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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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PIM-ST: a New Paraphrase Identification Model Incorporating Sequence and Topic information 4

PIM-ST: a New Paraphrase Identification Model Incorporating ...

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4th International Symposium on Computer technology and information Science, ISCTIS 2024

作者： Mao, Kaili Zhao, Qingjuan Beijing Institute of Control Engineering Beijing China University Beijing Computer School Beijing Information Science and Technology Beijing China

ISBN: (纸本)9798350354560

Paraphrase identification, with the objective to determine whether two sentences are paraphrases of each other, has fostered many applications including natural language inference and document retrieval. Traditional machine learning models for paraphrase identification are generally based on hand-crafted features. Recent deep neural network models generally take semantic vectors as inputs and generate classification results directly. However, existing solutions still have much room for improvement. First, existing models do not fully exploit sequence information (i.e., the order of words) and topic information, which are important for text under-standing. Second, although deep models achieve promising results, they require a large amount of labelled data and have low interpretability. To cope with these problems, we propose a new model called PIM-ST (Paraphrase Identification Model Incorporating Sequence and Topic information). Specifically, besides semantic information, PIM-ST extracts sequence information and topic information, and adopts a simple machine learning model LightGBM as the classifier. We conduct comparative experiments and ablation studies on two public datasets Microsoft Research Paraphrase Corpus (MSRP) and Quora Question Pairs. Experimental results show that PIM-ST achieves better results than existing state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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Failure modes determination and load-bearing capacity evaluation of concrete columns under seismic loads by ANNs

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Soft Computing 2024年第13-14期28卷 8361-8377页

作者： Hao, Meijing Chang, Wei Xu, Chonghao School of Architecture and Civil Engineering Harbin University of Science and Technology Harbin150080 China Key Lab of Structures Dynamic Behavior and Control Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology 202 Haihe Road Nangang District Harbin China

The failure modes of concrete columns can be applied to judge the ductility of concrete columns under seismic loads. In addition, the load-bearing capacity of concrete columns was an important parameter in evaluating the seismic performance of concrete columns under seismic loads. However, it was difficult to determine the failure modes and evaluate the load-bearing capacity of concrete columns under seismic loads with the application of high-strength concrete. In this paper, two ANNs, including a genetic algorithm optimization-based support vector machine (GA-SVM) and an ANFIS, were developed to determine the failure modes of concrete columns under seismic loads. The main investigated parameters, including specimens' shear span ratio, the compressive strength of concrete, the yield strength and volumetric ratio of stirrups, and the yield strength and ratio of longitudinal reinforcement, were applied as input variables to the models. The proposed models were trained and tested using 247 data sets from previous studies. The proposed models had high reliability and predictability to determine the failure modes of concrete columns under seismic loads. Furthermore, an ANFIS network was developed to evaluate the load-bearing capacity of concrete columns, which had high reliability and predictability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Failure modes

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Video Visibility Prediction Method for Foggy Weather Based on Spatio-temporal Variation Features 4

Video Visibility Prediction Method for Foggy Weather Based o...

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4th International Conference on Computer Vision and Data Mining, ICCVDM 2023

作者： Li, He Dai, Haoran Yao, Yunxing Cao, Wen Institute of Geospatial Information Information Engineering University Zhengzhou China North Information Control Research Institute Group Co. LTD Nanjing China School of Geoscience and Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9781510674448

Visibility prediction affects travel safety and is a major concern for highway and airline authorities. However, current visibility prediction methods based on video data usually capture some features in static images, which are relatively simple and are not commonly used for continuous variation information of video data. This study proposes a visibility prediction method based on the spatio-temporal variation features of a video. Through the spatial division of the images from the video, the optimal spatial distribution of different features in the image is mapped using the correlation matrix, and the spatio-temporal variation information of the video is extracted based on the short-term stationary features of fog spatio-temporal evolution. Finally, the relationship between the spatiotemporal variation features of the video and visibility is constructed by combining the time-frequency localization features of the wavelet transform and the self-learning ability of the neural network. Compared with the predicted results of traditional static image features, the R2, RMSE, and MAE of the visibility prediction results were improved by 0.1698 and reduced by 58.4142 and 20.0427, respectively. In addition, the R2, RMSE, and MAE of the prediction results based on wavelet neural networks can reach 0.9817, 28.3365, and 19.2098, respectively, compared to current mainstream prediction methods. This fully proves the scientificity and effectiveness of the method, which can be applied to the accurate monitoring of visibility in fog weather based on video data. © 2024 SPIE.

关键词： Fog

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Measuring Transverse Relaxation Time of Xenon Atoms Based on Single Beam of Laser in Nuclear Magnetic Resonance Gyroscope

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Journal of Shanghai Jiaotong university(Science) 2023年第5期28卷 569-576页

作者：钟国宸刘华郭阳李绍良赵万良成宇翔 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Aerospace Control Technology Institute Shanghai Academy of Spaceflight TechnologyShanghai 201109China

Nuclear magnetic resonance gyroscope (NMRG) has the characteristics of high precision and miniaturization, and is one of the main applications of quantum technology in the field of navigation. The transverse relaxation time (T_(2)) of the xenon nuclear spin in the atomic cell of the NMRG directly affects the angular random walk of the gyro. Accurate and rapid measurement of T_(2) is conducive to further improvement of gyroscope. At present, for the measurement of T_(2), the schemes of two orthogonal lasers for pumping and detecting are usually used. By applying two fast-switching orthogonal static magnetic fields and a single beam of circularly polarized laser with corresponding wavelength to pump the atomic cell, the xenon nuclear macroscopic magnetic moment Larmor precession is generated. The cesium atoms parametric magnetometer in cell is formed to detect the free induction decay signal generated by nuclear spin precession of xenon atoms. The measurement of T_(2) by a single laser simplifies the measurement equipment compared with traditional method with two lasers. The experimental results show that the T_(2) of xenon atoms is more than 10 s, and the effects of temperature are studied, which lay the foundation for the subsequent improvement of gyro performance.

关键词： nuclear magnetic resonance gyroscope(NMRG) transverse relaxation time magnetometer free-induction decay

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Cross-modal three-dimensional intrinsic image decomposition for hyperspectral and LiDAR image joint classification 43

Cross-modal three-dimensional intrinsic image decomposition ...

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43rd Chinese control Conference, CCC 2024

作者： Yu, Wenbo Zhang, Miao Soochow University School of Electronics and Information Engineering Suzhou215021 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China

ISBN: (纸本)9789887581581

Existing remote sensing technologies capture discriminant information of on-ground objects and materials from a distance for accurate land-cover identification. Specifically, hyperspectral and light detection and ranging (LiDAR) images record spectral and spatial information and elevation information, respectively. Aligning and fusing both RS modalities comprehensively is beneficial to improving classification performance. However, this alignment and fusion are always challenged by the enormous RS modality gap. In this paper, a cross-modal three-dimensional intrinsic image decomposition (CM3DIID) is presented for hyperspectral and LiDAR image joint classification. Intrinsic image decomposition contributes to enhancing semantic information and making RS modalities clear. Our motivation is to construct a unique cross-modal intrinsic image decomposition for three-dimensional data cubes specifically, facilitating robust discriminant information extraction. Specifically, the hyperspectral image contributes to restricting the decomposed reflectance component, making the samples with similar spectral curves very close. The LiDAR image is considered to search samples with similar elevations, which should be closed in the shading component. The whole optimization process is clearly analyzed and achieved by constructing a deep learning network. The proposed CM3DIID constructs a connection bridge between hyperspectral and LiDAR images for information switching and modality fusing. Multiple experiments are conducted on two widely used hyperspectral and LiDAR datasets to verify the effectiveness and feasibility of the proposed CM3DIID. The final results clearly prove that the proposed CM3DIID is capable of generating better classification performance than other state-of-the-art comparison techniques. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image enhancement

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Design of Vehicle Bus Signal Simulation Device 5

Design of Vehicle Bus Signal Simulation Device

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Chen, Sirui Wang, Shiji Chen, Wenke Li, Baohua Qiao, Jiaqing School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Beijing Aerospace Measurement and Control Technology Co. Ltd Beijing China

ISBN: (纸本)9798331529192

As the functions of automobiles continue to increase and become more intelligent, the data exchange and communication between onboard electronic devices have become increasingly important. To ensure the reliability and stability of these systems in practical applications, it is necessary to manually simulate the testing and verification of ECUs with erroneous bus data packets. This paper designs a vehicular bus signal simulation device that can simulate the generation of CAN, LIN, FlexRay, CXPI, and SENT bus signals and their fault data packets. The hardware part of the device consists of an arbitrary waveform generator and an upper computer, while the software part uses QT to design the user interface and C++ to write the programmable software. Finally, experiments have verified the performance and accuracy of the simulation device. The waveforms captured by the digital storage oscilloscope are consistent with the expected input parameters, proving the effectiveness of the device. This device has significant application value in the development, teaching, and training of automotive electronic systems, capable of reducing the reliance on real-vehicle testing, and reducing test costs and time. © 2024 IEEE.

关键词： C++ (programming language)

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Multi-perspective regional continuity alignment network for hyperspectral and LiDAR image fusion and classification 43

Multi-perspective regional continuity alignment network for ...

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43rd Chinese control Conference, CCC 2024

ISBN: (纸本)9789887581581

In these decades, remote sensing (RS) techniques have been widely used when facing diverse real-world tasks. These unique RS theories tend to observe on-ground objects and materials by several techniques from different perspectives. Generally, several works have verified that land cover classification tasks are always hampered when using one single RS modality owing to the lack of diverse information. In this paper, we propose a multi-perspective regional continuity aligning network (MPRCAnet) for hyperspectral and light detection and ranging (LiDAR) image fusion and classification. Our motivation is to capture and align the regional spatial-spectral continuities from different perspectives for better performance. Specifically, the whole network consists of three parallel streams, including 1) the HS spatial stream, 2) the HS spectral stream and 3) the LiDAR spatial stream. This network aims to discuss the potential of the continuous information extraction strategy in related tasks. Multiple experiments are conducted on two publicly available hyperspectral and LiDAR datasets to prove the effectiveness and feasibility of the proposed MP-RCAnet. Compared with several state-of-the-art techniques, MP-RCAnet is capable of generating fusion and classification results with higher evaluation accuracies. Its classification map has less scattered noise than other techniques. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image fusion

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Identification of Pests and Diseases in Greenhouse Rice Based on ConvNeXt-T Neural Network 2

Identification of Pests and Diseases in Greenhouse Rice Base...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Li, Dan Zhang, Chao Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin Province Jilin City China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Province Jilin City China

ISBN: (纸本)9798350382952

Rice cultivation in greenhouses is a key agricultural form in our agricultural development. Timely detection and prevention of pests and diseases in rice in greenhouses has a significant impact on improving rice yield. Deep learning models excel in image recognition and can be used to monitor rice-induced growth conditions in rice in greenhouses and quickly identify diseases and pests. Image data analysis enables farmers to take timely measures to prevent the spread of pests and diseases. Various environmental factors affect the collected dataset, resulting in an insufficient number of available images, and the training process can easily fail to extract effective features. Addressing these problems, this paper proposes a rice pest and disease recognition model based on the ConvNeXt-T neural network. Data enhancement techniques, such as mirroring and cropping, and data preprocessing steps, including the addition of Gaussian noise, random brightness, and random masking, were applied to the dataset. The initially acquired 5,932 rice pest images were expanded to 21,340 images. These augmented images were then used to train a ConvNeXt-T neural network model for recognizing four of the most common diseases of rice: leaf blight, rice bacterial streak, brown mottle, and rice dong quai virus disease. The experimental results demonstrate that the ConvNeXt-T neural network performs optimally, achieving the highest level of disease recognition accuracy (99.86%) compared to the classical AlexNet, GoogLeNet, ResNet34, and VGG16 networks in the same experimental environment. Its excellent recognition accuracy provides strong support for the prevention of pests and diseases in greenhouse riceacterial streak, brown mottle, and rice dong quai virus disease). The experimental results show that the ConvNeXt-T neural network performs optimally and achieves the highest level of disease recognition accuracy (99.86%) compared with the classical AlexNet, GoogLeNet, ResNet34 and VGG16 netwo

关键词： Greenhouses

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