• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.97-97
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• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.

• 정보교육학회논문지
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• 제21권4호
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• pp.393-401
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• 2017

본 연구의 목적은 학습자의 데이터 분석적 사고력 향상을 위한 딥러닝 기반 학습 시스템 개발 연구이다. 연구의 내용은 다음과 같다. 첫째, 데이터 분석적 사고력 향상을 위해 발견학습 모형에 딥러닝 기법을 적용하였다. 이는 데이터의 관계를 나타내주는 모델을 딥러닝 기법을 사용하여 생성하고, 새로운 데이터를 이 모델에 적용하여 데이터를 분석하는 과정을 경험할 수 있는 학습 방법이다. 둘째, 이 학습 방법에 따른 수업을 위한 딥러닝 기반 학습 시스템을 개발하였다. 딥러닝 기법을 사용하여 학습자가 입력한 데이터의 모델을 생성하고 적용할 수 있는 시스템을 개발하였다. 딥러닝을 적용한 발견학습 및 시스템 설계 연구는 데이터의 중요성이 더욱 커지는 미래 사회에서 학습자의 데이터 분석적 사고력을 향상시킬 수 있는 새로운 접근이 될 것으로 기대한다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

• 대한임베디드공학회논문지
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• 제16권5호
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• pp.163-169
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• 2021

In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.

• 반도체디스플레이기술학회지
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• 제18권2호
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• pp.1-5
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• 2019

As the number of IOT devices is growing rapidly, various 'see-thru connection' techniques have been reported for efficient communication with them. In this paper, we propose a deep learning based IOT device recognition system for interaction with these devices. The overall system consists of a TensorFlow based deep learning server and two Android apps for data collection and recognition purposes. As the basic neural network model, we adopted Google's inception-v3, and modified the output stage to classify 20 types of IOT devices. After creating a data set consisting of 1000 images of 20 categories, we trained our deep learning network using a transfer learning technology. As a result of the experiment, we achieve 94.5% top-1 accuracy and 98.1% top-2 accuracy.

• 전기전자학회논문지
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• 제28권3호
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• pp.474-478
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• 2024

본 논문에서는 의약 용기의 다중카메라 인라인 검사 시스템에서의 품질 검사를 위한 딥러닝 네트워크를 제안한다. 제안하는 딥러닝 네트워크는 현장에서 생산되는 의약 용기의 데이터를 사용하여 의약 용기에 특화된 딥러닝 네트워크로 더욱 정확하게 품질을 검사한다. 또한, 인라인 검사가 가능한 딥러닝 네트워크를 사용하여 품질 검사의 속도를 증대시킬 수 있다. 다중카메라 인라인 검사 시스템에서의 품질 검사를 위한 딥러닝 네트워크의 개발은 3단계로 나뉜다. 첫 번째로 실제 의약 용기 생산 현장에서 1개의 이물검사용 line 카메라, 3개의 치수검사용 area 카메라를 통해 얻은 약 10,000장의 이미지로 데이터셋을 구축한다. 두 번째로 의약 용기 데이터 전처리에서는 이물 검사, 치수검사의 용도에 맞게 불량이 일어날 수 있는 곳에 ROI를 지정하여 데이터를 전처리한다. 세 번째로 전처리된 데이터를 이용하여 딥러닝 네트워크를 학습한다. 딥러닝 네트워크는 적은 채널 수를 적용하여 linear layer를 사용하지 않아 판정 속도를 향상하고, PReLU와 residual learning를 적용하여 정확도를 향상한다. 이를 통해 4개의 카메라에서 구축한 데이터셋에 맞는 4개의 딥러닝 모듈을 제작한다. 제안된 의약 용기의 다중카메라 인라인 검사 시스템에서의 품질 검사를 위한 딥러닝 네트워크의 성능을 평가하기 위하여 공인시험기관에서 실험한 결과는, 딥러닝 모듈의 판별 정확도가 99.4%로 세계 최고 수준인 95% 보다 우수한 성적을 달성하였고, 평균 판별 속도가 0.947초로 측정되어 세계 최고 수준인 1초보다 우수한 성적을 달성하였다. 따라서, 본 논문에서 제안한 의약 용기의 다중카메라 인라인 검사 시스템에서의 품질 검사를 위한 딥러닝 네트워크의 효용성이 입증되었다.

• ETRI Journal
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• 제38권6호
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• pp.1207-1217
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• 2016

The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately

• 한국컴퓨터정보학회논문지
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• 제24권3호
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• pp.11-17
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• 2019

In this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by $45^{\circ}$ increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users' moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.

• Design & Manufacturing
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• 제16권4호
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• pp.60-66
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• 2022

Recently, with the development of industry and the improvement of living standards, various wastes are generated along with the production of various products. Most of these wastes are used as containers for products, and plastic or aluminum is used. Various attempts are being made to automate the classification of these wastes due to the high labor cost, but most of them are solved by manpower due to the geometrical shape change due to the nature of the waste. In this study, in order to automate the waste sorting task, Deep Learning technology is applied to a robot system for waste sorting and a vision system for waste sorting to effectively perform sorting tasks according to the shape of waste. As a result of the experiment, a Deep Learning parameter suitable for waste sorting was selected. In addition, through various experiments, it was confirmed that 99% of wastes could be selected in individual & group image learning. It is expected that this will enable automation of the waste sorting operation.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.