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IEEE General Meeting Power& Energy Society

作者： Eric Segerstrom Andrea Pinceti Katelynn Vance Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY Electric Transmission Operations Special Studies Dominion Energy Richmond VA

The increasing presence of high-bandwidth inverter-based resources, FACTS devices, and other components with fast dynamic characteristics requires more robust simulation methods to accurately capture the interactions of these components with more traditional power system components and their effects on the behavior of the bulk system. Many phenomena cannot be modeled with positive sequence programs alone, such as transformer saturation and power electronic device switching. Real-time electromagnetic transient (EMT) simulation architectures provide the potential to capture these dynamics in interactive, large-scale simulation models; however, developing, validating, and initializing new models for each individual study can be prohibitively time-consuming. This paper outlines the development of a reusable, modular large-scale, real-time EMT simulation model of Dominion Energy Virginia’s transmission system that can facilitate quick, iterable execution of transmission planning and training studies using architecture from RTDS Technologies. This work also outlines the development of an automated pipeline to initialize this model using data measured from the energy management system (EMS). The efficacy of these tools is demonstrated in a case study of the synchronization of two stable, isolated islands during system restoration.

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A Novel Model for Driver Lane Change Prediction in Cooperatice Adaptive Cruise Control Systems

A Novel Model for Driver Lane Change Prediction in Cooperati...

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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Armin Nejadhossein Qasemabadi Saeed Mozaffari Mahdi Rezaei Majid Ahmadi Shahpour Alirezaee Electrical and Computer Engineering Department University of Windsor Windsor Canada Mechanical Automotive and Material Engineering Department University of Windsor Windsor Canada Institute for Transport Studies University of Leeds UK

Accurate lane change prediction can reduce potential accidents and contribute to higher road safety. Adaptive cruise control (ACC), lane departure avoidance (LDA), and lane keeping assistance (LKA) are some conventional modules in advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle communication (V2V), vehicles can share traffic information with surrounding vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies on the vehicle’s sensors to obtain the position and velocity of the leading vehicle, CACC also has access to the acceleration of multiple vehicles through V2V communication. This paper compares the type of information (position, velocity, acceleration) and the number of surrounding vehicles for driver lane change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD dataset to predict lane change intention. Results indicate a significant improvement in accuracy with an increase in the number of surrounding vehicles and the information received from them. Specifically, the proposed model can predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and CACC scenarios, respectively.

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On Security-Constrained Bidding of Shared Transportation Hubs for Renewable Fuels and Refined Oil Participating Within Energy, Regulation and Pipeline Logistics Markets

On Security-Constrained Bidding of Shared Transportation Hub...

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IEEE Student Conference on Electric Machines and Systems

作者： Shengshi Wang Hongyi Li Danji Huang Kun Li Shichang Cui Xiaomeng Ai Jiakun Fang Jinyu Wen State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China School of Engineering Cardiff University Cardiff UK Department of Electrical and Computer Engineering Iowa State University Ames United States Department of Electrical and Computer Engineering University of Macau Macau China PetroChina ShenZhen New Energy Research Institute Co. Ltd. Shenzhen China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

This paper investigates the security-constrained bidding strategy of the emerging shared transportation hubs for renewable fuels and refined oil (STH-RRs) participating within energy, regulation and pipeline logistics markets. Firstly, to excavate more flexible capability for the power system as well as to increase the revenue of the STH-RR operation company, the model for STH-RR participating in regulation market is established. Secondly, the security-constrained two-stage bidding model is first proposed for STH-RRs to balance the revenue across energy, regulation and pipeline logistics markets. Particularly, to guarantee secure operation of STH-RRs and meet the delivery demand, the rigorous multi-product transportation constraints are considered in both stages. Case studies based on the modified real-world multi-product pipelines in South China validate the proposed method, with the revenue and turnover increase of 14% and 4.7%, respectively, compared with conventional methods.

关键词： Renewable energy sources Uncertainty Oils Pipelines Transportation Regulation Fuels Frequency control Logistics Testing

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TshFNA-Examiner:A Nuclei Segmentation and Cancer Assessment Framework for Thyroid Cytology Image

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 945-957页

作者： KE Jing ZHU Junchao YANG Xin ZHANG Haolin SUN Yuxiang WANG Jiayi LU Yizhou SHEN Yiqing LIU Sheng JIANG Fusong HUANG Qin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Life Sciences and Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China School of Engineering Shanghai Ocean UniversityShanghai 201306China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Department of Computer Science Johns Hopkins UniversityBaltimore 21218USA Department of Thyroid Breast and Vascular Surgery Shanghai Fourth People’s HospitalShanghai 200434China Department of Endocrinology and Metabolism Shanghai Sixth People’s HospitalShanghai 200233China Department of Pathology Shanghai Sixth People’s HospitalShanghai 200233China

Examining thyroid fine-needle aspiration(FNA)can grade cancer risks,derive prognostic information,and guide follow-up care or *** digitization of biopsy and deep learning techniques has recently enabled computational ***,there is still lack of systematic diagnostic system for the complicated gigapixel cytopathology images,which can match physician-level basic *** this study,we design a deep learning framework,thyroid segmentation and hierarchy fine-needle aspiration(TshFNA)-Examiner to quantitatively profile the cancer risk of a thyroid FNA *** the TshFNA-Examiner,cellular-intensive areas strongly correlated with diagnostic medical information are detected by a nuclei segmentation neural network;cell-level image patches are catalogued following The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)system,by a classification neural network which is further enhanced by leveraging unlabeled data.A cohort of 333 thyroid FNA cases collected from 2019 to 2022 from I to VI is studied,with pixel-wise and image-wise image patches ***,TshFNA-Examiner is evaluated with comprehensive metrics and multiple tasks to demonstrate its superiority to state-of-the-art deep learning *** average performance of cellular area segmentation achieves a Dice of 0.931 and Jaccard index of *** cancer risk classifier achieves a macro-F1-score of 0.959,macro-AUC of 0.998,and accuracy of 0.959 following *** corresponding metrics can be enhanced to a macro-F1-score of 0.970,macro-AUC of 0.999,and accuracy of 0.970 by leveraging informative unlabeled *** clinical practice,TshFNA-Examiner can help cytologists to visualize the output of deep learning networks in a convenient way to facilitate making the final decision.

关键词： thyroid fine-needle aspiration(FNA)cytology The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC) diagnostic system nuclei segmentation cancer risk classification

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DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s)

DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or M...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yun Su Jeong Hye Bin Yoo Il Yong Chun Department of Electrical and Computer Engineering Sungkyunkwan University Republic of Korea Departments of Artificial Intelligence and Advanced Display Engineering Sungkyunkwan University Republic of Korea Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Computational tomography (CT) provides high-resolution medical imaging, but it can expose patients to high radiation. X-ray scanners have low radiation exposure, but their resolutions are low. This paper proposes a new conditional diffusion model, DX2CT, that reconstructs three-dimensional (3D) CT volumes from bi or mono-planar X-ray image(s). Proposed DX2CT consists of two key components: 1) modulating feature maps extracted from two-dimensional (2D) X-ray(s) with 3D positions of CT volume using a new transformer and 2) effectively using the modulated 3D position-aware feature maps as conditions of DX2CT. In particular, the proposed transformer can provide conditions with rich information of a target CT slice to the conditional diffusion model, enabling high-quality CT reconstruction. Our experiments with the bi or mono-planar X-ray(s) benchmark datasets show that proposed DX2CT outperforms several state-of-the-art methods. Our codes and model will be available at: https://***/intyeger/DX2CT.

关键词： Three-dimensional displays Computed tomography Two-dimensional displays Diffusion models Transformers Feature extraction Speech processing X-ray imaging Image reconstruction Biomedical imaging

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GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR Genie Datasets

arXiv

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arXiv 2023年

作者： Bidollahkhani, Michael Atasoy, Ferhat Abedini, Elnaz Davar, Ali Hamza, Omid Sefaoğlu, Fırat Jafari, Amin Yalçın, Muhammed Nadir Abdellatef, Hamdan Department of Computer Engineering Institute of Graduate Studies Karabuk University Turkey Göttingen Germany Department of Biomedical Engineering Institute of Graduate Studies Karabuk University Turkey Department of Genetics and Bioengineering Faculty of Engineering and Architecture Kastamonu University Turkey Department of Medical Science Faculty of Medicine Karabuk University Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and pathogenic variables. We evaluated the proposed method using a dataset from the AACR GENIE project and compared its performance with modern approaches. Our proposed approach outperformed existing models with 99.86% accuracy. We also conducted NF1 and interpretable AI tests to validate our approach. Our work provides an explainable approach model using logistic regression and explanatory stimulus as well as a black-box model. The explainable models help to explain the predictions of black-box models while the glass-box models provide information about the best-fit features. Overall, our study presents an interpretable AI approach for diagnosing patients with neurofibromatosis and demonstrates the potential of AI in the medical field. © 2023, CC BY-NC-ND.

关键词： Machine learning

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Robust Deep Learning-Based Secret Key Generation in Dynamic LiFi Networks Against Concept Drift

Robust Deep Learning-Based Secret Key Generation in Dynamic ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Elmahedi Mahalal Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Nei Katoll Department of Computer Science Tennessee Technological University Cookeville TN USA College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Western University London ON Canada Graduate School of Information Sciences Tohoku University Sendai Japan

This paper explores secret key generation in 5G and beyond LiFi networks using visible light in the downlink and infrared in the uplink. Unlike the existing works, we focus on a realistic indoor environment with multi-user mobility. Given inaccuracies in high-frequency channel models, we introduce the first deep learning model that combines the channel probing and quantization phases to generate initial secret keys with a minimal key disagreement rate (KDR) of 16% between the uplink and downlink, leading to a key generation rate (KGR) of 79 bits/s after information reconciliation. We show that LiFi channel statistics suffer from concept drifts with user density changes in the room. This increases the KDR by 28% - 44% and the generated keys fail to pass the NIST randomness tests. As a countermeasure, we introduce a voting ensemble model that mitigates concept drifts, maintaining a stable 16% KDR, 79 bits/s KGR, and passing NIST tests, despite the varying user densities.

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Shape Sensing with Electrostatic Differential Capacitance for Ultrasound Imaging by Flexible Array Transducer

Shape Sensing with Electrostatic Differential Capacitance fo...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Chisato Hojo Hiroki Kawagishi Hiroki Shigemune Ryosuke Tsumura Graduate school of Electrical Engineering and Computer Science at Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan Health and Medical Research Institute at National Institute of Advanced Industrial Science and Technology Ibaraki Japan

Flexible ultrasound (US) transducer, which has a potential to fit various regions of human body for diagnosis, need to have its geometry accurately measured for US image reconstruction. This paper serves a shape sensing system with the electrostatic differential capacitance for the imaging with the flexible US transducer. The shape sensing system is composed of two strips as a pair, each end of which is fixed, and focuses on the relative shift between capacitance sensors embedded in the inner and outer strips when bending the sensing system. For increasing the capacitance, we applied a silicon oil to the sensor substrate and changed the size of electrodes. Experimental results showed that the estimation error was improved by the average of 52.8% when applying the silicon oil and the average of 10.4% by increasing the size of electrodes. Additionally, US simulation was performed for investigating the influence of image reconstruction due to the sensing error. The simulation results enabled to visualize all point targets and demonstrated the feasibility that the developed sensing system are applicable for the flexible US transducer.

关键词： Strips Transducers Ultrasonic imaging Shape Robot sensing systems Capacitance Silicon

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Benchmarking the User-Centric Clustering and Pilot Assignment Problems in Cell-Free Networks

Benchmarking the User-Centric Clustering and Pilot Assignmen...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ahmed Aboulfotouh Zubair Md Fadlullah Mostafa M. Fouda Muhammad Ismail Dusit Niyato Department of Computer Science Lakehead University Thunder Bay ON Canada Department of Computer Science Western University London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Tennessee Technological University Cookeville TN USA School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

This paper addresses the user-centric clustering and pilot assignment problems in cell-free networks, recognizing the need to solve both problems simultaneously. The motivation of this research stems from the absence of benchmarks, general formulations, and the reliance on subjectively designed objective functions and heuristic algorithms prevalent in existing literature. To tackle these challenges, we formulate stochastic non-linear binary integer programs for both the user-centric clustering and pilot assignment problems. We specifically design the pilot assignment formulation to incorporate user-centric clusters when evaluating the desirability of pilot assignments, resulting in improved efficiency. To solve the problems, the proposed methodology employs sample average approximation coupled with surrogate optimization for the user-centric clustering problem and the genetic algorithm for the pilot assignment problem. Numerical experiments demonstrate that the optimized solutions outperform baseline solutions, leading to significant gains in spectral efficiency.

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Secure Multi-Party Computation for Collaborative Data Analysis in Network Security

Secure Multi-Party Computation for Collaborative Data Analys...

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Intelligent and Innovative Technologies in Computing, electrical and Electronics (IITCEE), International Conference

作者： Tamilselvi P V. Lathika S. Jayachitra S Arunkumar M. Balasubramani V Kalaichelvi Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of CSE Sona College of Technology (Autonomous) Salem India Department of ECE PSNA College of Engineering and Technology Dindigul India Department of ECE Coimbatore Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering VSB Engineering College Karur India Department of CSE Srinivasa Ramanujan Centre SASTRA Deemed to be University Kumbakonam India

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. advanced threat analysis may also benefit.

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