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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是411-420 订阅

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the Equivalence Induced by Unifying Fitness Mappings in Frequency-Dependent Moran Process

The Equivalence Induced by Unifying Fitness Mappings in Freq...

引用

ieee data driven control and learning systems conference

作者： Feng Huang Xiaojie Chen Long Wang Center for Systems and Control College of Engineering Peking University School of Mathematical Sciences University of Electronic Science and Technology of China

ISBN: (纸本)9781538626191

As one of the most prevalent microscopic mechanisms, frequency-dependent Moran process is widely adopted to model the traits' evolution of agents in a well-mixed population, where an individual is chosen for reproduction proportional to its fitness. Using a pair of specific fitness mappings, it has been demonstrated that the Moran process leads to identical evolutionary outcomes under weak selection. But does the equivalent relation hold for any two fitness functions? In this paper, by introducing a general mapping that fitness is an arbitrary non-negative function of payoff and selection intensity, the fitness mappings is unified. And accordingly we investigate that whether the unifying fitness mappings can lead to equivalent evolutionary outcomes in frequency-dependent Moran process. By calculating fixation probabilities and fixation times under weak selection, we find that the effect of different fitness mappings on these two quantities just embody in a constant factor under mild conditions. In particular, this constant factor can be absorbed into the selection intensity by proper rescaling or by making the payoff matrix change a scale. thus, in spite of the scaling factor, any two fitness functions are equivalent under weak selection for frequency-dependent Moran process.

关键词： Stochastic game dynamics Frequency-dependent Moran process Fitness function

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Wind Turbine Unit Power Prediction Based on Wavelet Neural Network Optimized by Brain Storm Optimization Algorithm

Wind Turbine Unit Power Prediction Based on Wavelet Neural N...

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ieee data driven control and learning systems conference

作者： Qiang Guo Zhiwei Xue Longying Zhang Xiaohui Lu Yue Yin Congzhi Huang State Grid Shanxi Electric Power Research Institute Taiyuan 030001 China State Grid Shanxi Electric Power Company Taiyuan 030001 China School of Control and Computer Engineering North China Electric Power University Beijing 102206 China

ISBN: (纸本)9781538626191

the construction of the wind power curve is of great significance to the wind turbines. Based on the accurate model of wind power curve developed, it can be employed for the wind power prediction and fault diagnosis. Normally, the wind turbine manufacturer provides the standard wind power curve, which is measured at standard conditions. However, the actual situation of the wind turbine is different from the standard state and is constantly changing. the wind power curve needs to be modified. the wind power curve essentially establishes a functional relationship between wind speed and active power. the neural networks have the ability to approximate function. In this paper, based on the actual data from a wind farm in Shanxi Province, the wavelet neural network is used to model the wind power curve, and the initial parameters are determined by using the brain storm optimization algorithm. the probability of the non-convergence in the learning process of the wavelet neural network is greatly reduced. Extensive experimental results are presented to validate the effectiveness of the proposed approach.

关键词： Wind power curve Wavelet neural network Brain storm optimization algorithm Power prediction

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A Novel System Decomposition Method Based on Pearson Correlation and Graph theory*

A Novel System Decomposition Method Based on Pearson Correla...

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data driven control and learning systems (ddcls)

作者： Jing Jin Shu Zhang Lijuan Li Tao Zou College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China Industrial Control Networks and Systems Department Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781538626191

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In the traditional system decomposition methods based on graph theory, the weight on each edge of the graph is set by state space equation to reflect the mutual influence of variables in the system. But in the actual industrial process, the acquisition of state space equation is more difficult. In this paper, a system decomposition method based on Pearson correlation coefficient and graph theory is proposed to avoid the use of state space equations. At first, a directed graph is established to represent the actual process of the industrial system and the weights on corresponding edges in the directed graph are set by the Pearson correlation coefficients between two nodes connected by these edges. then the directed graph is decomposed into several initial subgraphs and the subgraphs will be fused according to a certain rule. Here, a fusion index is defined to select the optimal fusion results in each fusion process. After each fusion process, the termination condition is required to determine whether to continue the next round of fusion process. When the fusion process ends, the subsets obtained at this time are the results of the system decomposition. When the system decomposition is finished, the online subsystems modeling will be carried out by RPLS algorithm. Finally, the proposed algorithm is applied in the Tennessee Eastman process to verify the validity.

关键词： Correlation Correlation coefficient Graph theory Mathematical model Input variables Heuristic algorithms Indexes

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Energy Management Of Cloud data Center using Neural Networks 7

Energy Management Of Cloud Data Center using Neural Networks

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7th ieee International conference on Cloud Computing in Emerging Markets (CCEM)

作者： Niranjan, U., V Pillai, Kishore Kumar G. IBM Corp India Syst Dev Lab Armonk NY 10504 USA

ISBN: (纸本)9781538694411

the cost effective deployment of applications into cloud has resulted in significant increase of cloud based services. this has in turn led to large number of data centers in delivering such services at scale, offering myriad of user experiences and minimal downtime. Such commitments of providing differentiated services at scale, invites the necessity to manage energy and performance of constituting nodes in data centers without impacting service level agreements (SLAs). Ensuring energy efficiency in these data centers is a major problem in cloud computing. Many optimization policies like workload consolidation, machine placement etc. helps in containing the energy requirement of servers in data centers. In this paper, we introduce a data-driven prognostic neural network based framework that will consider power consumed by all the components in server beyond incoming request load and effectively forecast it at any point in future.

关键词： energy savings cloud computing data centers machine learning neural networks optimization feedback control

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An Improved Stop and Go Model Considering Exhaust Emissions for Connected Vehicles

An Improved Stop and Go Model Considering Exhaust Emissions ...

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data driven control and learning systems (ddcls)

作者： Pangwei Wang Yue Ma Hongbin Yu Li Wang Wei Zhang Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Beijing China China Academy of Transportation Science Beijing China

ISBN: (纸本)9781538626191

Increasing vehicle ownership causes more and more serious urban traffic jam, meanwhile it also brings about much more idle time and exhaust emissions. the Stop and Go model is used to reduce stop time when vehicles drive at low speed status. With the development of connected vehicles, in this paper, an improved Stop and Go model is established considering low speed status of the leading vehicle and the following vehicle at the same time. Firstly, by combining the advantage of connected vehicles, the improved Stop and Go model chooses the minimum emissions as the control target. According to the emissions evaluation principle of Comprehensive Modal Emissions Model (CMEM), the objective function is established; secondly, in accordance with the actual traffic conditions and the operating conditions of the sample vehicle, the constraints are established; finally, the improved Stop and Go model is simulated based on Octave software. To prove the feasibility, we use CMEM to calculate the emissions of the following vehicle in the Stop and Go model and the linear Car-Following model. the results of exhaust emissions under two different models has been analyzed. Comparing with the linear Car-Following model, the HC emissions are reduced by 28%, CO emissions are reduced by 16%, and NOx emissions are reduced by 30%. therefore, the improved Stop and Go model can effectively reduce the emissions of vehicle exhaust.

关键词： Engines data models Fuels Acceleration Combustion

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the Methods of Extracting Spatiotemporal Characteristics of Travel Based on Mobile Phone data

The Methods of Extracting Spatiotemporal Characteristics of ...

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data driven control and learning systems (ddcls)

作者： Jiyuan Tan Luxi Dong Jian Gao Weiwei Guo Zhengxi Li Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Beijing China Research Institute of Highway Ministry of Transport Beijing China

ISBN: (纸本)9781538626191

With the rapid development of urbanization in China, the problem of traffic congestion is mainly due to the rapid increase in traffic demand. Compared with a variety of travel behaviors and origin-destination spatiotemporal distribution, which is helpful for us to explore the cause of traffic congestion. Traditionally, travel surveys are time consuming and huge economic investment. the accuracy of the results were existed large errors. In recent years, data acquisition techniques and storage capabilities are developed rapidly, more and more human travel related data have been collected. these "Big data" is brought both opportunities and challenges for extracting valid travel information. In this paper, the different trajectories of travel mode are match with traffic analysis zones through using geography information system. And then stay points are identified by clustering spatiotemporal characteristics of trajectories. Moreover, the OD matrix is established by different stay regions. the indices of travel and OD desire lines are chosen to analyze travel behaviors. Meanwhile, the OD volume distribution in rush hours are used to explain traffic demand in different urban area. the findings could be helped government make the appropriate decision of urban traffic system and made residents the better daily travel planning, which has significant reference value.

关键词： Spatiotemporal phenomena Trajectory Mobile handsets data mining Base stations Space exploration Geographic information systems

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data-driven Risk Assessment for Peer-to-Peer Network Lending Agencies 5

Data-driven Risk Assessment for Peer-to-Peer Network Lending...

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5th ieee International conference on Cloud Computing and Intelligence systems (CCIS)

作者： Zhao, Tianyuan Li, Lei Xie, Yang Lv, Yue Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781538660058

With the rapid development of Peer-to-Peer(P2P) network lending in the financial field, more data of lending agencies have appeared. P2P agencies also have problems such as absconded with ill-gotten gains and out of business. therefore, it is necessary to assess their risks based on P2P company data. this paper proposes a framework of data-driven Risk Assessment for P2P(DRAP2P) network lending agencies based on unstructured natural language data. First, use the natural language processing technology, such as word segmentation, keyword, LDA topic model, word2vec and doc2vec, to process and extract features of company profile which reflect its business status. then, seven machine learning classifiers and three deep learning models are used for analysis. Since keywords show good performance in machine learning models, we improve Convolutional Neural Network(CNN) with keywords and propose two CNN+Keyword models, namely CNN+Keyword(static+BP) and CNN+Keyword (Expand word embedding). Experiments have shown that CNN+Keyword(static+BP) can achieve the best performance. Finally, we use the method of meta-learning to integrate CNN+Keyword(static+BP) and logistic regression classifier to further strengthen the performance.

关键词： data-driven P2P risk assessment Machine learning Deep learning Meta-learning

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Design of missile longitudinal controller based on deep deterministic policy gradient learning algorithm 7

Design of missile longitudinal controller based on deep dete...

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7th ieee Information Technology and Mechatronics Engineering conference, ITOEC 2023

作者： Zhang, Wanchao Chen, Guangshan Ni, Hao Tong, TingShuai Zhou, YiWen Shanghai Aerospace Control Technology Institute Shanghai China

ISBN: (纸本)9798350334197

For the longitudinal control problem of missiles under highly nonlinear conditions, traditional controllers designed by fixed points have good static characteristics, but in the face of highly nonlinear systems, the control parameters designed at a certain characteristic point will not be able to satisfy the entire control system. In this paper, a combination of reinforcement learning and deep neural network architecture is used to propose an intelligent adaptive controller design scheme based on a deep deterministic policy gradient algorithm. Firstly, the control problem is reconstructed in the framework of deep reinforcement learning. the environment is composed of a two-degree-of-freedom nonlinear model of missile longitudinal dynamics. Secondly, the design of the reward function not only considers the stability of the longitudinal dynamics, but also uses the ITAE index as the reward function. the penalty item is to comprehensively consider the balance of error and execution output. Finally, simulation results are shown to check the ability of the proposed data-driven method to achieve the specified closed-loop response in a fully model-free manner under a complete flight condition, and the method is verified by model parameter-pull Monte Carlo simulations. Feasibility and effectiveness. © 2023 ieee.

关键词： Reinforcement learning

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Structured Joint Sparse Principal Component Analysis for Fault Detection and Isolation

Structured Joint Sparse Principal Component Analysis for Fau...

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data driven control and learning systems (ddcls)

作者： Yi Liu Jiusun Zeng Lei Xie Shihua Luo Hongye Su Institute of Cyber Systems and Control Zhejiang University Hangzhou P. R. China College of Metrology and Measurement Engineering China Jiliang University Hangzhou P. R. China School of Statistics Jiangxi University of Finance and Economics Nanchang P. R. China

ISBN: (纸本)9781538626191

Principal component analysis (PCA) has been widely applied in process monitoring of modern industrial systems. PCA performs fault detection by mapping the process data into a low dimensional subspace and tracking the process behavior using T 2 and SPE statistics, whilst in fault isolation, it heavily relies on contribution plot or reconstruction based approaches. However, conventional methods based on contribution plot and reconstruction suffer from insufficient fault isolation capabilities. In order to improve the fault isolation performance, this article proposes a novel fault detection and isolation approach based on the Structured Joint Sparse PCA (SJSPCA). the objective function of SJSPCA involves two regularization terms: l 2,1 norm and the graph Laplacian. By imposing the l 2,1 norm term, SJSPCA is able to achieve row-wise sparsity, introducing the graph Laplacian regularization term can incorporate structured variable correlation information. In fault detection, conventional T 2 and SPE statistics are constructed to detect abnormal situations. Once a fault is detected, a two stage fault isolation strategy is considered and a score index is calculated for each variable. the row-sparsity property of l 2,1 norm ensures that the score indices associated to normal variables approach zero and the graph Laplacian constraint helps isolation of correlated faulty variables. the validity of SJSPCA in fault detection and isolation is illustrated by a process fault observed in an industrial blast furnace iron-making process.

关键词： Principal component analysis Laplace equations Fault detection Monitoring Linear programming Loading Optimization

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Adaptive Bayesian Knowledge Tracing Based on Personalized Characteristics 7

Adaptive Bayesian Knowledge Tracing Based on Personalized Ch...

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7th ieee International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Li, Zhijun Jiao, Yuhang Xu, Jining Zhang, Xiaoping School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (纸本)9798350350760

—Bayesian Knowledge Tracing (BKT) model is a student knowledge state model that currently widely used in intelligent teaching system. However, BKT still has limitations in capturing dynamic changes during the student learning process and handling multiple characteristics as well as individualized needs. To address the above problems, this paper proposes an adaptive BKT model based on students' personalized features. the core of the model lies in constructing a feature set based on students' previous personalized question-answering data, and dynamically classifying students' current learning situation accordingly. the dynamic classification method classifies students by analyzing their question-answering performance in real time and adjusts the BKT parameters accordingly to more accurately reflect their current knowledge status. On this basis, it predicts students' future learning status and question-answering performance. Comparative experiments show that the model can better adapt to individual learning differences and significantly improve the accuracy of predicting students' future question-answering correctness. ©2024 ieee.

关键词： Question answering

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