This study develops a model to determine the input rate of the chemical for coagulation and flocculation process (i.e. coagulant) at industrial water treatment plant, based on real-world data. To detect outliers among the collected data, a two-phase algorithm with standardization transformation and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied. In addition, both of the missing data and outliers are revised with linear interpolation. To determine the coagulant rate, various kinds of machine learning models are tested as well as linear regression. Among them, the random forest model with min-max scaled data provides the best performance, whose MSE, MAPE, R2 and CVRMSE are 1.136, 0.111, 0.912, and 18.704, respectively. This study demonstrates the practical applicability of machine learning based chemical input decision model, which can lead to a smart management and response systems for clean and safe water treatment plant.

This study aims to develop a comprehensive predictive model for Digital Quality Management (DQM) and to analyze the impact of various quality activities on different levels of DQM. By employing the Classification And Regression Tree (CART) methodology, we are able to present predictive scenarios that elucidate how varying quantitative levels of quality activities influence the five major categories of DQM. The findings reveal that the operation level of quality circles and the promotion level of suggestion systems are pivotal in enhancing DQM levels. Furthermore, the study emphasizes that an effective reward system is crucial to maximizing the effectiveness of these quality activities. Through a quantitative approach, this study demonstrates that for ventures and small-medium enterprises, expanding suggestion systems and implementing robust reward mechanisms can significantly improve DQM levels, particularly when the operation of quality circles is challenging. The research provides valuable insights, indicating that even in the absence of fully operational quality circles, other mechanisms can still drive substantial improvements in DQM. These results are particularly relevant in the context of digital transformation, offering practical guidelines for enterprises to establish and refine their quality management strategies. By focusing on suggestion systems and rewards, businesses can effectively navigate the complexities of digital transformation and achieve higher levels of quality management.

of hazardous risk factors, risk estimation and determination steps by reflecting the trend of overseas risk assessment. METHODS : In deriving, estimating and determining risk factors, comparing the procedures presented by the ILO with the domestic guidline to find out the differences in procedural. and, According to the domestic manual, after setting the criteria for determining a deterministic perspective, analyze the risk assessment data of a specific domestic company and three overseas risk assessment research data to analyze the differences in methodology domestic and abroad. RESULTS : Within the country, there is a possibility that a deterministic view may be applied to all stages of procedure, and certain corporate data to the risk estimation and determination stage. In the case of overseas, the trend of applying deterministic perspectives to the risk determination stage was confirmed. CONCLUSIONS : Present the need for a standard model for improving deterministic methods in the other two stages, excluding risk determination in the domestic evaluation procedure.

It is difficult to optimize the process parameters of directly preparing carbonaceous mesophase (CMs) by solvothermal method using coal tar as raw material. To solve this problem, a Decision Tree model for CMs preparation (DTC) was established based on the relationship between the process parameters and the yields of CMs. Then, the importance of variables in the preparation process for CMs was predicted, the relationship between experimental conditions and yields was revealed, and the preparation process conditions were also optimized by the DTC. The prediction results showed that the importance of the variables was raw material type, solvothermal temperature, solvothermal time, solvent amount, and additive type in order. And the optimized reaction conditions were as follows: coal tar was pretreated by decompress distillation and centrifugation, the solvent amount was 50.0 ml, the solvothermal temperature was 230 °C, and the reaction time was 5 h. These prediction results were consistent with the actual experimental results, and the error between the predicted yields and the actual yields was about − 1.1%. Furthermore, the prediction error of DTC method was within the acceptable range when the data sample sets were reduced to 100 sets. These results proved that the established DTC for chemical process optimization can effectively lessen the experimental workload and has high application value.

PURPOSES : The aim of this study is to develop a decision-making model for safety countermeasures based on the characteristics of water deer roadkills. METHODS : Through field investigation, 113 water deer roadkills with factors related to environment, geometry, and ecology were collected from May 2018 to April 2019. From the collected database, the characteristics of water deer roadkills were analyzed. An analytic hierarchy process was applied to establish a decision-making model to prevent water deer roadkills from appearing. RESULTS : The likelihood of water deer roadkills increases in summer and winter, road sections in suburban areas with low traffic volume, surrounding farmlands, and areas without illumination. The results show that factors such as safety, ecology, road geometry, policy consistency, and the willingness of the local government are critical factors for establishing the decision-making model. CONCLUSIONS : Appropriate safety countermeasures for water deer roadkills can be developed if the roadkill frequency, degree of sight distance restriction, speed limit for controlling overspeeding, roadkill severity, degree of forest area, traffic volume, and willingness of competent authorities are considered as essential variables.

Background: Scapular winging (SW) could be caused by tightness or weakness of the periscapular muscles. Although data mining techniques are useful in classifying or predicting risk of musculoskeletal disorder, predictive models for risk of musculoskeletal disorder using the results of clinical test or quantitative data are scarce. Objects: This study aimed to (1) investigate the difference between young women with and without SW, (2) establish a predictive model for presence of SW, and (3) determine the cutoff value of each variable for predicting the risk of SW using the decision tree method. Methods: Fifty young female subjects participated in this study. To classify the presence of SW as the outcome variable, scapular protractor strength, elbow flexor strength, shoulder internal rotation, and whether the scapula is in the dominant or nondominant side were determined. Results: The classification tree selected scapular protractor strength, shoulder internal rotation range of motion, and whether the scapula is in the dominant or nondominant side as predictor variables. The classification tree model correctly classified 78.79% (p = 0.02) of the training data set. The accuracy obtained by the classification tree on the test data set was 82.35% (p = 0.04). Conclusion: The classification tree showed acceptable accuracy (82.35%) and high specificity (95.65%) but low sensitivity (54.55%). Based on the predictive model in this study, we suggested that 20% of body weight in scapular protractor strength is a meaningful cutoff value for presence of SW.

Recently, the continuing operation of nuclear power plants has become a major controversial issue in Korea. Whether to continue to operate nuclear power plants is a matter to be determined considering many factors including social and political factors as well as economic factors. But in this paper we concentrate only on the economic factors to make an optimum decision on operating nuclear power plants. Decisions should be based on forecasts of plant accident risks and large and small accident data from power plants. We outline the structure of a decision model that incorporate accident risks. We formulate to decide whether to shutdown permanently, shutdown temporarily for maintenance, or to operate one period of time and then periodically repeat the analysis and decision process with additional information about new costs and risks. The forecasting model to predict nuclear power plant accidents is incorporated for an improved decision making. First, we build a one-period decision model and extend this theory to a multi-period model. In this paper we utilize influence diagrams as well as decision trees for modeling. And bayesian statistical approach is utilized. Many of the parameter values in this model may be set fairly subjective by decision makers. Once the parameter values have been determined, the model will be able to present the optimal decision according to that value.

As a system complexity increases and technology innovation progresses rapidly, leasing the equipment is considered as an important issue in many engineering areas. In practice, many engineering fields lease the equipment because it is an economical way to lease the equipment rather than to own the equipment. In addition, as the maintenance actions for the equipment are costly and need a specialist, the lessor is responsible for the maintenance actions in most leased contract. Hence, the lessor should establish the optimal maintenance strategy to minimize the maintenance cost. This paper proposes two periodic preventive maintenance policies for the leased equipment. The preventive maintenance action of policy 1 is performed with a periodic interval, in which their intervals are the same until the end of lease period. The other policy is to determine the periodic preventive maintenance interval minimizing total maintenance cost during the lease period. In addition, this paper presents two decision-making models to determine the preventive maintenance strategy for leased equipment based on the lessor’s preference between the maintenance cost and the reliability at the end of lease period. The structural properties of the proposed decision-making model are investigated and algorithms to search the optimal maintenance policy that are satisfied by the lessor are provided. A numerical example is provided to illustrate the proposed model. The results show that a maintenance policy minimizing the maintenance cost is selected as a reasonable decision as the lease term becomes shorter. Moreover, the frequent preventive maintenance actions are performed when the minimal repair cost is higher than the preventive maintenance cost, resulting in higher maintenance cost.

Selecting suppliers in the global supply chain is the very difficult and complicated decision making problem particularly due to the various types of supply risk in addition to the uncertain performance of the potential suppliers. This paper proposes a multi-phase decision making model for supplier selection under supply risks in global supply chains. In the first phase, the model suggests supplier selection solutions suitable to a given condition of decision making using a rule-based expert system. The expert system consists of a knowledge base of supplier selection solutions and an “if-then” rule-based inference engine. The knowledge base contains information about options and their consistency for seven characteristics of 20 supplier selection solutions chosen from articles published in SCIE journals since 2010. In the second phase, the model computes the potential suppliers’ general performance indices using a technique for order preference by similarity to ideal solution (TOPSIS) based on their scores obtained by applying the suggested solutions. In the third phase, the model computes their risk indices using a TOPSIS based on their historical and predicted scores obtained by applying a risk evaluation algorithm. The evaluation algorithm deals with seven types of supply risk that significantly affect supplier’s performance and eventually influence buyer’s production plan. In the fourth phase, the model selects Pareto optimal suppliers based on their general performance and risk indices. An example demonstrates the implementation of the proposed model. The proposed model provides supply chain managers with a practical tool to effectively select best suppliers while considering supply risks as well as the general performance.

For game design planning education, we researched step by step learning method from storyline setting to game content evaluation. In this process, we developed 'Content Generated Tree' educational model applying the segmentation, classification, and prediction process of decision tree theory. This model is divided into the trunk stage as a story setting, the node generation stage as a content branch, and the conformity assessment. In the node generation stage, there are 'Game Theme' stage for determining the overall direction of the game, 'Interest Element' stage for finding the unique joy of the development game, and 'Game Format' stage for setting the visualization direction. The learner creates several game content combinations through content branching, and evaluates each content combination value. The education model was applied to 19 teams, and the efficiency of the step by step learning process was confirmed.

본 논문은 개인의 비합리적인 이타주의 행동을 설명하는 규범활성화모델을 활용하여 공정무역제품 구매행동을 촉진하는 개인적, 규범적, 문화적 요인을 통합적으로 다루고 있다. 모델 관련, 예기된 감정(예기된 자부심과 예기된 죄책감)과 두 가지 상충하는 개인가치(박애주의 가치와 권력 가치)를 공정무역 관련 개인규범 활성화 선행요인으 로 개념화 하였다. 그리고 활성화된 개인규범이 공정무역제품 구매행동에 영향을 주는 것으로 보았고 특히 이들 간의 관계를 문화 클러스터(유교, 라틴 유럽)가 조절하는 것으로 개념화 하였다. 구조방정식을 통해 얻은 실증결 과는 예기된 자부심이 개인규범에 미치는 영향력이 예기된 죄책감 보다 크고, 상충되는 두 가지 개인가치 중 이 타적 가치인 박애주의 가치만이 개인규범에 유의한 영향을 미치는 것으로 나타났다. 그리고 활성화된 개인규범은 공정무역제품구매 행동에 유의한 영향을 주는 것으로 조사되었다. 문화 클러스터의 조절효과는 다집단비교 구조 방정식을 통해 분석하였다. 검증결과, 해당 경로에 대한 영향력이 라틴 유럽 클러스터 보다 유교 클러스터에서 더 강한 것으로 나타났다. 이러한 결과는 유교 클러스터가 라틴 유럽 클러스터에 비해 직접적인 비용지불에 따른 자기희생 정도가 더 크고, 사회 구성원 기대에 상응하는 도의적 의무감을 강하게 느끼기 때문에 공정무역제품 구 매 행위가 더 강하게 나타난 것으로 해석 가능하다. 본 논문은 규범활성화모델을 활용하여 공정무역제품 구매행 동을 비교 문화적 관점에서 접근하여 윤리적 소비자의 의사결정과정을 실증적으로 구명했다는데 의의가 있다.

As a system complexity increases and technology innovation progresses rapidly, it tends to lease a system rather than own one. This paper deals with a decision-making model to determine the preventive maintenance strategy for leased equipment. Various maintenance options are presented and formulated via the non-homogeneous Poisson process. During the lease period, the preventive maintenance strategy that minimizes the total cost among the presented maintenance options is selected. A numerical example is provided to illustrate the proposed model.

This research is the result on calculating the logical speed limit through certain process which some elements must be considered on selecting the speed limit of harbour and waterway. The suggested speed limit select model on this research is processed from 1~6 steps by forming a professional group of experts. Each step has its information which 1st step(water division), 2nd step(selecting the model vessel and vessel applied with speed limit.), 3rd step(selecting the maximum and minimum speed range on each section), 4th step(evaluation on the safeness of traffic), 5th step(suggesting the appropriate speed limit), 6th step(execution and evaluation.). The appropriate speed limit was decided on consideration of the safety of maritime traffic on the range of the maximum speed and the minimum speed. This model was used to derive the appropriate speed limit on the harbour water and Busan harbour entrance waterway. As the result, the harbour water was calculated to be 6.9 knots, the appropriate speed limit of Busan entrance harbour was 9.3 knots. The present calculation of the speed limit on the approaching channel area is 10 knots, inner harbour area is 7 knots, which are similar to the result of the speed limit. This research is the first research on selecting the speed limit model and has its limits on finding the perfect speed limit result. More detailed standards on the safeness of traffic evaluation must be found and additional study is necessary on discriminating consideration of the elements. This research has its value which it provides instances of aboard cases on guidelines of selecting the speed limit.

본 연구는 제한속력 설정 모델 개발을 위한 1차 연구로서 제한속력 설정 시 고려요소에는 어떤 것이 있으며, 각 요소별 중요도는 얼마나 되는지에 대한 연구의 결과이다. 고려요소 식별과 중요도 산정을 위해 델파이(Delphi)기법과 AHP(Analytic Hierarchy Process)기법을 이용하였다. 델파이 설문은 3차에 걸쳐 수행하였고 3차에 걸친 설문을 통해 5개 상위요소(Level 1)와 23개 하위요소(Level 2)가 식별되었다. 3차 델파이 설문과정에서 내용타당도비율(CVR)값이 0.4~1.0 범위에 있으면 제한속력 설정 시 고려요소로서 반영하였고 0.4 미만의 값은 고려요소에서 제외하였다. 2차 델파이 설문과정에서 33개 고려요소가 식별되었으나 3차 설문을 통해 23개 항목으로 축소되었다. 델파이 3차 분석을 통해 얻은 23개 항목을 대상으로 AHP 설문을 수행하였다. AHP 설문 결과, 5개 상위요소(Level 1)에 대한 중요도는 교통조건이 가장 중요한 요소로 평가되었고, 선박조건, 항로조건, 자연조건, 외부지원 및 기타 조건 순으로 평가되었다. 23개 하위요소 중에서 시정이 가장 중요(1위)하다고 평가되었으며, 선박조종성능, 항로 내 수중장애물, 교통량 및 밀도, 통항선박간 거리, 타효가능속력, 조류 순으로 평가되었다.

Proteomics may help to detect subtle pollution-related changes, such as responses to mixture pollution at low concentrations, where clear signs of toxicity are absent. Also proteomics provide potential in the discovery of new sensitive biomarkers for environmental pollution. We utilized SELDI-TOF MS (surface enhanced laser desorption. / ionization time-of-flight mass spectrometry) to analyze the proteomic profile of Heterocypris incongruens exposed to several heavy metals (lead, mercury, copper, cadmium and chromium) and pesticides (emamectin benzoate, endosulfan, cypermethrin, mancozeb and paraquat dichloride). Several highly significant biomarkers were selected to make a model of classification analysis. data sets obtained from H. incongruens exposed to pollutants were investigated for differential protein expression by SELDI-TOF MS and decision tree classification. Decision tree model was developed with training set, and then validated with test set from profiling data of H. incongruens. Machine learning techniques provide a promising approach to process the information from mass spectrometry data. Even thought the identification of protein would be ideal, class discrimination does not need it. In the future, this decision tree model would be validated with various levels of pollutants to apply field samples.

본 논문은 영상 부호화에 적용되는 전역 움직임 보상 유무를 판정하는 방법을 제안한다. 전역 움직임을 이용한 영상 부호화에서의 기존 방법은 항상 전역 움직임이 존재한다는 가정하에서 전역 움직임을 추정하고 추정된 전역 움직임을 이용하여 전역 움직임 보상을 행한다. 그러나 전역 움직임이 없고 단지 지역 움직임(local motion)만이 존재하는 경우 기존의 방식은 무조건 전역 움직임 보상을 행함으로써 부호화 효율을 떨어뜨릴 뿐만 아니라 전역 움직임 보상 과정에서 불필요한 계산량을 요구하게 된다. 따라서 항상 전역 움직임 보상을 하는 것이 아니라 사전에 전역 움직임 보상 유무를 판정하는 방식이 필요하다. 본 논문은 2차원 병진 움직임 벡터 및 선형 회기법(linearregression)을 이용하여 전역 움직임 추정을 행한 뒤, 전역 움직임 모델 적합성 판정(test for model adequacy)을 함으로써 전역 움직임 보상 유무를 판정하게 된다. 실험결과, 제안 방법을 적용함으로써 부호화 효율이 크게 향상되었다.

We derive priority decision making on healthcare service technology standardization in the home network through the decision support process with industry professionals. We configured a research group with 4 industrial areas including Industry, Academic,

  Supply Chain Management (SCM) system is a critical investment that can affect the competitiveness and performance of a company. Selection of a right SCM system is one of the critical issues. This paper presents the characteristic factors of SCM system a