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\n                                \n                                \n                                    \n                                         社会职务 \n                                    \n                                     陕西省计算机学会软件工程专委会委员","陕西",false,"http:\u002F\u002Fjs.chd.edu.cn\u002Fxxgcxy\u002Fysx\u002Flist.htm",true,[],[],{"id":402,"name":403,"orgName":404,"school":389,"gender":390,"title":391,"degree":392,"honor":10,"position":10,"researchDirection":405,"intro":406,"province":395,"completeness":229,"avatarAuthorized":396,"sourceUrl":407,"nameValid":398,"tags":408,"tagNames":409},"2ff0ebfb-839b-60f8-690e-11293630005e","张海伦","长安大学汽车学院","关注智能网联汽车中驾驶人认知决策、人机交互以及控制领域，如下：（1）在人机交互方面：提出了一种基于分层聚类算法、墨西哥帽小波变换法、非参数检验法以及层次狄利克雷过程与隐马尔可夫模型相结合的多层次驾驶行为分析方法。通过该方法，能够系统地捕捉网联环境下驾驶人和HMI的交互频率、注意力分布及响应特性，为驾驶行为的定量分析和模式提取提供新的思路。采用创新的文本聚类算法对驾驶模式进行定量分析，提供了全新的网联环境下驾驶行为的科学理解和应用模型。学术影响方面，为智能交通系统中的驾驶行为建模提供了理论支持，还为交通安全管理及车联网技术的发展提供了实践依据，具有较高的学术价值与应用潜力(论文1：Zhang Hailun, Fu Rui, Wang Jiangqiang, Simeon C. Calvert, Hans van Lint. Driving Patterns in Connected Environments: A Case Study of Intersection-Approaching Behavior of Professional and Non-Professional Drivers [J]. Transportation Research Part F: Traffic Psychology and Behavior. 103 (2024) 230–259. 论文2：网联环境下驾驶人响应机制—一种交叉路口接近行为定性效应分析与定量驾驶模式提取案例[J\u002FOL]. 机械工程学报, 2024, 60 (10): 22-39.) （2）在车辆稳定性控制方面：提出了一种新型的IPC（干预预测与控制）框架，解决了公交车因转向滞后而导致的横向稳定性问题。该框架结合了干预标准、车辆状态预测和在线控制，通过多因素耦合的BP神经网络实时评估稳定性边界，采用EKF观测器和OARIMA模型预测车速与侧滑角的短期趋势，并通过AFS策略与Smith-SMC设计稳定性控制系统。仿真结果表明，该框架在极端转向条件下表现出色，显著降低了转向滞后的影响，为公交车的主动安全控制提供了新思路（论文：Fu Rui, Yang Bing, Zhang Hailun. Lateral Stability Control of Buses: A framework Considering the Steering Hysteresis Response Using Interleaved Predictive-Control [J]. IEEE Transactions on Vehicular Technology. 2024, 23(1), 216-231.）（3）在驾驶人意图识别方面：拓展在线迁移学习的应用场景，通过将在线迁移学习引入换道意图识别领域，研究展示了如何在交通行为建模中应用动态适应性技术。为交通流量管理与智能驾驶系统中的行为预测提供了新的方法，推动了在线迁移学习在复杂交通环境中的应用研究。此外，为提高意图识别模型的可扩展性与训练效率，通过引入字典学习（稀疏表示）与图Lasso方法，研究解决了传统深度学习在建模速度上的不足，提升了模型在处理不同驾驶行为时的扩展能力。这一创新对提升智能驾驶系统的实时响应能力具有重要意义(论文：Zhang Hailun, Fu Rui, et al. Turning Maneuver Prediction of Connected Vehicles at Signalized Intersections: A Dictionary Learning-Based Approach[J]. IEEE Internet of Things Journal, 2022, 9(22): 23142-23159.)（4）在驾驶行为感知方面：增强车辆行为感知模型的实用性与鲁棒性，将集成学习与半监督学习相结合，并设计了针对数据流中的异常值和概念漂移的自适应策略，使得模型在面对现实世界中的动态数据时表现出更强的鲁棒性与适应性。这为智能交通系统中车辆行为识别与预测的研究提供了更具实用价值的技术方案。此外，提出的基于微簇特征的在线自适应方法，进一步拓展了模型在应对概念演化和新类检测方面的能力，增强了模型在不断变化的交通环境中的适应性与准确性。这为自适应半监督学习在智能交通中的应用研究提供了新方向（论文：Zhang Hailun, Fu Rui, Wang Chang, et al. Driving Maneuver Detection at Intersections for Connected Autonomous Vehicles: A Micro-Cluster-Based Online Adaptable Approach [J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(2): 1178-1199, Feb. 2024.）","张海伦，1992年8月生，博士，副教授，长安大学青年英才A，博士生导师。于长安大学获得博士学位，期间赴荷兰代尔夫特理工大学进行为期一年的访问研究，后进入清华大学车辆与运载学院完成博士后研究工作。长期致力于智能网联汽车、驾驶人认知与决策、人机交互与智能控制等前沿交叉领域的研究 。具备出色的科研创新能力与扎实的学术功底，在博士与博士后期间取得了一系列标志性创新成果（对我个人来说挺标志创新的(●'◡'●)）。以第一作者身份在交通运输工程和车辆工程领域发表高水平期刊论文20余篇，其中多篇发表于IEEE Transactions on Intelligent Transportation Systems、IEEE Internet of Things Journal、Transportation Research Part F 等国际顶级期刊。此外，还申请了10余项国家发明专利（7项授权）。主持了国家自然科学基金、国家重点研发计划子任务、陕西省自然科学基金、国家资助博士后研究人员计划（博新计划上会没中，给了个B档，难受）、中国博士后科学基金面上项目、北京市自然科学基金青年项目（这个很难，我也是侥幸获批）等多项国家级及省部级课题。同时，作为项目骨干组织申报1项并深度参与了3项国家重点研发计划项目。研究工作系统地解决了智能座舱人机交互、驾驶意图识别和行为建模等领域的关键问题。例如，提出了创新的多层次驾驶行为分析方法，为理解网联环境下的人机交互特性提供了新模型；将在线迁移学习与字典学习等方法引入驾驶意图识别，有效提升了模型的扩展性与实时性；设计的集成学习与半监督学习融合框架，显著增强了车辆行为感知模型的鲁棒性与自适应能力。这些研究成果兼具理论深度与应用价值，为智能驾驶系统的发展提供了关键技术支撑。每年有3-4个硕士招生名额和1个博士招生名额（硕士生名额确定，但是博士生名额与到账经费挂钩），欢迎对本方向有兴趣的本科生、硕士生和博士生同学联系我！无性别和第一学历歧视！邮箱：zhanghailun@chd.edu.cn          iszhanghailun@outlook.com我能提供的：1. 科研方面：提供前沿科研idea，至少每周1次的手把手指导算法建模、论文撰写、专利申报、科研项目申请书撰写（指导过的研究生同学均可以发领域内顶刊）；2. 交流方面：国际（荷兰）和国内（清华大学、吉林大学、北京理工大学、华南理工大学、重庆大学等）一流学府交流机会，还能带你去开会吃茶歇；3. 硬件方面：团队具有基座式驾驶模拟器2台、多自由度驾驶模拟器1台、自动驾驶汽车3辆，依托于长安大学车联网与智能汽车试验场，可有效进行驾驶人认知和自动驾驶控制决策课题研究；4. 运动方面：本人热爱健身、跑步（配速515）、骑行（9小时200公里）、越野（每年一次，50公里6.5小时）、登山徒步（之前每周一山，现在太忙，求带）等运动项目，具有多年运动基础和常年运动习惯，可帮助同学成为合格的“体育生”。对你的期待：1. 坐的住、能吃苦、有奋斗精神；2. 对学术科研有追求，致力于基础研究和工程应用落地；3. 熟练Matlab、Python等软件，学习过《机器学习》、《深度学习》、《强化学习》等课程；4. 对自动驾驶、智能交通、认知心理科学感兴趣并有过论文发表经历（不强制）。 \n                                \n                                \n                                    \n                                         社会职务 \n                                    \n                                     给IEEE Transactions on Intelligent Transportation Systems、Accident Analysis and Prevention、《机械工程学报》、《汽车工程》、IEEE Internet of Things、Transportation Research Part C\u002FF等期刊投过稿，所以被列为了审稿人（没啥社会职务，空着也不好，但是我看其他老师都写了，那我也这么写）","http:\u002F\u002Fjs.chd.edu.cn\u002Fqcxy\u002Fzhl1\u002Flist.htm",[],[],{"id":411,"name":412,"orgName":413,"school":389,"gender":390,"title":414,"degree":415,"honor":10,"position":10,"researchDirection":416,"intro":417,"province":395,"completeness":95,"avatarAuthorized":396,"sourceUrl":418,"nameValid":398,"tags":419,"tagNames":420},"7694407a-a422-3ab5-b16b-a0e05863f78a","左琛","长安大学运输工程学院","讲师","工学博士","1. 基于视觉基础模型的公路交通设施无人机巡检与智能图像识别方法研究伴随着飞控能力和计算平台的发展，无人机已经成为进行公路交通基础设施巡检的新兴工具。然而，无人机图像中扰动因素多、场景变化大，给后继的图像分析与智能识别方法带来巨大挑战。本课题组针对路面病害、标线标识、沿线设施、道路边坡等多种细分养护场景，以海量无人机航拍图像为数据基础，运用视觉基础模型、图像分割大模型、多模态数据处理等人工智能和计算机视觉前沿技术，探索各类异常结构的视觉响应特征，求解行业规范文本和巡检图像之间的相互对应关系，建立兼具计算效率和识别精度的智能图像分析方法，显著提高深度神经网络的训练速度、标注效率和泛化能力，为公路交通基础设施的状态检测和性能评估提供重要依据。1.1 基于基础模型的路面病害识别与搜索方法及时准确地发现裂缝、坑槽、修补等路面病害，是公路预防性养护和智慧化养护的重要内容。近年来，以卷积神经网络为核心的深度学习技术已经成为了分析巡检图像的主流方法。然而，当前技术多基于有监督学习框架，难以有效应对道路病害多样的形态特征，识别方法的泛化能力有限。本课题组基于无人机高分辨率图像，以视觉基础模型为核心技术手段，探索各类路面异常样例的高阶视觉特征，建立高性能路面病害识别方法。具体内容包括：(1)使用DINOv2、Web-SSL等视觉基础模型，充分利用自监督学习、Vision Transformer和海量无标注数据集，提取图像视觉特征；(2)运用距离度量、流形学习和无监督聚类技术，发现道路路面区域，移除不相关图像块；(3)使用XGBoost、TabPFN等有监督分类器，建立主动学习框架，促使计算机理解视觉特征与病害样本之间的对应关系，预测病害的发生位置、损坏类型和损坏程度；(4)基于典型病害样例库，运行近似近邻搜索方法，求解路面病害的类内多样性和场景变化性，实现异常结构的精细搜索与快速排查，探索道路病害的时空变化规律。1.2 图像分割大模型支持的交通标线缺损定位方法保障交通标线的可见性和清晰度，是道路设施养护的重要环节。基于深度神经网络的目标检测和语义分割方法是目前进行破损标线识别的主流技术手段。然而，神经网络的性能高度依赖人工标注的质量和规模，如何有效建立高质量数据集，提高神经网络的训练效率，减少人工标注工作量，成为了重要且紧迫的科研话题。本课题组引入图像分割大模型技术，依据标线颜色等先验信息，引导图像大模型理解交通设施养护场景，建立交通标线缺损快速检测和准确评估的技术方法。具体内容包括：(1)基于图像分割大模型SAM,启动全局分析模式，自动划分无人机巡检图像，依据颜色信息确定道路路面区域，划定研究范围；(2)使用DINOv2基础模型，运用Hausdorff距离和余弦距离，量化无人机图像序列的相似性，获得代表性图像样例；(3)基于代表性图像，训练YOLO和DETR目标检查网络，准确定位减速标线、车道线、导向箭头和导流线破损；(4)启动图像分割大模型SAM，分析交通标线样例，预测标线初始状态。基于阈值分割方法，提取标线的现存可见区域。计算标线的缺失区域比例，定量评估标线健康状态。1.3 多模态学习赋能的交通沿线设施快速识别方法交通标志、交安设施、交通监控等沿线设施是交通系统的基本组成，定位和维护沿线设施是进行基础设施养护的基本要素。基于YOLO等目标检测网络，计算机能够实现无人机航拍图像的高效处理。然而，当前的有监督学习方法高度依赖人工标注数据集，能以有效应对光照条件、巡检场景和飞控参数的显著变化，识别方法的泛化能力有限。本课题组引入多模态分析技术，探索交通标志图像和行业规范文本之间的映射关系，在共享特征空间内实现多模态样例的准确对齐和配准，提高交通沿线设施的快速定位和精细分类。具体内容如下：(1)启动Grounding DINO、YOLO-World、YOLOE等开放词汇目标检测程序，合理设定关键字提示词，建立沿线设施样例数据集；(2)运用知识蒸馏技术，建立教师-学生结构，去除任务无关神经元，获得小尺寸高效率目标检测网络；(3)使用视觉基础模型分析无人机图像，使用文本编码器分析行业规范文本，运用线性映射层实现共享特征空间内的样本对齐，实现交通沿线设施的精细化分类。1.4 光学、红外与三维点云联合驱动的道路边坡隐患识别方法快速发现边坡落石、岩体开裂、植被侵入等异常案例，是维护道路边坡安全的重要因素。在无人机智能巡检框架内，如何有效整合光学图像、红外图像、三维点云等多模态数据采集手段，提高边坡异常结构的识别质量，成为了重要的研究话题。本课题组针对山区公路和沿海公路场景，有机结合设备、数据、技术和交通养护部门需求，以海量无人机图像为数据基础，运用深度神经网络、视觉基础模型、多模态数据处理等手段，建立高性能道路边坡智能巡检方法。具体内容如下：(1)启动EfficientNet、Vision Transformer、ConvNeXt、Mamba等多种前沿深度神经网络，探索各类神经网络的性能边界，实现边坡落石和岩体开裂的快速定位和分类；(2)针对水库边坡场景中的少样本学习难题，使用DINOv2视觉基础模型分析RGB、红外和点云图像，编程实现特征级融合和决策级融合框架，实现边坡落石和植被侵入的快速识别；(3)基于海量无标注图像，使用自监督学习和对比学习技术探索光学-红外-点云图像之间的匹配关系，促使计算机自主发掘边坡结构之间的内在关系和发展趋势，实现多模态数据对齐，获得与巡检场景相匹配的高质量图像编码器。2. 基于开放词汇目标检测和视觉基础模型的危化品车辆识别方法从海量交通监控视频中快速发现危化品车辆，是智慧交通系统的关键环节，是提高通行效率、保障交通安全、促进绿色发展的基本前提。近年来，深度学习和计算机视觉技术成为了实现交通感知的主流方法。然而，多样的视频采集设备、繁杂的交通通行场景和稀缺的危化品车辆样例，给深度神经网络的推广应用带来了巨大挑战。为了提高泛化能力并简化训练流程，本课题组将开放词汇目标检测和视觉基础模型引入到交通视频分析领域，充分利用通用视觉特征，建立高性能危化品车辆识别方法。具体内容包括：(1)启动YOLO-World目标检测程序，合理设置关键字提示词，快速提取交通监控视频中的车辆样例。为了消除冗余信息，使用ByteTrack和SAM2技术，完成车辆的轨迹跟踪；(2)使用DINOv2视觉基础模型，提取车辆样例的视觉特征，使用UMAP和谱聚类技术理解车辆样例之间的相似性关系；(3)使用k近邻搜索去除YOLO-World的噪声样例，使用XGBoost分类器预测车辆类型，有效应对夜间、降雨等复杂场景，建立具有高泛化能力和强鲁棒性的危化品车辆识别方法。3. 孔隙结构与物理属性联合约束的非均质数字岩心建模方法3.1 基于通用图像特征的非均质岩心表征方法3.2 基于机器学习技术的三维数字岩心快速建模方法3.3 贝叶斯证据学习驱动的地质模型空间不确定性量化方法4. 基于视觉基础模型的城市更新区域与建成环境识别方法对于具有复杂建成环境的老城区，采用人工智能技术可以提高更新规划的科学性，然而现有的目标识别方法高度依赖人工标注数据集，面临更新实践不足、分析精度受限和训练成本过高的局限。将视觉基础模型引入城市更新领域，探讨视觉基础模型支持的老城区更新规划方法：(1)通过等间隔采样构建图像块数据集，使用视觉基础模型提取高维视觉特征；(2)启动余弦距离、流形学习、高斯混合模型等机器学习技术，实现图像块数据集的智能分组；(3)对图像块进行空间转译，精准识别重点区域；结合多维要素综合评判，优化智能识别结果，指导后续更新规划实践。以陕西省榆林市清涧县老城区为例，探索该技术方法在城市更新规划实践中的应用，以期为老城区的城市更新提供新的思路","左琛，本科毕业于四川大学电子信息工程专业，博士毕业于西安交通大学信息与通信工程专业，美国斯坦福大学访问学者。现任长安大学运输工程学院讲师，数据与系统工程系副主任，数据与系统工程党支部宣传委员。申请人长期从事机器学习与计算机视觉相关研究工作，目前主持国家自然科学基金青年科学基金项目1项、陕西省自然科学基础研究计划一般项目(青年)1项、陕西省交通运输厅交通运输科研项目1项、山东省交通运输厅先行试验项目1项、中央高校基本科研业务费专项资金资助项目2项。作为技术负责人，参与国家自然科学基金面上项目2项、陕西省科学技术厅重点研发计划项目1项、陕西省交通运输厅重点项目2项、河北省交通厅科技项目1项。以第一作者和通信作者身份，累计发表学术论文10余篇，包括《IEEE Transaction on Geoscience and Remote Sensing》《Water Resources Research》《Remote Sensing》等，参加国内外高水平学术会议并做专题报告6次，已授权发明专利2项。 \n                                \n                                \n                                    \n                                         社会职务","http:\u002F\u002Fjs.chd.edu.cn\u002Fysgcxy\u002Fzc5\u002Flist.htm",[],[],{"id":422,"name":423,"orgName":388,"school":389,"gender":390,"title":414,"degree":392,"honor":10,"position":10,"researchDirection":424,"intro":425,"province":395,"completeness":426,"avatarAuthorized":396,"sourceUrl":427,"nameValid":398,"tags":428,"tagNames":429},"3f635a77-d23d-d8fd-0535-bcd77654118d","张朝阳","图像处理与模式识别研究聚焦：图像特征设计与特征匹配方法研究；图像局部及全局结构描述与信息鉴别；图像变化检测；图像矫正与相机标定；大范围多相机时间同步数据采集及其应用；多相机图像空间重构及其应用。谱聚类分析与特征挖掘研究聚焦：图谱方法与数据聚类；图谱与图像高阶统计特征提取；谱方法与形变目标变化检测；图模型及其应用。机器学习方法与交通视频智能分析研究聚焦：机器学习方法与建模；目标3D重建及其应用；机器学习模型设计及其在交通目标检测、跟踪中的应用；长距离、大范围、多交通目标轨迹长时感知与应用；长大隧道交通目标轨迹与安全态势分析；中小桥梁车辆目标车道坐标系重建与定位","张朝阳，博士，硕导。2007年西北工业大学应用数学专业本科毕业，同年进入理学院攻读博士学位，研究方向应用数学与时间序列分析，2011年到2012年在加拿大阿尔伯塔大学计算机系图像处理实验室做联合培养博士生，2013年07月在西北工业大学理学院获应用数学专业理学博士学位，2013年9月至今工作于长安大学信息工程学院人工智能与交通信息工程系。自参与研究工作以来，一直从事图像处理与模式识别，谱聚类方法与特征挖掘，机器学习方法与交通视频智能分析等研究方向的工作。目前独立完成和联合发表论文20余篇，获得国家发明专利6项，指导与协助指导硕士研究生及博士研究生毕业共31人，主持及参与中央高校、省部级研究项目8项。 \n                                \n                                \n                                    \n                                         社会职务",88,"http:\u002F\u002Fjs.chd.edu.cn\u002Fxxgcxy\u002Fzcy101\u002Flist.htm",[],[],{"id":431,"name":432,"orgName":433,"school":434,"gender":390,"title":435,"degree":10,"honor":10,"position":10,"researchDirection":436,"intro":437,"province":395,"completeness":141,"avatarAuthorized":396,"sourceUrl":438,"nameValid":398,"tags":439,"tagNames":455},"ef966e56-2240-4505-97ff-a6050b89c6bd","卢明飞","西安建筑科技大学人工智能与机器人学院","西安建筑科技大学","教授","统计机器学习；智能信息处理；模式识别与智能控制；多元广义度量理论与应用（多视图学习；多源域自适应；多目标聚类；多智能体强化学习；多任务学习；联邦学习","【产业方向】人工智能","https:\u002F\u002Ffaculty.xauat.edu.cn\u002Flumingfei\u002Fzh_CN\u002Findex.htm",[440,443,446,449,452],{"code":441,"name":442},"SEI-01","新一代信息技术产业",{"code":444,"name":445},"SEI-02","高端装备制造产业",{"code":447,"name":448},"CH-20","高端装备",{"code":450,"name":451},"CH-27","建筑",{"code":453,"name":454},"CH-33","人工智能",[442,445,448,451,454],{"id":457,"name":458,"orgName":459,"school":459,"gender":10,"title":435,"degree":10,"honor":10,"position":10,"researchDirection":460,"intro":437,"province":461,"completeness":141,"avatarAuthorized":396,"sourceUrl":462,"nameValid":398,"tags":463,"tagNames":469},"c41a2743-21ea-4138-8bc4-e38eebc40591","万中","中南大学","[1] 在数据驱动建模和机器学习优化算法研究方面，提出了刻画复杂数据分布的新型不确定性集构造优化方法，它能在一定置信水平下用最小区域覆盖尽量多样本点，克服了已有鲁棒优化存在的过保守性；构建了基于正则化技术的非负矩阵分解优化模型和算法，能精准聚类、降维和特征辨识。主要成果发表于《Pattern Recognition》，《EAAI》","湖南","http:\u002F\u002Ffaculty.csu.edu.cn\u002Fwanzhong\u002Fzh_CN\u002Findex.htm",[464,467,468],{"code":465,"name":466},"GB-73","研究和试验发展",{"code":441,"name":442},{"code":453,"name":454},[466,442,454],{"id":471,"name":472,"orgName":473,"school":473,"gender":10,"title":391,"degree":10,"honor":10,"position":10,"researchDirection":474,"intro":437,"province":475,"completeness":476,"avatarAuthorized":396,"sourceUrl":477,"nameValid":398,"tags":478,"tagNames":481},"92a442e5-cb53-451f-bdd5-50cd7e77ad5d","赵钦佩","同济大学","聚类算法, 多媒体处理, 数据挖掘等","上海",67,"http:\u002F\u002Ffaculty.tongji.edu.cn\u002Fzhaoqinpei\u002Fzh_CN\u002Findex.htm",[479,480],{"code":441,"name":442},{"code":453,"name":454},[442,454],{"id":483,"name":484,"orgName":485,"school":485,"gender":10,"title":435,"degree":10,"honor":10,"position":10,"researchDirection":486,"intro":437,"province":487,"completeness":488,"avatarAuthorized":396,"sourceUrl":489,"nameValid":398,"tags":490,"tagNames":493},"e0b6231b-d519-4e87-b8b4-c3db0e067744","周劲","济南大学","智能计算与科学建模 机器学习 数据挖掘 数据聚类","山东",64,"http:\u002F\u002Ffaculty.ujn.edu.cn\u002Fzhoujin\u002Fzh_CN\u002Findex.htm",[491,492],{"code":441,"name":442},{"code":453,"name":454},[442,454],{"id":495,"name":496,"orgName":497,"school":498,"gender":10,"title":414,"degree":10,"honor":10,"position":10,"researchDirection":499,"intro":500,"province":395,"completeness":76,"avatarAuthorized":396,"sourceUrl":501,"nameValid":398,"tags":502,"tagNames":522},"4fa417d9-3ed1-4f4c-8d83-53b80f18800c","宋超","西安交通大学人居环境与建筑工程学院","西安交通大学","科研成果 1. 期刊论文 Song Chao , Zhao Tengyuan*, Xu Ling, Huang Xiaolin. Probabilistic prediction of uniaxial compressive strength for rocks from sparse data using Bayesian Gaussian process regression with Synthetic Minority Oversampling Technique (SMOTE). Computers and Geotechnics , 2024, 165: 105850. DOI: 10.1016\u002Fj.compgeo.2023.105850 Song Chao , Peng Hongzhen, Xu Ling , Zhao Tengyuan *, Guo Zhiqian, Chen Wenwu. Probabilistic evaluation of cultural soil heritage hazards in China from extremely imbalanced site investigation data using SMOTE-Gaussian process classification. Journal of Cultural Heritage , 2024, 67: 121-133. DOI: 10.1016\u002Fj.culher.2024.02.014 Song Chao , Zhao Tengyuan *, Xu Ling . Physics-informed explainable AI and SMOTE-GPC for the classification of surrounding rock mass in tunneling. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering , 2025, 11(2): 04025021. DOI: 10.1061\u002FAJRUA6.RUENG-1519 Song Chao , Zhao Tengyuan *, Xu Ling . Probabilistic classification of surrounding rock mass of tunnels based on fully Bayesian Gaussian process classification (fB-GPC) with consideration of influencing factors selection. Geological Journal , 2024, 59(9): 2442-2458. DOI: 10.1002\u002Fgj.4925 Zhao Tengyuan , Song Chao , Lu Shifeng, Xu Ling *. Prediction of uniaxial compressive strength using fully Bayesian Gaussian process regression (fB-GPR) with model class selection. Rock Mechanics and Rock Engineering , 2022, 55(10): 6301-6319. DOI: 10.1007\u002Fs00603-022-02964-y Zhao Tengyuan , Song Chao , Xu Ling *, Huang Xiaolin . Bayesian selection of the optimal composite kernels for probabilistic estimation of UCS with explicit consideration of measurement errors and the anisotropic characteristic of input features. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards , 2025. DOI : 10.1080\u002F17499518.2025.2491847 Song Chao *, Zhao Tengyuan . Uncertainty quantification in predicting UCS using fully Bayesian Gaussian process regression with consideration of model class selection. In: Geo-risk 2023 , Arlington, Virginia, 23-26 July, 2023, pp 9-19. 宋超 , 赵腾远, 高重阳. 基于机器学习的黄土关键力学参数概率预测统一框架体系. 地球科学 , 2024. 宋超 , 赵腾远*, 许领. 基于贝叶斯高斯过程回归与模型选择的岩石单轴抗压强度估计方法. 岩土工程学报 , 2023, 45(08): 1664-1673. DOI：10.11779\u002FCJGE20220734 赵腾远, 宋超 , 何欢*. 小样本条件下江苏软土路基回弹模量的贝叶斯估计——基于静力触探数据与高斯过程回归的建模分析. 岩土工程学报 , 2021, 43(S2): 137-141. DOI：10.11779\u002FCJGE2021S2033 宋超 , 赵腾远*. 粘土路基回弹模量预测及贝叶斯模型选择研究. 长沙理工大学学报（自然科学版） , 2024, 21(01): 1-12. DOI: 10.19951\u002Fj.cnki.1672-9331.20231201001 赵腾远, 宋超 , 谌文武, 郭志谦, 许领*. 基于k-means聚类与高斯过程分类的土遗址裂隙病害发育等级概率预测. 石窟与土遗址保护研究 , 2023, 2(04): 75-86. 朱文清, 赵腾远*, 宋超 , 王宇, 许领. 基于吉布斯采样与压缩感知的二维非平稳CPT数据快速插值方法. 土木与环境工程学报(中英文) , 2022, 44(05): 98-108. DOI: 10.11835\u002Fj.issn.2096-6717.2021.194 2. 软件著作权 宋超 , 赵腾远, 许领. 隧道围岩智能分级与可视化平台V1.0. 登记号：2024SR0347851 宋超 , 赵腾远. 岩石关键力学指标智能预测平台V1.0. 登记号：2023SR0614930 赵腾远, 宋超 , 许领. 全国土遗址病害等级发育概率与空间分布预测平台V1.0. 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