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Learning robust and discriminative low-rank representations for face recognition with occlusion

Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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Adaptive Islanding Detection and Diagnosis Using Wide Area Monitoring

Adaptive Islanding Detection and Diagnosis Using Wide Area M...

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IEEE PES Innovative Smart Grid Technologies Conference Europe

作者： Mark Rafferty Xueqin (Amy) Liu David Laverty Lei Xie Sean McLoone School of Electronics Electrical Engineering and Computer Science Queen's University Belfast State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou PR China

This paper proposes a method for the detection and diagnosis of power system islanding events using phase-angle monitoring. The method is based on multiblock principal component analysis (MB-PCA), an extension of PCA that allows the underlying relationship between data sets to be identified. MB-PCA divides the large-scale system data into several blocks thus enhancing the model's ability to explain and diagnose power system events. Also incorporated into the method is a moving window technique. This allows the method to update over time enhancing power system situational awareness, by using data that best represents the current condition of the time-varying power system. Using Hotelling's T~2 and Q statistics allows the method to detect specific power system events with the main emphasis placed on the detection of power system islands. Further diagnostic approaches, such as contribution plots, are used to locate and isolate the detected events. The reliability of the proposed method is demonstrated using simulated case studies showing the method's ability to detect a genuine islanding event and its avoidance of nuisance tripping.

关键词： Islanding detection and diagnosis Wide Area Monitoring Multiblock principal component analysis Moving window

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Inverse extreme learning machine for learning with label proportions

Inverse extreme learning machine for learning with label pro...

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IEEE International Conference on Big Data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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Localization of pallets in warehouses using passive RFID system

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Journal of Central South University 2015年第8期22卷 3017-3025页

作者：周俊儒张宏建周洪亮 State Key Laboratory of Industrial Control Technology Department of Control Science and EngineeringZhejiang University

Warehouse operation has become a critical activity in supply chain. Position information of pallets is important in warehouse management which can enhance the efficiency of pallets picking and sortation. Radio frequency identification(RFID) has been widely used in warehouse for item identifying. Meanwhile, RFID technology also has great potential for pallets localization which is underutilized in warehouse management. RFID-based checking-in and inventory systems have been applied in warehouse management by many enterprises. Localization approach is studied, which is compatible with existing RFID checking-in and inventory systems. A novel RFID localization approach is proposed for pallets checking-in. Phase variation of nearby tags was utilized to estimate the position of added pallets. A novel inventory localization approach combing angle of arrival(AOA) measurement and received signal strength(RSS) is also proposed for pallets inventory. Experiments were carried out using standard UHF passive RFID system. Experimental results show an acceptable localization accuracy which can satisfy the requirement of warehouse management.

关键词： warehouse management radio frequency identification （RFID） localization tag phase

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On Trajectory Tracking for an Under-actuated Helicopter 35

On Trajectory Tracking for an Under-actuated Helicopter

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第35届中国控制会议

作者： LI Junfang LI Yi Gao Qiang SUN Mingwei CHEN Zengqiang Tianjin Key Laboratory of Intelligent Robotics Nankai University Dept of Automation College of Computer and Control Engineering Nankai University Dept of Automation College of Automation Tianjin University of Technology

ISBN: (纸本)9781509009107

This paper tackles the problem of the attitude tracking control for the 3-DOF helicopter. A robust tracking control scheme is proposed based on active disturbance rejection control(ADRC). Aiming at the 3-DOF helicopter manufactured by Quanser Inc, a high order nonlinear ESO is designed to implement the real-time estimation and online compensation of the external disturbance. Simulation results show high performance and good robustness in the presence of the external disturbance.

关键词： helicopter active disturbance rejection controller high order ESO trajectory tracking control

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Nuclear norm regularized coding with local position-patch and nonlocal similarity for face hallucination

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Digital Signal Processing 2017年 64卷 107-120页

作者： Guangwei Gao Jian Yang Zhihui Lai Pu Huang Jielin Jiang Hao Gao Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 PR China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 PR China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 PR China

Learning-based face hallucination methods have received much attention and progress in past few decades. Specially, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based ones. As opposed to the existing patch based methods, where the input image patch matrix is converted into vectors before combination coefficients calculation, in this paper, we propose to directly use the image matrix based regression model for combination coefficients computation to preserve the essential structural information of the input patch matrix. For each input low-resolution (LR) patch matrix, its combination coefficients over the training image patch matrices at the same position can be computed. Then the corresponding high-resolution (HR) patch matrix can be obtained with the LR training patches replaced by the corresponding HR ones. The nonlocal self-similarities are finally utilized to further improve the hallucination performance. Various experimental results on standard face databases indicate that our proposed method outperforms some state-of-the-art algorithms in terms of both visual quantity and objective metrics.

关键词： Face hallucination Position-patch Matrix based regression Nuclear norm Nonlocal similarity

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Distributed Kalman Filters with state equality constraints: Time-based and event-triggered communications

arXiv

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arXiv 2017年

作者： He, Xingkang Hu, Chen Hong, Yiguang Shi, Ling Fang, Haitao Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Rocket Force University of Engineering Shaanxi China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology

In this paper, we investigate a distributed estimation problem for multi-agent systems with state equality constraints (SEC). First, under a time-based consensus communication protocol, applying a modified projection operator and the covariance intersection fusion method, we propose a distributed Kalman filter with guaranteed consistency and satisfied SEC. Furthermore, we establish the relationship between consensus step, SEC and estimation error covariance in dynamic and steady processes, respectively. Employing a space decomposition method, we show the error covariance in the constraint set can be arbitrarily small by setting a sufficiently large consensus step. Besides, we propose an extended collective observability (ECO) condition based on SEC, which is milder than existing observability conditions. Under the ECO condition, through utilizing a technique of matrix approximation, we prove the boundedness of error covariance and the exponentially asymptotic unbiasedness of state estimate, respectively. Moreover, under the ECO condition for linear time-invariant systems with SEC, we provide a novel event-triggered communication protocol by employing the consistency, and give an offline design principle of triggering thresholds with guaranteed boundedness of error covariance. More importantly, we quantify and analyze the communication rate for the proposed event-triggered distributed Kalman filter, and provide optimization based methods to obtain the minimal (maximal) successive non-triggering (triggering) times. Two simulations are provided to demonstrate the developed theoretical results and the effectiveness of the filters. Copyright © 2017, The Authors. All rights reserved.

关键词： Multi agent systems

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Identification of Nonlinear State-Space Models Using Joint State Particle Smoothing 35

Identification of Nonlinear State-Space Models Using Joint S...

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第35届中国控制会议

作者： GENG Li-Hui NINNESS Brett Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education School of Electrical Engineering and Computer Science The University of Newcastle

ISBN: (纸本)9781509009107

On the basis of a previous expectation maximization(EM) algorithm, this paper applies the particle Markov chain Monte Carlo(MCMC) technique to estimate nonlinear state-space models(SSMs). The smoothed simultaneous particles for joint states are generated by importance resampling(IR) and are directly used to compute a log-likelihood function and its Jacobian vector and Hessian matrix. As a result, a relatively less computational load can thus be obtained, which is crucial to practical applications of the particle MCMC. In addition, the SSM parameters can be recursively iterated by the more efficient Newton method, in which an optimal step length for parameter update in each iteration is optimized by a 1-D search method. This presented identification method has a good global parameter convergence in case iteration parameters are initialized from quite large intervals of their respective true values. Finally, a numerical simulation for identification of a classical SSM is used to show the effectiveness of the studied identification method.

关键词： Nonlinear state-space models Smoothed simultaneous particles Importance resampling Newton method

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Memory partition for SIMD in streaming dataflow architectures 7

Memory partition for SIMD in streaming dataflow architecture...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Shen, Xiaowei Ye, Xiaochun Tan, Xu Wang, Da Zhang, Zhimin Tang, Zhimin Fan, Dongrui State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509051175

The high parallelism feature of scientific applications makes SIMD very suitable for streaming dataflow architectures. However, the splitting of SIMD memory requests increases the messages in on-chip networks and decreases the efficiency of streaming dataflow architectures. To process SIMD memory requests without splitting, a memory partition mechanism is proposed for SIMD in streaming dataflow architectures. The mechanism partitions input data of scientific applications into N (SIMD width) independent sub-spaces equally and groups operations on different data with same locations in N sub-spaces into SIMD operation. Moreover, the mechanism merges data with the same location in different sub-spaces to SIMD data and stores SIMD data in same SPMs to process SIMD memory requests as a whole. Simulation experimental results show that the proposed mechanism improves the performance per watt of streaming dataflow architectures by 2.5× on average on typical scientific applications. © 2016 IEEE.

关键词： Memory architecture

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A framework for energy-efficient optimization on multi-cores 7

A framework for energy-efficient optimization on multi-cores

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Zhu, Yatao Ye, Xiaochun Wang, Da Li, Wenming Zhang, Yang Fan, Dongrui Zhang, Zhimin Tang, Zhimin State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509051175

In recent years, many studies on energy-efficient optimization with performance constraint have focused on 'power-performance' assignment in multi-core processors. To find the solution of 'power-performance' assignment, progressive search for optimal configuration of core number and frequency can be implemented in two dimensions (core number and frequency) according to power-performance models. However, the existing methods of searching for optimal energy-efficient configuration in the space of core number and frequency have the faults of slow convergence speed, great overhead and poor scalability. In this paper, a search method based on feasible direction method is developed to quickly reduce search space in the two dimensions of core number and frequency and converge to the minimum point of energy consumption in the iterative process. Simultaneously, the practical energy and performance of each feasible configuration can revise model computation. The experimental results show that compared with Hill-climbing Heuristic, the best one of the existing search methods for energy-efficient optimization, in the number of execution, execution time and energy overhead, our framework makes an average reduction by 38.6%, 43.9% and 46.7% respectively, 47.6%, 50.2% and 49.3% when doubling cores of a multi-core processor, and 44.7%, 49.1%, 53.2% when doubling the frequency levels. © 2016 IEEE.

关键词： Energy utilization

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