Fuzzy Set-Based Risk Evaluation Model for Real Estate Projects

With the rapid development of residential real estate market, risk evaluation has been an important task in the process of project. This paper describes a risk evaluation method for residential real estate projects based on fuzzy set theory which uses linguistic variables and respective fuzzy numbers to evaluate the factors. The primary weights of factors and evaluation of alternatives are determined by applying linguistic variables and fuzzy numbers. The notion of Shapley value is used to determine the global value of each factor in accomplishing the overall objective of the risk evaluation process, so the primary weights are revised, thus the importance of factors can be reflected more precisely. A major advantage of the method is that it allows experts and engineers to express their opinions on project risk evaluation in linguistic variables rather than crisp values. An illustration is presented to demonstrate the application of the method in risk evaluation. The results are consistent with the results calculated by conventional risk evaluation method. The research demonstrates that the method is objective and accurate, and is of an application value in the risk evaluation for residential real estate project.     

Research on Construction Knowledge Resources Framework of Real Estate Enterprises

<正>Many studies have shown that knowledge resources have been the real source of innovation and competitiveness of real estate enterprises.Therefore,it is the basic work to establish the real estate enterprise knowledge resources framework for implementing knowledge management(KM),and it should be given enough attention.A sort of the real estate enterprise knowledge resources framework system has been built in this paper based on the thought of Clark Henderson' s enterprise knowledge classification,and the contents have been analyzed and expounded.Thus,the real estate enterprises can grasp clearly the basic contents of its knowledge resources,then depict the knowledge system,in the end the solid foundation should be settled for implementing knowledge management.     

Risk Assessment,Management and Application in Nuclear Power Plant Operation

To introduce the basic concepts of technical specification of nuclear power plant,a risk assessment and management technique based on the probabilistic safety analysis（ PSA） method was proposed. The risk-informed method was used,and an example was given to show how to use some specific risk metrics like CDF / LERF /ICDP / ILERP to analyze and manage the risk associated with activities in nuclear power plant operation. The advantage of this technique can be concluded from this paper,and this technique should be used more widely and deeply in nuclear industry.     

Risk Management of the Escalator Overturned Accident

Escalator is a vehicle related to people’s daily life closely while it incurs safety problems frequently.Therefore,developing a new methodology for escalator safety assessment is very important.In this paper,the related suitable risk analysis models for the escalator overturned accident were proposed on different risk management stages.Firstly,field of the escalator was surveyed and information was collected to identify the danger sources.Secondly,a safety assessment process was done for escalator risk evaluation.Then the dangerous levels were devised based on the LEC evaluation method.According to the results,targeted management measures suggestions and effective safety strategy were proposed finally to solve the escalator overturned problems.The practice has proven that the safety assessment method in this paper is convenient,efficient and effective,which helps to improve the elevators safety level in China and supports the government management.     

Stability and Accuracy Analysis for Taylor Series Numerical Method

The Taylor series numerical method (TSNM) is a time integration method for solving problems in structural dynamics. In this paper, a detailed analysis of the stability behavior and accuracy characteristics of this method is given. It is proven by a spectral decomposition method that TSNM is conditionally stable and belongs to the category of explicit time integration methods. By a similar analysis, the characteristic indicators of time integration methods, the percentage period elongation and the amplitude decay of TSNM, are derived in a closed form. The analysis plays an important role in implementing a procedure for automatic searching and finding convergence radii of TSNM. Finally, a linear single degree of freedom undamped system is analyzed to test the properties of the method.     

A Steady-State Errors Correction Method for Eliminating Measurement Errors

This paper proposes a steady-state errors correction （SSEC） method for eliminating measurement errors.This method is based on the detections of error signal E（s） and output C（s） which generate an expected output R （s）.In comparison with the conventional solutions which are based on detecting the expected output R （s） and output C （s） to obtain error signal E （s）,the measurement errors are eliminated even the error might be at a significant level.Moreover,it is possible that the individual debugging by regulating the coefficient K for every member of the multiple objectives achieves the optimization of the open loop gain.Therefore,this simple method can be applied to the weak coupling and multiple objectives system,which is usually controlled by complex controller.The principle of eliminating measurement errors is derived analytically,and the advantages comparing with the conventional solutions are depicted.Based on the SSEC method analysis,an application of this method for an active power filter （APF） is investigated and the effectiveness and viability of the scheme are demonstrated through the simulation and experimental verifications.     

Neural Network Based Model for Predicting Housing Market Performance

The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home developers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the shortterm in order to make appropriate financial decisions. This paper presents the development of artificial neural network based models to support real estate investors and home developers in this critical task. The paper describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to predict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.     

Research advance in failure risk and local strength failure for high arch dams

To develop national economy and use the water resources and hydropower resources sufficiently,a lot of high arch dams,with the height of more than 200 m,have been and will be built in China.Although arch dams have good mechanical behavior,there is still failure possibility due to the huge water pressure and high stress level in dam,complex topographic and geologic conditions,formidable environment and high intensity earthquake.As one of the three main aspects concerning the safety of high arch dam,the study on global destruction,has been elaborated in the literatures,and research advance in the other two aspects,namely the failure risk and local damage of high arch dams,will be reviewed in this paper.In recent years,the failure risk of high arch dams has been investigated,and the model for identifying dam failure risk factors has been established.It is shown that the foundation deterioration and strong earthquake are the major risk sources for high dam failure.With the fault tree method,the failure mode and failure probability of high arch dams are studied,and the principle for determining failure mode and the method of calculating failure probability are proposed.Meanwhile,the determination principle of acceptable risk standard for high arch dam was proposed,and the acceptable risk rate and the acceptable standard value of various risk losses were given.For the local damage of arch dam,it is pointed out that the local damage belongs to the strength failure at material level.The study on local failure mechanism of arch dam is reviewed,based on the theories that from traditional strength theory to damage mechanics and meso-mechanics theory.Aiming at the cracking,the main pattern of local failure of high concrete dam,the research advances in the analysis methods and cracking criteria for smeared crack model and discrete crack model are summarized,and the research findings of preventive measures for local failure are shown.     

Predictive PI control and its robust stability analysis

It is difficult to analyze robust BIBO stability of predictive PI (PPI) control system due to the time delay of process. A novel technique for stability analysis of this kind of system is proposed in this paper. The state space form of PPI control system is given, and its characteristic polynomial is derived by a simple method, so the robust BIBO stability analysis of this kind of system is also the analysis of corresponding characteristic polynomial. Applying Kharitonov theorem and edge theorem, the BIBO stability can be judged for arbitrarily given process parameter intervals. The system robust stability with variable process parameters is described respectively, and some beneficial conclusions for the design of PPI controller are obtained.     

Study of Commercial Bank Risk Monitoring Model in Individual Consumption Credit

With the development of individual consumption credit (ICC) in China, commercial banks have been exposed to more and more risks. The loan failure has been an important problem that the banking must face and revolve. This paper develops a factor system to explain how the borrower's risk is affected, and then establishes a risk monitoring model with AHP to pre-warn the banks how much the risk is.     

Development of the Driving Simulation System MOVIC-T4 and Its Validation Using Field Driving Data

Underground urban expressways are a possibility for solving many existing transportation-related problems, such as traffic congestion in high density areas and the division of neighborhoods due to elevated roadways. However, they may also pose high risks regarding traffic safety. Therefore, it is important for a pre-analysis of traffic safety to be made. This paper describes recent efforts to develop a driving simulation system, MOVIC-T4, for traffic safety analysis of underground urban expressways. In order to develop a small portable simulator, a small-sized motion-base with two-degrees-of-freedom is used to duplicate accel-eration cueing together with a head-mounted-display (HMD) for the visual system. An overview of this sys-tem is given and the reliability of driving data obtained from the experiments using MOVIC-T4 is discussed through a validation study using field driving data. The results of validation indicate that the perceived speed, distance headway, and physiological data in the simulator show the almost same trend as that in the real world, but larger decelerations tend to be produced in the simulator.     

A Project Risk Ranking Approach Based on Set Pair Analysis

Set Pair Analysis （SPA） is a new methodology to describe and process system uncertainty. It is different from stochastic or fuzzy methods in reasoning and operation, and it has been applied in many areas recently. In this paper, the application of SPA in risk ranking is presented, which includes review of risk ranking, introduction of Connecting Degree （CD） that is a key role in SPA., Arithmetic and Tendency Grade （TG） of CDs, and a risk ranking approach proposed. Finally a case analysis is presented to illustrate the reasonability of this approach. It is found that this approach is very convenient to operate, while the ranking result is more comprehensible.     

Ocean primary productivity estimation of China Sea by remote sensing

Ocean primary productivity is a key parameter in the research of globalcarbon cycle, ocean biological resources, and in evaluation of the feature and quality of ocean biological environment. Traditional shipboard measurement which is costly and time-consuming is impossible to obtain the spatial and temporal information on primary productivity on a large scale in a short period of time. Satellite remote sensing is an effective strategy to acquire the ocean information in near real time. Here we propose a model special for China Sea based on the concept of primary productivity using in situ primary productivity and environmental data from 1984 to 1990, and discuss every modeling parameter which can be retrieved by remote sensing in detail. The reliability of this model is tested by in situ data, and the comparison of other primary productivity models is made. We also analyze the temporal and spatial distribution of China Sea primary productivity in 2000. From our analysis the satellite remote sensing data have been proved very useful for ocean primary productivity study.     

Urban Fire Risk Clustering Method Based on Fire Statistics

Fire statistics and fire analysis have become important ways for us to understand the law of fire, prevent the occurrence of fire, and improve the ability to control fire. According to existing fire statistics, the weighted fire risk calculating method characterized by the number of fire occurrence, direct economic losses, and fire casualties was put forward. On the basis of this method, meanwhile having improved K-mean clustering arithmetic, this paper established fire risk K-mean clustering model, which could better resolve the automatic classifying problems towards fire risk. Fire risk cluster should be classified by the absolute distance of the target instead of the relative distance in the traditional cluster arithmetic. Finally, for applying the established model, this paper carried out fire risk clustering on fire statistics from January 2000 to December 2004 of Shenyang in China. This research would provide technical support for urban fire management.     

DESIGN SENSITIVITY ANALYSIS OF ROTATING MACHINERY BY MODAL SYNTHESIS METHOD

A new method which is a combination of sensitivity analysis and modal synthesis technique was applied to the simple calculation of design sensitivities of the dynamic characteristics of substructurally combined rotating systems. In this paper the free interface mode method was introduced as a basic tool for the modal synthesis technique. As an example, the sensitivities of the critical speeds of a high-specd winding mechanism on textile machine with respect to design modifications were obtained by the new method introduced in this paper and compared with the values obtained by using another method.     

Efficiency and Feasibility of an Integrated Algorithm for Distributed Hydrological Model Calibration

Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization for DHM calibration.Latin Hypercube-once at a time （LH-OAT） was adopted in global parameter SA to obtain relative sensitivity of model parameter,which can be categorized into different sensitivity levels.Two comparative study cases were conducted to present the efficiency and feasibility by combining SA with MO（SA-MO）.WetSpa model with non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） algorithm and EasyDHM model with multi-objective sequential complex evolutionary metropolis-uncertainty analysis （MOSCEM-UA）algorithm were adopted to demonstrate the general feasibility of combining SA in optimization.Results showed that the LH-OAT was globally effective in selecting high sensitivity parameters.It proves that using parameter from high sensitivity groups results in higher convergence efficiency.Study case Ⅰ showed a better Pareto front distribution and convergence compared with model calibration without SA.Study case Ⅱ indicated a more efficient convergence of parameters in sequential evolution of MOSCEM-UA under the same iteration.It indicates that SA-MO is feasible and efficient for high dimensional DHM calibration.     

Optimization Design for Fixed Table of Gantry Machining Center Based on Sensitivity and Topology Analyses

In order to decrease the deformation and stress and increase the natural frequency of the fixed table,a method of optimization driven by the sensitivity and topology analyses is proposed.The finite element model of the fixed table is constructed and analyzed by using ANSYS software.Based on the results of static analysis and modal analysis,the maximum deformation,the maximum stress,and natural frequencies are obtained.Then,the sensitivity analysis and topology optimization are carried out to find out the parameters to be optimized.The fixed table is reconstructed according to optimal design scheme.In the comparison of the results between original model and the optimized one,the maximum deformation and stress are decreased by 71.73％ and 60.27％ respectively.At the same time,the natural frequencies from the first mode to the sixth mode are increased by 30.28％,29.57％,29.51％,31.52％,22.19％,and 21.80％,respectively.The method can provide technology guide for the design and optimization of machining structure.     

Utilizing Statistical Semantic Similarity Techniques for Ontology Mapping——with Applications to AEC Standard Models

The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.     

Temperature Prediction Model Identification Using Cyclic Coordinate Descent Based Linear Support Vector Regression

Temperature prediction plays an important role in ring die granulator control, which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonlinear, and large time-delay characteristics. Support vector machine （SVM） has been successfnlly based on small data. But its accuracy is not high, in contrast, if the number of data and dimension of feature increase, the training time of model will increase dramatically. In this paper, a linear SVM was applied combing with cyclic coordinate descent （ CCD） to solving big data regression. It was mathematically strictly proved and validated by simulation. Meanwhile, real data were conducted to prove the linear SVM model＇s effect. Compared with other methods for big data in simnlation, this algorithm has apparent advantage not only in fast modeling but also in high fitness.