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Reduction to the pole near the equator with a Fourier-domain equivalent dipole layer

GEOPHYSICS 2023年第6期88卷 G115-G124页

作者： Luo, Yao Wu, Meiping China Aero Geophys Survey & Remote Sensing Ctr Na Beijing Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China

Reduction to the pole (RTP) transforms the phase of the observed total-field anomaly, making magnetic interpretation easier. However, RTP operations are notoriously unstable at low latitudes and produce singularities at the equator. The equivalent-source approach is an appropriate method to address the well-known difficulties of RTP at low latitudes. Although we routinely use equivalent-source RTP algorithms in the spatial domain, they consume massive computational resources. In contrast, the equivalent-source method in the Fourier domain is more efficient and also invalid for RTP because of the inevitable use of the RTP operator in the inversion. To overcome this problem, we introduce a simple approximation of the RTP operator instead and then develop a novel algorithm to calculate the magnetic moments of the equivalent dipole layer from the total-field anomaly near the equator. Because the calculations are mainly performed in the Fourier domain, this algorithm is more efficient than the conventional spatial-domain method. We also impose a nonnegative constraint on the dipole layer to improve the results and significantly reduce the undesired striation associated with the low-latitude RTP. We evaluate the effectiveness of our approach with synthetic data near the equator, and the results indicate that the algorithm can produce an RTP field that is an accurate representation of the real field at the pole, even when accounting for the existence of noise. The new approach is applied to field data collected near the geomagnetic equator in the South China Sea. In the central part of the anomaly, we obtain a robust spindle shape of the RTP field, which is identical to the shape of a basalt formation in that location. Recent 1:1,000,000 scale marine geologic mapping has confirmed this presence, proving that our solution is an applicable approach to the problem.

关键词： approximation algorithms

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A 2-D Autofocus Algorithm for Long Synthetic Aperture Time SAR in Low-Contrast Scenarios Based on Deep Neural Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Xie, Yi Chen, Longyong Tang, Haibo Zhang, Fubo Jiang, Tao Li, Zhenhua Chinese Acad Sci Aerosp Informat Res Inst Natl Key Lab Microwave Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Naval Univ Engn Sch Elect Engn Wuhan 430033 Peoples R China

As synthetic aperture radar (SAR) technology continues to evolve with a focus on miniaturization, reduced weight, and lower costs, its range of applications has broadened to encompass lightweight and slow-moving platforms, such as small autonomous aerial vehicles (AAVs) and airships. However, this advancement introduces a new challenge for SAR systems in the form of excessively long synthetic aperture time (LSAT). LSAT-SAR faces significant challenges caused by its longer integration time (10-1000 times more than conventional airborne SAR's synthetic aperture time), including more stringent error tolerance of navigation system and more demanding trajectory control requirements. These issues often result in severe 2-D defocusing. Existing high-precision navigation systems and traditional autofocus algorithm, though adequate for conventional airborne SAR, often fail to meet the stringent requirements of LSAT-SAR, especially in low-contrast scenarios, necessitating more efficient and more robust compensation methods. To address these challenges, we propose a 2-D autofocus algorithm for LSAT-SAR using deep neural networks (DNNs). The proposed approach treats the 2-D focusing problem as a series of coupled 1-D curve estimation tasks, employing a weighted entropy loss function. The solution is optimized in an unsupervised manner using a DNN. Finally, the additional refinement is performed through a specialized fine-tuning correction module. Experiments on real LSAT-SAR images with synthetic aperture times of 150-270 s show that the proposed method substantially exceeds the performance of traditional approaches in low-contrast scenarios. It demonstrates excellent robustness, offering valuable insights and technical guidance for LSAT-SAR autofocusing.

关键词： Synthetic aperture radar Apertures Accuracy Artificial neural networks Motion compensation Trajectory Error analysis Entropy Costs approximation algorithms 2-D autofocus deep neural network (DNN) long synthetic aperture time (LSAT) low contrast

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ABA*-Adaptive Bidirectional A* Algorithm for Aerial Robot Path Planning

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IEEE ACCESS 2023年 11卷 103521-103529页

作者： Wang, Pengkai Mutahira, Husna Kim, Jonghoek Muhammad, Mannan Saeed Sungkyunkwan Univ Coll Informat & Commun Engn Dept Elect & Comp Engn Nat Sci Campus Suwon 16419 South Korea Sogang Univ Dept Comp Sci & Engn Seoul 04107 South Korea Sejong Univ Dept Def Syst Engn Seoul 05006 South Korea

The extensive use of robots has led to the rapid development of related technologies, such as automatic guidance and the use of drones in various applications. The challenge in path planning for robots involves finding collision-free paths that satisfy physical and kinematic constraints. The A* algorithm is commonly used for path planning in mobile robot, but its efficiency is reduced in large 3D environments due to high computational demands. This study introduces an improved version of the A* algorithm, called the Adaptive Bidirectional A* algorithm, specifically designed for path planning in 3D environments. The algorithm adapts to 3D environment variables and utilizes bidirectional search to find the optimal path. Compared to the original A* algorithm, the new algorithm significantly reduces planning time, up to 91.2%. The algorithm is applied to quadcopter path planning, and the simulation results demonstrate its superior performance. Furthermore, theoretical analysis validates the efficiency of the algorithm. Overall, the proposed ABA* algorithm shows great potential for real-world applications that require proposed path planning in 3D environments with limited computational resources.

关键词： Path planning Heuristic algorithms Robots Drones Three-dimensional displays Costs approximation algorithms Bidirectional control A* algorithm adaptive bidirectional A* algorithm quadcopter path planning

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Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 995-1009页

作者： Ma, Lijia Shao, Zengyang Li, Xiaocong Lin, Qiuzhen Li, Jianqiang Leung, Victor C. M. Nandi, Asoke K. Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH Middx England Shenzhen Univ Shenzhen 518060 Peoples R China

Influence maximization (IM) in complex networks tries to activate a small subset of seed nodes that could maximize the propagation of influence. The studies on IM have attracted much attention due to their wide applications such as item recommendation, viral marketing, information propagation and disease immunization. Existing works mainly model the IM problem as a discrete optimization problem, and use either approximate or meta-heuristic algorithms to address this problem. However, these works are hard to find a good tradeoff between effectiveness and efficiency due to the NP-hard and large-scale network properties of the IM problem. In this article, we propose an evolutionary deep reinforcement learning algorithm (called EDRL-IM) for IM in complex networks. First, EDRL-IM models the IM problem as a continuous weight parameter optimization of deep Q network (DQN). Then, it combines an evolutionary algorithm (EA) and a deep reinforcement learning algorithm (DRL) to evolve the DQN. The EA simultaneously evolves a population of individuals, and each of which represents a possible DQN and returns a solution to the IM problem through a dynamic markov node selection strategy, while the DRL integrates all information and network-specific knowledge of DQNs to accelerate their evolution. Systematic experiments on both benchmark and real-world networks show the superiority of EDRL-IM over the state-of-the-art IM methods in finding seed nodes.

关键词： Optimization Integrated circuit modeling Complex networks Computational modeling approximation algorithms Reinforcement learning Metaheuristics Complex networks influence maximization deep reinforcement learning evolutionary algorithm optimization

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Design of Polar Codes and PAC Codes for SCL Decoding

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第5期71卷 2587-2601页

作者： Chiu, Mao-Ching Su, Yi-Sheng Natl Chung Cheng Univ Dept Commun Engn Chiayi 621 Taiwan

The performance of a code under the maximum-likelihood (ML) decoder highly depends on the weight enumerating function (WEF). However, how to compute efficiently the WEF of a polar code or a polarization-adjusted convolutional (PAC) code is still an open problem. For the design of stand-alone polar codes, we consider enumerating the number of minimum-weight non-zero codewords of the polar code. The block error rate (BLER) under the ML decoder can be approximated as a function of the minimum weight of non-zero codewords and its multiplicity. On the other hand, for the design of PAC codes, we consider enumerating the WEF averaged over the ensemble of random PAC codes. The ML-BLER upper bound can be represented as a function of the WEF. The bit-channel selection algorithms for polar codes and PAC codes are proposed, which take both utilization of the polarization effect and the ML decoding performance as selection criteria. Simulation results show that the proposed stand-alone polar codes are competitive when the block length gets larger. Also, simulation results show that the proposed PAC codes yield excellent performance for a wide range of code rates and block lengths and outperform the 5G polar codes.

关键词： Codes Decoding Polar codes Picture archiving and communication systems Convolutional codes Simulation approximation algorithms polarization-adjusted convolutional codes weight enumerating function minimum distance

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A Novel Value Iteration Scheme With Adjustable Convergence Rate

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第10期34卷 7430-7442页

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this article, a novel value iteration scheme is developed with convergence and stability discussions. A relaxation factor is introduced to adjust the convergence rate of the value function sequence. The convergence conditions with respect to the relaxation factor are given. The stability of the closed-loop system using the control policies generated by the present VI algorithm is investigated. Moreover, an integrated VI approach is developed to accelerate and guarantee the convergence by combining the advantages of the present and traditional value iterations. Also, a relaxation function is designed to adaptively make the developed value iteration scheme possess fast convergence property. Finally, the theoretical results and the effectiveness of the present algorithm are validated by numerical examples.

关键词： Convergence Iterative algorithms Stability criteria Heuristic algorithms approximation algorithms Optimal control Numerical stability Adaptive dynamic programming (ADP) admissible control policy convergence rate discrete-time nonlinear systems reinforcement learning (RL) value iteration

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AutoDock Koto: A Gradient Boosting Differential Evolution for Molecular Docking

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第6期27卷 1648-1662页

作者： Ji, Junkai Zhou, Jin Yang, Zhangfan Lin, Qiuzhen Coello, Carlos A. Coello Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China CINVESTAV IPN Dept Comp Sci Evolutionary Computat Grp Mexico City 07300 DF Mexico Tecnol Monterrey Sch Engn & Sci Monterrey 64849 Mexico

Molecular docking plays a vital role in modern drug discovery, by supporting predictions of the binding modes and affinities of ligands at the binding site of target proteins. Several docking programs have been developed for both commercial and academic applications. Typically, a docking program's performance depends on the sampling algorithm used to generate the ligand's potential conformations and the scoring function applied to evaluate and rank these conformations. Evolutionary algorithms are widely used as sampling algorithms in docking programs. However, both the linkage problem and the dimensionality degenerate the search ability of evolutionary algorithms in the docking process. Therefore, a newly designed docking program named AutoDock Koto was developed in this study, which adopts a novel gradient boosting differential evolution algorithm to effectively address these issues. Experimental results show that compared with commonly used docking programs, AutoDock Koto yields dramatic improvements in docking performance based on an extensive dataset of 285 protein-ligand complexes. In addition, due to its strong docking ability, AutoDock Koto was used to identify potential drugs for COVID-19 based on a virtual screening of all approved drugs in our experiments. Sixteen drugs are found to possess low binding energy to the main target protease of SARS-CoV-2 and, thus, have the potential to treat COVID-19 as antiviral drugs. The source code of AutoDock Koto can be downloaded for free from https://***/codezhouj/Molecular_Docking.

关键词： Drugs Proteins Couplings Genetic algorithms COVID-19 Three-dimensional displays approximation algorithms Differential evolution drug design molecular docking SARS-CoV-2 virtual screening

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Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第11期72卷 13985-14001页

作者： Hu, Zijian Gao, Xiaoguang Wan, Kaifang Wang, Qianglong Zhai, Yiwei Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China

Unmanned aerial vehicles (UAVs) have been widely used in military warfare, and realizing safely autonomous motion control (AMC) in complex unknown environments is a challenge to face. In this article, we formulate the AMC problem as a Markov decision process (MDP) and propose an advanced deep reinforcement learning (DRL) method that allows UAVs to execute complex tasks in different environments. Aiming to overcome the limitations of the prioritized experience replay (PER), the proposed asynchronous curriculum experience replay (ACER) uses multithreads to asynchronously update the priorities and assigns the true priorities to increase the diversity of experiences. It also applies a temporary pool to enhance learning fromnew experiences and changes the fashion of experience pool to first-in-useless-out (FIUO) to make better use of old experiences. In addition, combined with curriculum learning (CL), a more reasonable training paradigm is designed for ACER to train UAV agents smoothly. By training in a large-scale dynamic environment constructed based on the parameters of a real UAV, ACER improves the convergence speed by 24.66% and the convergence result by 5.59% compared to the twin delayed deep deterministic policy gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities further demonstrate the strong robustness and generalization ability of the ACER agents.

关键词： Autonomous aerial vehicles Training Convergence Task analysis approximation algorithms Deep learning Complexity theory Autonomous motion control curriculum learning deep reinforcement learning experience replay UAV

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Fast and High Accuracy 3D Point Cloud Registration for Automatic Reconstruction From Laser Scanning Data

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IEEE ACCESS 2023年 11卷 42497-42509页

作者： Xu, Anran Rao, Lei Fan, Guangyu Chen, Niansheng Shanghai Dianji Univ Lab Robot & Intelligent Technol Shanghai 201306 Peoples R China Shanghai Dianji Univ Coll Elect Informat Shanghai 201306 Peoples R China

Point cloud registration from laser scanning data is a technique to establish the mapping relationship between source and target point clouds, which has been widely used in automatic 3D reconstruction, pose estimation, localization, and navigation. While algorithms like Super4PCS and MSSF-4PCS can achieve registration without initial poses, they are relatively slow, less accurate, and require iterations. To address these issues, we propose a 3D point cloud registration algorithm based on interval segmentation and multi-dimensional feature. Firstly, the source and target point clouds are segmented internally and the point cloud curvature is designed to narrow down the search range for the registration between the segmented point clouds. Secondly, the corresponding four-point sets in the segmented areas of the source and target point clouds are determined using affine invariance constraints. Finally, a multi-dimensional feature vector based on curvature features and fast point feature histogram is established to determine the unique corresponding four-point set pairs, and the rigid body transformation matrix is solved accordingly. Our algorithm is tested on publicly available 3D point cloud data models Bunny, Dino, Dragon, and Horse from Stanford University. Results showed that our algorithm improved registration accuracy by 24.39% and registration efficiency by 46.21% compared to the MSSF-4PCS point cloud registration algorithm. Multiple sets of experimental results confirmed the effectiveness of our algorithm. The proposed 3D point cloud registration is proved to be fast with high accuracy, which can be utilized for automatic segmentation, reconstruction, and modelling from Laser Scanning Data.

关键词： Point cloud compression Three-dimensional displays Feature extraction Real-time systems approximation algorithms Matrix converters Histograms Point cloud registration point cloud curvature affine invariance multi-dimensional features

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Fleet Sizing for Reservation-Based Car-Sharing Services Allowing Cruise Parking

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2023年第4期15卷 106-120页

作者： Guo, Ge Hou, Yuqin Sun, Tianyu Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

Fleet sizing is an important problem in the planning procedure of one-way car-sharing systems. This article investigates the fleet-sizing problem for reservation-based car-sharing systems allowing cruise parking. The system with traffic congestion is modeled as a mixed queuing network with its servers mapping the remaining road capacity. The optimal fleet size with maximum operating profit is derived, taking into account asymptotic performance metrics like reservation, vehicle availability and utilization, and occupancy of parking spaces of each of the stations. The solution is obtained based on an exact mean value analysis (MVA) algorithm and an approximate Schweitzer-Bard MVA (SB-MVA) algorithm. Numerical experiments demonstrate that the proposed method with cruise parking can meet the travel demands with greatly reduced operating cost. Another interesting finding is that a larger reservation time window implies lower parking capacity and smaller fleet size, whereas, as the time window exceeds a threshold, reducing the fleet size may damage the quality of service.

关键词： Servers Automobiles Queueing analysis Space vehicles Roads Routing approximation algorithms

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