• 한국정밀공학회지
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• 제34권1호
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• pp.23-27
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• 2017

This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

• 한국대기환경학회지
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• 제15권5호
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• pp.625-638
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• 1999

Air quality modeling for coastal urban region has been composed of a complex system including meteorological, chemical and physical processes and emission characteristics in complex terrain. In this study, we studied about an effect prediction for deposition phenomenon affecting on air quality in Pusan metopolitan metropolitan city. In air quality modeling including ship sources, a situation considered deposition process habe better result than not considered when compared with observed value. Air pollutants emitted into urban air during the daytime nearly removed through urban atmosphere polluted. Also these phenomena correlated concentration variation connent with sea/land breezes and terrain effect. Therefore we conclude that the concentration was low at daytime when deposition flux is high, and deposition effect on industrial complex and Dongrae region is considerable in particular.

• 대한산업공학회지
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• 제39권4호
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

• 산업공학
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• 제25권1호
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• pp.40-51
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• 2012

Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.

• 한국축산식품학회지
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• 제42권4호
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• pp.655-671
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• 2022

There are differences of spectral characteristics between different types of meat cut, which means the model established using only one type of meat cut for meat quality prediction is not suitable for other meat cut types. A novel portable visible and near-infrared (Vis/NIR) optical system was used to simultaneously predict multiple quality indicators for different commercial meat cut types (silverside, back strap, oyster, fillet, thick flank, and tenderloin) from Small-tailed Han sheep. The correlation coefficients of the calibration set (R_c) and prediction set (R_p) of the optimal prediction models were 0.82 and 0.81 for pH, 0.88 and 0.84 for L^*, 0.83 and 0.78 for a^*, 0.83 and 0.82 for b^*, 0.94 and 0.86 for cooking loss, 0.90 and 0.88 for shear force, 0.84 and 0.83 for protein, 0.93 and 0.83 for fat, 0.92 and 0.87 for moisture contents, respectively. This study demonstrates that Vis/NIR spectroscopy is a promising tool to achieve the predictions of multiple quality parameters for different commercial meat cut types.

• 한국식품저장유통학회지
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• 제30권2호
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• pp.224-234
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• 2023

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. R_c² values of ≥0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (R_p²=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model's accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• 대한간호학회지
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• 제39권4호
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• pp.518-527
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• 2009

Purpose: The purpose of this study was to identify factors influencing quality of life in kidney transplant recipients and to understand the concrete pathway of influence and the power of each variable, so that integrated prediction model to promote the quality of life of kidney transplant recipients could be developed. Methods: The sample was composed of 218 patients in follow-up care after a kidney transplant in one of 4 university hospitals in the Honam area. A structured questionnaire was used and the collected data were analyzed for fitness, using the LISREL program. Results: This model was concise and extensive in predicting the quality of life of kidney transplant recipients. Conclusion: The research verified the factors influencing quality of life for kidney transplant recipients and it verified that direct factors such as perception of health state, compliance, self-efficacy, stress and indirect factors such as self-efficacy and social support can be important factors to predict the quality of life for recipients. Moreover, those variables represent 87% of variance in explaining quality of life in a prediction model so that the variables can be utilized to predict quality of life for kidney transplant recipients.

• 환경영향평가
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• 제5권1호
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• pp.95-105
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• 1996

Meteorological Joint Frequency Function required indispensably in long-term air quality prediction models were discussed for practical application in Korea. The algorithm, proposed by Turner(l964), is processed with daily solar insolation and cloudiness and height basically using Pasquill's atmospheric stability classification method. In spite of its necessity and applicability, the computer program, called STAR(STability ARray), had some significant difficulties caused from the difference in meteorological data format between that of original U.S. version and Korean's. To cope with the problems, revised STAR program for Korean users were composed of followings; applicability in any site of Korea with regard to local solar angle modification; feasibility with both of data which observed by two classes of weather service centers; and examination on output format associated with prediction models which should be used.

• 한국신뢰성학회지:신뢰성응용연구
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• 제11권4호
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• pp.399-407
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• 2011

Reliability evaluation of One-Shot system which flies at speed of Mach must be evaluated as the result of many firing tests. But many firing tests are impossible because of budget deficit. Consequently the reliability prediction which substitutes firing tests is used. The accuracy of reliability prediction is decided according to a quantity of accumulated test data. If the test data is insufficient, the direction of prediction can not be set. So we propose the reliability prediction method which applies MIL-HDBK-217 Plus. MIL-HDBK-217 Plus is described about reliability prediction method without sufficient test data. So we apply MIL-HDBK-217 Plus to the rocket motor system, and we accomplish a modeling and a reliability prediction about the system.