NEURAL NETWORK ASSESSMENT OF ROCKBURST RISKS FOR DEEP GOLD MINES IN SOUTH AFRICA

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Assessing the Risk Situation of Network Security for Active Defense

The risk situation assessment and forecast technique of network security is a basic method of active defense techniques. In order to assess the risk of network security two methods were used to define the index of risk and forecast index in time series, they were analytical hierarchy process （AHP） and support vector regression （SVR）. The module framework applied the methods above was also discussed. Experiment results showed the forecast values were so close to actual values and so it proved the approach is correct.

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.

Artificial Neural Network Modeling of Healthy Risk Level Induced by Aircraft Pollutant Impacts around Soekarno Hatta International Airport

Aircraft pollutant emissions are an important part of sources of pollution that directly or indirectly affect human health and ecosystems. This research suggests an Artificial Neural Network model to determine the healthy risk level around Soekarno Hatta International Airport-Cengkareng Indonesia. This ANN modeling is a flexible method, which enables to recognize highly complex non-linear correlations. The network was trained with real measurement data and updated with new measurements, enhancing its quality and making it the ideal method for this research. Measurements of aircraft pollutant emissions are carried out with the aim to be used as input data and to validate the developed model. The obtained results concerned the improved ANN architecture model based on pollutant emissions as input variables. ANN model processes variables—hidden layers—and gives an output variable corresponding to a healthy risk level. This model is characterized by a 4-10-1 scheme. Based on ANN criteria, the best validation performance is achieved at epoch 28 from 34 epochs with the Mean Squared Error (MSE) of 9 × 10-3. The correlation between targets and outputs is confirmed. It validated a close relationship between targets and outputs. The network output errors value approaches zero. Further research is needed with the aim to enlarge the scheme of the ANN model by increasing its input variables. This is one of the major key defining environmental capacities of an airport that should be applied by Indonesian airport authorities. These would institute policies to manage or reduce pollutant emissions considering population and income growth to be socially positive.

Network analysis using organizational risk analyzer

The tool system of the organizational risk analyzer (ORA) to study the network of East Turkistan terrorists is selected. The model of the relationships among its personnel, knowledge, resources and task entities is represented by the meta-matrix in ORA, with which to analyze the risks and vulnerabilities of organizational structure quantitatively, and obtain the last vulnerabilities and risks of the organization. Case study in this system shows that it should be a shortcut to destroy effectively the network of terrorists by recognizing the caucus persons of the terrorism organization for the first and eliminating them when strikes the terror organization. It is vital to ensure effective use of the resources and control the risks of terrorist attacks.

Risk Index Prediction of Civil Aviation Based on Deep Neural Network

Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe data are considered in predicting safety risks.A deep learning method is adopted for extracting reactions in safety risks.The deep neural network(DNN)model for safety risk prediction is shown to extract complex data characteristics better than a shallow network model.Using extended unsafe data and monthly risk indices,hidden layers and iterations are determined.The effectiveness of DNN is also revealed in comparison with the traditional neural network.Through early risk detection using the method in the paper,airlines and the government can mitigate potential risk and take proactive measures to improve civil aviation safety.

An Artificial Neural Network Approach for Credit Risk Management

The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a litera-ture review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to an-other one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models.

Designing of Commercial Bank Loans Risk Early Warning System Based on BP Neural Networks

According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.

故障树和模糊贝叶斯网络在管廊运维风险评估中的应用研究

地下综合管廊是城市的生命线,一旦出现问题就会对人们生命财产安全造成巨大损害。为了系统地分析管廊运维风险,建立了基于模糊贝叶斯网络的风险评估框架。首先,通过分析管廊运维风险源与风险形成的原因以确定风险事件和风险类别;其次,建立管廊运维风险故障树来梳理风险因素之间的逻辑关系,将故障树映射为贝叶斯网络;最后,结合专家模糊评价,构建地下综合管廊运维风险评估模型。案例分析结果显示:中间事件“火灾、爆炸”与“危害气体浓度过高”的发生概率较高。敏感性分析结果显示:“运维人员操作和维护不当”是导致管廊运维风险发生的根本事件,因此需要制定严格管理措施及规范,加强对运维人员的素质培训,以降低管廊运维过程中各种风险的发生概率。

P2P network lending in the credit risk rating of the individual

P2P lending network is person to person lending network, lnternet-based applications, individuals lending financial model to others through the network intermediary,＇ platform. Currently P2P lending network has developed rapidly, but the P2P network lending platform also are lacing increasing risks, the biggest risk is credit risk. This article from the credit rating perspective, comparative analysis of the existing credit rating methodology, Analysis to establish a relatively sound credit rating mechanisms, thus reducing credit risk.

机场飞行区多层异质链序风险传播模型研究

为探究机场飞行区风险发生机制及风险传播特性,增强机场飞行区安全管控能力,采用复杂网络理论,依据风险致因间的因果链序关系,构建多层异质网络风险传播模型。采用多层次因果图(AcciMap)理论分析机场飞行区运行风险传播的致因链;基于风险致因特征构建3层异质风险传播网络并根据网络特征设计概率-触发传播模型、容量-阻抗传播模型、容量-负荷传播模型;利用复杂网络理论设计风险传播评价指标,对风险网络传播特征进行分析。结果表明:多层风险网络中节点风险传播能力与节点度弱相关,节点风险灵敏指数可提升风险节点排序准确性;对风险灵敏度指数排序前15%的节点进行风险控制,可有效降低风险扩散近32%;网络风险传播结构存在高连通和松散状态,对风险扩散指数排序前15%的节点进行控制,可以有效降低网络结构鲁棒性指数使风险网络结构由高连通进入到松散状态;所建模型可有效识别风险扩散过程并进行精准控制,以提升机场飞行区风险控制水平。

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.

行业指数波动同步性、风险溢出与传染渠道研究--基于中国股票市场的经验证据

基于2005—2021年的申万一级行业指数,采用GED-GJR-GARCH-DCC模型考察行业波动的同步性,并借助VAR模型的广义方差分解方法构建波动风险溢出层次网络,系统性分析风险的跨行业传染问题。研究发现,行业波动的动态变化具有明显的“事件驱动”特征,极端事件不仅加剧了行业间波动的同步性、非对称性和溢出效应,而且破坏了行业波动关联网络的原有结构。从行业波动的方向性溢出来看,位于产业链中游的制造类行业具有较强的风险溢出能力,上游资源类行业和金融类支撑性行业容易受到其他行业的冲击,具有风险净吸收效应。行业波动溢出层次网络揭示出风险的跨行业传染沿循“风险输出型行业—风险中介型行业—风险吸收型行业”的传导渠道。鉴于此,监管部门应当根据行业风险传染机制进行分类监管,平抑行业波动同步性,切断风险传染渠道,提高风险监控和处理能力。

Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network

Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.

Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control

Managing TG-51 reference dosimetry in a large hospital network can be a challenging task. The objectives of this study are to investigate the effectiveness of using Statistical Process Control (SPC) to manage TG-51 workflow in such a network. All the sites in the network performed the annual reference dosimetry in water according to TG-51. These data were used to cross-calibrate the same ion chambers in plastic phantoms for monthly QA output measurements. An energy-specific dimensionless beam quality cross-calibration factor, , was derived to monitor the process across multiple sites. The SPC analysis was then performed to obtain the mean, , standard deviation, σk, the Upper Control Limit (UCL) and Lower Control Limit (LCL) in each beam. This process was first applied to 15 years of historical data at the main campus to assess the effectiveness of the process. A two-year prospective study including all 30 linear accelerators spread over the main campus and seven satellites in the network followed. The ranges of the control limits (±3σ) were found to be in the range of 1.7% - 2.6% and 3.3% - 4.2% for the main campus and the satellite sites respectively. The wider range in the satellite sites was attributed to variations in the workflow. Standardization of workflow was also found to be effective in narrowing the control limits. The SPC is effective in identifying variations in the workflow and was shown to be an effective tool in managing large network reference dosimetry.

A Software Risk Analysis Model Using Bayesian Belief Network

The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.

基于动态贝叶斯网络的装配式建筑吊装施工安全风险分析

针对装配式建筑吊装施工期间风险影响因素多、不确定性高及风险状态随时间动态变化等问题,提出基于动态贝叶斯网络的装配式建筑吊装施工安全风险动态分析方法。首先,根据装配式建筑吊装施工特点和事故致因理论,从“人、物、环、管”4个方面建立风险指标体系。其次,引入时间维度建立动态贝叶斯网络模型,利用模糊理论和专家打分法量化网络节点的概率,并结合Leaky Noisy-or Gate扩展模型修正条件概率。最后,利用动态贝叶斯网络的双向推理功能对装配式建筑吊装施工安全风险进行动态风险分析。由实例分析得到某装配式建筑吊装施工安全风险的时序变化曲线,通过反向推理得到导致吊装事故发生的关键风险因素。研究成果可为分析和有效控制装配式建筑吊装施工安全风险提供新思路。

Nonhomogeneous Risk Rank Analysis Method for Security Network System

Security measures for a computer network system can be enhanced with better understanding the vulnerabilities and their behavior over the time. It is observed that the effects of vulnerabilities vary with the time over their life cycle. In the present study, we have presented a new methodology to assess the magnitude of the risk of a vulnerability as a “Risk Rank”. To derive this new methodology well known Markovian approach with a transition probability matrix is used including relevant risk factors for discovered and recorded vulnerabilities. However, in addition to observing the risk factor for each vulnerability individually we have introduced the concept of ranking vulnerabilities at a particular time taking a similar approach to Google Page Rank Algorithm. New methodology is exemplified using a simple model of computer network with three recorded vulnerabilities with their CVSS scores.

基于复杂网络的高速公路交通事故风险动态演化模型

为有效挖掘高速公路交通事故规律信息,基于复杂网络理论提出高速公路交通事故致因网络构建方法.从风险结构与风险模式两方面定义风险传播机制,通过高速公路交通事故致因网络与风险传播机制的有机结合,建立了高速公路交通事故风险演化模型.以高速公路重特大交通事故为例,对201份高速公路重特大交通事故调查报告进行风险分析.结果表明,基于复杂网络的风险演化模型适用于高速公路的交通事故风险分析;通过高速公路交通事故风险演化模型可对各致因的风险传播特性进行解析与归类,从而制定出针对不同情形致因的控制策略,为高速公路交通事故风险演化研究提供新思路.