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Research Progress in Solar Flare Prediction Methods

Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： Sun: activity Sun: flares (Sun:) sunspots Sun: magnetic fields magnetohydrodynamics (MHD)

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High performance of Mg_(3)Bi_(1.5)Sb_(0.5) based materials for power generation:Revealing the counter-intuitive effect of tuning Bi content on the thermoelectric properties

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Journal of Magnesium and Alloys 2024年第11期12卷 4538-4546页

作者： Nuo Qu Yuke Zhu Jianbo Zhu Kuai Yu Fengkai Guo Zihang Liu Qian Zhang Wei Cai Jiehe Sui State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Department of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055China

Recently,Mg_(3)Sb_(2)-xBixalloys have attracted intensive attention,with the aim of maximizing the electrical transport performance via donor element doping,increasing the grain size,as well as electronic band structure ***,less attention has been paid to other significant factors,like how these intrinsic point defects and accompanied secondary phases influence thermoelectric *** this study,the microstructure and thermoelectric transport properties of Mg_(3.2)Sb_(0.5)Bi_(1.495-x)Te_(0.005)(x=0~0.2)compounds were systematically investigated,where tuning the Bi content shows the counter-intuitive impact on the thermoelectric *** was found that the Bipoor environment associated with Bi impurities facilitated the increment of cation vacancy formation energy and then increased the carrier concentration,leading to the enhancement of power ***,the reduction of Bi-rich second phase content weakened the carrier scattering by grain boundaries whereas high carrier mobility was ***,the bipolar thermal conductivity decreased obviously due to the increased majority carrier concentration to suppress the intrinsic *** synergistic optimization pushes the average ZT value(300-573 K)up to 0.95 in the Mg_(3.2)Sb_(0.5)Bi_(1.295)Te_(0.005)***,the calculated single-leg conversion efficiency is increased up to 9.7%with the hot-side temperature of 573 K,as the record-high value in this system.

关键词： Mg_(3)Sb_(2)-xBix alloys Nanoscale Bi-rich phase Bipolar thermal conductivity Thermoelectric performance

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Interpretable machine learning for chiral induced symmetry breaking of spin density boosting hydrogen evolution

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Journal of Energy Chemistry 2025年第4期103卷 68-78页

作者： Xin Song Zhonghua Li Li Sheng Yang Liu School of Chemistry and Chemical Engineering Key Laboratory of Microsystems and Microstructures ManufacturingMinistry of EducationHarbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

The integration of machine learning and electrocatalysis presents nota ble advancements in designing and predicting the performance of chiral materials for hydrogen evolution reactions(HER).This study utilizes theoretical calculations and machine learning techniques to assess the HER performance of both chiral and achiral M-N-SWCNTs(M=In,Bi,and Sb)single-atom catalysts(SACs).The stability preferences of metal atoms are dependent on chirality when interacting with chiral *** HER activity of the right-handed In-N-SWCNT is 5.71 times greater than its achiral counterpart,whereas the left-handed In-N-SWCNT exhibits a 5.12-fold *** calculated hydrogen adsorption free energy for the right-handed In-N-SWCNT reaches as low as-0.02 *** enhancement is attributed to the symmetry breaking in spin density distribution,transitioning from C_(2V)in achiral SACs to C_(2)in chiral SACs,which facilitates active site transfer and enhances local spin ***-handed M-N-SWCNTs exhibit superiorα-electron separation and transport efficiency relative to left-handed variants,owing to the chiral induced spin selectivity(CISS)effect,with spin-upα-electron density reaching 3.43×10^(-3)e/Bohr^(3)at active *** learning provides deeper insights,revealing that the interplay of weak spatial electronic effects and appropriate curvature-chirality effects significantly enhances HER performance.A weaker spatial electronic effect correlates with higher HER activity,larger exchange current density,and higher turnover *** curvature-chirality effect undersco res the influence of intrinsic structures on HER *** findings offer critical insights into the role of chirality in electrocatalysis and propose innovative approaches for optimizing HER through chirality.

关键词： Symmetry breaking Machine learning Spin density CISS DFT Hydrogen evolution reaction

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Adversarial attacks and defenses for digital communication signals identification

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Digital Communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150000China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators

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Stabilization With Prescribed Instant via Lyapunov Method

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 557-559页

作者： Jiyuan Kuang Yabin Gao Yizhuo Sun Aohua Liu Jianxing Liu Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China

Dear Editor,This letter presents a prescribed-instant stabilization approach to high-order integrator systems by the Lyapunov method. Under the presented controller, the settling time of controlled systems is independ... 详细信息

关键词： method. prescribed instant

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mVLINS: A Multilevel Visual-LiDAR-Inertial Navigation System with Completely Decoupled Odometry and Adaptive Environmental Mapping

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Zhang, Bing Yao, Weiran Wang, Yankun Li, Peng Shao, Xiangyu Sun, Guanghui department of control science and engineering Harbin Institute of Technology Harbin China

The core component of the navigation system is the process of multi-sensor fusion localization in a challenging environment with limited GNSS constraints. This process is designed to robustly and precisely estimate the system state and map the traversed area. In this paper, we propose a low-drift, highly resilient multilevel navigation system with completely decoupled odometry and adaptive environmental mapping. A novel method for visual-LiDAR-inertial frame-to-frame odometry is presented to leverage the complementary strengths of these sensors. This odometry approach involves decoupling the 6-degree-of-freedom (6-DoF) state to allocate each state component to an appropriate submodule for estimation. An adaptive environmental mapping module that aims to align the target frame with the local map is proposed to refine the rough odometry pose. This module is achieved through the utilization of an adaptive keyframe strategy and the feature consistency constraint. Enhance the matching of keyframes to the map by reducing the point-to-feature error between frame points and map features. Additionally, the state is further refined by minimizing the feature consistency error on lines in the adjacent corner map and the local map at each keyframe. Our proposed algorithm is validated using both public and self-collected datasets, demonstrating superior results compared to state-of-the-art algorithms. IEEE

关键词： Optical radar

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Generating Long Financial Report Using Conditional Variational Autoencoders With Knowledge Distillation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1669-1680页

作者： Wang, Ziao Ren, Yunpeng Zhang, Xiaofeng Wang, Yiyuan Harbin Institute of Technology Department of Computer Science Engineering Shenzhen518055 China

Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a conditional variational autoencoders (CVAEs)-based approach that distills external knowledge from a set of news-report data. Specifically, we design an encoder-decoder architecture to learn the latent variable distribution from this set of news-report data to provide background knowledge. Next, a teacher-student network is employed to distill knowledge to refine the output of the decoder component. To evaluate the model performance, extensive experiments have been performed on two public datasets using evaluation criteria like BLEU, ROUGE, METEOR, and human evaluation. Our promising experimental results demonstrate that our proposed approach outperforms existing state-of-the-art approaches. © 2020 IEEE.

关键词： Natural language processing systems

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Effects of La and Ce on the microstructure,thermal conductivity and strength synergy of the as-extruded Mg-Mn-RE alloys

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Journal of Magnesium and Alloys 2025年第2期13卷 654-667页

作者： Huafeng Liu Taiki Nakata Chao Xu Guangze Tang Danyang Li Xiaojun Wang Guisong Wang Shigeharu Kamado Lin Geng State Key Laboratory of Precision Welding&Joining of Materials and Structures Harbin Institute of TechnologyHarbin150001PR China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001PR China Department of Mechanical Engineering Nagaoka University of TechnologyNagaoka940-2188Japan Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin150001PR China School of Physics Harbin Institute of TechnologyHarbin150001PR China

High thermal conductivity and high strength Mg-1.5Mn-2.5Ce alloy with a tensile yield strength of 387.0 MPa,ultimate tensile strength of 395.8 MPa,and thermal conductivity of 142.1 W/(m·K)was successfully fabricated via hot *** effects of La and Ce additions on the microstructure,thermal conductivity,and mechanical properties of the Mg-1.5Mn alloy were *** results indicated that both the as-extruded Mg-1.5Mn-2.5La and Mg-1.5Mn-2.5Ce alloys exhibited a bimodal grain structure,with dynamically precipitated nano-scaleα-Mn *** comparison with La,the addition of Ce enhanced the dynamic precipitation more effectively during hot extrusion,while its influence on promoting the dynamic recrystallization was relatively *** high tensile strength obtained in the as-extruded Mg-1.5Mn-2.5RE alloys can be attributed to the combined influence of the bimodal grain structure(with fine dynamic recrystallized(DRXed)grain size and high proportion of un-dynamic recrystallized(unDRXed)grains),dense nano-scale precipitates,and broken Mg12RE phases,while the remarkable thermal conductivity was due to the precipitation of Mn-rich phases from the Mg matrix.

关键词： Mg-Mn-RE alloys Thermal conductivity Mechanical properties Dynamic precipitation

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Content-Aware Dynamic In-Loop Filter With Adjustable Complexity for VVC Intra Coding

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6114-6128页

作者： Man, Hengyu Wang, Hao Lu, Riyu Wan, Zhaolin Fan, Xiaopeng Zhao, Debin Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China City University of Hong Kong Department of Computer Science Hong Kong

Recently, neural network-based in-loop filters have been rapidly developed, effectively improving the reconstruction quality and compression efficiency in video coding. Existing deep in-loop filters typically employed networks with fixed structures to process all image blocks. However, under various bitrate conditions, compressed image blocks with different textures exhibit varying degradations, which poses a challenge for high-quality and low-complexity filtering. Additionally, different complexity requirements for coding tools in various scenarios limit the versatility of fixed models. To address these problems, a content-aware dynamic in-loop filter (dubbed DILF) with adjustable complexity is proposed in this paper. Specifically, DILF comprises a policy network and a filtering network. For each reconstructed image block, the policy network dynamically generates a filtering network topology based on pixel information and the quantization parameter (QP), guiding the filtering network to skip redundant layers and conduct content-aware image enhancement, thereby improving the filtering performance. In addition, by introducing a user-defined balancing factor into the policy network, the content-aware filtering network topology can be further adjusted according to user’s requirements, facilitating adjustable complexity with a single model. We integrate DILF into Versatile Video Coding (VVC) to replace the built-in deblocking filter. Extensive experiments demonstrate the efficiency of DILF in processing image blocks with varying degrees of degradation and its flexibility in controlling complexity. When the balancing factor is set to 2e-5, DILF achieves bitrate savings of 8.07%, 17.97%, and 20.93% on average for YUV components over VVC reference software VTM-11.0 under all-intra configuration. Compared to static networks with fixed structures, DILF demonstrates superior performance and lower computational complexity. © 1991-2012 IEEE.

关键词： Image coding

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