• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.121-132
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• 1994

A GIS based tactical terrain analysis system named ATTAS(Army Tactical Terrain Analysis Software) has been designed and implemented to support the field commanders for enhancing the capabiliy of their unit and efficiency of weapon system. This system is designed to provide computer graphics environment in which the analyst can interactively operate the entire analyzing process such as selecting the area of interest, performing analysis functions, simulating required battlefield operation and display the results. This system can be divided into three major sections; the terrain analysis modules, utilites, and graphic editor. The terrain analysis module inclused surface analysis, line of sight analysis, enemy disposition, 3D display, radar coverage, logistic route analysis, shortest path analysis, atmospheric phenomena prediction, automated IPB (Inteligence preparation of Battlefield), and other applied analysis. A combination of 2D and 3D computer graphics techniques using the X-window system with OSF/Motif in UNIX workstation was adopted as the user interface. The integration technique of remotely sensed images and GIS data such as precision registration, overlay, and on-line editing was developed and implemented. An efficient image and GIS data management technique was also developed and implemented using Oracle Database Management System.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.625-636
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• 2019

Soil moisture is a representative factor that plays a key role in hydrological cycle. It is involved in the interaction between atmosphere and land surface, and is used in fields such as agriculture and water resources. Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced SCATterometer (ASCAT), and European Space Agency Climate Change Initiative (ESACCI) data were used to analyze the applicability and uncertainty of satellite soil moisture product in the Korean peninsula. Cumulative distribution function (CDF) matching and triple collocation (TC) analysis were carried out to investigate uncertainty and correction of satellite soil moisture data. Comparisons of pre-calibration satellite soil moisture data with the Automated Agriculture Observing System (AAOS) indicated that ESACCI and ASCAT data reflect the trend of AAOS well. On the other hand, AMSR2 satellite data showed overestimated values during the freezing period. Correction of satellite soil moisture data using CDF matching improved the error and correlation compared to those before correction. Finally, uncertainty analysis of soil moisture was carried out using TC method. Clearly, the uncertainty of the satellite soil moisture, corrected by CDF matching, was diminished in both freezing and thawing periods. Overall, it is expected that using ASCAT and ESACCI rather than AMSR2 soil moisture data will give more accurate soil moisture information when correction is performed on the Korean peninsula.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.539-544
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• 2021

The tactical data link is a military data network to improve the ability to recognize battlefield situations. The ROK military is promoting the tactical data link performance improvement programs. Tactical data link is essential to combine and integrate various platforms, sensor data, and command and control (C2) systems. Therefore, the research on related technical fields is required. However, the tactical data link has not disclosed detailed technical information due to the characteristics of military operation. In this paper, we propose a data-based automated analysis methodology using intellectual property information to understand the technology trend of tactical data link. In this paper, data related to intellectual property is automatically collected and pre-processed, and analyzed in terms of time series. In addition, the current status of each institution of patent technology information was generated, and the process of identifying key-researchers through network analysis was presented with providing results of our approach in this paper.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.125-131
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• 2022

The diversity and complexity of criminal, administrative and civil cases resolved by the courts makes it difficult to develop universal automated tools for the analysis and evaluation of justice. However, big data generated in the scope of justice gives hope that this problem will be resolved as soon as possible. The big data applying makes it possible to identify typical options for resolving cases, form detailed rules for the individualization of a court decision, and correlate these rules with an abstract provisions of law. This approach allows us to somewhat overcome the contradiction between the abstract and the concrete in law, to automate the analysis of justice and to model e-justice for scientific and practical purposes. The article presents the results of using dimension reduction, SHAP value, and p-value to identify, analyze and evaluate the individualization of justice and the differentiation of legal regulation. Processing and analysis of arrays of court decisions by computational methods make it possible to identify the typical views of courts on questions of fact and questions of law. This knowledge, obtained automatically, is promising for the scientific study of justice issues, the improvement of the prescriptions of the law and the probabilistic prediction of a court decision with a known set of facts.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.61-69
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• 2001

We present a probabilistic reasoning method for inferring knowledge about mathematical truth before an automated theorem prover completes a proof. We use a Bayesian analysis to update beleif in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.

• Journal of the Korea Society for Simulation
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• v.13 no.2
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• pp.23-34
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• 2004

Regenerative simulation (RS) is a method of stochastic steady-state simulation in which output data are collected and analysed within regenerative cycles (RCs). Since data collected during consecutive RCs are independent and identically distributed, there is no problem with the initial transient period in simulated processes, which is a perennial issue of concern in all other types of steady-state simulation. In this paper, we address the issue of experimental analysis of the quality of sequential regenerative simulation in the sense of the coverage of the final confidence intervals of mean values. The ultimate purpose of this study is to determine the best version of RS to be implemented in Akaroa2 [1], a fully automated controller of distributed stochastic simulation in LAN environments.

• Journal of the Korea Safety Management & Science
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• v.8 no.2
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• pp.91-102
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• 2006

Korea Railroad Research Institute developed the driverless rubber-tired Korean-AGT system from 1999 to 2005 and has done its performance and reliability tests on the test line at Gyeongsan-city. We made the reliability management program to control required the RAMS(reliability, availability, maintainability & safety) of the K-AGT system. Therefore, we demonstrated the development and application of Reliability DB program. The main functions of K-AGT Reliability DB program manages failures and maintenance data systematically from the test line through test period and provides various analysis results based on the inputted data.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.627-630
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• 2022

Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.986-1005
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• 2020

In this paper, we propose AnoVid, an automated video annotation tool based on deep neural networks, that automatically generates various meta data for each scene or shot in a long drama video containing rich elements. To this end, a novel meta data schema for drama video is designed. Based on this schema, the AnoVid video annotation tool has a total of six deep neural network models for object detection, place recognition, time zone recognition, person recognition, activity detection, and description generation. Using these models, the AnoVid can generate rich video annotation data. In addition, AnoVid provides not only the ability to automatically generate a JSON-type video annotation data file, but also provides various visualization facilities to check the video content analysis results. Through experiments using a real drama video, "Misaeing", we show the practical effectiveness and performance of the proposed video annotation tool, AnoVid.

• Journal of Korean Society for Quality Management
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• v.30 no.3
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• pp.111-119
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• 2002

Gears are essential elements for mechanical power transmission. Geometric precision is the main factor for characterizing gear grade and qualify. Gear pitch is one of the crucial measurements, which is defined as a distance between two adjacent gear teeth. It is well-known that variability in gear pitches may causes wear-out and vibration noise. Therefore maintaining pitch errors at a low level plays a key role in assuring the gear quality to customers. This paper is concerned with a case study, which presents a computerized system for Inspecting pitch errors in a gear machining process. This system consists of a PC and window-based programs. Although the start and stop is manually accomplished, the process of measuring and analyzing pitch data is automatically conducted in this system. Our purpose lies in reducing inspection cost and time as well as Increasing test reliability. Its operation is briefly illustrated by example. Sometimes a strong autocorrelation is observed from pitch data. We also discuss a process monitoring scheme taking account of autocorrelations.