• Journal of information and communication convergence engineering
• /
• v.7 no.1
• /
• pp.35-40
• /
• 2009

Many researchers strive to research on ways on how to improve the performance of RFID system and many papers were written to solve one of the major drawbacks of potent technology related with data management. As RFID system captures billions of data, problems arising from dirty data and large volume of data causes uproar in the RFID community those researchers are finding ways on how to address this issue. Especially, effective data management is important to manage large volume of data. Data reduction techniques in attempts to address the issues on data are also presented in this paper. This paper introduces readers to a new data reduction algorithm that might be an alternative to reduce data in RFID Systems. A process on how to extract data from the reduced database is also presented. Performance study is conducted to analyze the new data reduction algorithm. Our performance analysis shows the utility and feasibility of our categorization reduction algorithms.

• KEPCO Journal on Electric Power and Energy
• /
• v.8 no.1
• /
• pp.9-14
• /
• 2022

Social infrastructure facilities such as production, transportation, gas and electricity facilities may experience poor performance depending on time, load, temperature, etc. and may require maintenance, repair and management as they are used. In particular, in the case of transformers, the process of managing them for the purpose of preventing them from failing is necessary because a failure can cause enormous social damage. The management of transformers should consider both technical and economic aspects and strategic aspects at the same time. Thus, it applies the Asset Management concept, which is widely used in the financial industry as an advanced method of transformer management techniques worldwide. In this paper, the operation and power outage data were secured for the asset management of the transformer for distribution, and the asset status was analyzed. Analysis of asset status using actual operation and power outage data is essential for assessing the statistical life and failure rate of the facility. Through this paper, the status of transformer assets for arbitrary A group distribution was analyzed, and the end of life and replacement life were calculated.

• Journal of the Korea Institute of Building Construction
• /
• v.11 no.3
• /
• pp.319-325
• /
• 2011

During a construction project, construction managers must be provided with material information to help them to make decisions more efficiently without delaying the delivery of material. Construction work can be smoothly performed with the proper material supply. Construction duration depends on several material-related decisions, including the order, delivery, and allocation of material to the correct work location. Hence, it is worthwhile to introduce data warehouse techniques that generate subject-oriented and integrated data to construction material management. The data warehouse for construction material management can perform multidimensional analysis and then define KPIs (Key Performance Index) in order to provide construction managers with construction material information such as lead time, material delivery rate, material installation rate and so on. This research proposes a method of effectively facilitating large amounts of data in the operating systems during the construction management process. In other words, the proposed method can supply structured and multi-perspective material-related information using data warehouse techniques.

• 제어로봇시스템학회:학술대회논문집
• /
• 1988.10a
• /
• pp.273-278
• /
• 1988

This paper represents the computer application techniques of initial and modification machining for dies with 3-dimensional scluptured surfaces. All procedures from die design to die machining and measurement are covered. The component of modelling is data management and modification (extrapolation and smoothing), surface modelling, and nc program preparation. Also this paper introduces the utility for successful and efficient operation of system such as map generation, data communication, tool path verification, contour map generation, graphic processing of extrapolation and smoothing results, and CAD/CAM system interface. Examples are given to illustrate the modelling.

• Proceedings of the IEEK Conference
• /
• 2002.07b
• /
• pp.1244-1247
• /
• 2002

This paper discusses various algorithms to the fusion of multi-spectral image. These fusion techniques have a wide variety of applications that range from hospital pathology to battlefield management. Different algorithms in each fusion level, namely data, feature, and decision are compared. The PCT-Based algorithm, which has the characteristic of data compression, is described. The algorithm is experimented on a foliated aerial scene and the fusion result is presented.

• 시스템엔지니어링워크숍
• /
• s.4
• /
• pp.86-89
• /
• 2004

A web-based program life-cycle management(PLM) system is introduced to implement the system engineering processes and to provide the development teaceability online. This information system aims for the realization of essential system engineering management techniques currently applied to the space launch system development program including management of configuration and data based on the work breakdown structure(WBS), WBS, bill of materials and properties. The system enhances communication and gives access to the development data with relevant information such as data-to-data relation and approval status/history through the web, and preserve all of the development data throughout the program life-cycle. Further improvement of the system is planned to implement the program managrment pricesses and to provide integrated information useful for the programmatic decision making.

• Journal of Information Technology Applications and Management
• /
• v.17 no.2
• /
• pp.71-90
• /
• 2010

Electronic learning(or e-learning) has created hype for companies, universities, and other educational institutions. It has led to the phenomenal growth in the use of web-based learning and experimentation with multimedia, video conferencing, and internet-based technologies. Many researchers are interested in the factors that affect to the performance of e-learning or e-learning services. In this sense, this study is aimed at proposing e-learning system reuse prediction models in which e-learner intention to reuse influence factors(i.e., system accessibility, system stability, information clarity, information validity, self-regulated efficacy, computer self-efficacy, perceived usefulness, perceived ease of use, flow, and parental expectation) affect e-learner intention to reuse positively. A web survey was conducted for the full members of the e-learning education institute A in Seoul, Republic of Korea, an exclusive e-learning company that provides real time video lectures via the desktop conferencing system. The web survey was conducted for 20 days from November 5, 2009, through the e-learning web site of the company A. In this study, three data mining techniques were used : the multivariate discriminant analysis, CART, and C5.0 algorithm. This study was conducted to provide the e-learning service providers, e-learning operators, and contents developers with marketing and management strategies for improving the e-learning service companies, based on the data mining analysis results.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.920-927
• /
• 2009

Many schedule delays and cost overruns in interior construction are caused by a lack of understanding in detailed and complicated interior works. To minimize these potential impacts in interior construction, a systematic approach for project managers to detect discrepancies at early stages and take corrective action through use of visualized data is required. This systematic implementation is still challenging: monitoring is time-consuming due to the significant amount of as-built data that needs to be collected and evaluated; and current interior construction progress reports have visual limitations in providing spatial context and in representing the complexities of interior components. To overcome these issues, this research focuses on visualization and computer vision techniques representing interior construction progress with photographs. The as-planned 3D models and as-built photographs are visualized in a 3D walk-through model. Within such an environment, the as-built interior construction elements are detected through computer vision techniques to automatically extract the progress data linked with Building Information Modeling (BIM). This allows a comparison between the as-planned model and as-built elements to be used for the representation of interior construction progress by superimposing over a 3D environment. This paper presents the process of representing and detecting interior construction components and the results for an ongoing construction project. This paper discusses implementation and future potential enhancement of these techniques in construction.

• Journal of Information Technology Application
• /
• v.1
• /
• pp.173-211
• /
• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Journal of Intelligence and Information Systems
• /
• v.7 no.1
• /
• pp.177-197
• /
• 2001

This paper is aimed at proposing a data mining-driven analysis to manage the customer defection rate in the bank. After 1997 IMF crisis, Korean banks were suffering from hard-pressed restructuring. At the heart of such restructuring effects, there was the need to manage the customer more effectively than ever. So far, many banks in Korea used to a poor management of customers without any highly-skillful techniques. In line with this argument, we propose several data mining techniques to determine more effective technique far managing customer deflection. We applied three data mining techniques such as logit model, neural network, and C5.0. Experiment data were collected from personal deposit account data of a specific bank in Korea. After experiments, we found that C5.0 showed more robust performance compared to other two techniques. On the basis of those experiment results, we proposed customer defection management policy.