• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• 한국전자통신학회논문지
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• 제9권7호
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• pp.791-797
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• 2014

빅데이터의 병렬분산처리 시스템을 위한 아파치 하둡 환경을 구축하기 위해서는 다수의 컴퓨터를 연결하여 노드를 구성하거나, 하나의 컴퓨터에 다수의 가상 노드 구성을 통해 클라우딩 환경을 구축하여야 한다. 그러나 이러한 시스템을 교육 환경에서 실습용으로 구축하는 것은 복잡한 시스템 구성과 비용적인 측면에서 많은 제약이 따른다. 따라서 빅데이터 처리 분야의 입문자들과 교육기관의 실습용으로 사용할 수 있는 실용적이고 저렴한 학습 시스템의 개발이 시급하다. 본 연구에서는 라즈베리파이 보드를 기반으로 하둡과 NoSQL과 같은 빅데이터 처리 및 분석 실습이 가능한 빅데이터 병렬분산처리 학습시스템을 설계 및 구현하였다. 구현된 빅데이터 병렬분산처리시스템은 교육현장과 빅데이터를 시작하는 입문자들에게 유용한 시스템이 될 것으로 기대된다.

• 제어로봇시스템학회논문지
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• 제18권5호
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• pp.465-470
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• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.

• 한국산업정보학회논문지
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• 제25권2호
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• pp.57-72
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• 2020

철광석의 가격은 여러 국가와 기업들의 수요와 공급에 따라서 높은 변동성이 지속되고 있다. 이러한 비즈니스 환경에서 철광석의 가격을 예측하는 것은 중요해졌다. 본 연구는 머신러닝 기법을 이용하여 철광석이 거래되는 시점으로부터 한 달 전에 철광석 거래가격을 미리 예측하는 모형을 개발하고자 하였다. 예측 모형은 시계열 데이터를 활용한 예측 방법론으로 많이 활용되고 있는 시차분포 모형과 다층신경망 (Multi-layer perceptron), 순환신경망 (Recurrent neural network), 그리고 장단기 기억 네트워크 (Long short-term memory)와 같은 딥 러닝(Deep Learning) 모형을 사용하였다. 측정지표를 통해 개별 모형을 비교한 결과에 따르면, LSTM 모형이 예측 오차가 가장 낮은 것으로 나타났다. 또한, 앙상블 기법을 적용한 모형들을 비교한 결과, 시차분포와 LSTM의 앙상블 모형이 예측오차가 가장 낮은 것으로 나타났다.

• 산업과 과학
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• 제2권3호
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• pp.8-14
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• 2023

대다수의 IoT 기기들은 이미 AIoT를 사용하고 있지만, AI 애플리케이션을 구축하기 위해서는 아직 해결해야 할 문제가 많이 남아 있다. 본 연구에서는 IoT 에지 자원을 보다 효과적으로 분산하기 위해 머신러닝 기반의 IoT 에지 자원 관리 기법을 제안한다, 제안 기법은 머신러닝을 이용하여 IoT 에지 자원 동향을 파악함으로써 IoT 자원의 할당을 지속적으로 개선하며, 최적화된 IoT 자원은 머신러닝 컨볼루션을 활용하여 항상 변화하는 IoT 에지 자원을 안정적으로 유지한다, 제안 기법은 각각의 머신러닝 기반 IoT 에지 자원을 이전 패턴의 자원과 함께 해시값으로 저장함으로써 분산된 AIoT 맥락에서 공격 패턴으로 자원을 효과적으로 검증한다. 실험 결과에서는 IoT Edge 리소스의 무결성을 검증하기 위해서 이질적인 계산 하드웨어가 있는 복잡한 환경에서 잘 동작하는지 세 가지 다른 테스트 시나리오에서 에너지 효율성을 평가하였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 가을 학술발표논문집 Vol.29 No.2 (2)
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• pp.391-393
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• 2002

최근 가상교육에서 학습 기술의 상호 운용성을 기반으로 한 표준화의 필요성을 인식하고 여러 국제표준기관을 통해서 기반 환경과 컨텐트 명세 및 활용에 대한 표준화 작업이 이루어지고 있다. 이로 인해 e-learning 분야에서는 국제표준을 소개하고 체제적으로 AICC(Aviation Industry CBT Committee), IMS(Instructional Management Systems) Global Learning Consortium, ADL(Advanced Distributed Learning)을 중심으로 진행되어 오고 있다. 특히, 미래의 진보적인 e-learning 환경 개발로, 기능별 5계층으로 구성된 LTSA(Learning Technology Standard Architecture)를 제정하고 이를 통한 개발을 지원하고 있다. 하지만, 이는 시스템 구성요소를 정의한 계층 3의 경우 데이터 흐름 모델로 작성되어 현재 개발 수준과 일치하지 않는 문제점을 가지는 모델로 인정한다. 본 논문에서는 표준 모델링 언어인 UML(Unified Modeling Language)을 통해 모델을 재정의하고, 각 프로세스별 단계를 메타모델로 제시하여 개발과 아키텍처의 이해에 대한 문제점을 해결하고자 한다. 또한, 재정의된 모델을 기반으로 e-learning 지원을 위한 분석, 설계 프로세스를 정의하여 이에 대한 사례를 제시한다. 이는 아키텍처를 기반으로 한 메타모델과 프로세스를 통한 교육영역의 질의 응답 학습 도구인 QALT(Query-Answer Learning Tool)에 적용한다. 모델의 재정의로 아키텍처의 이해성 및 이를 기반으로 하는 교육용 애플리케이션 개발의 용이성의 증대를 기대할 수 있으며, 모델의 재사용성을 보장할 수 있다.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• 한국환경복원기술학회지
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• 제24권6호
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• pp.89-96
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• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

• 한국멀티미디어학회논문지
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• 제6권4호
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• pp.685-697
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• 2003

The Web based multimedia programmes/courses are becoming widely available in recent years. Most of these courses focus on Behaviorist way of learning, which does not promote deep learning in any way. For Adults this approach further incapacitated, as it does not satisfy Andragogical needs. The search for Constructivist way of learning through the web applied to Indian conditions led to need for developing a curriculum development approach that would promote construction of knowledge through web based collaboration. This paper attempts to reengineer existing curriculum development processes and lays out a framework of‘Problem Based Online Learning (PBOL)’curriculum design. In this context, entire curriculum development life cycle is evolved and explained. This is a part of doctoral work (Ph.D), which is in progress and being undertaken by K.James Mathai, and guided of Dr.D.S.Karaulia.

• 한국임상약학회지
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• 제33권3호
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• pp.202-209
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• 2023

Background: With the emergent transition of online learning during the COVID-19 pandemic, the need for online/offline blended learning that can effectively be utilized in a team-based learning (TBL) course has emerged. Methods: We used the online metaverse platforms, Gather and Zoom, along with face-to-face teaching methods in a team-based Introductory Pharmacy Practice Experience (IPPE) course and examined students' learning satisfaction and achievement, as well as their preferences to the learning platforms. A survey questionnaire was distributed to the students after the IPPE course completion. All data were analyzed using Excel and SPSS. Results: Students had high levels of course satisfaction (4.61±0.57 out of 5) and achievement of course learning objectives (4.49±0.70 out of 5), and these were positively correlated with self-directed learning ability. While students believed that the face-to-face platform was the most effective method for many of the class activities, they responded that Gather was the most effective platform for team presentations. The majority of students (64.3%) indicated that blended learning was the most preferred method for a TBL course. Conclusion: Students in a blended TBL IPPE course had high satisfaction and achievements with the use of various online/offline platforms, and indicated that blended learning was the most preferred learning method. In the post-COVID-19 era, it is important to utilize the blended learning approach in a TBL setting that effectively applies online/offline platforms according to the learning contents and activities to maximize students' learning satisfaction and achievement.