Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Reliability Risk Evaluation Method for Complex Mechanical System Based on Optimal Bayesian Network

In order to reduce the calculation of the failure probability in the complex mechanical system reliability risk evaluation,and to implement importance analysis of system components effectively,the system fault tree was converted into five different Bayesian network models. The Bayesian network with the minimum conditional probability table specification and the highest computation efficiency was selected as the optimal network. The two heuristics were used to optimize the Bayesian network. The fault diagnosis and causal reasoning of the system were implemented by using the selected Bayesian network. The calculation methods of Fussel-Vesely( FV),risk reduction worth( RRW),Birnbaum measure( BM) and risk achievement worth( RAW) importances were presented. A certain engine was taken as an application example to illustrate the proposed method. The results show that not only the correlation of the relevant variables in the system can be accurately expressed and the calculation complexity can be reduced,but also the relatively weak link in the system can be located accurately.

Clues from networks:quantifying relational risk for credit risk evaluation of SMEs

Owing to information asymmetry,evaluating the credit risk of small-and mediumsized enterprises(SMEs)is difficult.While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs,our study considers both intrinsic and relational risks generated by neighbor firms’publicly available risk events.We propose a framework for quantifying relational risk based on publicly available risk events for SMEs’credit risk evaluation.Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type,risk event type,and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk.Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs,providing valuable managerial and practical implications for financial institutions.

Network security equipment evaluation based on attack tree with risk fusion

Network security equipment is crucial to information systems, and a proper evaluation model can ensure the quality of network security equipment. However, there is only a few models of comprehensive models nowadays. An index system for network security equipment was established and a model based on attack tree with risk fusion was proposed to obtain the score of qualitative indices. The proposed model implements attack tree model and controlled interval and memory(CIM) model to solve the problem of quantifying qualitative indices, and thus improves the accuracy of the evaluation.

METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Risk evaluation for the task transfer of an aircraft maintenance program based on a multielement connection number

This paper proposes a framework for evaluating the efficacy and suitability of maintenance programs with a focus on quantitative risk assessment in the domain of aircraft maintenance task transfer. The analysis is anchored in the principles of Maintenance Steering Group-3 (MSG-3) logic decision paradigms. The paper advances a holistic risk assessment index architecture tailored for the task transfer of maintenance programs. Utilizing the analytic network process (ANP), the study quantifies the weight interrelationships among diverse variables, incorporating expert-elicited subjective weighting. A multielement connection number-based evaluative model is employed to characterize decision-specific data, thereby facilitating the quantification of task transfer-associated risk through the appraisal of set-pair potentials. Moreover, the paper conducts a temporal risk trend analysis founded on partial connection numbers of varying orders. This analytical construct serves to streamline the process of risk assessment pertinent to maintenance program task transfer. The empirical component of this research, exemplified through a case study of the Boeing 737NG aircraft maintenance program, corroborates the methodological robustness and pragmatic applicability of the proposed framework in the quantification and analysis of mission transfer risk.

RiskRank:一种网络风险传播分析方法

通过研究网络风险传播途径和规律,提出一种RiskRank网络风险传播分析方法。通过计算网络节点间相似关系和临近关系,以构建网络风险传播图谱,并基于随机游走方法迭代计算网络风险传播模型,以动态分析网络风险传播过程并量化评估网络风险程度,最后采用密度聚类算法识别高风险簇,通过隔离高风险簇以控制安全态势。实验结果表明,提出的RiskRank网络风险传播模型的准确率为97.4、精度为98.1%、召回率为86.4%。

Bayesian network-based risk evaluation model for the operational requirements of the China Railway Express under the Belt and Road initiative

The operation of the China Railway Express features numerous links across several regions and countries. Effectively controlling therisks involved in the operation of the China Railway Express is crucial for ensuring safety and efficiency and promoting the sustainabledevelopment of the China Railway Express. The Bayesian network-based risk-management model was built corresponding to theactual operation of the China Railway Express;the model used an advanced risk-management theory. The sensitivity analysis of riskfactors and Bayesian network inference were realized using the expectation-maximization and clique-tree propagation algorithms.Using the risk-checklistmethod, 17 risk-related factors were analysed on 17 nodes of the Bayesian network fromthree perspectives—safety, efficiency and effectiveness—based on expert opinions and the actual operating conditions of the China Railway Express.Data from a sensitivity analysis and evidence inference of the Bayesian network model indicated that the sensitivity coefficients ofnodes N01, N04, N07, N08 and N17 of the network were high. Moreover, the risk-occurrence probabilities for nodes N01, N04, N06,N07 and N09 were higher in the case of reverse inference. Our results revealed the crucial factors influencing the risk. The identifiedrisk factors included the stability of the political environment in countries along the route, conditions of station infrastructures andthe complexity of the process of changing rails and reloading. Further, risk-management suggestions were provided. By establishinga sound risk-management framework, reliable assessment and management could be realized in accordance with changes in theoperating conditions of the China Railway Express.

A Credit Risk Evaluation Approach to Neural Network Training by Means of Financial Ratios

In recent years artificial neural networks are used to recognize the risk category of investigated companies. The research is based on data from 81 listed enterprises that applied for credit in domestic regional banks operating in China. The backpropagation algorithm-the multilayer feedforward network structure is described. Each firm is described by 9 diagnostic variables and potential borrowers are classified into four classes. The efficiency of classification is evaluated in terms of classification errors calculated from the actual classification made by the credit officers. The results of the experiments show that LevenbergMarque training error is smallest among 4 learning algorithms and its performance is better, and application of artificial neural networks and classification functions can support the creditworthiness evaluation of borrowers.

民航中小机场运行风险管控措施效能评估研究

机场作为关键的交通枢纽和经济引擎,必须实施安全风险管理,以确保其安全运营,为此,提出了危险源管控措施效能评估模型。采集多个机场的危险源数据,对其进行量化分析;利用GloVe将管控措施文本处理成词向量,通过文本卷积神经网络(TextCNN)模型,评估出风险管控措施的效果。通过对比实验,确定危险源管控措施效能评估模型的优劣。结果表明基于TextCNN的效能评估模型的准确率达到74.36%,而基于TextRNN的模型为69.24%。因此,TextCNN更适合用于此类评估任务。本研究为机场管理部门提供了一个新的视角和方法,以更有效地评估和管理各种危险源,从而提升整个机场的安全性能。

基于生成对抗网络的驾驶行为风险监测方法

针对真实驾驶场景下驾驶行为风险难以准确判断的问题,提出一种基于生成对抗网络的生成序列评估框架,旨在通过车辆行驶状态数据与驾驶场景信息匹配度评估来提高驾驶行为风险判断的准确性。构建真实驾驶场景数据匹配序列框架,利用生成对抗网络增加不同驾驶场景的数据量,提出一个驾驶员个性化情景感知样本数据集,对比分析真实数据和生成数据,并验证生成对抗网络检索与生成框架在匹配车辆行驶状态数据与驾驶场景信息方面的有效性和准确性。采用无监督学习生成评估框架,结合自回归模型中的监督训练控制策略来解决数据扩增问题,并通过迭代更新样本序列进行策略集成以保持模型鲁棒性。实验结果表明,相较于现有方法,所提研究框架在识别危险驾驶状态方面具有明显优势,为车辆行驶状态数据与驾驶场景信息的匹配评估提供了一种新的视角。

基于有向网络的航空安全事故风险识别与评估

针对航空安全事故风险识别与评估问题,基于2019年航空安全事故,通过事件树分析方法分析风险事件间的因果关系,建立了有向加权航空安全风险网络模型。面对航空安全风险传播的情况,引入传染病模型对航空安全风险网络风险传播进行建模,对风险事件进行排序以实现航空安全风险识别,提出了航空安全风险网络风险指标与航空安全事故发生风险指标,以评估航空系统安全风险。结果发现,能见度差、飞机系统故障、设备维护不足、违规操作、疲劳、资源配置不当、应急响应措施不当、飞行准备不足、教育培训不足等应当引起管理者的关注。

基于边缘计算的光通信网络安全风险等级评估方法

以提升光通信网络安全性为目的,提出基于边缘计算的光通信网络安全风险等级评估方法。首先构建光通信网络安全风险评估指标,并利用各边缘计算节点计算各终端指标数据。以各评估指标数据计算结果为基础,在云中心计算层内构建模糊综合评估模型,以模糊数学理论内的隶属度为基础,实现光通信网络安全风险多层次模糊评估。实验结果表明,该方法的光通信网络安全风险等级评估精度最高为97%,评估误差率最低为3%,该方法能够有效实现评估过程,基于评估结果进行网络优化能够提升光通信网络安全性能。

底板突水危险性评价研究进展

回顾了煤层底板突水危险性评价的发展历程,提出其危险性评价的体系:指标(指标体系的建立)—方法(评价方法的选取)—工具(处理工具的革新),并对3个环节进行总结。指出指标体系的发展不再是因素集的扩充,而是因素与因素之间非线性关系的处理以及对以开采条件及地质条件为两大基本要素集的化繁为简;将现有方法依据处理数据的逻辑分为3类:以数据的基础信息为基准,将原始数据对评价对象所产生的大小、高低、优劣性影响进行考量、排序及综合形成评价结果的第一类方法;对数据列进行人工评判、加工分析、拓展和延伸,挖掘数据的潜在信息,并形成最终评价结果的第2类方法;整理具有相同指标的数据集,通过数据信息处理技术发现数据间的共有信息,从而获得最终评价结果的第3类方法。指出未来评价方法的发展方向一方面是对突水系数法的传承,修正其在厚、巨厚、极薄隔水层的不良表现,另一方面是对机器学习新型方法的创新,对其本身及组合模型进行开发与应用。提出了处理工具所需实现的三大目标:矿井立体化模型的建立、评价结果的动态化演示、“定位、定量、定概率”三定指标的实现。分别探讨了三者面临的问题并阐述具体解决手段。在上述基础上,总体阐明了煤层底板突水危险性评价体系各环节的研究展望。

基于反距离权重插值法与BP神经网络对浙江某电镀厂遗留地块土壤重金属健康风险评价及预测

为电镀厂拆迁遗留地块污染恢复提供理论支撑,以浙江省湖州某已停产的电镀厂为研究对象,采用内梅罗指数法、人体健康风险评估模型对研究区域内土壤重金属浓度空间分布进行特征分析,并对区域内土壤重金属污染对人类的健康风险进行评价。通过MATLAB构建的BP(Back Propagation)神经网络预测模型预测深层土壤对人体的健康风险,运用ArcGIS软件使用反距离权重插值法绘制地块健康风险预测分布图。预测结果显示Cr、Ni为研究区主要污染物,地下储罐埋地Cr的致癌风险达到0.35属于高致癌风险,表层土壤中重金属超标主要集中在地下储罐埋地、镀锌镀镍车间及临时废弃物堆积区,可能是由电镀作业地面防渗措施不足及电镀废弃物处置不当造成的。构建的BP神经网络预测模型显示,调查地块内Cr、As、Ni在深层土壤(2.0~2.5 m深度)中仍然具有致癌风险,且Cr的致癌风险属于较高水平,Ni、Cr、As、Zn、Cu等在2.0~2.5 m处土壤中已无非致癌风险。

基于AHP-模糊综合评价的蒸汽管网风险评估方法及应用

蒸汽管网是石化和电力行业中实现能源利用率最大化的重要途径,风险的准确评估对其安全运行具有重要意义,而当前针对石油化工的风险评估方法对蒸汽管网适用性有限。本文通过考虑蒸汽管网潜在风险类型对失效可能性的影响,提出基于层次分析-模糊综合评价的蒸汽管网风险评估方法,并在某企业蒸汽管网的风险评估中进行应用。结果表明该评估方法更接近蒸汽管网实际情况,从而提高了蒸汽管网风险评估的准确性。

基于DA-TCN-BiGRU的坡面泥石流预测研究

为解决当前坡面泥石流预测中存在的多因素数建模问题,并提高预测的精确度,提出一种融合双注意力机制、时间卷积神经网络和双向门控循环单元(DA-TCN-BiGRU)的坡面泥石流风险预测方法。通过模拟平台进行坡面泥石流模拟实验,采集多类传感器数据得到风险度大小,并以此表征所处的风险阶段。实验结果表明,所提模型短期预测的均方根误差、平均百分比误差和平均绝对百分比误差分别为0.013 59、0.010 407和1.182 64,中期预测的均方根误差、平均百分比误差和平均绝对百分比误差分别为0.019 01、0.015 17和1.729 46,优于其他比较模型。

基于GA-WNN的桥梁基础结构局部冲刷风险模糊评估方法

针对难以获取冲刷灾害危险性指数、冲刷风险评估精度较低、桥梁基础结构突发坍塌风险较大的问题,提出了基于GA-WNN的冲刷风险模糊评估方法。在分析桥梁基础结构局部冲刷机理后,采用GA-WNN网络构建冲刷风险评估指标体系,通过冲刷风险评估指标数据的无量纲化处理,计算模糊评估指标权值;采用冲刷灾害危险性指数来反映冲刷风险的程度,结合GA-WNN网络,校验矩阵的最大特征根,计算随机一致性比率,实现了冲刷风险模糊评估算法设计。结果表明:文中方法在评估冲刷风险时,可以将相对误差控制在2%以内,大幅度提高了评估精度。

基于在线监测与模拟优化雨水截流设施运行应对内涝风险的应用

基于在线监测和排水模型对平原河网地区雨水管网及截流设施开展系统评估分析。研究区域的雨水管网受末端闸门井的控制,旱天均存在一定的水深,部分闸门井降雨后开启不及时,开启水位与旱天水位相差较大,可能导致上游排水不畅和内涝风险,也会影响下游污水系统运行。通过模拟分析得到积水风险分布图,并与闸门井及其上游管网分布图进行叠加,识别了对上游内涝积水及下游污水泵站抽排负荷冲击影响较大的闸门井,提出了以应对内涝风险为主要目标的闸门井运行调度建议。结合在线监测与模型模拟评估可有效支撑排水管网运行诊断评估、规划改造和管理维护决策等。