• 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.

• New & Renewable Energy
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• v.16 no.4
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• pp.1-8
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• 2020

The possibility of conversion to the RC-MAT propulsion system (gasoline engine → electric motor) was studied. However, as commercial battery capacities are low. it is not possible to change the propulsion system. Nevertheless, development of nex-generation batteries is necessary for high capacity and high energy density. Although Li/S batteries are theoretically suitable as new generation batteries, these batteries are not composed of only Li and S. Hence, ensuring high energy density can be difficult. Moreover, electrolytes are important components in the study of energy density; hence, the battery by Li2S8 Molarity was sorted. There are no studied on its various electrode components. In this study, a Li/S battery was fabricated using an assorted 3D sulfur electrode of high energy density and its electrochemical properties were studied. The Li/S battery has a high energy density of 468 Wh/kg at 1.28 M Li2S8 (A805-1.28). Its capacity rapidly decreased after 1 cycle with more than 1 M Li2S8.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.397-404
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• 2016

Illegal and harmful contents on the Internet has been an issue and been increased in Korea. They are often posted on the billboard and website of small enterprise and government office. Those illegal and harmful contents can relate to crime and suspicious activity, so, we need a detection system. However, to date the detection itself has been conducted manually by a person. In this paper, we develop an automated URL detection scheme for detecting a drug trafficking by using Google. This system works by analyzing the frequently used keywords in a drug trafficking and generate a keyword dictionary to store words for future search. The suspected drug trafficking URL are automatically collected based on the keyword dictionary by using Google search engine. The suspicious URL can be detected by classifying and numbering each domain from the collection of the suspected URL. This proposed automated URL detection can be an effective solution for detecting a drug trafficking, also reducing time and effort consumed by human-based URL detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1759-1768
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• 2022

This paper proposes a model to detect depression-related emotions in a user's speech using wellness dialogue scripts provided by AI Hub, topic-specific daily conversation datasets, and chatbot datasets published on Github. There are 18 emotions, including depression and lethargy, in depression-related emotions, and emotion classification tasks are performed using KoBERT and KOELECTRA models that show high performance in language models. For model-specific performance comparisons, we build diverse datasets and compare classification results while adjusting batch sizes and learning rates for models that perform well. Furthermore, a person performs a multi-classification task by selecting all labels whose output values are higher than a specific threshold as the correct answer, in order to reflect feeling multiple emotions at the same time. The model with the best performance derived through this process is called the Depression model, and the model is then used to classify depression-related emotions for user utterances.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.411-425
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• 2023

Today, AI (Artificial Intelligence) technology is widely used in various fields, performing classification and regression tasks according to the purpose of use, and research is also actively progressing. Especially in the field of security, unexpected threats need to be detected, and unsupervised learning-based anomaly detection techniques that can detect threats without adding known threat information to the model training process are promising methods. However, most of the preceding studies that provide interpretability for AI judgments are designed for supervised learning, so it is difficult to apply them to unsupervised learning models with fundamentally different learning methods. In addition, previously researched vision-centered AI mechanism interpretation studies are not suitable for application to the security field that is not expressed in images. Therefore, In this paper, we use a technique that provides interpretability for detected anomalies by searching for and comparing optimization references, which are the source of intrusion attacks. In this paper, based on reference, we propose additional logic to search for data closest to real data. Based on real data, it aims to provide a more intuitive interpretation of anomalies and to promote effective use of an anomaly detection model in the security field.

• Journal of IKEEE
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• v.28 no.2
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• pp.123-130
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• 2024

As technology advances swiftly and the lifespan of products becomes increasingly short, there is a demand to fasten the pace of research outcomes, product development, and market introduction. As a result, the researchers and developers need a computational experiment environment that enables rapid verification of the experiment and application of research findings. Such an environment must efficiently harness all available computational resources, manage simulations across diverse test scenarios, and support the experimental data collection. This research introduces the design and implementation of an experimental frame based on a microservice architecture. The experimental frame leverages scripts to utilize computing resources optimally, making it more straightforward for users to conduct simulations. It features an experimental frame capable of automatically deploying scenarios to the computing components. This setup allows for the automatic configuration of both the computing environment and experiments based on user-provided scenarios and experimental software, facilitating effortless execution of simulations.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.821-832
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• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.9-22
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• 2024

Mobility data is important to understand social phenomena and problem. Previous studies have utilized Origin-Destination (OD) visualization methods to represent human's mobility. However, the effectiveness of visualization tools as a method for understanding social phenomena remains unexplored. Therefore, in this study, we developed a visualization tool called SeoulOD-Vis to facilitate understanding social issues. It included three different modules: map visualization, condition selection, and detailed information presentation. We recruited 28 participants to evaluate the SeoulOD-Vis and compared it with a publicly available visualization tool. The results suggested that the SeoulOD-Vis had higher usability and problem-solving performances. Interview results suggested that it attributed to its 'condition selection' and 'detailed information presentation' modules. Our results will contribute to develop visualization tools to solve social problems using mobility data.