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IEEE International Conference on systems, Man, and Cybernetics (SMC)

作者： Li, Juanjuan Ni, Xiaochun Yuan, Yong Qin, Rui Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha Hunan Peoples R China

ISBN: (纸本)9781509018970

With the rapid development of big data analytics in online marketing, real-time bidding (RTB) has emerged as a promising business model in recent years and now becomes one of the major online advertising channels. Based on analysis of Web Cookies, RTB platforms are able to precisely identify the features and preferences of target audiences visiting publishers' websites, and forward the information to competing advertisers submitting bids for their best-matched audience in real-time ad auctions. As the supplier of ad impressions, publishers typically have multiple channels to sell their ad impressions (i.e., ad inventory), making their strategies for allocating ad inventory one of the most critical research problems. In this paper, we strive to study publishers' optimal strategy of allocating ad inventory across online channel of RTB-based auctions and offline channel prevailingly realized in the form of guaranteed contracts. Considering the ad reserve price as the control variable, we establish the optimization model. We also explicitly take the default penalty in offline channels into consideration, so as to balance the short-term online revenue and long-term offline revenue. In our work, we analyze altogether three kinds of strategies for publishers to allocate their ad inventory in pursuit of the optimal strategy, and validate our model and analysis via computational experiments. We find that there is no dominant strategy that can outperform others in all cases, and interestingly, publishers using the hybrid-channel strategy do not always gain more revenues than those using the single-channel strategy.

关键词： real-time bidding guaranteed contract publisher ad inventory allocation ad impression

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Exploring Optimal Frequency Caps in Real Time Bidding Advertising 6

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

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6th IEEE Int Conf on Big Data and Cloud Computing (BDCloud)/9th IEEE Int Conf on Soc Comp and Networking (SocialCom)/IEEE Int Conf on Sustainable Comp and Commun (SustainCom) (BDCloud-SocialCom-SustainCom)

作者： Qin, Rui Yuan, Yong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha Hunan Peoples R China

ISBN: (纸本)9781509039364

Benefited front 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 1-4 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： real time bidding frequency capping computational experiment approach demand side platforms computational advertising

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Crowdsourcing in ITS: The state of the Work and the Networking

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2016年第6期17卷 1596-1605页

作者： Wang, Xiao Zheng, Xinhu Zhang, Qingpeng Wang, Tao Shen, Dayong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Hong Kong Peoples R China Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China Chinese Acad Sci Beijing 100190 Peoples R China

In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems.

关键词： Crowdsourcing location-based services network analysis urban traffic management

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Learning Time-optimal Anti-swing Trajectories for Overhead Crane systems 13th

Learning Time-optimal Anti-swing Trajectories for Overhead C...

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13th International Symposium on Neural Networks (ISNN)

作者： Zhang, Xuebo Xue, Ruijie Yang, Yimin Cheng, Long Fang, Yongchun Nankai Univ IRAIS Tianjin 300071 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300071 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9783319406633;9783319406626

Considering both state and control constraints, minimum-time trajectory planning (MTTP) can be implemented in an 'offline' way for overhead crane systems [1]. In this paper, we aim to establish a real-time trajectory planning model by using machine learning approaches to approximate those results obtained by MTTP. The fusion of machine learning regression approaches into the trajectory planning module is new and the application is promising for intelligent mechatronic systems. In particular, we first reformulate the considered trajectory planning problem in a three-segment form, where the acceleration and deceleration segments are symmetric. Then, the offline MTTP is applied to generate a database of minimum-time trajectories for the acceleration stage, based on which several regression approaches including Extreme Learning Machine (ELM) and Backpropagation Neural Network (BP) are adopt to approximate MTTP results with high accuracy. More important, the resulting model only contains a set of parameters, rather than a large volume of offline data, and thus machine learning based approaches could be implemented in low-cost digital signal processing chips required by industrial applications. Comparative evaluation results are provided to show the superior performance of the selected regression approach.

关键词： Overhead crane systems Minimum-time trajectory planning Machine learning Regression techniques

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An erlang-based simulation approach of Artificial Transportation systems

An erlang-based simulation approach of Artificial Transporta...

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International Conference on intelligent Transportation

作者： Songhang Chen Fenghua Zhu Fei-Yue Wang 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 Shandong China

ISBN: (纸本)9781509018901

Artificial Transportation systems (ATS) can support a variety of computational experiments for different purposes, and have become important tools for transportation research. However, when the road network modeled in ATS is large or there are a lot of vehicles, ATS will run very slow or even cannot be started due to the memory limit of a standalone computer. With the acceleration of urbanization process and rapid increase of car ownership, it is necessary to find a high-performance computing method for running large-scale ATS. Therefore, the paper presents an Erlang-based approach to realize concurrent and distributed computing of ATS. To verify the feasibility, a prototype is built, and a performance test is conducted. The results show the method can achieve the efficiency utilization of computing resources.

关键词： Roads Computational modeling Object oriented modeling Computers Sociology Statistics

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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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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

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

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 *** 250 experimental tasks training a SAE model and evaluating its performance at daytime and nighttime with 3different 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 *** 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： SAE MAE Performance Evaluation of the Deep Learning Approach for Traffic Flow Prediction at Different Times

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Scene-specific pedestrian detection based on parallel vision

arXiv

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arXiv 2017年

作者： Zhang, Wenwen Wang, Kunfeng Qu, Hua Zhao, Jihong Wang, Fei-Yue School of Software Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Communication and Information Engineering Xi’an Jiaotong University Xi’an710049 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of lab.led training data manually. While the models trained with generic dataset work bad when they are directly used in specific scenes. With special viewpoints, flow light and backgrounds, datasets from specific scenes are much different from the datasets from generic scenes. In order to make the generic scene pedestrian detectors work well in specific scenes, the lab.led data from specific scenes are needed to adapt the models to the specific scenes. While lab.ling the data manually spends much time and money, especially for specific scenes, each time with a new specific scene, large amounts of images must be lab.led. What’s more, the lab.ling information is not so accurate in the pixels manually and different people make different lab.ling information. In this paper, we propose an ACP-based method, with augmented reality’s help, we build the virtual world of specific scenes, and make people walking in the virtual scenes where it is possible for them to appear to solve this problem of lacking lab.led data and the results show that data from virtual world is helpful to adapt generic pedestrian detectors to specific scenes. Copyright © 2017, The Authors. All rights reserved.

关键词： Virtual reality

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A Probabilistic Price Mechanism Design for Online Auctions

A Probabilistic Price Mechanism Design for Online Auctions

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IEEE International Conference on systems, Man, and Cybernetics (SMC)

作者： Zhang, Jie Li, Linjing Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Shandong Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781509018970

Recently, there is a rapid growth of the online auctions in e-commerce platforms, in which small and medium-sized enterprises (SMEs) heavily depend on the advertising systems. We need to design flexible price mechanisms to reduce the competition of SMEs without affecting competitive large companies. In this paper, a probabilistic price mechanism design approach is investigated for online auctions. Utilizing this approach, we first introduce simple mechanisms as a tool for designing new mechanisms. Based on a simple and a classical mechanism probabilistic price mechanisms are designed for online auctions and their properties are analysed. Furthermore, two mechanism design algorithms are suggested for different online auction scenarios. Experiments are presented to demonstrate the flexibility and the effictiveness of the proposed probabilistic mechanism design approach.

关键词： mechanism design online auctions probabilistic mechanism computational experiments e-commerce

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Performance Evaluation for Multi-antenna Vehicular Communication Based on IEEE 802.11p Standard

Performance Evaluation for Multi-antenna Vehicular Communica...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Han, Shuangshuang Zhao, Kai Yang, Liu-Qing Cheng, Xiang Chinese Acad Sci Inst Automat Chinese State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Shandong Peoples R China Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China

ISBN: (纸本)9781467385794

Multi-input multi-output (MIMO) has been a promising technique for wireless communication areas, such as intelligent transportation systems. In this paper, we present performance evaluation of a multi-antenna transceiver for Wireless Access in Vehicular Environments (WAVE) according to IEEE 802.11p standard. Herein, MIMO WAVE is performed by employing three MIMO schemes, namely, Alamouti, vertical BLAST (V-BLAST) and space shift keying (SSK) scheme. The above three MIMO schemes are discussed and compared, considering the influence of different driving velocity and with/without line of sight (LOS) on the system performance. Based on the analysis and simulation results, it can be found that compared with single antenna systems, V-BLAST scheme would significantly increase the transmission data rate, while Alamouti and SSK scheme could improve the reliability and robustness even under poor receive conditions.

关键词： MIMO 802.11p Alamouti V-BLAST SSK

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NFLB Dropout:Improve Generalization Ability by Dropping out the Best-A Biologically Inspired Adaptive Dropout Method For Unsupervised Learning

NFLB Dropout:Improve Generalization Ability by Dropping out ...

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International Joint Conference on Neural Networks

作者： Peijie Yin Lu Qi Xuanyang Xi Bo Zhang Hong Qiao Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Science State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781509006212

Generalization ability is widely acknowledged as one of the most important criteria to evaluate the quality of unsupervised models. The objective of our research is to find a better dropout method to improve the generalization ability of convolutional deep belief network (CDBN), an unsupervised learning model for vision tasks. In this paper, the phenomenon of low feature diversity during the training process is investigated. The attention mechanism of human visual system is more focused on rare events and depresses well-known facts. Inspired by this mechanism, No Feature Left Behind Dropout (NFLB Dropout), an adaptive dropout method is firstly proposed to automatically adjust the dropout rate feature-wisely. In the proposed method, the algorithm drops well-trained features and keeps poorly-trained ones with a high probability during training iterations. In addition, we apply two approximations of the quality of features, which are inspired by theory of saliency and optimization. Compared with the model trained by standard dropout, experiment results show that our NFLB Dropout method improves not only the accuracy but the convergence speed as well.

关键词： Training Feature extraction Brain models Adaptation models Neural networks Unsupervised learning

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