declines as the UAV height increases. The proposed ANN model has higher prediction accuracy than the classic empirical model, and can achieve precise prediction of the PL in water scenarios. © 2024 IEEE.

关键词： Neural networks

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Research on the Remaining Useful Life Prediction Model of Complex Equipment 4

Research on the Remaining Useful Life Prediction Model of Co...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Tang, Lei Tang, Yifei Zhang, Junhao Liu, Mengjuan Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331541729

With the rapid advancement of artificial intelligence technology, the development of intelligent manufacturing has become an inevitable trend. Utilizing AI technology to ensure the safe operation of factories is a crucial aspect of smart factories. In particular, research focused on fault diagnosis and remaining useful life (RUL) prediction models based on machine learning has garnered significant attention from both academia and industry. This paper explores RUL prediction models for complex equipment, introduces four representative models, and analyzes their performance using two benchmark datasets, C-MAPSS and N-CMAPSS. Finally, the paper discusses the future research and application trends of artificial intelligence technology in complex equipment operation and maintenance. © 2024 IEEE.

关键词： Smart manufacturing

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Research on Shipborne Laser Communication Optical Stability Control technology 7

Research on Shipborne Laser Communication Optical Stability ...

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7th International Conference on Imaging, Signal Processing and Communications, ICISPC 2023

作者： Shen, Shao-Nan Zhao, Zhan-Feng Wang, Xiao-Yan Wang, Zhen-Xing Zhang, Ting-Ting Zhang, Chen-Yang Harbin Institute of Technology School of Information Science and Engineering Weihai China Aerospace System Engineering Shanghai First Project Sector Shanghai China

ISBN: (纸本)9798350325072

Free space laser communication is a promising technology with numerous advantages, such as high resistance to electromagnetic interference, excellent confidentiality, and fast information transmission rates. It has broad application prospects in both military and civilian fields. However, the application of this technology on ships poses many challenges. For instance, the ship's motion trajectory and speed cannot be determined precisely, making it difficult to initially align the line of sight. Furthermore, the ship's motion is strongly affected by waves at sea, causing six degrees of freedom attitude disturbance and making it challenging to maintain stable line-of-sight alignment. To address these issues, we propose a shipborne wireless laser communication line-of-sight stabilization control technology that provides a robust guarantee for ships to establish and maintain stable communication links at sea. Our approach relies on GPS navigation positioning equipment, attitude sensors, and servo turntables to build a shipborne wireless laser communication line-of-sight stabilization control platform. Specifically, we design methods for initial alignment and attitude disturbance compensation of the line of sigh. Our experimental results demonstrate that our method effectively solves the problem of attitude disturbance in shipborne wireless laser communication systems and improves the speed of initial alignment of the line of sight. Overall, our proposed technology offers a promising solution for enhancing the reliability and stability of laser communication systems on ships. © 2023 IEEE.

关键词： Stabilization

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Kaolid: A Lid-Type Olfactory Interface to Present Retronasal Smell towards Beverage Flavor Augmentation 13

Kaolid: A Lid-Type Olfactory Interface to Present Retronasal...

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13th International Conference on the Internet of Things, IoT 2023

作者： Mayumi, Daiki Nakamura, Yugo Matsuda, Yuki Misaki, Shinya Yasumoto, Keiichi Grad. Sch. of Science and Technology/Division of Information Science/Ubiquitous Computing System Lab Nara Institute of Science and Technology Japan Kyushu University Japan Nara Institute of Science and Technology Japan

ISBN: (纸本)9798400708541

In this paper, we introduce Kaolid, an olfactory interface that uses a lid mechanism to augment the flavor of beverages by delivering scents as retronasal smell. Kaolid aims to promote the consumption of healthier beverages by intensifying their perceived taste through the release of scents during drinking. The system features a compact olfactory display and an IMU sensor, triggering scents in response to drinking movements. It comes in two models: A straw-Type for cold beverages and a cup-Type for hot drinks. We tested the interface using sparkling and hot water and measured its efficacy in enhancing perceived sweetness when paired with scents. Results showed significant enhancements in all evaluation metrics (taste satisfaction, perceived sweetness, and preference) with the straw-Type device. Notably, the perceived sweetness increased by an amount equivalent to about 2.88 grams of sugar when a retronasal smell was introduced compared to when no scent was present. This innovative interface holds promise in elevating the flavor of sugar-free drinks and could support those aiming to limit sugar consumption. Furthermore, this research contributes to the future of IoT systems for health support by harnessing the power of scent, opening avenues for novel approaches in sensory-driven well-being advancements. © 2023 ACM.

关键词： Beverages

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