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Class-conditional domain adaptation for semantic segmentation

Computational Visual Media 2024年第5期10卷 1013-1030页

作者： Yue Wang Yuke Li James H.Elder Runmin Wu Huchuan Lu School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science York UniversityToronto M3J 1P3Canada Department of Computer Science the University of Hong KongHong Kong 999077China

Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.

关键词： domain adaptation generative adversarial networks semantic segmentation cityscapes

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A Half-Bridge/Dual Stacked-Switches Structured AC/DC Electrolytic Capacitor-Less Bidirectional Converter With Soft-Switching for High-Voltage EV Charger

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IEEE Transactions on Power Electronics 2025年第8期40卷 10912-10930页

作者： Derakhshan, Siamak Lam, John York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada

This article presents a half-bridge (HB)/dual stacked switches, fully soft-switched bidirectional electrolytic capacitor-less ac/dc resonant converter, tailored for high-voltage (HV) electric vehicle (EV) systems. The proposed converter integrates a closed-loop power factor correction (PFC)mechanism based on the dq model of the input LCL filter, ensuring high-quality sinusoidal input current and reduced total harmonic distortion in both ac/dc conversion and dc/ac inversion modes. The front-end HB PFC circuit operates in continuous conduction mode with soft-switching operation, which eliminates the high peak current issue that happens in the switches in discontinuous conduction mode. Moreover, the dual stacked switches configuration of the proposed converter reduces the voltage stress on the switches to half of the dc-link and output voltage. A dedicated duty ratio-based closed-loop control system in both primary and secondary sides of the dual-stacked-switches resonant converter drastically reduces the double-line frequency voltage ripple, thereby eliminating the need for bulky electrolytic storage capacitors. Output voltage regulation in ac/dc conversion mode is achieved through variable frequency modulation, and soft-switching is maintained for all switches for all the operating condition in both modes. The performance of the proposed bidirectional converter is validated through a 1 kW, 120 V rms/750 V dc proof-of-concept prototype. © 1986-2012 IEEE.

关键词： Capacitor bank

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Optimal Design of a Planning and Contracting Framework to Enable Vehicle to Building and Grid Services via Demand Response

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7542-7556页

作者： Abd Elaziz Elsayed, Ahmed Saxena, Shivam Essa Zidan Farag, Hany York University Lassonde School of Engineering Electrical Engineering and Computer Science Department Keele Campus TorontoONM3J 1P3 Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B5A3 Canada

Bidirectional electric vehicle (EV) charging enables stored energy to reduce peak loads for vehicle to buildings (V2Bs) and the vehicle to grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2G services face challenges in planning and coordinating with EV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2B and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2B and V2G, resulting in inconsistent participation, this article proposes a novel planning and contracting framework that enables building owner to determine the optimal contract parameters with EV owners. These parameters include minimum participation time in DR events, minimum arrival state of charge (SoC), and permitted emergency departure hours. The framework supports V2B aggregation with on-site distributed energy resources (DERs) for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while compensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses Monte Carlo simulations to generate EV owner profit assessments, select optimal EV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators (PSOs), building owners, and EV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a 3-day case study with real-world datasets that demonstrates net revenue generation of {\} 209 for each EV owner and {\} 950 for building owners during DR events with a return on investment (ROI) of 134.5% and 130.7% for the EVs and Building owners, respectively. © 2015 IEEE.

关键词： Vehicle-to-grid

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A Hybrid P&O-Fuzzy-Based Maximum Power Point Tracking (MPPT) Algorithm for Photovoltaic Systems under Partial Shading Conditions

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IEEE Access 2025年 13卷 86046-86056页

作者： Karimi, Hamed Siadatan, Alireza Rezaei-Zare, Afshin Khatam University Department of Electrical Engineering Tehran19916-33357 Iran York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada

Photovoltaic arrays receive varying levels of solar radiation due to factors such as shadows created by clouds, surrounding buildings, and other obstructions. Therefore, an effective Maximum Power Point Tracking (MPPT) algorithm should be used to maximize power generation from non-uniform arrays. Most MPPT algorithms do not perform well during partial shadow conditions, as they can become trapped in local maxima and fail to identify the absolute Global Maximum Power Point (GMP). This paper proposes a new MPPT approach that combines Perturb & Observe (P&O) and Fuzzy Logic Controller (FLC) with the Particle Swarm Optimization (PSO) algorithm. The FLC algorithm is then used to maximize search accuracy. The configurations of this system and the study results demonstrate the dynamic and promising performance of the proposed method under various environmental and shading conditions. In addition to its simplicity and ease of implementation, the proposed algorithm exhibits high accuracy in tracking the maximum power point. This has been evidenced through both simulation and laboratory results of the hardware. © 2013 IEEE.

关键词： Particle swarm optimization (PSO)

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Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

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Tsinghua science and Technology 2024年第1期29卷 86-98页

作者： Rui Yuan Shunmei Meng Ruihan Dou Xinna Wang Department of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210056China Faculty of Mathematics University of WaterlooWaterloo N2L 3G1Canada

Edge computing platforms enable application developers and content providers to provide context-aware services(such as service recommendations)using real-time wireless access network *** to recommend the most suitable candidate from these numerous available services is an urgent ***-through rate(CTR)prediction is a core task of traditional service ***,many existing service recommender systems do not exploit user mobility for prediction,particularly in an edge computing *** this paper,we propose a model named long and short-term user preferences modeling with a multi-interest network based on user *** uses a logarithmic network to capture multiple interests in different fields,enriching the representations of user short-term *** terms of long-term preferences,users'comprehensive preferences are extracted in different periods and are fused using a nonlocal *** experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.

关键词： recommender system logarithmic network nonlocal network

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Decoding the Script: A Bi-LSTM Based Journey into Doctor's Handwriting for Precise Prescription Understanding

Decoding the Script: A Bi-LSTM Based Journey into Doctor's H...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Khan, Mohd Ayan Arif, Hamza Singh, Vishal Sharma, Dolly Amity University Department of Computer Science and Engineering Noida India 3rd Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350394412

Deciphering a doctor's handwritten prescription has been a complex task for human beings. Research has been done to automate the understanding of handwritten text, but transcribing doctor's handwritten prescriptions still requires the advancement to build a method that can be applied in real life. This paper presents a new hybrid model built on a fusion of CNN and Bi-LSTM architectures trained with CTC loss for the difficult task of decoding non-printed handwritten physicians' prescriptions. The unification of signals such as spatial feature extraction (CNN) as well as sequential dependency decrease (Bi-LSTM) with the aim of misinterpretation mitigation retrieves a very high recognition accuracy over a single, segmented test collection. With iterative training methodology, which focuses on computational limitations, the approach results in continuous learning, thus eliminating the problem of overfitting. The given model is very good at understanding the heterogeneity of handwriting and contextual relationships in prescription texts. This scientific addition leads to improvements in patient care, streamlined workflows, and healthcare information management improvements as the ultimate performance of the model is the astonishing 98.40% accuracy level it offers. © 2024 IEEE.

关键词： Deep learning

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Two-Dimensional Golay Complementary Array Sets with Arbitrary Lengths for Omnidirectional MIMO Transmission

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IEEE Transactions on Communications 2024年第2期72卷 1135-1145页

作者： Zhao, You-Qi Pai, Cheng-Yu Huang, Zhen-Ming Liu, Zilong Chen, Chao-Yu National Cheng Kung University Department of Engineering Science Tainan701 Taiwan National Cheng Kung University Department of Engineering Science and the Institute of Computer and Communication Engineering Tainan701 Taiwan University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom National Cheng Kung University Department of Electrical Engineering and the Institute of Computer and Communication Engineering Tainan701 Taiwan

This paper presents a coding approach for achieving omnidirectional transmission of certain common signals in massive multi-input multi-output (MIMO) networks such that the received power at any direction in a cell remains constant for any given distance. Specifically, two-dimensional (2D) Golay complementary array set (GCAS) can be used to design the massive MIMO precoding matrix so as to achieve omnidirectional transmission due to its complementary autocorrelation property. In this paper, novel constructions of new 2D GCASs with arbitrary array lengths are proposed. Our key idea is to carefully truncate the columns of certain larger arrays generated by 2D generalized Boolean functions. Finally, the power radiation patterns and numerical results are provided to verify the omnidirectional property of the GCAS-based precoding. The error performances of the proposed precoding scheme are presented to validate its superiority over the existing alternatives. © 1972-2012 IEEE.

关键词： Peak to average power ratio

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computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

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computer Systems science & engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

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Experimental and Statistical Study on Mechanical Behavior of H13 Tool Steel Additively Manufactured and Laser Surface Treated

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Lasers in Manufacturing and Materials Processing 2024年第2期11卷 193-217页

作者： Baali, Lamya Dehghan, Shayan Iltaf, Asim Barka, Noureddine Dassylva-Raymond, Véronique Department of Mathematics Computer Science and Engineering University of Quebec at Rimouski RimouskiQCG5L 3A1 Canada

H13 tool steel is used in hot work tooling applications where favorable mechanical properties and surface quality are required. This material has high hardness at elevated temperatures, high wear resistance, and good toughness. The potential for complex mold designs and conformal cooling channels using additive manufacturing technology makes it an attractive option for tool steel fabrication. This study uses the SLM technology to fabricate H13 tool steel, with laser power set at 125 W, scanning speed at 800 mm/s, layer thickness at 40 mm, and hatch space at 0.12 mm. After the SLM process, a specific heat treatment is carried out, including stress relieving, austenitization, quenching, and tempering. Laser surface heat treatment is performed to control laser power, scanning speed, and defocus distance. The results showed that the ultimate tensile strength (UTS) is improved compared to heat-treated samples, but the elongation is reduced. Fractography analysis applied by SEM revealed a combination of ductile and brittle fracture behaviors in different zones. Finally, the findings of this work contributed to the improvement of laser treatment parameters and the enhancement of mechanical properties of H13 tool steel molds. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Tensile strength

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EVA2.0:Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training

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Machine Intelligence Research 2023年第2期20卷 207-219页

作者： Yuxian Gu Jiaxin Wen Hao Sun Yi Song Pei Ke Chujie Zheng Zheng Zhang Jianzhu Yao Lei Liu Xiaoyan Zhu Minlie Huang The Conversational AI Group Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical Engineering and Computer Science York UniversityToronto M3J1P3Canada

Large-scale pre-training has shown remarkable performance in building open-domain dialogue ***,previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model,ignoring the discussion of some key factors towards a powerful human-like chatbot,especially in Chinese *** this paper,we conduct extensive experiments to investigate these under-explored factors,including data quality control,model architecture designs,training approaches,and decoding *** propose EVA2.0,a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters,and will make our models and codes publicly *** and human evaluations show that EVA2.0 significantly outperforms other open-source *** also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.

关键词： Natural language processing deep learning(DL) large-scale pre-training dialogue systems Chinese open-domain conversational model

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