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PyionNet: Pyramid Progressive Cross-Fusion Network for Joint Classification of Hyperspectral and LiDAR Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12042-12058页

作者： Pan, Haizhu Zhang, Quanxiu Ge, Haimiao Liu, Moqi Shi, Cuiping Qiqihar University College of Computer and Control Engineering Qiqihar161000 China Qiqihar University Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar161000 China Xi'an Jiaotong University School of Information and Communications Engineering Shanxi Xi'an710049 China Huzhou University College of Information Engineering Huzhou313000 China

Joint hyperspectral (HS) and Light Detection and Ranging (LiDAR) data for land cover classification have received widespread attention. Nevertheless, existing methods often struggle with multi-scale feature extraction and multi-source feature fusion. To address these issues, this paper proposes a pyramid progressive cross-fusion network, named PyionNet, for joint classification of HS and LiDAR data. Technically, the proposed PyionNet incorporates a pyramid multi-scale extraction (PME) module cascaded with a progressive cross-fusion (PCF) module, which are designed to extract multi-scale features and fuse multi-source features, respectively. Specifically, the PME module employs pyramid spectral and spatial convolutional blocks to extract and aggregate features from HS and LiDAR data, respectively, then uses an expansion-compression strategy to eliminate redundant information, and thus achieve a compact multi-scale feature representation. Moreover, the PCF module utilizes the transformer architecture and introduces two innovative feature fusion blocks, i.e., the discrepancy feature fusion (DFF) block and the common feature fusion (CFF) block, to progressively integrate multi-source and multi-scale features extracted by the PME module. The DFF block is designed to integrate discrepancy features between HS and LiDAR data to enhance complementary information. Subsequently, the CFF block focuses on fusing common features to progressively enrich the fused representation of the DFF block and further enhance the representation power. Experiments on three real-world datasets demonstrate that the proposed PyionNet outperforms state-of-the-art methods in both qualitative and quantitative results. © 2008-2012 IEEE.

关键词： Feature Selection

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Research on Preventing Commutation Failure in MMC and LCC Parallel DC Systems

Research on Preventing Commutation Failure in MMC and LCC Pa...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Xiong, Lingfei Zheng, Kuan Wu, Yucheng Liu, Yiqi State Grid Economic and Technology Research Institute Co. Ltd. Beijing102209 China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin150040 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

ISBN: (纸本)9789819618675

Offshore wind power transmission systems require high capacity, and LCC-based HVDC transmission systems are well-suited to meet this demand. Nevertheless, LCCs are dependent on the grid to supply commutation voltage, and in the event of voltage dips or distortions in the grid, commutation failures can readily occur. This results in significant oscillations between the AC and DC sides, thereby compromising the system’s safe and stable operation. This paper introduces a method to enhance the system’s resilience to commutation failures by integrating an MMC-based VSC-HVDC system in parallel with an LCC-HVDC inverter station. The MMC offers the advantage of decoupled control of active and reactive power, enabling it to stabilize the AC bus voltage. By maintaining the stability of the AC bus voltage, the resilience of the parallel system to commutation failures is augmented, thereby improving the overall stability of the combined transmission system. Subsequently, a parallel hybrid HVDC system model was developed in PSCAD/EMTDC to simulate AC grid faults, and a coordinated control strategy was formulated. The simulation results demonstrate that the proposed control method significantly enhances the system’s capability to withstand commutation failures. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： HVDC power transmission

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Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning

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Sensors 2025年第9期25卷 2733-2733页

作者： Sun, Ming Zhong, Yihe He, Xiaoou Zhang, Jie College of Computer and Control Engineering Qiqihar University Qiqihar161006 China

Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of the multi-cell NOMA system. In addition, due to the complex and dynamically changing wireless communication environment and the lack of the near-optimal labels, conventional supervised learning methods cannot be directly applied. To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. Furthermore, the joint action (JA) derived from the MDRLNN for channel allocation is used as a representation to be fed into the ULNN for power allocation. In order to maximize the energy efficiency of the multi-cell NOMA system, the expectation of the energy efficiency is used to train both the MDRLNN and the ULNN. Simulation results indicate that the proposed MDRL-UL can achieve higher energy efficiency and transmission rates than other algorithms. © 2025 by the authors.

关键词： Deep reinforcement learning

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Computational imaging based on single-photon detection: a survey

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Artificial Intelligence Review 2025年第8期58卷 1-54页

作者： Pu, Yanyun Zhu, Chengyuan Yao, Gongxin Li, Chao Pan, Yu Yang, Kaixiang Yang, Qinmin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational imaging applications, including bioimaging and remote sensing. In this review, we utilize the CiteSpace tool to create knowledge maps and perform a bibliometric analysis of this research area. Furthermore, we provide a comprehensive overview of the key challenges associated with computational imaging using temporal single-photon counting. We also highlight how these challenges have been addressed under extreme conditions to establish a reference model for future imaging solutions. We examine the performance evaluation parameters of single-photon detectors to enhance the understanding of detector array scaling and their application in constructing efficient computational imaging systems. Lastly, we aim to elucidate the current technical challenges in single-photon detector-based computational imaging and explore their potential future developments.

关键词： Computational Imaging

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Security Offloading Scheduling and Caching Optimization Algorithm in UAV Edge Computing

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IEEE Systems Journal 2025年第1期19卷 96-106页

作者： Qiu, Jianli Kuang, Zhufang Huang, Zhenqi Lin, Siyu Central South University of Forestry and Technology College of Computer and Mathematics Changsha410004 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China

Mobile edge computing, a prospective wireless communication framework, can contribute to offload a large number of tasks to unmanned aerial vehicle (UAV) mobile edge servers. Besides, the demand for server computational resources increasingly ascends as the volume of processing tasks grows. However, in reality, many devices have similar computing tasks and require the same computing data. Therefore, servers can effectively reduce server computing latency and bandwidth costs by caching task data. This investigation explores task security offloading and data caching optimization strategies in scenarios with multiple interfering devices. With the goal of minimizing the total energy consumption, the UAV trajectories, transmission power, task offloading scheduling strategies, and caching decisions is jointly optimized. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To make this problem solved, the original problem is decomposed into three tiers, and an iterative algorithm named CDSFS which is based on the coordinate descent, successive convex approximation, and flow shop scheduling is proposed. Simulation results demonstrate the stability and superiority of the proposed algorithm. © 2007-2012 IEEE.

关键词： Nonlinear programming

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FineBID: Fine-Grained Protocol Reverse engineering for Bit-Level Field IDentification

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2670-2686页

作者： Huang, Tao Gao, Yansong Zheng, Yifeng Wang, Zhanfeng Hu, Chao Fu, Anmin Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China University of Western Australia CrawleyWA6009 Australia Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China Lexbell Company Ltd. Nanjing210014 China Army Engineering University of PLA College of Command and Control Engineering Nanjing210007 China

Protocol Reverse engineering (PRE) serves as the foundation for numerous security analysis techniques, such as vulnerability mining and intrusion detection, etc. The PRE analysis precision can directly affect the accuracy of these downstream techniques. The network-trace-based PRE technique has become the mainstream PRE technique attributed to its ease of implementation. However, without the prerequisite of additional dedicated devices or knowledge of information, the analysis precision of existing network-trace-based PRE methods is often achievable at only byte or half-byte level but not the fine-grained bit-level, which makes it increasingly challenging to meet the precision requirements of those downstream security applications. In this work, we propose a fine-grained PRE scheme, named FineBID, which makes the identification capability in a fine-grained manner for existing network-trace-based PRE methods into bit-level fields. FineBID follows the global characteristics of protocol fields and constructively models the bit-level field identification problem as a multi-objective decision model, which thus effectively overcomes the insufficient representativeness of bit-level fields’ local characteristics. Then, the multi-objective decision model is solved to obtain the Pareto solution set for different field segmentation levels, and the utility value per bit is further computed. The utility value can be used as the immediate indicator to determine whether each bit is a field boundary or not. Meanwhile, we propose an Actual Ground Truth that is more in line with the actual usage of each bit. With extensive experiments on the Internet, wireless, and industrial protocols, we affirm that FineBID can not only significantly reduce the search space for Ground Truth or Actual Ground Truth with a space reduction of 95.3% compared to exhaustive search, but also identify Ground Truth or Actual Ground Truth more accurately than other similar methods. © 2004-2012 IEEE.

关键词： Reverse engineering

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Reference Model Design in Multiple Timescale Adaptive control

Reference Model Design in Multiple Timescale Adaptive Contro...

引用

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Eves, Kameron Valasek, John Electrical and Computer Engineering Engineering Department Utah Tech University Saint GeorgeUT84770 United States Vehicle Systems and Control Laboratory Aerospace Engineering Department Texas A and M University College StationTX77843-3141 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The method [K]control of Adaptive Multiple-timescale Systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior research on KAMS has shown a relationship between the asymptotic behavior of the reference models and the behavior of the full-order closed-loop system. Poor reference model design can cause steady-state error or instability even if that reference model is acceptable in a single timescale context, due to adverse feedback between the reference models and the fast-state manifold. This paper simplifies KAMS control design and provides insight into multiple timescale reference models by identifying design considerations for the fast and slow reference models. For fast reference models, it is shown that the stability proofs used in KAMS control design necessarily require the manifold to be an asymptotically stable equilibrium of the fast reference model, despite prior work that allowed other possibilities. In the case of a slow reference model that is not asymptotically stable to its reference signal, it is common for the inner connection conditions to be dependent on the reference signal. This can cause the interconnection conditions to be ill-formatted for the application of the stability proofs used in KAMS control design. This paper addresses that case and proposes the use of Raffoul’s condition as a method to prove stability in the sense of Lyapunov. A linear quadrotor attitude stabilization problem is used as a framework for the slow reference model analysis. Results presented in the paper show that 1) the asymptotic stability of the fast reference model is a necessary condition for the KAMS stability proofs used in prior work and 2) Raffoul’s condition is a sufficient condition for Lyapunov sense stability even when the inner connection conditions are ill-formatted for the KAMS stability proofs used in prior work. © 2025, American Institute of Aeronautics

关键词： Model reference adaptive control

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Ptychographic characterization of spatiotemporal wavepackets

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Optica 2025年第4期12卷 498-504页

作者： Liu, Yuanyuan Xu, Yingming Zhang, Nianjia Liu, Dawei Wei, Lijin Cao, Qian Shi, Junhui Zhan, Qiwen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Zhangjiang Laboratory 100 Haike Road Shanghai201204 China Research Center for Novel Computational Sensing and Intelligent Processing Zhejiang Lab. Hangzhou311100 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

Exciting advances in spatiotemporal wavepackets have shown great potential in a range of applications, including particle manipulation, time-resolved imaging, and unique light–matter interactions. In particular, spatiotemporal optical vortice (STOV) pulse beams characterized by a helical phase structure in space and time are a recently emerging type of complex structured ultrashort pulse. However, the high-resolution measurement and manipulation of ultrashort spatiotemporal wavepackets still face major challenges due to their inseparable properties in space and time. In this study, based on spectra-encoded ptychography, we implement a simple, compact, reference-free STOV characterization technique with a temporal resolution of 6 fs and a spatial resolution of 11.05 µm. This technique can be employed to analyze various intricate spatiotemporal structural beams, contributing to the growing field of spatiotemporally engineered ultrafast laser pulse beams. Additionally, as a lensless measurement technique, this characterization method may be extended to other spectral ranges, including the extreme ultraviolet and X-ray domains. © 2025 Optica Publishing Group.

关键词： Wave packets

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HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection

arXiv

引用

arXiv 2025年

作者： Zheng, Qizhi Luo, Zhongze Guo, Meiyan Wang, Xinzhu Wu, Renqimuge Meng, Qiu Dong, Guanghui College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

Accurate and real-time object detection is crucial for anomaly behavior detection, especially in scenarios constrained by hardware limitations, where balancing accuracy and speed is essential for enhancing detection performance. This study proposes a model called HGO-YOLO, which integrates the HGNetv2 architecture into YOLOv8. This combination expands the receptive field and captures a wider range of features while simplifying model complexity through GhostConv. We introduced a lightweight detection head, OptiConvDetect, which utilizes parameter sharing to construct the detection head effectively. Evaluation results show that the proposed algorithm achieves a mAP@0.5 of 87.4% and a recall rate of 81.1%, with a model size of only 4.6 MB and a frame rate of 56 FPS on the CPU. HGO-YOLO not only improves accuracy by 3.0% but also reduces computational load by 51.69% (from 8.9 GFLOPs to 4.3 GFLOPs), while increasing the frame rate by a factor of 1.7. Additionally, real-time tests were conducted on Raspberry Pi4 and NVIDIA platforms. These results indicate that the HGO-YOLO model demonstrates superior performance in anomaly behavior detection. © 2025, CC BY.

关键词： Object detection

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