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A Novel Reinforcement learning Framework for Adaptive Routing in Network-on-Chips 23

A Novel Reinforcement Learning Framework for Adaptive Routin...

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23rd ieee International conference on High Performance Computing and Communications, 7th ieee International conference on data Science and systems, 19th ieee International conference on Smart City and 7th ieee International conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Changhong Wang, Zicong Dong, Dezun Zhang, Xiaoyun Zhao, Zhenyu National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

Adaptive routing is crucial to the overall performance of network-on-chips (NoCs), and still faces great challenges, especially when emerging applications on many-core architecture exhibit complicated and time-varying traffic patterns. When witnessing most existing heuristic adaptive routing algorithms fail to address multi-objective optimization for complex traffic well, we decide to try and explore a new approach of thinking and extracting insights from network behaviors. Reinforcement learning methods have demonstrated promising opportunity applied to architecture design exploration, however not been well applied on adaptive routing design. We make the first attempt to propose a novel and comprehensive reinforcement learning framework for adaptive routing on NoCs, called RELAR. RELAR is suitable for diversified traffic patterns and resolve multi-objective optimization simultaneously. It is able to effectively isolate endpoint congestion when facing adversary hot-spot and bursty traffic, and achieve dynamic load-balancing and mitigate network congestion when meeting heavy uniform traffic. We utilize state-of-the-art high-performance interconnection benchmark, GPCNeT, as traffic generators to generate rich network congestion workloads and thus enhance online-training efficiency of RELAR. We conduct extensive experiments against state-of-the-art routing algorithms to evaluate our design. the results show that RELAR achieves 14.82% and 9.86% reduction in packet latency on average, and reduces packet latency by up to 34.24% and 16.82% under heavy synthetic traffic workload and high-performance interconnection benchmark, respectively. We also perform cost analysis to validate potential implementation of RELAR on NoCs with low computation, storage and power. © 2021 ieee.

关键词： Adaptive systems Heuristic algorithms Reinforcement learning Computer architecture Traffic control Benchmark testing Routing

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Short-Term Traffic Flow Prediction Based on XGBoost

Short-Term Traffic Flow Prediction Based on XGBoost

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data driven control and learning systems (ddcls)

作者： Xuchen Dong Ting Lei Shangtai Jin Zhongsheng Hou Advanced Control Systems Lab Beijing Jiaotong University Beijing China

ISBN: (纸本)9781538626191

Fast and accurate short-term traffic flow prediction is an important precondition for traffic analysis and control. Due to the fact that the short-term traffic flow has nonlinear characteristic and changes randomly, concurrent computation is difficult for traditional machine learning algorithms. In this paper, a traffic flow prediction model combining wavelets decomposition and reconstruction with the extreme gradient boosting (XGBoost) algorithm is proposed to predict the short-term traffic flow. First, in the training part, wavelet de-noising algorithm is utilized to obtain the high and low frequency information of target traffic flow. Secondly, the high frequency information of traffic flow is processed by threshold method. After that, the high and low frequency information is reconstituted as the training label. Finally, the de-noised target flow is sent to the XGBoost algorithm for training to predict traffic flow. In this way, the trend of the traffic flow in each sample period is retained, and the influence of the short-term high frequency noise is reduced. the proposed traffic flow prediction method is tested base on the traffic flow detector data collected in Beijing, and the proposed method is compared with support vector machine (SVM) algorithm. the result shows that the prediction accuracy of the proposed algorithm is much higher than SVM, which is of great importance in the field of traffic flow prediction.

关键词： Training Prediction algorithms Predictive models Support vector machines Linear programming Boosting

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Iterative learning Based Model Identification and State of Charge Estimation of Lithium-Ion Battery

Iterative Learning Based Model Identification and State of C...

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data driven control and learning systems (ddcls)

作者： Qiao Zhu Meng’en Xu Meng’qian Zheng School of Mechanical Engineering Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781538626191

this work focuses on the accurate identification of Lithium-ion battery's nonlinear parameters by using an iterative learning method. First, the 2nd-order RC model is introduced. then, when the battery repeatedly implements a discharging trial from SOC 100% to 0%, an iterative learning based recursive least square (IL-RLS) algorithm is presented to accurately identify the nonlinear parameters of the regression model. the essential idea of IL-RLS algorithm is to improve the current parameter estimations by learning the estimation errors of the previous trails. Notably, the IL-RLS algorithm needs to be implemented offline for the long-time repetitive trials, which is the price worth paying to accurately identify the nonlinear parameters. After that, the parameters are identified as the functions of SOC by using the IL-RLS, which are verified by comparing with the result of the classic identification method for current pulses. Finally, by using the classic extended Kalman filter (EKF) as well as the parameters identified by the IL-RLS to estimate the SOC, three dynamic operation conditions are given to show the efficiency of the IL-RLS, where all the SOC estimation errors are less than 2%.

关键词： State of charge Batteries Estimation Voltage measurement Electronic countermeasures Current measurement Iterative methods

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Dynamic State Estimation Using Event-Trigger Master-Slave Nonlinear Filter for WAMS Applications

Dynamic State Estimation Using Event-Trigger Master-Slave No...

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data driven control and learning systems (ddcls)

作者： Qing Yuan Fengdi Zhang Hengheng Gong Luyu Li Sen Li Xiaozhong Liao Zhen Li Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing P. R. China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing P. R. China Beijing Institute of Radio Measurement Beijing P. R. China

ISBN: (纸本)9781538626191

the real-time state estimation becomes greatly important with the wide application of phasor measurement unit (PMU) in distributed generation (DG) for wide-area measurement systems (WAMS). In view of estimation, particle filter (PF) is capable of providing the best performance but at the cost of heavy computation burden. Besides, the growing grid size sustainably boosts the amount of data communication from PMU, causing the congestion. An event-trigger master-slave nonlinear filter (ET-MSNF) is proposed to guarantee the estimation accuracy and get the communication bandwidth relieved. the local slave filter at the generator node carries out the local estimation and event-trigger strategy using unscented transformation, which is identical to the center slave. the master filter at the center is designed using Monte Carlo method to improve the center's estimation accuracy by the cooperation with the center slave. Such master-slave filtering structure can fully utilize the computation capability both at the center and node. Simulation on the standard ieee 39-bus system verify the performance of ET-MSNF.

关键词： Generators Monte Carlo methods Phasor measurement units Master-slave Wide area measurements State estimation

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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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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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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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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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Big data Mining Method of thermal Power Based on Spark and Optimization Guidance

Big Data Mining Method of Thermal Power Based on Spark and O...

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data driven control and learning systems (ddcls)

作者： Mingcheng Song Li Jia Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai P.R. China Shanghai Key Laboratory of Power Station Automation Technology Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai P. R. China

ISBN: (纸本)9781538626191

With the increasing degree of information technology in the electric-power industry, the amount of big data in thermal power has increased geometrically. To address the problem of the computational bottlenecks in traditional data mining deal with big data of thermal power, big data mining of thermal power method based on Spark is presented in this paper. According to the characteristics of the actual operation of the unit, the proposed method determines the steady-state conditions of big data of thermal power and divides the working conditions based on external constraints. In addition, data mining method based on distributed computing is used to mine big data of thermal power to get the strong association rules, thus the best value of the parameters under each working condition can be got. Lastly, the historical knowledge base is established, which can guide the operation of the unit by the proposed method. this method is applied to a 300 MW unit in a power plant in Anhui Province, and mines the operation data of the unit for 10 days in a month. the results of simulation show that the proposed method can effectively mine big data of thermal power and has the advantage of computational efficiency compared with traditional data mining for big data.

关键词： Big data data mining Sparks Employee welfare Indexes Optimization Knowledge based systems

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Design of teaching system for computer network application learning

Design of teaching system for computer network application l...

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10th ieee International conference on Computer and Information Technology, CIT-2010, 7th ieee International conference on Embedded Software and systems, ICESS-2010, 10th ieee Int. Conf. Scalable Computing and Communications, ScalCom-2010

作者： Li, Liu Chen, Tianzhou Shi, Qingsong Hu, Wei Instructional Division of Computer Technology Zhejiang Sci-Tech Universcity HangZhou China College of Computer Science Zhejiang University Hangzhou Zhejiang 310027 China

ISBN: (纸本)9780769541082

driven by continuing scaling of Moore's law, computer system becomes more and more complicated. the Network application technology of the computer is a basic course in computer architecture. Computer assisted teaching system is used to make the students understand the Network application technology of the computer more easily. A multimedia teaching system is introduced in this paper. the system consists of netmeeting subsystem, streaming media subsystem, TCP/IP control subsystem, FTP subsystem, remote management subsystem, process management subsystem, and user management interface. Some further optimizations, such as load balance and data compression, are employed in the system to improve the user experience. © 2010 ieee.

关键词： Teaching

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