• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제22권2호
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• pp.195-211
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• 2023

For the commercialization For the commercialization of autonomous vehicles (AV), the operational design domain (ODD) of automated driving systems (ADS) is to be clearly defined. A common language and consistent format must be prepared so that AV-related stakeholders can understand ODD at the same level. Therefore, overseas countries are presenting a standardized ODD framework and developing scenarios that can evaluate ADS-specific functions based on ODD. However, ODD includes conditions reflecting the characteristics of each country, such as road environment, weather environment, and traffic environment. Thus, it is necessary to clearly understand the meaning of the items defined overseas and to harmonize them to reflect the specific domestic conditions. Therefore, in this study, domestic optimization of the ODD classification system was performed by analyzing the domestic driving environment based on international standards. The driving environment of currently operating self-driving car test districts (Sangam, Seoul, and Gwangju) was investigated using the developed domestic ODD items. Then, based on the results obtained, the ranges of the ODDs in each test district were determined and compared.

• Journal of Intelligence and Information Systems
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• 제29권2호
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Journal of KIISE:Computing Practices and Letters
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• 제9권3호
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• 제20권4호
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• pp.282-294
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• 2010

The purpose of SAICM (Strategic Approach to International Chemicals Management) is to minimize the health and environmental hazards from the production and the consumption of chemicals by improving the chemicals management capability of developing countries and implementing a system of the risk assessment and the management based on the precautionary principle until 2020. To achieve this purpose, the UN has prescribed the principles, objectives and establishment of an action plan for the chemicals management strategy which must be carried out at international, local, and national levels, and requested the implementation of the Global Plan of Action (GPA) comprising of 273 recommendations in 36 work areas. SAICM is currently based on voluntary participation, but is expected to become the basic framework of international order in relation to chemicals management in the future. This study aims to analyze the practice in the occupational safety and health area relating to implement 273 recommendations of the GPA, and propose complementary measures for the system in order to provide political advices for establishing future plans to manage industrial chemicals. Twenty three areas of total 36 work areas and 161 items of 273 recommendations have relevance to occupational safety and health areas. We have found that, as a national implementation level, 157 of 161 industrial safety and health items are being implemented at a satisfactory level in regard to the implementation of the GPA, while 4 items, including the ratification of the ILO Conventions 170, 174, 184, and support for GHS (Globally Harmonized System of Classification and Labeling of chemicals) implementation of developing countries, require additional complementary measures for the system and operation.

• Journal of Science and Technology Studies
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• 제10권2호
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• pp.143-174
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• 2010

How can a regulatory policy to address potential hazards be made legitimate in the face of scientific uncertainty? The precautionary principle has been gradually regarded as the most persuasive answer to this intricate question in Europe since the 1970s and generally recognized as a guiding principle in international environmental law. This principle, however, has often been subject to diverse concerns and criticisms due to its vague definition. This article tries to elaborate the precautionary principle while reviewing both the validity and unreasonableness of these criticisms over this principle. Then, this article explores the policy relevance of this principle by applying this elaborated definition to the concrete case of risk governance such as the risk assesment of food safety. In the end, this paper emphasizes the fact that the precautionary principle can be applied in the field of risk governance, refuting the argument that the precautionary principle is only a moral attitude or a political position.

• Information Systems Review
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• 제8권1호
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• pp.159-179
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• 2006

With the advances in information technologies and the wide acceptance of IT outsourcing practices, subscription-based information & telecommunications services(ITS) become more available. Convergence and intensified industry competition have made it an imperative for the ITS providers to keep their current customers and acquire new customers at the same time. In this study, we developed a framework for effective customer management based on the factors influencing the post-purchase customer choice: stay with the present provider or switch to another one. Specifically, we classified the factors into four categories: Holding factors, Defect factors, Inducement factors, and Hurdle factors depending on the characteristics of the influence and direction of the influence. Based on the classification, we developed a post-purchase customer choice model for the subscription-based ITS providers. Then, we illustrated a possible application of the model in the context of the broadband Internet access service. The model could be used to increase the competitive advantage of service providers through the effective customer management in the subscription-based ITS market.

• Journal of the Korea Institute of Information Security & Cryptology
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• 제28권5호
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• pp.1197-1207
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• 2018

PowerShell is command line shell and scripting language, built on the .NET framework, and it has several advantages as an attack tool, including built-in support for Windows, easy code concealment and persistence, and various pen-test frameworks. Accordingly, malwares using PowerShell are increasing rapidly, however, there is a limit to cope with the conventional malware detection technique. In this paper, we propose an improved monitoring method to observe commands executed in the PowerShell and a deep learning based malware classification model that extract features from commands using Convolutional Neural Network(CNN) and send them to Recurrent Neural Network(RNN) according to the order of execution. As a result of testing the proposed model with 5-fold cross validation using 1,916 PowerShell-based malwares collected at malware sharing site and 38,148 benign scripts disclosed by an obfuscation detection study, it shows that the model effectively detects malwares with about 97% True Positive Rate(TPR) and 1% False Positive Rate(FPR).

• The KIPS Transactions:PartD
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• 제8D권2호
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• pp.117-124
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• 2001

A data mining system includes various data mining functions such as aggregation, association and classification, among others. To express these data mining function, a powerful data mining query language is needed. In addition, a graphic user interface(GUI) based on the data mining query language is needed for users. In addition, processing a data mining query targeted for a data warehouse, which is the appropriate data repository for decision making, is needed. In this paper, we first build a GUI to enable users to easily define data mining queries. We then propose a data mining query processing framework that can be used to process a data mining query targeted for a data warehouse. We also implement a schema generate a data warehouse schema that is needed to build a data warehouse. Lastly, we show the implementation details of a query processor that can process queries that discover association rules.

• The Journal of the Korea Contents Association
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• 제10권7호
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• pp.198-207
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• 2010

Formation of optical meaning shown in the picture book screen is the original expression method in that it makes you understand properties or characteristics of subjects more easily and faster adding another images to them, exaggerating or reducing them as well as shows the phenomenon that you try to deliver. In this study, I first prepared the framework of classification on metaphorical expression methods of the picture books' illustration through theoretical study on the optical metaphorical characteristics and expression forms to analyze how the metaphorical expression methods shown in the picture books for children works optically and psychologically to them. Also, I divided the expression form into 2 types to derive optical psychological perceptional response of children through the empirical survey and analyzed linguistic psychological response of children by type utilizing it as the survey materials of in-depth interviews. Finally, I think that the result of this study will help screen production based on personality and creativity of illustrators and, in addition, it will be utilized in studies and tests of the effective expression methods for students who learn illustration.

• Journal of the Korean Operations Research and Management Science Society
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• 제23권2호
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• pp.131-156
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• 1998

Cooperative query answering is a research effort to develop a fault-tolerant and intelligent database system using the semantic knowledge base constructed from the underlying database. Such knowledge base has two aspects of usage. One is supporting the cooperative query answering Process for providing both an exact answer and neighborhood information relevant to a query. The other is supporting ongoing maintenance of the knowledge base for accommodating the changes in the knowledge content and database usage purpose. Existing studies have mostly focused on the cooperative query answering process but paid little attention on the dynamic knowledge base maintenance. This paper proposes a multi-level knowledge representation framework called Knowledge Abstraction Hierarchy (KAH) that can not only support cooperative query answering but also permit dynamic knowledge maintenance. The KAH consists of two types of knowledge abstraction hierarchies. The value abstraction hierarchy is constructed by abstract values that are hierarchically derived from specific data values in the underlying database on the basis of generalization and specialization relationships. The domain abstraction hierarchy is built on the various domains of the data values and incorporates the classification relationship between super-domains and sub-domains. On the basis of the KAH, a knowledge abstraction database is constructed on the relational data model and accommodates diverse knowledge maintenance needs and flexibly facilitates cooperative query answering. In terms of the knowledge maintenance, database operations are discussed for the cases where either the internal contents for a given KAH change or the structures of the KAH itself change. In terms of cooperative query answering, database operations are discussed for both the generalization and specialization Processes, and the conceptual query handling. A prototype system has been implemented at KAIST that demonstrates the usefulness of KAH in ordinary database application systems.