• Journal of IKEEE
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• v.26 no.2
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• pp.319-328
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• 2022

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.19-27
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• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• Analytical Science and Technology
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• v.5 no.2
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• pp.1096-1113
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• 1992

분자구조, 동력학, 그리고 분자들의 화학분응에 관한 정확한 지식은 분자들의 기능과 성질을 이해하는 데 중요한 정보를 제공한다. 2D NMR 분광법의 개발은 용액상의 분자들에 관한 이러한 의문을 해결하는 데 결정적인 역할을 하게 되었다. 그동안 아주 다양한 NMR기술들이 개발되어 왔으며 현재 그들에 대한 이용이 활발하게 진행되고 있다. 그러나 성공적인 2D NMR 분광법의 적용을 위해서는 적당한 기계뿐만 아니라 실험실의 정확한 선택 및 최적 조건의 변수들을 선택해야 하며 스펙트럼의 세밀하고도 정확한 해석을 필요로 한다. 곱연산자 방식(product operator formalism)의 도입은 펄스 FT NMR 분광학을 정성, 정량적으로 이해하도록 하는 것을 가능케 했으며, 이번 해설은 연속적으로 주어지는 펄스의 이해를 위해서 필요로 하는 상의 순환(phase cycle) 및 곱연산자 방식을 이용하여 다양한 2D NMR 기술의 이해를 돕고, 분석기기로서 2D NMR 분광법이 널리 사용 및 활용되어지고자 하는 데 목적이 있다.

• 전기의세계
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• v.34 no.4
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• pp.213-219
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• 1985

2중층 압전변압기를 공진주파수로 동작시키면 부하저항이 클수록 전압비가 커지고 부하저항의 변동에 대하여도 안정되며, 특히 무부하시의 전압비는 전기기계결합계수의 제곱과 기계적 품질계수의 곱에 비례하고, 또 그 크기나 형상에는 관계없으므로 설계시 제약이 적은 장점이 있다. 한편 이 변압기의 공진주파수는 압전진동자의 직력공지주파수에 크게 의존하고, 진동자의 고정용량, 전기기계결합계수, 절연판의 정전용량 등에 의해서도 영향을 받으며, 부하저항이 큰 경우의 공진주파수는 부하저항의 변동에 대하여 안정됨을 알 수 있다. 따라서 이러한 2중층 압전변압기를 이용하여 높은 전압비를 얻으려면, 변압기를 전기기계 결합계수와 기계적 품질계수가 큰 압접진동자로 제작하고, 또 그 변압기를 저항이 큰 부하에서 공진주파수로 동작시키는 것이 바람직하다.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.554-558
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• 2020

최근 사전학습 모델의 발달로 기계독해 시스템 성능이 크게 향상되었다. 하지만 기계독해 시스템은 주어진 단락에서 질문에 대한 정답을 찾기 때문에 단락을 직접 검색해야하는 실제 환경에서의 성능 하락은 불가피하다. 즉, 기계독해 시스템이 오픈 도메인 환경에서 높은 성능을 보이기 위해서는 높은 성능의 검색 모델이 필수적이다. 따라서 본 논문에서는 검색 모델의 성능을 보완해 줄 수 있는 오픈 도메인 기계독해를 위한 단락 재순위화 모델을 제안한다. 제안 모델은 합성곱 신경망을 이용하여 질문과 단락을 구절 단위로 표현했으며, N-gram 구절 사이의 상호 주의 집중을 통해 질문과 단락 사이의 관계를 효과적으로 표현했다. KorQuAD를 기반으로한 실험에서 제안모델은 MRR@10 기준 93.0%, Top@1 Precision 기준 89.4%의 높은 성능을 보였다.

• Progress in Medical Physics
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• v.7 no.1
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• pp.53-64
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• 1996

Purpose: The purposes of this paper are to develop a theoretical basis that the beam directions should be considered when the mechanical accuracy of teletherapy machine is evaluated by the star pattern test, to develop methods using asymmetric field in length to simulate beam direction for the case that beam direction does not appear on film. Method: In evaluating mechanical rotational accuracy of the gantry of teletherapy unit by the star pattern test, the direction of radiation beams was considered. A star pattern using some narrow beams was made. Density profiles at 10cm far from estimated gantry axis on the star pattern were measured using an optical densitometer. On each profile, one coordimate of a beam axis was determined. A pair of coordinates on a beam axis form an equation of the axis. Assume that a unit vector equation omitted is with same direction as radiation beam and a vector equation omitted is a vector directing to the beam axis from the estimated gantry axis. Then, a vector product equation omitted ${\times}$ equation omitted is an area vector of which the absolute value is equal to the distance from the estimated gantry axis to the beam axis. The coordinate of gantry axis was obtained by using least-square method for the area vectors relative to the average of whole area vectors. For the axis, the maximum of absolute value of area vectors would be an accuracy of the gantry rotation axis. For the evaluation of mechanical accuracies of collimator and couch axes for which beam direction could not be depicted on a star pattern test film, narrow beams asymmetric in field length was used to simulate beam direction. Result: For a star test pattern to evaluate the mechanical accuracy of rotational axes of a telectherapy machine, the result considering beam direction was different from that ignoring beam direction. For the evaluation of mechanical accuracies of collimator and couch axes by means of a star pattern test, narrow asymmetric beams could simulate beam direction. Conclusion: When a star pattern test is used to evaluate the mechanical accuracy of a teletherapy unit, beam direction must be considered or simulated, and quantitatively evaluated.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.211-218
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• 2021

A recent spread of personal mobility devices such as electric kickboards has brought about a rapid increase in accident cases. Such vehicles are susceptible to falling accidents due to their low dynamic stability and lack of outer protection chassis. This paper presents the development of an automatic emergency braking system and a safe starting system as driving assistance devices for electric kickboards. The braking system employed artificial intelligence to detect nearby threaening objects. The starting system was developed to disable powder to the motor until when the driver's boarding is confirmed. This study is meaningful in that it proposes the convergence technology of advanced driver assistance systems specialized for personal mobility devices.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Smart Media Journal
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• v.11 no.1
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• pp.58-66
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• 2022

As Internet technology develops, SNS users are increasing. As SNS becomes popular, SNS-type crimes using the influence and anonymity of social networks are increasing day by day. In this paper, we propose a fake account classification method that applies machine learning and deep learning to statistical and image data for fake accounts classification. SNS account data used for training was collected by itself, and the collected data is based on statistical data and image data. In the case of statistical data, machine learning and multi-layer perceptron were employed to train. Furthermore in the case of image data, a convolutional neural network (CNN) was utilized. Accordingly, it was confirmed that the overall performance of account classification was significantly meaningful.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.43-53
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• 2019

BigCloneBench has recently been used for performance evaluation of code clone detection tool using machine learning. However, since BigCloneBench is not a benchmark that is optimized for machine learning, incorrect learning data can be created. In this paper, we have shown through experiments using machine learning that the set of Type-4 clone methods provided by BigCloneBench can additionally be found. Experimental results using Tree-Based Convolutional Neural Network show that our proposed method is effective in improving BigCloneBench's dataset.