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Optimal Evasion Against Dual Pure Pursuit *

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Optimal Evasion Against Dual Pure Pursuit *

American control Conference (ACC)

作者： Alexander Von Moll Zachariah Fuchs Meir Pachter Control Science Center of Excellence Air Force Research Laboratory WPAFB OH USA Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH USA Department of Electrical Engineering Air Force Institute of Technology WPAFB OH USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

The Pure Pursuit strategy is ubiquitous both in the control literature but also in real-world implementation. In this paper, we pose and solve a variant of Isaacs' Two Cutters and Fugitive Ship problem wherein the Pursuers' strategy is fixed to Pure Pursuit, thus making it an optimal control problem. The Pursuers are faster than the Evader and are endowed with a finite capture radius. All agents move with constant velocity and can change heading instantaneously. Although capture is inevitable, the Evader wishes to delay capture as long as possible. The optimal trajectories cover the entire state space. Regions corresponding to either solo capture or isochronous (dual) capture are computed and both types of maximal time-to-capture optimal trajectories are characterized.

关键词： Games Optimal control Marine vehicles Delays Trajectory Aerospace electronics Missiles

Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation

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Journal of computer science & Technology 2019年第2期34卷 476-493页

作者： Xiang-Chu Feng Chen-Ping Zhao Si-Long Peng Xi-Yuan Hu Zhao-Wei Ouyang School of Mathematics and Statistics Xidian University Xi’an 710126 China School of Mathematical Science Henan Institute of Science and Technology Xinxiang 453003 China Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China Research Center of Beijing Visystem Co. Ltd. Beijing 100190 China

Different from a general density estimation,the crime density estimation usually has one important factor:the geographical *** this paper,a new crime density estimation model is formulated,in which the regions where crime is impossible to happen,such as mountains and lakes,are *** further optimize the estimation method,a learning-based algorithm,named Plug-and-Play,is implanted into the augmented Lagrangian scheme,which involves an off-the-shelf filtering *** selections of the filtering operator make the algorithm correspond to several classical estimation ***,the proposed Plug-and-Play optimization based estimation algorithm can be regarded as the extended version and general form of several classical *** the experiment part,synthetic examples with different invalid regions and samples of various distributions are first *** under complex geographic constraints,we apply the proposed method with a real crime dataset to recover the density *** state-of-the-art results show the feasibility of the proposed model.

关键词： crime density estimation augmented Lagrangian strategy Plug-and-Play filtering operator

Decision support system based on pattern detection in iterative dermoscopic screening

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computer Methods and Programs in Biomedicine 2025年 269卷

作者： Natalia Salwowska Aleksander Kempski Piotr Zielonka Paweł Pietkiewicz Damian Borys Department of Dermatology School of Medicine Medical University of Silesia Katowice Poland Department of Systems Biology and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Dermatology Private Practice Polish Dermatoscopy Group Poznań Poland Biotechnology Centre Silesian University of Technology Gliwice Poland

Background and Objective: Videodermoscopy is the gold standard in melanoma diagnosis. A new research trend aims to support specialists’ work by automating this examination method, but most research focuses on developing detectors to replace dermatologists. Our study aims to present a novel idea which focuses on supporting the examination process instead of completely automating it. Instead of detecting and classifying all naevi each time, our approach focuses on full screening at the first visit and shortening the examination at subsequent visits. During the following examinations, images of all naevi are taken by supporting staff and are compared with previously taken images using feature-matching or co-registration-based algorithms. An unmatched naevus needs to be checked by the specialist, meaning that a new naevus appeared or an old one had changed. Methods: The similarity between images (ranging from 0% to 100%) was obtained with characteristic points searching (ORB method) and co-registration-based methods. No dataset presenting changes of naevi over time exists, so augmenting the images from PH2 and HAM10000 datasets was required to simulate the time flow. Results: The decision bound was set, so the true positive rate of each method was equal to 99%, giving the specificity of ORB equal to 97% (PH2) and 89% (HAM10000). Similar tests were performed using a co-registration algorithm giving specificity equal to 23% (PH2) and 19% (HAM10000). Conclusions: We believe that employing our approach will allow supporting staff to perform screening without experts’ help, decreasing examination time and costs and increasing the accessibility of dermoscopic examinations.

关键词： Dermoscopy Template matching Pattern recognition Machine vision Melanoma

P-WAE: Generalized patch-wasserstein autoencoder for anomaly screening

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arXiv 2021年

作者： Zhang, Hui Chen, Yurong Wang, Yaonan Cao, Yihong Wu, Q.M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha Hunan410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University OntarioP7B 5E1 Canada Vector Institute TorontoM5G 1M1 Canada

To mitigate the inspector’s workload and improve the quality of the product, computer vision-based anomaly detection (AD) techniques are gradually deployed in real-world industrial scenarios. Recent anomaly analysis benchmarks progress to generative models. The aim is to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need researchers and deployers to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability;(ii) the collapsed mutual-dependent features resulting in poor generalization. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness and complexity of the latent manifold. This alleviates the blurry reconstruction problem. In addition, the Hilbert-Schmidt Independence Criterion (HSIC) bottleneck is introduced to constrain the over-regularization representation. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Anomaly detection

On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs

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arXiv 2020年

作者： Gargiani, Matilde Diehl, Moritz Zanelli, Andrea Hutter, Frank Automatic Control Laboratory ETH Zurich Department of Microsystems Engineering Department of Mathematics University of Freiburg Department of Microsystems Engineering University of Freiburg Department of Computer Science University of Freiburg Bosch Center for Artificial Intelligence

Following early work on Hessian-free methods for deep learning, we study a stochastic generalized Gauss-Newton method (SGN) for training deep neural networks. SGN is a second-order optimization method, with efficient iterations, that we demonstrate to often require substantially fewer iterations than standard SGD to converge. As the name suggests, SGN uses a Gauss-Newton approximation for the Hessian matrix, and, in order to efficiently compute an approximate search direction, relies on the conjugate gradient method combined with forward and reverse automatic differentiation. Despite the success of SGD and its first-order variants in deep learning applications, and despite Hessian-free methods based on the Gauss-Newton Hessian approximation having been already theoretically proposed as practical methods for training neural networks, we believe that SGN has a lot of undiscovered and yet not fully displayed potential in big mini-batch scenarios. For this setting, we demonstrate that SGN does not only substantially improve over SGD in terms of the number of iterations, but also in terms of runtime. This is made possible by an efficient, easy-to-use and flexible implementation of SGN we propose in the Theano deep learning platform, which, unlike Tensorflow and Pytorch, supports forward automatic differentiation. This enables researchers to further study and improve this promising optimization technique and hopefully reconsider stochastic second-order methods as competitive optimization techniques for training DNNs;we also hope that the promise of SGN may lead to forward automatic differentiation being added to Tensorflow or Pytorch. Our results also show that in big mini-batch scenarios SGN is more robust than SGD with respect to its hyperparameters (we never had to tune its step-size for our benchmarks!), which eases the expensive process of hyperparameter tuning that is instead crucial for the performance of first-order methods. Copyright © 2020, The Authors. All righ

关键词： Stochastic systems

Visual SLAM based on dynamic object removal

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TechRxiv

TechRxiv 2020年

作者： Liu, Guoliang Liu, Hanjie Tian, Guohui Xin, Shiqing Ji, Ze School of Control Science and Engineering Shandong University Shandong Jinan China School of Computer Science and Technology Shandong University Shandong Qingdao China School of Engineering Cardiff University CardiffCF24 3AA United Kingdom

Visual simultaneous localization and mapping (SLAM) is the core of intelligent robot navigation system. Many traditional SLAM algorithms assume that the scene is static. When a dynamic object appears in the environment, the accuracy of visual SLAM can degrade due to the interference of dynamic features of moving objects. This strong hypothesis limits the SLAM applications for service robot or driverless car in the real dynamic environment. In this paper, a dynamic object removal algorithm that combines object recognition and optical flow techniques is proposed in the visual SLAM framework for dynamic scenes. The experimental results show that our new method can detect moving object effectively and improve the SLAM performance compared to the state of the art methods. © 2020, CC BY.

关键词： Object detection

Visual SLAM based on dynamic object removal

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Visual SLAM based on dynamic object removal

2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Liu, Hanjie Liu, Guoliang Tian, Guohui Xin, Shiqing Ji, Ze Shandong University School of Control Science and Engineering Jinan Shandong China Shandong University School of Computer Science and Technology Qingdao Shandong China Cardiff University School of Engineering CardiffCF24 3AA United Kingdom

ISBN: (纸本)9781728163215

关键词： Object detection

Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection

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arXiv 2021年

作者： Chen, Yurong Zhang, Hui Wang, Yaonan Wu, Q. M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Hunan Changsha410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University ONP7B 5E1 Canada Vector Institute TorontoONM5G 1M1 Canada

Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension turns the traditional representation and statistical-based outlier detection method into challenging. In this paper, we leverage the generative model in hyperspectral images anomaly detection. The gist is to model the distribution of the normal data, while the out-of-distribution sample can be viewed as the outlier. At first, the variational inference-based anomaly detection methods are investigated. We theoretically and empirically find that they are unstable due to the strong notion of distance (f-divergence) served as the regularization. Secondly, this paper introduces sliced Wasserstein distance, which is a weaker distribution measure compared with f-divergence. However, the number of randomly slicing poses a difficulty to estimate the true distance. In the end, we propose a projected sliced Wasserstein (PSW) autoencoder-based anomaly screening method. In particular, we leverage a computation-friendly eigen-decomposition method to find the principal component as slicing the high-dimensional data. Furthermore, our proposed distance can be calculated with the closed-form, even the prior distribution is not Gaussian. Comprehensive experiments conducted on various real-world hyperspectral anomaly detection benchmarks demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Spectroscopy

QoE-driven UAV-enabled pseudo-analog wireless video broadcast: a joint optimization of power and trajectory

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arXiv 2020年

作者： Tang, Xiao-Wei Huang, Xin-Lin Hu, Fei Department of Control Science and Engineering Tongji University Shanghai201804 China Department of Information and Communication Engineering Tongji University Shanghai201804 China Department of Electrical and Computer Engineering University of Alabama TuscaloosaAL35487 United States

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAVs total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6dB PSNR gains in terms of GUs minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal to noise ratio