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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

引用

24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Generative adversarial networks

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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Adjustable Current Limiting Function with a Monolithically Integrated SiC Circuit Breaker Device

Adjustable Current Limiting Function with a Monolithically I...

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作者： Takamori, Taro Wada, Keiji Boettcher, Norman Erlbacher, Tobias Saito, Wataru Nishizawa, Shin-Ichi Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo192-0397 Japan Fraunhofer Institute for Integrated Systems and Device Technology IISB Erlangen91058 Germany Kyushu University Research Institute for Applied Mechanics Fukuoka816-8580 Japan

This article proposes a current limiting function for a self-sensing and self-triggering monolithically integrated SiC circuit breaker device. The proposed function provides the device not only with a fast-response current breaking operation but also with the current limiting operation, including supplying a constant current to the load and preventing the detection of inrush currents. This function is suitable for replacing mechanical conductors in initial charging circuits within rectifiers and safety equipment in case of overcurrent phenomena. The proposed method achieves certain constant currents through a simple variation of the gate-drive circuit with the adjustable parameters of an additional MOSFET. The mechanism of the current limiting function is verified using TCAD simulations based on certain conditions. Consequently, experimental results verified that the circuit breaker device with the current limiting function reduces the inrush current by up to 76.9% using a 500 VDC distribution circuit system. © 1972-2012 IEEE.

关键词： Silicon carbide

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Multi-Stage Model Predictive Control with Enhanced Discrete-Time Models for Multilevel Inverters

Multi-Stage Model Predictive Control with Enhanced Discrete-...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Hoang Le Apparao Dekka Deepak Ronanki Abdul R. Beig Department of Electrical and Computer Engineering Lakehead University Thunder Bay Canada Department of Engineering Design Indian Institute of Technology Madras Chennai India Advanced Power and Energy Systems Electrical Engineering Department Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Model accuracy, computational complexity, and weighting factor dependency are three primary challenges associated with multi-stage model predictive control (MS-MPC) of multilevel inverters (MLIs). Typically, existing MS-MPC employs the forward Euler integration method to discretize continuous-time models, due to the ease of implementation in real-time controllers. This approach faces a significant deterioration in computational accuracy as sampling period increases. To address this issue, this paper proposes a new formulation of MS-MPC for MLIs. Also, an enhanced discrete-time model of MLI by using Heun’s integration method is proposed to implement the proposed MS-MPC, resulting in a more precise formulation and indirect minimization of common-mode voltage (CMV). Unlike the existing methods, the proposed method does not employ a cost function with weighting factor or offline selection of voltage vectors (VVs) to minimize the CMV. Furthermore, it does not increase computational complexity compared to the existing MS-MPCs. Simulation studies are conducted on a four-level MLI system to validate the efficacy of the proposed MS-MPC, and its performance is compared with the existing methods.

关键词： Accuracy Computational modeling Predictive models Multilevel inverters Mathematical models Vectors Real-time systems Power electronics Computational complexity Predictive control

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Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

Renderable Street View Map-Based Localization: Leveraging 3D...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

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Backstepping-Based Adaptive Cooperative Control For Multiple Euler-Lagrange systems Under Unknown Harmonic Disturbances

Backstepping-Based Adaptive Cooperative Control For Multiple...

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2023 China Automation Congress, CAC 2023

作者： Chen, Ying Zhao, Zi-Yi Sun, Peng Guo, Xiang-Gui Wang, Jian-Liang Coutinho, Daniel School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Hangzhou Innovation Institute of Beihang University Collaborative Flying Robotics HangZhou China Universidade Federal de Santa Catarina Department of Automation and Systems Florianópolis Brazil

ISBN: (纸本)9798350303759

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system. © 2023 IEEE.

关键词： Backstepping

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Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia Tallinn University of Technology Department of Computer Systems Akadeemia tee 15a Tallinn12618 Estonia Technion - Israel Institute of Technology Faculty of Electrical & Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9798350327748

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening 'knowledge-action gap'. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user's energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction. © 2023 IEEE.

关键词： Energy efficiency

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Nonvolatile transmissive metasurface with phase-only modulation

Nonvolatile transmissive metasurface with phase-only modulat...

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2023 Laser Science, LS 2023

作者： Fang, Zhuoran Chen, Rui Fröch, Johannes E. Tanguy, Quentin A.A. Khan, Asir Intisar Wu, Xiangjin Tara, Virat Manna, Arnab Sharp, David Munley, Christopher Zhao, Yang Böhringer, Karl Reynolds, Matthew Pop, Eric Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Physics University of Washington SeattleWA98195 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States School of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China University of Washington Institute for Nano-engineered Systems SeattleWA United States

We demonstrate non-volatile phase-only modulation by switching low-loss phase change material Sb2Se3 on a high-Q silicon metasurface. 17 meta-molecules are independently addressed with electrical control, enabling hig... 详细信息

关键词： II-VI semiconductors

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NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network 3

NOMA Cooperative Spectrum Sharing Average Capacity Improveme...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering P.O. Box 00000 Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Department of Electrical and Computer Engineering Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

One of the most effective approaches to satisfying the requirements of the next generation of wireless services is non-orthogonal multiple access (NOMA). Spectrum efficiency and average capacity are two aspects of wireless communication that have become increasingly important as the number of mobile services in use has increased. Using both traditional MIMO and Massive MIMO (M-MIMO), this study proposes two new methods for increasing the average capacity of a 5G network's downlink (DL) NOMA power domain (PD) in conjunction with a Cooperative Cognitive Radio Network (CCRN). The initial method involves NOMA users competing for available rivalry channels (R-CH) on the CCRN. The second technique creates a dedicated channel (D-CH) for NOMA users. The MATLAB program is utilized to evaluate the proposed methods in three scenarios with varying distances, power location coefficients, and transmission power. Successive interference cancellation (SIC) and unstable channel conditions are also considered while assessing the analysis of the proposed system under the assumption of Rayleigh fading. Using DL NOMA PD systems, The results reveal that the higher average capacity of best at transmit power DL NOMA with CCRN (dedicated channel) improved U4 average capacity performance by M-MIMO DL NOMA with CCRN (D-CH) improved by when compared with convention DL NOMA. Formulas in closed form were found for average capacity. The resulting formulas are verified using a Monte Carlo simulation. © 2023 IEEE.

关键词： 5G mobile communication systems

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Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350396782

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort. © 2023 IEEE European Union.

关键词： Consumer comfort Demand side recommender system Distributed power systems Hybrid digital twins Industry 5.0

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