近年来,随着高速铁路的迅速发展,全国高速铁路运营总里程及高速动车组数量不断增加。高速动车组是复杂程度最高、运行速度最快的地面运输装备。为保障动车组安全稳定运行,减小故障率和维修成本,应引入动车组故障预测与健康管理(Prognost...近年来,随着高速铁路的迅速发展,全国高速铁路运营总里程及高速动车组数量不断增加。高速动车组是复杂程度最高、运行速度最快的地面运输装备。为保障动车组安全稳定运行,减小故障率和维修成本,应引入动车组故障预测与健康管理(Prognostics and Health Management,PHM)的智能运维体系到动车组全寿命周期管理。本文对轨道交通领域PHM系统现状及特征进行了梳理,详细分析了动车组车载PHM系统的需求和关键功能。针对动车组内部组件众多、结构复杂,PHM系统面对的数据体量巨大、数据格式多样、难以实现对数据实时处理与数据存储的问题,本文提出了基于“边缘—中心”分布式架构的高速动车组车载故障预测与健康管理平台,并对该架构的业务逻辑和解决方案框架进行了阐述。展开更多
随着列车通信技术的发展,列车控制网络的环境愈发复杂,车载设备对网络带宽和网络拓扑复杂度的要求愈发严格,列车互联网的物理层载体逐渐从串行通信总线网过渡到了以太网。为了保证以太网协议栈在列车互联网中的正常运行,结合列车互联网...随着列车通信技术的发展,列车控制网络的环境愈发复杂,车载设备对网络带宽和网络拓扑复杂度的要求愈发严格,列车互联网的物理层载体逐渐从串行通信总线网过渡到了以太网。为了保证以太网协议栈在列车互联网中的正常运行,结合列车互联网的独特需求,列车通信网络(train communication network,TCN)联盟提出了列车实时数据协议(train real-time data protocol,TRDP),现已成为车载设备数据传输中不可或缺的重要一环。然而,在引入以太网架构的同时,传统计算机网络面临的安全风险也被带入了列车通信网络中。架构在以太网之上的TRDP,缺乏有效的安全防护机制,面临着恶意攻击、数据泄露、拒绝服务等一系列风险。在TRDP的础上,分析了TRDP消息数据、过程数据两种协议模式的区别和安全需求,结合列车互联网的实际情况,选取合适的加密协议,改进了协议细节,设计了基于加密协议的安全防护机制。并在此基础上搭建了一个模型系统,系统展示了如何将这些关键的密码技术组合起来,构建一个安全的列车互联网信息系统,并证明了系统在保证协议安全性的情况下,能够满足列车互联网对TRDP的性能需求。展开更多
Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological ...Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological conditions.Traditional sampling strategies commonly used in landslide susceptibility models can lead to a misrepresentation of the distribution of negative samples,causing a deviation from actual geological conditions.This,in turn,negatively affects the discriminative ability and generalization performance of the models.To address this issue,we propose a novel approach for selecting negative samples to enhance the quality of machine learning models.We choose the Liangshan Yi Autonomous Prefecture,located in southwestern Sichuan,China,as the case study.This area,characterized by complex terrain,frequent tectonic activities,and steep slope erosion,experiences recurrent landslides,making it an ideal setting for validating our proposed method.We calculate the contribution values of environmental factors using the relief algorithm to construct the feature space,apply the Target Space Exteriorization Sampling(TSES)method to select negative samples,calculate landslide probability values by Random Forest(RF)modeling,and then create regional landslide susceptibility maps.We evaluate the performance of the RF model optimized by the Environmental Factor Selection-based TSES(EFSTSES)method using standard performance metrics.The results indicated that the model achieved an accuracy(ACC)of 0.962,precision(PRE)of 0.961,and an area under the curve(AUC)of 0.962.These findings demonstrate that the EFSTSES-based model effectively mitigates the negative sample imbalance issue,enhances the differentiation between landslide and non-landslide samples,and reduces misclassification,particularly in geologically complex areas.These improvements offer valuable insights for disaster prevention,land use planning,and risk mitigation strategies.展开更多
滑坡易发性评价是滑坡灾害防治的重要手段之一,而不合理的滑坡负样本会影响滑坡易发性评价,从而影响到滑坡灾害的防治,因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例,选择高程、坡度、坡向、坡位、距道路距...滑坡易发性评价是滑坡灾害防治的重要手段之一,而不合理的滑坡负样本会影响滑坡易发性评价,从而影响到滑坡灾害的防治,因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例,选择高程、坡度、坡向、坡位、距道路距离、距断层距离、距水系距离、地形起伏度、地层岩性、土地利用类型10类环境因子,使用Relief算法计算环境因子的贡献值并依据贡献值优化选择环境因子;基于环境因子优化的目标空间外向化采样法(target space exteriorization sampling,简称TSES)选择负样本,作为性能优异的随机森林模型的输入变量;之后结合优化的环境因子和正或负样本预测米林市的滑坡易发性,并用混淆矩阵和ROC曲线评价构建模型的性能。为检验环境因子优化的TSES法的有效性和先进性,采用耦合信息量法和TSES法选择滑坡负样本并构建随机森林模型,与环境因子优化的TSES法构建的随机森林模型进行对比研究。结果表明,环境因子优化的TSES法构建的随机森林模型的评价效果较好,其ACC为93.7%、AUC为0.987,均高于耦合信息量、TSES法构成的模型。环境因子优化的TSES法能够提高模型的精度,解决多因子作为约束条件取样中因子选取的问题,为滑坡易发性评价采集负样本提供了新的思路。展开更多
In recent years, the coastal region of Southeast China has witnessed a significant increase in the frequency and intensity of extreme rainfall events associated with landfalling typhoons. The hilly and mountainous ter...In recent years, the coastal region of Southeast China has witnessed a significant increase in the frequency and intensity of extreme rainfall events associated with landfalling typhoons. The hilly and mountainous terrain of this area, combined with rapid rainfall accumulation, has led to a surge in flash floods and severe geological hazards. On August 10, 2019, Typhoon Lekima made landfall in Zhejiang Province, China, and its torrential rainfall triggered extensive landslides, resulting in substantial damage and economic losses. Utilizing high-resolution satellite images, we compiled a landslide inventory of the affected area, which comprises a total of 2,774 rainfallinduced landslides over an area of 2965 km2. The majority of these landslides were small to mediumsized and exhibited elongated, clustered patterns. Some landslides displayed characteristics of high-level initiation, obstructing or partially blocking rivers, leading to the formation of debris dams. We used the inventory to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to elevation, slope angle, faults, and road density. The landslides were predominantly located in hilly and low mountainous areas, with elevations ranging from 150 to 300 m, slopes of 20 to 30 degrees, and a NE-SE aspect. Notably, we observed the highest Landslide Number Density(LND) and Landslide Area Percentage(LAP) in the rhyolite region. Landslides were concentrated within approximately 4 km on either side of fault zones, with their size and frequency negatively correlated with distances to faults, roads, and river systems. Furthermore, under the influence of typhoons, regions with denser vegetation cover exhibited higher landslide density, reaching maximum values in shrubland areas. In areas experiencing significantly increased concentrated rainfall, landslide density also showed a corresponding rise. In terms of spatial distribution, the rainfall-triggered landslides primarily occurred in the northeastern part of the study area, particularly in regions characterized by complex topography such as Shanzao Village in Yantan Town, Xixia Township, and Shangzhang Township. The research findings offer crucial data on the rainfallinduced landslides triggered by Typhoon Lekima, shedding light on their spatial distribution patterns. These findings provide valuable references for mitigating risks and planning reconstruction in typhoon-affected area.展开更多
文摘近年来,随着高速铁路的迅速发展,全国高速铁路运营总里程及高速动车组数量不断增加。高速动车组是复杂程度最高、运行速度最快的地面运输装备。为保障动车组安全稳定运行,减小故障率和维修成本,应引入动车组故障预测与健康管理(Prognostics and Health Management,PHM)的智能运维体系到动车组全寿命周期管理。本文对轨道交通领域PHM系统现状及特征进行了梳理,详细分析了动车组车载PHM系统的需求和关键功能。针对动车组内部组件众多、结构复杂,PHM系统面对的数据体量巨大、数据格式多样、难以实现对数据实时处理与数据存储的问题,本文提出了基于“边缘—中心”分布式架构的高速动车组车载故障预测与健康管理平台,并对该架构的业务逻辑和解决方案框架进行了阐述。
文摘随着列车通信技术的发展,列车控制网络的环境愈发复杂,车载设备对网络带宽和网络拓扑复杂度的要求愈发严格,列车互联网的物理层载体逐渐从串行通信总线网过渡到了以太网。为了保证以太网协议栈在列车互联网中的正常运行,结合列车互联网的独特需求,列车通信网络(train communication network,TCN)联盟提出了列车实时数据协议(train real-time data protocol,TRDP),现已成为车载设备数据传输中不可或缺的重要一环。然而,在引入以太网架构的同时,传统计算机网络面临的安全风险也被带入了列车通信网络中。架构在以太网之上的TRDP,缺乏有效的安全防护机制,面临着恶意攻击、数据泄露、拒绝服务等一系列风险。在TRDP的础上,分析了TRDP消息数据、过程数据两种协议模式的区别和安全需求,结合列车互联网的实际情况,选取合适的加密协议,改进了协议细节,设计了基于加密协议的安全防护机制。并在此基础上搭建了一个模型系统,系统展示了如何将这些关键的密码技术组合起来,构建一个安全的列车互联网信息系统,并证明了系统在保证协议安全性的情况下,能够满足列车互联网对TRDP的性能需求。
基金supported by Natural Science Research Project of Anhui Educational Committee(2023AH030041)National Natural Science Foundation of China(42277136)Anhui Province Young and Middle-aged Teacher Training Action Project(DTR2023018).
文摘Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological conditions.Traditional sampling strategies commonly used in landslide susceptibility models can lead to a misrepresentation of the distribution of negative samples,causing a deviation from actual geological conditions.This,in turn,negatively affects the discriminative ability and generalization performance of the models.To address this issue,we propose a novel approach for selecting negative samples to enhance the quality of machine learning models.We choose the Liangshan Yi Autonomous Prefecture,located in southwestern Sichuan,China,as the case study.This area,characterized by complex terrain,frequent tectonic activities,and steep slope erosion,experiences recurrent landslides,making it an ideal setting for validating our proposed method.We calculate the contribution values of environmental factors using the relief algorithm to construct the feature space,apply the Target Space Exteriorization Sampling(TSES)method to select negative samples,calculate landslide probability values by Random Forest(RF)modeling,and then create regional landslide susceptibility maps.We evaluate the performance of the RF model optimized by the Environmental Factor Selection-based TSES(EFSTSES)method using standard performance metrics.The results indicated that the model achieved an accuracy(ACC)of 0.962,precision(PRE)of 0.961,and an area under the curve(AUC)of 0.962.These findings demonstrate that the EFSTSES-based model effectively mitigates the negative sample imbalance issue,enhances the differentiation between landslide and non-landslide samples,and reduces misclassification,particularly in geologically complex areas.These improvements offer valuable insights for disaster prevention,land use planning,and risk mitigation strategies.
文摘滑坡易发性评价是滑坡灾害防治的重要手段之一,而不合理的滑坡负样本会影响滑坡易发性评价,从而影响到滑坡灾害的防治,因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例,选择高程、坡度、坡向、坡位、距道路距离、距断层距离、距水系距离、地形起伏度、地层岩性、土地利用类型10类环境因子,使用Relief算法计算环境因子的贡献值并依据贡献值优化选择环境因子;基于环境因子优化的目标空间外向化采样法(target space exteriorization sampling,简称TSES)选择负样本,作为性能优异的随机森林模型的输入变量;之后结合优化的环境因子和正或负样本预测米林市的滑坡易发性,并用混淆矩阵和ROC曲线评价构建模型的性能。为检验环境因子优化的TSES法的有效性和先进性,采用耦合信息量法和TSES法选择滑坡负样本并构建随机森林模型,与环境因子优化的TSES法构建的随机森林模型进行对比研究。结果表明,环境因子优化的TSES法构建的随机森林模型的评价效果较好,其ACC为93.7%、AUC为0.987,均高于耦合信息量、TSES法构成的模型。环境因子优化的TSES法能够提高模型的精度,解决多因子作为约束条件取样中因子选取的问题,为滑坡易发性评价采集负样本提供了新的思路。
基金supported by National Natural Science Foundation of China (42277136)Natural Science Research Project of Anhui Educational Committee (2023AH030041)National Key Research and Development Program of China (2021YFB3901205)。
文摘In recent years, the coastal region of Southeast China has witnessed a significant increase in the frequency and intensity of extreme rainfall events associated with landfalling typhoons. The hilly and mountainous terrain of this area, combined with rapid rainfall accumulation, has led to a surge in flash floods and severe geological hazards. On August 10, 2019, Typhoon Lekima made landfall in Zhejiang Province, China, and its torrential rainfall triggered extensive landslides, resulting in substantial damage and economic losses. Utilizing high-resolution satellite images, we compiled a landslide inventory of the affected area, which comprises a total of 2,774 rainfallinduced landslides over an area of 2965 km2. The majority of these landslides were small to mediumsized and exhibited elongated, clustered patterns. Some landslides displayed characteristics of high-level initiation, obstructing or partially blocking rivers, leading to the formation of debris dams. We used the inventory to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to elevation, slope angle, faults, and road density. The landslides were predominantly located in hilly and low mountainous areas, with elevations ranging from 150 to 300 m, slopes of 20 to 30 degrees, and a NE-SE aspect. Notably, we observed the highest Landslide Number Density(LND) and Landslide Area Percentage(LAP) in the rhyolite region. Landslides were concentrated within approximately 4 km on either side of fault zones, with their size and frequency negatively correlated with distances to faults, roads, and river systems. Furthermore, under the influence of typhoons, regions with denser vegetation cover exhibited higher landslide density, reaching maximum values in shrubland areas. In areas experiencing significantly increased concentrated rainfall, landslide density also showed a corresponding rise. In terms of spatial distribution, the rainfall-triggered landslides primarily occurred in the northeastern part of the study area, particularly in regions characterized by complex topography such as Shanzao Village in Yantan Town, Xixia Township, and Shangzhang Township. The research findings offer crucial data on the rainfallinduced landslides triggered by Typhoon Lekima, shedding light on their spatial distribution patterns. These findings provide valuable references for mitigating risks and planning reconstruction in typhoon-affected area.
