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第28届中国控制与决策会议

作者： Xuejin Gao Rong Ma College of Electronic Information and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

When we use the traditional multi-scale independent component analysis method to extract independent component ICA on each scale,and then,using the ICA decomposition on the reconstructed data to construct monitoring statistics,however,data of the reconstruction on the nature was already independent component,it is meaningless to extract them by *** on the shortcoming,this paper proposes a MSICA-OCSVM method that was combined with Multi-scale Independent Component Analysis(MSICA) and One-class Support Vector Machine(OCSVM) to monitor the ***,we can use the wavelet transform decomposition to monitor data at different *** then,the data was processing by threshold denoising,and was monitored on each scale extraction by using ICA independent principal ***,we can use the wavelet transform coefficients for each scale would scale back on the reconstruction of the new signal matrix(?) Finally,new OCSVM model was constructed by the reconstructed matrix *** can make the use of determined hyper-plane to construct a nonlinear statistic,and the appropriate control limits was determined by using kernel density *** is more,this method is applied to penicillin fermentation process simulation platform,the experimental results show that this method can effectively utilize the structure information data compared to traditional MSICA fault monitoring method,the failure rate of false positives,false negative rate was significantly reduced.

关键词： batch process one-class support vector machines(OCSVM) fault detection Multi-scale independent component analysis(MSICA)

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Optimizing Market Segmentation Granularity in RTB Advertising: A Computational Experimental Study

Optimizing Market Segmentation Granularity in RTB Advertisin...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Rui Qin Yong Yuan Fei-Yue Wang Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China

ISBN: (纸本)9781509039371

Real Time Bidding (RTB) is a novel business model of online computational advertising with the integration of Internet economy and big data analysis. It can help advertisers achieve the precision marketing through the market segmentation strategies of Demand Side Platforms (DSPs). Based on a mathematical programming approach, this paper studied DSPs' strategies for market segmentation, and established a selection model of the granularity for segmenting RTB advertising markets. We proposed to validate our model using the computational experiment approach, and the experimental results show that: 1) with the increasing refinement of the market segmentation granularity, the total revenue has a tendency of a rise first followed by a decline, 2) the optimal granularity of market segmentation will be significantly influenced by the number of advertisers on the DSP, but less influenced by the number of ad requests. Our findings show the crucial role of market segmentation on the RTB advertising effect, and indicate that the DSPs should adjust their market segmentation strategies according to their total number of advertisers. Our findings also highlight the importance of advertisers as well as the characteristics of the target audiences in DSPs' market segmentation decisions.

关键词： Digital signal processing Computational modeling Advertising Numerical models Big data Complex systems Automation

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Exploring Optimal Frequency Caps in Real Time Bidding Advertising

Exploring Optimal Frequency Caps in Real Time Bidding Advert...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

ISBN: (纸本)9781509039371

Benefited from the high-speed development of Internet and the popularization of big data technology, Real Time Bidding (RTB) emerged and developed quickly, and has become one of the most important and popular model for online computational advertising. In the pay-per-impression based RTB advertising, frequency capping is undoubtedly one of the most crucial issues faced by advertisers and Demand Side Platforms (DSPs), since there are generally vast amount of ad requests in RTB markets, among which many may be triggered by the same target audience. As such, choosing the optimal frequency cap considering all the advertisers becomes a critical issue faced by DSPs. Considering that displaying one advertisement multiple number of times to the same user will diminish the advertising effect, we introduce the concept of discount factor and establish an optimization model of frequency capping to seek for the optimal frequency cap for each advertiser. We also design some experiments to validate our proposed model by utilizing the computational experiment approach, and the experimental results show that the optimal frequency cap can be influenced by the values of the discount factors, and higher value of the discount factor can deduce the advertising effect of larger frequency cap. Moreover, when the discount factors of the advertisers take different values, there also exists an optimal frequency cap, at which the advertisers can get the expected maximum revenue in the long run.

关键词： Advertising Digital signal processing Computational modeling Big data Time-frequency analysis Radio spectrum management Real-time systems

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Performance evaluation of the deep learning approach for traffic flow prediction at different times

Performance evaluation of the deep learning approach for tra...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yanjie Duan Yisheng Lv Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao Shandong China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509029280

Traffic flow prediction is very important in the deployment of intelligent transportation system. Based on our previous research on deep learning approach for traffic data prediction, we further evaluates the performance of the SAE model for traffic flow prediction at daytime and nighttime. Through 250 experimental tasks training a SAE model and evaluating its performance at daytime and nighttime with 3 different criteria, we obtain the best combination of hyper parameters for each criterion at different times on weekday and non-weekday, respectively. Experimental results show that the MAE and RMSE at daytime are larger than that at nighttime, while the MRE at daytime are smaller than that at nighttime. For different criteria, the hyper parameters of the SAE model should vary accordingly. The results in this paper indicate that in real applications, traffic flow prediction using the deep learning approach can be a combination of multiple SAE models with different parameters suitable for different periods, which is of significance in future research.

关键词： Predictive models Machine learning Training Data models Roads Mathematical model Prediction algorithms

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Stackelberg Game Model of Wind Farm and Electric Vehicle Battery Switch Station

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IOP Conference Series: Materials Science and engineering 2017年第1期199卷

作者： Zhe Jiang Zhimin Li Wenbo Li Mingqiang Wang Mengxia Wang Department of Electrical Engineering Harbin Institute of Technology Harbin 150001 China. Electric Power Research Institute State Grid Shandong Electric Power Company Jinan 250003 China. Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan 250061 China. Collaborative Innovation Center for Global Energy Interconnection (Shandong) Jinan 250061 China.

In this paper, a cooperation method between wind farm and Electric vehicle battery switch station (EVBSS) was proposed. In the pursuit of maximizing their own benefits, the cooperation between wind farm and EVBSS was formulated as a Stackelberg game model by treating them as decision makers in different status. As the leader, wind farm will determine the charging/discharging price to induce the charging and discharging behavior of EVBSS reasonably. Through peak load shifting, wind farm could increase its profits by selling more wind power to the power grid during time interval with a higher purchase price. As the follower, EVBSS will charge or discharge according to the price determined by wind farm. Through optimizing the charging /discharging strategy, EVBSS will try to charge with a lower price and discharge with a higher price in order to increase its profits. Since the possible charging /discharging strategy of EVBSS is known, the wind farm will take the strategy into consideration while deciding the charging /discharging price, and will adjust the price accordingly to increase its profits. The case study proved that the proposed cooperation method and model were feasible and effective.

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research on Event-B based formal modeling and verification of automatic production line

Research on Event-B based formal modeling and verification o...

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第28届中国控制与决策会议

作者： Kaiming-FU Bin Fang Yafen-LI Huijie-LI College of Electronic and Control Engineering Beijing University of Technology The Ministry of Education P.R.C.Engineering Research Center of Digital Community Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Before the automatic production line put into practical use in industrial domain,it must go through a large number of long-term rigorous test to detect errors in the design *** in the actual test process,the same mistake leads to different results due to the different test methods and there are some special conditions resulting from the limitation of field test environment what can't be used to be *** formal method,by using the method of discrete mathematics to practical system mathematical modeling and validation,can replace the methods what can not been tested under the conditions of system *** formal method is also suitable for developing large reactive and distributed *** this paper,the specific formal method which is called Event-B improves the high security and reliability of *** the related tools such as Rodin are used for modeling,refinement and verification in the PLC(Programmable Logic controller) automatic production *** result in this paper shows that our approach contributes to reducing system details during the early development stage and leads to simpler proofs and more automated *** it provides a new method for reference for higher requirements in the reliability in engineering projects,so as to ensure the correctness of the designed software.

关键词： automatic production line PLC model verify Event-B Formal method

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Utilizing Fullerenols as Surfactant for Carbon Nanotubes Dispersions Preparation

Journal of Materials

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Journal of Materials 2017年第1期2017卷

作者： Jianlong Ji Chang Qiao Yali Liu Wendong Zhang Shengbo Sang Xing Yang Aoqun Jian Qianqian Duan Qiang Zhang Ying Liu Micro Nano System Research Center Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education College of Information Engineering Taiyuan University of Technology Taiyuan 030024 *** Advanced Coal Mine Machinery and Equipment Collaborative Innovation Center of Shanxi Province College of Information Engineering Taiyuan University of Technology Taiyuan 030024 *** MEMS Laboratory Department of Precision Instruments and Mechanology Tsinghua University Beijing 100084 *** College of Materials Science and Engineering Taiyuan University of Technology Taiyuan 030024 ***

Dispersions of individual carbon nanotubes (CNTs) are crucial for nanodevices and polymer/CNTs nanocomposites. In this paper, stable and homogenous dispersions of individual multiwalled carbon nanotubes (MWCNTs) have been synthesized. The factors influencing the dispersibility mechanism, including the surfactant concentration and the pH value, have been investigated. SEM images display the impurities sticking on MWCNTs which have been removed. The oxygen-containing groups on the surface of MWCNTs sample have been detected through FT-IR and Raman spectra. All experimental results illustrate that using fullerenols as surfactant can greatly improve the dispersibility of MWCNTs. Moreover, the prepared dispersions exhibit good stability that the sediment percentage of fullerenols-MWCNTs is only 5.2% after 5 days.

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A Sophisticated RMPC Design for LPV systems based on the mixed multi-step feedback control 34

A Sophisticated RMPC Design for LPV Systems based on the mix...

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第三十四届中国控制会议

作者： ZHENG Pengyuan LI Dewei XI Yugeng LI Xiaoli College of Automation Engineering Shanghai University of Electric PowerShanghai Engineering Research Center of Intelligent Management and Control for Power Process Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing Ministry of Education School of Information Science and Technology Donghua University

ISBN: (纸本)9781467374439

This paper studies a sophisticated robust model predictive control(RMPC) design for linear parameter varying(LPV)systems with the varying parameter unmeasurable. Based on a mixed multi-step feedback control, a sophisticated control design which utilizes both multi-step control sets and parameter-dependent feedback control is developed to introduce more freedom and thus reduces the conservativeness. The proposed method achieves good control performance with guaranteed robust and stability properties, which is verified by simulation examples.

关键词： Linear parameter varying systems,Robust model predictive control,Parameter-dependent feedback control,Mixed multi-step feedback control,Linear matrix inequalities

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Cover Feature: Ultrasensitive, Sustainable, and Selective Electrochemical Hydrazine Detection by ALD-Developed Two-Dimensional WO3(ChemElectroChem 2/2018)

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ChemElectroChem 2017年第2期5卷

作者： Zihan Wei Zhenyin Hai Mohammad Karbalaei Akbari Prof. Jie Hu Lachlan Hyde Prof. Stephen Depuydt Prof. Francis Verpoort Prof. Serge Zhuiykov Ghent University Global Campus Department of Applied Analytical & Physical Chemistry 119 Songdomunhwa-ro Yeonsu-gu Incheon South Korea Micro and Nano System Research Center Key Lab of Advanced Transducers and Intelligent Control System (Ministry of Education) & College of Information Engineering Taiyuan University of Technology Taiyuan 030024 Shanxi PR China Factory of the Future Swinburne University of Technology Hawthorn Australia National Research Tomsk Polytechnic University Lenin Avenue 30 634050 Tomsk Russian Federation Laboratory of Organometallics Catalysis and Ordered Materials State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Chemical and Material Engineering Wuhan University of Technology Wuhan P.R. China

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