• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.239-245
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• 2000

This study is aimed to develop the historical data management system named "ACON"(Actual COst Network system) which can be used for collecting, analysis and modification of the historical data. ACON_Local is used for collecting the historical data at each spot place. It redueces the work potential of managers, spending time and cost. ACON_Analysis is to analyze the collection , modifies the data′s error and updates the historical database. ACON_Network is developed to transfer data from ACON to Web server or opposites. The proposed model, ACON, provide the easy way for the cost estimate system, "HiCOMS" to collect, analyze and modify the historical cost evaluated data, which is used to compose the historical data base.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.468-471
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• 2001

The importance of early project planning is broadly recognized for construction projects. From the planning step, If extensive historical data related with the project is applied effectively, It can be major resource for estimating cost and project scope. However, The accumulation, analysis, and application of historical data is not sufficient in Korea. So useful information of construction project has disappeared. In order to solve the problems, Project Historical Data Management Systems is need to be developed. The purpose of this study is to analyze current problems and to find the method to utilize historical data in similar project.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.119-126
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• 2004

Location management is very important issue in wireless communication system to trace mobile users' exact location. In this study, we propose a dynamic location area management scheme which determines the size of dynamic location area considering each user's characteristic. In determining the optimal location area size, we consider the measurement data as well as the historical data, which contains call arrival rate and average speed of each mobile user. In this mixture of data, the weight of historical data is derived by linear searching method which guarantees the minimal cost of location management. We also introduce the regularity index which can be calculated by using the autocorrelation of historical data itself. Statistical validation shows that the regularity index is the same as the weight of measurement data. As a result, the regularity index is utilized to incorporate the historical data into the measurement data. By applying the proposed scheme, the location management cost is shown to decrease. Numerical examples illustrate such an aspect of the proposed scheme.

• IE interfaces
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• v.18 no.4
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• pp.382-389
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• 2005

Location management is very important issue in wireless communication system to trace mobile users' exact location. In this study, we propose a dynamic location area management scheme which determines the size of dynamic location area considering each user's characteristics. In determining the optimal location area size, we consider the measurement data as well as the historical data, which contains call arrival rate and average speed of each mobile user. In this mixture of data, the weight of historical data is derived by linear searching method which guarantees the minimal cost of location management. We also introduce the regularity index which can be calculated by using the autocorrelation of historical data itself. Statistical validation shows that the regularity index is the same as the weight of measurement data. As a result, the regularity index is utilized to incorporate the historical data into the measurement data. By applying the proposed scheme, the location management cost is shown to decrease. Numerical examples illustrate such an aspect of the proposed scheme.

• Asian review of World Histories
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• v.4 no.1
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• pp.3-34
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• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.114-125
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• 2006

Since wireless Internet services and small mobile communication devices come into wide use as well as the use of GPS is rapidly growing, researches on moving object, whose location information shifts sequently in accordance with time interval, are being carried out actively. Especially, the researches on vehicle moving object are applied to Advanced traveler information system, vehicle tracking system, and distribution transport system. These systems are very useful in searching previous positions, predicted future positions, the optimum course, and the shortest course of a vehicle by managing historical data of the vehicle movement. In addition, vehicle historical data are used for distribution transport plan and vehicle allocation. Vehicle historical data are stored at regular intervals, which can have a pattern. For example, a vehicle going repeatedly around a specific section follows a route very similar to another. If historical data of the vehicle with a repeated route course are stored at regular intervals, many redundant data occur, which result in much waste of storage. Therefore this thesis suggest a vehicle historical data store scheme for vehicles with a repeated route course using similar trajectory which efficiently store vehicle historical data.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.11
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• pp.1680-1691
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• 2008

This study examines how the development and value of dresses shown in MBC historical dramas have changed with the overall change of the dramas. As for the research method, the second data analysis was done with literature study which was supplemented with interviews with the wardrobe team of MBC production design center. Historical dramas produced by MBC have gone through the developmental period, legitimate historical drama-oriented period, stagnant period, and historical dramas-modernized period. The value of costume in each period is as follows: Costume in the developmental period is considered only as part of drama setting. During legitimate historical drama-oriented period, it carries value as educational data and historical replica produced by historical research. Production design including costume obtains its own value in the stagnant period although the production of historical dramas decreases dramatically. And lastly, in the historical dramas-modernized period, costume starts to have commercial value as cultural contents. Historical drama costume may contain important value in terms of education and history, even though there has been controversy on TV historical dramas' role: they should focus on reproducing historical facts or they should support writers' creativity.

• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.3
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• pp.35-41
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• 1998

Cost estimate system Will be changed from the prime. cost calculation to the historical cost data because the present system has some problems. In this situation, each owner should prepare his own cost estimate system based on the historical cost data. In this study, the standard work items were classified and the criteria of their work amount computation were established for rural improvement projects. And also the historical cost data were collected in all range of rural improvement projects, and the database system, "HICOMS" (HIstorical COst data Management System) was built. In order to test the applicability of the HICOMS, standard work cost and contractor cost were compared. The results by HICOMS showed high significance and it was concluded that the HICOMS could be applicable for the cost estimate of the rural improvement projects. projects.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.13-26
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• 2008

Inference on the present data will be more reliable when the data arising from previous similar studies are available. The data arising from previous studies are referred as historical data. The power prior is defined by the likelihood function based on the historical data to the power $a_0$, where $0\;{\le}\;a_0\;{\le}\;1$. The power prior is a useful informative prior for Bayesian inference such as model selection and model comparison. We utilize the historical data to perform multiple comparison in the one-way ANOVA model. We demonstrate our results with some simulated datasets under a simple order restriction between the treatments.