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arXiv 2019年

作者： Oliveira, Rui Lima, Pedro F. Pereira, Goncalo Collares Martensson, Jonas Wahlberg, Bo Department of Automatic Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Scania Autonomous Transport Solutions Sodertalje Sweden

Driving in urban environments often presents difficult situations that require expert maneuvering of a vehicle. These situations become even more challenging when considering large vehicles, such as buses. We present a path planning framework that addresses the demanding driving task of buses in urban areas. The approach is formulated as an optimization problem using the road-aligned vehicle model. The road-aligned frame introduces a distortion on the vehicle body and obstacles, motivating the development of novel approximations that capture this distortion. These approximations allow for the formulation of safe and nonconservative collision avoidance constraints. Unlike other path planning approaches, our method exploits curbs and other sweepable regions, which a bus must often sweep over in order to manage certain maneuvers. Furthermore, it takes full advantage of the particular characteristics of buses, namely the overhangs, an elevated part of the vehicle chassis, that can sweep over curbs. Simulations are presented, showing the applicability and benefits of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion planning

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Convex Relaxation with Log-Determinant Divergence-L1 Regularization for 3D Shape Reconstruction

Convex Relaxation with Log-Determinant Divergence-L1 Regular...

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第三十八届中国控制会议

作者： Wei Cao Luefeng Chen Min Wu Witold Pedrycz School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta

Investigated is the problem of estimating the 3 D shape of an object defined by a set of 3 D landmarks with their 2 D correspondences in a single image. To solve this problem, we use a dictionary of the basic shape with LDD-L1 regularization,which is the construction of the shape space model. Based on the proposed convex optimization method, 3 D human pose reconstruction by shape space model and 3 D variable shape model was carried out on the mocap database. To improve accuracy and reduce the number of iterations, we use PSO algorithm to optimize initial value of the key parameter. The experimental results show that the improved algorithm exhibits less iterations but higher accuracy, which can be much helpful in practical applications.

关键词： LDD-L1 regularization 3D reconstruction PSO

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Secure Distributed Filtering for Unstable Dynamics Under Compromised Observations

Secure Distributed Filtering for Unstable Dynamics Under Com...

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IEEE Annual Conference on Decision and control

作者： Xingkang He Xiaoqiang Ren Henrik Sandberg Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Automation Shanghai University Shanghai China

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

In this paper, we consider a secure distributed filtering problem for linear time-invariant systems with bounded noises and unstable dynamics under compromised observations. A malicious attacker is able to compromise a subset of the agents and manipulate the observations arbitrarily. We first propose a recursive distributed filter consisting of two parts at each time. The first part employs a saturation-like scheme, which gives a small gain if the innovation is too large. The second part is a consensus operation of state estimates among neighboring agents. A sufficient condition is then established for the boundedness of estimation error, which is with respect to network topology, system structure, and the maximal compromised agent subset. We further provide an equivalent statement, which connects to 2s-sparse observability in the centralized framework in certain scenarios, such that the sufficient condition is feasible. Numerical simulations are finally provided to illustrate the developed results.

关键词： Observability Estimation error Technological innovation control systems State estimation Robustness

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Performance Comparison of Distributed State Estimation Algorithms for Power Systems

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Journal of Systems Science & Complexity 2017年第3期30卷 595-615页

作者： SUN Yibing FU Minyue ZHANG Huanshui School of Control Science and Engineering Shandong University School of Electrical Engineering and Computer Science University of Newcastle School of Automation Guangdong University of Technology

A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.

关键词： Distributed MAP estimation distributed state estimation extended Kalman filter power systems.

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Snow Cover Mapping for Mountainous Areas by Fusion of MODIS L1B and Geographic Data Based on Stacked Denoising Auto-Encoders

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computers, Materials & Continua 2018年第10期57卷 49-68页

作者： Xi Kan Yonghong Zhang Linglong Zhu Liming Xiao Jiangeng Wang Wei Tian Haowen Tan School of Atmospheric Science Nanjing University of Information Science and TechnologyNanjing210044China School of Information and Control Nanjing University of Information Science and TechnologyNanjing210044China School of Atmospheric Physics Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Chosun UniversityGwangju501-759South Korea

Snow cover plays an important role in meteorological and hydrological ***,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex *** at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan *** this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free ***,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological *** proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and *** comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.

关键词： Snow cover remote sensing deep learning Qinghai-Tibetan Plateau MODIS L1B

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Learning structured twin-incoherent twin-projective latent dictionary pairsfor classification

arXiv

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arXiv 2019年

作者： Zhang, Zhao Sun, Yulin Zhang, Zheng Wang, Yang Liu, Guangcan Wang, Meng School of Computer Science and Technology Soochow University Suzhou215006 China Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extendthe popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured ***, a novel framework called Twin-Projective Latent Flexible DPL(TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction errorto avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPLand the joint embedding of codesas well assalient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. As a result, TP-DPL unifies the salient feature extraction, representation and classification. The twin-incoherence constrainton codesand features can explicitly ensure high intra-class compactness and inter-class separation overthem. TP-DPL also integrates the adaptive weighting to preserve the local neighborhood of the coefficientsand salient features with in each class explicitly. For efficiency, TP-DPL uses Frobenius-normand abandonsthe costly l0/l1-norm for group sparse representation. Another byproductis that TP-DPL can directly apply the class-specific twin-projective reconstruction residualto computethe label of data. Extensive results on publicdatabases show that TP-DPL can deliver the state-of-the-art performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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A Hierarchical Fine-Tuning Based Approach for Multi-label Text Classification

A Hierarchical Fine-Tuning Based Approach for Multi-label Te...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Qiang Zhang Bo Chai Bochuan Song Jingpeng Zhao Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory (GEIRI) Global Energy Interconnection Research Institute Co. Ltd SGCC-HUST Future Grid Research Institute Beijing China School of Control and Computer Engineering North China Electric Power University Changpin District Beijing China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

Hierarchical Text classification has recently become increasingly challenging with the growing number of classification labels. In this paper, we propose a hierarchical fine-tuning based approach for hierarchical text classification. We use the ordered neurons LSTM (ONLSTM) model by combining the embedding of text and parent category for hierarchical text classification with a large number of categories, which makes full use of the connection between the upper-level and lower-level labels. Extensive experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.

关键词： Training Text categorization Logic gates Neurons Computational modeling Mathematical model Taxonomy

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Finite impulse response models: A non-asymptotic analysis of the least squares estimator

arXiv

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arXiv 2019年

作者： Djehiche, Boualem Mazhar, Othmane Rojas, Cristian R. Department of Mathematics School of Engineering Sciences Kth Royal Institute of Technology Stockholm100 44 Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology StockholmSE-100 44

We consider a finite impulse response system with centered independent sub-Gaussian design covariates and noise components that are not necessarily iden-tically distributed. We derive non-asymptotic near-optimal estimation and prediction bounds for the least squares estimator of the parameters. Our results are based on two concentration inequalities on the norm of sums of dependent covariate vectors and on the singular values of their covariance operator that are of independent value on their own and where the dependence arises from the time shift structure of the time series. These results generalize the known bounds for the independent case. Copyright © 2019, The Authors. All rights reserved.

关键词： Covariance matrix

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Face detection and recognition based on deep learning in the monitoring environment 4th

Face detection and recognition based on deep learning in the...

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4th International Conference of Pioneer computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Zhu, Chaoping Yang, Yi School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China Detection and Control of Integrated Systems Engineering Laboratory Chongqing400076 China

ISBN: (纸本)9789811322020

In the construction of smart cities and public safety, the number of cameras has increased dramatically, the artificial video management cannot meet the demand of urban construction. Therefore, intelligent video monitoring technology has become a research hotspot. In the actual video monitoring,the monitoring environment is very complex, Face recognition in video is often difficulty. This paper research the face recognition problem of monitoring video in the city, propose a mothed of detection and recognition method based on deep learning. The detection part adopts the fast speeding YOLO2 algorithm, the recognition part adopts high accuracy ResNet algorithm. Using WIDERFACE face detection database as training data set, Use the CASIA_Webface database for validation experiments. The experimental data show that the mothod YOLO2 algorithm and ResNet algorithm can complete detection and recognition of human face in monitoring video. the result is better. To further verify the recognition effect, the data set collected by the actual camera was tested. the test results show that the real time and accuracy of the system can meet the needs of practical engineering application. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Face recognition

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Nonlinear Virtual Inertia control of WTGs for Enhancing Primary Frequency Response and Suppressing Drive-Train Torsional Oscillations

arXiv

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arXiv 2020年

作者： Liu, Bi Huang, Qi Zhao, Junbo Milano, Federico Zhang, Yingchen Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States School of Electrical and Electronic Engineering University College Dublin Dublin Ireland National Renewable Energy Laboratory GoldenCO80401 United States

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce low frequency torsional oscillations of the drive train of WTGs. This paper addresses this issue and develops a new nonlinear VIC based on objective holographic feedbacks theory. This approach allows transforming the original nonlinear control into a completely controllable system of Brunovsky’s type. Simulation results under various scenarios demonstrate that the proposed method outperforms existing VICs in terms of suppression of low frequency drive-train torsional oscillations, enhancement of system frequency nadir as well as fast and smooth recovery of WTG rotor speed to the original MPP point before the disturbance. The proposed method is also able to coordinate multiple WTGs. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency response

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