As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Artificial intelligence is reshaping radiology by enabling automated report generation,yet evaluating the clinical accuracy and relevance of these reports is a challenging task,as traditional natural language generati...Artificial intelligence is reshaping radiology by enabling automated report generation,yet evaluating the clinical accuracy and relevance of these reports is a challenging task,as traditional natural language generation metrics like BLEU and ROUGE prioritize lexical overlap over clinical relevance.To address this gap,we propose a novel semantic assessment framework for evaluating the accuracy of artificial intelligence-generated radiology reports against ground truth references.We trained 5229 image–report pairs from the Indiana University chest X-ray dataset on the R2GenRL model and generated a benchmark dataset on test data from the Indiana University chest X-ray and MIMIC-CXR datasets.These datasets were selected for their public availability,large scale,and comprehensive coverage of diverse clinical cases in chest radiography,enabling robust evaluation and comparison with prior work.Results demonstrate that the Mistral model,particularly with task-oriented prompting,achieves superior performance(up to 91.9%accuracy),surpassing other models and closely aligning with established metrics like BERTScore-F1(88.1%)and CLIP-Score(88.7%).Statistical analyses,including paired t-tests(p<0.01)and analysis of variance(p<0.05),confirm significant improvements driven by structured prompting.Failure case analysis reveals limitations,such as over-reliance on lexical similarity,underscoring the need for domain-specific fine-tuning.This framework advances the evaluation of artificial intelligence-driven(AI-driven)radiology report generation,offering a robust,clinically relevant metric for assessing semantic accuracy and paving the way for more reliable automated systems in medical imaging.展开更多
Described and exemplified a semantic scoring system of students' on-line English-Chinese translation. To achieve accurate assessment, the system adopted a comprehensive method which combines semantic scoring with ...Described and exemplified a semantic scoring system of students' on-line English-Chinese translation. To achieve accurate assessment, the system adopted a comprehensive method which combines semantic scoring with keyword matching scoring. Four kinds of words-verbs, adjectives, adverbs and "the rest" including nouns, pronouns, idioms, prepositions, etc., are identified after parsing. The system treats different words tagged with different part of speech differently. Then it calculated the semantic similarity between these words of the standard versions and those of students' translations by the distinctive differences of the semantic features of these words with the aid of HowNet. The first semantic feature of verbs and the last semantic features of adjectives and adverbs are calculated. "The rest" is scored by keyword matching. The experiment results show that the semantic scoring system is applicable in fulfilling the task of scoring students' on-line English-Chinese translations.展开更多
The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pu...The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.展开更多
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2024-RS-2024-00436773).
文摘Artificial intelligence is reshaping radiology by enabling automated report generation,yet evaluating the clinical accuracy and relevance of these reports is a challenging task,as traditional natural language generation metrics like BLEU and ROUGE prioritize lexical overlap over clinical relevance.To address this gap,we propose a novel semantic assessment framework for evaluating the accuracy of artificial intelligence-generated radiology reports against ground truth references.We trained 5229 image–report pairs from the Indiana University chest X-ray dataset on the R2GenRL model and generated a benchmark dataset on test data from the Indiana University chest X-ray and MIMIC-CXR datasets.These datasets were selected for their public availability,large scale,and comprehensive coverage of diverse clinical cases in chest radiography,enabling robust evaluation and comparison with prior work.Results demonstrate that the Mistral model,particularly with task-oriented prompting,achieves superior performance(up to 91.9%accuracy),surpassing other models and closely aligning with established metrics like BERTScore-F1(88.1%)and CLIP-Score(88.7%).Statistical analyses,including paired t-tests(p<0.01)and analysis of variance(p<0.05),confirm significant improvements driven by structured prompting.Failure case analysis reveals limitations,such as over-reliance on lexical similarity,underscoring the need for domain-specific fine-tuning.This framework advances the evaluation of artificial intelligence-driven(AI-driven)radiology report generation,offering a robust,clinically relevant metric for assessing semantic accuracy and paving the way for more reliable automated systems in medical imaging.
基金The National Natural Science Foundution of China(No60496326)The Second Phase of 985 Project of Shanghai Jiaotong University
文摘Described and exemplified a semantic scoring system of students' on-line English-Chinese translation. To achieve accurate assessment, the system adopted a comprehensive method which combines semantic scoring with keyword matching scoring. Four kinds of words-verbs, adjectives, adverbs and "the rest" including nouns, pronouns, idioms, prepositions, etc., are identified after parsing. The system treats different words tagged with different part of speech differently. Then it calculated the semantic similarity between these words of the standard versions and those of students' translations by the distinctive differences of the semantic features of these words with the aid of HowNet. The first semantic feature of verbs and the last semantic features of adjectives and adverbs are calculated. "The rest" is scored by keyword matching. The experiment results show that the semantic scoring system is applicable in fulfilling the task of scoring students' on-line English-Chinese translations.
基金National Natural Science of China(No.42201463)Guangxi Natural Science Foundation(No.2023GXNSFBA026350)+1 种基金Special Fund of Guangxi Science and Technology Base and Talent(Nos.Guike AD22035158,Guike AD23026167)Guangxi Young and Middle-aged Teachers’Basic Scientific Research Ability Improvement Project(No.2023KY0056).
文摘The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.
