The solution and duality of imprecise network problems

Duality properties have been investigated by many researchers in the recent literature. They are introduced in this paper for a fully fuzzified version of the minimal cost flow problem, which is a basic model in network flow theory. This model illustrates the least cost of the shipment of a commodity through a capacitated network in terms of the imprecisely known available supplies at certain nodes which should be transmitted to fulfil uncertain demands at other nodes. First, we review on the most valuable results on fuzzy duality concepts to facilitate the discussion of this paper. By applying Hukuhara’s difference, approximated and exact multiplication and Wu’s scalar production, we exhibit the flow in network models. Then, we use combinatorial algorithms on a reduced problem which is derived from fully fuzzified MCFP to acquire fuzzy optimal flows. To give duality theorems, we utilize a total order on fuzzy numbers due to the level of risk and realize optimality conditions for providing some efficient combinatorial algorithms. Finally, we compare our results with the previous worthwhile works to demonstrate the efficiency and power of our scheme and the reasonability of our solutions in actual decision-making problems.     

上海世博会信息化运作模式与风险防范的探究

阐述上海世博会信息化运营架构、上海世博会信息化总体框架、世博会信息化运作模式,并分析世博会信息化运作存在的风险,以及上海世博会信息化运作风险的防范取向等内容。     

普适计算环境下信任管理模型的研究

普适计算环境下,各种资源、设备、应用以及环境均是高度动态变化的,因此如何衡量实体间的信任关系成为了一个十分重要的问题.传统的安全和认证方法基于可信第三方,而在动态的普适计算环境下,可信第三方的设置是不现实的,也是不可行的.基于以上问题,提出了一个新的普适计算信任管理模型,该模型集成了信誉和风险分析机制,考虑了多种相关因素,可以有效建立和度量各实体间的信任关系.相关仿真结果证明,该模型是行之有效的.     

Approximate probabilistic optimization using exact-capacity-approximate-response-distribution (ECARD)

Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses). The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact, thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members. It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic optimization.     

基于神经网络的安全风险概率预测模型

网络安全风险概率预测对分布式网络环境及其内在的不确定事件进行动态分析和评价,是构建网络安全保障体系的重要环节.深入研究网络态势感知中的特征提取、聚类分析、相似性度量和预测方法,提出了一种基于神经网络的安全风险概率预测模型.采用入侵检测数据进行了实例验证,仿真实验结果验证了风险预测方法的可行性与有效性.     

跨国公司研发项目风险评估方法研究

日益众多的跨国企业越来越重视研发项目的风险评估。在分析跨国公司研发项目风险特点的基础上,提出了一套切实可行的跨国公司研发项目的风险评估指标体系。基于TOPSIS（Technique for Order Performance by Similarity to Ideal Solution）的定量评估方法,提出了一种跨国公司研发项目风险评估方法。数据实例表明,方法是可行的、正确的和有效的。在跨国公司研发项目风险管理过程中,可以运用该方法对其进行评估,根据评估结果对研发项目进行优选排序,为管理决策提供有效的科学依据。     

基于BP模型的商业银行贷款风险预测

国有商业银行不良贷款严重束缚了商业银行的发展.防范金融风险,降低不良贷款,增强商业银行的风险识别能力,其关键是风险预测.商业银行贷款本身是一个复杂的非线性系统,用一般的线性理论难以客观反映其规律,为此,采用人工神经网络方法进行研究.在简述概念的基础上,通过反向传播网络(BP模型)对贷款企业的经营能力进行预测,从而对商业银行的贷款决策提供理论支持,使商业银行贷款风险能被控制在可控范围内.并对代表性的A股份有限公司进行预测,所得结果表明:建立的预测模型具有良好的风险预测能力.     

A quick identification model for assessing human anxiety and thermal comfort based on physiological signals in a hot and humid working environment

We propose a simple and quick method for quantifying workers' anxiety and thermal comfort levels using physiological signals. Nine subjects enrolled in a series of controlled laboratory experiments involving varying temperature, relative humidity, and labor intensities. A total of 40 experiments were conducted, and 1592 groups of anxiety data and 1624 groups of thermal comfort data were obtained, respectively. During 2-h-working trials, Electroencephalogram (EEG), photoplethysmography (PPG), and pupil diameter of each subject were collected synchronously, and the State-Trait Anxiety Inventory (STAI) and thermal comfort vote (TCV) were completed in stages. Random Forest was adopted to screen out the appropriate sensitivity feature indicators of anxiety levels and thermal comfort levels from the 70 features of the 10 EEG channels. Finally, Random Forest, Gradient Boosting Decision Tree, K-nearest Neighbor Algorithm, and Support Vector Machine were used to determine relevant physiological data combinations and modeling algorithms. The Precision of the anxiety level and thermal comfort level quick identification model based on Random Forest Algorithm can reach 81.04% and 84.79%, respectively. This suggests that the proposed quick identification method for assessing workers' anxiety and thermal comfort levels holds promise. Physiological data need to be obtained by monitoring only PPG, pupil diameter, and 5 EEG channels. By processing these data, the workers' anxiety and thermal comfort level could be judged realistically to ensure their safety. It is suggested that PPG, pupil diameter, and EEG should be considered all together in the future study of anxiety and thermal comfort.     

A Multi-Criteria Decision Analysis Technique for Stochastic Task Criticality in Project Management

Inherent in project management is the risk that a project fails to meet planned completion deadlines due to delays experienced in individual tasks. As such, certain critical tasks may be candidates for risk management (e.g., the allocation of additional resources such as labor, materials, and equipment) to prevent delays. A common means to identify such critical tasks is with the critical path method (CPM), which identifies a path of tasks in a project network that, when delayed, result in project delays. This work offers a complementary, stochastic approach to CPM that ranks tasks according to their effect on the project completion time distribution, when the distributions of task completion time are delayed. The new hybrid approach is based on the use of a Monte Carlo simulation and a multi-criteria decision analysis technique. Monte Carlo simulation allows for approximating the cumulative distribution function of the total duration of the project, while the multi-criteria decision analysis technique is used to compare and rank the tasks across percentiles of the resulting project completion time distributions. Doing so allows for different percentile weighting schemes to represent decision maker risk preferences. The suggested approach is applied to two project network examples. The examples illustrate that the proposed approach highlights some tasks as risky, which may not always lie on the critical path as identified by CPM. This is valuable for practicing managers as it allows them to properly consider their risk preferences when determining task criticality based on the distribution of project completion time (e.g., emphasizing median vs. upper tail completion time).     

Critical factors and paths influencing construction workers’ safety risk tolerances

While workers’ safety risk tolerances have been regarded as a main reason for their unsafe behaviors, little is known about why different people have different risk tolerances even when confronting the same situation. The aim of this research is to identify the critical factors and paths that influence workers’ safety risk tolerance and to explore how they contribute to accident causal model from a system thinking perceptive. A number of methods were carried out to analyze the data collected through interviews and questionnaire surveys. In the first and second steps of the research, factor identification, factor ranking and factor analysis were carried out, and the results show that workers’ safety risk tolerance can be influenced by four groups of factors, namely: (1) personal subjective perception; (2) work knowledge and experiences; (3) work characteristics; and (4) safety management. In the third step of the research, hypothetical influencing path model was developed and tested by using structural equation modeling (SEM). It is found that the effects of external factors (safety management and work characteristics) on risk tolerance are larger than that of internal factors (personal subjective perception and work knowledge & experiences). Specifically, safety management contributes the most to workers’ safety risk tolerance through its direct effect and indirect effect; while personal subjective perception comes the second and can act as an intermedia for work characteristics. This research provides an in-depth insight of workers’ unsafe behaviors by depicting the contributing factors as shown in the accident causal model developed in this research.