This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for...This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).展开更多
The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and em...The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.展开更多
Through the practice of developing an expert system FBCDES(Feed Box Conceptual De- sign Expert System)for the planomillingmachine in CIMS ERC(CIMS Engineering Research Center) in China,the paper intends to introduce a...Through the practice of developing an expert system FBCDES(Feed Box Conceptual De- sign Expert System)for the planomillingmachine in CIMS ERC(CIMS Engineering Research Center) in China,the paper intends to introduce a common problem-solving strategy for the mechanical product conceptual design expert system.Meanwhile some problems such as system architecture,knowledge representations and inference engine sets are also described.A yet more detailed discussion is given to the technical problems for realizing two key modules,that is concept design module and layout module.展开更多
Photonics is promising to handle extensive vector multiplications in artificial intelligence(AI)techniques due to natural bosonic parallelism and high-speed information transmission.However,the dimensionality of curre...Photonics is promising to handle extensive vector multiplications in artificial intelligence(AI)techniques due to natural bosonic parallelism and high-speed information transmission.However,the dimensionality of current photonic linear operation is limited and tough to improve due to the complex beam interaction for implementing optical matrix operation and digital-analog conversions.Here,we propose a programmable and reconfigurable photonic linear vector machine with extreme scalability formed by a series of emitter-detector pairs as the independent basic computing units.The elemental values of two high-dimensional vectors are prepared on emitter-detector pairs by bit encoding and analog detecting method without requiring large-scale analog-to-digital converter or digital-to-analog converter arrays.Since there is no interaction among light beams inside,extreme scalability could be achieved by simply multiplicating the independent emitter-detector pair.The proposed architecture is inspired by the traditional Chinese Suanpan or abacus,and thus is denoted as photonic SUANPAN.Experimentally,the computing fidelities for vector inner products could achieve>98%in our implementation with an 8×8 vertical cavity surface emission laser(VCSEL)array and an 8×8 MoTe_(2)two-dimensional material photodetector array.Furthermore,such implementation is applied on two typical AI tasks as 1024-dimensional optimization problem is successfully solved and competitive classification accuracy of 88%is achieved for handwritten digit dataset.We believe that the photonic SUANPAN could serve as a fundamental linear vector machine and enhance various future AI applications.展开更多
文摘This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).
基金The authors are grateful to the Universiti Malaysia Pahang Al-Sultan Abdullah and the Malaysian Ministry of Higher Education for their generous support and funding provided through University Distinguished Research Grants(Project No.RDU223016)as well as financial assistance provided through the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UMP/02/35).
文摘The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.
文摘Through the practice of developing an expert system FBCDES(Feed Box Conceptual De- sign Expert System)for the planomillingmachine in CIMS ERC(CIMS Engineering Research Center) in China,the paper intends to introduce a common problem-solving strategy for the mechanical product conceptual design expert system.Meanwhile some problems such as system architecture,knowledge representations and inference engine sets are also described.A yet more detailed discussion is given to the technical problems for realizing two key modules,that is concept design module and layout module.
基金Funding from the National Key Research and Development Program of China(2023YFB2806703)the National Natural Science Foundation of China(Grant Nos.U22A6004,92365210,and 62175124)is greatly acknowledgedsupported by Beijing National Research Center for Information Science and Technology(BNRist),Frontier Science Center for Quantum Information,Beijing Academy of Quantum Information Science,and Tsinghua University Initiative Scientific Research Program.
文摘Photonics is promising to handle extensive vector multiplications in artificial intelligence(AI)techniques due to natural bosonic parallelism and high-speed information transmission.However,the dimensionality of current photonic linear operation is limited and tough to improve due to the complex beam interaction for implementing optical matrix operation and digital-analog conversions.Here,we propose a programmable and reconfigurable photonic linear vector machine with extreme scalability formed by a series of emitter-detector pairs as the independent basic computing units.The elemental values of two high-dimensional vectors are prepared on emitter-detector pairs by bit encoding and analog detecting method without requiring large-scale analog-to-digital converter or digital-to-analog converter arrays.Since there is no interaction among light beams inside,extreme scalability could be achieved by simply multiplicating the independent emitter-detector pair.The proposed architecture is inspired by the traditional Chinese Suanpan or abacus,and thus is denoted as photonic SUANPAN.Experimentally,the computing fidelities for vector inner products could achieve>98%in our implementation with an 8×8 vertical cavity surface emission laser(VCSEL)array and an 8×8 MoTe_(2)two-dimensional material photodetector array.Furthermore,such implementation is applied on two typical AI tasks as 1024-dimensional optimization problem is successfully solved and competitive classification accuracy of 88%is achieved for handwritten digit dataset.We believe that the photonic SUANPAN could serve as a fundamental linear vector machine and enhance various future AI applications.
