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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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A Visual Kinematics Calibration Method for Manipulator Based on Nonlinear Optimization

A Visual Kinematics Calibration Method for Manipulator Based...

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2022 Chinese Automation Congress, CAC 2022

作者： Peng, Gang Wang, Zhihao Guan, Shangbin Deng, Zhang Guo, Wentao Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing China Wuhan Lisai Technology Co. Ltd. Wuhan China

ISBN: (纸本)9781665465335

The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera's parameters affects the calibration accuracy of the kinematics of the robot arm. In order to solve this problem, this paper reduces the dimensionality of the experimental data and redesigns the objective function to be optimized. That is, the three-dimensional positions of the feature points of the calibration plate under each manipulator attitude corresponding to the actual kinematic model and the classic D-H kinematic model are mapped into the pixel coordinate system, and the sum of Euclidean distance errors of the pixel coordinates of the two is used as the objective function to be optimized. And then, nonlinear optimization and parameter compensation are performed. The experimental results show that the pixel deviation of the end pose corresponding to the optimized D-H kinematic model proposed in this paper and the end pose corresponding to the actual kinematic model in the pixel coordinate system is 0.99 pixels. Compared with the 7.9 deviation pixels between the pixel coordinates calculated by the classic D-H kinematic model and the actual pixel coordinates, the deviation is reduced by nearly 7 pixels for an 87% reduction in error. Therefore, the proposed method can effectively avoid system errors caused by camera parameters in visual calibration, can improve the absolute positioning accuracy of the end of the robotic arm, and has good economy and universality. © 2022 IEEE.

关键词： Pixels

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Research and application of Transformer based anomaly detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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A Sensitivity Analysis Method of Soil Erosion Based on Land Use Type 6th

A Sensitivity Analysis Method of Soil Erosion Based on Land ...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Zhang, Jinxin Li, Hui Zhang, Xiufang Yu, Hua Wang, Min Shen, Yazhi Jiang, Miaohua Zhang, Fuquan Ocean College Minjiang University Fuzhou350108 China College of Forestry Fujian Agricultural and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9789811650352

Soil erosion is one of the main environmental problems in the world. In this paper, the Minjiang river basin was taken as the research area. Based on the land use map, a comprehensive soil erosion modulus map is generated by referring to the soil erosion evaluation index and the revised soil loss equation (RUSLE). The results showed that the Minjiang river basin was dominated by mild erosion and moderate erosion, accounting for 41.50% and 54.81% of the total basin area, respectively, and the high erosion accounted for only 3.69%. The soil erosion intensity of each land use type was unused land > garden land > cultivated land > forest land > water body > construction land. By discussing the sensitivity evaluation of soil erosion in Minjiang river basin, this paper provides a reference for relevant departments to provide soil and water conservation work in Minjiang river basin. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Erosion

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LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Point Cloud Heat Map

arXiv

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arXiv 2024年

作者： Wu, Xinrui Xu, Jianbo Hu, Puyuan Wang, Guangming Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

— Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method. Our code is available at: https://***/IRMVLab/LHMaploc. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation

DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spa...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Huixin Zhang Guangming Wang Xinrui Wu Chenfeng Xu Mingyu Ding Masayoshi Tomizuka Wei Zhan Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Department of Engineering University of Cambridge Cambridge U.K. UC Berkeley Berkeley CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a 3D point cloud sequence learning model based on inconsistent spatio-temporal propagation for LiDAR odometry, termed DSLO. It consists of a pyramid structure with a spatial information reuse strategy, a sequential pose initialization module, a gated hierarchical pose refinement module, and a temporal feature propagation module. First, spatial features are encoded using a point feature pyramid, with features reused in successive pose estimations to reduce computational overhead. Second, a sequential pose initialization method is introduced, leveraging the high-frequency sampling characteristic of LiDAR to initialize the LiDAR pose. Then, a gated hierarchical pose refinement mechanism refines poses from coarse to fine by selectively retaining or discarding motion information from different layers based on gate estimations. Finally, temporal feature propagation is proposed to incorporate the historical motion information from point cloud sequences, and address the spatial inconsistency issue when transmitting motion information embedded in point clouds between frames. Experimental results on the KITTI odometry dataset and Argoverse dataset demonstrate that DSLO outperforms state-of-the-art methods, achieving at least a 15.67% improvement on RTE and a 12.64% improvement on RRE, while also achieving a 34.69% reduction in runtime compared to baseline methods. Our implementation will be available at https://***/IRMVLab/DSLO.

关键词： Point cloud compression Solid modeling Laser radar Accuracy Three-dimensional displays Runtime Logic gates Robot sensing systems Real-time systems Odometry

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Two-Dimensional Metal-Organic Framework Nanosheets Coated Solid-Phase Microextraction Arrow Coupled with Uplc-Q-Tof-Ms for the Determination of Three Veterinary Residues in Milk and Pork

SSRN

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SSRN 2024年

作者： Du, Xuanhua Lan, Hangzhen Wu, Zhen Pan, Daodong Wu, Yichun State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agroproducts Zhejiang Key Laboratory of Intelligent Food Logistic and Processing Zhejiang-Malaysia Joint Research Laboratory for Agricultural Product Processing and Nutrition College of Food Science and Engineering Ningbo University Ningbo315800 China Zhoushan Institute for Food and Drug Control Zhoushan316012 China

This study presents a method utilizing solid-phase microextraction Arrow (SPME Arrow) combined with ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) for the selective detection of three veterinary drugs-thiabendazole, sulfamethazine, and clenbuterol-in milk and pork. Two-dimensional metal-organic framework nanosheets (2D-MOFs) were employed as coating materials for the SPME Arrow. Three types of 2D-MOFs (Ni, Mn, and Co based) were synthesized and characterized using Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, and a physical adsorption analyzer. The 2D-MOF coatings were fabricated using the electrospinning technique, with polyacrylonitrile (PAN) serving as the binder. Comparative analysis of the three 2D-MOF coatings revealed that 2D-Ni-MOF was the optimal coating material for the SPME Arrow. Optimization of the coating preparation conditions and SPME procedures included determining the optimal mass ratio of 2D-Ni-MOF to PAN, electrospinning time, and extraction and desorption parameters. Equilibrium extraction was achieved within 60 minutes, and desorption was completed within 30 minutes. Subsequently, the 2D-Ni-MOF-SPME Arrow-UPLC-Q-TOF-MS method was established and validated under optimal conditions, demonstrating high precision with inter-day precision ranging from 3.8% to 9.5% and intra-day precision ranging from 5.1% to 11.5%. The reusability study indicated that the extraction performance of the new SPME Arrow remained consistent after 90 adsorption-desorption cycles. The method exhibited linearity in milk and pork over the ranges of 0.002–5 μg L–1 and 0.01–5 μg L–1, respectively. The detection limits in milk and pork were 0.001–0.004 μg L–1 and 0.003–0.007 μg L–1, respectively. This method demonstrated excellent applicability for determining residues of the three veterinary drugs in milk and pork. © 2024, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Specific Design and Implementation of the Characteristic Species Database in Wuling Mountain Area

Specific Design and Implementation of the Characteristic Spe...

引用

Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Shaowei Deng Yujun Yang Yimei Yang College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua Hunan China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

This article aims to design and implement a database system for characteristic species in the Wuling Mountain area. The system will collect, organize, store, and analyze information on characteristic species in the Wuling Mountain area, providing data support for ecological research, biodiversity conservation, agricultural production, and other fields. This article will provide a detailed introduction to database requirements analysis, data model design, database structure implementation, data security management, and system testing and optimization.

关键词： System testing Data security Ecosystems Production Database systems Data models Biodiversity Information technology Optimization

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Distributed Real-Time and Fair Resource Allocation for 5G Dense Cellular Networks Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Chen, Yong Liu, Zhixin Yuan, Yazhou Guan, Xinping Hebei Key Laboratory of Industrial Computer Control Engineering China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Qinhuangdao 066004 China Shanghai Jiaotong University School of Electronic Information and Electrical Engineering Shanghai 200240 China

This paper considers a 5G dense cellular network scenario where wireless channels are dynamically changing. To address the resource allocation problems in 5G dense networks, a distributed real-time and fair resource allocation algorithm based on a single deep Q-network (DQN) and multiple local deep neural networks (DNNs) architecture (DRFRA-SDML) is proposed. Specifically, each downlink is modeled as an agent. Each downlink is equipped with a local deep neural network (DNN), allowing each agent to input locally observed information into the local DNN to select spectral subband and transmission power in real-time. In the core network, the global experience replay buffer is utilized to collect local experiences collected by all local agents to train a global weight vector of the train DNN, which is shared by all local DNNs. This ensures that the complexity of the algorithm does not depend on the size of the cellular network, resulting in excellent scalability. To achieve fairness between user devices, a reward function based on fairness has been designed. The simulation results how that, while ensuring fairness between user devices, DRFRA-SDML significantly outperforms IFP-FRA, FP-FRA-D, and Random in both sum rate and real-time performance. © 2014 IEEE.

关键词： cellular network deep reinforcement learning (DRL) DQN multi-agent resource allocation

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