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3D-RPDM: A Method for Measuring Packing Density of Gas-Solid Two-Phase Flow Based on 3-D Reconstruction

IEEE Sensors Journal 2025年第9期25卷 15511-15524页

作者： Ma, Qihang Peng, Gaoliang Zhang, Wei Harbin Institute of Technology School of Mechatronics Engineering Harbin150001 China Eastern Institute of Technology School of Information Science and Technology Ningbo315200 China

This article introduces the 3D-RPDM framework, a method based on 3-D reconstruction for measuring the packing density of gassolid two-phase flow using a structured light system. Packing density, crucial for the manufacturing of gassolid two-phase flow materials, presents challenges in terms of intrusiveness, efficiency, and precision in sensing. The 3D-RPDM comprises calibration, volume estimation, and density calculation. Calibration aligns the structured light sensors system with container and measurement coordinates, capturing accurate particle surface data. Volume estimation is divided into irregular and regular types, accommodating various material shapes for volume calculation. Incorporating material mass data into density models yields accurate packing density measurements. This article evaluates the 3D-RPDM's performance in terms of measurement, robustness, parameter ablation, and time consumption. The experiments confirmed an average packing density measurement error of up to 0.95%, with a maximum deviation of 2.9%, while achieving an average efficiency improvement of 77.21%, underscoring the enhanced efficiency, accuracy, and reliability of the proposed method over traditional approaches. © 2001-2012 IEEE.

关键词： Density of gases

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Effective elastic modulus and energy absorption performance evaluations of a novel re-entrant chiral hybrid honeycomb

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Applied Mathematics and Mechanics(English Edition) 2025年第5期46卷 781-794页

作者： Youjiang CUI Zhihui XU Que ZHOU Baolin WANG Kaifa WANG Biao WANG Research Institute of Interdisciplinary Science Dongguan University of TechnologyDongguan 523808Guangdong ProvinceChina Graduate School at Shenzhen Harbin Institute of TechnologyHarbin 150001China

Re-entrant honeycombs are widely used in safeguard structures due to their geometric simplicity and excellent energy absorption ***,traditional re-entrant honeycombs exhibit insufficient stiffness and stability owing to the lack of internal *** paper proposes a new hybrid honeycomb by integrating a chiral component inside the re-entrant *** Young's modulus is a key parameter to evaluate the energy absorption performance and stiffness,an analytical model is given to predict the effective Young's modulus of the proposed hybrid *** is found that the optimal design scheme is to directly insert a circular ring inside the re-entrant *** normalized specific energy absorption(SEA)of the hybrid honeycomb is 95%larger than that of the traditional re-entrant *** normalized SEA first increases to a peak value and then decreases with the cell wall *** optimal thickness of the cell wall for the maximum SEA is derived in terms of the geometric configuration of the unit *** normalized SEA first decreases to a valley value and then increases with the re-entrant angle.A longer horizontal cell wall results in a smaller normalized *** paper provides a new design method for safeguard structures with high stiffness and energy absorption performance.

关键词： re-entrant honeycomb chiral metamaterial effective Young’s modulus energy absorption performance

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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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High strength-ductility synergy in refractory multi-principal element alloys via special deformation mechanisms and dislocation behaviors

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Rare Metals 2025年第1期44卷 608-622页

作者： Zhi-Wen Li Bao-Xian Su Liang Wang Chen Liu Zhe Li Qing-Da Zhang Bin-Bin Wang Xiang Xue Rui-Run Chen Yan-Qing Su National Key Laboratory for Precision Hot Processing of Metals School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150001China Zhengzhou Research Institute Harbin Institute of TechnologyZhengzhou 450000China Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin 150001China

Ti-Zr-Nb refractory multi-principal element alloys(RMPEAs)have attracted increased attention due to their excellent mechanical *** this study,(TiZr)_(80-x)Nb_(20)Mo_(x)(x=0,5 and 10)alloys were designed,and the intrinsic conflicts between strength and ductility were overcome via composition optimization and *** causes of the superior strength-ductility synergy were investigated in terms of their deformation mechanism and dislocation *** results show that the strength improvement can be attributed to the deformation mechanism transition caused by local chemical fluctuations and lattice ***,the slip band widths decrease after Mo addition,and the measured slip traces in the fracture samples are associated with high-order{112}and{123}slip ***,the grain refinement achieved via recrystallization promotes multi-slip system activation and shortens the slip-band spacing,which reduces the stress concentration and inhibits crack source formation,thereby allowing the alloy to ensure sufficient ***,the Ti_(35)Zr_(35)Nb_(20)Mo_(10)alloy annealed at 900℃ exhibits high yield strength and *** findings provide a new strategy for designing high-strength RMPEAs and addressing room-temperature brittleness.

关键词： Refractory multi-principal element alloy Mechanical property Strengthening mechanism Deformation substructure Dislocation behavior

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Machine learning-driven insights into biaxial strain-induced anomalous thermal conductivity enhancement of boron arsenide

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science China Technological sciences 2025年第5期68卷 5-18页

作者： Yikun LIU Yurong HE Tianqi TANG Jiaqi ZHU School of Energy Science and Engineering Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology Heilongjiang Key Laboratory of New Energy Storage Materials and Processes National Key Laboratory of Science and Technology on Advanced Composites in Special Environments

Boron arsenide(BAs) is a promising integrated circuit thermal management material with ultra-high thermal conductivity(κ) and exceptional semiconductor properties. In practical devices, mechanical stress and strain caused by thermal expansion inevitably affect the thermodynamic properties of heat sink materials. Consequently, investigation of the effects of stress and strain on the κ of BAs is essential. Accurate assessment and regulation of the κ and phonon heat transport of BAs requires consideration of both three-phonon(3ph) and four-phonon(4ph) interactions. However, anharmonicity computation using density functional theory is computationally expensive, particularly for the fourth-order interatomic force constants. This work uses a machine learning-driven moment tensor potential method to evaluate higher-order anharmonicity and extends it to include fourth-order forces, accelerating the calculations significantly. Results show that the κ of BAs calculated using the moment tensor potential method when considering both 3ph and 4ph processes agrees well with experimental data. In contrast,considering the 3ph interaction alone causes κ to be overestimated. The machine learning approach reduces computational costs by approximately 94% while maintaining comparable accuracy to density functional theory. By solving the phonon Boltzmann transport equation, the thermal transport properties of BAs under biaxial tensile strains ranging up to 7% are evaluated. BAs shows an anomalous κ enhancement when including both 3ph and 4ph interactions, with a maximum enhancement of 15% at a small strain of 1%, primarily because of the weakening of the 3ph scattering. Beyond 2% strain, both the 3ph and 4ph scattering rates increase significantly, causing shorter phonon lifetimes and a monotonic reduction in κ. Additionally, the κ temperature dependence under strain is explored, highlighting temperature's role in modulating phonon scattering. This study provides machine learning-driven i

关键词： boron arsenide machine learning potential biaxial strain thermal conductivity enhancement phonon transport

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Improving Multimodal Large Language Models through Combining Resampler and MLP Projections

Improving Multimodal Large Language Models through Combining...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, Zixing Bai, Yuting School of Computer Science and Technology Fudan University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

Current multimodal large language models (LLMs) achieve impressive performance through connecting visual encoders and LLMs by resampler or MLP projections. Although the MLP projections are effective and widely used in recent works when compared to resampler projections, it has the flaw of the inability to change the number of tokens, which limits the application scenarios. In this paper, we propose a new projection structure that inherits the efficiency of traditional MLP projections and has the ability to flexibly change the number of tokens. Furthermore, we propose a new multimodal LLM with the proposed projection applied. To validate the performance, we evaluate the proposed model on the first large-scale multimodal science question dataset scienceQA, and the first multimodal LLM evaluation benchmark MME. Our model achieves a new state-of-the-art (SoTA) result of 94.27% on scienceQA, which is higher than the previous SoTA result of 92.53% achieved by the LLaVA+GPT-4 judge model. On the MME benchmark, our model achieves better performance when compared to representative chatbot models BLIP-2 and InstructBLIP, with significantly lower training costs. © 2025 IEEE.

关键词： large language models Multimodal vision and language

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Dynamic Layered Clustering Routing Protocol Based on Hybrid-Optimized Neural Networks for UWSNs

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IEEE Internet of Things Journal 2025年第11期12卷 16155-16170页

作者： Lu, Xinmiao Yang, Longyue Wu, Qiong Zhu, Yuna Zou, Yixin Shi, Yunbo Harbin University of Science and Technology School of Measurement-Control Technology and Communications Engineering Harbin150000 China Heilongjiang Network Space Research Center School of Computer Science and Technology Harbin150000 China

To address the limitations of traditional flat routing in large-scale Underwater Wireless Sensor Networks (UWSNs), and to tackle challenges such as long delays, low bandwidth, and high error rates encountered by sensor nodes in underwater environments, this paper proposes a dynamic layered clustering routing protocol based on a hybrid optimized backpropagation neural network (PSB-NN), referred to as PSBDR. The protocol introduces non-uniform vertical layering of the network based on water depth and assigns different communication and sensing radii to nodes according to their initial layer. A trained hierarchical prediction model dynamically adjusts the virtual layer assignments of nodes throughout the network's operation. Additionally, a method is implemented for isolated nodes to join clusters via relay nodes, and a priority mechanism, along with a dynamic weight update strategy, is employed to select the optimal next hop during routing. This protocol effectively balances the workload across nodes at varying depths and maximizes the utilization of residual energy in the nodes, contributing to an extending network lifetime. Simulation results demonstrate that PSBDR enhances network performance by improving load balancing, prolonging network longevity, reducing end-to-end delay, and increasing data collection reliability. © 2014 IEEE.

关键词： Network layers

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Vector bending sensor based on low-cost single-channel measurement and highly reliable three-peak reconstruction algorithm

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Journal of Lightwave technology 2025年第11期43卷 5276-5282页

作者： Li, Xiyu Liu, Yi Li, Yan Qu, Shiliang Harbin Institute of Technology School of Physics Harbin150001 China Harbin Institute of Technology Department of Optoelectronics Science Weihai264209 China

A vector bending sensor based on low-cost single-channel measurement and a highly reliable three-peak reconstruction algorithm is proposed. The sensor consists of single-mode fiber (SMF), multimode fiber (MMF) and two side-hole fibers (SHFs) of different lengths, with a 90° offset angle. The lengths of MMF and SHFs are 375 μm, 400 μm and 300 μm. Fresnel reflection occurs at the interface due to the difference in refractive index (RI) between air and fiber cladding, creating three reflective surfaces. Three Fabry-Perot interferometers (FPIs) with different optical path difference (OPD) are constructed using only two SHFs. A low-cost single-channel measurement method is employed to simultaneously demodulate the interference signals from the three FPIs. When bending is applied to the sensor, each FPI exhibits different strain variation. Experimental results show that the bending responses of FPIs are highly dependent on the bending directions, and each of them exhibits its maximum sensitivity at a different bending direction. The maximum positive sensitivities of FPIs are 155.11 pm/m-1 (0°), 210.45 pm/m-1 (225°), and 91.78 pm/m-1 (255°), respectively. Thus, the three FPIs are located at different spatial positions, making them fitted for vector bending sensing. In addition, the application of the three-peak reconstruction algorithm significantly enhances the stability and reliability of the curvature and bending direction reconstruction. © 2025 IEEE. All rights reserved.

关键词： Fabry-Perot interferometers

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Enhancing microwave absorption performance of MoS_(2)by synergistic effect of Fe doping

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Journal of Advanced Ceramics 2025年第3期14卷 79-90页

作者： Yuefeng Yan Guangyu Qin Kaili Zhang Boshi Gao Guansheng Ma Xiaoxiao Huang Yu Zhou National Key Laboratory of Precision Welding&Joining of Materials and Structures Harbin Institute of Technology Harbin 150001China School of Materials Science and Engineering Harbin Institute of Technology Harbin 150001China MIIT Key Laboratory of Advanced Structural-Functional Integration Materials&Green Manufacturing TechnologyHarbin Institute of TechnologyHarbin 150001China

Owing to its unique two-dimensional structure and tunable electronic properties,MoS_(2)has emerged as a promising electromagnetic wave(EMW)-absorbing material that can be extensively combined with various other substances to construct effective EMW ***,research on cation substitution doping in MoS_(2)remains relatively limited,which impedes the design and development of high-performance MoS_(2)-based EMW absorbing *** this study,MoS_(2)was synthesized with various concentrations of doped Fe via a facile hydrothermal *** thoroughly investigated the effects of Fe doping,which induced lattice distortion and collapse,triggered a 1T‒2H phase transition,and led to the formation and evolution of second *** modulation of phase transitions,coupled with doping-induced lattice defects that enhance polarization and interfacial polarization from second phases,enabled the Fe-doped MoS_(2)samples to exhibit remarkable EMW absorption ***,the sample FM3 achieved an effective absorption bandwidth(EAB)of 5.1 GHz and a minimum reflection loss(RLmin)of−60.6 dB,underscoring the critical role of Fe doping in increasing the EMW absorption *** research provides valuable pathways and unique insights for the advancement of transition metal dichalcogenides(TMDs)as high-performance EMW-absorbing materials.

关键词： microwave absorption Fe-doped MoS_(2) dielectric property impedance characteristic second phase

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