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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Investigating structural features of dimeric SARS-CoV-2 Mpro catalytic site with bound covalent ligands at physiological temperature 47

Investigating structural features of dimeric SARS-CoV-2 Mpro...

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47th Vietnam Conference on Theoretical Physics, VCTP 2022

作者： Lai, Hien T. T. Nguyen, Toan T. Key Laboratory for Multiscale Simulation of Complex Systems Department of Theoretical Physics Faculty of Physics University of Science Vietnam National University - Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi11400 Viet Nam

The SARS-CoV-2 main protease (Mpro) plays an important role in the viral transcription and replication of the SARS-CoV-2 virus that is causing the Covid-19 pandemic worldwide. Therefore, it represents a very attractive target for drug development for treatment of this disease. It is a cysteine protease because it has in the active site the catalytic dyad composed of cysteine (C145) and histidine (H41). The catalytic site represents the binding site for inhibitors, many of them bind to Mpro with a covalent bond. In this research, structural and physiochemical characteristics of the Mpro binding site are investigated when the ligand 11a is covalently and non-covalently bound. All-atom molecular dynamics (MD) simulations were run for 500 ns at physiological temperature (310 K). It is found that conformations of both the Mpro protein and the ligand are stable during the simulation with covalently bound complex showing stronger stability. When the ligand is covalently bound (its final state), residues that stably interact with the ligand are H41, C145, H163, H164 and E166. The optimal conformation of these residues is stabilized also via the Hbond interactions with the catalytic water present in the Mpro binding site. In the case of the non-covalently bound ligand (state before the covalent bond is formed), the binding site residues retain their conformations similar to the covalent binding site, and they still form Hbonds with the catalytic water, except H41. This residue, instead, adopts a different conformation and looses the Hbond with the catalytic water, leaving more freedom to move to the ligand. We hypothesize that H41 could play a role in guiding the ligand to the optimal position for final covalent bonding. Further analyses are in process to check this hypothesis. These results represent an important basis for studying drug candidates against SARS-CoV-2 by means of computer aided drug design. © Published under licence by IOP Publishing Ltd.

关键词： Ligands

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A system-of-systems architecture-driven modeling method for combat system effectiveness simulation 2

A system-of-systems architecture-driven modeling method for ...

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2nd Annual IEEE International Symposium on systems Engineering, ISSE 2016

作者： Li, Xiaobo Wang, Weiping Shu, Zhe Zhu, Ning He, Hua Liao, Tianjun College of Information Systems and Management National University of Defense Technology Changsha Hunan410073 China Department of Mathematics and Computer Science University of Antwerp Antwerp2020 Belgium State Key Laboratory of Complex System Simulation Beijing Institute of Systems Engineering Beijing China

ISBN: (纸本)9781509007936

Under the background of system-of-systems (SoS) counterwork, it is more apparent that the simulation modeling of the equipment should be domain-specific, formal, automatic and composable. Current equipment effectiveness simulation modeling approaches are mainly based on the methods and techniques from the modeling and simulation (M&S) community and software engineering community, and they are not capable to satisfy these four requirements systematically. Thus the systems engineering architecture model and domain knowledge should be used to support effectiveness simulation modeling more effectively. In this research, we propose an architecture-driven modeling method which conforms to the principle of architecture-driven development, uses ontology techniques to build equipment SoS architecture model and sub-domain ontology. This method performs architecture driven simulation modeling to realize the transformation from architecture models to simulation model frameworks;employs ontological metamodeling to design domain-specific modeling languages (DSML) based on the comprehensive usage of architecture models, sub-domain ontologies and M&S formalisms;integrates domain-specific simulation models from various domains using the model framework, and supports the composable development of simulation applications. © 2016 IEEE.

关键词： System of systems

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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作者： Huawei ZHU Yihao MAO Yiming ZHAO Liyuan SHI Yanli SHAO School of Information and Electrical Engineering Zhejiang University City College Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and TechnologyHangzhou Dianzi University

With the development of urbanization,the number of residents' motor vehicles has increased sharply,and traffic congestion problem has become increasingly *** construction of Intelligent Traffic System(ITS) has become the main means to alleviate traffic ***-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation,and has become one of the research hotspots in intelligent transportation ***,A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction ***,a graph convolutional network(GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow ***,the long and short-term memory(LSTM) neural network is used to process the time ***,the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow *** results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method,and can provide a basis for intelligent traffic control and smart city construction.

关键词： Traffic Flow Prediction Spatio-temporal GCN LSTM

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A Fast Algorithm for Solving a Class of the Linear Complementarity Problem in a Finite Number of Steps

Abstract and Applied Analysis

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Abstract and Applied Analysis 2020年第none期2020卷 1-8页

作者： Achik, Yamna Idmbarek, Asmaa Nafia, Hajar Agmour, Imane El Foutayeni, Youssef Analysis Modeling and Simulation Laboratory Hassan II University Morocco Unit for Mathematical and Computer Modeling of Complex Systems IRD France

The linear complementarity problem is receiving a lot of attention and has been studied extensively. Recently, El foutayeni et al. have contributed many works that aim to solve this mysterious problem. However, many results exist and give good approximations of the linear complementarity problem solutions. The major drawback of many existing methods resides in the fact that, for large systems, they require a large number of operations during each iteration;also, they consume large amounts of memory and computation time. This is the reason which drives us to create an algorithm with a finite number of steps to solve this kind of problem with a reduced number of iterations compared to existing methods. In addition, we consider a new class of matrices called the E-matrix. © 2020 Yamna Achik et al.

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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BthyDra+: Towards a Comprehensive Model to Conduct Cross-project Defect Prediction

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Journal of Computers (Taiwan) 2018年第6期29卷 52-65页

作者： Huang, Ning Fang, Jinglong Wei, Dan Chen, Bin Wang, Xingqi School of Computer Science Hangzhou Dianzi University ZheJiang Province China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education School of Computer Science Hangzhou Dianzi University ZheJiang Province China

Cross-project defect prediction (CPDP) is a field of study where researchers need to build a universal model by using within-project data to predict defects on other projects. However, variations in the distribution of source and target projects have an influence on the performance of classifiers. To enable effective cross-project defect prediction, we propose a comprehensive model containing data preprocessing and classifier transferring to construct a better classification space and strengthen the performance of classifiers. In preprocessing step, one baseline is calculated for every dataset from its non-defective samples based on the distance to all other non-defective samples and the data is transformed by using rank function. Genetic algorithm and ensemble learning are selected as the way in transferring step to extract effective representation from source projects and boost the capability of weak classifiers. We use Naive Bayes, Support Vector Machine and Classification and Regression Trees as classifiers and apply this model on five open resource projects (one Apache and four Eclipse projects) and NASA MDP dataset. Selecting accuracy as fitness in genetic algorithm improves the performance of classification. The model we proposed yields similar results and obtains higher precision comparing the within-project models. Meanwhile, it obtains better performance than the state-of-the-art methods on cross-project defect prediction. These results show that our model provides an opportunity to training classifiers by using more samples from different projects. © 2018 Computer Society of the Republic of China. All rights reserved.

关键词： Genetic algorithms

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Machine Learning Security Analysis Framework Based on Threat Matrix

Machine Learning Security Analysis Framework Based on Threat...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hu Li Qiongmin Ma Ming Zhang Xiaohui Kuang National Key Laboratory of Science and Technology on Information System Security Beijing China National Key Laboratory for Complex Systems Simulation Beijing China

Machine Learning and Artificial Intelligence technology accelerates technological progress and promotes social development, but also brings many security problems. Machine learning models may be affected, deceived, controlled or destroyed at different stages, e.g. training and inference, which may lead to serious consequences. In view of the main stages of the life cycle of machine learning, we summarizes the related attack and defense technologies based on threat matrix, makes a comprehensive and systematic analysis on the security issues of machine learning, and puts forward several security protection suggestions for intelligent information systems.

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Decadal climate variability during the pre-industrial Common Era: characteristics and mechanisms

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Science Bulletin 2025年

作者： Ning, Liang Liu, Zhengyu Mann, Michael E. Liu, Jian Yan, Mi Sun, Weiyi Chen, Kefan Qin, Yanmin Wen, Qin State Key Laboratory of Climate System Prediction and Risk Management Nanjing210023 China Key Laboratory for Virtual Geographic Environment Ministry of Education Nanjing210023 China State Key Laboratory Cultivation Base of Geographical Environment Evolution and Regional Response of Jiangsu Province Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China School of Geography Nanjing Normal University Nanjing210023 China Department of Geography The Ohio State University ColumbusOH43210 United States Department of Earth and Environmental Science Annenberg School for Communication University of Pennsylvania PhiladelphiaPA19104 United States Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical Science Nanjing Normal University Nanjing210023 China

In this article, we present a comprehensive review of decadal to multi-decadal climate variability during the Common Era (CE), focusing on their characteristics and mechanisms. We begin by summarizing recent advances in proxy reconstructions that reveal the paleo-evidence of decadal to multi-decadal climate variability during the CE. Decadal to multi-decadal variability has been observed in extensive sets of proxy records in the CE. Despite improvements in proxy records in the type, temporal resolution, and temporal coverage, there remains a lack of clear consistency in the preferred time scales and phases of the variability among different records. The agreements of decadal characteristics between proxy records and model simulations are higher during the periods with strong external forcings, but lower during periods of weak external forcing. We subsequently describe the recent modeling studies on the influences of external forcings and internal variability on decadal to multi-decadal climate variability with associated physical mechanisms, and some emerging research topics. Despite the improved understanding of climate variability and regional climate changes, especially over the eastern Asia summer monsoon region, several inconsistencies still exist, such as the amplitudes of responses to external forcings and relative contributions from external forcings and internal variability. The review ends with perspectives for future directions to reconcile discrepancies of decadal climate variability, such as applications of paleoclimate data assimilation and isotope-enabled transient climate modeling, and implications for projecting future decadal to multi-decadal climate changes and for improving the accuracy of decadal predictions. © 2025 Science China Press

关键词： Global warming

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Research on Multi-agent Based C4ISR Effectiveness simulation and Evaluation

Research on Multi-agent Based C4ISR Effectiveness Simulation...

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International Conference on Computer modeling and simulation, ICCMS

作者： Zhang Ying-chao Ye Feng Yu Qin-zhang Chen Xin National Key Laboratory of Science and Technology on Complex Systems Simulation Beijing Institute of System Engineering Beijing China

The effectiveness evaluation of C4ISR is playing a more and more important role in its designing, developing and using. According to its characters, such as complexity and intelligence, the traditional evaluation methods and systems are not feasible and effective enough. In this paper, a multi-agent based simulation and evaluation framework of C4ISR is advanced, which includes the architecture and the working procedure. The agent-based simulation model of C4ISR is put forward, according to which the C2 Agent, Armament Agent and the Evaluation Agent are founded. The interaction model between each agent is also designed. At final, based on the Swarm, a multi-agent based C4ISR effectiveness simulation and evaluation prototype is developed, through which NATO C2 effectiveness evaluation is thoroughly analyzed.

关键词： Analytical models Computational modeling Computer simulation Adaptive systems Intelligent agent Design engineering systems engineering and theory Laboratories Virtual prototyping Information technology

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