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Improving Auction Mechanisms for Online Real-Time Bidding Advertising with a Two-stage Resale Model

IFAC-PapersOnLine 2017年第1期50卷 13575-13580页

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

Real Time Bidding (RTB), widely publicized as one of the most promising big-data-driven business models for online computational advertising, has the potential of effectively monetizing user clicks and impressions via two-stage auctions with resale. Demand side platforms (DSPs) play a key role as intermediators in this auction process. Typically, the first-stage auctions will be conducted separately in each DSP to determine one winning advertiser registered on it, and in the second-stage auction, each DSP submits a bid based on its winning advertiser's bid to the ad exchange platform (AdX). The highest-bid DSP wins the ad impression from AdX and resells it to its winning advertiser in pursuit of the intermediate fee. This two-stage resale auction is a critical component in maintaining the effectiveness and efficiency of RTB ecosystems. In this paper, we strive to identify and design potential improvements for this auction mechanism with the aim of enhancing the total revenue of both advertisers and DSPs. We also validated our proposed auction mechanisms using the computational experiment approach. The experimental results indicate that our proposed mechanisms can make both advertisers and DSPs better off. Our work represents the first step towards a new research area of optimal mechanism design for RTB auctions, and is expected to provide useful managerial insights in RTB market practice. © 2017

关键词： Sales Commerce Machine design Auction mechanisms Computational advertisings Computational experiment Critical component Demand side Effectiveness and efficiencies Optimal mechanism designs Real time

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Suppressing the pressure fluctuations caused by drill string vibration by SP-EID method

Suppressing the pressure fluctuations caused by drill string...

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Asian control Conference

作者： Yang Zhou Xin Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

During drilling process, the axial vibration of the drill string always causes the fluctuations of the bottom hole pressure, which may lead to serious accidents. At the same time the bottom hole pressure control system also has the phenomenon of parameter change and the feedback delay, so that the pressure is difficult to control. In view of this situation, we develop a method combining Smith Predictor with equivalent input disturbance(SP-EID) approach to achieve the disturbance suppression and delay compensation. First, considering the changes in the model parameters, we improve the existing hydraulic model to describe the dynamic characteristics of the bottom hole pressure, at same time the mathematical form of the disturbance is given. Then we use the Smith Predictor to compensate the delay of the feedback channel and apply EID method to suppress pressure disturbances. Finally the SP-EID method is used to adjust the mud flow on the ground to control the bottom hole pressure. The simulations are conducted to compare our method with SMC method, in order to show the superiority and effectiveness of our method.

关键词： Mathematical model Delays Matrix decomposition Vibrations State feedback Symmetric matrices

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A GA-SVM and D-S evidence theory based method for shunt-fault diagnosis in power systems

A GA-SVM and D-S evidence theory based method for shunt-faul...

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Asian control Conference

作者： Xiaomin Wu Dianhong Wang Weihua Cao Min Ding School of Automation China University of Geosciences Wuhan China Hubei KEY Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

In this paper, a novel genetic algorithm support vector machine (GA-SVM) and Dempster Shafer (D-S) evidence theory based diagnostic method is proposed to improve the efficiency and accuracy for power system fault diagnosis. The fault diagnosis method proposed consists of two steps, first step is the preliminary diagnosis, the second step is the fault fusion. The preliminary diagnosis with two GA-SVM classifiers decreases the information dimension and improve the efficiency. The fault fusion based on D-S evidence theory reduces the conflict of the preliminary diagnosis faults. The voltage phasor and current phasor information are utilized together in the method, the diagnosis accuracy is improved by weakening the detection uncertainties. The method proposed applied in an actual power system in hubei province in China. The practical results verified the feasibility and effectiveness of the method.

关键词： Support vector machines Genetic algorithms Fault diagnosis Substations Training Uncertainty

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Nesterov Accelerated Gradient Descent-based Convolution Neural Network with Dropout for Facial Expression Recognition

Nesterov Accelerated Gradient Descent-based Convolution Neur...

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Asian control Conference

作者： Wanjuan Su Luefeng Chen Min Wu Mengtian Zhou Zhentao Liu Weihua Cao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

Nesterov accelerated gradient descent-based convolution neural network (NAGDCNN) with dropout is proposed for facial expression recognition, which fuses the convolution neural network (CNN) with Softmax regression to construct a deep convolution neural network (DCNN) that can excavate high-level expression features and classify them. The dropout layer is added after the sub-sampling layer which can effectively reduce overfitting and the network's training time, moreover, the Nesterov accelerated gradient descent (NAGD) is used to optimize the network weights that can predictably prevent the algorithm from going too fast or too slow and enhance the response capability of the network. To verify the effectiveness of the proposal, experiments on benchmark database are conducted, and the experimental results show that the proposal outperforms the state-of-the-art methods. Futhermore, the application experiment is also curried out and the results indicate the feasibility of the proposal in practical applications.

关键词： Deep learning Facial expression recognition Nesterov accelerated gradient descent Dropout Principal component analysis

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A Model Reference Adaptive control with ADP-to-SMC Strategy for Unknown Nonlinear systems

A Model Reference Adaptive Control with ADP-to-SMC Strategy ...

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Asian control Conference

作者： Hao Fu Xin Chen Wei Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

In this paper, a novel online learning controller based on model reference adaptive control (MRAC) is presented to simplify the complexity of controlling a class of discrete nonlinear systems with a little prior knowledge, using approximate dynamic programming (ADP). Without the identifier and the assumption of a command generator model, the proposed approach employs a modified direct heuristic dynamic programming (HDP) to acquire two types of the bounded tracking errors, which are available to transform the system into a linear system with the uncertain bounded persistent disturbance. In addition, toggling between the learning and the sliding mode control (SMC) flexibly, an ADP-to-SMC strategy is developed to guarantee the better performance in MRAC. Finally, Simulation results demonstrate the effectiveness of the proposed control scheme.

关键词： Mathematical model Nonlinear systems Adaptation models Linear systems Computational modeling Linear programming Generators

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New constructing method of Lyapunov-Krasovskii functionals for stability of time-varying delay systems

New constructing method of Lyapunov-Krasovskii functionals f...

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Annual Conference of Industrial Electronics Society

作者： Gao Zhenman He Yong Wu Min School of Automation China University of Geosciences Wuhan Chian Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

This paper investigates the stability of linear systems with a time-varying delay. We propose a new approach to construct Lyapunuv-Krasovskii functional (LKF). Compared with other traditional approach, the proposed one can provide a higher time-delay upper bound and lower computation complexity. Six stability criteria by linear matrix inequalities (LMIs) are established by proposed two novel LKFs in this paper. Based on one numerical example, the advantages of the proposed approach are illustrated.

关键词： Stability criteria Symmetric matrices Linear matrix inequalities Numerical stability Delays Linear systems

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Automatic design of hybrid optimizers based on particle swarm optimization and differential evolution 5

Automatic design of hybrid optimizers based on particle swar...

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5th International Workshop on advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Wang, Yipeng Xin, Bin School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

Hybridization is termed as an efficient strategy to improve the performance of many optimizers, and the design of efficient hybrids for difficult optimization problems remains a challenging field. In this study, an automatic method is proposed to generate various hybrid optimizers based on particle swarm optimization and differential evolution for a given problem. Based on the unified framework for hybridization proposed in our recent work, the proposed method can provide plenty of hybrid optimizers for a specific problem with given parent optimizers. Besides, an approach which can provide the unified representation of different kinds of hybridization levels is developed. Moreover, all the generated hybrids are evaluated on the CEC2017 benchmark problems, and the results of the statistic analysis and the Wilcoxon's rank-sum tests demonstrate that the hybrid optimizer utilizing the hybridization strategy performs obviously better than the others on the test functions.

关键词： Particle swarm optimization (PSO)

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Distributed Full Order Sliding Mode control for Finite-Time Attitude Synchronization and Tracking of Spacecraft

Distributed Full Order Sliding Mode Control for Finite-Time ...

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Chinese control Conference (CCC)

作者： Nsubuga Latifu Zhuoyue Song Chao Duan Zhen Li Housheng Su Qinghe Wu Xiangdong Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China School of Automation Huazhong University of Science and Technology Wuhan People's Republic of China

This paper investigates a distributed finite-time attitude synchronization and tracking problem of multiple spacecraft when a time varying reference signal is available to only a subset of group members. Modified Rodrigues parameters are used for attitude representation. A two layer sliding mode surface with fast non-singular terminal sliding mode surface (FNTSMS) as the inner layer and terminal sliding mode surface (TSMS) as the outer layer is used to create a full order sliding mode surface. The proposed distributed attitude coordination control law guarantees attitude synchronization and tracking as long as their exists atleast one spacecraft that has a direct access to the virtual leader's time varying attitude. Finite-time stability of the closed loop system is guaranteed by Lyapunov-based stability method. Simulation results are presented to validate the performance of the proposed control strategy.

关键词： Space vehicles Attitude control Synchronization Sliding mode control Decentralized control Symmetric matrices Stability analysis

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Crowdsensing game with demand uncertainties: A deep reinforcement learning approach

arXiv

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arXiv 2018年

作者： Zhan, Yufeng Xia, Yuanqing Zhang, Jiang Li, Ting Wang, Yu School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Lab Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States

Currently, explosive increase of smartphones with powerful built-in sensors such as GPS, accelerometers, gyroscopes and cameras has made the design of crowdsensing applications possible, which create a new interface between human beings and life environment. Until now, various mobile crowdsensing applications have been designed, where the crowdsourcers can employ mobile users (MUs) to complete the required sensing tasks. In this paper, emerging learning-based techniques are leveraged to address crowdsensing game with demand uncertainties and private information protection of MUs. Firstly, a novel economic model for mobile crowdsensing is designed, which takes MUs' resources constraints and demand uncertainties into consideration. Secondly, an incentive mechanism based on Stackelberg game is provided, where the sensing-platform (SP) is the leader and the MUs are the followers. Then, the existence and uniqueness of the Stackelberg Equilibrium (SE) is proven and the procedure for computing the SE is given. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience without any prior knowledge about MUs' information. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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IEEE Transactions on Evolutionary Computation Special Issue on Parallel Evolution for Large Scale Optimization

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IEEE Transactions on Evolutionary Computation 2018年第4期22卷 633-633页

作者： Wang, Fei-Yue Wei, Qinglai Tang, Ke Coello Coello, Carlos A. State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Qingdao Academy of Intelligent Industries China Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science CINVESTAV-IPN Mexico

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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