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第30届中国控制与决策会议

作者： Xiangqian Xu Yajie Dou Zhexuan Zhou Tianjun Liao Yanjing Lu Yuejin Tan College of Systems Engineering National University of Defense Technology State Key Laboratory of Complex System Simulation Beijing Institute of System Engineering

With the rapid development of high-speed railway in China, the research of railway passenger flow forecasting has become a key research direction. The forecast of railway passenger flow can help formulate a reasonable price, improve the organization of passenger terminals, optimize the allocation of railway vehicles resources and improve the service capability of passenger transport equipment, which are of great significance to improve the efficiency of railway passenger transport. In this study, a comprehensive forecasting model based on time series analysis is proposed for railway passenger flow forecasting. To solve problems which cannot be handled with traditional programming model in the context of big data, time series analysis is introduced into the solution. Railway Passenger Flow Forecasting model based on Time Series Analysis is established with the combination of the long-term trend factor, the seasonal factor and the weather factor. Railway passenger flow data obtained from the Railway Bureau are used for the case study. The change rule of passenger flow was researched under different conditions, the railway passenger flow in the next two weeks was forecast, and the corresponding optimization of vehicle configuration and station docking scheme are proposed. Sensitivity analysis shows good stability and robustness of the model.

关键词： Time-Series Forecasting Big Data modeling

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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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Message from the Chairs

Proceedings - 2015 IEEE/ACM 19th International Symposium on ...

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Proceedings - 2015 IEEE/ACM 19th International Symposium on Distributed simulation and Real Time Applications, DS-RT 2015 2015年 ix-x页

作者： Turner, Stephen J. Yao, Yiping You, Guangrong Li, Bohu Cai, Wentong Nanyang Technological University Singapore National University of Defense Technology China National Key Laboratory for Complex Systems Simulation China BeiHang University China

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Multi2human: Controllable Human Image Generation with Multimodal Controls

SSRN

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SSRN 2023年

作者： Gu, Xiaoling Xu, Shengwenzhuo Wong, Yongkang Wu, Zizhao Yu, Jun Fan, Jianping Kankanhalli, Mohan S. Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China National University of Singapore Singapore School of Digital Media Technology Hangzhou Dianzi University China

Generating high-quality and diverse human images presents a substantial difficulty within the field of computer vision, especially in developing controllable generative models that can utilize input from various modalities. Such models could enable innovative applications like digital human, fashion design, and content creation. In this study, we introduce Multi2Human, a two-stage image synthesis framework for controllable human image generation with multimodal controls. In the first stage, a novel WAvelet-vqVaE (WAVE) architecture is designed to embed human images using a learnable codebook. The WAVE model enhances the conventional Vector Quantized Variational Autoencoder (VQVAE) by integrating wavelets throughout the encoder, thereby enhancing the quality of image reconstruction and synthesis. In the second stage, a new Multimodal Conditioned Diffusion Model (MCDM) is de- signed to estimate the underlying prior distribution within the discrete latent space using a discrete diffusion process, thus allowing for human image generation condi- tioned on multimodal controls. Quantitative and qualitative analysis demonstrates that the proposed method has the ability to create high-quality, lifelike full-body human images while satisfying the specified multimodal controls. Our code is available at https://***/gxl-groups/Multi2Human. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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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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Impacts of major volcanic eruptions over the past two millennia on both global and Chinese climates:A review

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Science China Earth Sciences 2024年第1期67卷 61-78页

作者： Weiyi SUN Deliang CHEN Guonian LÜ Liang NING Chaochao GAO Renhe ZHANG Bin WANG Jian LIU Key Laboratory for Virtual Geographic Environment Ministry of EducationState Key Laboratory Cultivation Base of Geographical Environment Evolution of Jiangsu ProvinceJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationSchool of Geography ScienceNanjing Normal UniversityNanjing 210023China Regional Climate Group Department of Earth SciencesUniversity of GothenburgGothenburg 40530Sweden College of Environmental and Resource Science Zhejiang UniversityHangzhou 310058China Department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences Fudan UniversityShanghai 200437China Department of Atmospheric Sciences and Atmosphere-Ocean Research Center University of Hawaii at ManoaHonoluluHI 96825USA Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical ScienceNanjing Normal UniversityNanjing 210023China Open Studio for the Simulation of Ocean-Climate-Isotope Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China

Major volcanic eruptions(MVEs)have attracted increasing attention from the scientific *** studies have explored the climatic impact of MVEs over the past two ***,proxy-based reconstructions and climate model simulations indicate divergent responses of global and China’s regional climates to ***,we used multiple data from observations,reconstructions,simulations,and assimilations to summarize the historical facts of MVEs,the characteristics and mechanisms of their climatic impact,and directions for future *** reviewed volcanic datasets and determined intensive MVE periods;these periods corresponded to the years 530–700,1200‒1460,and 1600‒1840 *** tropical MVEs,a substantial cooling effect is observed throughout the globe and China on the interannual-interdecadal time scales but an inconsistent cooling magnitude is detected between reconstructions and *** the first summer after tropical MVEs,a decrease in global and monsoonal precipitation is *** reconstructions and simulations,an increased precipitation is seen for the Yangtze River Basin,while large uncertainties in precipitation changes are present for other regions of *** drought can be induced by frequent eruptions and volcanism superimposed on low solar irradiation and internal *** affect climate directly through the radiative effect and indirectly by modulating internal variability,such as the El Niño‒Southern Oscillation(ENSO)and Atlantic Multidecadal Oscillation(AMO).However,changes in the phase,amplitude,and periodicity of ENSO and AMO after MVEs and the associated mechanisms remain controversial,which could account for model-reconstruction ***,other internal variability,uncertainties in reconstruction methods and aerosol‒climate models,and climate background may also induce model-reconstruction *** gaps and directions for future research are also discussed.

关键词： Major volcanic eruption Climate variability Mechanism Past two millennia Internal variability

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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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Evaluation of Colchicine's interaction with the ATP-binding region of mice NLRP3-NACHT domain using molecular docking and dynamics simulation 46

Evaluation of Colchicine's interaction with the ATP-binding ...

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46th Vietnam Conference on Theoretical Physics, VCTP 2021

作者： Lai, Hien T. T. Do, Ha M. Nguyen, Toan T. Key Laboratory for Multiscale Simulation of Complex Systems Department of Theoretical Physics Faculty of Physics VNU University of Science Vietnam National University Ͽ Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi Viet Nam The Key Laboratory of Enzyme and Protein Technology VNU University of Science Vietnam National University Ͽ Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi Viet Nam

Since the discovery of the role of NLRP3 in microbial infection in 2001, many studies have shown that NLRP3 play a key role in causing many mammal acute and chronic diseases. However, a full understanding of the mechanism of NLRP3 activation is still lacking. Our previous theoretical work and experimental evidence show the role of ATP in interacting with and activating the NATCH region of NLRP3. In this study, we continue to use bioinformatics and molecular dynamic (MD) simulation to evaluate the competitive impact of the interaction the ligand ATP and colchicine (COL) with this NACHT protein. The later ligand is a medication to treat gout attacks. Our results show that COL bind stably to the ATP binding pocket of mice NACHT domain with high numbers of hydrophobic and van der Waals interactions, while hydrogen bond and electrostatic interactions are important types of contact for keeping ATP at its NACHT pocket. Our results assist in building in-silico screening model for natural compounds with pharmacological effects to NLRP3 similar to colchicine with few side effects. In addition, this work helps to better understand the balance between this inflammasome activation and inhibition, which will help in the improvement and development of new therapies for related diseases. © Published under licence by IOP Publishing Ltd.

关键词： Chemical activation

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Hazard-evaluation-oriented Moving Horizon Parallel Steering Control for Driver-Automation Collaboration During Automated Driving

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1062-1073页

作者： Hongyan Guo Linhuan Song Jun Liu Fei-Yue Wang Dongpu Cao Hong Chen Chen Lv Partick Chi-Kwong Luk State Key Laboratory of Automotive Simulation and Control and the Department of Con-trol Science and Engineering Jilin University(Campus Nanling)Changchun130025China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense TechnologyChangsha 410073China Mechanical and Mechatronics Engineering Department at the University ot Waterloo 200 University Avenue West WaterlooONNZL3G1Canada Department of Automotive Engineering Cranfield UniversityBedfordshire MK43 0ALUK School of Water Energy and EnvironmentCranfield UniversityBedfordshire MK43 0ALUK

Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon *** framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation,the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable *** automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.

关键词： Hazard evaluation intelligent vehicle atera stability moving horizon optimization paralle steering control

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On Exploring Undetermined Relationships for Visual Relationship Detection

On Exploring Undetermined Relationships for Visual Relations...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yibing Zhan Jun Yu Ting Yu Dacheng Tao Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney

ISBN: (纸本)9781728132945

In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undetermined relationships. Although a vast body of literature exists, few methods exploit these undetermined relationships for visual relationship detection. In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection. We propose a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieve great improvements in relationship detection. In detail, our MF-URLN automatically generates undetermined relationships by comparing object pairs with human-notated data according to a designed criterion. Then, the MF-URLN extracts and fuses features of object pairs from three complementary modals: visual, spatial, and linguistic modals. Further, the MF-URLN proposes two correlated subnetworks: one subnetwork decides the determinate confidence, and the other predicts the relationships. We evaluate the MF-URLN on two datasets: the Visual Relationship Detection (VRD) and the Visual Genome (VG) datasets. The experimental results compared with state-of-the-art methods verify the significant improvements made by the undetermined relationships, e.g., the top-50 relation detection recall improves from 19.5% to 23.9% on the VRD dataset.

关键词： Visualization Fuses Dataset

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