• Journal of Energy Engineering
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• v.26 no.3
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• pp.11-29
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• 2017

In recent years, the use of artificial intelligence technology such as deep neural net machine learning(deep learning) is becoming an effective and practical option in industrial manufacturing process. This study focuses on recent deep learning development environments and their applications in the manufacturing field.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.449-453
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• 2006

This paper wishes to present way that can take advantage of parts information of image drawing for efficient maintenance management of facilities efficiently. Information for parts that compose facilities to facilities design drawing has been expressed, and legend character has been written to divide each parts. This paper applies Artificial Intelligence techniques for legend character cognition of image drawing. Finally, apply artificial intelligence techniques to drawing management system to evaluate efficiency of method that propose in this paper that see.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.75-80
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• 2021

Artificial intelligence is becoming an important part of our lives providing incredible benefits. In this respect, facial expression recognition has been one of the hot topics among computer vision researchers in recent decades. Classifying small dataset of low resolution images requires the development of a new small scale deep CNN model. To do this, we propose a method suitable for small datasets. Compared to the traditional deep CNN models, this model uses only a fraction of the memory in terms of total learnable weights, but it shows very similar results for the FER2013 and FERPlus datasets.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.243-250
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• 2010

As the wind has become one of the fastest growing renewable energy sources, the key issue of wind energy conversion systems is how to efficiently operate the wind turbines in a wide range of wind speeds. Compared to fixed speed turbines, variable speed wind turbines feature higher energy yields, lower component stress and fewer grid connection power peaks. Generally, measurement of wind speed is required for the control of variable speed wind turbine system. However, wind speed measured by anemometers is not accurate owing to various reasons. In this work, a new control algorithm for variable speed wind turbine system based on Kalman filter which can be used for the estimation of wind speed and artificial neural network which can generate optimum rotor speed is proposed. Also, to verify the feasibility of the proposed scheme, various simulation studies are carried out by using Simulink in Matlab.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.603-609
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance procedures. Condition Monitoring System(CMS) can be used to aid plant owners in achieving these goals. Its aim is to provide operators with information regarding the health of their machines, which in turn, can help them improve operational efficiency. In this work, systematic design procedure for artificial neural network based normal behavior model which can be applied for fault detection of various devices is proposed. Furthermore, to verify the design method SCADA(Supervisor Control and Data Acquisition) data from 850KW wind turbine system installed in Beaung port were utilized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1612-1617
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• 2020

Recently, research to reduce the accident rate by actively adopting artificial intelligence technology in the field of disaster safety technology is spreading. In particular, it is important to quickly search the Rescue Requester in order to effectively perform rescue activities at the disaster site. However, it is difficult to search for Rescue Requester due to the nature of the disaster environment. In this paper, We intend to develop an artificial intelligence system that can be operated in a smart helmet for firefighters to search for a rescue requester. To this end, the optimal SoC was selected and developed as an embedded system, and by testing a general-purpose artificial intelligence S/W, the embedded system for future smart helmet research was verified to be suitable as an artificial intelligence S/W operating platform.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.886-888
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• 2020

ONNX 은 인공신경망 모델 교환을 위한 표준 중 하나이다. 인공신경망 모델을 구현하는 연구자 입장에선 ONNX 형태로 모델을 배포함으로써 이질적인 플랫폼 간의 호환성을 보장받을 수 있다. 서로 다른 플랫폼에서 ONNX 표준에선 ONNX 모델을 실행하는 엔진을 ONNX Runtime 이라고 하는데, ONNX Runtime 은 순수 S/W 형태이거나, 다양한 H/W 가속 기술과 결합된 형태가 있다. 본 논문에선 ONNX Backend Scoreboard 에 등록 되어있는 3 종류의 엔진과 본 논문에서 새롭게 제안하는 C-ONNX 의 성능을 웍스테이션과 임베디드 기기에서 비교해보고 임베디드 기기에 특화된 C-ONNX 의 가능성에 대해 알아본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.497-500
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• 2004

가상현실은 현실세계에서 경험하기 어려운 환경을 간접적으로 경험할 수 있는 가상의 공간이다. 이러한 가상현실에는 건물, 지형, PC(Playable Character), NPC(Non-Playable Character)등의 다양한 객체들이 존재하게 되며, PC와 NPC와 같은 객체들은 현실감을 주기 위해 인공지능을 가지게 된다. 현재까지 인공지능에 대한 많은 연구가 진행되었으며, 다양한 분야에서 활용되고 있다. 가상현실에서는 유한상태 기계(Finite Sate Machine, FSM), 유전자 알고리즘, 신경망 알고리즘 $A{\ast}$ 알고리즘 등이 활용되고 있으며, FSM은 비교적 알고리즘이 간단하고, 다른 알고리즘에 비해 구현이 간단해 널리 이용되고 있다. 본 논문에서는 FSM을 활용하여 여러 행동 패턴을 정의하고 행동 패턴간 천이가 이루어 짐으로, 객체의 행동을 다양하게 나타낼 수 있는 Multiple FSM은 제안한다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.667-669
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• 2021

본 논문에서는 벡터 기반 데이터 증강 기법(Data augmentation)을 제안하여 학습 데이터를 구축한 뒤, 이를 합성곱 신경망(Convolutional Neural Networks, CNN)으로 실제 균열과 가까운 패턴을 표현할 수 있는 프레임워크를 제안한다. 건축물의 균열은 인명 피해를 가져오는 건물 붕괴와 낙하 사고를 비롯한 큰 사고의 원인이다. 이를 인공지능으로 해결하기 위해서는 대량의 데이터 확보가 필수적이다. 하지만, 실제 균열 이미지는 복잡한 패턴을 가지고 있을 뿐만 아니라, 위험한 상황에 노출되기 때문에 대량의 데이터를 확보하기 어렵다. 이러한 데이터베이스 구축의 문제점은 인위적으로 특정 부분에 변형을 주어 데이터양을 늘리는 탄성왜곡(Elastic distortion) 기법으로 해결할 수 있지만, 본 논문에서는 이보다 향상된 균열 패턴 결과를 CNN을 활용하여 보여준다. 탄성왜곡 기법보다 CNN을 이용했을 때, 실제 균열 패턴과 유사하게 추출된 결과를 얻을 수 있었고, 일반적으로 사용되는 픽셀 기반 데이터가 아닌 벡터 기반으로 데이터 증강을 설계함으로써 균열의 변화량 측면에서 우수함을 보였다. 본 논문에서는 적은 개수의 균열 데이터를 입력으로 사용했음에도 불구하고 균열의 방향 및 패턴을 다양하게 생성하여 쉽게 균열 데이터베이스를 구축할 수 있었다. 이는 장기적으로 구조물의 안정성 평가에 이바지하여 안전사고에 대한 불안감에서 벗어나 더욱 안전하고 쾌적한 주거 환경을 조성할 것으로 기대된다.