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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiaolu Ling Yong Xue School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization China University of Mining and Technology Xuzhou China School of Electronics Computing and Mathematics College of Engineering and Technology University of Derby Derby UK

Vegetation parameter, especially the leaf area index (LAI), is a key variable that take part in the land-atmosphere interaction. Many studies have found that by updating LAI in land surface models can improve the simulation ability for land surface variables. This study used the coupled Community Land Model with explicit carbon and nitrogen components (CLM4CN) and Community Atmosphere Model (CAM) as well as Data Assimilation Research Testbed (DART) to update assimilated LAI, to improve the simulation ability for air temperature and precipitation. The Global Land Surface Data Assimilation System (GLDAS) reanalysis data were used to estimate the coupled CLM4CN/CAM/DART performance.

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Robust Multiple-Structure Geometric Model Fitting by Preserving Neighborhood Consistency

SSRN

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Yan, Yan Lin, Shuyuan Wang, Hanzi Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361005 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China College of Information Science and Technology College of Cyber Security Jinan University Guangzhou510632 China

Geometric model fitting has been widely used in various computer vision applications. However, it remains a challenging task when handling the data that contains multiple model instances (i.e, structures) and a large number of false matches (i.e., outliers) between image pairs. In this paper, we propose a novel Neighborhood-Consistency-based Multiple-structure Fitting method (NCMF) to robustly estimate multiple model instances from the data with outliers, by preserving the neighborhood relationships of potential inliers. Specifically, we first propose a neighborhood consistency preservation algorithm, which explores both the motion pattern consistency and residual pattern consistency of input data, to alleviate the sensitivity to outliers. Consequently, we can produce the significant neighborhood set for each of input data and the reliable neighborhood relationships for data clustering. Then, we propose a robust neighborhoodconsistency-based clustering algorithm by an agglomerative clustering procedure, to effectively distinguish the inliers belonging to different model instances from outliers. In addition, we propose a neighborhood-relationship-based measurement function for data similarity to further make the clustering phase robust to outliers. Overall, NCMF is able to effectively estimate the number and the parameters of model instances from input data simultaneously. Extensive experimental results on several challenging datasets show that the proposed NCMF method can obtain significant superiority over several other state-of-theart model fitting methods on both fitting accuracy and computational efficiency. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Numerical simulation of water entry of a wedge into waves 30

Numerical simulation of water entry of a wedge into waves

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30th International Ocean and Polar engineering Conference, ISOPE 2020

作者： Wang, Kai Ma, Xin Bai, Wei Li, Yibin School of Control Science and Engineering Shandong University JinanShandong Province China Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom

ISBN: (纸本)9781880653845

The open source CFD library OpenFOAM is used to simulate the process of the crane lifting cargo into the water. In this paper, overset mesh strategy and the overInterDyMFoam solver are used to solve the process of two-dimensional water entry of a wedge. Firstly, the pressure coefficient distribution and free surface profile of wedges with different deadrise angles into the calm water with constant velocity or free-falling are calculated. Furthermore, water entry of a wedge into a wave was studied. The accuracy of the code is verified by comparing with the published academic data and experimental results. © 2020 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Arctic engineering

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Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Connected Inverters

Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Conne...

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Annual Conference of Industrial Electronics Society

作者： Pooyan Alinaghi Hosseinabadi Mostefa Kermadi Hemanshu Pota Saad Mekhilef Ali Soltani Sharif Abadi The School of Engineering and Information Technology The University of New South Wales Canberra ACT Australia The Power Electronics and Renewable Energy Research Laboratory (PEARL) University of Malaya Kuala Lumpur Malaysia The School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia Faculty of Mechatronics Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland

This paper proposes and experimentally implements new phase-locked loop algorithms using non-adaptive and adaptive terminal sliding modes for the three-phase grid-connected inverters. The proposed algorithms aim to extract the grid voltage phase and frequency and provide them for the inverter controller. The proposed sliding mode-based PLL algorithms provide faster, more robust, and more accurate detection of the grid voltage information compared to the conventional PI-PLL. This leads to better current controller performance and subsequently, improves the quality of the injected currents into the grid. The finite-time stability notion is incorporated with robust sliding modes to ensure a fast convergence rate. An adaptive law is proposed to determine a proper value for the switching gain which leads to a better overall performance of the PLL compared with a non-adaptive one. The Lyapunov stability theory is used to obtain the stability proofs of the closed-loop systems under both designed sliding modes-based PLLs. The proposed PLL algorithms are validated by simulation and experimental results for the three-phase grid-connected inverter under normal and various abnormal grid conditions.

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A Resilient Decentralized Learning Approach against Byzantine Attack via Reputation Evaluation

A Resilient Decentralized Learning Approach against Byzantin...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Heng Zhang Wenting Wang Xuguo Jiao Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan P. R. China College of Control Science and Engineering Zhejiang University Hangzhou China State Grid Shandong Electric Power Research Institute Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Machine learning is a key component of many applications, among which decentralized learning has attracted wide attention because of its cost-effectiveness and high efficiency. However, decentralized learning is vulnerable to various Byzantine attacks. Existing defense models face challenges in defending against complex attacks and are often constrained by strict network topologies. To address this issue, a parameter aggregation rule based on reputation evaluation (RPV) is proposed in this paper. This rule establishes a reputation model for neighboring nodes and continuously updates it based on their distance performance during each iteration. By applying the reputation model of neighboring nodes to the Beta distribution, their reliability is obtained. Based on the reliability of neighboring nodes, the set of reliable nodes for this iteration and select parameter values from them for aggregation are determined. Finally, we gradually eliminate the influence of Byzantine nodes. Our experimental results on the MNIST dataset demonstrate that the proposed algorithm is resilient to attacks from any number of Byzantine nodes and outperforms previous defense models in terms of network topology constraints, training accuracy, and computational costs.

关键词： Training Learning systems Accuracy Network topology Computational modeling Machine learning Classification algorithms

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Exploring the GLIDE model for Human Action-effect Prediction

arXiv

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arXiv 2022年

作者： Li, Fangjun Hogg, David C. Cohn, Anthony G. School of Computing University of Leeds United Kingdom Luzhong Institute of Safety Qingdao University of Science and Technology China College of Electronic and Information Engineering Tongji University China School of Mechanical and Electrical Engineering Qingdao University of Science and Technology China School of Control Science and Engineering Shandong University China

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing this task. GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text. Our idea is to mask-out a region of the input image where the effect of the action is expected to occur. GLIDE is then used to inpaint the masked region conditioned on the required action. In this way, the resulting image has the same background context as the input image, updated to show the effect of the action. We give qualitative results from experiments using the EPIC dataset of ego-centric videos labelled with actions. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Numerical simulation of cylinder falling in flow using openfoam 30

Numerical simulation of cylinder falling in flow using openf...

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30th International Ocean and Polar engineering Conference, ISOPE 2020

作者： Chen, Wenshu Ma, Xin Bai, Wei Li, Yibin School of Control Science and Engineering Shandong University JinanShandong Province China Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom

ISBN: (纸本)9781880653845

In the process of offshore working, payloads of offshore cranes may suffer from flow disturbances. In this work, OpenFOAM is used to simulate the interaction between payloads and the ocean flow after it enters water. Firstly, two-dimensional model of flow around cylinder at a low Reynolds and the cylinder free-falling in fluids are verified with the existing data to verify the model. On this basis, the overset dynamic grid technique is used to simulate the free fall of the horizontal cylinder in flow. The extensive simulation results can be used to further study the safe movement control of underwater payloads. © 2020 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Circular cylinders

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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House Price Prediction System Using Ensemble Learning Approach

House Price Prediction System Using Ensemble Learning Approa...

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Abhishek Kumar Paras Jain Priyank Pandey Shyamalendu Das Vishan Kumar Gupta Dhiraj Kumar Gupta Department of CSE MMHA & PU Patna Bihar India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kothrikalan Bhopal MP India Department of CSE Graphic Era Deemed to be University Dehradun India Humanities Department SRMCEM Lucknow India Amity School of Engineering and Technology Amity University Mohali Punjab India Department of Quality Control Sun Pharmaceutical Industrial Limited Halol Vadodara Gujrat India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The real estate industry is among the most active and critically important industries for any economy. An accurate forecast of the prices of houses is very important to the buyers, sellers, investors and the policy makers. In years gone by, other methods of valuation have been used, but new advancements have been made in machine learning meaning the prediction speeds and the level of accuracy have been boosted. In this article, I would like to know how the development of various sophisticated models of ML can be used to forecast house prices: the most used models, the most favorite datasets and informative features, as well as the appropriate evaluation measures. Real estate is a continually expanding business that is prominently involved in the world economy. To precisely estimate house prices is always a daunting task due to various factors that vary from physical to non-physical factors include size, location, market, and economic conditions. Digital advancements especially, the incorporation of machine learning in the analysis of financial markets, housing and other sectors has enabled real estate specialists, banks and other institutions and government bodies analyze trends and develop sophisticated models to predict housing prices more effectively than conventional analytical models. In the context of this article’s expanse, we provide a deeper look into the components of house price prediction, with additional information on datasets, feature engineering, specific algorithms, optimization tactics, known difficulties, and future developments.

关键词： Training Analytical models Accuracy Biological system modeling Computational modeling Machine learning Predictive models Stakeholders Optimization Testing

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An Algorithm of Object Detection Based on Regression Learning for Remote Sensing Images

An Algorithm of Object Detection Based on Regression Learnin...

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2021 International Conference on Applied Mathematics, Modelling and Intelligent computing, CAMMIC 2021

作者： Wang, Jiajia Wu, Ran Yu, Xiaqiong Beijing Jinghang Research Institute of Computing and Communication Beijing100076 China Applied Mathematics Research Center of CASIC Beijing100076 China Beijing Air Traffic Control Engineering Technology Co. Ltd Beijing100621 China 32021 Troops Beijing100094 China

Saliency detection method is widely used in the field of object detection. It can greatly improve the efficiency of detection by extracting the region that may contain the objects and reduce the processing of background area. It is very difficult to detect the airports in remote sensing image for its changeable structure and complex background. Considering this situation, this paper proposes a object detection method of saliency model based on regression learning, this method extracts salient regions that may contain airports, and then detects salient regions through feature bag model. Our method uses super-pixel segmentation, minimization of absolute shrinkage and selection operator and hierarchical learning theory based on back-propagation. The salient regions that contain potential objects are extracted and sent to the feature bag model for classification. and it can location the final objects. This paper constructs the airport training dataset and test dataset, and the experimental results fully prove the effectiveness and superiority of this method. © Published under licence by IOP Publishing Ltd.

关键词： Airports

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