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3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Hang, Yu Kai, Zhang Jilin Agr Univ Inst Informat Technol Changchun 130122 Peoples R China Changchun Univ Finance & Econ Changchun Peoples R China

ISBN: (纸本)9783038352679

TSP problem is a class of classical problems in the combinatorial optimization problem;it has important applications in gene sequencing, robot control and other areas, especially in the computer domain, applied more widely. This paper considers abstracting the problem of stitching and reduction for scraps of paper as a class of TSP problem, and use the optimized greedy algorithm, achieve automatic image stitching shredding by the use of computer graphics technology. Contents of this paper make a useful attempt to study the automatic stitching algorithm for scraps of paper.

关键词： TSP greedy algorithm image processing paper splicing

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Constrained Attitude Maneuvering via Modified-Rodrigues-Parameter-Based Motion Planning algorithms

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第4期59卷 1342-1356页

作者： Calaon, Riccardo Schaub, Hanspeter Univ Colorado Ann & HJ Smead Dept Aerosp Engn Sci 3775 Discovery Dr429 UCB CCAR Boulder CO 80303 USA

The attitude dynamics and control of a spacecraft becomes complex in the presence of constraints in its orientation. Such constraints consist in not pointing sensitive payload toward bright objects in space or, vice versa, keeping the same bright objects within some instrument's field of view. This paper investigates the applicability of nonsingular attitude motion planning algorithms to navigate a three-dimensional grid in the Modified Rodrigues Parameter configuration space. The aim is to compute a constraint-compliant reference trajectory in Modified Rodrigues Parameter space that can be tracked to reorient a spacecraft from an initial attitude to a final attitude while also attempting at minimizing the required control effort. Nonzero initial and final angular rates are considered to yield a general solution. The path is constructed with B-spline curves that pass through each attitude waypoint. Further, a constant angular maneuver rate is imposed, which influences the speed at which the reference attitude traverses the B-spline solution. Simulations illustrate that constraint-compliant, control-torque-minimizing paths are found if the grid spacing is chosen to be fine enough.

关键词： Motion Planning Attitude Dynamics Spline Interpolation Search algorithm Reaction Wheels Boundary Value Problems Numerical Integration Computing Cost greedy algorithm Power Generation

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Pattern search method for accelerating Stope boundary optimization problem in underground mining operations

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ENGINEERING OPTIMIZATION 2022年第5期54卷 881-893页

作者： Nikbin, Vahid Mardaneh, Elham Asad, Mohammad Waqar Ali Topal, Erkan Amirkabir Univ Technol Dept Min Engn Tehran Iran Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia Curtin Univ WA Sch Mines Minerals Energy & Chem Engn Perth WA Australia

The Stope Boundary Optimization (SBO) problem refers to the layout design of production areas (stopes) in an underground mining operation. It falls within the domain of strategic mine planning and a model that addresses this problem aims to maximize profitability, offers optimal management of mineral resources and facilitates decision making around capital investments. The SBO problem is computationally complex and a method that offers solution within a reasonable computation time does not exist. In this article, a combination of a new greedy algorithm and a pattern search technique with an integer programming model is proposed. An implementation of the proposed method on three realistic case studies confirms its efficiency in generating robust solutions with a better optimality gap and considerably less computational effort.

关键词： Stope boundary optimization optimization greedy algorithm pattern search integer programming

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Complexity Optimized Digital Predistortion Model of RF Power Amplifiers

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2022年第3期70卷 1490-1499页

作者： Cao, Wenhui Wang, Siqi Landin, Per N. Fager, Christian Eriksson, Thomas Chalmers Univ Technol Dept Elect Engn SE-41296 Gothenburg Sweden Ericsson AB SE-16440 Kista Sweden Chalmers Univ Technolo Dept Microtechnol & Nanosci MC2 SE-41296 Gothenburg Sweden

A novel behavioral modeling technique for digital predistortion of radio frequency power amplifiers (PAs) is proposed. The proposed basis-propagating selection (BAPS) model has an advantage on complexity since its basis functions are constructed as delay or a combination of existing basis functions. The BAPS model effectively optimizes the running time complexity while maintaining the generality of a full Volterra series. A greedy search procedure is applied to select the optimal basis functions. Compared with truncated Volterra models and pruned models obtained by popular pruning techniques, the superior performance of the BAPS model, in terms of accuracy and identification stability, has been fairly justified. The linearization measurements on different types of PAs illustrate that the BAPS model can robustly outperform other models from literature while achieving a low running complexity.

关键词： Complexity theory Adaptation models Predistortion Predictive models Delays Radio frequency Costs Basis-propagating selection (BAPS) digital predistortion (DPD) greedy algorithm low-cost complexity modeling and or linearization techniques power amplifiers (PA)

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Improved algorithms for non-submodular function maximization problem

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THEORETICAL COMPUTER SCIENCE 2022年 931卷 49-55页

作者： Liu, Zhicheng Jin, Jing Chang, Hong Du, Donglei Zhang, Xiaoyan Beijing Univ Technol Beijing Inst Sci & Engn Comp Beijing 100124 Peoples R China Nanjing Normal Univ Coll Taizhou Tiazhou 225300 Peoples R China Nanjing Normal Univ Inst Math Sch Math Sci Nanjing 210023 Peoples R China Univ New Brunswick Fac Management Fredericton NB E3B5A3 Canada

The concept of submodularity finds wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms to solve different submodular optimization problems arising from a diversity of applications. However pure submodular or supermodular problems only represent a small portion of the problems we are facing in real life applications. The main focus of this work is to consider a non-submodular function maximization problem subject to a cardinality constraint, where the objective function is the sum of a monotone gamma-weakly submodular function and a supermodular function. This problem includes some previously studied problems as special cases, such as the submodular+supermodular maximization problem when gamma =1, and the gamma-weakly submodular function maximization problem when the supermodular function is void. We present greedy algorithms for this generalized problem under both offline and streaming models, improving existing results.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Non-submodular function maximization greedy algorithm Cardinality constraint Offline model Streaming model

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A solution for the greedy approximation of a step function with a waveform dictionary

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2023年 116卷

作者： Rivero, Jorge Andres Vellucci, Pierluigi Univ Washington Deptartment Econ Savery Hall305 Spokane LnBox 353330 Seattle WA 98105 USA Roma Tre Univ Dept Econ Via Silvio Am 77 I-00145 Rome Italy

In this paper we consider a step function characterized by an arbitrary sequence of real -valued scalars and approximate it with a matching pursuit (MP) algorithm. We utilize a waveform dictionary with rectangular window functions as part of this algorithm. We show that the waveform dictionary is not necessary when all of the scalars are either non positive or non negative and the parameters of a wavelet dictionary on an integer lattice yields a closed-form solution for the initial optimization problem as part of the MP. Additionally, for any real-valued scalar sequence, we provide a solution with a related wavelet dictionary at each iteration of the algorithm. This allows for practical calculation of the approximating function, which we use to provide examples on simulated and real univariate time series data that display discontinuities in its underlying structure where the step function can be thought of as a sample from a signal of interest.(c) 2022 Elsevier B.V. All rights reserved.

关键词： greedy algorithm Hilbert space Wavelet Step function Projection pursuit Time series Data

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Optimal planning of indoor laser scans based on continuous optimization

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AUTOMATION IN CONSTRUCTION 2022年 143卷

作者： Zeng, Yan Liu, Jiepeng Cao, Qian Wu, Zhou Chen, Bo Li, Dongsheng Cheng, Guozhong Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Chengdu Construct Engn Grp Co Ltd Chengdu 610000 Sichuan Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Guangdong Peoples R China Chongqing Univ Sch Automat Chongqing 400045 Peoples R China

The automatic scanning system of terrestrial laser scanning requires an efficient scanning solution owing to the time-consuming and labor-intensive data acquisition process. Most existing methods tend to achieve a prior scan planning by using a discrete solving process but need a user-defined discretization resolution, which often leads to the challenge of finding the scanning locations. This study describes a two-dimensional (2D) based scheme for scan planning. In the proposed method, the scanning location consensus based on multi-objective optimization is designed to guarantee the registrability and completeness of scan planning. A breadth-first strategy combined with a backtracking process is employed to find a minimal scanning location set. Validation experiments have been conducted on two real-world scenes and verified the effectiveness and feasibility of the proposed method. The proposed method achieves the optimal scan planning and yields almost 90%, 70% improvement on planning time compared with greedy approach and expert plans separately.

关键词： Planning for scanning Multi -objective Heuristic optimization greedy algorithm Terrestrial laser scanning

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The sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第1期44卷 74-93页

作者： Zhang, Qiao Guan, Xiucui Jia, Junhua Qian, Xinqiang Southeast Univ Sch Math Nanjing 210096 Peoples R China

Network interdiction problems by upgading critical edges/nodes have important applications to reduce the infectivity of the COVID-19. A network of confirmed cases can be described as a rooted tree that has a weight of infectious intensity for each edge. Upgrading edges (nodes) can reduce the infectious intensity with contacts by taking prevention measures such as disinfection (treating the confirmed cases, isolating their close contacts or vaccinating the uninfected people). We take the sum of root-leaf distance on a rooted tree as the whole infectious intensity of the tree. Hence, we consider the sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees (SDIPT-UE/N). The problem (SDIPT-UE) aims to minimize the sum of root-leaf distance by reducing the weights of some critical edges such that the upgrade cost under some measurement is upper-bounded by a given value. Different from the problem (SDIPT-UE), the problem (SDIPT-UN) aims to upgrade a set of critical nodes to reduce the weights of the edges adjacent to the nodes. The relevant minimum cost problem (MCSDIPT-UE/N) aims to minimize the upgrade cost on the premise that the sum of root-leaf distance is upper-bounded by a given value. We develop different norms to measure the upgrade cost. Under weighted Hamming distance, we show the problems (SDIPT-UE/N) and (MCSDIPT-UE/N) are NP-hard by showing the equivalence of the two problems and the 0-1 knapsack problem. Under weighted l1 norm, we solve the problems (SDIPT-UE) and (MCSDIPT-UE) in O(n) time by transforimg them into continuous knapsack problems. We propose two linear time greedy algorithms to solve the problem (SDIPT-UE) under unit Hamming distance and the problem (SDIPT-UN) with unit cost, respectively. Furthermore, for the the minimum cost problem (MCSDIPT-UE) under unit Hamming distance and the problem (MCSDIPT-UN) with unit cost, we provide two O(n log n) time algorithms by the binary search methods. Finally, we perform some numeri

关键词： Network interdiction problem Upgrading critical edges Upgrading critical nodes Tree Knapsack problem greedy algorithm

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Model Order Reduction for Delayed PEEC Models With Guaranteed Accuracy and Observed Stability

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第10期69卷 4177-4190页

作者： Feng, Lihong Lombardi, Luigi Benner, Peter Romano, Daniele Antonini, Giulio Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Micron Semicond I-67051 Avezzano Italy Univ Aquila Dept Ind & Informat Engn & Econ UAq EMC Lab I-67100 Laquila Italy

In this work, a stable and accurate model order reduction method for large-scale partial element equivalent circuit (PEEC) models with many delays is proposed. The method reduces the dimension of the original model by interpolating the original transfer function. The interpolation points are iteratively selected using a greedy algorithm. An efficient error estimator for the reduced transfer function makes the greedy algorithm very successful in both, properly selecting the interpolation points and tightly measuring the error of the reduced transfer function. The resulting reduced-order model is accurate both in the frequency and in the time domain. Numerical tests have shown that the reduced-order model successfully filters the instability behavior of the original model and exhibits stability over a large time interval.

关键词： Transfer functions Read only memory Time-domain analysis Integrated circuit modeling Delays Interpolation greedy algorithms Partial element equivalent circuit (PEEC) method model order reduction greedy algorithm error estimation

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Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第1期15卷 194-205页

作者： Chen, Zheyi Zheng, Hongqiang Zhang, Jianshan Zheng, Xianghan Rong, Chunming Univ Exeter Dept Comp Sci Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Univ Stavanger Dept Elect Engn & Comp Sci N-4036 Stavanger Norway

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

关键词： Mobile edge computing Unmanned aerial vehicle deployment Computation offloading Particle swarm optimization Genetic algorithm greedy algorithm

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