• Smart Structures and Systems
• /
• v.21 no.2
• /
• pp.249-255
• /
• 2018

Many studies in risk management have been focused on management process, contract relation, and risk analysis in the past decade, but very few studies have addressed project risks from the perspective of risk efficiency. This study started with using Fault Tree Analysis to develop a framework for the decision-making support system of risk management from the perspective of risk efficiency, in order for the support system to find risk strategies of optimal combination for the project manager by the trade-off between project risk and cost of project strategies. Comprehensive and realistic risk strategies must strive for optimal decisions that minimize project risks and risk strategies cost while addressing important data such as risk causes, risk probability, risk impact and risk strategies cost. The risk management in the construction phase of building projects in Taiwan upon important data has been analyzed, that provided the data for support system to include 247 risk causes. Then, 17 risk causes were extracted to demonstrates the decision-making support system of risk management from the perspective of risk efficiency in building project of Taiwan which could reach better combination type of risk strategies for the project manager by the trade-off between risk cost and project risk.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.10 no.4
• /
• pp.21-30
• /
• 2007

This study aims to understand the characteristics of dangsan forests and rural community forests (RCF), and seek for a landscape management scheme. Dangsan forests and RCF have been maintained by local residents since hundreds of years ago. However, many of them have been disturbed. The case sites were derived from the twenty villages previous investigated where dangsan forests and RCF's remainrd. The two sites were remodelled to restore what have been degraded. Hanbam village has maintained its dangsan forest with good management practices, whereas the dangsan forest and RCF of Goiran village showed relatively poor management. The size of dangsan forest at hanbam village was 13,784$m^2$, and major tree species was Pinus densiflora. In total, 151 trees with more than 30cm in DBH were standing on the site. As a cultural activity, the dangsan festival have been held in January $5^{th}$ by lunar calendar to the 2005 at the Jindongdan, a dangsan tree made of stone. The RCF of Hanbam village has disappeared due to the event of landslide in 1930, which needs to be restored. Goiran village has a dangsan forest and a RCF. The forests in Goiran village revealed many problems due to bad management practice. The prototype of the dangsan forest was deteriorated by introduced Prunus serrulata and the facilities for physical training. A systematic management scheme for dangsan forests and RCF's should be established with a close partnership among local residents, experts, and local government.

• Journal of Korean Society for Quality Management
• /
• v.46 no.3
• /
• pp.739-752
• /
• 2018

Purpose: The purpose of this study was to investigate the relationship between kiln processing parameters and NOx emissions that occur in the sintering and calcination steps of the cement manufacturing process and to derive the main factors responsible for producing emissions outside emission limit criteria, as determined by category models and classification rules, using data mining techniques. The results from this study are expected to be useful as guidelines for NOx emission control standards. Methods: Data were collected from Precalciner Kiln No.3 used in one of the domestic cement plants in Korea. Thirty-four independent variables affecting NOx generation and dependent variables that exceeded or were below the NOx emiision limit (>1 and <0, respectively) were examined during kiln processing. These data were used to construct a detection model of NOx emission, in which emissions exceeded or were below the set limits. The model was validated using SPSS MODELER 18.0, artificial neural network, decision treee (C5.0), and logistic regression analysis data mining techniques. Results: The decision tree (C5.0) algorithm best represented NOx emission behavior and was used to identify 10 processing variables that resulted in NOx emissions outside limit criteria. Conclusion: The results of this study indicate that the decision tree (C5.0) can be applied for real-time monitoring and management of NOx emissions during the cement firing process to satisfy NOx emission control standards and to provide for a more eco-friendly cement product.

• Journal of Intelligence and Information Systems
• /
• v.14 no.4
• /
• pp.147-160
• /
• 2008

BPMS(business process management system) facilitates defining new processes or updating existing processes. However, processing of exceptional or nonroutine task requires the intervention of domain experts or introduction of the situation specific resolution process. This paper assumes sufficient amount of business process exception handling cases are stored in the process repository. Since the retrieval of the best exception handling process requires a good understanding about the exceptional situation, context awareness is an important issue. To facilitate the understanding of exceptional situation and to enable the efficient selection of the best exception handling process, we adopted the 'situation variable' and 'decision variable' construct. A case example for exception handling in the e-commerce delivery process is provided to illustrate how the proposed construct works. Application of the C5.0 algorithm guarantees the construction of an optimum search tree. It also implies that an efficient search path has been identified for the context aware selection of the best exception handling process.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.1278-1282
• /
• 2009

In apartment type of housing, if resident's vehicle is registered in central control office and RFID TAG is issued, identification can be recognized from the time of entrance into parking lot and intelligent parking guide system can be activated based on the residents' profile. Parking Guide System leads a vehicle to the available parking space which is closest to the entrance gate of the vehicle's owner. And when residents forget where they parked their cars, they can query to the Parking Guide and Management System and get responses about the location. For the correct operation of this system, it is necessary to find out where the residents' cars have parked in real time and which lot is available for parking of other cars. RFID is very fancy solution for this system. RFID reader gathers the ID information in RFID TAGs in parked cars and updates the DB up to date. But, when non-residents' cars are parked inside apartment, RFID reader cannot identify them nor know the exact empty/occupied status of parking spaces because they don't react to RFID reader's query. So for the exact detection of empty/occupied status, we suggest the combined use of ultrasonic sensors and RFID. We designed a tree topology with intermediate data aggregators. The depth of tree is normally more than 3 from root (central office) to leaves (individual parking lots). The depth of 2 in tree topology brings about the bottleneck in communication and maintenance. We also designed the information fields used in RFID networks and Sensor Networks.

• Journal of Intelligence and Information Systems
• /
• v.9 no.1
• /
• pp.139-155
• /
• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.3
• /
• pp.518-523
• /
• 2006

Due to keen competition among companies, they have segmented customers and they are trying to offer specially targeted customer by means of the distinguished method. In accordance, data mining techniques are noted as the effective method that extracts useful information. This paper explores customer segmentation of the home study company using a hybrid decision tree and artificial neural network model. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship. The case study shows that the predicted accuracy of the proposed model is higher than those of regression, decision tree (CART), artificial neural networks.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.123-129
• /
• 2016

In the manufacturing industry fields, thousands of quality characteristics are measured in a day because the systems of process have been automated through the development of computer and improvement of techniques. Also, the process has been monitored in database in real time. Particularly, the data in the design step of the process have contributed to the product that customers have required through getting useful information from the data and reflecting them to the design of product. In this study, first, characteristics and variables affecting to them in the data of the design step of the process were analyzed by decision tree to find out the relation between explanatory and target variables. Second, the tolerance of continuous variables influencing on the target variable primarily was shown by the application of algorithm of decision tree, C4.5. Finally, the target variable, loss, was calculated by a loss function of Taguchi and analyzed. In this paper, the general method that the value of continuous explanatory variables has been used intactly not to be transformed to the discrete value and new method that the value of continuous explanatory variables was divided into 3 categories were compared. As a result, first, the tolerance obtained from the new method was more effective in decreasing the target variable, loss, than general method. In addition, the tolerance levels for the continuous explanatory variables to be chosen of the major variables were calculated. In further research, a systematic method using decision tree of data mining needs to be developed in order to categorize continuous variables under various scenarios of loss function.

• Journal of Korean Society for Quality Management
• /
• v.48 no.2
• /
• pp.297-307
• /
• 2020

Purpose: The purpose of this study is to analyze the factors that affect ammunition performance by applying data mining techniques to the Ammunition Stockpile Reliability Program (ASRP) data of the 155mm propelling charge. Methods: The ASRP data from 1999 to 2017 have been utilized. Logistic regression and decision tree analysis were used to investigate the factors that affect performance of ammunition. The performance evaluation of each model was conducted through comparison with an artificial neural networks(ANN) model. Results: The results of this study are as follows; logistic regression and the decision tree analysis showed that major defect rate of visual inspection is the most significant factor. Also, muzzle velocity by base charge and muzzle velocity by increment charge are also among the significant factors affecting the performance of 155mm propelling charge. To validate the logistic regression and decision tree models, their classification accuracies have been compared with the results of an ANN model. The results indicate that the logistic regression and decision tree models show sufficient performance which conforms the validity of the models. Conclusion: The main contribution of this paper is that, to our best knowledge, it is the first attempt at identifying the significant factors of ASPR data by using data mining techniques. The approaches suggested in the paper could also be extended to other types ammunition data.

• Journal of KIISE:Software and Applications
• /
• v.26 no.12
• /
• pp.1418-1427
• /
• 1999

시간지원 데이타베이스 시스템은 자료의 과거 및 현재, 그리고 미래의 상태까지 관리함으로써, 사용자에게 시간에 따라 변화하는 자료에 대한 저장 및 질의 수단을 제공한다. 시간지원 데이타베이스는 경향 분석, 버전 관리, 의료 기록 관리 및 비디오 데이타 관리 등과 같이 자료의 시간적 특성이 중요시 되는 모든 분야에 폭 넓게 응용될 수 있다. 시간지원 데이타베이스에서의 집계는 시간 애트리뷰트를 고려하지 않은 기존의 집계와는 큰 차이가 있으며, 기존의 집계 처리 기법을 이용하여 효과적으로 처리될 수 없다. 본 논문에서는 시간지원 집계를 효율적으로 처리하기 위한 새로운 자료 구조인 PA-트리를 제안하고, 이를 이용한 시간지원 집계 처리 기법을 제안한다. 또한 본 논문에서는 제안된 PA-트리를 이용한 기법과 기존의 집계 트리를 이용한 기법의 성능을 최악 경우 분석과 실험을 통해 비교한다.Abstract Temporal databases manage time-evolving data. They provide built-in supports for efficient recording and querying of temporal data. Many application area such as trend analysis, version management, and medical record management have temporal aspects, and temporal databases can handle these temporal aspects efficiently. The aggregate in temporal databases, that is, temporal aggregate is an extension of conventional aggregate on the domain and range of aggregation to include time concept. The basic techniques behind computing aggregates in conventional databases are not efficient when applied to temporal databases. In this paper, we propose a new tree structure for temporal aggregation, called PA-tree, and aggregate processing method based on the PA-tree. We compare the PA-tree with the existing aggregation tree which has been proposed for temporal aggregate.