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29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017

作者： Gao, Min Yin, Yu Wang, Xingfei Li, Ying Chen, Zaidie Guo, Lipeng Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education College of Electrical Engineering Hangzhou Dianzi University Zhejiang China College of Computer Science Zhejiang University Zhejiang Hangzhou China College of Computer Science Fudan University Shanghai China Hangzhou Housing Management Bureaus Hangzhou Zhejiang China

ISBN: (纸本)1891706411

As a new service paradigm of Software-As-A-Service (SaaS),Business Process-As-A-Service (BPaaS). BPaaS is used to build a cost-effective Business Process Management (BPM) system. Based on universal Artifacts, we develop a framework named SeGA (Self-Guided Artifact). In SeGA, a BPM system is capable of executing business processes from multiple clients, and responding query at runtime. what's more, by the template-based cascading data mapping method, entity to be synchronized with database automatically, so each BP instance can modify its process entity without worrying about the database access. In this paper, we conduct a deep research and implement a prototype system for Artifact process design.

关键词： Process design

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食品系统与气候变化、空气污染之间的相互作用综述（英文）

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Engineering 2025年第1期 215-233页

作者： Chaopeng Hong Rui Zhong Mengyao Xu Peidong He Huibin Mo Yue Qin Danna Shi Xinlei Chen Kebin He Qiang Zhang Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex College of Environmental Sciences and Engineering Peking University Institute of Data and Information Shenzhen International Graduate School Tsinghua University State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University

Food systems are deeply affected by climate change and air pollution, while being key contributors to these environmental challenges. Understanding the complex interactions among food systems, climate change, and air pollution is crucial for mitigating climate change, improving air quality, and promoting the sustainable development of food systems. However, the literature lacks a comprehensive review of these interactions, particularly in the current phase of rapid development in the field. To address this gap, this study systematically reviews recent research on the impacts of climate change and air pollution on food systems, as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution. In addition, this study summarizes various strategies for mitigation and adaptation, including adjustments in agricultural practices and food supply chains. Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions. This review offers a critical overview of current research on the interactions among food systems, climate change, and air pollution and highlights future research directions to support the transition to sustainable food systems.

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Air pollution characteristics and their relationship with emissions and meteorology in the Yangtze River Delta region during 2014–2016

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Journal of Environmental Sciences 2019年第9期31卷 8-20页

作者： Tao Ma Fengkui Duan Kebin He Yu Qin Dan Tong Guannan Geng Xuyan Liu Hui Li Shuo Yang Siqi Ye Beiyao Xu Qiang Zhang Yongliang Ma State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing Key Laboratory of Indoor Air Quality Evaluation and Control Tsinghua University Beijing 100084 China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing 100084 China National Satellite Meteorological Center Beijing 100081 China College of Resources and Environmental Sciences China Agricultural University Beijing 100094 China

With rapid economic growth and urbanization, the Yangtze River Delta(YRD) region in China has experienced serious air pollution challenges. In this study, we analyzed the air pollution characteristics and their relationship with emissions and meteorology in the YRD region during 2014–2016. In recent years, the concentrations of all air pollutants, except O3,decreased. Spatially, the PM2.5, PM10, SO2, and CO concentrations were higher in the northern YRD region, and NO2 and O3 were higher in the central YRD region. Based on the number of non-attainment days(i.e., days with air quality index greater than 100), PM2.5 was the largest contributor to air pollution in the YRD region, followed by O3, PM10, and ***, particulate matter pollution has declined gradually, while O3 pollution *** conditions mainly influenced day-to-day variations in pollutant concentrations. PM2.5 concentration was inversely related to wind speed, while O3 concentration was positively correlated with temperature and negatively correlated with relative *** air quality improvement in recent years was mainly attributed to emission *** 2014–2016, PM2.5, PM10, SO2, NOx, CO, NH3, and volatile organic compound(VOC)emissions in the YRD region were reduced by 26.3%, 29.2%, 32.4%, 8.1%, 15.9%, 4.5%, and0.3%, respectively. Regional transport also contributed to the air pollution. During regional haze periods, pollutants from North China and East China aggravated the pollution in the YRD region. Our findings suggest that emission reduction and regional joint prevention and control helped to improve the air quality in the YRD region.

关键词： Air pollution Spatiotemporal variation Meteorological condition Emission inventory Regional transport Yangtze River Delta

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Convolutional neural networks for intestinal hemorrhage detection in wireless capsule endoscopy images

Convolutional neural networks for intestinal hemorrhage dete...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Panpeng Li Ziyun Li Fei Gao Li Wan Jun Yu Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University School of Communication Engineering Hangzhou Dianzi University College of Computer Science Chongqing University

ISBN: (纸本)9781509060689

Wireless capsule endoscopy (WCE) can painlessly capture a large number of images inside the intestine. However, only a small portion of these WCE images contain hemorrhage. It is thus critical to develop automated hemorrhage detection method to facilitate the diagnosis of intestinal diseases. However, automated hemorrhage detection is complicated by 1) the extreme imbalance between the amount of hemorrhage images and that of normal images; and 2) the variety of the appearance, texture, and luminance inside the intestine. In this paper, we proposed to learn a robust intestinal hemorrhage detection model via Convolutional Neural Networks (CNNs), because of CNNs' extraordinary performance in solving various image understanding tasks. Specially, we explored different CNN architectures and data augmentation methods. Besides, we investigated the correlation between hemorrhage detection accuracy and image quality. Across about 1.3k hemorrhage images and 40k normal images, the learned CNN model achieves an F-measure of 98.87%.

关键词： Hemorrhaging Optical wavelength conversion Endoscopes Machine learning Biomedical imaging Training Wireless communication

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Research on Prediction Method for Pivotal Indicator of Hospital Medical Quality Using Decision Tree

Research on Prediction Method for Pivotal Indicator of Hospi...

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Liujian Chen Lujia Bi Huayou Si Jilin Zhang Yongjian Ren Key Laboratory of Complex System Modeling and Simulation Ministry of Education Department of Computer Science and Technology Hangzhou Dianzi University Department of Information First Hospital of Jiaxing

Indicators of medical quality is the main basis of medical quality evaluation, predictive analyzing the indicators by using data mining technology, we can find and solve the problems of hospital quality management more timely. The study, basing on the medical homepage records, analyzes the key indicators of the hospital medical quality by decision tree C4.5 algorithm. Decision tree is a prediction model which can assist hospital to make related decision on medical quality management issues. By using decision tree C4.5 algorithm with appropriate modification, a high quality model of decision tree can be made. It can not only do the statistic of medical quality indicators, but also predict the indicators with high relative accuracy to improve hospital’s medical quality management.

关键词： medical quality data mining decision tree C4.5

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Analysis of target inventory via discrete-event simulation

Analysis of target inventory via discrete-event simulation

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20th European Conference on Modelling and simulation: Modelling Methodologies and simulation Key Technologies in Academia and Industry, ECMS 2006

作者： Zottolo, Marcelo Williams, Edward J. Ülgen, Onur M. Production Modeling Corporation 1006 Parklane Towers West Dearborn MI 48126 United States 2310 Engineering Complex Industrial and Manufacturing Systems Engineering University of Michigan Dearborn 4901 Evergreen Road Dearborn MI 48128 United States Institute of Industrial Engineers IIE Society for Computer Simulation International SCS United States

ISBN: (纸本)0955301807

Discrete-event process simulation, originally the benefactor of the manufacturing sector of the economy, has expanded aggressively into the service sector of the economy, much to the benefit and gratitude of its new cadre of industrial engineers and management strategists. The study documented in this paper originated within a large health-care insurance provider seeking optimal strategies relative to target inventories of pending inquiries concerning insurance policy coverage and concomitant staffing levels of policy analysts. Since several clients of this insurance provider were large companies within the automotive industry, the provider dedicated significant staffing segments to the service of these accounts (hence to the employees of those automotive companies who thereby held insurance coverage). The simulation study worked within this constraint to provide management valuable strategic recommendations. Most specifically, the insurance provider wished to develop a model capable of predicting service levels (average time required to answer specific questions submitted on behalf of two major clients and average inventory level of these questions pending) as a function of number of full-timeequivalent analysts assigned to each of those clients. © ECMS, 2006.

关键词： Automotive industry

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Virtual Page Behavior Based Page Management Policy for Hybrid Main Memory in Cloud Computing 12

Virtual Page Behavior Based Page Management Policy for Hybri...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Huang, Jie Zhang, Xuan Han, Guangjie Jia, Gangyong Liyou, Huizi Wan, Jian Department of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou310018 China Department of Information and Communication Systems Hohai University Changzhou213022 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9781509056965

A new generation memory, Non-Volatile Memory (NVM), such as Phase-Change Memory (PCM), has been adopted together with DRAM in the main memory to form the hybrid main memory for low energy consumption and high capacity. The biggest challenge of hybrid memory is how to decrease the average memory access cost for the higher cost of NVM's read/write operation. Currently, most researches are based on migration. However, the page migration itself is a high cost operation. And the migration based policy produces many migration operations, which induces high cost in memory access. Therefore, in order to decrease the cost, we present a virtual page behavior based page management policy (VBPM) in this paper. According to the virtual pages' behavior, we allocate virtual pages into DRAM or PCM physical pages correspondingly. The whole process is migration independent. The experimental results show our VBPM decreases the average memory access time by 24%, moreover, VBPM improves real-time performance in critical path. © 2016 IEEE.

关键词： Phase change memory

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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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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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