• 전기학회논문지
• /
• 제65권6호
• /
• pp.1001-1006
• /
• 2016

As consumption of energy is increasing rapidly, energy saving is emphasized in nowadays. Thermal power plant occupies a large proportion in various type of power plant. Major causes of decreased power generation efficiency on thermal power stations is deposition of fly ash. Soot Blower is a facility to remove the ash which is deposited outside of tube by steam blowing on boiler. Residual stream which caused by lance tube in soot blower cannot be discharged steam effectively in lance tube causes reducing the thickness of lance tube. On the contrary, increasing discharge ratio of steam, lance tube cannot sustain proper pressure to remove ash on tube. This study suggests increasing discharge ratio of steam with proper pressure to remove ash on tube by optimization on shape of lance tube nozzle. To optimize shape of nozzle, discharge ratio and maximum blowing pressure on nozzle is selected as object functions. Diameter of nozzle, distance between nozzles, angle of nozzle and gap between nozzle is selected as design parameters. Then the design of experiment (DOE) with an orthogonal array is performed to analyze the effect of design parameters. And grey relational analysis and analysis of mean (ANOM) is performed to optimize shape of lance tube.

• Journal of Information Processing Systems
• /
• 제15권2호
• /
• pp.305-319
• /
• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• Journal of Information Processing Systems
• /
• 제8권4호
• /
• pp.621-652
• /
• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Industrial Engineering and Management Systems
• /
• 제11권2호
• /
• pp.165-169
• /
• 2012

There has been a growing interest in firms' environmental sustainability activities to improve environmental practices in their supply chain. This study aims to deal with supplier evaluation of firm's green supply chain management (GSCM) criteria with incomplete information. Nevertheless, the suitable supplier is a key strategic direction in eliminating environmental impact on supply chain management for manufacturing firms. The firm's GSCM criteria and supplier selection need to be unified as a system to improve the firm's performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권11호
• /
• pp.4611-4624
• /
• 2020

Future network scenario will be a heterogeneous wireless network environment composed of multiple networks and multimode terminals (MMT). Seamless switching and optimal connectivity for MMT among different networks and different services become extremely important. Here, a vertical handoff algorithm combined an improved entropy weighting method based on grey relational analysis (GRA) is proposed. In which, the improved entropy weight method is used to obtain the objective weights of the network attributes, and GRA is done to rank the candidate networks in order to choose the best network. Through simulation and comparing the results with other vertical handoff decision algorithms, the number of handoffs and reversal phenomenon are reduced with the proposed algorithm, which shows a better performance.

• 한국인터넷방송통신학회논문지
• /
• 제19권5호
• /
• pp.187-194
• /
• 2019

시장의 급속한 변화와 개별 수요자 요구의 다양화로 인하여 전통적인 예측 방식은 기업의 요구사항을 충족시키기 어렵다. 다변화하는 생산 환경에서의 올바른 수요예측은 원활한 수율관리를 위한 중요한 요소이다. 현재 산업에서 보편적으로 사용되는 기존의 많은 예측 모델은 조금씩 기능에 제한이 있다. 제안된 모델은 각 모델이 개별적으로 더 잘 수행하는 부분을 고려하여 이러한 한계를 극복하도록 설계 되었다. 본 논문에서는 동적 프로세스 분석에 적합한 Grey Relational 분석을 통한 변수 추출을 하고, ARIMA 예측값을 통하여 산출되는 과거 수요 데이터의 특징을 포함하는 통계적으로 예측된 데이터를 생성한다. 이후, LSTM 모델과 결합하여 신경망모델이 가지는 특성인 유연성, 장기적인 의존성 문제를 피하도록 구성되어진 구조를 통하여 수요예측에 영향을 주는 많은 요인들을 특징을 반영하여 수요예측을 산출할 수 있다.

• Structural Engineering and Mechanics
• /
• 제31권1호
• /
• pp.57-74
• /
• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• The Journal of Asian Finance, Economics and Business
• /
• 제7권7호
• /
• pp.113-121
• /
• 2020

Multi-criteria stock selection is a critical issue for effective investment since the improper stock investment might cause many problems affecting investors negatively. Investors need a range of financial indicators while they are choosing the optimal set of stocks to invest. This study aims to rank the stock of agriculture companies indexed on the Vietnam Stock Exchange Market. The data of 13 agriculture companies during the 2016-2019 periods was analyzed by analytical hierarchy process (AHP) integrated with grey relational analysis (GRA), multi-objective optimization ratio analysis (MOORA), and technique for order performance by similarity to ideal solution (TOPSIS). The AHP method is employed to determine the weights of the proposed financial ratios, and GRA, TOPSIS, and MOORA approaches are used to obtain final ranking. The results indicated that HSL is the top stock with the highest rank and GRA, MOORA, and TOPSIS rankings have strong correlation values between 0.78-1. The findings suggest that the integrated model could be implemented effectively to specific analysis of industries such as oil and gas, textiles, food, and electronics in future research. Further, other techniques like COPRAS, KEMIRA, and EDAS could be employed to evaluate the financial performance of other companies to solve investment problems.

• Advances in materials Research
• /
• 제5권2호
• /
• pp.67-80
• /
• 2016

The objective of the present work is to optimize process parameters namely, cutting speed, feed rate, and depth of cut in milling of AISI 304 stainless steel. In this work, experiments were carried out as per the Taguchi experimental design and an $L_{27}$ orthogonal array was used to study the influence of various combinations of process parameters on surface roughness (Ra) and material removal rate (MRR). As a dynamic approach, the multiple response optimization was carried out using grey relational analysis (GRA) and desirability function analysis (DFA) for simultaneous evaluation. These two methods are considered in optimization, as both are multiple criteria evaluation and not much complicated. The optimum process parameters found to be cutting speed at 63 m/min, feed rate at 600 mm/min, and depth of cut at 0.8 mm. Analysis of variance (ANOVA) was employed to classify the significant parameters affecting the responses. The results indicate that depth of cut is the most significant parameter affecting multiple response characteristics of GFRP composites followed by feed rate and cutting speed. The experimental results for the optimal setting show that there is considerable improvement in the process.

• Geomechanics and Engineering
• /
• 제23권1호
• /
• pp.51-59
• /
• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.