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Micro defects formation and dynamic response analysis of steel plate of quasi-cracking area subjected to explosive load

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Defence technology（防务技术） 2024年第1期31卷 580-593页

作者： Zheng-qing Zhou Ze-chen Du Xiao Wang Hui-ling Jiang Qiang Zhou Yu-long Zhang Yu-zhe Liu Pei-ze Zhang Research institute of Macro-Safety Science University of Science and TechnologyBeijing100083China School of Civil and Resource Engineering University of Science and Technology BeijingBeijing100083China School of Mechanical Engineering Nantong UniversityNantong226019China China North Industry advanced technology generalization institute Beijing100089China China Academy of Ordnance Science China

As the protective component,steel plate had attracted extensive attention because of frequently threats of explosive *** this paper,the evolution of microstructure and the mechanism of damage in the quasi-cracking area of steel plate subjected to explosive load were discussed and the relationships between micro defects and dynamic mechanical response were *** the explosion experiment,five observation points were selected equidistant from the quasi-cracking area of the section of the steel plate along the thickness direction,and the characteristics of micro defects at the observation points were analyzed by optical microscope(OM),scanning electron microscope(SEM) and electron backscattered diffraction(EBSD).The observation result shows that many slip bands(SBs) appeared,and the grain orientation changed obviously in the steel plate,the two were the main damage types of micro *** addition,cracks,peeling pits,grooves and other lager micro defects were appeared in the lower area of the *** stress parameters of the observation points were obtained through an effective numerical *** mechanism of damage generation and crack propagation in the quasicracking area were clarified by comparing the specific impulse of each observation point with the corresponding micro *** result shows that the generation and expansion of micro defects are related to the stress area(*** upper compression area,the neutral plane area,and the lower tension area).The micro defects gather and expand at the grain boundary,and will become macroscopic damage under the continuous action of tensile ***,the micro defects at the midpoint of the section of the steel plate in the direction away from the explosion center(*** horizontal direction) were also *** was found that the specific impulse at these positions were much smaller than that in the thickness direction,the micro defects were only SBs and a few micro cracks,and the those decrea

关键词： Explosive load Quasi-cracking area Micro defects Steel plate Dynamic response Numerical simulation

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Microscopic defects formation and dynamic mechanical response analysis of Q345 steel plate subjected to explosive load

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Defence technology（防务技术） 2024年第2期32卷 430-442页

作者： Zhengqing Zhou Zechen Du Yulong Zhang Guili Yang Ruixiang Wang Yuzhe Liu Peize Zhang Yaxin Zhang Xiao Wang Research institute of Macro-Safety Science University of Science and TechnologyBeijing100083China School of Civil and Resource Engineering University of Science and TechnologyBeijing100083China China North Industry advanced technology generalization institute Beijing100089China College of Field Engineering PLA Army Engineering UniversityNanjing210007China School of Mechanical Engineering Nantong UniversityNantong226019China

As the basic protective element, steel plate had attracted world-wide attention because of frequent threats of explosive loads. This paper reports the relationships between microscopic defects of Q345 steel plate under the explosive load and its macroscopic dynamics simulation. Firstly, the defect characteristics of the steel plate were investigated by stereoscopic microscope(SM) and scanning electron microscope(SEM). At the macroscopic level, the defect was the formation of cave which was concentrated in the range of 0-3.0 cm from the explosion center, while at the microscopic level, the cavity and void formation were the typical damage characteristics. It also explains that the difference in defect morphology at different positions was the combining results of high temperature and high pressure. Secondly, the variation rules of mechanical properties of steel plate under explosive load were studied. The Arbitrary Lagrange-Euler(ALE) algorithm and multi-material fluid-structure coupling method were used to simulate the explosion process of steel plate. The accuracy of the method was verified by comparing the deformation of the simulation results with the experimental results, the pressure and stress at different positions on the surface of the steel plate were obtained. The simulation results indicated that the critical pressure causing the plate defects may be approximately 2.01 GPa. On this basis, it was found that the variation rules of surface pressure and microscopic defect area of the Q345 steel plate were strikingly similar, and the corresponding mathematical relationship between them was established. Compared with Monomolecular growth fitting models(MGFM) and Logistic fitting models(LFM), the relationship can be better expressed by cubic polynomial fitting model(CPFM). This paper illustrated that the explosive defect characteristics of metal plate at the microscopic level can be explored by analyzing its macroscopic dynamic mechanical response.

关键词： Explosive load Q345 steel Micro defect Finite element simulation Dynamic response Data fitting

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Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of Computer Science and technology Beijing Institute of Technology advanced Reasearch institute of Multidisciplinary Science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

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Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of Mechanical Transmission for advanced Equipment&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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Science China(Information Sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift cyberspace security intrusion detection malicious traffic identification variational autoencoder

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Initial costates derived by near-optimal reference sequence and least-squares method

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Chinese Journal of Aeronautics 2024年第5期37卷 377-391页

作者： Shaozhao LU Yao ZHANG Quan HU School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China

In this paper,we present a novel initial costates solver for initializing time-optimal trajectory problems in relative motion with continuous low *** proposed solver consists of two primary components:training a Multilayer Perceptron(MLP)for generating reference sequence and Time of Flight(TOF)to the target,and deriving a system of linear algebraic equations for obtaining the initial *** overcome the challenge of generating training samples for the MLP,the backward generation method is proposed to obtain five different training *** training database and sample form are determined by analyzing the input and output correlation using the Pearson correlation *** best-performing MLP is obtained by analyzing the training results with various hyper-parameter combinations.A reference sequence starting from the initial states is obtained by integrating forward with the near-optimal control vector from the output of ***,a system of linear algebraic equations for estimating the initial costates is derived using the reference sequence and the necessary conditions for *** results demonstrate that the proposed initial costates solver improves the convergence ratio and reduce the function calls of the shooting ***,Monte-Carlo simulation illustrates that the initial costates solver is applicable to different initial velocities,demonstrating excellent generalization ability.

关键词： Relative motion Expanding training database Multilayer perceptron Initial costates Least-squares method

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Ensemble successor representations for task generalization in offline-to-online reinforcement learning

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Science China(Information Sciences) 2024年第7期67卷 240-255页

作者： Changhong WANG Xudong YU Chenjia BAI Qiaosheng ZHANG Zhen WANG Space Control and Inertial technology Research Center Harbin Institute of Technology Shanghai Artificial Intelligence Laboratory Shenzhen Research institute of Northwestern Polytechnical University School of Cybersecurity Northwestern Polytechnical University

In reinforcement learning(RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides a promising solution by giving an initialized offline policy, which can be refined through online interactions. However, existing approaches primarily perform offline and online learning in the same task, without considering the task generalization problem in offline-to-online adaptation. In real-world applications, it is common that we only have an offline dataset from a specific task while aiming for fast online-adaptation for several tasks. To address this problem, our work builds upon the investigation of successor representations for task generalization in online RL and extends the framework to incorporate offline-to-online learning. We demonstrate that the conventional paradigm using successor features cannot effectively utilize offline data and improve the performance for the new task by online fine-tuning. To mitigate this, we introduce a novel methodology that leverages offline data to acquire an ensemble of successor representations and subsequently constructs ensemble Q functions. This approach enables robust representation learning from datasets with different coverage and facilitates fast adaption of Q functions towards new tasks during the online fine-tuning *** empirical evaluations provide compelling evidence showcasing the superior performance of our method in generalizing to diverse or even unseen tasks.

关键词： offline reinforcement learning online fine-tuning task generalization successor representations ensembles

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Towards robustness and generalization of point cloud representation:A geometry coding method and a large-scale object-level dataset

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Computational Visual Media 2024年第1期10卷 27-43页

作者： Mingye Xu Zhipeng Zhou Yali Wang Yu Qiao Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518000China University of Chinese Academy of Sciences Beijing065001China Shanghai AI Laboratory Shanghai200001China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen518000China Alibaba DAMO Academy Hangzhou242332China

Robustness and generalization are two challenging problems for learning point cloud *** tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengraph to group points with similar geometric information,even when such points are far from each *** also introduce a large-scale point cloud dataset,*** consists of 184 categories and 51,915 synthetic objects,which brings new challenges for point cloud classification,and provides a new benchmark to assess point cloud cross-domain ***,we perform extensive experiments on point cloud classification,using ModelNet40,ScanObjectNN,and our PCNet184,and segmentation,using ShapeNetPart and *** method achieves comparable performance to state-of-the-art methods on these datasets,for both supervised and unsupervised *** and our dataset are available at https://***/MingyeXu/PCNet184.

关键词： geometry coding self-supervised learning point cloud classification segmentation 3D analysis

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The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

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Nuclear Science and Techniques 2024年第2期35卷 170-182页

作者： Gao-Yi Cheng Qian-Min Su Xi-Guang Cao Guo-Qiang Zhang School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620China Shanghai advanced Research institute Chinese Academy of SciencesShanghai 201210China Shanghai institute of Applied Physics Chinese Academy of SciencesShanghai 201800China

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate *** urgently need solutions to the dilemma of traditional particle identification *** study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate *** intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 ***-based machine learning algorithms and deep learning algorithms *** show excellent performance and generalization *** additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is *** learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.

关键词： Heavy-ion collisions at low and intermediate energies Machine learning Ensemble learning algorithm Particle identification Data imbalance

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Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

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Computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen institutes of advanced technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

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