• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2913-2927
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• 2013

A data hiding scheme in HTML files is presented in this paper. Web pages are a very popular medium for broadcasting information and knowledge nowadays, and web pages are a good way to achieve the goal of secret message delivery because the different HTML coding codes will render the same screen in any of the popular browsers. The proposed method utilizes the HTML special space codes and sentence segmentation to conceal secret messages into a HTML file. The experimental results show that the stego HTML file generated by the proposed method is imperceptible. Also, the proposed method can conceal one more secret bit in every between-word location.

• 한국경영과학회지
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• 제30권4호
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• 대한산업공학회지
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• 제40권4호
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• pp.375-381
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• 2014

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.

• 한국컴퓨터정보학회논문지
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• 제24권6호
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• pp.89-97
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• 2019

This study proposes a method for investigating current patents related to information communication technology and smart mobility to provide insights into future technology trends. The method is based on text mining clustering analysis. The method consists of two stages, which are data preparation and clustering analysis, respectively. In the first stage, tokenizing, filtering, stemming, and feature selection are implemented to transform the data into a usable format (structured data) and to extract useful information for the next stage. In the second stage, the structured data is partitioned into groups. The K-medoids algorithm is selected over the K-means algorithm for this analysis owing to its advantages in dealing with noise and outliers. The results of the analysis indicate that most current patents focus mainly on smart connectivity and smart guide systems, which play a major role in the development of smart mobility.

• 간호행정학회지
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• 제9권3호
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• pp.415-427
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• 2003

Purpose: The purpose of this study was to investigate the degree and pattern of knowledge management process, and to identify the relationship between knowledge management process and organizational effectiveness in clinical nurses. Method: Participants were 665 regular clinical nurses who had worked for over 1 year in general units of 9 tertiary medical hospitals including 2 national university hospitals, 5 university hospitals, and 2 hospitals founded by business enterprises. Data were collected from March to May 2003 through questionnaires. Four structured instruments were used to collect the data: Knowledge Management Process Scale(Jeong, Lee, Lee, & Kim, 2003), cCommitment Questionnaire(Mowday, Steers, & Porter, 1979), General Satisfaction Scale(CooK, Hepworth, Wall, & Warr, 1981), and one for general characteristics. The data were analyzed using factor analysis, reliability analysis, descriptive analysis, cluster analysis, one-way ANOVA, Scheffe test, correlation analysis with the SPSS for Windows 10.0 program. Result: 1) The average score for knowledge management process in nurses was $3.08{\pm}.54$ on a 5-point Likert scale. In order from highest mean score, the elements of knowledge management process, were Knowledge $Utilization(3.35{\pm}.57)$, Knowledge $Sharing(3.07{\pm}.58)$, Knowledge $Creation(2.99{\pm}.63)$, and Knowledge $Storage(2.91{\pm}.82)$. 2) Four knowledge management patterns for nurses, which were derived from cluster analysis, were inactivate pattern, delayed pattern, activate pattern, and high-activate pattern of knowledge management. 3) The degree of knowledge management process activation and 4 elements of knowledge management process, Knowledge Creation, Knowledge Storage, Knowledge Sharing, and Knowledge Utilization, were significantly correlated with nurses' organizational commitment and job satisfaction(p=.000). 4) The nurses' organizational commitment and job satisfaction showed significant differences according to the knowledge management patterns derived from cluster analysis of high-activate pattern, activate pattern, delayed pattern, inactivate pattern(p=.000). Conclusion: These results suggest that there are four knowledge management patterns for nurses, and knowledge management process positively affects the nurses' organizational commitment and job satisfaction. From the above findings, knowledge management process is empirically verified as a useful and effective method to increase organizational effectiveness, and develop the organization.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2002년도 추계학술대회 논문집
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• pp.101-105
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• 2002

In a traditional hierarchical inventory system, direct orders are the only information for inventory management that is exchanged between the firms involved. But due to the rapid development of modern information technology, it becomes possible for the firms to share more information in real time, e.g. demand and inventory status data. And so the term Supply Chain has emerged because it is seen as an important source of competitive advantage. Now it is possible to challenge traditional approaches to inventory management. In the past, one of the de-facto assumptions for inventory management was that the demand pattern follows a specific distribution function. However, it is undesirable to apply this assumption in real situations because the demand information in the supply chain tends to be distorted due to the bullwhip effect in a supply chain. To overcome this weakness, we propose a new solution method using NN (Neural Network). Our method proceeds in three steps. First, we find the patterns of optimal reorder points by analyzing past data. Second. train the NN using these pattern data and finally decide the reorder point. Using simulation experiment, we show that the proposed solution method gives better result than that of traditional research.

• Management Science and Financial Engineering
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• 제18권2호
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• pp.13-17
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• 2012

Set covering has widely been accepted as a staple tool for feature selection in data mining. We present a generalized version of this classical combinatorial optimization model to make it better suited for the purpose and propose a surrogate relaxation-based procedure for its meta-heuristic solution. Mathematically and also numerically with experiments on 25 set covering instances, we demonstrate the utility of the proposed model and the proposed solution method.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1999년도 Proceedings of the 1999 Annual Conference The Korean Society of Agricutural Engineers
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• pp.354-358
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• 1999

In practicing the refinement project of agriuctural production basys, many researches have been done until now, but they are not reused because management is not easily accomplished. We analysed and designed the Components of refinement project by the object -orientation technique, and presented a method of accumulation and management of object's data using relational database. In result, management of new data is easy and reusibility are increased compared to structural analysis technique.

• 제어로봇시스템학회논문지
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• 제17권11호
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• pp.1074-1081
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• 2011

As robotics systems are becoming more complex there is the need to promote component based robot development, where systems can be constructed as the composition and integration of reusable building block. One of the most important challenges facing component based robot development is safeguarding against software component failures and malfunctions. The health monitoring of the robot software is most fundamental factors not only to manage system at runtime but also to analysis information of software component in design phase of the robot application. And also as a lot of monitoring events are occurred during the execution of the robot software components, a simple data treatment and efficient memory management method is required. In this paper, we propose an efficient events monitoring and data management method by modeling robot software component and monitoring factors based on robot software framework. The monitoring factors, such as component execution runtime exception, Input/Output data, execution time, checkpoint-rollback are deduced and the detail monitoring events are defined. Furthermore, we define event record and monitor record pool suitable for robot software components and propose a efficient data management method. To verify the effectiveness and usefulness of the proposed approach, a monitoring module and user interface has been implemented using OPRoS robot software framework. The proposed monitoring module can be used as monitoring tool to analysis the software components in robot design phase and plugged into self-healing system to monitor the system health status at runtime in robot systems.

• 한국수자원학회논문집
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• 제52권3호
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• pp.195-206
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• 2019

본 연구에서는 주 단위 지하수자원 관리 취약시기 평가 방법을 개발하였다. 강수의 지하수위에 대한 영향을 고려하기 위하여 한계 침투량을 고려한 강우이동평균 방법을 통해 지하수위와의 상관계수를 산정하였다. 취약 시기 평가 기준을 개발하고 평가 기준에 대한 가중치를 엔트로피 방법을 이용하여 산정하였다. 강수와의 상관계수와 산정된 가중치를 이용한 주 단위 지하수자원 관리 취약시기 평가 방법을 개발하였으며, 개발한 방법을 통하여 소규모 행정구역을 대상으로 취약시기를 평가하였다. 본 연구에서 개발된 방법은 지역적일뿐만 아니라 계절적인 지하수자원의 효율적 관리 대책 수립의 근거가 될 수 있을 것이다.