Applying the AHP-FUZZY method to evaluate the measure effect of rubble roadbed engineering in permafrost regions of Qinghai-Tibet Plateau: a case study of Chaidaer-Muli Railway

This article attempts to investigate the measure effect of rubble roadbed engineering in permafrost regions of Qinghai-Tibet Plateau. As a case study, Chaidaer-Muli Railway is used to evaluate the measure effect of rubble roadbed engineering in permafrost regions. The AHP(Analytic Hierarchy Process) method is thus employed to establish the evaluation indicator system. The evaluation factor is selected by analyzing the mutual relation between the permafrost environment and roadbed engineering. Thus, a hierarchical structure model is established based on the selected evaluation indices. Each factor is weighted to determine the status in the evaluation system, and grading standards are built for providing a basis for the evaluation. Then, the fuzzy mathematical method is introduced to evaluate the measure effect of rubble roadbed engineering in permafrost regions along the Chadaer-Muli Railway. Results show that most of the permafrost roadbed is in a preferable condition(b) along the Chaidaer-Muli Railway due to rubble engineering measures. This proportion reaches to 86.1%. The proportion in good(a), general(c) and poor states(d) are 0.0%, 7.5% and 6.4%, respectively, in all the evaluation sections along the Chaidaer-Muli Railway. Ground-temperature monitoring results are generally consistent with AHP-FUZZY evaluation results. This means that the AHP-FUZZY method can be applied to evaluate the effect of rubble roadbed engineering measures in permafrost regions. The effect evaluation of engineering measures will provide timely and effective feedback information for further engineering design. The series of engineering measures will more effectively protect permafrost stability.

Comparison of GUF and Monte Carlo methods to evaluate task-specific uncertainty in laser tracker measurement

Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.

Decision-Making Method for Problem of Goal Type Based on Evaluation Criterion

The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we found some defects of the method and pointed out these defects. To overcome these defects we absorbed the spirit and exploited concepts of evaluation criterion and the fault measure of evaluation criterion. We proposed and applied a method with an evaluation criterion, after which we also p...

Multi Objective Decision Making Method Based on Evaluation Criterion for Problems with Linear Value Function

A class of interactive multi objective decision making method by means of evaluation criterion is proposed for problems with linear value function,in which case,the decision maker(DM) usually has only unwhole information of weights for objectives. The concept of fault measure of the evaluation criterion is proposed to measure the deviation of the evaluation criterion from the DMs preference structure.The approach to obtain an upper boundary of fault measure of an evaluation criterion,and the approach to modify the evaluation criterion to be one with smaller fault measure,and the approach to obtain a pre optimized objective set by evaluation criterion with certain fault measure are also proposed.

First Evaluation and Frequency Measurement of the Strontium Optical Lattice Clock at NIM

An optical lattice clock based on 87Sr is built at National Institute of Metrology （NIM） of China. The systematic frequency shifts of the clock are evaluated with a total uncertainty of 2.3×10-16. To measure its absolute frequency with respect to NIM＇s cesium fountain clock NIM5, the frequency of a flywheel H-maser of NIM5 is transferred to the Sr laboratory through a 50-kin-long fiber. reference frequency of this H-maser, is used for the optical this Sr clock is measured to be 429228004229873.7（1.4）Hz. A fiber optical frequency comb, phase-locked to the frequency measurement. The absolute frequency of

Evaluation methods of man-machine-environment system for clean and safe production in phosphorus mines: A case study

Phosphorus is an essential element in agricultural production and chemical industry. However, since the risk of casualties and economic loss by mining accidents, the application of clean and safe production in phosphorus mines encounters great challenges. For this purpose, a man-machine-environment system composed of evaluation indexes was established, and the grading standards of indexes were defined. Firstly, the measurements of 39 qualitative indexes were obtained through the survey data. According to the measured values of 31 quantitative indexes, the measurements of quantitative indexes were calculated by linear measurement function(LM) and other three functions. Then the singleindex measurement evaluation matrixes were established. Secondly, the entropy weight method was used to determine the weights of each index directly. The analytic hierarchy process(AHP) was also applied to calculate the weights of index and index factor hierarchies after the established hierarchical model. The weights of system hierarchies were given by the grid-based fuzzy Borda method(GFB). The comprehensive weights were determined by the combination method of AHP and GFB(CAG). Furthermore, the multi-index comprehensive measurement evaluation vectors were obtained.Thirdly, the vectors were evaluated by the credible degree recognition(CDR) and the maximum membership(TMM)criteria. Based on the above functions, methods, and criteria, 16 combination evaluation methods were recommended.Finally, the clean and safe production grade of Kaiyang phosphate mine in China was evaluated. The results show that the LM-CAG-CDR is the most reasonable method, which can not only determine the clean and safe production grade of phosphorus mines, but also improve the development level of clean and safe mining of phosphorus mines for guidance.In addition, some beneficial suggestions and measures were also proposed to advance the clean and safe production grade of Kaiyang phosphorus mine.

Corrosion Resistance of Ni-P Composite Coating on Mg Alloy:Critical Role of the Intermediate Phosphate Conversion Coating Layer for Suppressing Galvanic Couple Formation

Electroless nickel(EN)plating can give rise to the severe galvanic corrosion of the magnesium(Mg)alloy matrix,owing to its nobler electrochemical potential than Mg alloy.To hinder the formation of galvanic couple,an intermediate phosphate conversion coating(PCC)layer is introduced between the EN layer and the Mg alloy matrix.Since the ceramic-like PCC layer cannot be catalyzed,a low-cost Ag-activation technique is used to process the PCC layer before electroless plating.The cross-section morphology and element distribution of the PCC-EN composite coating indicate that the PCC intermediate layer can effectively separate the Mg alloy from the EN layer.Moreover,the results of electrochemical tests suggest that the PCC-EN composite coating has a better corrosion resistance in comparison with the EN coating and AZ91D Mg alloy.

Multilevel Tests and Measurement Evaluation Methods for the Application of Composite Materials in Spacecraft Structures

With the implementation of new-generation launch vehicles,space stations,lunar and deep space exploration,etc.,the development of spacecraft structures will face new challenges. In order to reduce the spacecraft weight and increase the payload,composite material structures will be widely used. It is difficult to evaluate the strength and life of composite materials due to their complex mechanism and various phenomena in damage and failure.Meanwhile,the structures of composite materials used in spacecrafts will bear complex loads,including the coupling loads of tension,pressure,bending,shear,and torsion. Static loads,thermal loads,and vibration loads may occur at the same time,which asks for verification requirements to ensure the structure safety. Therefore,it is necessary to carry out a systematic multi-level experimental study. In this paper,the building block approach (BBA) is used to investigate the multilevel composite material structures for spacecrafts. The advanced measurement technology is adopted based on digital image correlation (DIC) and piezoelectric and optical fiber sensors to measure the composite material structure deformation. The virtual experiment technology is applied to provide sufficient and reliable data for the evaluation of the composite material structures of spacecrafts.

Heart Disease Risk Prediction Expending of Classification Algorithms

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early on.Heart failure is on the rise as a result of today’s lifestyle.The healthcare business generates a vast volume of patient records,which are challenging to manage manually.When it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful information.Several methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment measures.However,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning repository.In a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from UCI.The SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

A Software Quality Evaluation System: JT SQE

JT SQE system is a software quality and measurement system. Its design was based on the Chinese national standards of software product evaluation and quality characteristics. The JT SQE system consists of two parts. One is the model for software quality measurement, which is of hierarchical structure. The other is the process of requirements definition, measurement and rating. The system is a feasible model for software quality evaluation and measurement, and it has the advantage of a friendly user interface, simple operation, ease of revision and maintenance, and expansible measurements.

The Evaluating Method of Technology Innovation Risk

The thesis analyzes risk factors of enterprise＇s technology innovation, adopts the undetermined measuring model to evaluate technology innovation risk and testifies it through an example.

Investigating of Classification Algorithms for Heart Disease Risk Prediction

Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVMare evaluated using MAE, RAE, precision, recall, f-measure, and accuracy.The overall finding indicates that for the dataset taken from UCI, the SVMclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 forMAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for MAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.

Evaluation of myocardial viability during cold storage by measurement of myocardual dielectric properties tanδm in radio frequency

Objective To study the mechanism of myocardial dielectric property changes in radio frequency during hypothermic preservation and explore myocardial viability evaluative method. Methods Hybrid young pigs ( 20 - 30 kg) were used in the experiment. Heart arrest was in-

Bug Prioritization to Facilitate Bug Report Triage

The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine （SVM） classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives （NFN） and Nearest False Positives （NFP）,which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.

A measurable evaluation method of visual comfort in underground space by intelligent sorting and classification algorithms

Based on human perception and machine learning methods,this study proposes a measurable method for evaluating visual comfort in underground spaces.First,a comfort evaluation index based on the characteristics of human visual perception is proposed,and color features and segmentation extraction methods for intelligent methods are given.Then,using probability statistics and machine learning methods,a multi-class intelligent sorting and classification algorithm for ranking visual comfort levels is constructed and a comparison is made of the suitability of different intelligent methods for evaluating visual comfort.The random forest algorithm is then selected as the most effective measurable intelligent evaluation algorithm for underground spaces.Finally,the proposed method is compared to intelligent methods employed by previous research,and a case study,the Wujiaochang underground space in Shanghai,China,is applied as the background.Results show that the proposed method can effectively improve the quantification and refinement of human perception and evaluation of comfort in underground spaces;this method will also be useful in computer-aided generative design in the future.

Objective visual performance evaluation with visual evoked potential measurements based on an adaptive optics system

An objective visual performance evaluation with visual evoked potential （VEP） measurements was first inte- grated into an adaptive optics （AO） system. The optical and neural limits to vision can be bypassed through this system. Visual performance can be measured electrophysiologically with VEP, which reflects the objective func- tion from the retina to the primary visual cortex. The VEP ts without and with AO correction were preliminarily carried out using this system, demonstrating the great potential of this system in the objective visual performance evaluation. The new system will provide the necessary technique and equipment support for the further study of human visual function.

Basic Evaluation of the Process of China’s Industrial Modernization1

As China will change from a large industrial power into a strong industrial power in the 21stcentury, theinevitabledemand isfor industrialmodernization. Thispaperputs forward three criteria for judging the degree of realization of industrialmodernization: industrial growth efficiency, industrial structure and industrial environment. Together with these, it constructsan indicatorsystem and indicesforevaluating thelevelofindustrialmodernization, and uses this indicator system to evaluate the level of China’s industrial modernization at theturn ofthecentury. TheresultingcomprehensiveindexofChina’sindustrialmodernization is28.72. This indicatesthat, through the rapid industrialization which hastaken place over more than 20 years of reform and opening up, China has commenced its industrial modernization, which is now in the primarystage of development. Thecore of thestrategy forfurther development should be to accelerate the process of industrial modernization.