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TopoChat:Enhancing Topological Materials Retrieval with Large Language Model and Multi-Source Knowledge 认领 引用
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作者 Huang-Chao Xu Bao-Hua Zhang +3 位作者 Zhong Jin Tian-Nian Zhu Quan-Sheng Wu Hong-Ming Weng 《Journal of Computer Science & Technology》 SCIE EI CSCD 2026年第2期684-697,共14页
Large language models(LLMs)perform well in general text tasks but face challenges in specialized fields like materials science.We present TopoChat,a knowledge-enhanced question-answering framework for materials scienc... Large language models(LLMs)perform well in general text tasks but face challenges in specialized fields like materials science.We present TopoChat,a knowledge-enhanced question-answering framework for materials science,which combines a domain-specific knowledge graph(TopoKG,Topological Materials Knowledge Graph)and a literature clustering module.TopoChat retrieves both relevant subgraphs and literature information for each query,integrating structured and unstructured knowledge to support LLM reasoning.Experiments on two benchmarks,MaScQA and TopoQA,show that TopoChat improves answer accuracy across multiple LLMs.These results demonstrate that integrating knowledge graphs and literature context enhances reliability in scientific question answering.TopoChat provides an effective approach for adapting LLMs to complex domains,narrowing the gap between general language abilities and domain expertise. 展开更多
关键词 information retrieval large language model(LLM) knowledge graph prompt learning
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LLM-KE: An Ontology-Aware LLM Methodology for Military Domain Knowledge Extraction 认领 引用 被引量：2
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作者 Yu Tao Ruopeng Yang +3 位作者 Yongqi Wen Yihao Zhong Kaige Jiao Xiaolei Gu 《Computers, Materials & Continua》 SCIE EI 2026年第1期2045-2061,共17页
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati... Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development. 展开更多
关键词 Knowledge extraction natural language processing knowledge graph large language model
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Multi-source errors evaluation of machine tools:from research gaps to methodologies and applications 认领 引用 被引量：1
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作者 Jingang Sun Yanbin Zhang +14 位作者 Xiao Ma Benkai Li Min Yang Liandi Xu Haiyuan Xin Qinglong An Lida Zhu Qingfeng Bie Xianxin Yin Shouhai Chen Guanqun Li Yusuf Suleiman Dambatta Rui Xue Zhenwei Yu Changhe Li 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期44-96,共53页
Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-re... Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-reliability machining in modern manufacturing systems.However,these errors typically exhibit complex characteristics such as strong coupling,time-variance,and nonlinearity,which challenge traditional methods of error identification,modeling,and compensation in terms of adaptability,real-time capability,and integration.Therefore,it is imperative to establish a systematic and intelligent multi-source error control framework.Firstly,this work systematically reviews typical error sources and their evolution mechanisms,evaluates multi-scale detection technologies including laser interferometry,double ball-bar systems,multi-sensor fusion,and vision-based systems,and constructs an intelligent error identification and evaluation framework.Next,it reviews classical modeling methods such as homogeneous transformation matrices,screw theory,thermal equilibrium models,finite element analysis,and modal analysis,compares physical modeling,data-driven,and hybrid modeling strategies,and develops an integrated multi-source error modeling architecture centered on digital twin technology and artificial intelligence.Furthermore,key technologies,including geometric error mapping and real-time compensation,online thermal error prediction and active temperature control,dynamic error suppression,and adaptive control,are summarized.A multi-level integrated error compensation architecture is proposed by combining physical models,data models,and cyber-physical synchronization.This architecture encompasses core processes such as error traceability and decoupling,dynamic prediction,real-time compensation,and closed-loop optimization,emphasizing engineering implementation mechanisms based on cyber-physical collaboration,multi-physics coupling,and multi-scale fusion,thereby effectively enhancing accuracy stability and control robustness under complex operating conditions.Finally,frontier challenges such as constructing high-fidelity coupled models from heterogeneous multi-source data,edge-cloud collaborative control,and cross-platform interoperability are discussed.The application prospects of multi-source error evaluation are also envisioned,providing theoretical foundations and technical support for the precise management and optimization of the entire lifecycle accuracy of machine tools. 展开更多
关键词 multi-source error evaluation error coupling integrated error modeling traceability and decoupling hybrid prediction closed-loop error compensation
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Hybrid Deep Learning for Hydraulic Cylinder Fault Diagnosis under Complex Conditions via Multi-Source Signal Fusion 认领 引用 被引量：1
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作者 Chen Yang Jianwen Yan +2 位作者 Yixiong Feng Lei Li Jianrong Tan 《Instrumentation》 2026年第1期40-56,共17页
Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinder... Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinders are difficult to diagnose due to the scarcity of labeled data,the complexity of fault mechanisms,and the limited representation capability of single-signal methods under variable operating conditions.To address these issues,a hybrid deep learning feature fusion model based on displacement error and pressure signal,including convolutional autoencoder,multi-head attention mechanism,residual network and bidirectional long short time series neural network(CAEMRAB),is proposed for the diagnosis and classification of leakage faults in hydraulic cylinders.A hydraulic cylinder test system simulates heavy load,variable speed,and nonlinear motion under actual operating conditions.Through the all-round deep feature decoupling of the proposed model,the multi-source signal representation ability in complex and multi-noise environments is enhanced,effectively extracting the local and global features of displacement error and pressure signal fault data and achieving efficient classification.Experimental results indicate that the proposed model achieves at least a 3.95%improvement in diagnostic accuracy compared with ablation models.In addition,it exhibits high diagnostic stability across other models,single-signal diagnosis,varying sample sizes,and complex noise conditions.These experiments fully validate the superior performance of the proposed method in terms of diagnostic accuracy,reliability,and robustness. 展开更多
关键词 hydraulic cylinder feature fusion multi-source signal hybrid deep learning deep feature decoupling
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Thoughts on the Agricultural Practice of the Party s Governance Strategy for Xinjiang in the New Era:Based on the Traditional Knowledge and Cultural Heritage 认领 引用 被引量：2
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作者 Zhiguo SUN Dongbing XING +2 位作者 Yulan BAI Jinjin WANG Meng LIU 《Asian Agricultural Research》 2026年第6期1-12,共12页
Traditional knowledge, biological genetic resources and folk literature and art are intellectual property rights of cultural heritage that can be shared by regional groups without time limit. This paper studies the tr... Traditional knowledge, biological genetic resources and folk literature and art are intellectual property rights of cultural heritage that can be shared by regional groups without time limit. This paper studies the traditional knowledge and cultural heritage of Xinjiang agriculture from the aspects of traditional knowledge, important agricultural heritage system, intangible cultural heritage, biological genetic resources, tangible cultural heritage, frontier development and defense culture, and cultural tourism resources. It analyzes the main problems existing in the protection and inheritance of them, and puts forward suggestions such as inheriting and sharing intellectual property rights of cultural heritage, improving the protection system of biological germplasm resources, establishing national-level cultural ecological protection (experimental) zones, promoting agricultural science and technology cultural exchanges, creating Xinjiang's characteristic Great Wall culture, deeply integrating "agriculture+culture+tourism", building national and autonomous region cultural parks, and dynamically inheriting agricultural cultural heritage. 展开更多
关键词 Traditional knowledge Biological genetic resources Agricultural intellectual property Cultural enrichment of Xinjiang The Party's governance strategy for Xinjiang
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Hybrid knowledge reasoning over knowledge hypergraph:Inductive,deductive,and abductive 认领 引用
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作者 Ling Tian Lei Gao +3 位作者 Ben Zhang Xiao Liu Yi-Nong Shi Hui Gao 《Journal of Electronic Science and Technology》 EI CAS CSCD 2026年第2期11-26,共16页
Traditional knowledge reasoning methods,which are predominantly reliant on static rules and structured data,often struggle to adapt to the ambiguity and dynamic evolution of real-world scenarios.To overcome these limi... Traditional knowledge reasoning methods,which are predominantly reliant on static rules and structured data,often struggle to adapt to the ambiguity and dynamic evolution of real-world scenarios.To overcome these limitations,this study proposes a novel reasoning framework based on a three-layered knowledge hypergraph.Core innovation lies in the synergy of inductive,deductive,and abductive reasoning mechanisms to enhance both reliability and interpretability.Specifically,hypergraph-based inductive reasoning extracts robust evolutionary patterns by mining the historical subgraph structures.Deductive reasoning ensures transparency by constructing tree-shaped inference paths,whereas abductive reasoning establishes causal traceability by forming evidence chains from historical contexts.Experimental evaluations on the Integrated Crisis Early Warning System(ICEWS)dataset demonstrate that the proposed approach significantly outperforms existing methods in terms of accuracy and interpretability,thereby offering a scalable solution for complex event analysis. 展开更多
关键词 Abductive reasoning Deductive reasoning Inductive reasoning Knowledge hypergraphs Knowledge reasoning
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Q&A system for international construction contracts driven by large language model and knowledge graph 认领 引用
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作者 Yongcheng FU Tong LYU +1 位作者 Yongqiang CHEN Ziyun LV 《ENGINEERING Management》 CSCD 2026年第1期124-145,共22页
As international construction projects continue to expand,construction enterprises are accumulating vast amounts of contract‑related text data,making the effective management and extraction of knowledge from these den... As international construction projects continue to expand,construction enterprises are accumulating vast amounts of contract‑related text data,making the effective management and extraction of knowledge from these dense texts essential to mitigate knowledge loss and ensure efficient contract management.The advent of large language models(LLMs)presents a promising avenue for enhancing contract knowledge management through intelligent systems.However,challenges such as hallucination,inflexibility,and lack of interpretability often diminish practitioners’confidence in applying these models to real‑world scenarios.This study seeks to develop a knowledge‑based question‑and‑answer(Q&A)system for international construction contracts by integrating both the knowledge graph(KG)and the LLM.Built upon a domain‑specific KG derived from the 2022 edition of the Fédération Internationale des Ingénieurs‑Conseils(FIDIC)Yellow Book and the NEC4 Conditions of Contract,the system leverages LLM to conduct synergistic reasoning with the KG,enabling it to answer complex queries using both tacit knowledge and external sources.Experimental results demonstrate that the proposed approach markedly enhances the model’s performance in Q&A tasks of contract knowledge,achieving an average success rate exceeding 87%in terms of both accuracy and interpretability.This model provides a specialized Q&A system for international construction enterprises,facilitating flexible knowledge acquisition and task‑oriented analysis in contract management,while also introducing a novel framework for integrating AI technologies into the management of international construction contracts. 展开更多
关键词 international construction contract knowledge graph large language model knowledge management
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A review on research of system dynamics and multi-source fault diagnosis of key components in high-speed train 认领 引用
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作者 Baosen Wang Yongqiang Liu +4 位作者 Qilan Li Min Wang Qiaoying Ma Yingying Liao Shaopu Yang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期496-507,共12页
As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-... As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-nomic and trade interactions.Nonetheless,the safety of train operations remains a paramount concern,prompting extensive research into the dynamic behavior of critical components,which is essential to ensuring seamless and secure transportation services.This article commences by comprehensively reviewing the current landscape and evolutionary trajectory of dynamic model analysis for both traditional bearings and axle box bearings.Emphasis is placed on elucidating the profound influence of diverse bearing fault types on the system's kinematic state,alongside delving into the research methodologies employed in developing multi-physics field coupling models.Subsequently,it expounds on the content of investigations focusing on various wheel and track impairments,grounded in the dynamic modeling of the bearing vehicle coupling system.Concurrently,the intricate interplay between wheel-rail excitation and axle box bearing faults on the system's performance is elucidated.Concludingly,the article underscores the inadequacy of current multi-source fault diagnosis meth-odologies in tackling the intricacies of complex train operating environments,thereby highlighting its sig-nificance as a pressing and vital research agenda for the future. 展开更多
关键词 High-speed train Axle box bearing Dynamic model Wheel rail excitation Multi-source fault
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Efficient user scheduling in mm Wave networks:leveraging knowledge transfer with channel knowledge map 认领 引用
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作者 Chunlong He Peihong He Xingquan Li 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期319-331,共13页
This paper proposes a Deep Reinforcement Learning(DRL)algorithm for user scheduling in Millimeter Wave(mmWave)networks,which utilizes Channel Knowledge Map(CKM)for knowledge transfer to enhance the learning of schedul... This paper proposes a Deep Reinforcement Learning(DRL)algorithm for user scheduling in Millimeter Wave(mmWave)networks,which utilizes Channel Knowledge Map(CKM)for knowledge transfer to enhance the learning of scheduling strategies.The user scheduling and link configuration problems are modeled as a multiqueue system.Each queue represents the data demand of an individual user.This setup allows the base station to make dynamic scheduling decisions based on changing environmental conditions.This approach facilitates efficient management of user-specific requirements while addressing the challenges posed by dynamic network environments.Our model incorporates relay selection,codebook selection,and beam tracking to support flexible and efficient resource allocation.In contrast to traditional channel model-based optimization,we design algorithms for scheduling policy pre-training using CKMs,which provide information about the channel between specific pairs of locations.Specifically,we assume that the CKM is fully available to allow the complex scheduling network to have a better starting point or follow a more favorable gradient direction through knowledge migration.This integration of CKM with knowledge transfer significantly accelerates DRL convergence and enhances performance stability.Simulation results confirmed the effectiveness of the proposed approach.Relative to the baseline methods,integrating CKM with knowledge transfer accelerated the convergence of the DRL algorithm by approximately 20%,maintained the delay within 30 milliseconds,and reduced the average queue length by nearly 30%. 展开更多
关键词 Millimeter wave User scheduling Knowledge transfer Channel knowledge map Deep reinforcement learning
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Drive-by spatial offset detection for high-speed railway bridges based on fusion analysis of multi-source data from comprehensive inspection train 认领 引用
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作者 Chuang Wang Jiawang Zhan +4 位作者 Nan Zhang Yujie Wang Xinxiang Xu Zhihang Wang Zhen Ni 《Railway Engineering Science》 EI 2026年第1期128-148,共21页
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ... The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges. 展开更多
关键词 High-speed railway bridge Drive-by inspection Spatial offset Multi-source data fusion Deep learning
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Changes in knowledge and attitude after communitybased first-aid training:a prospective study with a 12-month follow-up 认领 引用
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作者 Shijiao Yan Wenning Fu +5 位作者 Ping Tan Chang Su Xuan Deng Jinlong Wang Chuanzhu Lyu Yanhong Yang 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2026年第3期215-222,共8页
BACKGROUND:This study aims to evaluate the immediate and 12-month effects of communitybased first-aid training on public knowledge and attitude,assess satisfaction,and identify factors associated with score changes.ME... BACKGROUND:This study aims to evaluate the immediate and 12-month effects of communitybased first-aid training on public knowledge and attitude,assess satisfaction,and identify factors associated with score changes.METHODS:This was a prospective study.In 2022-2023,a total of 2,010 community residents in Hainan Province received first-aid training and completed structured questionnaires at baseline,immediately after training,and at 3,6,and 12 months after training.First-aid knowledge was assessed through 33 items,with a maximum total score of 33.First-aid attitude was evaluated using seven items,totaling a maximum score of 21.Satisfaction was measured on a 5-point Likert scale.Paired-sample t-tests were used to compare baseline and post-training scores,repeated-measures analysis of variance(ANOVA)was used to examine time effects,and multiple linear regression was used to analyze factors associated with score changes.RESULTS:The satisfaction score with the training was high(mean score>4.3 for all items).The first-aid knowledge and attitude scores increased significantly after training(first-aid knowledge at baseline,14.90±7.63;immediately after training,20.70±5.72,P<0.001;and attitude at baseline,17.52±2.29;immediately after training,17.89±1.54,P<0.001).At 12 months after training,knowledge scores declined slightly compared with those immediately after training but remained above baseline(time effect P<0.001),whereas attitude scores remained stable(time effect P<0.001).Knowledge improvement was greater among middle-income participants and less among participants with lower levels of education,those in professional occupations,widowed individuals,or those who had previously received first-aid training.Attitude improvements were more pronounced among male participants,younger participants,and those in the agriculture or sales/service sectors.CONCLUSION:Community-based first-aid training improved public first-aid knowledge and attitude and was well received by participants.While knowledge levels declined somewhat over time,attitude remained relatively stable,highlighting the importance of continuous reinforcement training.Personalized reinforcement strategies may be particularly beneficial for individuals with lower levels of education,specific professional backgrounds,widowhood,or prior training experience to further enhance training effectiveness. 展开更多
关键词 First aid Knowledge Attitude Practice Training Longitudinal studies
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Data-driven iterative calibration method for prior knowledge of earth-rockfilldam wetting model parameters 认领 引用
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作者 Shaolin Ding Jiajun Pan +4 位作者 Yanli Wang Lin Wang Han Xu Yiwei Lu Xudong Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1621-1632,共12页
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a... Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments. 展开更多
关键词 Earth-rockfilldam Wetting deformation Prior knowledge Data-driven Bayesian inversion
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Exploring cognitive process of construction engineering tacit knowledge transfer based on interpersonal brain synchronization 认领 引用
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作者 Xiaotong GUO Shuailong ZHANG +2 位作者 Heyang ZHAO Mengmeng WANG Hanliang FU 《ENGINEERING Management》 CSCD 2026年第2期541-558,共18页
The efficient transfer of tacit knowledge is crucial for enhancing teamwork resilience and promoting collaborative innovation in construction projects.This study used functional near-infrared spectroscopy(fNIRS)hypers... The efficient transfer of tacit knowledge is crucial for enhancing teamwork resilience and promoting collaborative innovation in construction projects.This study used functional near-infrared spectroscopy(fNIRS)hyperscanning technology to measure interpersonal brain synchronization(IBS)during the transfer of different classifications of tacit knowledge.This study explored the relationships among types of tacit knowledge,IBS during the transfer process,and the performance of tacit knowledge transfer.Finally,the role of knowledge behavioral characteristics in the process of tacit knowledge transfer was revealed.The results show that i)there is a significant IBS between the sender and receiver during the transfer task,with the IBS level of the cognitive tacit knowledge group being significantly lower than that of the technical tacit knowledge group.ii)There is a significant causal relationship between the IBS level of the transferring subjects and transfer performance,and the type of tacit knowledge influences transfer performance through IBS.iii)The tacit knowledge learning willingness of the receiver and the tacit knowledge sharing willingness of the sender moderate the relationship between the classification of tacit knowledge and the IBS level,and the absorptive capacity of the receiver moderates the relationship between the IBS level and tacit knowledge transfer performance.This study identifies the transfer mechanism of engineering tacit knowledge and provides a reliable predictor for the performance of tacit knowledge transfer with strong hysteresis. 展开更多
关键词 construction tacit knowledge transfer interpersonal brain synchronization(IBS) functional nearinfrared spectroscopy(fNIRS)hyperscanning knowledge behavior characteristics transfer performance
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In-situ monitoring of layer-wise process quality and signal analysis for laser powder bed fusion using multi-source optical signal 认领 引用
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作者 Di Wang Tao Tang +10 位作者 Tingyi Wang Renwu Jiang Xiaoqiang Zheng Long Zhou Laizhu Chen Wenlong Chen Pan Wang Zhiguang Zhou Ying Ma Yongqiang Yang Linqing Liu 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期59-77,共19页
Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic de... Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic defect formation of the process.Current quality assessment is constrained by the inherent latency of offline methods and the diagnostic limitations of single-sensor monitoring.To address these challenges,this study developed a multi-source optical signal monitoring system integrating coaxial photodiodes and an off-axis industrial camera to achieve simultaneous powder spreading detection and radiation signal monitoring during LPBF layer-wise process quality monitoring.Based on the successful identification and analysis of typical detectable features,the YOLOv5s deep learning model was employed to achieve rapid and accurate detection of lack-of-powder defects during the printing process.The training results indicated that the model exhibited good performance metrics.The relationships between process parameters,typical defects,and multi-channel monitoring data were also investigated.The monitoring system achieved a spatial resolution of 300μm for in-process monitoring and demonstrated high accuracy in detecting various defect types,including lack of powder,pores,warping,stitching seams,and printing failures.Furthermore,the algorithm-detected signal anomalies exhibited good spatial correlation with the actual surface defects.Simultaneously,wavelet time-frequency analysis was employed to evaluate molten pool dynamic stability under different process parameters and to analyze energy distribution for different defects.Furthermore,3D model reconstruction from signals enabled effective correlation with actual part defects.Based on the signal-driven process optimization,complex conformal cooling molds were successfully fabricated with a grafting accuracy error of less than 0.12 mm on high-performance substrates,demonstrating the practical efficacy of the developed monitoring methodology.This study provides both a technological and a theoretical foundation for intelligent quality control in LPBF and its practical implementation in industry. 展开更多
关键词 Laser powder bed fusion Multi-source optical signal Signal analysis Layer-wise quality monitoring
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Multi-source remote sensing unveils hydrological dynamics and drought escalation in Central Asia's arid regions 认领 引用
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作者 WANG Yuhao ZHENG Zhijie +13 位作者 ZHU Guofeng HUANG Enwei MENG Gaojia LU Siyu QIU Dongdong CHEN Longhu LI Rui JIAO Yinying ZHAO Ling QI Xiaoyu WANG Qinqin LI Wenmin MIAO Yuxin WANG Qingyang 《Journal of Geographical Sciences》 SCIE CSCD 2026年第5期1105-1129,共25页
Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with m... Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with machine learning to analyze drought dynamics from 2003 to 2022 using the Water Storage Deficit Index(WSDI).Results reveal significant declines in terrestrial water storage(TWS)particularly in the Tianshan Mountains(–10.20 mm/yr)and the Central Desert(–6.19 mm/yr).Drought severity has intensified since 2014,with 67%of subregions transitioning to moderate drought.Random Forest modeling indicates that drought is no longer solely climate-driven but is increasingly dominated by anthropogenic factors(GDP,urbanization,cropland),which explain over 85%of the variability.Furthermore,the WSDI outperformed traditional indices by identifying 12 major drought events linked to deep aquifer depletion—a“hidden”structural deficit often overlooked by surface-based metrics.These findings challenge the optimistic“warming-wetting”narrative,highlighting the urgent need for storage-based management strategies to address anthropogenic groundwater depletion. 展开更多
关键词 Central Asia’s arid regions drought dynamics multi-source remote sensing GRACE hydrometeorological drivers Random Forest
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From data to insight:Building a knowledge graph for risk analysis of hazardous chemical accidents 认领 引用
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作者 Kai Zhao Xilei Lu +7 位作者 Lining Wan Linlin Zhang Yulong Jin Pengtao Wen Jinhao Gao Miao He Qibo Wang Li Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第3期92-110,共19页
A substantial amount hazardous chemical accident(HCA)data have been accumulated in the form of unstructured textual data,making systematic analysis and utilization challenging.More importantly,manually identifying and... A substantial amount hazardous chemical accident(HCA)data have been accumulated in the form of unstructured textual data,making systematic analysis and utilization challenging.More importantly,manually identifying and analyzing key information from a considerable quantity of accident data is inefficient and highly susceptible to subjective bias.To efficiently unlock the value of HCA investigation reports and uncover underlying accident patterns,a semi-automated method for knowledge graph(KG)construction has been developed to model the HCA data.First,an ontology that accurately expresses key factors of HCAs is established.Second,an automated method is developed for the identification,standardization,and enhancement of accident factors,which combines deep learning(DL)and natural language processing(NLP)techniques.Specifically,the deep neural network model,named interaction region and type information(IRTI)is proposed to extract accident factors and their relationships from lengthy HCA data with complex overlapping issues.Non-standard accident factors are standardized using ChatGPT-4 in combination with the proposed text clustering model,named contrastive learningbased short text clustering(CLSTC).The processed accident factors are used to construct the hazardous chemical accident knowledge graph(HCAKG).Finally,the risk factors in the HCAKG are statistically analyzed,and their internal topological relationships are explored to facilitate quantitative analysis.Data from HCA investigation reports are used to demonstrate the effectiveness of this method.The result shows that it improves the accuracy and efficiency of accident data analysis,promoting better risk assessment and management strategies. 展开更多
关键词 Chemical processes Safety Neural networks Knowledge graph Hazardous chemical accident Risk analysis
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Multi-source hydrocarbon accumulation in the faulted and deeply depressed Tangdong area,Qikou Sag,Bohai Bay Basin 认领 引用
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作者 Xiugang Pu Guochao Xia +6 位作者 Jiangchang Dong Zhannan Shi Wenzhong Han Qianru Shi Wei Zhang Yueqi Dong Xiongying Dong 《Energy Geoscience》 EI CAS CSCD 2026年第2期85-100,共16页
Based on organic geochemical analysis data,this study examined the characteristics of multi-source hydrocarbon accumulation in the Tangdong area of Qikou Depression,Bohai Bay Basin.The results indicate that the Tangdo... Based on organic geochemical analysis data,this study examined the characteristics of multi-source hydrocarbon accumulation in the Tangdong area of Qikou Depression,Bohai Bay Basin.The results indicate that the Tangdong area contains four source rock sequences:the third member of the Dongying Formation(Ed3),the middle and lower sub-members of the first and third member of Shahejie Formation(Es 1M,Es 1L,Es 3).Both the salinity of sedimentary water bodies and the contribution of bacteria in the four source rock sequences gradual increase in the order of Es 3,Es 1M,Es 1L and Ed3,as indicated by the Ga/C30H,ETR,sterane/C30 hopane,C24TeT/C26TT,C23TT/C30H,and the carbon isotope data of group components.Additionally,certain quantities of 4-methylsterane have been detected in source rocks in the Es 3,Es 1M,and Ed3,suggesting the special contribution of dinoflagellates to these source rocks.Based on biomarker parameters and carbon isotope data,the crude oil can be categorized into classes A to D,which show complex multi-source accumulation characterized by different sources in the same well and even in the same layer.Class A crude oil occurring in the higher part of the Ed3 originates from source rocks in the Es 1L and Es 3.Class B oil existing in the lower part of the Ed3 is derived from source rocks in the Ed3.Class C oil found in the Es 1U is sourced from source rocks in the Es 1M.Class D oil present in the Es 1L is contributed indigenously by the source rocks in the Es 1L,establishing this unit as a lithologic hydrocarbon reservoir with source rocks and reservoirs in the same layer.Under the guidance of the understanding of multi-source hydrocarbon accumulation,currently,three wells have been drilled,all yielding satisfactory outcomes. 展开更多
关键词 Oil-source rock correlation Multi-source hydrocarbon accumulation Petroleum geochemistry Source rock Deeply depressed area Qikou sag Exploration well emplacement
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A Knowledge-Distilled CharacterBERT-BiLSTM-ATT Framework for Lightweight DGA Detection in IoT Devices 认领 引用
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作者 Chengqi Liu YongtaoLi +1 位作者 Weiping Zou Deyu Lin 《Computers, Materials & Continua》 SCIE EI 2026年第4期2049-2068,共20页
With the large-scale deployment of the Internet of Things(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random do... With the large-scale deployment of the Internet of Things(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment. 展开更多
关键词 IoT security DGA detection knowledge distillation lightweight model edge computing
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A Survey of Key Technologies for Multi-source Heterogeneous Data in Intelligent Manufacturing 认领 引用
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作者 Minghao Zhu Pengfei Yang +2 位作者 Bo Gao Xuehan Li Letian Wang 《Instrumentation》 2026年第1期26-39,共14页
Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large v... Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large volumes of multi-source heterogeneous data across all stages of the product life cycle.However,current data utilization methods remain simplistic,and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized.To address this issue,this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing,based on an analysis of its relationship with industrial big data.Then,integrating principles from data science,a technological framework for multi-source heterogeneous data is proposed.The key technologies involved in each stage of data processing are investigated,and typical applications of such data in intelligent manufacturing are discussed.Finally,this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing.The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science. 展开更多
关键词 intelligent manufacturing multi-source heterogeneous data feature fusion data system technological framework
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High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data 认领 引用
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作者 Qiuhua TANG Ningning LI +4 位作者 Yujie ZHANG Zhipeng DONG Yongling ZHENG Jingjing BAO Jingyu ZHANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第1期99-108,共10页
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications... Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs. 展开更多
关键词 Wuzhizhou Island marine remote sensing coastal mapping multi-spectral remote sensing shallow water reef seabed sediment classification benthic habitat mapping multi-source data fusion random forest(RF)
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