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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431-440 订阅

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A Novel Improved Grey Wolf Optimization Algorithm for Numerical Optimization and PID controller Design

A Novel Improved Grey Wolf Optimization Algorithm for Numeri...

引用

data driven control and learning systems (ddcls)

作者： Tao Zhang Xin Wang Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Center of Electrical & Electronic Technology Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538626191

the grey wolf optimization (GWO) algorithm, one of the recently proposed bio-inspired algorithms, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. the GWO has a good performance in some optimization tasks, but its search capacity decreases with the increasing search scope and dimension. this paper proposes an improved GWO (IGWO) algorithm, in which Levy flight strategy and a sine cosine operator with adaptive step are incorporated to significantly improve the performance of the algorithm. the Levy flight strategy is used to strengthen the efficiency of global search. the adaptive sine cosine operator is introduced to improve the local search ability. Experimental results based on twenty unconstrained benchmark problems show the superiority of the proposed IGWO. Furthermore, the IGWO is utilized in PID controller design. the comparison results show that the IGWO algorithm is better than, or at least comparable to, other well-established swarm intelligence algorithms.

关键词： Optimization Power system stability Sociology Statistics Prediction algorithms Particle swarm optimization Genetic algorithms

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Direction of Arrival Estimation Based on Generalized Reference Curve Model

Direction of Arrival Estimation Based on Generalized Referen...

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data driven control and learning systems (ddcls)

作者： Lizhi Cui Xuhui BU Junqi Yang Yi Yang Weina He Key Laboratory of control engineering of Henan Province Henan Polytechnic University Jiaozuo Henan China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China School of computer Pingdingshan University Pingdingshan Henan China

ISBN: (纸本)9781538626191

Currently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which needed to know the number of sources in advance. In this paper, a new model based on the Generalized Reference Curve Model (GRCM) for the DOA estimation was proposed, which do not need to know the sources number in advance. And the comparison of the performance between the proposed model and the MUSIC model was given to demonstrate the effectiveness of our method. the algorithm of Multi-target Intermittent Particle Swarm Optimization (MIPSO) was adopted to solve the model proposed in this paper, and the performance of the MIPSO was analyzed through a simulation. the result shown that:(1) the GRCM was an effective model to solve the DOA estimation without prior knowledge of the sources number; (2) the MIPSO was an efficient algorithm to solve the DOA estimation with much shorter operation time and high precision.

关键词： Direction-of-arrival estimation Estimation Multiple signal classification Signal to noise ratio Sensors Particle swarm optimization Analytical models

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Based on Improved Semi-Supervise Clustering Method Training Classifier for Analog Circuit Fault Classification

Based on Improved Semi-Supervise Clustering Method Training ...

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data driven control and learning systems (ddcls)

作者： Aihua Zhang Kailun Huang Gang Luo Zhiqiang Zhang College of Engineering BHU Jinzhou Jinzhou Normal College Jinzhou China

ISBN: (纸本)9781538626191

In recent years, semi-supervised clustering as an important research subject has significance in dealing with lack of training sample sets. However, formerly semi-supervised clustering usually cannot attend satisfactory consequence in precision and training time at the same time. Aimed to the problem of clustering method assist training classifier to label the samples, produce the time optimization algorithm. Based on prior knowledge, mining the acquired unlabeled sample sets deeply of their potential data structure and combine semi-supervised fuzzy C-means(SS-FCM) arithmetic with similarity coefficient to sort out the samples for training time improvement. On the basis of little influence on classification result accuracy, gain the fuzzy similarity matrix from Euclidean distance and assess the maximum dependable sample point with its neighborhood for their similarity degree, will avoid searching the maximum dependable sample point one by one and optimize holistic clustering time costing from reduce the iterations of classifier to some extent. through artificial circuit simulation experiment, using improvement SS-FCM assist SVM classifier and single SVM and SS-FCM assist SVM classifier to make a comparison, verify the algorithm from classify precision and arithmetic speed and the result of experiment can prove the validity of the improvement.

关键词： Support vector machines Training Clustering algorithms Classification algorithms data models Linear programming Clustering methods

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Preliminary data Processing by Means of Onboard Computer systems of Spacecraft 7

Preliminary data Processing by Means of Onboard Computer Sys...

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7th Mediterranean conference on Embedded Computing (MECO)

作者： Konkin, Yuriy, V Kolesenkov, Aleksandr N. Sablina, Victoria A. Ashapkina, Maria S. RSREU Dept Space Technol Ryazan Russia RSREU Dept Elect Comp Ryazan Russia RSREU Dept Micro & Nanoelect Ryazan Russia

ISBN: (纸本)9781538656839

the article is devoted to the solution of scientific problems of developing stand-alone storage technologies as well as data processing methods oriented to the usage of small spacecraft in onboard computer systems while solving the tasks of aggregating information from technical vision system sensors, machine learning and intellectual data processing. the relevance of the project is caused by the necessity to solve the tasks requiring large amount of calculations on board, in the conditions of limited computing resources concerning memory volume and performance. the work solves the task of developing and unifying effective built-in onboard control systems and database control systems with low requirements to computer power on board the small spacecraft. Scientific significance of such problem solution is to receive new efficient technologies of control onboard systems design on the basis of open source operating systems and software. the implementation of the project will allow receiving scientific and technological results as well as creating basic technologies for innovation development of local and international markets of goods and services in the sphere of intellectual built-in systems.

关键词： processing storage onboard system ERS data format data database DBMS ultrasmall spacecraft satellite

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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...

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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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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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A Novel Scalable Semi-supervised GMM and Its Application for Multimode Process Quality Prediction with Big data

A Novel Scalable Semi-supervised GMM and Its Application for...

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data driven control and learning systems (ddcls)

作者： Le Yao Zhiqiang Ge Weiming Shao Zhihuan Song State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou P. R. China

ISBN: (纸本)9781538626191

In this paper, a novel variational inference semi-supervised GMM (VI-S 2 GMM) model is firstly proposed for multimode process predictive modeling with semi-supervised data. Since all the labeled and unlabeled data samples are involved in each iteration of parameter updating, an intractable computing problem occurs when facing a high-dimension and large-scale dataset. To tack this problem, a scalable Stochastic Variational Inference semi-supervised GMM (SVI-S 2 GMM) is further proposed for massive semi-supervised data. through taking advantage of stochastic gradient optimization algorithm to maximize the Evidence of Lower Bound (ELBO), the VI-based algorithm becomes scalable. In SVI-S 2 GMM, only one or a mini-batch of samples is randomly selected to update parameters in each iteration, which is more efficient than VI-S 2 GMM. In this way, a large number of unlabeled process data can be useful in the modeling, which will benefit the parameter identification. the SVI-S 2 GMM is then exploited for the prediction of quality-related key performance index (KPI). Two modeling cases with large scale of semi-supervised datasets demonstrate the feasibility and effectiveness of the proposed algorithms.

关键词： data models Inference algorithms Big data Predictive models Adaptation models Computational modeling Stochastic processes

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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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Subject Features and Hash Codes for Multi-label Image Retrieval

Subject Features and Hash Codes for Multi-label Image Retrie...

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data driven control and learning systems (ddcls)

作者： Changzhen Xiong Yanmei Shan Beijing Key Laboratory of Urban Intelligent Control North China University of Technology Beijing

ISBN: (纸本)9781538626191

In order to solve the problem that existing hashing methods cannot describe multi-label images accurately, this paper proposes a multi-label image retrieval method based on hashing codes and main part detection of an image. this method adds a fully connected layer after the region proposal network (RPN) in Faster-RCNN to learn the binary hash codes of the detected region proposals. through the network, the region proposals in the query image and their class probabilities, spatial positions and hash codes can be obtained. then, the main instance of query and its hash codes are extracted according to the class probability and the spatial relations of the proposals. Finally, the images with the same class label of the query are retrieved from the database and ranked by the Hamming distance of hash codes. the top N images with the highest score are returned as the final results. Experimental verification is carried out on 2000 query images selected randomly from VCO2007 dataset, the results show that the NDCG(normalized discounted cumulative gain) is 0.8967 and the ACG(average cumulative gain) is 0.7970 when the top 1000 images selected in retrieval result. Compared with IAH(Instance aware hashing) method, they respectively increased by 1.02 and 3.04 percent. the proposed method has a good retrieval effect on the multi-label query images.

关键词： Proposals Feature extraction Image retrieval Binary codes Filtering algorithms Hamming distance

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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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