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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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Research on Boot Problem and Automatic Repair of an Embedded Real-Time Operating system

Research on Boot Problem and Automatic Repair of an Embedded...

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Computer Technologies (ICCTech), International Conference on

作者： Haoshang Zhou Zhaoling Gao Zhongfu Wang Xiaopeng Wang State Key Laboratory for Traction and Control System of EMU and Locomotive CRRC Dalian Electric Traction R&D Center Co. Ltd. School of Intelligence and Electronic Engineering Dalian Neusoft University of Information

At present, in the communication between Electric Multiple Units (EMU) and locomotive network control system, in order to ensure the stability of vehicle control and the accuracy and reliability of data recording, its key components - Central control Unit (CCU) and Event Record machine (ERM) adopt an embedded real-time operation system. In order to facilitate debugging, the system needs to start Microsoft's DOS system before being called to start. Based on the feedback of the embedded real-time operating system startup failure from each locomotive and rolling stock depot, this paper will elaborate on its startup process and propose a solution that can automatically repair the startup problem, which has been tested in the laboratory to prove that this method can effectively avoid the system startup failure.

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Natural Gas Load Forecasting Based on Multi-Stage Model Identification

Natural Gas Load Forecasting Based on Multi-Stage Model Iden...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Pengbin Zhang Hongrui Wang Kunpeng Xing Mingzhe Yuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China GUANGZHOU INSTITUTE OF INDUSTRIAL INTELLIGENCE Guangzhou China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Natural gas load forecasting is crucial for the safe operation of urban natural gas. Based on the system identification method, the short-term natural gas load forecasting is studied in the paper. Employing a multi-stage identification framework, the initial values of the model parameters using the least squares and the instrumental variables method, while the model parameters are further optimized by the prediction error method to improve the accuracy of the model identification. The proposed method is validated by the simulation based on the public natural gas data in the U.S., demonstrating the algorithm's effectiveness in forecasting the daily natural gas load effectively.

关键词： Load forecasting Simulation Load shedding Predictive models Prediction algorithms system identification Reliability Forecasting Natural gas Load modeling

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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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Chalcogenide Integrated Nanophotonics and Its Potential for Quantum Photonics

Chalcogenide Integrated Nanophotonics and Its Potential for ...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Kunhao Lei Kai Xu Kangjian Bao Jiawei Weng Lan Li Hongtao Lin The State Key Lab of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou China The Key Laboratory of 3D Micro/ Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou Zhejiang China Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou Zhejiang China

Due to the high optical nonlinearity and nonvolatile phase-changing properties, chalcogenide integrated nanophotonics are emerging for entangling photon generation and nonvolatile quantum states operation. We will rev... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Integrated optics Chalcogenides Nanophotonics Reviews Optical device fabrication Optical resonators Quantum state Optical network units Optical materials Quantum networks

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Learning Hierarchical Color Guidance for Depth Map Super-Resolution

arXiv

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arXiv 2024年

作者： Cong, Runmin Sheng, Ronghui Wu, Hao Guo, Yulan Wei, Yunchao Zuo, Wangmeng Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong Jinan250061 China Artificial Intelligence Institute Beijing Normal University Beijing100875 China Changsha410073 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Lingnan University Hong Kong

Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively. The code and results can be found from the link of https://***/HCGNet_TIM2024. © 2024, CC BY-NC-SA.

关键词： Restoration

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Slewing control of Overhead Crane Based on State-enhance Coupling Method

Slewing Control of Overhead Crane Based on State-enhance Cou...

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第43届中国控制会议

作者： Rui Liu Biao Lu Jinyang Dong Weili Ding The 6th Research Institute of China Electronic Corporation Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Yanshan University National Key Laboratory of Key Technologies for Lifting Machinery Yanshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Cranes which are frequently utilized as sizable machinery, are widely used across a range of settings, including warehouses, distribution hubs, harbors, steel plants, and manufacturing facilities. However, the current researches on cranes have paid insufficient attention to the rotation of loads during transportation. Additionally, some of the proposed control methods fail to effectively integrate information from both actuated DOFs(degrees of freedom) and unactuated DOFs, leading to a loss of controller robustness. In this paper, we establish an overhead crane model with a slewing mechanism, focusing on the analysis of crane's lifting and rotating motion. Based on that, a nonlinear controller is proposed for load slewing control. The stability of the controller is proven using Lyapunov techniques and LaSalle's invariance theorem. The effectiveness and robustness of the controller are validated through numerical simulations.

关键词： Load Rotation Nonlinear controller Robustness

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Small-Size Zif-8/In2o3 Core-Shell Nanocomposites for Highly Specific Detection of Ppb-Level No2

SSRN

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SSRN 2024年

作者： Wang, Hongtao Zhang, Yaodan Xie, Yuanchao Han, Dan Ma, Jianan Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificiat Intelligence Biomedical Science Taiyuan030024 China

The enhanced sensitivity and selectivity of nitrogen dioxide (NO2) gas sensors are paramount for atmospheric environmental monitoring. In this work, a novel small-size ZIF-8/In2O3 core-shell nanocubes gas sensor was fabricated using a straightforward solvothermal method, complemented by in situ modification. The ZIF-8 film markedly increased the specific surface area of In2O3 nanocubes from 60.64 m²/g to 140.84 m²/g. Concurrently, there was a rise in the content of adsorbed oxygen ions on the surface. The gas-sensing results demonstrated that the ZIF-8/In2O3 composites achieved a response of 56.3 to 1 ppm of NO2, with an exceptionally low detection limit of 1 ppb. Importantly, it exhibited unique selectivity for NO2 over other interfering gases. The outstanding NO2 gas-sensing performance of the ZIF-8/In2O3 composites can be attributed to the grain model effect inherent in the In2O3 (with side lengths of approximately 40-50 nm), along with the sensitization influence and filtering efficacy of ZIF-8. This advancement in gas sensor technology has significant potential for applications in the rapid detection of low-concentration NO2 gas. © 2024, The Authors. All rights reserved.

关键词： Indium compounds

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A Novel Magnetoelastic Biosensor Based on Pdms/Fesib/Qds Composite Film for African Swine Fever Virus Detections

SSRN

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SSRN 2024年

作者： Liu, Yuanhang Sang, Shengbo Zhao, Dong Ge, Yang Xue, Juanjuan Duan, Qianqian Guo, Xing Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of The Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China

African swine fever (ASF) is a highly contagious and severe hemorrhagic disease caused by the African swine fever virus (ASFV). The continuous spread of ASFV affects the safety of global meat supply, so the establishment of sensitive and specific detection methods for ASFV has become an important hot spot in food safety. Herein, we developed a flexible magnetoelastic (ME) biosensor based on PDMS/FeSiB/QDs composite films for the detection of ASFV. Based on the high luminescence performance of CsPbBr3 quantum dots and the excellent magnetoelastic effect of FeSiB, flexible ME biosensors convert stress signals generated by antibody-antigen-specific binding into optical and electromagnetic signals. The nanostructures covalently linked by quantum dots and PDMS provide biomodification sites for ASFV antibodies, simplifying the functionalization modification process compared to conventional biosensors. The deformation of the PDMS film is amplified and conversion of surface stress signal to electrical signal is enhanced by exposing the biosensor to a uniform magnetic field. The experimental results proved that the flexible ME biosensor has wide linear range of 10 ng/mL - 100 μg/mL, and the detection limit is as low as 0.7933 ng/mL. Moreover, the flexible ME biosensor have also shown to good stability, sensitivity and specificity. © 2024, The Authors. All rights reserved.

关键词： Biosensors

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