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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 392-406页

作者： Li, Jinhao Wang, Changlong Zhang, Yanru Wang, Hao Monash University Department of Data Science and AI Faculty of IT and Monash Energy Institute MelbourneVIC3800 Australia Monash University Department of Civil Engineering MelbourneVIC3800 Australia University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional 'black-box' DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation. © 2023 IEEE.

关键词： Real time systems

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An Amalgamated CNN-Transformer Network for Lightweight Image Super-Resolution

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1376-1387页

作者： Fang, Jinsheng Lin, Hanjiang Zeng, Kun School of Computer Science and Engineering Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China School of Computer and Control engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Recently, Transformer-based methods for single image super-resolution (SISR) have achieved better performance advantages than the methods based on convolutional neural network (CNN). Exploiting self-attention mechanism to model global context definitely improves the SR results. However, the neglect of local information will bring inevitable reduction of the network performance. In this work, we propose an Amalgamated CNN-Transformer network for lightweight SR, namely ACTSR. Specifically, an amalgamated CNN-Transformer block (ACTB) is developed to extract the useful information of both local and global features. By employing stacked ACTBs, our ACTSR extracts more informative deep features beneficially for super-resolution reconstruction to improve network performance while keeps lightweight and flexible enough. Extensive experiments on commonly used benchmark datasets validate our ACTSR outperforms the advanced competitors. Our codes are available at: https://***/ginsengf/ACTSR. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Towards property-preserving JPEG encryption with structured permutation and adaptive group differentiation

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Visual computer 2024年第9期40卷 6421-6447页

作者： Su, Guo-Dong Chang, Ching-Chun Lin, Chia-Chen Chang, Chin-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Fujian Province University Fuzhou China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

The security and privacy of digital images are a major concern in cyberspace. JPEG is the most widely used image compression standard and yet there are problems with format compatibility and file size preservation in most of the modern encryption schemes for JPEG images. To address these problems, we propose a novel properties-preserving encryption scheme that obfuscates JPEG images by scrambling AC and DC coefficients. The key observation is that an innovative structured permutation has a remarkable effect on enciphering the coefficients and simultaneously prevents value overflow as well as size increment. On this basis, we first leverage the row-wise reordering and reversing technique to disorganize the DC coefficients. Afterward, a series of groups of consecutive DC differential values are produced using adaptive group differentiation and each of them is independently permuted inside. Finally, we perform block-wise ACCs permutation and intra-block ACCs shuffling to further confuse the outline of the original image. Moreover, invariant features regarding the coefficients are incorporated in the key generation process to resist various cryptanalytic attacks. Experimental results demonstrate a significant improvement in terms of format compatibility and file size preservation, and verify the security against various attacks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Cryptography

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Grassland Mouse Hole Recognition Model Based on UAV Remote Sensing and Improved YOLOv7

Grassland Mouse Hole Recognition Model Based on UAV Remote S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Han, Yu Bai, Xiangyu Gao, Xiaochen Cheng, Haoran Zhou, Kexin College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China School of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (纸本)9781665410205

As the most widely distributed vegetation type on Earth, grasslands are the second largest ecosystem after forests, and are known as the 'skin of the earth'. However, the frequency of rodent infestation has been increasing year by year in recent years due to climate change, loose soil and other factors. As the current traditional monitoring methods are time-consuming and laborious, while the deep learning method has the advantages of low cost, high efficiency and high accuracy. Therefore, this paper proposes a combination of UAV remote sensing and deep learning methods to monitor grassland mouse hole. First, because there is no publicly available grassland mouse hole dataset, this study used a UAV to capture images to create a grassland mouse hole dataset. Second, an improved YOLOv7 model, YOLOAPM, is proposed with the goal of improving the accuracy of small target recognition. Through ablation and comparison experiments, the validity of the improved model as well as the method of this paper are verified to be improved compared to other models. Finally, in order to facilitate the monitoring task of the researchers, a monitoring system is established and the mouse hole identification of this paper is embedded into the system. © 2024 IEEE.

关键词： Abiotic

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Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

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Frontiers of computer science 2022年第4期16卷 131-146页

作者： Guoshuai Zhou Xiuxia Tian Aoying Zhou School of Computer Science and Technology ShangHai University of Electric PowerShanghai200090China School of Data Science and Engineering East China Normal UniversityShanghai200062China

Image forgery detection remains a challenging *** the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further *** the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected *** method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery ***,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be ***,the dual feature matching is used to match the features and locate the forgery ***’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high *** experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.

关键词： image copy-move forgery passive detection self-selected sub-images pulse coupled neural network(PCNN) dual feature matching

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Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same *** work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a *** our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： Privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

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A Survey on Evolutionary Computation Based Drug Discovery

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Diagnosing Dementia Disorder Detection Using an Improved Eliminate Particle Swarm Optimization (IEPSO) Based on Convolutional Neural Networks

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SN computer science 2024年第6期5卷 695页

作者： Duraipandian, Kavitha Ambigapathy, Murugan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur Chennai India

Dementia is an un-repairable and continuous disease that affects a person's mental health. Symptoms of Dementia may vary from one person to another, there are no effective treatments for detecting Dementia. Machine learning algorithm has proven to be effectively used by many researchers in assisting optimal detection of Dementia. So, this research introduces an improved eliminate particle swarm optimization (IEPSO) based Convolutional Neural Network (IEPSO-CNN) for diagnosing Dementia without causing severe effects effectively. IEPSO is used to find suitable parameters while training the model to improve Dementia detection accuracy. This IEPSO is used for tuning the parameter of the two-level Visual Geometry Group 16 (VGG 16) model. The integration of IEPSO and two hierarchical layers of VGG 16 increases the model's Dementia detection performance. The IEPSO-CNN model performance is evaluated using 6400 Dementia and non-Dementia images. The performance analysis shows that the IEPSO-CNN obtained a maximum accuracy of 99% for detecting the dementia stages. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural network Deep learning Dementia detection Improved eliminate particle swarm optimization VGG 16

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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