• Spatial Information Research
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• v.21 no.6
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• pp.91-98
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• 2013

Government introduces a Total Maximum Daily Loads(TMDL) which can be implemented for total pollutant amounts in 2004. Normally, the local governments have been calculated the amounts of pollutant discharge of each watershed using a water quality model. However, among the input data to use the water quality model, creating a schematic diagram of the stream or the modeling usually requires considerable amount of time and efforts due to the manual work. Therefore, this study tried to develop an algorithm which automates the creation of a schematic diagram for water quality modeling using the Korean Reach File capable of river network analysis. Further, this study creates a schematic diagram with the shape of a stream utilizing GIS capabilities. The diagram can be easily analyzed with overlapping various spatial information such as pollution sources and discharge points. This study mainly has automated element segmentation algorithm to divide streamflows into equal distance using line graphic data of Koran Reach File. Also, automated attribute input algorithm has also been developed to enable to insert element order and type into elements using point graphic data of Korean Reach File. For the verification of the developed algorithm, the algorithm was applied to kyungan stream basin to see the acceptable results. To conclude, it was possible to automate generating of schematic diagram of water quality model and it is expected to be able to save time and cost required for the water modeling. In future study, it is necessary to develop an automatic creation system of various types of input data for water quality modeling and this will lead to relatively easier and simple water quality modeling.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.131-143
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• 2009

Based on the fact that a human being walks naturally and stably with consuming a minimum energy, this paper proposes a new method of generating a natural gait of 5-link biped robot like human by analyzing a COG (Center Of Gravity) trajectory of human's gait. In order to generate a natural gait pattern for 5-link biped robot, it considers the COG trajectory measured from human's gait images on the sagittal and frontal plane. Although the human and 5-link biped robot are similar in the side of the kinematical structure, numbers of their DOFs(Degree Of Freedom) are different. Therefore, torques of the human's joints cannot are applied to robot's ones directly. In this paper, the proposed method generates the gait pattern of the 5-link biped robot from the GA algorithm which utilize human's ZMP trajectory and torques of all joints. Since the gait pattern of the 5-link biped robot model is generated from human's ones, the proposed method creates the natural gait pattern of the biped robot that minimizes an energy consumption like human. In the side of visuality and energy efficiency, the superiority of the proposed method have been improved by comparative experiments with a general method that uses a inverse kinematics.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.10
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• pp.979-986
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• 2011

In this paper, an embedded program of a flight control system for a small high performance UAV is introduced. The program consists of modules for device management and guidance and control. The device management system handles navigation sensors and mission equipments. The program for the guidance and control system is used to accomplish various kinds of missions and realize automation of flight control. Driver programs embedded in the device management system for operation of sensors and external devices are based on Texas Instrument's DSP/BIOS RTOS(realtime operating system). The on-board programs for the guidance and control system is obtained by using the model-based auto code generation technology.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.378-387
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• 2007

To enhancing the exercise effect, exercise management systems are introduced and generally used. They create the proper exercise program through exercise prescription after determining the personal body status. When the exercise programs are created, they will consider $2weeks{\sim}3months$ period. And, existing exercise programs cannot respect with personal exercise habits or exercise period which are changing variedly. If exercise period is long, it can be caused inappropriate exercise about user current status. To solve these problems in legacy systems, this paper proposes a Context Aware Exercise Model (CAEM) to provide the exercise program considering the user context. Also, we implemented that as Intelligent Fitness Guide (IFG) System. The IFG system is selectively received necessary measurement values as input values according to user's context. If exercise kinds, frequency and strength of user are changing, that system creates the exercise program through exercise optimization algorithm and exercise knowledge base. As IFG is providing the exercise program in a real time, it can be managed the effective exercise according to user context.

• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.13-23
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• 2021

In this paper, we proposed a framework that generates the trajectory of a single rigid body based on its COM configuration and contact pose. Because we use a smaller input dimension than when we use a full body state, we can improve the learning time for reinforcement learning. Even with a 68% reduction in learning time (approximately two hours), the character trained by our network is more robust to external perturbations tolerating an external force of 1500 N which is about 7.5 times larger than the maximum magnitude from a previous approach. For this framework, we use centroidal dynamics to calculate the next configuration of the COM, and use reinforcement learning for obtaining a policy that gives us parameters for controlling the contact positions and forces.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.451-461
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• 2024

Recently, various AI chatbots based on large language models have been released. Chatbots have the advantage of providing users with quick and easy information through interactive prompts, making them useful in various fields such as question answering, writing, and programming. However, a vulnerability in chatbots called "prompt injection attacks" has been proposed. This attack involves injecting instructions into the chatbot to violate predefined guidelines. Such attacks can be critical as they may lead to the leakage of confidential information within large language models or trigger other malicious activities. However, the vulnerability of Korean prompts has not been adequately validated. Therefore, in this paper, we aim to generate malicious Korean prompts and perform attacks on the popular chatbot to analyze their feasibility. To achieve this, we propose a system that automatically generates malicious Korean prompts by analyzing existing prompt injection attacks. Specifically, we focus on generating malicious prompts that induce harmful expressions from large language models and validate their effectiveness in practice.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.693-698
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• 2003

Every baseball game generates various records and on the basis of those records, win/lose prediction about the next game is carried out. Researches on win/lose predictions of professional baseball games have been carried out, but there are not so good results yet. Win/lose prediction is very difficult because the choice of features on win/lose predictions among many records is difficult and because the complexity of a learning model is increased due to overlapping factors among the data used in prediction. In this paper, learning features were chosen by opinions of baseball experts and a heuristic function was formed using the chosen features. We propose a hybrid model by creating a new value which can affect predictions by combining multiple features, and thus reducing a dimension of input value which will be used for backpropagation learning algorithm. As the experimental results show, the complexity of backpropagation was reduced and the accuracy of win/lose predictions on professional baseball games was improved.

• Journal of the Korean Wood Science and Technology
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• v.42 no.4
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• pp.367-375
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• 2014

A simplified material model, which was efficiently implemented in a three-dimensional finite solid element (3D FE) analysis for wood was developed. The bi-linear elasto-plastic anisotropic material theory was adopted to describe constitutive relations of wood in three major directions including longitudinal, radial and tangential direction. The assumption of transverse isotropy was made to reduce the requisite 27 material constants to 6 independent constants including elastic moduli, yield stresses and Poisson's ratios in the parallel, and perpendicular to grain directions. The results of Douglas fir compression tests in the three directions were compared to the 3D FE simulation incorporated with the wood material model developed in this study. Successful agreements of the results were found in the load-deformation curves and the permanent deformations. Future works and difficulties expected in the advanced application of the model were discussed.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.178-185
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• 2021

Sequence to sequence(S2S) 기반 음성인식 후처리기를 훈련하기 위한 학습 데이터 구축을 위해 (음성인식 결과(speech recognition sentence), 전사자(phonetic transcriptor)가 수정한 문장(Human post edit sentence))의 병렬 말뭉치가 필요하며 이를 위해 많은 노동력(human-labor)이 소요된다. BackTranScription (BTS)이란 기존 S2S기반 음성인식 후처리기의 한계점을 완화하기 위해 제안된 데이터 구축 방법론이며 Text-To-Speech(TTS)와 Speech-To-Text(STT) 기술을 결합하여 pseudo 병렬 말뭉치를 생성하는 기술을 의미한다. 해당 방법론은 전사자의 역할을 없애고 방대한 양의 학습 데이터를 자동으로 생성할 수 있기에 데이터 구축에 있어서 시간과 비용을 단축 할 수 있다. 본 논문은 BTS를 바탕으로 제주어 도메인에 특화된 음성인식 후처리기의 성능을 향상시키기 위하여 모델 수정(model modification)을 통해 성능을 향상시키는 모델 중심 접근(model-centric) 방법론과 모델 수정 없이 데이터의 양과 질을 고려하여 성능을 향상시키는 데이터 중심 접근(data-centric) 방법론에 대한 비교 분석을 진행하였다. 실험결과 모델 교정없이 데이터 중심 접근 방법론을 적용하는 것이 성능 향상에 더 도움이 됨을 알 수 있었으며 모델 중심 접근 방법론의 부정적 측면 (negative result)에 대해서 분석을 진행하였다.