The paper aims to develop a risk assessment model with the fuzzy temporal information. The traditional model assess risk with the risk matrix method. And the method rank the risks without regard to the with the temporal information to assess the risk. The model integrates the method of 2-tuple linguistic and temporal linguistic variable. The improved concept defines the transition symbols operator as the projection of temporal term on the fuzzy linguistic variable. The new model may deal with the temporal information in the fuzzy linguistic judgements. The emprical research give a example by applying the new method. The result of example show that the new model can provide the worthwhile temporal information in the assessment result. temporal element temporal element temporal element temporal element

Monte Carlo Simulation is a general method for evaluating a deterministic model by iteratively generating inputs so as to get the natural distribution of outputs, which has often been employed for risk analysis of development cost estimation under uncertain environment. However, the traditional way of implementing Monte Carlo Simulation on cost risk analysis is always based on deterministic Cost Estimation Relation (CER) model and does not take the uncertainty of history cost data used to build CER into account, which will considerably affect the cost risk analysis. In this paper, we extend Monte Carlo Simulation model to make its simulating process cover the stage of building model so that not only the inputs are iteratively generated but also the model is iteratively rebuilt. An example is carried out to compare the extended model to the traditional one on analyzing aircraft development cost risk, which shows that the risk distribution gotten by Extended Monte Carlo Simulation is considerably different to that gotten by traditional one.

The paper proposed a practice teaching mode by making analysis on Didi data set. There are more and more universities have provided the big data analysis courses with the rapid development and wide application of big data analysis technology. The theoretical knowledge of big data analysis is professional and hard to understand. That may reduce students' interest in learning and learning motivation. And the practice teaching plays an important role between theory learning and application. This paper first introduces the theoretical teaching part of the course, and the theoretical methods involved in the course. Then the practice teaching content of Didi data analysis case was briefly described. And the study selects the related evaluation index to evaluate the teaching effect through questionnaire survey and verify the effectiveness of teaching method. The results show that 78% of students think that practical teaching can greatly improve students' interest in learning, 89% of students think that practical teaching can help them learn theoretical knowledge, 89% of students have basically mastered the method of big data analysis technology introduced in the course, 90% of students think that the teaching method proposed in this paper can greatly improve students' practical ability. The teaching mode is effective, which can improve the learning effect and practical ability of students in data analysis, so as to improve the teaching effect.

The paper aims to solve the problem of insufficient high risk data in risk assessment of R&D projects. A one-class classification method called support vector data description (SVDD) is studied, and an intelligent risk assessment model based on SVDD with fuzzy regression information is also proposed. The model comes into being a new approach. Applying this approach, firstly verify the conversional risk evaluation indexes by fuzzy regression technique to develop a sensitive index system. Secondly the study uses the historical risk data referring to these indexes to train the SVDD one-class classifier. Unlike previously proposed intelligent methods of risk assessment, with this model the risk level can be distinguished only by training of low risk data. The results of its application on an example show that the method is feasible for risk assessment with the fuzzy high risk data.

This paper aims to expand the concept of linguistic variable. The study analyzes the common conclusion forms in the evaluation project. The evaluation often makes the final conclusion by analyzing the fuzzy number and its membership function. However, the information of modified word or phrase (“largely”, “preliminary” and “unclear”) has got lost in analysis. The study develops the extended linguistic variable by integrating the modified and traditional linguistic variable. It is useful to understand the means of ambiguity exactly in the manufacturing processing.