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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441-450 订阅

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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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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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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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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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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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Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals 7

Compact Deep Neural Networks for Computationally Efficient G...

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7th ieee International conference on Biomedical Robotics and Biomechatronics (BIOROB)

作者： Hartwell, Adam Kadirkamanathan, Visakan Anderson, Sean R. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S13JD S Yorkshire England

ISBN: (纸本)9781538681831

Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines (SVMs) use manually extracted features based on e.g. wavelets. these features tend to be fixed and non-person specific, which is a key limitation due to high person-to-person variability of myography signals. Deep neural networks, by contrast, can automatically extract person specific features - an important advantage. However, deep neural networks typically have the drawback of large numbers of parameters, requiring large training data sets and powerful hardware not suited to embedded systems. this paper solves these problems by introducing a compact deep neural network architecture that is much smaller than existing counterparts. the performance of the compact deep net is benchmarked against an SVM and compared to other contemporary architectures across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. the accuracy of the compact deep net was found to be 84.2 +/- 0.06% versus 70.5 +/- 0.07% for the SVM on the Myo, and 80.3 +/- 0.07% versus 67.8 +/- 0.09% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5,889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance.

关键词： Electrodes Neural networks Feature extraction Support vector machines Graphics processing units Convolution Embedded systems

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Context-Aware learning from Demonstration: Using Camera data to Support the Synergistic control of a Multi-Joint Prosthetic Arm 7

Context-Aware Learning from Demonstration: Using Camera Data...

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7th ieee International conference on Biomedical Robotics and Biomechatronics (BIOROB)

作者： Vasan, Gautham Pilarski, Patrick M. Univ Alberta Dept Comp Sci Edmonton AB T6G 2E1 Canada Univ Alberta Dept Med Edmonton AB T6G 2E1 Canada

ISBN: (纸本)9781538681831

Muscle synergies in humans are context-dependent-they are based on the integration of vision, sensorimotor information and proprioception. In particular, visual information plays a significant role in the execution of goal-directed grasping movements. Based on a desired motor task, a limb is directed to the correct spatial location and the posture of the hand reflects the size, shape and orientation of the grasped object. Such contextual synergies are largely absent from modern prosthetic robots. In this work, we therefore introduce a new algorithmic contribution to support the context-aware, synergistic control of multiple degrees-of-freedom of an upper-limb prosthesis. In our previous work, we showcased an actor- critic reinforcement learning method that allowed someone with an amputation to use their non-amputated arm to teach their prosthetic arm how to move through a range of coordinated motions and grasp patterns. We here extend this approach to include visual information that could potentially help achieve context-dependent movement. To study the integration of visual context into coordinated grasping, we recorded computer vision information, myoelectic signals, inertial measurements, and positional information during a subject's training a robotic arm. Our approach was evaluated via prediction learning, wherein our algorithm was tasked with accurately distinguishing between three different muscle synergies involving similar myoelectric signals based on visual context from a robot-mounted camera. these preliminary results suggest that even simple visual data can help a learning system disentangle synergies that would be indistinguishable based solely on motor and myoelectric signals recorded from the human user and their robotic arm. We therefore suggest that integrating learned, vision-contingent predictions about movement synergies into a prosthetic control system could potentially allow systems to better adapt to diverse situations of daily-life prosthesis

关键词： Prosthetics Muscles Electromyography Grasping Robots Training Wrist

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Knowledge at first glance: A model for a data visualization recommender system suited for non-expert users 7

Knowledge at first glance: A model for a data visualization ...

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7th International conference on data Science, Technology and Applications, data 2018

作者： Kubernátová, Petra Friedjungová, Magda Van Duijn, Max Leiden Institute of Advanced Computer Science Leiden University Netherlands Faculty of Information Technology Czech Technical University in Prague Czech Republic

ISBN: (纸本)9789897583186

In today's age, there are huge amounts of data being generated every second of every day. through data visualization, humans can explore, analyse and present it. Choosing a suitable visualization for data is a difficult task, especially for non-experts. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. the aim of this study is to create a model for a data visualization recommender system for non-experts that resolves these issues. Based on existing work and a survey among data scientists, requirements for a new model were identified and implemented. the result is a question-based model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristics-driven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclusively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. the presented model can be applied in the development of new data visualization software or as part of a learning tool. Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： data visualization

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Design and Implementation of a Fire Detection and control System with Enhanced Security and Safety for Automobiles Using Neuro-Fuzzy Logic 7

Design and Implementation of a Fire Detection and Control Sy...

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7th ieee International conference on Adaptive Science and Technology (ieee ICAST)

作者： Sowah, Robert Ofoli, Abdul Koumadi, Koudjo Osae, George Nortey, Gilbert Bempong, Amewugah M. Agyarkwa, Benedict Apeadu, Kwaku O. Univ Ghana Comp Engn Dept PMB 25 Legon Accra Ghana Univ Tennessee Chattanooga Elect & Comp Engn Dept Chattanooga TN USA

ISBN: (纸本)9781538642337

Automobiles provide comfort and mobility to owners. While they make life more meaningful they also pose challenges and risks in their safety and security mechanisms. Some modern automobiles are equipped with anti-theft systems and enhanced safety measures to safeguard its drivers. But at times, these mechanisms for safety and secured operation of automobiles are insufficient due to various mechanisms used by intruders and car thieves to defeat them. Drunk drivers cause accidents on our roads and thus the need to safeguard the driver when he is intoxicated and render the car to be incapable of being driven. these issues merit an integrated approach to safety and security of automobiles. In the light of these challenges, an integrated microcontroller-based hardware and software system for safety and security of automobiles to be fixed into existing vehicle architecture, was designed, developed and deployed. the system submodules are: (1) Two-step ignition for automobiles, namely: (a) biometric ignition and (b) alcohol detection with engine control, (2) Global Positioning System (GPS) based vehicle tracking and (3) Multisensor-based fire detection using neuro-fuzzy logic. All submodules of the system were implemented using one microcontroller, the Arduino Mega 2560, as the central control unit. the microcontroller was programmed using C++11. the developed system performed quite well with the tests performed on it. Given the right conditions, the alcohol detection subsystem operated with a 92% efficiency. the biometric ignition subsystem operated with about 80% efficiency. the fire detection subsystem operated with a 95% efficiency in locations registered with the neuro-fuzzy system. the vehicle tracking subsystem operated with an efficiency of 90%.

关键词： data fusion fire detection fuzzy logic control alcohol detection vehicle tracking biometric ignition neural networks

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Usage of Fully Convolutional Network with Clustering for Traffic Light Detection 7

Usage of Fully Convolutional Network with Clustering for Tra...

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7th Mediterranean conference on Embedded Computing (MECO)

作者： Yudin, Dmitry Slavioglo, Dmitry Belgorod State Technol Univ Dept Tech Cybernet Belgorod Russia

ISBN: (纸本)9781538656839

In this paper we consider a traffic light detector constructed on the basis of a fully convolutional neural network for segmenting traffic lights on image and subsequent clustering, which allows us to obtain bounding boxes for traffic lights. the proposed approach is compared with one of the most effective object detectors - Single Shot Multibox detector (SSD). We implemented algorithms for objects detection on an embedded system based on the NVidia Jetson TX2 platform. Traffic light detection recall for the proposed approach is better than SSD and higher than 0.9 on both the testing and training samples from relatively small data set (500 training images and 107 testing images). Time of traffic lights detection on one frame is about 50 ms. the results of the traffic light detection prove the possibility of applying the approach based on fully convolutional neural network with clustering for embedded autonomous vehicle control systems and driver assistance systems.

关键词： image recognition detection traffic light deep learning fully convolutional network clustering single shot multibox detector embedded system

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