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Research on Information Structure of Programmatic Advertising Markets

IFAC-PapersOnLine 2017年第1期50卷 13587-13592页

作者： Li, Juanjuan Yuan, Yong Zhao, Xueliang Wang, Fei-Yue The State Key Laboratory of Management 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 Beijing Qingdao China Research Center of Military Computational Experiments and Parallel Systems National University of Defense Technology Changsha China

With the abundance of online big data and computing resources, as well as the rapid develop-ment and diversification of business models, programmatic advertising (PA) has emerged and become the mainstream and one of the most promising advertising channels in recent years. As an effective way to precision marketing, PA relies heavily on the information elicited from analyzing Web users and pages, which can help advertisers precisely identify their best-matched audiences and evaluate the ad impressions in a real-time fashion In PA markets, publishers serve as the supplier of ad impressions, and have control on whether, what and how to reveal such key information to advertisers. These decisions play a central role in the information structure of data-driven PA markets, and attract intensive research interests. In this paper, we strive to investigate publishers’ rational preference over the symmetric and asymmetric information structures, with the aim of maximizing their revenues. Our model views publishers’ revenue as a function of the information structure among advertisers, and we hereby proved its convexity under an incentive compatible mechanism. This conclusion indicates that publishers prefer an asymmetric information structure rather than a symmetric one. Our research findings can help improve publishers’ revenue, and also can explain the underlying rationale for the hybrid PA markets mixed by public real-time bidding and private marketplaces. © 2017

关键词： Commerce Big data Marketing Advertising markets Asymmetric information Computing resource Incentive compatible mechanisms Information structures Precision marketings private marketplace Real time biddings

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Approximate Dynamic Programming for Event-Driven H8 Constrained control

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第9期53卷 5922-5932页

作者： Yang, Xiong Xu, Mengmeng Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Technol Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

We study the dynamic event-driven Hop constrained control problem through approximate dynamic programming (ADP). Differing from the existing literature considering systems with either symmetric constraints or asymmetric constraints, we consider the two different constraints simultaneously. Initially, by constructing a generalized nonquadratic value function, we trans -form the H-8 constrained control problem into an unconstrained two-player zero-sum game. Then, we present an event-driven Hamilton-Jacobi-Isaacs equation (ED-HJIE) corresponding to the zero-sum game for lowering down the computational load. To solve the ED-HJIE, we propose a dynamic triggering mech-anism together with a sole critic neural network (CNN) being built under the ADP framework. The CNN's weights are tuned via the gradient descent approach. After that, we prove uniform ultimate boundedness of the closed-loop system and the CNN's weight estimation error via Lyapunov's method. Finally, we sepa-rately use an F16 aircraft plant and an inverted pendulum system to validate the present theoretical claims.

关键词： Approximate dynamic programming (ADP) event-driven control H(8)control input constraint optimal control

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A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics 33

A Gait Events Detection Algorithm Based on the Invariant Cha...

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第33届中国控制与决策会议

作者： Ningcun Xu Xiwei Peng Liang Peng Zeng-Guang Hou Institute of Intelligent Sensing & Motion Control School of AutomationBeijing Institute of Technology The State Key Laboratory for Management and Control of Complex Systems(SKL-MCCS) Institute of AutomationChinese academy of Science

ISBN: (纸本)9781665431293

In order to make up for the shortcomings of a single inertial sensor,which is easily disturbed and unable to directly describe the periodic characteristics of gait,a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint *** healthy volunteers conducted a walking experiment over the ground,who were equipped with motion capture device and insole with *** hip angle derived from motion capture device were applied to detect gait events,heel stride(HS) and toe off(TO).And the gait events detected by ground reactor force(GRF) were taken as the reference *** mean absolute difference of HS is 28±42 ms,and the mean absolute difference of TO is 27 ± 43 ms. And the confidence levels of the two gait events are 97.5% and 99.2%,*** results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.

关键词： gait event inertial sensor hip joint kinematics statistical analysis

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Adaptive Dynamic Programming for Robust Event-Driven Tracking control of Nonlinear systems With Asymmetric Input Constraints

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第11期54卷 6333-6344页

作者： Yang, Xiong Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Techno Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article considers the robust dynamic event-driven tracking control problem of nonlinear systems having mismatched disturbances and asymmetric input constraints. Initially, to tackle the asymmetric constraints, a novel nonquadratic value function is constructed for the original system. This makes the asymmetrically constrained tracking control problem transformed into an unconstrained optimal regulation problem. Then, a dynamic event-driven mechanism is proposed. Meanwhile, the event-driven Hamilton-Jacobi-Bellman equation (ED-HJBE) is developed for the optimal regulation problem in order to acquire the optimal control with distinctly decreased computational burden. To solve the ED-HJBE, a single critic neural network (CNN) is designed in the adaptive dynamic programming framework. Meanwhile, the gradient descent method is employed to update the CNN's weights. After that, both the weight estimation error and the tracking error are proved to be uniformly ultimately bounded via Lyapunov's direct method. Finally, simulations of the spring-mass-damper system and the pendulum plant are separately utilized to validate the established theoretical claims.

关键词： Nonlinear systems control systems Optimal control Regulation Dynamic programming Perturbation methods Adaptive systems Adaptive dynamic programming (ADP) asymmetric constraint event-driven mechanism (EDM) neural network (NN) reinforcement learning (RL)

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OS-DS tracker: Orientation-variant Siamese 3D tracking with Detection based Sampling

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PATTERN RECOGNITION LETTERS 2023年第1期174卷 57-63页

作者： Zhang, Qiuyu Wang, Lipeng Li, Wanyi Meng, Hao Zhang, Wen Zhang, Zhi Wang, Peng Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

LIDAR point clouds are less affected by the weather and possess more depth of field than 2D images. However, sparsity and disorder of point clouds bring challenges for 3D object tracking due to the difficulty in detecting and encoding. In addition, ineffective candidate proposals increase the phenomenon of the loss of target tracking or wrong target tracking. In this paper, we propose an Orientation-variant Siamese 3D object tracking network that utilizes a Detection based Sampling module to generate candidate proposals (OS-DS tracker). The Detection based Sampling module is used to cut the excessive and useless proposals. And the multivariate Gaussian sampling generates the candidate proposals when the objects are not detected. Concretely, we first pre-train PointRCNN Network to globally detect objects from LIDAR point clouds. Then the 3D detected objects are refined by Candidate Regions Sampling to generate candidate proposals. Meanwhile, to make the feature vectors more discriminative, we design an Orientation-variant Siamese Auto-encoder, i.e., tracking loss regress to the intersection over union (IOU) of the template box and the candidate region boxes. Our method is tested on the KITTI dataset and the SHIP Tracking dataset. Compared with the previous state-of-the-arts, the proposed method outperforms in vessel tracking with 5.2%/2.1% improvement in Success and Precision.

关键词： LIDAR point clouds Object tracking Gaussian sampling Candidate regions Auto-encoder

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Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis

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IEEE TRANSACTIONS ON FUZZY systems 2020年第12期28卷 3204-3218页

作者： Shen, Tianyu Wang, Jiangong Gou, Chao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Social Comp Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Pattern Recognit & Intelligent Syst Beijing 100190 Peoples R China 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 Chinese Acad Sci Beijing 100049 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Breast cancer diagnosis based on medical imaging necessitates both fine-grained lesion segmentation and disease grading. Although deep learning (DL) offers an emerging and powerful paradigm of feature learning for these two tasks, it is hampered from popularizing in practical application due to the lack of interpretability, generalization ability, and large labeled training sets. In this article, we propose a hierarchical fused model based on DL and fuzzy learning to overcome the drawbacks for pixelwise segmentation and disease grading of mammography breast images. The proposed system consists of a segmentation model (ResU-segNet) and a hierarchical fuzzy classifier (HFC) that is a fusion of interval type-2 possibilistic fuzzy c-means and fuzzy neural network. The ResU-segNet segments the masks of mass regions from the images through convolutional neural networks, while the HFC encodes the features from mass images and masks to obtain the disease grading through fuzzy representation and rule-based learning. Through the integration of feature extraction aided by domain knowledge and fuzzy learning, the system achieves favorable performance in a few-shot learning manner, and the deterioration of cross-dataset generalization ability is alleviated. In addition, the interpretability is further enhanced. The effectiveness of the proposed system is analyzed on the publicly available mammogram database of INbreast and a private database through cross-validation. Thorough comparative experiments are also conducted and demonstrated.

关键词： Image segmentation Biomedical imaging Fuzzy sets Breast cancer Breast cancer deep learning (DL) fuzzy classifier (FC) interval type-2 possibilistic fuzzy c-means (IT2PFCM)

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The CASTlab in CASIA [ITS Research lab]

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IEEE intelligent TRANSPORTATION systems MAGAZINE 2020年第2期12卷 85-94页

作者： Olaverri-Monreal, Cristina Yisheng Lv is currently an associate professor with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences. His research interests include artificial intelligence intelligent control intelligent transportation systems and parallel traffic management and control systems. He is currently an ITS Society Board of Governors member. He is an associate editor of IEEE Transactions on Intelligent Transportation Systems and is on the editorial board of Acta Automatica Sinica. He received the 2015 IEEE ITS Outstanding Application Award. Contact him at yisheng.lv@***.

The complex Adaptive systems for Transportation laboratory (CASTlab) was established by Prof. Fei-Yue Wang in July 1999 for the task of designing and implementing the proposed intelligent traffic system for the city of Xinxiang, Henan, one of the first initiatives in intelligent transportation systems (ITS) in China. At the end of 1999, the CASTlab became a part of the newly created Center for intelligent control and systems in the Institute of Automation, Chinese Academy of Sciences (CASIA), one of the premier and oldest national research organizations in information, automation, and artificial intelligence in China and worldwide.

关键词： Research laboratories

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Malicious code detection based on CNNs and multi-objective algorithm

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2019年 129卷 50-58页

作者： Cui, Zhihua Du, Lei Wang, Penghong Cai, Xingjuan Zhang, Wensheng TaiYuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100190 Peoples R China

An increasing amount of malicious code causes harm on the internet by threatening user privacy as one of the primary sources of network security vulnerabilities. The detection of malicious code is becoming increasingly crucial, and current methods of detection require much improvement. This paper proposes a method to advance the detection of malicious code using convolutional neural networks (CNNs) and intelligence algorithm. The CNNs are used to identify and classify grayscale images converted from executable files of malicious code. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is then employed to deal with the data imbalance of malware families. A series of experiments are designed for malware image data from Vision Research lab. The experimental results demonstrate that the proposed method is effective, maintaining higher accuracy and less loss. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Malicious code Deep learning CNN Imbalance data NSGA-II

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Parallel intelligence： toward lifelong and eternal developmental AI and learning in cyber-physical-social spaces

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Frontiers of Computer Science 2018年第3期12卷 401-405页

作者： Fei-Yue WANG Jun Jason ZHANG Xiao WANG The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Electrical Engineering Wuhan University Wuhan 430072 China Qingdao Academy of Intelligent Industries Qingdao 266109 China

Dear editor, Fundamentally, there are only two main approaches so far in artificial intelligence （AI）： reasoning-oriented formal logic approach and function-oriented computational intelligence approach, so called Neats vs. Scuruffies, which is a reflection of the historical fight between two schools of thought for formalism and empiricism respectively in the field of AI that is continuing even today.

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Improved NSGA-III with selection-and-elimination operator

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 49卷 23-33页

作者： Cui, Zhihua Chang, Yu Zhang, Jiangjiang Cai, Xingjuan Zhang, Wensheng Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 003024 Shanxi Peoples R China Chinese Acad Sci State Key Lab Intelligent Control & Management Co Inst Automat Beijing 100190 Peoples R China

A fast non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is a well-known many-objective optimization algorithm in which the reference-point strategy is incorporated to maintain population diversity. However, the convergence capability of NSGA-III is poor in many cases. In this paper, a new selection-and-elimination operator is designed to balance convergence and diversity. First, a selection operator is employed to identify the reference point with the minimum niche count, and then one individual with the shortest penalty-based boundary intersection distance is chosen. Second, a reference point with the maximum niche count is identified, and one individual with the longest penalty-based boundary intersection distance is removed by the elimination operator. To test performance, this modification is verified on benchmark problems with up to 15 objectives, and compared with five other state-of-the-art algorithms. Simulation results demonstrate that our modified strategy can achieve the better performance.

关键词： Many-objective optimization NSGA-III Reference-point strategy Selection operator Elimination operator

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