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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Deng, Jiewen Jiang, Wei Zhao, Hongda Ren, Qinghua Zhang, Li School of Software Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798350345094

The process of research activities in the electric power field produces a large number of articles, patents, and other results, which contain a large amount of information. Named entity recognition is a common method for obtaining textual information, and this study establishes a model that can efficiently recognize named entities of electric power research results. Firstly, the keywords of electric power literature are obtained for preprocessing and organization, and then the annotated dataset of keyword named entities of electric power scientific research is constructed. After that, this paper enhances the ability of the model to obtain contextual information through the Transformer encoder and enriches the expression of textual information by obtaining textual semantic structure information through dependent syntactic analysis. Meanwhile, in order to strengthen the feature extraction ability, the BILSTM-Multi-Head-Attention-CRF model is used to obtain text context information. IDCNN model is also used to make this paper's model acquire the ability to extract text semantic structure information. Finally, ablation experiments are conducted to show that the model proposed in this paper has a better recognition effect compared with the named entity recognition methods in the generic domain. © 2023 IEEE.

关键词： Semantics

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Fuzzy based Fall Risk Prediction in Older Adults 3

Fuzzy based Fall Risk Prediction in Older Adults

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohan, Deepika Al-Hamid, Duaa Zuhair Chong, Peter Han Joo Gutierrez, Jairo Li, Hui Auckland University of Technology Department of Electrical and Electronic Engineering Auckland New Zealand Auckland University of Technology Department of Computer Science and Software Engineering Auckland New Zealand Peking University School of Electronic & Computer Engineering Shenzhen China

ISBN: (纸本)9798350372120

The global elderly population is increasing rapidly, leading to a rise in chronic illnesses and co-existing conditions, which in turn results in higher healthcare expenses. Accidental falls are among the leading causes of injury-related deaths in elderly individuals. This study aims to create a real-time monitoring system using vital signs to foresee a future fall in older adults by identifying abnormalities through continuous monitoring. The proposed fall prediction technique, employing the Fuzzy-based Fall Prediction Algorithm, utilizes Fuzzy rules to learn and execute tasks. The obtained results are then classified according to different levels of predicted risk indicators. The developed model is tested using data from older adults sourced from a public repository and compared with the results of the theoretical evaluation. The simulated outcomes demonstrate that the proposed algorithm achieves 96% accuracy, 93.75% sensitivity, and 100% specificity. Utilizing these advancements in the proposed heterogeneous technology allows for the early prediction of falls in the elderly and can potentially save lives. © 2024 IEEE.

关键词： Forecasting

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Constructing Knowledge Graph for Electricity Keywords Based on Large Language Model 7

Constructing Knowledge Graph for Electricity Keywords Based ...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Zhao, Honda Jiang, Wei Deng, Jiewen Ren, Qinghua Zhang, Li School of Software Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798350345094

In the information age, the electric power industry, as a crucial pillar of modern society, has accumulated a wealth of valuable research literature. Knowledge graph technology offers the potential to tap into this knowledge repository, providing a better understanding of research outcomes in the electric power domain. However, due to the diversity and complexity of knowledge in the power industry, it is difficult to build a comprehensive and complete knowledge graph of power keywords. In recent years, large language models (LLMs) have made significant advancements. This paper harnesses LLM technology along with text similarity analysis and co-occurrence frequency analysis to establish a comprehensive framework for processing keyword knowledge in the field of electric power. Within this framework, various forms of information found in electric power research can be processed. This includes creating a thesaurus of electric power domain keywords;obtaining the individual attributes implied by the information keywords in this thesaurus and their interconnections;and generating a knowledge graph of electric power domain keywords. This knowledge graph includes attributes, interpretations, relationships, and associated literature for keywords. It serves as a valuable reference for the effective utilization of research outcomes in the electric power domain. © 2023 IEEE.

关键词： Knowledge graph

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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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BPREC: A Bayesian Personalized Recommendation Based on User Preferences for Mining 21

BPREC: A Bayesian Personalized Recommendation Based on User ...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Qiu, Laixiang Feng, Jianping Zhou, Wang College of Computer Science Chongqing College of Mobile Communication Chongqing401420 China Mechanical and Electrica Engineering College Hainan Vocational University of Science and Technology Hainan571126 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798331519254

Faced with overwhelming product information, users often struggle to make choices, impacting their shopping experience and time. To address this, recommendation systems provide precise suggestions that streamline decision-making. The BPRec model, based on user preferences, enhances recommendation relevance and quality. Experiments on realworld datasets show that BPRec significantly improves recommendation effectiveness, thus enhancing the shopping experience. © 2024 IEEE.

关键词： Matrix factorization

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TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science Guangdong China Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. © 2024 IEEE.

关键词： Zero-shot learning

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Optimization Performance Analysis of Different Initialization Methods in Single-Objective and Multi-Objective Chaotic Evolution 13

Optimization Performance Analysis of Different Initializatio...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Zhang, Xiaoyi Pei, Yan Jiang, Huiyan Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu965-8580 Japan University of Aizu Computer Science Division Aizuwakamatsu965-8580 Japan Northeastern University College of Software Shenyang110819 China

ISBN: (纸本)9798350373332

Chaotic Evolution (CE) is a significantly faster and more robust method for solving single-objective and multi-objective optimization problems. However, there are various factors that can impact the performance of CE, with population initialization being one of them. A well-executed initialization method can contribute to the discovery of better solutions and enhance both convergence rate and accuracy. This paper conducts a comparative experiment on CE algorithms using six different initialization methods. The performance of the CE algorithms utilizing these various initialization methods is analyzed and discussed. To assess convergence rate and accuracy, eight single-objective optimization benchmark functions and five multi-objective optimization benchmark functions are employed in our analysis. The evaluation results reveal significant disparities among the six initialization methods, indicating that different approaches to initialization have a substantial influence on the optimization performance of the CE algorithm. © 2024 IEEE.

关键词： Optimization algorithms

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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Diverse Hazy Image Synthesis via Coloring Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3703-3713页

作者： Zhang, Shengdong Zhang, Xiaoqin Wan, Shaohua Ren, Wenqi Zhao, Liping Zhao, Li Shen, Linlin Shaoxing University Department of Computer Science and Engineering Shaoxing312000 China Shenzhen United Aircraft Technology Company Ltd. Guangdong Shenzhen518129 China Wenzhou University College of Computer and Artificial Intelligence Wenzhou325035 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518110 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Convolutional neural network (CNN)-based dehazing methods have achieved great success in single image dehazing. However, the absence of real-world haze image datasets hinders the deep development of single image dehazing. To address this issue, we propose a diverse hazy image synthesis method based on generative adversarial network (GAN) and matting. Specially, we train a GAN-based model that can transform a gray image into a hazy image. To boost the diversity of hazy images, we propose to simulate hazy images via image matting, which can fuse a real haze image with another image containing diverse objects. To evaluate the performance of dehazing methods, we propose two novel metrics: part-based peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). Extensive experiments are conducted to show the effectiveness of the proposed model, dataset, and criteria. © 2020 IEEE.

关键词： Three dimensional displays

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Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline 18th

Profinder: Towards Professionals Recognition on Mobile Devi...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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