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Lateral Vehicle Trajectory Planning Using a Model Predictive Control Scheme for an Automated Perpendicular Parking System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第2期70卷 1820-1829页

作者： Kim, Dae Jung Jeong, Yong Woo Chung, Chung Choo Hanyang Univ Dept Elect Engn Seoul 04763 South Korea NAVER LABS Seongnam Si 13561 South Korea Hanyang Univ Div Elect & Biomed Engn Seoul 04763 South Korea

This article proposes a lateral vehicle trajectory planning and control algorithm using a model predictive control (MPC) scheme for an automated perpendicular parking system. Previous work showed that approximated clothoid-based local path planning using a virtual towing distance provides low computational time and the stability of lateral vehicle motion in a parking system. However, this approach cannot function in certain parking situations and may cause undesirable steering maneuvers due to state-dependent planning. We present a new approximated clothoid path based on lateral vehicle kinematics to cope with these problems. Using the proposed kinematic model, we formulate an MPC problem for path planning. Then, the proposed lateral vehicle trajectory planning becomes an MPC problem under constraints. Besides, we show that lateral vehicle motion for vertical parking can be implemented using simple kinematic lateral motion control. Experimental results show that the vehicle with the proposed method moves smoothly and completes the given parking mission under constraints, even under tight parking space conditions.

关键词： Kinematics Path planning Planning Trajectory planning approximation algorithms Trajectory Predictive models Automated perpendicular parking system constrained optimization model predictive control (MPC) trajectory planning vehicle lateral control

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A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第2期10卷 606-618页

作者： Li, Weiling Wang, Renfang Luo, Xin Zhou, MengChu Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China Zhejiang Wanli Univ Coll Big Data & Software Engn Ningbo 315100 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Precise representation to undirected weighted network (UWN) is the foundation of understanding connection patterns inside a massive node set. It can be addressed via a Symmetric Non-negative Latent Factor (SNLF) model with a non-convex learning objective. However, existing SNLF models commonly adopt a first-order learning algorithm that cannot well handle such a non-convex objective, thereby leading to inaccurate UWN representation. Aiming at addressing this issue, this study incorporates an efficient second-order learning algorithm into an SNLF model, thereby establishing a Second-order Symmetric Non-negative Latent Factor ((SNLF)-N-2) model with two-fold ideas: a) applying the single latent factor-related mapping function to the non-negativity constrained optimization parameters to achieve an unconstrained learning objective, and b) optimizing this learning objective with its optimization parameters through an efficient second-order learning algorithm to achieve accurate representation to the target UWN with affordable computational burden. Empirical studies indicate that owing to its efficient incorporation of the second-order optimization technique, the proposed (SNLF)-N-2 model outperforms state-of-the-art SNLF models when they are used to gain highly accurate representation to UWNs emerging from real applications.

关键词： Optimization Computational modeling Symmetric matrices Analytical models approximation algorithms Jacobian matrices Convergence Conjugate gradient descent hessian-vector product latent factor analysis undirected weighted network representation learning symmetric second-order optimization

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Throughput Maximization of Delay-Aware DNN Inference in Edge Computing by Exploring DNN Model Partitioning and Inference Parallelism

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第5期22卷 3017-3030页

作者： Li, Jing Liang, Weifa Li, Yuchen Xu, Zichuan Jia, Xiaohua Guo, Song Australian Natl Univ Sch Comp Canberra ACT 0200 Australia City Univ Hong Kong Dept Comp Sci 83 Tat Chee Ave Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Liaoning Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading compute-intensive tasks to MEC networks for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a user request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed in the local IoT device of the request, and another part is processed in a cloudlet (edge server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet to which the request is assigned. In this paper, we study a novel delay-aware DNN inference throughput maximization problem with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN partitioning and multi-thread execution parallelism. Specifically, we consider the problem under both offline and online request arrival settings: a set of DNN inference requests is given in advance, and a sequence of DNN inference requests arrives one by one without the knowledge of future arrivals, respectively. We first show that the defined problems are NP-hard. We then devise a novel constant approximation algorithm for the problem under the offline setting. We also propose an online algorithm with a provable competitive ratio for the problem under the online setting. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising

关键词： Inference algorithms Delays Partitioning algorithms Computational modeling Task analysis approximation algorithms Parallel processing Mobile edge computing (MEC) DNN model inference provisioning throughput maximization Intelligent IoT devices approximation and online algorithms delay-aware DNN inference DNN partitioning inference parallelism computing and bandwidth resource allocation and optimization algorithm design and analysis

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Pricing Social Visibility Service in Online Social Networks: Modeling and algorithms

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第2期10卷 859-870页

作者： Zheng, Shiyuan Xie, Hong Lui, John C. S. Chinese Univ Hong Kong Dept Comp Sci Engn Cent Ave Hong Kong Peoples R China Chinese Acad Sci Chongqing Inst Green Intelligent Technol Beijing 100045 Peoples R China

Online social networks (OSNs) such as YouTube, Instagram, Twitter, Facebook, etc., serve as important platforms for users to share their information or content to friends or followers. Oftentimes, users want to enhance their social visibility, as it can make their contents, i.e., opinions, videos, pictures, etc., attract attention from more users, which in turn may bring higher commercial benefits to them. Motivated by this, we propose a mechanism, where the OSN operator provides a "social visibility boosting service" to incentivize "transactions" between requesters (users who seek to enhance their social visibility via adding new "neighbors") and suppliers (users who are willing to be added as a new "neighbor" of any requester when certain "rewards" is provided). We design a posted pricing scheme for the OSN provider to charge the requesters who use such boosting service and reward the suppliers who make contributions. The OSN operator keeps a fraction of the payment from requesters and distributes the remaining part to participating suppliers "fairly" via a rewarding rule based on Shapley value. We consider two different objectives/problems of the OSN provider, i.e., to select the optimal prices and supplier set to maximize (1) the revenue or (2) the welfare increase, under the requesters' budget constraint on suppliers. We first show that the problems are not simpler than NP-hard. We then decomposed each problem into two sub-routines, where one focuses on selecting the optimal set of suppliers, and the other one focuses on selecting the optimal prices. We prove the hardness of each sub-routine, and eventually design a computationally efficient approximation algorithm to solve the problems with provable theoretical guarantee on the revenue/welfare increase gap. We conduct extensive experiments on four public datasets to validate the performance of our proposed algorithms.

关键词： approximation algorithms revenue maximization social visibility welfare maximization

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Deep Filtering With Adaptive Learning Rates

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第6期68卷 3285-3299页

作者： Qian, Hongjiang Yin, George Zhang, Qing Univ Connecticut Dept Math Storrs CT 06269 USA Univ Georgia Dept Math Athens GA 30602 USA

This article develops a new deep learning framework for general nonlinear filtering. Our main contribution is to present a computationally feasible procedure. The proposed algorithms have the capability of dealing with challenging (infinitely dimensional) filtering problems involving diffusions with randomly-varying switching. First, we convert it to a problem in a finite-dimensional setting by approximating the optimal weights of a neural network. Then, we construct a stochastic gradient-type procedure to approximate the neural network weight parameters, and develop another recursion for adaptively approximating the optimal learning rate. The convergence of the combined approximation algorithms is obtained using stochastic averaging and martingale methods under suitable conditions. Robustness analysis of the approximation to the network parameters with the adaptive learning rate is also dealt with. We demonstrate the efficiency of the algorithm using highly nonlinear dynamic system examples.

关键词： Switches Mathematical models Deep learning Filtering Adaptive learning approximation algorithms Training filtering stochastic approximation (SA)

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Multi-UAV Cooperative Mission Assignment Based on Hybrid WAFC-RRAS Algorithm

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IEEE ACCESS 2023年 11卷 98132-98152页

作者： Huang, Gang Hu, Min Yang, Xueying Huang, Feiyao Space Engn Univ Dept Aerosp Sci & Technol Beijing 101416 Peoples R China

Multi-UAV cooperative mission assignment is an important research direction in the field of UAV research. In the planning process, the assignment conflict between UAVs and mission points and the solution efficiency are the key difficulties in multi-UAV cooperative mission assignment. Based on the Weighted Approximate Flight Cost (WAFC) method and the Reduced Redundant Assignment Scheme (RRAS) algorithm, a multi-UAV cooperative mission problem considering different assignment models is proposed. The contributions of this study are: firstly, the approximate flight cost matrix is constructed based on the vertical distance between the UAV and the mission point, and the matrix is applied to calculate the initial flight distance of the UAV, which fully takes into account the effects of mountain type and radar threat on the mission assignment. Secondly, the auxiliary flight cost method and the spatial mapping matrix are constructed, which maps the discrete approximation of the flight cost to the continuous space and solves the UAV's local conflict problem in the assignment process. Finally, an adaptive selection mutation strategy based on iterative individual fitness values is proposed to reduce the redundancy of candidate assignment schemes and improve the efficiency of the planning system. The simulation results verify that the algorithm has high cooperative capability, good robustness and fast solution speed when dealing with cooperative multi-UAV mission assignment planning.

关键词： Autonomous aerial vehicles Planning Costs Heuristic algorithms Computational modeling Decision making approximation algorithms Vertical cut method approximate flight cost method mapping strategies assignment conflicts multi-UAV cooperative mission assignment

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Smooth Distance approximation 31

Smooth Distance Approximation

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31st Annual European Symposium on algorithms, ESA 2023

作者： Abdelkader, Ahmed Mount, David M. Google LLC Mountain ViewCA United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9783959772952

Traditional problems in computational geometry involve aspects that are both discrete and continuous. One such example is nearest-neighbor searching, where the input is discrete, but the result depends on distances, which vary continuously. In many real-world applications of geometric data structures, it is assumed that query results are continuous, free of jump discontinuities. This is at odds with many modern data structures in computational geometry, which employ approximations to achieve efficiency, but these approximations often suffer from discontinuities. In this paper, we present a general method for transforming an approximate but discontinuous data structure into one that produces a smooth approximation, while matching the asymptotic space efficiencies of the original. We achieve this by adapting an approach called the partition-of-unity method, which smoothly blends multiple local approximations into a single smooth global approximation. We illustrate the use of this technique in a specific application of approximating the distance to the boundary of a convex polytope in Rd from any point in its interior. We begin by developing a novel data structure that efficiently computes an absolute Ε-approximation to this query in time O(log(1/Ε)) using O(1/Εd/2) storage space. Then, we proceed to apply the proposed partition-of-unity blending to guarantee the smoothness of the approximate distance field, establishing optimal asymptotic bounds on the norms of its gradient and Hessian. © Ahmed Abdelkader and David M. Mount.

关键词： approximation algorithms

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Fast Frequent Patterns Mining by Multiple Sampling With Tight Guarantee Under Bayesian Statistics

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第5期53卷 2993-3006页

作者： Zhang, Zhongjie Huang, Jian Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Hunan Peoples R China

Sampling from large dataset is commonly used in the frequent patterns (FPs) mining. To tightly and theoretically guarantee the quality of the FPs obtained from samples, current methods theoretically stabilize the supports of all the patterns in random samples, despite only FPs do matter, so they always overestimate the sample size. We propose an algorithm called multiple sampling-based FPs mining (MSFP). The MSFP first generates the set of approximate frequent items (AFI), and uses the AFI to form the set of approximate FPs without supports ( AFP*), where it does not stabilize the value of any item's or pattern's support, but only stabilizes the relationship >= or < between the support and the minimum support, so the MSFP can use small samples to successively obtain the AFI and AFP*, and can successively prune the patterns not contained by the AFI and not in the AFP*. Then, the MSFP introduces the Bayesian statistics to only stabilize the values of supports of AFP*'s patterns. If a pattern's support in the original dataset is unknown, the MSFP regards it as random, and keeps updating its distribution by its approximations obtained from the samples taken in the progressive sampling, so the error probability can be bound better. Furthermore, to reduce the I/O processes in the progressive sampling, the MSFP stores a large enough random sample in memory in advance. The experiments show that the MSFP is reliable and efficient.

关键词： Bayes methods approximation algorithms Upper bound Tools Indexes Cybernetics Task analysis Bayesian statistics frequent pattern (FP) sampling tight guarantee

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PAC Reinforcement Learning Algorithm for General-Sum Markov Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第5期68卷 2821-2831页

作者： Zehfroosh, Ashkan Tanner, Herbert G. Univ Delaware Dept Mech Engn Newark DE 19716 USA

This article presents a theoretical framework for probably approximately correct (PAC) multi-agent reinforcement learning (MARL) algorithms for Markov games. Using the idea of delayed Q-learning, this article extends the well-known Nash Q-learning algorithm to build a new PAC MARL algorithm for general-sum Markov games. In addition to guiding the design of a provably PAC MARL algorithm, the framework enables checking whether an arbitrary MARL algorithm is PAC. Comparative numerical results demonstrate the algorithm's performance and robustness.

关键词： Games Markov processes Picture archiving and communication systems Nash equilibrium Q-learning approximation algorithms Convergence Markov game multiagent system probably approximately correct (PAC) reinforcement learning

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Analysis and Compensation for Systematical Errors in Airborne Microwave Photonic SAR Imaging by 2-D Autofocus

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 2221-2236页

作者： Chen, Min Qiu, Xiaolan Li, Ruoming Li, Wangzhe Fu, Kun Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Suzhou Aerosp Informat Res Inst Suzhou 215124 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Natl Key Lab Microwave Imaging Technol Beijing 100190 Peoples R China

In the area of synthetic aperture radar (SAR), the ultrahigh resolution is always a continuous pursuit for researchers. To realize ultrahigh resolution, microwave photonics technology is an excellent solution since it has advantages of low transmission loss, ultrawide bandwidth (UWB), etc. However, with the improvement of resolution, new problems arise as the impact of more systematical errors becomes non-negligible, for example, the synchronization error between the transmit channel and the mixing channel, unknown system delay during signal reception, and the fluctuation of frequency for UWB signals. These factors will bring the degradation of the focusing quality of SAR images together with the motion errors and the trajectory measurement errors, causing not only residual range cell migration and azimuth phase error, but also higher order phase error along the range frequency dimension. In this article, we discuss the adverse influence of the above factors and give a detailed analysis of the 2-D phase error of the coarse focused image processed by range migration algorithm. Then, to compensate for the above unknown systematical errors, a 2-D autofocus method is proposed, and the effectiveness is validated by experiments on both simulated and real data.

关键词： Synthetic aperture radar Signal resolution Bandwidth Trajectory Synchronization approximation algorithms Image resolution 2-D autofocus microwave photonic (MWP) synthetic aperture radar (SAR) range migration algorithm (RMA) systematical errors ultrawide bandwidth (UWB)

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