• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.595-597
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• 2015

최근, 전 세계 주요 국가에서 전략산업으로 육성되고 있는 사물인터넷(IoT : Internet of Things) 기술은 사물과 사물간의 소통이 가능한 상황인식 기반의 미래 인터넷 환경이다. 기존의 연구에는 사람이 직접 명령을 내려 디바이스를 제어하는 P2M(Person to Machine) 방식과 단순 센서 데이터를 통한 단일 디바이스 제어방식이 있다. 하지만, IoT의 일종인 Machine Socialization은 각각의 디바이스들이 디바이스 내부 기능정보를 이용하고, Device Manager를 통하여 전체적인 시나리오를 전개하는 M2M(Machine to Machine)방식의 디바이스 협업 시스템이다. 본 논문에서는 기존의 P2M의 시스템을 M2M의 시스템으로 변화시키기 위한 Machine Socialization의 시스템의 알고리즘을 제안한다.

• 디지털융복합연구
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• 제13권4호
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• pp.205-210
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• 2015

자동차산업의 발전과 함께 M2M(Machine-to-Machine)통신이 자동차 산업분야에서 많은 관심이 되고 있다. M2M은 기상, 환경, 물류, 국방, 농.축산 등에서 사용하기 시작하여 장비들이 자동으로 상황에 맞추어 통신을 하고 상황에 맞는 동작을 함으로써 운영해가는 시스템이다. 자동차에서도 차량내부 장치 간, 차대 차, 차와 교통시설물, 차와 주변의 환경 등에 적용되고 있다. 그러나 통신시스템의 특성상 전송구간에서 공격자의 공격에 대한 문제가 있으며 자동차의 운행, 제어계통 및 엔진제어 등에 공격자의 공격이 진행되면 안전에 심각한 문제가 발생하게 된다. 따라서 디바이스 간 보안통신에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 차량의 디바이스간 안전한 통신을 위해 해시함수 및 수학적 복잡한 공식을 이용하여 프로토콜을 설계하였으며 프로토콜 정형검증 도구인 Casper/FDR을 이용하여 실험하였으며 제안한 프로토콜이 각종 공격에 안전하게 동작되며 실제 적용할 때 효과적임을 확인하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• 전자공학회논문지CI
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• 제41권2호
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• pp.1-8
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• 2004

자동차 텔레매틱스 용 음성 HMI(Human Machine Interface) 기술은 차량 내 음성정보기술 활용을 위하여 차량 잡음환경에 강인한 내장형 음성 기술을 통합한 음성 HMI 기반 텔레매틱스 용 DSP 시스템의 개발을 포함한다. 개발된 내장형 음성 인식엔진을 바탕으로 통합 시험을 위한 자동차 텔레매틱스 용 DSP 시스템 구현 개발을 수행하는 본 논문은 자동차용 음성 HMI의 요소 기술을 통합하는 기술 개발로 자동차용 음성 HMI 기술 개발에 중심이 되는 연구이다.

• 대한산업공학회지
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• 제29권3호
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• pp.213-221
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• 2003

In this paper, we consider a multi-objective part-machine grouping problem, in which part types have several alternative part routings and each part routing has a machining sequence. This problem is characterized as optimally determining part type sets and its corresponding machine cells such that the sum of inter-cell part movements and the sum of machine workload imbalances are simultaneously minimized. Due to the complexity of the problem, a two-stage heuristic algorithm is proposed, and experiments are shown to verify the effectiveness of the algorithm.

• 한국전자통신학회논문지
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• 제18권1호
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• pp.85-90
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• 2023

보건의료분야 데이터 분석 방법론이 기존의 통계 중심의 연구방법에서 머신러닝을 이용한 예측 연구로 전환되고 있다. 본 연구에서는 다양한 머신러닝 도구들을 살펴보고, 보건의료분야에서 많이 사용하고 있는 통계 도구인 R을 빅데이터 머신러닝에 적용하기 위해 R과 스파크를 연계한 프로그래밍 모델들을 비교한다. 그리고, R을 스파크 환경에서 수행하는 SparkR을 이용한 선형회귀모델 학습의 성능을 스파크의 기본 언어인 스칼라를 이용한 모델과 비교한다. 실험 결과 SparkR을 이용할 때의 학습 수행 시간이 스칼라와 비교하여 10~20% 정도 증가하였다. 결과로 제시된 성능 저하를 감안한다면 기존의 통계분석 도구인 R을 그대로 활용 가능하다는 측면에서 SparkR의 분산 처리의 유용성을 확인하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1998년도 춘계학술발표회논문집(1)
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• pp.291-296
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• 1998

The DAta management and Communication NETworks (DACONET), Which it is designed as a subsystem for Man-Machine Interface System of Korea Advanced LIquid MEtal Reactor(KALIMER MMIS) and advanced design concept is approached, is described. The DACONET has its roles of providing the real-time data transmission and communication paths between MMIS systems, providing the quality data for protection, monitoring and control of KALIMER and logging the static and dynamic behavioral data during KALIMER operation. The DACONET is characterised as the distributed real-time system architecture with high performance, Future direction, in which advanced technology is being continually applied to Man-Machine interface System Development of Nuclear Power Plants, will be considered for designing data management and communication networks of KALIMER MMIS

• 한국정보통신학회논문지
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• 제24권1호
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• pp.130-133
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• 2020

Smart pig farm which is based on IoT has been widely adopted by many pig farmers. In order to achieve optimal control of smart pig farm, the relation between environmental conditions and performance metric should be characterized. In this study, the relation between multiple environmental conditions including temperature, humidity and various performance metrics, which are daily gain, feed intake, and MSY, is analyzed based on data obtained from 55 real pig farm. Especially, based on preprocessing of data, various regression based machine learning algorithms are considered. Through performance evaluation, we show that the performance can be predicted with high precision, which can improve the efficiency of management.

• Journal of information and communication convergence engineering
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• 제20권4호
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.