This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of lim...This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.展开更多
In this paper,we present a new method of intelligent back analysis(IBA)using grey Verhulst model(GVM)to identify geotechnical parameters of rock mass surrounding tunnel,and validate it via a test for a main openings o...In this paper,we present a new method of intelligent back analysis(IBA)using grey Verhulst model(GVM)to identify geotechnical parameters of rock mass surrounding tunnel,and validate it via a test for a main openings of−600 m level in Coal Mine“6.13”,Democratic People's Republic of Korea.The displacement components used for back analysis are the crown settlement and sidewalls convergence monitored at the end of the openings excavation,and the final closures predicted by GVM.The non-linear relation between displacements and back analysis parameters was obtained by artificial neural network(ANN)and Burger-creep viscoplastic(CVISC)model of FLAC3D.Then,the optimal parameters were determined for rock mass surrounding tunnel by genetic algorithm(GA)with both groups of measured displacements at the end of the final excavation and closures predicted by GVM.The maximum absolute error(MAE)and standard deviation(Std)between calculated displacements by numerical simulation with back analysis parameters and in situ ones were less than 6 and 2 mm,respectively.Therefore,it was found that the proposed method could be successfully applied to determining design parameters and stability for tunnels and underground cavities,as well as mine openings and stopes.展开更多
This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate ...This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate change in both the thickness and length directions via power-law distributions and Mori-Tanaka model. The governing equation of motion of BDFG plate in the fluid-plate system is formulated basing on Hamilton's principle and the refined quasi three-dimensional (3D) plate theory with improved function f(z). The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to determine the added mass. The discrete system of equations is derived from the Galerkin weak form and numerically analyzed by IGA. The accuracy and reliability of the proposed solutions are verified by comparing the obtained results with those published in the literature. Moreover, the effects of the various parameters such as the interaction boundary condition, geometric parameter, submerged depth of plate, fluid density, fluid level, and the material volume control coefficients on the free vibration behavior of BDFG plate in the fluid medium are investigated in detail. Some major findings regarding the numerical results are withdrawn in conclusions.展开更多
With the high-tech industrialization of earth observation satellite remote sensing and the implementation of digital earth strategy,the energy and natural resources have been decided to be the key research fields in C...With the high-tech industrialization of earth observation satellite remote sensing and the implementation of digital earth strategy,the energy and natural resources have been decided to be the key research fields in China.In these fields,from the model based on topology data,through simple feature data model to rule-based data model,the basic spatial analysis algorithms have been developed展开更多
深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制。大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言...深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制。大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域。文章总结了LLM在医疗领域的应用,涵盖了LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用。同时,进一步讨论了LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略。最后,文章展望了LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响。展开更多
以基因、转录、蛋白质等生命组学为主体的生物大数据快速积累和以深度学习为代表的人工智能技术迅猛发展,催生出各种类别的生物大模型(biological large models)。复杂的深度学习架构、巨大的参数量和算力需求、以及海量的预训练数据等...以基因、转录、蛋白质等生命组学为主体的生物大数据快速积累和以深度学习为代表的人工智能技术迅猛发展,催生出各种类别的生物大模型(biological large models)。复杂的深度学习架构、巨大的参数量和算力需求、以及海量的预训练数据等是大模型技术的主要特征。预训练数据类别及参数量一定程度上决定了大模型所具备的能力强弱,而不同的模型架构则可支撑不同类别的下游任务。近两年,围绕DNA/RNA/蛋白质等生物序列与单细胞表达图谱等组学数据分析挖掘、大分子结构预测、新型药物设计和功能机制解析等多种应用场景,涌现了多种通用或专用大模型,展示出其在生物医学研究及转化应用等领域的巨大潜力。本文旨在结合不同类别的生物数据特点和研究应用需求,概述生物数据特征及其用于生物大模型训练的技术方法,并进一步综述现有大模型在生物医学研究及疾病诊疗中的应用进展,为提升生物大模型能力、拓展应用范围提供新的思路。展开更多
基金Projects supported by the China Scholarship Council
文摘This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.
基金Project(32-41)supported by the National Science and Technical Development Foundation of DPR of Korea。
文摘In this paper,we present a new method of intelligent back analysis(IBA)using grey Verhulst model(GVM)to identify geotechnical parameters of rock mass surrounding tunnel,and validate it via a test for a main openings of−600 m level in Coal Mine“6.13”,Democratic People's Republic of Korea.The displacement components used for back analysis are the crown settlement and sidewalls convergence monitored at the end of the openings excavation,and the final closures predicted by GVM.The non-linear relation between displacements and back analysis parameters was obtained by artificial neural network(ANN)and Burger-creep viscoplastic(CVISC)model of FLAC3D.Then,the optimal parameters were determined for rock mass surrounding tunnel by genetic algorithm(GA)with both groups of measured displacements at the end of the final excavation and closures predicted by GVM.The maximum absolute error(MAE)and standard deviation(Std)between calculated displacements by numerical simulation with back analysis parameters and in situ ones were less than 6 and 2 mm,respectively.Therefore,it was found that the proposed method could be successfully applied to determining design parameters and stability for tunnels and underground cavities,as well as mine openings and stopes.
基金This research is funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant number 107.02-2019.330.
文摘This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate change in both the thickness and length directions via power-law distributions and Mori-Tanaka model. The governing equation of motion of BDFG plate in the fluid-plate system is formulated basing on Hamilton's principle and the refined quasi three-dimensional (3D) plate theory with improved function f(z). The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to determine the added mass. The discrete system of equations is derived from the Galerkin weak form and numerically analyzed by IGA. The accuracy and reliability of the proposed solutions are verified by comparing the obtained results with those published in the literature. Moreover, the effects of the various parameters such as the interaction boundary condition, geometric parameter, submerged depth of plate, fluid density, fluid level, and the material volume control coefficients on the free vibration behavior of BDFG plate in the fluid medium are investigated in detail. Some major findings regarding the numerical results are withdrawn in conclusions.
文摘With the high-tech industrialization of earth observation satellite remote sensing and the implementation of digital earth strategy,the energy and natural resources have been decided to be the key research fields in China.In these fields,from the model based on topology data,through simple feature data model to rule-based data model,the basic spatial analysis algorithms have been developed
文摘深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制。大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域。文章总结了LLM在医疗领域的应用,涵盖了LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用。同时,进一步讨论了LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略。最后,文章展望了LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响。
文摘以基因、转录、蛋白质等生命组学为主体的生物大数据快速积累和以深度学习为代表的人工智能技术迅猛发展,催生出各种类别的生物大模型(biological large models)。复杂的深度学习架构、巨大的参数量和算力需求、以及海量的预训练数据等是大模型技术的主要特征。预训练数据类别及参数量一定程度上决定了大模型所具备的能力强弱,而不同的模型架构则可支撑不同类别的下游任务。近两年,围绕DNA/RNA/蛋白质等生物序列与单细胞表达图谱等组学数据分析挖掘、大分子结构预测、新型药物设计和功能机制解析等多种应用场景,涌现了多种通用或专用大模型,展示出其在生物医学研究及转化应用等领域的巨大潜力。本文旨在结合不同类别的生物数据特点和研究应用需求,概述生物数据特征及其用于生物大模型训练的技术方法,并进一步综述现有大模型在生物医学研究及疾病诊疗中的应用进展,为提升生物大模型能力、拓展应用范围提供新的思路。
