• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.15 no.5
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• pp.115-127
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• 2012

This study was conducted on the measurement of soil hardness through a hardness testing machine in slopes of natural environments and artificial environments which is generally known as slope revegetation. The soil hardness as one of physicochemical soil properties is significantly associated with plant growth. Although another studies related to the slope revegetation was focused on herbaceous plants, studies related to soil properties for arbor appearance is lack. It was focused on the correlation analysis between the soil hardness and the plant appearance. the results were as follows : The higher the soil hardness is, the less the appearance of plants is as a result of survey. Species appearing in the high levels of the soil hardness represented mugwort and grass. The levels of the soil hardness in the slope of natural environments was good environmental conditions with various plants in the range of 6 to 12mm. The levels of the soil hardness in the slope revegetation was in the 6.88-30mm range. The soil hardness below 21mm showed a variety of plants with arbors and herbaceous plants, whereas it above 21mm represented a monotonous style of plant structure including Artemisia princeps, Lolium perenne, Poa pratensis L and Setaria viridis. The result of the correlation analysis between the soil hardness and the plant appearance was negatively correlated with justifiable significance levels. The result of a logistic regression analysis for tree appearance was statistically proved when the numerical value of the soil hardness is lower.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.6
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• pp.767-777
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• 2014

Characterizing local chicken types and their mostly rural production systems is prerequisite for designing and implementing development and conservation programs. This study evaluated the management practices of small-scale chicken keepers and the phenotypic and production traits of their chickens in Oman, where conservation programs for local livestock breeds have currently started. Free-range scavenging was the dominant production system, and logistic regression analysis showed that socio-economic factors such as training in poultry keeping, household income, income from farming and gender of chicken owners influenced feeding, housing, and health care practices (p<0.05). A large variation in plumage and shank colors, comb types and other phenotypic traits within and between Omani chicken populations were observed. Male and female body weight differed (p<0.05), being $1.3{\pm}0.65$ kg and $1.1{\pm}0.86$ kg respectively. Flock size averaged $22{\pm}7.7$ birds per household with 4.8 hens per cock. Clutch size was $12.3{\pm}2.85$ and annual production $64.5{\pm}2.85$ eggs per hen. Egg hatchability averaged $88{\pm}6.0%$ and annual chicken mortality across all age and sex categories was $16{\pm}1.4%$. The strong involvement of women in chicken keeping makes them key stakeholders in future development and conservation programs, but the latter should be preceded by a comprehensive study of the genetic diversity of the Omani chicken populations.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.241-250
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• 2005

New product development (NPD) is defined as the transformation of a market opportunity and a set of assumptions about product technology into a product available for sale. Managers charged with project selection decisions in the NPD process, such as go/no-go choices and specific resource allocation decisions, are faced with a complicated problem. Therefore, the ability to develop new successful products has identifies as a major determinant in sustaining a firm's competitive advantage. The purpose of this study is to develop a new evaluation model for NPD project selection in the high -tech industry using support vector machines (SYM). The evaluation model is developed through two phases. In the first phase, binary (go/no-go) classification prediction model, i.e. SVM for high-tech NPD project selection is developed. In the second phase. using the predicted output value of SVM, feasibility grade is calculated for the final NPD project decision making. In this study, the feasibility grades are also divided as three level grades. We assume that the frequency of NPD project cases is symmetrically determined according to the feasibility grades and misclassification errors are partially minimized by the multiple grades. However, the horizon of grade level can be changed by firms' NPD strategy. Our proposed feasibility grade method is more reasonable in NPD decision problems by considering particularly risk factor of NPD in viewpoints of future NPD success probability. In our empirical study using Korean NPD cases, the SVM significantly outperformed ANN and logistic regression as benchmark models in hit ratio. And the feasibility grades generated from the predicted output value of SVM showed that they can offer a useful guideline for NPD project selection.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.153-168
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• 2012

It has been recognized as an important issue to design optimally a firm's logistics network for minimizing logistics cost and maximizing customer service. It is, however, not easy to get an optimal solution by analyzing trade-off of cost factors, dynamic and interdependent characteristics in the logistics network decision making. Although there has been some developments in a system which helps decision making for logistics analysis, it is true that there is no system for enterprise-wise's on-site support and methodical logistics decision. Specially, E-biz process along with information technology has been made dramatic advance in a various industries, there has been much need for practical education closely resembles on-site work. The software developed by this study materializes efficient algorithm suggested by recent studies in key topics of logistics such as location and allocation problem, traveling salesman problem, and vehicle routing problem and transportation and distribution problem. It also supports executing a variety of experimental design and analysis in a way of the most user friendly based on Java. In the near future, we expect that it can be extended to integrated supply chain solution by adding decision making in production in addition to a decision in logistics.

• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.81-93
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• 2021

Through processors, wholesale markets, intermediate sellers, and retailers, agricultural products have been distributed in a multi-level customary manner for a long time as they are easy to deteriorate and no not have a standardized system of size and quality. However, with the advancement of Internet networks and logistic services during the 2000s that facilitated the development of offline markets, and the rise of the non-contact purchase preference in direct response to COVID-19, previous offline consumers flowed into the online market to purchase agricultural goods. In other words, the volume of online agricultural transactions exploded since the pandemic. Against this social backdrop, this study focused on the difference in distribution costs as a result of converting from conventional offline distribution channels to online channels, and analyzed the reduced distribution costs through a case study of garlic sales on the online platform "H" shopping mall. The analysis found that considerable economic effects occurred, some of the effects being an approximate 39% decrease in distribution cost when comparing direct online transactions of the online shopping mall with other more traditional means, a reduced distribution cost rate of approximately 28%p, and increased profit for farmers.

• Journal of Construction Engineering and Project Management
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• v.8 no.4
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• pp.1-24
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• 2018

In the last decade, the use of Building Information Modeling (BIM) as a new technology has been applied with traditional Computer-aided design implementations in an increasing number of architecture, engineering, and construction projects and applications. Its employment alongside construction management, can be a valuable tool in helping move these activities and projects forward in a more efficient and time-effective manner. The traditional stakeholders, i.e., Owner, A/E and the Contractor are involved in this BIM system that is used in almost every activity of construction projects, such as design, cost estimate and scheduling. This article extracts major features of the application of BIM from perspective of participating BIM components, along with the different phrases, and applies to them a logistic analysis using a fuzzy performance tree, quantifying these phrases to judge the effectiveness of the BIM techniques employed. That is to say, these fuzzy performance trees with fuzzy logic concepts can properly translate the linguistic rating into numeric expressions, and are thus employed in evaluating the influence of BIM applications as a mathematical process. The rotational fuzzy models are used to represent the membership functions of the performance values and their corresponding weights. Illustrations of the use of this fuzzy BIM performance tree are presented in the study for the uninitiated users. The results of these processes are an evaluation of BIM project performance as highly positive. The quantification of the performance ratings for the individual factors is a significant contributor to this assessment, capable of parsing vernacular language into numerical data for a more accurate and precise use in performance analysis. It is hoped that fuzzy performance trees and fuzzy set analysis can be used as a tool for the quality and risk analysis for other construction techniques in the future. Baldwin's rotational models are used to represent the membership functions of the fuzzy sets. Three scenarios are presented using fuzzy MEAN, AND and OR gates from the lowest to intermediate levels of the tree, and fuzzy SUM gate to relate the intermediate level to the top component of the tree, i.e., BIM application final performance. The use of fuzzy MEAN for lower levels and fuzzy SUM gates to reach the top level suggests the most realistic and accurate results. The methodology (fuzzy performance tree) described in this paper is appropriate to implement in today's construction industry when limited objective data is presented and it is heavily relied on experts' subjective judgment.

• Journal of Yeungnam Medical Science
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• v.37 no.4
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• pp.314-320
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• 2020

Background: A diabetic foot is the most common cause of non-traumatic lower extremity amputations (LEA). The study seeks to assess the risk factors of amputation in patients with diabetic foot ulcers (DFU). Methods: The study was conducted on 351 patients with DFUs from January 2010 to December 2018. Their demographic characteristics, disease history, laboratory data, ankle-brachial index, Wagner classification, osteomyelitis, sarcopenia index, and ulcer sizes were considered as variables to predict outcome. A chi-square test and multivariate logistic regression analysis were performed to test the relationship of the data gathered. Additionally, the subjects were divided into two groups based on their amputation surgery. Results: Out of the 351 subjects, 170 required LEA. The mean age of the subjects was 61 years and the mean duration of diabetes was 15 years; there was no significant difference between the two groups in terms of these averages. Osteomyelitis (hazard ratio [HR], 6.164; 95% confidence interval [CI], 3.561-10.671), lesion on percutaneous transluminal angioplasty (HR, 2.494; 95% CI, 1.087-5.721), estimated glomerular filtration rate (eGFR; HR, 0.99; 95% CI, 0.981-0.999), ulcer size (HR, 1.247; 95% CI, 1.107-1.405), and forefoot ulcer location (HR, 2.475; 95% CI, 0.224-0.73) were associated with risk of amputation. Conclusion: Osteomyelitis, peripheral artery disease, chronic kidney disease, ulcer size, and forefoot ulcer location were risk factors for amputation in diabetic foot patients. Further investigation would contribute to the establishment of a diabetic foot risk stratification system for Koreans, allowing for optimal individualized treatment.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5315-5320
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• 2016

Purpose: Tumor cell growth and sensitivity to chemotherapy depend on many factors, among which insulin-like growth factors (IGFs) may play important roles. The aim of the present study was to evaluate the levels of insulin-like growth factors (IGFs) and IGF binding proteins (IGFBPs) in primary tumors and ascites as predictors of response to neoadjuvant chemotherapy in ovarian cancer (OC) patients. Materials and Methods: Tumor tissue samples and ascitic fluid were obtained from 59 patients with advanced OC. The levels of IGF-I, IGF-II, IGFBP-3, IGFBP-4 and PAPP-A were determined using ELISA kits. Taking into account the data on expression of these IGF-related proteins and outcome, logistic regression was performed to identify predictors of response to neoajuvant chemotherapy. Results: Human ovarian tumors expressed IGFs, IGFBP-3, IGFBP-4 and PAPP-A and these proteins were also present in ascites fluid and associated with its volume. IGFs and IGFBPs in ascites and soluble PAPP-A might play a key role in ovarian cancer progression. However, levels of proteins of the IGF system in tumors were not significant predictors of objective clinical response (oCR). Univariate analysis showed that the level of IGF-I in ascites was the only independent predictor for oCR. Conclusion: The level of IGF-I in ascites was shown to be an independent predictor of objective clinical response to chemotherapy for OC patients treated with neoadjuvant chemotherapy and debulking surgery.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.