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Reweighted nuclear norm regularization: A SPARSEVA approach

IFAC-PapersOnLine 2015年第28期48卷 1172-1177页

作者： Ha, Huong Welsh, James S. Blomberg, Niclas Rojas, Cristian R. Wahlberg, Bo School of Electrical Engineering and Computer Science The University of Newcastle Technologiepark 913 B-9052 Gent Australia Department of Automatic Control and ACCESS School of Electrical Engineering KTH Royal Institute of Technology StockholmSE-100 44 Sweden

The aim of this paper is to develop a method to estimate high order FIR and ARX models using least squares with re-weighted nuclear norm regularization. Typically, the choice of the tuning parameter in the reweighting scheme is computationally expensive, hence we propose the use of the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) framework to overcome this problem. Furthermore, we suggest the use of the prediction error criterion (PEC) to select the tuning parameter in the SPARSEVA algorithm. Numerical examples demonstrate the veracity of this method which has close ties with the traditional technique of cross validation, but using much less computations. © 2015

关键词： Parameter estimation Digital control systems Discrete time control systems Identification (control systems) Least squares approximations Numerical methods Discrete time systems Nuclear norm regularizations Nuclear norms regularization sparse estimate Sparse estimation Traditional techniques Validation criteria

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Pruning and preprocessing methods for inventory-aware pathfinding

Pruning and preprocessing methods for inventory-aware pathfi...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Davide Aversa Sebastian Sardina Stavros Vassos Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Computer Science and Information Technology RMIT University Melbourne Australia

ISBN: (纸本)9781509018840

Inventory-Aware Pathfinding is concerned with finding paths while taking into account that picking up items, e.g., keys, allow the character to unlock blocked pathways, e.g., locked doors. In this work we present a pruning method and a preprocessing method that can improve significantly the scalability of such approaches. We apply our methods to the recent approach of Inventory-Driven Jump-Point Search (InvJPS). First, we introduce InvJPS+ that allows to prune large parts of the search space by favoring short detours to pick up items, offering a trade-off between efficiency and optimality. Second, we propose a preprocessing step that allows to decide on runtime which items, e.g., keys, are worth using thus pruning potentially unnecessary items before the search starts. We show results for combinations of the pruning and preprocessing methods illustrating the best choices over various scenarios.

关键词： Runtime Artificial intelligence Planning Electronic mail Games Navigation Search problems

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Theoretical Analysis of Biogeography Based Optimization

Theoretical Analysis of Biogeography Based Optimization

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and computer(MMEBC 2016)

作者： Aijun ZHU Cong HU Chuanpei XU Zhi Li School of Electronic Engineering and Automation Guilin University of Electronic Technology Department of Computer Science and Engineering Texas A&M University Guangxi Key Laboratory of Automatic Detecting Technology and Instruments

Since the computation ability of computer improves dramatically, a lot of new meta-heuristic methods ariseAll those algorithms are originated from some mechanisms in nature, and are similar in structure and widely used to solve global optimization problemsHowever, evolutionary algorithm, such as BBO, is lack of strict theory foundation and hard to be analyzed in theory, because it comes from heuristic idea and has complicated random behaviorTherefore, In this paper, we propose a Markov chain model of BBO to analyze the relationship between individual vector and Pop Size, and prove that a Markov population s=eries{ξ t, t0,1, 2,...}(ξ t∈?)in BBO is an absorbing Markov chainConvergence analysis of BBO is obtained, which is the *Markov population series in BBO converge to objective subspace B0 with probability one.

关键词： meta-heuristic global optimization NP hard problem

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Sum-Mse performance gain of DFT-based channel estimator over frequency-domain ls one in full-duplex OFDM systems with colored interference

arXiv

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arXiv 2017年

作者： Wang, Jin Shu, Feng Lu, Jinhui Yu, Hai Chen, Riqing Li, Jun Jayakody, Dushantha Nalin K. School of Electronic and Optical Engineering Nanjing University of Science and Technology 210094 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China China Research Institute of Radiowave Propagation National Key Laboratory of Electromagnetic Environment Qingdao266107 China National Research Tomsk Polytechnic University Department of Control System Optimization Institute of Cybernetics Russia

In this paper, we make an investigation on the summean-square-error (sum-MSE) performance gain achieved by DFT-based least-square (LS) channel estimator over frequencydomain LS one in full-duplex OFDM system in the presence of colored interference and noise. The closed-form expression of the sum-MSE performance gain is given. Its simple upper and lower bounds are derived by using inequalities of matrix eigen-values. By simulation and analysis, the upper lower bound is shown to be close to the exact value of MSE gain as the ratio of the number N of total subcarriers to the cyclic prefix length L grows and the correlation factor of colored interference increases. More importantly, we also find that the MSE gain varies from one to N/L as the correlation among colored interferences decreases gradually. According to theoretical analysis, we also find the MSE gain has very simple forms in two extreme scenarios. In the first extreme case that the colored interferences over all subchannels are fully correlated, i.e., their covariance matrix is a matrix of allones, the sum-MSE gain reduces to 1. In other words, there is no performance gain. In the second extreme case that the coloredinterference covariance matrix is an identity matrix, i.e, they are mutually independent, the achievable sum-MSE performance gain is N/L. A large ratio N/L will achieve a significant sum-MSE gain. Copyright © 2017, The Authors. All rights reserved.

关键词： Covariance matrix

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A Novel Under-sampling Algorithm based on Iterative-Partitioning Filters for Imbalanced Classification

A Novel Under-sampling Algorithm based on Iterative-Partitio...

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IEEE International Conference on Automation science and engineering

作者： XiaoShuang Chen Qi Kang MengChu Zhou Zhi Wei Department of Control Science and Engineering School of Electronics and Information Engineering Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark 07102 USA Department of Computer Science New Jersey Institute of Technology Newark 07102 USA

ISBN: (纸本)9781509024100

Many real-world datasets suffer from the problem of class imbalance, i.e., they have a minority class being only a small portion of the whole dataset. Under-sampling techniques, e.g., EasyEnsemble (EE), present an efficient approach to imbalanced classification problems. However, imbalance is not the only factor that harms the performance of conventional classifiers. The presence of noises is another issue that really complicates the process of classifier learning. This paper presents a new noise-filtered under-sampling algorithm by incorporating an Iterative-Partitioning Filter (IPF) into EE, named EE-IPF for short. IPF can remove noises from both majority and minority classes. Comprehensive experiments are performed to test its performance via eleven commonly-used benchmark datasets. The results show its outstanding performance in terms of popular ly-used metrics for imbalanced classification.

关键词： Classification algorithms Training Noise measurement Filtering algorithms Algorithm design and analysis Benchmark testing Filtering

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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An Event-Triggered Approach to State Estimation for Neural Networks with Individual Triggering Thresholds

An Event-Triggered Approach to State Estimation for Neural N...

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第35届中国控制会议

作者： Licheng Wang Zidong Wang Lei Zou Guoliang Wei Shanghai Key Lab of Modern Optical System Department of Control Science and EngineeringUniversity of Shanghai for Science and Technology Department of Computer Science Brunel University LondonUxbridge Research Institute of Intelligent Control and Systems Harbin Institute of Technology

ISBN: (纸本)9781509009107

In this paper, the state estimation problem is investigated for a class of discrete-time stochastic neural networks with event-triggered transmission(ETT) mechanism. Different from the traditional periodic communication mechanism, the ETT mechanism employed in this paper possesses the advantage of mitigating the network traf c resulting from the unnecessary sending and receiving data between the sensors and the estimators. Moreover, the proposed ETT mechanism is implemented individually for each neuron. That is, the measurement output of each neuron is transmitted to the estimator only when its corresponding triggering condition with the speci ed triggering threshold is satis ed, thereby re ecting the engineering practice in a more realistic way. The aim of the addressed problem is to design an estimator for the neural networks subject to the ETT mechanism such that the estimation error is asymptotically bounded in the mean square. Furthermore, the estimator gain matrix is explicitly parameterized in the sense of minimizing such an asymptotic error upper bound. Finally, a numerical example is presented to demonstrate the effectiveness of the results.

关键词： Event-triggered scheme Neural networks Individual triggering threshold State estimation Asymptotic boundedness

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Constraint Robust Model Predictive control for Jump Markov Linear Systems with Additive Disturbances

Constraint Robust Model Predictive Control for Jump Markov L...

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European control Conference

作者： Jens Tonne Olaf Stursberg Volkswagen AG Baunatal (Germany) Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel (Germany)

ISBN: (纸本)9781509025923

This paper proposes an approach to robust model predictive control (MPC) for discrete-time jump Markov linear systems (JMLS) considering polytopic constraints on the inputs and states. In a first design step, state feedback controllers, that stabilize the JMLS robustly, are calculated offline by means of a semi-definite program (SDP). These controllers are designed to satisfy the constraints and keep the states within disturbance invariant sets (DIS). To reduce the conservatism of the design approach, an SDP formulation in which the controller parameterization is independent of the Lyapunov matrices is employed. The resulting Lyapunov matrices and the DIS are used to formulate quadratic constraints for the first prediction step, that ensure robustness against additive disturbances, stability, constraint satisfaction and recursive feasibility. The stabilizing properties and low computation times of the resulting quadratically constraint quadratic program (QCQP) are illustrated by simulation.

关键词： JMLS SDP MPC

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On the Implementation of Compressive Sensing on Wireless Sensor Network

On the Implementation of Compressive Sensing on Wireless Sen...

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IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI)

作者： Dong-Yu Cao Kai Yu Shu-Guo Zhuo Yu-Hen Hu Zhi Wang Department of Control Science and Engineering Zhejiang University Hangzhou China MADYNES team INRIA Nancy Grand-Est Department of Electrical and Computer Engineering University of Wisconsin-Madison

Compressive sensing (CS) is applied to enable real time data transmission in a wireless sensor network by significantly reduce the local computation and sensor data volume that needs to be transmitted over wireless channels to a remote fusion center. By exploiting the sparse structure of commonly used signals in Wireless Sensor Network (WSN) applications, a Compressed Sensing on WSN (CS-WSN) framework is proposed. This is accomplished by (i) random sub-sampling of data collected at sensor node, (ii) transmitting only the sign-bit of the data samples over wireless channels. It is shown that this CS-WSN framework is capable of delivering similar performance as conventional local data compression method while greatly reduce the data volume and local computation. This proposed scheme is validated using a prototype wireless sensor network test bed. Preliminary experimental results clearly validate the superior performance of this proposed scheme.

关键词： Wireless sensor networks Compressed sensing Wireless communication Sensor arrays Direction-of-arrival estimation Hardware

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GPU Parallel Optimization of Hyperspectral Image Kernel Sparse Representation Classification Based on Spatial-Spectral Graph Regularization

GPU Parallel Optimization of Hyperspectral Image Kernel Spar...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Jida Zheng Zebin Wu Qicong Wang Jianjun Liu Zhihui Wei Wubin Wang Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China North Automatic Control Technology Institute Taiyuan China

ISBN: (纸本)9781509036783

With the development of hyperspectral remote sensing information processing, hyperspectral image classification becomes a hot topic. The algorithm of kernel sparse representation classification based on spatial-spectral graph regularization and sparsity concentration index (SSGSCI-KSRC) gains a good result. Due to the big scale of hyperspectral image data, time-critical requirement in the practical application makes it impossible to use the original SSGSCI-KSRC algorithm. This paper proposes a parallelization method for SSGSCI-KSRC algorithm. The optimization method achieves the efficient calculation operations of hyperspectral image matrix data, coalesces memory accesses to reduce the time of transferring data to the GPU devices, and designs proper kernel functions in the classification algorithm. The experimental results demonstrate that the parallel SSGSCI-KSRC algorithm obtains a better result in terms of computational performance when the accuracy stays the same.

关键词： Classification algorithms Graphics processing units Kernel Hyperspectral imaging Algorithm design and analysis Training

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