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5th International Conference on Robotics and computer Vision, ICRCV 2023

作者： Shao, Qi Han, Xiao Lei Fan, Jia Nan Zhang, Xin Long Wang, Nan Internet of Things Engineering Department Shanghai Business School Shanghai China Beigene Ltd Shanghai China Computer Science and Technology Shanghai Business School Shanghai China Digital Publishing、Academic Resources Import and Export China Science Publishing & Media Ltd Beijing China

ISBN: (纸本)9798350326369

Due to the decentralized nature of civil meteor monitoring and the lack of conditions for large-scale observations like in scientific astronomical meteor monitoring, the identification algorithms for scientific astronomical meteor monitoring cannot be applied to civil meteor monitoring. In response to the problem that the civil meteor monitoring system cannot effectively distinguish meteors from other moving objects during meteor recognition, a meteor candidate recognition algorithm has been designed and implemented. This algorithm mainly includes two parts: meteor trajectory recognition and light change curve shape analysis. By using this meteor recognition algorithm to process two months' worth of images from the civil meteor monitoring system, 7400 meteor trajectories were extracted, of which 25% were non-meteor objects. The algorithm classifies the observed moving objects into target sets, and by considering the features of the light variation curve's single-peak structure and multi-frame features, the final algorithm feature accuracy is 86%. © 2023 IEEE.

关键词： Trajectories

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A review of travel behavioural pattern using GPS dataset: A systematic literature review

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Measurement: Sensors 2024年 32卷

作者： Sivalingam, Prahaladhan Asirvatham, David Marjani, Mohsen Syed Masood, Jafar Ali Ibrahim Chakravarthy, N.S. Kalyan Veerisetty, Gopinath Lestari, Martha Tri Faculty of Innovation & Technology Taylor's University Subang Jaya Malaysia Department of Internet of Things School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India Center for Data Science School of Computer Science and Engineering QIS College of Engineering and Technology Andhra Pradesh Ongole523272 India Department of Mechanical Engineering QIS College of Engineering and Technology Andhra Pradesh Ongole523272 India Department of Business Communication Telkom University Indonesia

In the last few years, the Global positioning system (GPS) has been used to study travel patterns with the advancement of data collection techniques. A GPS device precisely records the time and the location data of an individual travel. The accuracy of travel studies is usually improved by integrating travel surveys or travel activity diaries with GPS data. GPS surveys are implemented to replace paper-based techniques and reduce human error on trip-misreporting issues. Several reviews have been performed previously on GPS travel studies. Still, a detailed systematic review on the comparison of different modes and methods on the entire procedure of GPS data processing has not been thoroughly studied. This review identified 116 peer-reviewed studies and journal papers from 2013 to 2020, and 53 papers were studied. The review focused on travel behavior pattern (TBP) analysis using GPS studies only and a combination of travel surveys. Our review reveals that 53 only 40 have quality assessment scores of >50 %, meaning the remaining TBP studies are conventional methods. The study also benefits data analysis and researchers to select the most appropriate data collection techniques for studying TBP and improvise them further for meta-analysis travel behavior studies. © 2024 The Authors

关键词： Global positioning system

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1248-1262页

作者： Dongdong Zhao Qiben Xu Fan Li Yiheng Mao Jianwen Xiang Huanhuan Li Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence and Chongqing Research Institute Wuhan University of Technology Wuhan China School of Computer Science School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

With the development of genome sequencing technology, genomic data have been widely collected and used in real-world scenarios, such as genomic medicine and similar patient query (SPQ) cases, based on similarity comparisons of genomic sequences. However, genomic data are unique for every person and contain a large amount of sensitive information involving personal privacy. Therefore, determining how to protect privacy while utilizing genomic data has become a key issue. In this paper, we mainly investigate the privacy protection of SPQs, and we propose five algorithms (NDB-ED, NDB-Band, NDB-Block, NDB-SIS and NDB-SDS) based on a promising technique called negative representation of information (NRI). The proposed algorithms use five kinds of similarity comparison approaches widely applied in SPQs and convert all genomic sequences into negative databases ( NDBs, among the most important NRI forms) for privacy protection. When performing an SPQ, NDB-ED approximates the edit distance between the sketches (the statistics of NDBs) of two genomic sequences to evaluate the dissimilarity. Banded edit distance and block-wise edit distance are two effective approximate edit distance algorithms, which can greatly reduce the time complexity. NDB-Band and NDB-Block are used to estimate the banded edit distance and the block-wise edit distance between the sketches of NDB pairs, respectively, to further improve query efficiency and reduce communication overhead. Besides, private genome set intersection size (SIS) and set difference size (SDS) can also be used instead of edit distance to evaluate the dissimilarity between genomic pairs during SPQs. NDB-SIS and NDB-SDS estimate the SIS and SDS between the sketches, for similarity comparison. The experimental results demonstrate that the proposed algorithms can achieve promising results in terms of accuracy and efficiency (Our best algorithm improves accuracy by at least 10% and query time is at least 700 times faster than existing a

关键词： Bioinformatics Genomics Protection Databases Protocols Privacy Data privacy Accuracy Time complexity Hamming distances

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Performance Enhancement and Secrecy Analysis of Hybrid RF/FSO Communication Links Under Combined κ-μ Fading and Exponential Turbulence Conditions 2

Performance Enhancement and Secrecy Analysis of Hybrid RF/FS...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Upadhyay, Deepak Prakhar Moray, Rashmy Sharma, Rashmi Venu, Nookala Shukla, Shipra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Roorkee Institute of Technology Pune India A constituent of Symbiosis International Deemed University India Nagpur Campus Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798331530389

In this study, the secrecy performance evaluation of hybrid RF/FSO communication systems over κ-μ fading and exponential turbulence channels are conducted. We analyse the secure information rate and evaluate the throughput and secrecy capacity of the system through simulations. The results indicate that the proposed hybrid system improves reliability and security by combining adaptive modulation, beamforming, and encryption to combat interference and secure data transmission. Geographical heat maps help to visualize the influence of environmental factors on system performance. Federated learning in hybrid RF/FSO systems have been investigated in detail through simulations and numerical results to show the considerable improvements in communication reliability and performance that the increased redundancy and robustness of the hybrid scheme provides. This helps gain valuable insights into a better design of wireless networks in the future by highlighting the benefits of hybrid communication techniques and simulation-driven design to enhance performance and security across diverse complex environments and conditions. © 2025 IEEE.

关键词： Adaptive modulation

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Digital-Twin-Driven PI Parameter Evaluation Method for Grid-Connected Converters 4

Digital-Twin-Driven PI Parameter Evaluation Method for Grid-...

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4th International Conference on Power engineering, ICPE 2023

作者： Cheng, Xu Chen, Jianfu Chen, Yong Zhao, Xiaoyan Zhou, Zhipeng Yang, Qifan Dai, Ning Yi Zhuhai Power Supply Bureau and DC Power Distribution and Consumption Technology Research Center Guangdong Power Grid Co. Ltd. Zhuhai China Zhuhai Power Supply Bureau Guangdong Power Grid Co. Ltd. Zhuhai China University of Macau SKL of Internet of Things for Smart City Department of Electrical and Computer Engineering Macao China University of Macau and Zhuhai UM Science & Technology Research Institute SKL of Internet of Things for Smart City Department of Electrical and Computer Engineering Macao China

ISBN: (纸本)9798350340020

Digital twin technology describes and models the characteristics, behavior, formation process and real-world performance of physical objects by using digital technologies such as sensors, internet of things (IoT), and artificial intelligence (AI). It is one of the key technologies to realize real-time perception and operation optimization of electric power equipment. Proportional-integral (PI) modules are the most commonly used blocks in the control system of power converters. But choosing the parameters with the best control effect requires repeated experiments. Therefore, this paper proposes a data-driven model that can evaluate the control performance of different PI parameters accurately and efficiently. First, the concept of digital twins and a general architecture applicable to the key power electronic equipment are demonstrated. Then, taking the grid-connected converter as an example, a data-driven model based on artificial neural network (ANN) and Levenberg-Marquardt (LM) algorithm is built to realize the evaluation of PI controller parameters. Comparisons with simulation models are also provided to show the validity of the digital-twin driven control parameter evaluation method. © 2023 IEEE.

关键词： Neural networks

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General Model for Manufacturing Defect Detection Crossing Multiple Products 22

General Model for Manufacturing Defect Detection Crossing Mu...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhou, Longfei Qin, Zhanghao Xia, Ping Liu, Jiabao Cheng, Weihao Li, Yunwei Ying, Leyi Liu, Qian Liu, Mengxin Song, Gezhang Huang, Zebo Hao, Zhou Gannon University Biomedical Industrial and Systems Engineering Department EriePA United States School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China School of Atmospheric Science Nanjing University of Information Science and Technology NanJing China School of Computer Science and Artificial Intelligence Suzhou City College Suzhou China School of Physics and Optoelectronic Engineering Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Communication Engineering Jiangsu University ZhenJiang China School of Mechanical Engineering Jiangnan University Wuxi China College of Artificial Intelligence and Computer Science Jiangnan University Wuxi China School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9798350345346

As one of the most important aspects of artificial intelligence, computer vision technologies enable intelligent systems to comprehend the real world through imaging. Despite this, there are still opportunities to enhance the development of machine vision technologies in the manufacturing industry. Many industrial systems still apply manual methods, particularly in small to medium-sized enterprises. Existing machine learning models also struggle with diverse datasets because models were mostly trained for particular datasets. Hence, creating a rational defect detection system that can be used across different imaging datasets is a crucial task for efficiency, precision, and labor relief. We present a novel machine learning model for defect detection of industrial products, fusing classification and segmentation, and crossing multiple datasets. A novel framework is established by combining classification and segmentation modules for multiple datasets. The classification uses a DenseNet model for distinct data categories, followed by the segmentation part. The developed model can benefit the industry by enhancing inspection accuracy and efficiency for multiple products. © 2023 IEEE.

关键词： Artificial intelligence computer vision Defect detection Industrial products Machine learning

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Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

Regional Medium-Term Hourly Electricity Demand Forecasting B...

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2023 IEEE Power and Energy Society General Meeting, PESGM 2023

作者： Sun, Hongfei Duan, Dongliang Zhang, Hongming Choi, Seong Luo, Jie Rockey Yang, Liuqing GoldenCO80401 United States University of Wyoming Department of Electrical and Computer Engineering LaramieWY82071 United States Nrel United States Colorado State University Department of Electrical and Computer Engineering Fort CollinsCO80523 United States Internet of Things Thrust and Intelligent Transportation Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9781665464413

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies. © 2023 IEEE.

关键词： Long short-term memory

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Synthetic data generation for medical by generative adversarial network

Synthetic data generation for medical by generative adversar...

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2022 International Conference on Electronics and Devices, Computational science, ICEDCS 2022

作者： Liu, Yaoxuan Xu, Hao Tang, Ziling Beijing Jiaotong University School of Computer and Information Technology Beijing China Internet of Things Engineering University College Dublin Beijing Dublin International Collage Dublin Ireland Monash University Faculty of Engineering Department of Mechanical and Aerospace Engineering Melbourne Australia

ISBN: (纸本)9781665455411

As the world develops, medical data privacy is becoming an important issue. Differential privacy can better ensure accuracy and reduce the reflection of personal privacy, while generative adversarial networks can generate data sets. Obviously, the combination of differential privacy and generative adversarial network (GAN) can synthesize sensitive medical data to form new data sets, which not only protects personal privacy but also does not affect medical research. We summarize the current difficulties in medical data, compare the common medical data types with typical DP methods of different GAN, analyze the advantages and disadvantages of each method, and discuss future development. At the same time, we also introduce GAN applied in the image data field. Because this approach is very mature, we only introduce some commonly used GANs, compare their improvements, and introduce their applications in data generation. This may help in the future development of medical data privacy algorithms. © 2022 IEEE.

关键词： Generative adversarial networks

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Arabic Dialect Identification in Social Media:A Comparative Study of Deep Learning and Transformer Approaches

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Intelligent Automation & Soft Computing 2024年第5期39卷 907-928页

作者： Enas Yahya Alqulaity Wael M.S.Yafooz Abdullah Alourani Ayman Jaradat Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityBuraydah51452Saudi Arabia Internet of Things Department Faculty of Science and Information TechnologyJadara UniversityIrbid21110Jordan

Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text *** difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social *** difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely *** dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media *** distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)*** Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also *** 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect ***,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested *** methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are *** suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial *** study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect *** model may prove crucial for future work in sociolinguistic studies and NLP.

关键词： Dialectal Arabic transformers deep learning natural language processing systems

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