A comprehensive evaluation method of fuzzy matter element for CNC equipment quality

A comprehensive evaluation method based on fuzzy matter element analysis is proposed to solve problems of multiple performance and quality evaluation for computer numerical control (CNC) equipment. The fuzzy matter element theory is utilized to analyze multi-scale quality characteristics and attributes. Then, the comprehensive quality evaluation model for CNC equipment is founded by using fuzzy matter element analysis method. Analytic hierarchy process (AHP) and information entropy are applied to obtain the objective index weights. Furthermore, the combinational weight is calculated with the optimal mathematics model based on the least square method. Finally, the fuzzy matter element method is used to determine the assessment result. The case study indicates that the method has a certain rationality and feasibility.     

a multi level fuzzy comprehensive evaluation analytical model of a regional manufacturing information engineering industry

Regional manufacturing information engineering industries are extremely large and complex systems. Based on the implemental integer effect and an evaluation requirement of regional manufacturing information engineering, we analyzed the objectives and characteristics of regional manufacturing information engineering, proposed a multiple evaluation index method of regional manufacturing information engineering. Based on a multiple integration evaluation method, we built a model to evaluate regional manufacturing information engineering. We proposed a measurement model for regional manufacturing information engineering based on multi level fuzzy comprehensive evaluation. Our quantitative research regarding the implementation effectiveness of regional manufacturing information engineering was based on fuzzy quantitative indicators. The model was grounded on collecting indexes of regional manufacturing industry information engineering, and used a fuzzy matrix to normalize the assessment results to a single fuzzy comprehensive evaluation level. Measurement was carried out step by step. Multi level comprehensive evaluation results for a regional manufacturing information engineering industry were obtained. The validity of the model was shown through application practice.     

Heuristic research and fuzzy evaluation for-post-faultrestoration in distribution networks

A heuristic research and fuzzy evaluation fault restoration algorithm for distribution network is proposed to quickly restore power supply for the regions where power supply is interrupted. The algorithm searches power supply paths by finding effective tie-switches and sectionalizing switches, and eliminates improper switch pair according to power flow calculation. During service restoration process, the problem is divided into self-healing restoration, whole-region restoration, sub-region restoration, load-transfer restoration and load shedding restoration. The algorithm takes switch operation times and customer priority into account, and consequently, obtains initial feasible schemes. Fuzzy technique is used to evaluate the schemes from minimal switch operation times, load transfer, feeder margin and maximal voltage drop. Weighted sum of four indices provides a good guideline for choosing the optimal scheme for restoration operation. Calculation results of a certain distribution network show that this algorithm is feasible.     

夏播绿豆品种资源试种鉴定及综合评价

对引进的10个绿豆品种（系）的农艺性状和产量性状观察比较,结果表明：引进夏播绿豆品种（系）中有3个绿豆品种（系）,L4、L5和L8比对照产量增产20％以上,其中k较对照增产51．65％,这些品种（系）在广西生产潜力很大,可在广西推广应用。     

鲜食糯玉米新品种引进和综合评价

对引进的27个鲜食糯玉米品种进行品比试验,调查生育期、植株性状、果穗性状、抗逆性、产量、品质性状6个方面内容.结果表明:金糯628和美玉5号2个品种品质优、产量高、综合性状好,适合在广东省汕头市及生态条件相近的地区推广种植.     

甘蓝型油菜种子萌发期耐铝毒特性综合评价及其种质筛选

种子萌发是油菜植株形态建成的重要阶段,铝毒是酸性土壤中影响其种子萌发的主要因素之一,因此筛选出种子萌发过程中耐铝毒种质对油菜生产及研究具有重要意义。本研究利用5份甘蓝型油菜品种(系)筛选出油菜萌发期耐铝毒种质资源处理的适宜胁迫浓度为90μg mL~(-1)。并以该浓度处理148份甘蓝型油菜品种(系)种子,于萌发期测定其发芽势、发芽率、鲜重、干重、根长和芽长等指标,通过铝毒胁迫耐性综合评价值(A值)、平均隶属函数值(ASF值)、对铝毒加权耐性系数(WAC值)、相关性、频数、主成分、灰色关联度、聚类和逐步回归分析等鉴定萌发期耐铝性,建立萌发期对铝毒耐性综合评价模型并筛选出适宜的评价鉴定指标。结果表明, 148份甘蓝型油菜的萌发期各指标在品种(系)间存在显著差异;筛选出萌发期耐铝毒甘蓝型油菜品种(系) 01188、WH-20、A109、甲预31棚等。根据灰色关联度及回归分析结果认为,在油菜萌发期测定其根长、芽长、鲜重、发芽率和发芽势,通过回归方程估算其A值,可以初步判断甘蓝型油菜种质的耐铝毒特性。     

Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment

In recent years, maize has become one of the main alternative crops for the Autumn–Winter growing season (off-season) in several regions of Brazil. Water deficits, sub-optimum temperatures and low solar radiation levels are some of the more common problems that are experienced during this growing season. However, the impact of variable weather conditions on crop production can be analyzed with crop simulation models. The objectives of this study were to evaluate the Cropping System Model (CSM)-CERES-Maize for its ability to simulate growth, development, grain yield for four different maturity maize hybrids grown off-season in a subtropical region of Brazil, to study the impact of different planting dates on maize performance under rainfed and irrigated conditions, and for yield forecasting for the most common off-season production system. The CSM-CERES-Maize model was evaluated with experimental data collected during three field experiments conducted in Piracicaba, SP, Brazil. The experiments were completely randomized with three replications for the 2001 experiment and four replications for the 2002 experiments. For the yield forecasting application, daily weather data for 2002 were used until the forecast date, complemented with 25 years of historical daily weather data for the remainder of the growing season. Six planting dates were simulated, starting on February 1 and repeated every 15 days until April 15. The evaluation of the CSM-CERES-Maize showed that the model was able to simulate phenology and grain yield for the four hybrids accurately, with normalized RMSE (expressed in percentage) less than 15%. The planting date analysis showed that a delayed planting date from February 1 to April 15 caused a decrease in average yield of 55% for the rainfed and 21% for the irrigated conditions for all hybrids. The yield forecasting analysis demonstrated that an accurate yield forecast could be provided at approximately 45 days prior to the harvest date for all four maize hybrids. These results are promising for farmers and decision makers, as they could have access to accurate yield forecasts prior to final harvest. However, to be able to make practical decisions for stock management of maize grains, it is necessary to develop this methodology for different locations. Future model evaluations might also be needed due to the release of new cultivars by breeders.     

Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment

In recent years, maize has become one of the main alternative crops for the Autumn–Winter growing season (off-season) in several regions of Brazil. Water deficits, sub-optimum temperatures and low solar radiation levels are some of the more common problems that are experienced during this growing season. However, the impact of variable weather conditions on crop production can be analyzed with crop simulation models. The objectives of this study were to evaluate the Cropping System Model (CSM)-CERES-Maize for its ability to simulate growth, development, grain yield for four different maturity maize hybrids grown off-season in a subtropical region of Brazil, to study the impact of different planting dates on maize performance under rainfed and irrigated conditions, and for yield forecasting for the most common off-season production system. The CSM-CERES-Maize model was evaluated with experimental data collected during three field experiments conducted in Piracicaba, SP, Brazil. The experiments were completely randomized with three replications for the 2001 experiment and four replications for the 2002 experiments. For the yield forecasting application, daily weather data for 2002 were used until the forecast date, complemented with 25 years of historical daily weather data for the remainder of the growing season. Six planting dates were simulated, starting on February 1 and repeated every 15 days until April 15. The evaluation of the CSM-CERES-Maize showed that the model was able to simulate phenology and grain yield for the four hybrids accurately, with normalized RMSE (expressed in percentage) less than 15%. The planting date analysis showed that a delayed planting date from February 1 to April 15 caused a decrease in average yield of 55% for the rainfed and 21% for the irrigated conditions for all hybrids. The yield forecasting analysis demonstrated that an accurate yield forecast could be provided at approximately 45 days prior to the harvest date for all four maize hybrids. These results are promising for farmers and decision makers, as they could have access to accurate yield forecasts prior to final harvest. However, to be able to make practical decisions for stock management of maize grains, it is necessary to develop this methodology for different locations. Future model evaluations might also be needed due to the release of new cultivars by breeders.     

A heritability-adjusted GGE biplot for test environment evaluation

Test environment evaluation has become an increasingly important issue in plant breeding. In the context of indirect selection, a test environment can be characterized by two parameters: the heritability in the test environment and its genetic correlation with the target environment. In the context of GGE biplot analysis, a test environment is similarly characterized by two parameters: its discrimination power and its similarity with other environments. This paper investigates the relationships between GGE biplots based on different data scaling methods and the theory of indirect selection, and introduces a heritability-adjusted (HA) GGE biplot. We demonstrate that the vector length of an environment in the HA-GGE biplot approximates the square root heritability (\( \sqrt H \)) within the environment and that the cosine of the angle between the vectors of two environments approximates the genetic correlation (r) between them. Moreover, projections of vectors of test environments onto that of a target environment approximate values of \( r\sqrt H \), which are proportional to the predicted genetic gain expected in the target environment from indirect selection in the test environments at a constant selection intensity. Thus, the HA-GGE biplot graphically displays the relative utility of environments in terms of selection response. Therefore, the HA-GGE biplot is the preferred GGE biplot for test environment evaluation. It is also the appropriate GGE biplot for genotype evaluation because it weights information from the different environments proportional to their within-environment square root heritability. Approximation of the HA-GGE biplot by other types of GGE biplots was discussed.     

The application of rough sets-neural network theory to mine ventilation system evaluation

To solve the instability problem of established sample in the neural network evaluation method for mine ventilation system, a comprehensive evaluation of the ventilation system is carried out based on rough sets and BP neural networks. Taking the ventilation system of a mine as an example, the classification quality of raw data samples are tested by using rough set data analysis system. Then, based on artificial neural network theory, a rough sets-neural network evaluation model of a mine ventilation system is established and a new rough sets-neural network evaluation method of mine ventilation system is formed. The results show that, after the model validation of data and application, its theoretical evaluation results are in line with the actual situation, and the network total error is less than 0.004. It shows that the comprehensive evaluation method based on rough sets-neural networks has a good effect in evaluating mine ventilation system in practical application.     

Comprehensive dynamic fuzzy evaluation for construction safety of manual digging pile in limestone area

Based on the analysis of construction characteristics of manual digging pile in karst region, a scientific, timely and dynamic safety assessment system is the established and fuzzy AHP(analytic hierarchy process)is used to conduct a comprehensive safety assessment. The results are accordant with the actual situation and the construction practice has proved that fuzzy AHP is feasible to evaluate manual digging pile construction safety and good results can be achieved.     

用模糊概率分析法对玉米杂交种综合评估的研究

该文介绍了模糊概率分析法的原理,并应用该法对7个玉米杂交种的12个性状进行了综合评估,结果与各杂交种以产量为指标的排序相吻合,而比单纯依靠产量结果对各杂交种的评估更具科学性。该方法计算简便,结论明确,是评价农作物新品种的又一新方法。     

煤炭企业科技进步水平综合评价研究

根据煤炭企业资源约束性、生产高危性以及环境破坏性等特点,建立了以综合环境条件、科技投入水平、经济效益水平、社会效益水平等为主线,包含9个二级指标、24个三级指标的煤炭企业科技进步水平评价指标体系;运用层次分析法,邀请42名不同工作单位和专业领域的专家采用1~9标度法得到指标权重值;权重分析表明,煤炭企业提高科技进步水平的工作重点是提升整体职工素质,引进高层次技术人才以及增加科技投入资金;最后,采用模糊综合评判方法对煤炭企业的科技进步水平进行了综合评价。实例分析表明,建立的评价体系具有较强的实用性,对煤炭企业的技术发展和管理优化具有一定的指导作用。     

Analysis of comprehensive evaluation method on safety of mountain tunnel initial support

Through collecting and collating information of 62 tunnels with initial support fracture,the risk grade is summed up,the factors causing initial support fracture are divided into two types：objective factors and subjective factors. The statistical results show that the proportion of controlled subjective factors’ reached as large as 25%. The comprehensive evaluation model on safety of mountain tunnel initial support by analytic hierarchy process (AHP)and fuzzy comprehensive evaluation is established. On this basis,the science rational risk response method is required. Then the evaluation model is proved credible by field measurements.     

Design and implementation of comprehensive evaluation model for long distance oil-gas pipeline

Existing pipeline safety evaluation models commonly use the subjective methods such as the expert evaluation method, but the subjective consciousness has serious influence on the determination of index weight. Therefore, the match degree of the evaluation and the real condition is low and the evaluation result lacks of comprehensiveness. This paper determines the major evaluation index of long-distance oil-gas pipelines through the analysis of the goals and features of the safety evaluation. Fuzzy mathematics method is applied to build the comprehensive evaluation model of long-distance oil-gas pipelines and to analyze the key technology of the evaluation model. Variation coefficient method and analytic hierarchy process are comprehensively utilized to make a quantitative analysis of the criteria layer weight, and preferential relation matrix and fuzzy consistent matrix are established to determine the index layer weight. Finally, some examples are given to verify the feasibility and accuracy of the proposed model.     

柱花草种质抗旱性综合评价

对我国目前主要推广种和部分引进的柱花草种质进行抗旱性综合评价。采用完全随机区组设计,4梯度人工控水干旱,测试其各项形态指标和生理指标,选出与水分胁迫达极显著相关的8项指标,用隶属函数值法,对44份柱花草种质的抗旱性综合评价。与抗旱性成正相关的指标为：相对生物量、相对株高、叶片抗旱级别、相对qP、相对脯氨酸含量、相对Fv/Fm、相对Fv’/Fm’;负相关的有：相对电导率。抗旱性综合评价显示,抗旱性最强的依次为：热研5号、USTPRC90139、爱德华①、爱德华②和CIAT11369;最弱的依次为：CPI18750A、TPRC90037②、TPRC90037③和TPRC90028。聚类分析将44份柱花草种质抗旱性可分为3类：高抗旱性10份、中抗旱性15份、低抗旱性19份。本研究为筛选柱花草优良种质和育种材料提供了理论和实践的科学数据。     

灰色系统理论在小麦新品种综合评判中的应用

根据灰色系统理论中关联度分析法,对2005年国家小麦新品种展示的17个品种进行综合评价.结果表明,矮抗58表现最优,温麦19号、周麦18、豫麦49-198、温麦18、郑麦004和濮麦9号也优于对照,其他品种较差,综合评判结果与各参试品种的生产实际表现基本一致.该分析方法为品种的综合评价提供了更直接的量化指标,对作物新品种的审定、筛选有重要指导意义.     

中国南北人群在中性环境下的生理适应性和主观评价

研究中国南北人群在中性环境下,生理适应的自主神经调节和主观评价差异。采用实验设计,遴选16名南方大学生和16名北方大学生,测试生理参数,包括皮肤温度、心率、心率变异性（HRV）、血容量（BVP）、肌电（EMG）,填写主观问卷。实验结果发现：1）中性环境,北方人群感觉是舒适的,各项评价指标接近中性和适中,数据较集中;南方人群感觉稍热,评价数据较离散,但热舒适（TCV）（p=0.106>0.05）、可接受程度（p=0.838>0.05）等几个主观评价与北方人群相比没有显著差异,说明南方人群有较宽的热中性温度区和较好的环境忍耐力;2）南北人群在中性环境下,平均皮肤温度有显著差异（p=0.006<0.05）,且身体躯干部位的皮肤温度显著高于四肢皮肤温度（p=0.009<0.05;p=0.043<0.05;p=0<0.05;p=0<0.05;p=0.002<0.05）,四肢皮肤温度差异不显著,肌电无显著差异（p=0.961>0.05）。心率和心率变异性的LF/HF比值,均值有差异,但不显著（（p=0.265>0.05;p=0.618>0.05）,但BVP有显著差异（p=0<0.05）。     

The water quality comprehensive evaluation and cluster analysis on drinking source water in Chongqing

Recent researches on the Yangtze River and the Jialing River water in Chongqing section of the Three Gorges Reservoir indicate that the pH value is relatively high and the CODMnconcentration is more than 4.5 mg/L in most of the time. Especially the proportion of dissolved molecule organic is high, as well as the high turbidity during the summer flood period. And the organic contamination is relatively serious. The fuzzy weighted water quality coefficient and hierarchical cluster analysis methods are applied to evaluate and sort the characteristic qualities of two-river water sources. During the research period, the results show that the Yangtze River water source quality is characterized by high turbidity and polluted of summer stage, middle temperature and micro-polluted of spring-autumn stage and low temperature and micro-polluted of winter stage. The Jialing River water source quality is characterized by low turbidity stage, high alga-polluted of early spring stage and high temperature and polluted of winter stage. Due to the various water qualities of two-river water sources at different periods, it is hard to meet the requirement of Standards for Drinking Water Quality (GB 5749-2006) by using traditional water treatment technologies. So the research on enhanced treatment is necessary.     

A water quality evaluation model based on AHP and variable fuzzy set

To provide objective and comprehensive evaluation of surface water environmental quality for management and engineering projects a Fuzzy-AHP evaluation model is introduced. Level 1 is comprised of several current evaluation systems. On level 2 variable fuzzy set is introduced to evaluate the accurate grade. The model is applied to evaluate Jialing River (Ciqikou section) and evaluation result is grade III which is reasonable and reliable. The model is well-developed and physically grounded makes use of information of monitoring data more scientifically and comprehensively and provides an alternative evaluation method for water quality assessment.