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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 536-551页

作者： Bienstock, Daniel Dvorkin, Yury Guo, Cheng Mieth, Robert Wang, Jiayi Columbia University Department of Industrial Engineering and Operations Research New YorkNY10027 United States Johns Hopkins University Department of Electrical and Computer Engineering Department of Civil and System Engineering Ralph O'Connor Sustainable Energy Institute BaltimoreMD21218 United States Clemson University School of Mathematical and Statistical Sciences ClemsonSC29634 United States Rutgers University Department of Industrial and Systems Engineering New BrunswickNJ08901 United States Stanford University Department of Management Science and Engineering StanfordCA94305 United States

We propose an enhancement to wholesale electricity markets to contain the exposure of consumers to increasingly large and volatile consumer payments arising as a byproduct of volatile real-time net loads - i.e., loads minus renewable outputs - and prices, both compared to day-ahead cleared values. We incorporate a trade-off, motivated by portfolio optimization methods, between standard day-ahead payments and a robust estimate of such excess payments into the day-ahead computation and specifically seek to account for volatility in real-time net loads and renewable generation. Our model features a data-driven uncertainty set based on principal component analysis, which accommodates both load and wind production volatility and captures locational correlation of uncertain data. To solve the model more efficiently, we develop a decomposition algorithm that can handle nonconvex subproblems. Our extensive experiments on a realistic NYISO data set show that the risk-aware model protects the consumers from potential high costs caused by adverse circumstances. © 2024 IEEE.

关键词： Principal component analysis

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From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

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Plant Phenomics 2023年第2期5卷 195-208页

作者： Xinlu Wu Xijian Fan Peng Luo Sruti Das Choudhury Tardi Tjahjadi Chunhua Hu College of Information Science and Technology Nanjing Forestry UniversityNanjing 210037China Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing 100091China Key Laboratory of Forestry Remote Sensing and Information System National Forestry and Grassland AdministrationBeijing 100091China Department of Computer Science and Engineering University of Nebraska-LincolnLincolnNE 68588USA School of Engineering University of WarwickCoventry CV47ALUK

Plant disease recognition is of vital importance to monitor plant development and predicting crop ***,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory *** environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across *** this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial ***,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty *** multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source *** effectively alleviates the problem of large interdomain *** adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass ***,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain *** was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.

关键词： Plant breakthrough details

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity 22

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023

作者： Al Sibahee, Mustafa A. Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Iraq University College Computer Technology Engineering Department Basrah61004 Iraq University of Basrah College of Education for Pure Sciences Department of Computer Science Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China

ISBN: (纸本)9798350381993

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections. © 2023 IEEE.

关键词： Cryptography

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Visualization Analysis and Trends in Indonesian Internet of Things Research Using Bibliometrics 1

Visualization Analysis and Trends in Indonesian Internet of ...

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1st International Conference on Information system and Information Technology, ICISIT 2022

作者： Maulana, Fairuz Iqbal Pangestu, Gusti Febriantono, M. Aldiki Arifuddin, Rahman Widartha, Vandha Pradwiyasma Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

The Internet of Things (IoT) has emerged as a critical issue and area of research for a variety of communities, academics, and industries worldwide. Due to the lack of research in IoT and understanding the direction of IoT development in the future. This research presents bibliometric analysis and analysis of science mapping on IoT. Data was taken from the Scopus database based on Indonesian state affiliation from 2012-2020. A total of 1415 documents (Conference proceedings, journals, book series, and books) were retrieved, which were then processed online via Scopus Web and by VOSviewer software to perform advanced bibliometric analysis and science mapping analysis. The method is divided into five stages: keyword selection, initial search results, search result refinement, initial compilation, and data analysis. Scopus's list of the most frequently published and indexed articles, the most published papers are in the subject area of computer science (29.0%) with 938 documents, engineering (20.7%) with 668 documents, Physics and Astronomy (9.5%) with 308 documents. The data processed and visualized on this topic offer exploratory information about the current status and trends in the IoT scientific literature and provide insight for established and novice researchers in understanding this research topic. © 2022 IEEE.

关键词： Mapping

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Robotic Slag Removal system with Position Compensation and Defect Detection based on YOLO Algorithm

Robotic Slag Removal System with Position Compensation and D...

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2023 International Conference on Advanced Robotics and Intelligent systems, ARIS 2023

作者： Shih, Chih-Hsuan Wu, Chih-Yi National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Industrial Technology Research Institute Mechanical and Mechatronics System Research Laboratories Hsinchu31040 Taiwan

ISBN: (纸本)9798350302714

Robotic arms play a crucial role in factory automation, offering immense versatility, precision, and the capability for autonomous operation, thereby eliminating the need for manual labor. In this study, we have developed a robotic slag removal system with automatic position compensation and defect detection capabilities, which is capable of locating the features of workpieces, perform deslagging, and detect defects using the YOLO (You Only Look Once) algorithm. The experimental workpiece in this study is a knee femoral implant. Firstly, a position compensation method is employed to precisely locate the clamping hole of the workpiece. The robot trajectory is then automatically generated by the software for the deburring of the clamping hole slag. Subsequently, the system utilizes the detected defect locations to generate a re-deslagging path, ensuring that all remnants are thoroughly cleaned. © 2023 IEEE.

关键词： Robotics

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Visualization and Interpretation of Mel-Frequency Cepstral Coefficients for UAV Drone Audio Data 13

Visualization and Interpretation of Mel-Frequency Cepstral C...

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13th International Conference on Data science, Technology and Applications, DATA 2024

作者： Wang, Mia Y. Chu, Zhiwei Entzminger, Conner Ding, Yi Zhang, Qian Department of Computer Science College of Charleston CharlestonSC United States Departmemt of Computer and Information System Purdue University West LafayetteIN United States Department of Biology College of Charleston CharlestonSC United States Department of Business Administration Anhui Polytechnic University China Department of Engineering College of Charleston CharlestonSC United States

ISBN: (纸本)9789897587078

Unmanned Aerial Vehicles (UAVs) have become a focal point in various fields, prompting the need for effective detection and classification methodologies. This paper presents a thorough investigation into UAV audio signatures using Mel-Frequency Cepstral Coefficients (MFCCs). We meticulously explore the influence of varying MFCC quantities on classification accuracy across diverse UAV categories. Our analysis demonstrates that employing 30 MFCCs produces promising outcomes, characterized by reduced variance and heightened discriminatory capability compared to alternative configurations. Moreover, we introduce a novel image-based dataset derived from our existing audio dataset, encompassing waveform, spectrogram, Mel filter bank, and MFCC plots for 26 UAV categories, each comprising 100 audio files. This dataset facilitates comprehensive analysis and the development of multimodal UAV detection systems. Our research highlights the significance of leveraging diverse datasets and identifies future paths for UAV detection and classification research. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Spectrographs

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Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Social Distance and Face Mask Detection using vision-based s...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： A. Vineela G. Lavanya Devi G. Vinay Deekshit Department of Computer Science and Engineering GMR Institute of Technology Rajam India Department of Computer Science and System Engineering AU College of Engineering(A) Vishakhapatnam India

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask.

关键词： Visualization Human factors Cameras Social factors Vaccines Object recognition IP networks

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Indian Supreme Court Judgement Dataset for Prediction Models

Indian Supreme Court Judgement Dataset for Prediction Models

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Intelligent Computing and Control for engineering and Business systems (ICCEBS)

作者： N. Sivaranjani Karthik R Jaya Sancith Siva P Department of Computer Science and Business System Sri Sairam Engineering College

The development of legal records requires extra time and their absurd length raises the need for programmed legal record handling frameworks. One of the handling steps is to recognize the essence of the reports expressions for legitimate case records. A computerized framework that could help a legal professional in anticipating the result of a case would help facilitate the legal interaction. For such a framework to be essentially valuable, expectations by the framework ought to be reasonable. To advance exploration in growing such a framework, this paper presents the Indian Legal Judgment Dataset (ILJD). ILJD is a huge corpus of 35k Indian Supreme Legal disputes commented on with unique court choices. A part of the corpus (a different test set) is commented on with best quality level clarifications by lawful specialists.

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Efficient Model-Based OPC via Graph Neural Network

Efficient Model-Based OPC via Graph Neural Network

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2023 International Symposium of Electronics Design Automation, ISEDA 2023

作者： Sun, Shuyuan Chen, Xuelian Yang, Fan Yu, Bei Li, Shang Zeng, Xuan School of Microelectronics Fudan University State Key Lab of ASIC and System China Cogenda Inc China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350304510

As feature size continues to shrink and light source wavelengths remain unchanged, the optical diffraction effects seriously degrade chip yield. Optical proximity correction (OPC) has become an essential step for chip manufacturability. However, OPC cannot achieve satisfactory mask correction results in an affordable number of iterations on some layouts, delaying the chip design cycle. In this paper, we propose to use the Graph Neural Network (GNN) to predict the initial mask correction and speed up model-based OPC. We design a graph model to represent the chip layout, with segments represented as graph nodes and the diffraction effects between them represented as graph edges. The GNN is used to obtain the embedding of the segment, and we predict the shift value based on its output. The proposed GNN-based predictor enhances the original OPC to find a good correction solution for full-chip-size masks in fewer iterations. compared with the original OPC method, experimental results on 55nm and 32nm full chip designs show that the proposed GNN-based OPC method reduces the number of iterations up to 75% and the acquired mask of comparable quality. © 2023 IEEE.

关键词： Diffraction

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Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 2897-2914页

作者： Faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of *** rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging ***,Android malware is increasing on a daily ***,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile *** this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud *** pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud *** presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter *** detect and classify the malware,the GCNN model is exploited in this *** order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

关键词： Cybersecurity IoT cloud malware detection graph convolution network

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