• 한국광학회:학술대회논문집
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• 한국광학회 1989년도 제4회 파동 및 레이저 학술발표회 4th Conference on Waves and lasers 논문집 - 한국광학회
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• pp.85-88
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• 1989

Introducing and optimizing it-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a 6x8 node system. Unlike many other neural networks models, this model has stronger error correction capability for correlated images such as "6", "8", "3", and "9". the bit-significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with another neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.uding the adaptive optimization networks is also introduced.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제30권3호
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• pp.281-294
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• 2016

본 연구에서는 '대화형(interactive) 공학수학 실습실'의 개발 과정과 내용 및 활용을 다룬다. 본 실습실에는 공학수학 전 범위에 걸쳐서 각 장별로 하나의 html 파일을 활용하여 공학수학 예제 및 연습문제, SageMath 명령어를 실행할 수 있는 SageMath 셀, 장별 요점 강의 녹화 파일로 연결되는 링크가 포함되어있다. 또한 기존에 만들어 놓은 SageMath 명령어가 동시에 제공되므로 실습 때마다 매번 명령어를 따로 입력해야하는 번거로움 없이 마우스로 [실행(Evaluate)]을 클릭하면서 미리 입력된 코드를 실습하고, 그와 유사한 다른 문제에 대하여는 함수와 조건을 바꾸면서 바로 사용할 수 있도록 하였다. 이렇게 웹 주소를 이용하여 공학수학을 지도하는 실습실의 장점은 첨단 모바일 기기의 사용이 증가하고 있는 추세에 맞추어 각 장 별 실습내용을 시간과 장소의 제약 없이 쉽게 프로그래밍을 학습 및 실행이 가능하므로 실습 효과를 높일 수 있다는 것이다. 본 실습실은 이론과 실습이 병행되어야 하는 공학수학 강의에서 학생들이 언제 어디서나 손쉽게 이용할 수 있으므로 공학수학 강좌의 효과적인 실습실 모델이 될 수 있다.

• Structural Engineering and Mechanics
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• 제66권6호
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• pp.677-691
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• 2018

Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.

• 생태와환경
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• 제43권1호
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제37권2호
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• pp.277-297
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• 2023

합성곱(convolution)은 인공지능(artificial intelligence)에서 컴퓨터 비전(computer vision), 심층학습(deep learning) 등의 분야를 이해하고 응용하려면 알아야 하는 중요한 수학적 연산이다. 그러나 현재의 공학수학 교과과정의 합성곱 내용은 독립적인 주제가 아니라 단편적으로 다루어지고 있어서 그 의미를 충분히 전달하지 못하고 있다. 이에 본 논문에서는 공학수학에서 인공지능 교육과 연계할 수 있도록 개발한 합성곱 교수·학습 자료를 제시한다. 먼저 기존 공학과 인공지능 기술의 통합적 관점에서 합성곱에 대한 배경지식과 응용 사례를 정리하고, 코딩을 이용한 교육이 가능하도록 파이썬(Python)/SageMath 코드를 개발하여 제공한다. 또한 합성곱 지식이 인공지능에서 어떻게 활용되는지 보여주는 구체적인 예시로, 이미지 분류에 사용되는 합성곱신경망(Convolutional Neural Network, CNN)을 개발된 코드와 함께 제공한다. 본 교수·학습자료는 합성곱 개념을 쉽고 효과적으로 교육할 수 있도록 공학수학의 보충 자료로 활용가능하며, 학습자는 코딩을 통해 합성곱을 배우고 본인의 전공과 관련된 인공지능 기술을 학습하는 데 이를 이용할 수 있다.

• 공학교육연구
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• 제18권3호
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• pp.59-68
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• 2015

This study was implemented for the purpose of analyzing the effects of Engineering Education Accreditation. Now, 15 years has passed adopting the engineering education system. We need to analyze the effect that this system has changed on the ground of engineering education, and it cultivated the human resource. In order to achieve the purpose of this study, the survey were done on the professors, graduates, and workers. The results and conclusions of this study are summarized as follows: First, it is urgent need to change the system of accreditation, and to get the public trust on assessment. Second, it is necessary to make circumstance that engineering education accreditation is advertised to the industries, and the industry can join the development, consulting, evaluation of curriculum. Third, government needs to make the policy that gives the incentive to the industries, if they give some merits to the accreditation graduates. Fourth, certificate of program graduate is desired to spread the accreditation proliferation. Fifth, government should systemize that accreditation program can get advantage to be selected for the public finance business(e.g. BK, LINC).. It will impact the quality Improvement and accountability of engineering programs.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2005년도 하계학술대회
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• pp.322-330
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• 2005

현대 사회는 정보의 홍수 시대로서, 날로 늘어나는 정보의 바다 속에 검색의 한계도 존재하며 사람들이 원하는 정보를 선별하는 능력이나 방법도 고도화 되어져야 한다. 본 논문에서는 이러한 문제점을 해결하기 위해서 교육용 문서에 적합한 카테고리를 가지고 있는 RSS에 대한 소개와 함께 RSS 리더를 설계하고 구현하고자 한다. RSS 신디케이션 포맷은 누구든지 코멘트, 뉴스 헤드라인, 최신기사에 대한 링크, 설명 그리고 이미지 등을 쉽게 공유할 수 있도록 해준다. 이러한 정보는 웹사이트뿐만 아니라 PDA, 핸드폰, 이메일 등에서도 자유롭게 볼 수 있다. 이러한 RSS 기술은 e-learning에서도 추후 변화를 가져다 줄 것으로 예상된다. 교수설계자가 설계한 학습단위의 완성을 위해 자료를 수집하여 초고와, 원고를 만들기 위해 교정, 교열을 하며, 디자이너를 통해 HTML 페이지를 만들던 기존의 프로세스를 대폭 간소화시켜 줄 수 있기 때문이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• 대한인간공학회지
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• 제31권4호
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• pp.577-583
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• 2012

Objective: This paper describes the process and results related to the development of gesture recognition-based natural user interface(NUI) for vehicle maintenance e-Training system. Background: E-Training refers to education training that acquires and improves the necessary capabilities to perform tasks by using information and communication technology(simulation, 3D virtual reality, and augmented reality), device(PC, tablet, smartphone, and HMD), and environment(wired/wireless internet and cloud computing). Method: Palm movement from depth camera is used as a pointing device, where finger movement is extracted by using OpenCV library as a selection protocol. Results: The proposed NUI allows trainees to control objects, such as cars and engines, on a large screen through gesture recognition. In addition, it includes the learning environment to understand the procedure of either assemble or disassemble certain parts. Conclusion: Future works are related to the implementation of gesture recognition technology for a multiple number of trainees. Application: The results of this interface can be applied not only in e-Training system, but also in other systems, such as digital signage, tangible game, controlling 3D contents, etc.