• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.20-20
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• 2017

최근 기후변화 영향에 따라 전 세계적으로 인명피해 및 재산피해를 유발하는 자연재난이 지속적으로 증가하고 있으며, 그로 인한 자연재해의 규모가 점점 더 커지고 있다. 실제로 우리나라에서도 지난 1994 년에서 2013 년까지 지난 20 년간 자연재해에 의한 피해액은 12조 3천억 원으로 집계되었으며, 이 중 강우와 태풍에 의한 피해가 85 % 이고, 대설에 의한 피해는 약 13 % 로 자연재해 중 대부분의 피해는 강우 및 태풍에서 발생하지만, 폭설에 의한 피해도 적지 않은 것으로 나타났다. 이에 따라, 정확한 예측을 위해 신뢰도 높은 자료 구축을 통한 대설피해 예측에 관한 연구가 필요한 시점이다. 본 연구에서는 대설피해액 예측을 위해 우리나라의 63개 기상 관측소에서 관측한 적설심 자료 및 기상관측 자료와 사회 경제 자료 총 11개를 대설피해 예측을 위한 입력변수로 선정하고, 이를 기상관측소가 속한 도시의 면적에 따라 3개의 지역으로 구분하였다. 주성분분석을 활용하여 선정된 입력변수들을 4개의 주성분으로 구분하고, 인공신경망 및 다중선형 회귀 모형을 구성하여 각 지역별 대설피해 예측의 오차를 분석하였다. 적용결과, 인공신경망 모형을 이용한 대설피해 예측의 수정결정계수는 22.8 %~48.2 %를 나타냈고, 다중선형회귀 모형의 수정결정 계수는 9.2 %~39.7% 로 나타났다. 그러므로 인공신경망 모형이 다중회귀 모형보다 선택된 입력자료를 활용하여 대설피해를 예측하는 목적으로 조금 더 우수한 결과를 나타내었다. 향후 자료를 보완 및 모형의 고도화를 통해 보다 정확한 대설피해 예측 함수 개발이 가능할 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Journal of dental hygiene science
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• v.20 no.4
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• pp.206-212
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• 2020

Background: A Teachable Machine is a kind of machine learning web-based tool for general persons. In this paper, the feasibility of Google's Teachable Machine (ver. 2.0) was studied in the diagnosis of the tooth-marked tongue. Methods: For machine learning of tooth-marked tongue diagnosis, a total of 1,250 tongue images were used on Kaggle's web site. Ninety percent of the images were used for the training data set, and the remaining 10% were used for the test data set. Using Google's Teachable Machine (ver. 2.0), machine learning was performed using separated images. To optimize the machine learning parameters, I measured the diagnosis accuracies according to the value of epoch, batch size, and learning rate. After hyper-parameter tuning, the ROC (receiver operating characteristic) analysis method determined the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of the machine learning model to diagnose the tooth-marked tongue. Results: To evaluate the usefulness of the Teachable Machine in clinical application, I used 634 tooth-marked tongue images and 491 no-marked tongue images for machine learning. When the epoch, batch size, and learning rate as hyper-parameters were 75, 0.0001, and 128, respectively, the accuracy of the tooth-marked tongue's diagnosis was best. The accuracies for the tooth-marked tongue and the no-marked tongue were 92.1% and 72.6%, respectively. And, the sensitivity (TPR) and specificity (FPR) were 0.92 and 0.28, respectively. Conclusion: These results are more accurate than Li's experimental results calculated with convolution neural network. Google's Teachable Machines show good performance by hyper-parameters tuning in the diagnosis of the tooth-marked tongue. We confirmed that the tool is useful for several clinical applications.

• Journal of Platform Technology
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• v.8 no.1
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• pp.16-23
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• 2020

For protecting copyright for trademark, convolutional neural network can be used to confirm genuine of trademark image. For this, repeated training one trademark image degrades the performance of machine learning because of overfitting problem. Therefore, this type of machine learning application generates training data in various way. But if genuine trademark image is rotated, this image is classified as not genuine trademark. In this paper, we propose the way for extending training data to confirm genuine of trademark image which is rotated. Our proposed way generates rotated image from genuine trademark image as training data. To show effectiveness of our proposed way, we use CNN machine learning model, and evaluate the accuracy with test image. From evaluation result, our way can be used to generate training data for machine learning application which confirms genuine of rotated trademark image.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.148-157
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• 2021

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.4
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• pp.183-194
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• 2020

Recently, the number of hip dysplasia (DDH) that occurs during infant and child growth has been increasing. DDH should be detected and treated as early as possible because it hinders infant growth and causes many other side effects In this study, two modelling techniques were used for multiple training techniques. Based on the results after the first transformation, the training was designed to be possible even with a small amount of data. The vertical flip, rotation, width and height shift functions were used to improve the efficiency of the model. Adam optimization was applied for parameter learning with the learning parameter initially set at 2.0 x 10e-4. Training was stopped when the validation loss was at the minimum. respectively A novel image overlay system using 3D laser scanner and a non-rigid registration method is implemented and its accuracy is evaluated. By using the proposed system, we successfully related the preoperative images with an open organ in the operating room

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.133-150
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• 2021

The purpose of this study is to identify the applicability of machine learning targeting titles in the classification of books in public libraries. Data analysis was performed using Python's scikit-learn library through the Jupiter notebook of the Anaconda platform. KoNLPy analyzer and Okt class were used for Hangul morpheme analysis. The units of analysis were 2,000 title fields and KDC classification class numbers (300 and 600) extracted from the KORMARC records of public libraries. As a result of analyzing the data using six machine learning models, it showed a possibility of applying machine learning to book classification. Among the models used, the neural network model has the highest accuracy of title classification. The study suggested the need for improving the accuracy of title classification, the need for research on book titles, tokenization of titles, and stop words.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1202-1208
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• 2020

In line with the current era of the 4th Industrial Revolution, it is necessary to prepare for the future by integrating AI elements in the ship sector. In addition, it is necessary to respond to this in the field of power management for the appearance of autonomous ships. In this study, we propose a battery-linked electric propulsion system (BLEPS) algorithm using machine learning's DNN. For the experiment, we learned the pattern of ship power consumption for each operation mode based on the ship data through LabView and derived the battery status through Python to check the flexibility of the generator and battery interlocking. As a result of the experiment, the low load operation of the generator was reduced through charging and discharging of the battery, and economic efficiency and reliability were confirmed by reducing the fuel consumption of 1% of LNG.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.22-27
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• 2020

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.