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accelerated algorithm for Back-Projection Fully Focused SAR Altimetry

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Hernandez-Burgos, Sergi Gibert, Ferran Broquetas, Antoni Villalvilla, Pol Egido, Alejandro Moyano, Gorka de la Cruz, Adrian Flores Fornari, Marco isardSAT SL Barcelona 08005 Catalonia Spain Univ Politecn Cataluna Signal Theory & Commun Dept Barcelona 08034 Catalonia Spain European Space Agcy European Space Res & Technol Ctr NL-2201 AZ Noordwijk Netherlands Stn Grp European Space Agcy ESA ESTEC NL-2201 AZ Noordwijk Netherlands

Fully focused (FF) synthetic aperture radar (SAR) back-projection algorithms for radar altimetry have gained attention in the altimetry community due to their improved azimuth resolution compared to traditional methods, such as the delay/Doppler algorithm. However, the significant computational cost of back-projection has limited its use in operational applications. This article presents an accelerated back-projection algorithm that reduces the runtime of the classic back-projection by a factor of 28 on a CPU-based architecture. When implemented on a GPU-based architecture, the accelerated back-projection achieves a speedup ranging from 8 to 13 times compared to the classic back-projection on the same GPU. In comparison to the classic back-projection running on a CPU, the combination of the accelerated back-projection and GPU processing results in an overall improvement of up to 1570 times. The accelerated algorithm maintains the accuracy of classic time-domain algorithms and allows for the processing of data in the order of the sensing time under a GPU-based architecture. The algorithm has been extensively validated using transponder and open ocean data. These results indicate that operational back-projection algorithms are feasible for current radar altimetry missions, such as Sentinel-6, and upcoming missions like CRISTAL and Sentinel-3 Next Generation.

关键词： Azimuth Synthetic aperture radar Focusing Approximation algorithms Altimetry Surface waves Transponders Sea surface Spaceborne radar Lighting accelerated algorithm accelerated back-projection back-projection delay/Doppler fully focused (FF) fully focused synthetic aperture radar (FF-SAR) GPU acceleration high-resolution radar altimetry omega-K open ocean radar altimetry real time altimetry synthetic aperture radar (SAR) transponder

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Performance optimization of solar-powered heat engine using a multiobjective accelerated algorithm

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HEAT TRANSFER 2023年第7期52卷 5041-5067页

作者： El Moudden, Mustapha Hssikou, Mohamed Chouaib Doukkali Univ Natl Sch Appl Sci Dept Sci & Ind Technol LabSIPE El Jadida Morocco Sultan Moulay Slimane Univ Polydisciplinary Fac Phys Dept LRPSI Beni Mellal Morocco

In this study, a multiobjective optimization approach was used to conduct a thermodynamic investigation of a solar Brayton and endoreversible heat engine. The thermo-economic performance capabilities of such machines with hybrid input power, solar-fuel, are examined numerically. Throughout this study, three performance indicators of the cycle, including the power output, the thermo-economic performance function, and the thermal efficiency are optimized concurrently employing a multiobjective steepest descent method, named the accelerated Diagonal Steepest Descent algorithm. Furthermore, to properly analyze the error, three strategies are employed in the decision-making step to identify the optimal compromise solution, and the deviation indices under these strategies are analyzed. The numerical experiments reveal that the present algorithm outperforms the two popular multiobjective algorithms: the multiobjective particle swarm optimization method and the elitist nondominated sorting genetic algorithm. The relevance of the presented algorithm with respect to the previous ones is examined by means of a deviation index. Finally, these experiments show the optimal design parameters which lead to the best performance of the heat engine.

关键词： accelerated algorithm Brayton heat engine multiobjective optimization solar steepest descent method

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An accelerated deterministic algorithm for maximizing monotone submodular minus modular function with cardinality constraint

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THEORETICAL COMPUTER SCIENCE 2024年 1016卷

作者： Gong, Shufang Liu, Bin Fang, Qizhi Ocean Univ China Sch Math Sci Qingdao 266100 Shandong Peoples R China

Submodular optimization not only covers some classical combinatorial optimization problems, but also has a wide range of applications in fields such as machine learning and artificial intelligence. For submodular maximization problems with constraints, some work has been done including the design of approximation algorithms, the measurement of approximation algorithms in terms of quality and efficiency, etc. In this paper, we consider the problem of maximizing a non-negative monotone submodular function minus a non-negative modular function with the cardinality constraint. This model has been applied to many scenarios, such as team formation problem, influence maximization problem, recommender systems problem, etc. We propose a threshold algorithm that achieve a (1/2 -..(..), 2)- bicriteria approximation ratio and query complexity..(.. log..). Our algorithm makes a small sacrifice in the approximation ratio but improves the best query complexity result of existing deterministic algorithms from..(..2) to..(.. log..) in the worst case.

关键词： Submodular minus modular Cardinality constraint accelerated algorithm Deterministic algorithm

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Research on three-step accelerated gradient algorithm in deep learning

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Statistical Theory and Related Fields 2022年第1期6卷 40-57页

作者： Yongqiang Lian Yincai Tang Shirong Zhou KLATASDS-MOE School of StatisticsEast China Normal UniversityShanghaiPeople's Republic of China

Gradient descent(GD)algorithm is the widely used optimisation method in training machine learning and deep learning *** this paper,based on GD,Polyak’s momentum(PM),and Nesterov accelerated gradient(NAG),we give the convergence of the algorithms from an ini-tial value to the optimal value of an objective function in simple quadratic *** on the convergence property of the quadratic function,two sister sequences of NAG’s iteration and par-allel tangent methods in neural networks,the three-step accelerated gradient(TAG)algorithm is proposed,which has three sequences other than two sister *** illustrate the perfor-mance of this algorithm,we compare the proposed algorithm with the three other algorithms in quadratic function,high-dimensional quadratic functions,and nonquadratic *** we consider to combine the TAG algorithm to the backpropagation algorithm and the stochastic gradient descent algorithm in deep *** conveniently facilitate the proposed algorithms,we rewite the R package‘neuralnet’and extend it to‘supneuralnet’.All kinds of deep learning algorithms in this paper are included in‘supneuralnet’***,we show our algorithms are superior to other algorithms in four case studies.

关键词： accelerated algorithm backpropagation deep learning learning rate momentum stochastic gradient descent

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Parallel accelerated algorithm Optimization for Speckle Matching in Deformation Measurement Based on Stereo Vision

Parallel Accelerated Algorithm Optimization for Speckle Matc...

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2021年第二届控制、机器人和智能系统国际会议

作者： Yunhe Liu Guiyang Zhang Lili Wang Jing Wang Zijian Zhu School of Mechanical Engineering Changshu Institute of Technology School of Electrical Engineering and Automation Harbin Institute of Technology

This paper is concerned with the efficiency of speckle match in vision deformation measurement,upon which the CUDA programming architecture,combined with the Visual Studio platform and Mex script files is utilized to implement parallel *** the aid of compiling the GPU parallel mode of the CUDA source program through NVCC,the scheme of speckle matching parallel computing are given,which is crucial to improve the real-time performance of vision-based deformation ***,the method in this paper completes the efficient calculation of match of the speckle image sub-regions in the three-dimensional deformation *** proposed strategy solves the obstacle problem when the Mex script and different programming languages interact,and is not restricted by overloaded functions,so that the overall computing performance of the deformation measurement program reaches a better ***,the experimental results show that the speckle matching has achieved a calculation speedup ratio of 20.39 times.

关键词： Stereo vision Speckle matching accelerated algorithm Deformation measurement

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Design of deep learning accelerated algorithm for online recognition of industrial products defects

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NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 4527-4540页

作者： Shu, Yufeng Huang, Yu Li, Bin HUST Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

With the defects of LED chip as the research object, in LED chip defect recognition, an efficient and scalable parallel algorithm is critical to the deep model using a large data set training. As the implementation of parallel in multi-machine is relatively difficult, the paper puts forward a recognition method based on convolution neural network algorithm, so as to improve the small batch stochastic gradient descent algorithm that is very popular in the industry. This method has overcome the shortcomings of the existing defects recognition algorithms: It requires to manually extract features and needs a heuristic method. Furthermore, this method has made the following improvements to traditional methods: (1) It adds a "copy of model parameters" of critical resources to reduce the waiting time of a GPU in requesting for model parameters in the process of model parameters updating;(2) distribution mechanism of small batch data is designed, which, training set is some small batch data that are randomly distributed at GPU side, and is selected by using the critical resource p variable;(3) a certain amount of memory space storage gradients are created on the GPU side and the parameter server side, and the gradient propagation is passed through a "gradient distribution" thread for scheduling. The experimental data show that the recognition rate of network for short-shot defects has reached 99.4%. In addition, compared with BP neural network, it can be seen from the experiment that the recognition rate of the proposed method is significantly better than that of BP neural network, so the method has a good application prospect.

关键词： Deep learning Online recognition Products defects accelerated algorithm

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An accelerated algorithm for Detecting Micro-Moving Objects of Radar Life Detector of Smart Campus Based on Block Data Analysis

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IEEE ACCESS 2018年 6卷 57994-58003页

作者： Liang, Buge Jin, Yanghao Yang, Degui Zhao, Dangjun Zhang, Yadong Cent S Univ Sch Aeronaut & Astronaut Changsha 410083 Hunan Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Gansu Peoples R China

As a non-contact life detecting equipment of smart campus, the ultra-wide band (UWB) radar life detector is widely used in finding victims of post-disaster search and rescue. However, due to the complex detection environment and the weak life characteristics of victims, the detecting time consuming by current detecting methods is long meanwhile the sensitivity is low, as a result, the process of search and rescue is slow and the probability of missed detection is high. To solve this problem, three works have been done in this research: first, a mathematical model of the echo of UWB radar is established;second, a concept, the critical speed of fast and slow objects, is put forward as a reference value of signal processing;and third, a new accelerated algorithm of which utilizes block data analysis to detect micro-moving objects is proposed in this paper. It has been verified both by simulation and experiment that the new algorithm can improve the detecting sensitivity and the speed of detection.

关键词： Smart campus UWB radar life detector mathematical model critical speed accelerated algorithm

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An accelerated algorithm for Ray Tracing Simulation Based on High-Performance Computation 11

An Accelerated Algorithm for Ray Tracing Simulation Based on...

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11th International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Wang, Longhe Guan, Ke Ai, Bo Li, Guangkai He, Danping He, Ruisi Tian, Li Dou, Jianwu Zhong, Zhangdui Beijing Jiaotong Univ Statc Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China ZTE Corp Prod R&D Syst Wireless Algorithm Dept Shanghai 201210 Peoples R China

ISBN: (纸本)9781509047437

Ray tracing tool has played an important role in communication channel modeling. However, accurate simulation with complex scenario will greatly challenge the computational capabilities of personal computer. In this paper, a novel accelerated algorithm for traditional ray tracing tool was proposed based on high-Performance distribute computers. The comparison between traditional ray tracing tool and extended ray tracing tool shown that the accelerating performance for later is highly significant. Numerical results of the simulations show that, jointly using ray tracing tool and high performance distribute computers have been illustrated to be high efficient way for high accurate simulation of channel.

关键词： accelerated algorithm channel modeling high-performance distribute computers ray-tracing

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A NOVEL accelerated FIXED POINT algorithm FOR CONVEX BILEVEL OPTIMIZATION PROBLEMS WITH APPLICATIONS TO MACHINE LEARNING FOR DATA CLASSIFICATION

JOURNAL OF NONLINEAR FUNCTIONAL ANALYSIS

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JOURNAL OF NONLINEAR FUNCTIONAL ANALYSIS 2025年 2025卷

作者： Sae-Jia, Puntita Suantai, Suthep Chiang Mai Univ Fac Sci Dept Math Chiang Mai 50200 Thailand Chiang Mai Univ Fac Sci Res Ctr Optimizat & Computat Intelligence Big Data Dept Math Chiang Mai 50200 Thailand

Fixed point theory and optimization play an important role in machine learning. In this paper, a novel common fixed point algorithm based on the viscosity approximation method and inertial technique is introduced and studied. The strong convergence of the proposed algorithm is established under some control and suitable conditions. As an application, the main result obtained in this work can be applied to solving some convex bilevel optimization problems. Finally, using extreme learning machine model, we employ and compare the performance of our algorithm with the others for data classification problems involving some noncommunication diseases. Our experiments demonstrate that the proposed algorithm has a better convergence behavior than the others in the literature.

关键词： accelerated algorithm Convex bilevel optimization Data classification Double inertial algorithm Machine learning Noncommunicable diseases

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accelerated Nash Equilibrium Seeking for Constrained Multi-Cluster Games With Time-Varying Communication

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Zhu, Rui Wang, Fuyong Liu, Zhongxin Chen, Zengqiang Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

This paper proposes a distributed accelerated Nash equilibrium (NE) seeking algorithm for constrained multi-cluster games with time-varying communication, where agents in the same cluster are cooperative, while there is competition among different clusters. Constraint sets and time-varying interaction networks are considered simultaneously, which is motivated by their widespread presence in practical applications, thereby the designed algorithm can deal with more scenarios where cooperation and competition coexist. To attain higher flexibility and faster convergence, two acceleration techniques, named the heavy-ball method and Nesterov momentum, are introduced into the algorithm with distinct parameters, and an average parameter is employed to handle constraints. Furthermore, the linear convergence of the algorithm to NE is confirmed based on the optimality gap, the state difference, the consensus error and the gradient tracking error, and explicit bounds for the step size and parameters are derived by the associated properties of the time-varying networks and cost function. Finally, the acceleration effect of the algorithm is demonstrated in the energy management system, which validates the feasibility of the designed algorithm.

关键词： Games Vectors Convergence Cost function Nash equilibrium Clustering algorithms Time-varying systems Costs Heuristic algorithms Circuits Constrained multi-cluster game accelerated algorithm time-varying communication

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