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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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Optimal Self-Learning Cooperative control for Continuous-Time Heterogeneous Multi-Agent systems 34

Optimal Self-Learning Cooperative Control for Continuous-Tim...

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第三十四届中国控制会议

作者： WEI Qinglai LIU Derong SONG Ruizhuo The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781467374439

In this paper, an optimal self-learning cooperative control for heterogeneous multi-agent systems by iterative adaptive dynamic programming(ADP) is developed. The main idea is to design an optimal control law by policy iteration based ADP technique which makes all the agents track a given dynamics and simultaneously makes the iterative performance index function reach the Nash equilibrium. The cooperative policy iteration algorithm for graphical differential games is developed to achieve the optimal control law for the agent of each node. Convergence properties are analyzed which make the performance index functions of heterogeneous multi-agent differential graphical games converge to the Nash equilibrium. Simulation example is given to show the effectiveness of the developed optimal self-learning control scheme.

关键词： Adaptive Critic Designs,Adaptive Dynamic Programming,Approximate Dynamic Programming,Heterogeneous Multi-Agents,Graphical Games,Policy Iteration,Synchronization

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Cloud Computing Platform and Big Data Service for Incubator Cluster

Cloud Computing Platform and Big Data Service for Incubator ...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Gang Xiong Chi Zhang Tongkai Ji Banji Guan Aobing Sun Yuexiang Hu Timo R.Nyberg Dongguan Research Institute of CASIA Cloud Computing CenterChinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Economic and Management Beijing University of Chemical Technology Department of Industrial Engineering and Management School of Sciences Aalto University

Hundreds of incubators get birthed worldwide every day, and many new ICT technologies developed continuously, like cloud computing and big data. Therefore, it's possible and necessary to develop a cloud computing platform that gathering a variety of data and resources information needed by thousands of incubators and millions of incubated enterprises. In this paper, after comparing three different countries incubator's management procedure, we mainly talk about the way to design a cloud-based incubator platform including its architecture, main functions, and the interface. We assume that the platform can gather and share a variety of information and resource, like money, human resource, equipment, office place and so on. So the platform is beneficial for the incubated enterprises, incubators, and the government. For example, the platform can provide individual services through big data technology. The platform can analysis the situation of incubators and warn the companies in dangerous condition with the history big data. Finally, this paper describes the use case of cloud-based incubator platform for Guangdong Province.

关键词： business incubator cluster open innovation cloud computing platform crowdsourcing big data service

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ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

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CAAI Transactions on Intelligence Technology 2016年第4期1卷 377-393页

作者： XiaoWang Lingxi Li Yong Yuan Peijun Ye Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Qingduo Academy of Intelligent Industries Qingduo 266100 China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN 46202 USA

Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.

关键词： Social computing Societies 5.0 Parallel intelligence Knowledge automation Cyber-physical-social system Artificial societies Computational ex-periments Parallel execution

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Transfer Classification for Distinct Manifestations with Shared Information

Transfer Classification for Distinct Manifestations with Sha...

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World Congress on Intelligent control and Automation

作者： Lu Qi Peijie Yin Xiayuan Huang Ken Chen Hong Qiao State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Applied Mathematics Academy of Science Beijing 100190 China State Key Laboratory of Tribology Department of Mechanical Engineering Tsinghua University Beijing 100084 China

ISBN: (纸本)9781467384155

An object often has many distinct manifestations in computer vision, which brings a great challenge to utilizing more comprehensive information. Inspired by some biological researches about edge sensitivity and global structure priority, our key insight is to establish unified transfer classification network with shared contour information. Combining two convolutional networks with three cascaded filters, we build a unified kernel SVM classifier based on shared contour features. Two convolutional networks are used for acquiring the contour information of objects exactly. Obtained by three cascaded filters, shared edge features are used by a unified kernels SVM classifier. Our transfer classification network (TCN) is trained and tested with distinct manifestations including real photos (imagenet dataset or cifar-10 dataset) and cartoon abstracts. The model is able to extract robust contour features and achieve considerable transfer recognition accuracy (40% relative improvement to some popular convolutional models).

关键词： Image edge detection Support vector machines Training Feature extraction Kernel Robustness Visualization Edge detection Support Vector Network Feature extraction Visualization Kernel Functional training Seeds contour Robustness Information Sharing Training

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GA-simplex algorithm and its application: A case study of gas emission estimation

GA-simplex algorithm and its application: A case study of ga...

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

作者： Hui Li Hongqiang Lv Qingqi Lin Jianwen Zhang Information Science and Technology College Beijing University of Chemical Technology Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Fluid Mechanics and Heat Transfer Laboratory Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781509029280

Among the mathematical optimization algorithms, simplex algorithm is a popular and practical algorithm which was listed as one of the top 10 algorithms of the twentieth century by the journal Computing in Science and Engineering. Although simplex algorithm is efficient in the linear programming, the quality of convergence is unacceptable in practice as a numerical breakdown of the algorithm, even for smooth and well-behaved functions. On the other hand, full convergence might be seen in genetic algorithms (GA) using only crossover. So in this paper we combine the GA and simplex algorithm by initializing simplex from the final individual in GA and getting the converged result through simplex algorithm thereafter. A case study in estimating gas emission shows noteworthy improvement of efficiency and stability, compared with GA or simplex algorithm.

关键词： Decision support systems Genetic algorithms Estimation Optimization Linear programming

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Cyber-physical-social System in Intelligent Transportation

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IEEE/CAA Journal of Automatica Sinica 2015年第3期2卷 320-333页

作者： Gang Xiong Fenghua Zhu Xiwei Liu Xisong Dong Wuling Huang Songhang Chen Kai Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Science the Cloud Computing Center Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Beijing Engineering Research Center for Intelligent Systems and Technology Institute of AutomationChinese Academy of Science

A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it's a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail. © 2014 Chinese Association of Automation.

关键词： Amphibious vehicles complex networks Embedded systems Intelligent systems Transportation

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A novel policy iteration based deterministic Q-learning for discrete-time nonlinear systems

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Science China Chemistry 2015年第12期58卷 143-157页

作者： WEI QingLai LIU DeRong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming Q-learning policy iteration neural networks nonlinear systems optimal control

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Parameter identification of plant growth models with stochastic development

Parameter identification of plant growth models with stochas...

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International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA)

作者： Mengzhen Kang Jing Hua Philippe de Reffye Marc Jaeger Qingdao Academy of Intelligent Sciences Qingdao China State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China AMAP Unit CIRAD Montpellier Languedoc-Roussillon FR AMAP Unit CIRAD Montpellier Languedoc-Roussillon France LIRMM ICAR University of Montpellier France

ISBN: (纸本)9781509016600

Plant architectures generally display structural variations among individuals. Stochastic FSPMs have been developed to capture such feature, but calibrating such models is a challenging issue. For GreenLab model, parameter identification has been achieved on several crops and trees, but the estimation of functional parameters is mostly limited to plants with deterministic development. In this work, we propose a methodological framework allowing the efficient FSPM parameter estimation for stochastic ramified plants. We focus on the randomness in three kinds of meristem activities in plant development: growth, death and branching. Concepts of organic series and potential structure are introduced to build the fitting target as well as corresponding model output. We show that, with a limited set of sampled plants (here from simulation), using a few organic series, the inverse method retrieves well the parameter values (the original parameter set being known here). Requiring the concept of physiological age and the assumption of common biomass pool, the proposed approach provides a solution of solving source-sink functions of complex plant architectures, with a novel simplified way of plant sampling. The proposed parameter estimation frame is promising, since this in silico process mimics the procedure of calibrating model for real plants in a stand. Estimating parameters on stochastic plant architectures opens a new range of coming applications.

关键词： Stochastic processes Biological systems Physiology Biological system modeling Computational modeling Vegetation Biomass

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