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11th Multidisciplinary International Social Networks Conference, MISNC 2024

作者： Hong, Tzung-Pei Chen, Da Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Taiwan Department of Computer Science and Engineering Department of Electrical and Control China Steel Inc. China Library and Information Center National University of Kaohsiung Taiwan

ISBN: (纸本)9798400717550

Erasable itemset mining is one of the most well-known methods in data mining for optimizing limited materials. After mining erasable itemsets, the manager can rearrange the production plan effectively. However, in real applications, traditional mining methods neglect some critical factors, such as costs or quantities of components forming a product. This thesis thus considers mining erasable itemsets from a product database with quantitative components. An erasable profit-based itemset mining (EPIM) algorithm is proposed to mine erasable itemsets with low profits by analyzing the relationships between products and components. In experiments, a synthetic dataset was used to compare the performance in execution speed and memory usage under various threshold settings. It can also be easily generalized to find erasable itemsets satisfying the other criteria with a quantitative product database. © 2024 ACM.

关键词： Mine safety

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Deep Multi-Patch Hierarchical Network-based Visibility Restoration Model for Autonomous Vehicles

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 1-11页

作者： Singh, Dilbag AlZubi, Ahmad Ali Kaur, Manjit Kumar, Vijay Lee, Heung-No Blockchain Intelligence Convergence Center Gwangju Institute of Science and Technology Gwangju South Korea Department of Computer Science Community College King Saud University Riyadh Saudi Arabia School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Information Technology Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab India School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea

Clear visibility of the road is crucial for autonomous vehicles, but adverse weather conditions can significantly reduce road visibility, impacting their performance. To address this challenge, a visibility restoration model is essential for autonomous driving and navigation. This paper proposes a Lightweight Visibility Restoration Network (LVR-Net) designed to restore images with improved spatial and spectral information despite severe weather degradation. Initially, a Deep MultiPatch Hierarchical Network (DMPHN) is employed, but its computational demands hinder deployment on autonomous vehicles. To overcome this, a Modified Adaptive distributed Differential Evolution (MADE) optimization technique is applied to enhance the network's size, computational speed, and overall performance. A multi-objective fitness function based on Peak Signal-to-Noise Ratio (PSNR), memory size, and computational speed is formulated. Benchmark datasets are utilized to train, validate, and test the LVR-Net. Experimental results demonstrate that the proposed LVR-Net outperforms other competitive models across various performance metrics. Additionally, its compact size (48.25MB) and faster scene restoration make it highly suitable for deployment in autonomous vehicles. IEEE

关键词： Image reconstruction

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Dual-Buck Bipolar Buck-Boost AC-AC Converter With Reduced Semiconductor Devices

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IEEE Transactions on Industrial Electronics 2024年第9期71卷 10495-10511页

作者： Ahmed, Hafiz Furqan Chien, Howard Khan, Ashraf Ali Islam, Shirazul Akbar, Fazal Alzaabi, Omar National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Memorial University of Newfoundland Faculty of Engineering And Applied Science Department of Electrical And Computer Engineering St. John'sNLA1B 3X9 Canada Qatar University Department of Electrical Engineering Doha2713 Qatar University of Edinburgh EdinburghEH9 3JL United Kingdom Khalifa University Department of Electrical Engineering And Computer Science Advanced Power And Energy Center Abu Dhabi127788 United Arab Emirates

Dual-buck (DB) structured ac-ac converters are becoming advanced due to their inherent protection from open- and short-circuit risks, and elimination of commutation issue. However, the existing DB ac-ac converters provide restricted bipolar voltage operations with a large semiconductor device count and underutilized inductors. This article proposed a new reduced switch DB ac-ac converter without commutation issue. The propose converter provides multiple buck-boost voltage operations with noninverting and inverting outputs, making it a flexible topology for various ac voltage applications. The propose converter exhibits the continuous input and output currents irrespective of operation mode, and performs well with nonunity power factor loads. The positive and negative voltage gains of the proposed converter can address the grid voltage sag and swell issues, respectively, making it right candidate for application as dynamic voltage restorer. A detailed comparative evaluation is carried out with existing DB converters, which validated the effectiveness of the proposed converter, including;reduced semiconductor device count and their power ratings, reduced inductor sizes with improved magnetic utilization, enhanced voltage compensation ranges, and better efficiency. A laboratory scale hardware prototype of the proposed converter is built and thoroughly tested to validate its operation. © 1982-2012 IEEE.

关键词： Semiconductor devices

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Peer-to-Peer Electricity-Carbon Coupling Trading Market Based on Multi-Agent Reinforcement Learning

Peer-to-Peer Electricity-Carbon Coupling Trading Market Base...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Li, Shunfeng Wang, Peng Zhu, Xiuli Li, Ning Yan, Weiwu Shanghai Jiao Tong University Department of Automation Shanghai China College of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350395075

With the the vigorous development of new power system, energy and carbon coupling trading has emerged as an effective way to promote the renewable generation and reduce carbon footprint. It is a challenge to design an effective market framework for joint electricity and carbon trading to achieve better strategies. This paper introduces a peer-to-peer electricity-carbon coupling trading (ECCT) market and develops a carbon-electricity virtual battery (CEVB) model to represent the relationship between dynamic carbon credits and energy consumption. The trading optimization problem is formulated as a partially observable Markov decision process, then multi-agent double soft actor-critic (MADSAC) algorithm is introduced to solve the problem. The algorithm makes the method more exploratory, protects personal privacy and solves the overestimation problems effectively. Result studies demonstrate the effectiveness of the proposed market and algorithm in optimizing total cost and reducing carbon emissions. © 2024 IEEE.

关键词： Electricity-carbon coupling trading market Multi-agent reinforcement learning Peer-to-peer

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InGaPχ^((2))integrated photonics platform for broadband,ultra-efficient nonlinear conversion and entangled photon generation

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Light(science & Applications) 2024年第12期13卷 3090-3098页

作者： Joshua Akin Yunlei Zhao Yuvraj Misra A.K.M.Naziul Haque Kejie Fang Holonyak Micro and Nanotechnology Laboratory and Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Nonlinear optics plays an important role in many areas of science and *** advance of nonlinear optics is empowered by the discovery and utilization of materials with growing optical *** we demonstrate an indium gallium phosphide(InGaP)integrated photonics platform for broadband,ultra-efficient second-order nonlinear *** InGaP nanophotonic waveguide enables second-harmonic generation with a normalized efficiency of 128,000%/W/cm^(2)at 1.55μm pump wavelength,nearly two orders of magnitude higher than the state of the art in the telecommunication C ***,we realize an ultra-bright,broadband time-energy entangled photon source with a pair generation rate of 97 GHz/mW and a bandwidth of 115 nm centered at the telecommunication C *** InGaP entangled photon source shows high coincidence-to-accidental counts ratio CAR>10^(4)and two-photon interference visibility>98%.The InGaP second-order nonlinear photonics platform will have wide-ranging implications for non-classical light generation,optical signal processing,and quantum networking.

关键词： nonlinear optics coincidence

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Single-shot phase retrieval for randomly fluctuated and obstructed vortex beams

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science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 72-80页

作者： Hao Zhang Jun’an Zhu Xingyuan Lu Zhiquan Hu Jianbo Gao Kun Liu Qiwen Zhan Yangjian Cai Chengliang Zhao School of Physical Science and Technology Soochow UniversitySuzhou 215006China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shandong Provincial Engineering and Technical Center of Light Manipulations Shandong Provincial Key Laboratory of Optics and Photonic DeviceSchool of Physics and ElectronicsShandong Normal UniversityJinan 250358China Joint Research Center of Light Manipulation Science and Photonic Integrated Chip of East China Normal University and Shandong Normal University East China Normal UniversityShanghai 200241China

Vortex beams with orbital angular momentum play a crucial role in increasing the information capacity in optical *** magnitude of orbital angular momentum determines the ability of information *** practice,a vortex beam can encounter random objects or turbulence during free-space propagation,resulting in information ***,accurately measuring the orbital angular momentum of a randomly fluctuated and obstructed vortex beam is a considerable ***,we propose a single-shot method for the phase retrieval of a randomly fluctuated and obstructed vortex beam by combining the phase-shift theorem and self-reference *** results reveal that the sign and magnitude of the initial orbital angular momentum can be simultaneously determined based on their quantitative relation with the number of coherence singularities on the observation plane,thus addressing the effects of random occlusion and atmospheric *** proposed method considerably improved the accurate decoding of orbital angular momentum information in nonideal freespace optical communications.

关键词： optical vortex phase retrieve orbital angular momentum measurement antiturbulence antidisturbance

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational Materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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Robust Deep Transfer Learning Based Object Detection and Tracking Approach

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Intelligent Automation & Soft Computing 2023年第3期35卷 3613-3626页

作者： C.Narmadha T.Kavitha R.Poonguzhali V.Hamsadhwani Ranjan walia Monia B.Jegajothi Electronics and Communication Engineering Periyar Maniammai Institute of Science&Technology(PMIST)Thanjavur613403India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Electrical Engineering Department Model Institute of Engineering&Technology(Autonomous)Jammu181122India Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatra182320India Center for Energy Research Chennai Institute of TechnologyChennai600069India

At present days,object detection and tracking concepts have gained more importance among researchers and business ***,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking *** paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)*** AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT *** AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast *** RPN is a full convolution network that concurrently predicts the bounding box and score of different *** RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection ***,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature *** performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight ***,softmax layer is applied to classify the *** performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes *** outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

关键词： Object detection tracking deep learning deep transfer learning image annotation

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer science 2025年第2期6卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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