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arXiv 2023年

作者： Xu, Dongsheng Huang, Qingbao Shuang, Feng Cai, Yi School of Electrical Engineering The Guangxi Key Laboratory of Multimedia Communications and Network Technology The Institute of Artificial Intelligence Guangxi University Nanning530004 China School of Electrical Engineering Guangxi University Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning530004 China School of Software Engineering South China University of Technology Guangzhou510006 China MOE of China Guangzhou510006 China

Text-based image captioning is an important but under-explored task, aiming to generate descriptions containing visual objects and scene text. Recent studies have made encouraging progress, but they are still suffering from a lack of overall understanding of scenes and generating inaccurate captions. One possible reason is that current studies mainly focus on constructing the plane-level geometric relationship of scene text without depth information. This leads to insufficient scene text relational reasoning so that models may describe scene text inaccurately. The other possible reason is that existing methods fail to generate fine-grained descriptions of some visual objects. In addition, they may ignore essential visual objects, leading to the scene text belonging to these ignored objects not being utilized. To address the above issues, we propose a DEpth and VIsual ConcEpts Aware Transformer (DEVICE) for TextCaps. Concretely, to construct three-dimensional geometric relations, we introduce depth information and propose a depth-enhanced feature updating module to ameliorate OCR token features. To generate more precise and comprehensive captions, we introduce semantic features of detected visual concepts as auxiliary information, and propose a semantic-guided alignment module to improve the model’s ability to utilize visual concepts. Our DEVICE is capable of generalizing scenes more comprehensively and boosting the accuracy of described visual entities. Sufficient experiments demonstrate the effectiveness of our proposed DEVICE, which outperforms state-of-the-art models on the TextCaps test set. Our code will be publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Ivomfuse: An Image Fusion Method Based on Infrared-to-Visible Object Mapping

SSRN

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SSRN 2022年

作者： Zhang, Xiangbo Huang, Lei Liu, Gang Ren, Qin Bavirisetti, Durga Prasad School of Automation Engineering Shanghai University of Electric Power Shanghai201306 China Library Shanghai University of Electric Power Shanghai201306 China School of Software and Microelectronics Peking University Beijing102600 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Due to the significant difference in the structure of infrared and visible images, most of the existing algorithms use globally consistent strategy, which may weaken the contrast of the target region. To solve this problem, we propose an infrared and visible image fusion method based on infrared-to-visible object mapping (IVOMFuse) First of all, we use the probability induced intuitionistic fuzzy C-means algorithm (PIIFCM) to process the infrared image, and extract the target region of the infrared image according to the image structure features. However, because of the poor contrast of the target of the visible image, it can not be segmented directly. For this reason, we map the infrared target region to the visible image, and the target region with the same coordinates of the two images can be obtained very quickly. Secondly, due to the significant differences in image structure, we set a hybrid fusion strategies for different regions. We use the fusion strategy based on the Expectation-Maximization (EM) algorithm to iterate and optimize the probability model for the target region. The fourth-order partial differential equation (FPDE) is used for the background region to decompose the source image into high-frequency texture and low-frequency approximation images respectively. Then, the principal component analysis (PCA) and the average fusion strategy are used to obtain the fused image. Finally, we evaluated our algorithm with thirteen other state-of-the-art image fusion methods on three commonly used datasets, namely TNO, CVC14, and RoadScene. Among the ten evaluation indexes, our algorithm achieved the best six times. © 2022, The Authors. All rights reserved.

关键词： Mapping

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Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks

arXiv

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arXiv 2023年

作者： Wang, Zhicheng Meng, Xuhui Jiang, Xiamo Xiang, Hui Karniadakis, George Laboratory of Ocean Energy Utilization of Ministry of Education School of Energy and Power Engineering Dalian University of Technology China Institute of Interdisciplinary Research for Mathematics and Applied Science School of Mathematics and Statistics Huazhong University of Science and Technology China School of Energy and Power Engineering State Key Lab of Structural Analysis Optimization and CAE Software for Industrial Equipment Research Institute for Carbon Neutrality Dalian University of Technology China Research Institute for Carbon Neutrality Dalian University of Technology China Division of Applied Mathematics School of Providence RI United States

Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for fluid flows and are especially effective for inverse and hybrid problems. However, vanilla PINNs often fail in forward problems, especially at high Reynolds (Re) number flows. Herein, we study systematically the classical lid-driven cavity flow at Re = 2, 000, 3, 000 and 5, 000. We observe that vanilla PINNs obtain two classes of solutions, one class that agrees with direct numerical simulations (DNS), and another that is an unstable solution to the Navier-Stokes equations and not physically realizable. We attribute this solution multiplicity to singularities and unbounded vorticity, and we propose regularization methods that restore a unique solution within 1% difference from the DNS solution. In particular, we introduce a parameterized entropy-viscosity method as artificial eddy viscosity and identify suitable parameters that drive the PINNs solution towards the DNS solution. Furthermore, we solve the inverse problem by subsampling the DNS solution, and identify a new eddy viscosity distribution that leads to velocity and pressure fields almost identical to their DNS counterparts. Surprisingly, a single measurement at a random point suffices to obtain a unique PINNs DNS-like solution even without artificial viscosity, which suggests possible pathways in simulating high Reynolds number turbulent flows using vanilla PINNs. © 2023, CC BY-NC-SA.

关键词： Machine learning

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power load prediction of smart grid based on deep learning

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Procedia Computer Science 2023年 228卷 762-773页

作者： Siyu Xiang Cao Zhen Jian Peng Linghao Zhang Zhengguo Pu State Grid Sichuan Electric Power Research Institute Chengdu Sichuan 610095 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611700 China College of Computer Science Sichuan University Chengdu 610065 China Anhui Jiyuan Software Co. Ltd. Hefei 230088 China

Real electricity costs, weather, and historical load data are added as new reference data when analyzing the characteristics of the load change policy to improve the forecasting model. A load forecasting model for smart grid based on convolutional neural network(CNN) is proposed. The model first analyzes the characteristics of the variable model and determines the forecast model inputs using two methods of correlation analysis to understand the impact of actual energy costs, weather and other factors on the changes. Second, since Feedforward neural network is deficient in processing the correlation information between loads, the prediction model developed by the author was studied as CNN. After completing the training, input two sets of real datasets for final data analysis and comparison, and the results show that, LSTM, BP, SVR[UNK]MAPE[UNK]RMSE respectively 0.49%, 0.57%, 0.83%, 1.44% and 106.08 MW, 113.82MW, 169.77 MW, 285.61 MW. It can be seen that the prediction model proposed by the author has the highest accuracy and the best fit with the trend of actual load changes. It is proved that the CNN model has certain advantages when dealing with load forecasting problems related to time series.

关键词： Real-time electricity price CNN Input features power load forecasting

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Application of Artificial Intelligence technology in power System Stability Assessment

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Procedia Computer Science 2023年 228卷 79-88页

作者： Siyu Xiang Cao zhen Jian Peng Linghao Zhang Zhengguo Pu State Grid Sichuan Electric Power Research Institute Chengdu Sichuan 610095 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611700China College of Computer Science Sichuan University Chengdu 610065 China Anhui Jiyuan Software Co. Ltd. Hefei 230088 China

In order to realize power system stability assessment, the author studies a deep Convolutional neural network algorithm for power system operation stability assessment. Combining short-term simulation with neural network prediction to reduce the time required for transient stability analysis can be used in various simulation analysis scenarios. A deep Convolutional neural network algorithm is used to improve the performance of the security system. A deep Convolutional Neural Network (CNN) is used to construct a multi-layer and multi-column neural network to evaluate the stability of electrical networks. The system significantly improves the efficiency of evaluating specific faults, particularly enhancing the specificity of evaluation judgments, and reducing the additional maintenance workload caused by providing stability warnings for the operation status of faultless power grids. The experimental results indicate that, the system has shown certain advantages in terms of sensitivity to the judgment of various types of faults mentioned above, especially improving the sensitivity to other faults with unknown causes, which is 10.8 percentage points higher than the previous system. The smallest difference in the improvement of sensitivity indicators is the sensitivity to ground faults. The previous system reached 95.4%, while this system increased by 3.9 percentage points to 99.3%. It is proved that the deep CNN is applicable to the task of power system operation stability assessment.

关键词： Deep Convolutional neural network power system operation Stability assessment Algorithm Innovation

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Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-enabled Multi-Agent Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Shengbo Lin, Chuan Han, Guangjie Zhu, Shengchao Li, Zhixian Wang, Zhenyu Ma, Yunpeng Software College Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China Department of Internet of Things Engineering Hohai University Changzhou213022 China Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai University China

In recent years, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration or underwater tracking, etc. In this paper, we propose a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account. We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process. Then, based on software-defined networking (SDN), we regard the AUV swarm as a underwater ad-hoc network and propose a hierarchical software-defined multi-AUV reinforcement learning (HSARL) architecture. Based on the proposed HSARL architecture, we propose the"Dynamic-Switching" mechanism, it includes"Dynamic-Switching Attention" and"Dynamic-Switching Resampling" mechanisms which accelerate the HSARL algorithm’s convergence speed and effectively prevents it from getting stuck in a local optimum state. Additionally, we introduce the reward reshaping mechanism for further accelerating the convergence speed of the proposed HSARL algorithm in early phase. Finally, based on a proposed AUV classification method, we propose a cooperative tracking algorithm called Dynamic-Switching-Based MARL (DSBM)driven tracking algorithm. Evaluation results demonstrate that our proposed DSBM tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of various important metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Enhancement of corrosion resistance of AZ31 Mg alloys by one-step in situ synthesis of ZnAl-LDH films intercalated with organic anions(ASP, La)

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Journal of Magnesium and Alloys 2021年第2期9卷 658-667页

作者： Yuhan Song Yan Tang Liang Fang Fang Wu XianGuang Zeng Jia Hu Shu Fang Zhang Bin Jiang HaiJun Luo State Key Laboratory of Power Transmission Equipment&System Safety and New Technology Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart MaterialsCollege of PhysicsChongqing UniversityChongqing 400044PR China School of Chemical&Environmental Engineering China University of Mining&TechnologyBeijing 100083PR China Material Corrosion and Protection Key Laboratory of Sichuan Province Institute of Materials Science and EngineeringSichuan University of Science and EngineeringZigongSichuan Province 643000PR China National Engineering Research Center for Magnesium Alloys College of Materials Science and EngineeringChongqing UniversityChongqing 400044PR China College of Software Chongqing College of Electronic EngineeringChongqing 401331PR China Key Laboratory on Optoelectronic Functional Materials College of Physics and Electronic EngineeringChongqing Normal UniversityChongqing 401331PR China

Inserting corrosion inhibitors into a lamellar protective coating is an effective way to improve the corrosion resistance of metal *** this work,two kinds of environmentally friendly organic acid anions,aspartic acid(ASP)and lauric acid(La),were explored as corrosion inhibitors to in situ intercalate into the Zn Al-layered double hydroxides(Zn Al-LDHs)on AZ31 magnesium alloys by a facile one-step hydrothermal *** morphology,composition,structure and the corrosion resistance of these two LDHs coating before and after corrosion experiment were *** is found that both the two kinds of Zn Al-LDHs films consist of uniform and dense layered nanosheets(NSs),and the NSs of ZnAl-ASP-LDHs films grow vertically,but those of ZnAl-La-LDHs films exhibit a staggered tilted *** corrosion current density of the two Zn Al-LDHs films are two orders of magnitude lower than that of Mg alloy *** one week’s immersion in NaCl solution,the NSs structure of two kind of ZnAl-LDHs films still remains the uniform and densely coverage on Mg *** to the larger d(003)spacing,the ZnAl-La-LDHs have better ability to absorb Cl-and release interlayer anions than Zn *** results indicate that the Zn Al-LDHs films intercalated with organic anions,specially La anions,can significantly improve the corrosion resistance of Mg alloy.

关键词： ZnAl-LDHs Magnesium alloy Organic anions Corrosion resistance Aspartic acid Lauric acid

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Fusionfrfcm: An Image Fusion Approach Driven by Non-Global Fuzzy Pre-Enhancement Framework

SSRN

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SSRN 2022年

作者： Zhang, Xiangbo Liu, Gang Huang, Lei Ren, Qin Bavirisetti, Durga Prasad School of Automation Engineering Shanghai University of Electric Power Shanghai201306 China School of Software and Microelectronics Peking University Beijing102600 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Most of the existing image fusion models adopt a global strategy, which usually reduces the contrast of infrared images. In this paper, we propose an image fusion approach driven by a non-global fuzzy pre-enhancement framework (FusionFRFCM), which is more suitable for the structure of the infrared (IR) image. A contrast equalization algorithm based on Fourth-order Partial Differential Equation (FPDE) algorithm is proposed, which is used to enhance the background region. Due to the differences between IR and visible (VIS) images, a hybrid fusion strategy based on Expectation Maximization algorithm (EM) and Principal Component Analysis algorithm (PCA) is designed. Compared with other state-of-the-art fusion methods, the experimental results show that the proposed algorithm has better fusion performance in both qualitative and quantitative results. In addition, to verify the effectiveness of our proposed FusionFRFCM in practical application, the fusion framework is embedded into the RGBT target tracking task under the VOT-RGBT2019 and OTCBVS datasets. Through comparative experiments, it is found that the FusionFRFCM frameworkcan easily be integrated with the tracking task and can improve the tracking accuracy in many scenes. © 2022, The Authors. All rights reserved.

关键词： Image fusion

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A fast and effective maximum power point tracking method under various weather conditions for grid-connected photovoltaic systems

A fast and effective maximum power point tracking method und...

引用

IEEE Conference on power Electronics and Renewable Energy (CPERE)

作者： Karam Khairullah Mohammed Saad Mekhilef Muhyaddin Rawa Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and power Systems King Abdulaziz University Jeddah Saudi Arabia Department Of Electrical and computer Engineering Faculty of engineering K. A Care Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.

关键词： Maximum power point trackers Renewable energy sources Fluctuations Simulation Optimization methods power electronics Solar panels

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Spatial distribution information extraction of winter wheat in Henan Province based on multi-source sentinel remote sensing data

Spatial distribution information extraction of winter wheat ...

引用

2024 International Conference on Remote Sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： Zhang, Ziqian Lv, Junye Cui, Tengfei Zhou, Qiongbo Jiang, Chuande Liu, Zhiyang Si, Qiliang Henan University of Science and Technology Software School No. 263 Kaiyuan Avenue Luolong District Henan Province Luoyang City471000 China School of Surveying and Land Information Engineering Henan Polytechnic University No. 2001 Shiji Road Jiaozuo454000 China Boston University Metropolitan College BostonMA02215 United States University of Southern California Gould School of Law CA90089 United States School of Water Conservancy North China University of Water Resources and Electric Power Zhengzhou450046 China College of Science Weihai264209 China

ISBN: (纸本)9781510685826

Timely and accurately acquiring spatial distribution information for winter wheat is crucial for formulating agricultural policies. However, the information extraction for traditional spatial distribution for winter wheat relies primarily on manual field surveys. This method is labour-intensive and subjective and less accurate. With that remote sensing technology continues to develop, new avenues for extracting distribution information for winter wheat have emerged. Nevertheless, previous studies often did not fully leverage the advantages of remote sensing data from multiple sources, resulting in lower extraction accuracy. To address this, this study focuses on Henan Province, where Sentinel-2 optical imagery and Sentinel-1 radar imagery. Then support vector machine and random forest algorithms were used to construct winter wheat extraction model. The results indicate that the extraction accuracy of Sentinel-2 optical imaging surpasses Sentinel-1 radar imaging, with an overall accuracy of 82.04%, the producer's accuracy for winter wheat reaches 86.25%, the accuracy of user is 85.19%, the Kappa coefficient is 0.770. Further integration within the Sentinel-2 optical imagery with Sentinel-1 radar imagery significantly enhances extraction's accuracy. The overall accuracy improves to 90.19%, the accuracy for producers increases to 96.25%, user's accuracy rises to 95.06%, and the Kappa coefficient increases to 0.874. Additionally, by comparing different machine learning algorithms, it is observed that the algorithm random forest consistently outperforms the algorithm support vector machine in extraction accuracy regardless of the dataset. In this way, this study provides valuable guidance for obtaining information on the spatial distribution of winter wheat in Henan Province. © 2024 SPIE.

关键词： Radar imaging

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