• 방송공학회논문지
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• 제27권3호
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• pp.341-350
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• 2022

본 논문은 온디바이스 환경에서 다중 시점 영상을 입력 받아 객체를 증강하고, 현실 공간에 의한 가려짐을 구현하는 딥러닝 기반의 증강현실 시스템을 제안한다. 이는 세부적으로 카메라 자세 추정, 깊이 추정, 객체 증강 구현의 세 기술적 단계로 나눠지며 각 기법은 온디바이스 환경에서의 최적화를 위해 다양한 모바일 프레임워크를 사용한다. 카메라 자세 추정 단계에서는 많은 계산량을 필요로 하는 특징 추출 알고리즘을 GPU 병렬처리 프레임워크인 OpenCL을 통해 가속하여 사용하며, 깊이 영상 추론 단계에서는 모바일 심층신경망 프레임워크 TensorFlow Lite를 사용하여 가속화된 단안, 다중 영상 기반의 깊이 영상 추론을 수행한다. 마지막으로 모바일 그래픽스 프레임워크 OpenGL ES를 활용해 객체 증강 및 가려짐을 구현한다. 제시하는 증강현실 시스템은 안드로이드 환경에서 GUI를 갖춘 애플리케이션으로 구현되며 모바일과 PC 환경에서의 동작 정확도 및 처리 시간을 평가한다.

• 한국정보시스템학회지:정보시스템연구
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• 제30권1호
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• pp.65-84
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• 2021

Purpose Based on the development of artificial intelligence and big data technologies, the RegTech has been emerged to reduce regulatory costs and to enable efficient supervision by regulatory bodies. The word RegTech is a combination of regulation and technology, which means using the technological methods to facilitate the implementation of regulations and to make efficient surveillance and supervision of regulations. The purpose of this study is to describe the recent adoption of RegTech and to provide basic examples of applying RegTech to capital market regulations. Design/methodology/approach English-based ontology and deep learning technologies are quite developed in practice, and it will not be difficult to expand it to European or Latin American languages that are grammatically similar to English. However, it is not easy to use it in most Asian languages such as Korean, which have different grammatical rules. In addition, in the early stages of adoption, companies, financial institutions and regulators will not be familiar with this machine-based reporting system. There is a need to establish an ecosystem which facilitates the adoption of RegTech by consulting and supporting the stakeholders. In this paper, we provide a simple example that shows a procedure of applying RegTech to recognize and interpret Korean language-based capital market regulations. Specifically, we present the process of converting sentences in regulations into a meta-language through the morpheme analyses. We next conduct deep learning analyses to determine whether a regulatory sentence exists in each regulatory paragraph. Findings This study illustrates the applicability of RegTech-based ontology and deep learning technologies in Korean-based capital market regulations.

• 한국인터넷방송통신학회논문지
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• 제22권6호
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• pp.173-178
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• 2022

최근, 소셜 네트워크 서비스에서 딥러닝을 활용한 추천시스템이 활발하게 연구되고 있다. 하지만 딥러닝을 이용한 추천시스템의 경우 콜드스타트 문제와 복잡한 연산으로 인해 늘어난 학습시간이 단점으로 존재한다. 본 논문에서는 사용자의 메타데이터를 활용하여 사용자 맞춤형 운동 루틴 추천 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘은 메타데이터(사용자의 키, 몸무게, 성, 등)를 입력받아 설계된 모델에 적용한다. 본 논문에서 제안한 운동 추천시스템 모델은 matrix factorization 알고리즘과 multi-layer perceptron을 활용한 neural collaborative filtering(NCF) 알고리즘을 기반으로 설계된다. 제안된 모델은 사용자 메타데이터와 운동 정보를 입력받아 학습을 진행한다. 학습이 완료된 모델은 특정 운동이 입력되면 사용자에게 추천도를 제공한다. 실험 결과에서 제안하는 운동 추천시스템 모델이 기존 NCF 모델보다 10% 추천 성능 향상과 50% 학습 시간 단축을 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.357-358
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• 2022

이 논문은 Deep Learning 을 이용해 의사의 진단의 도움을 줄 수 있는 Clinic Decision Support System(CDSS) Framework 를 제안한다. 당뇨병, 고혈압, 고지혈증 같은 대사질환은 증상이 있는 경우도 있지만 없는 경우가 대부분이다.[1] 그렇기 때문에 원격으로 진료할 경우 대사질환에 대한 부분을 놓칠 수 있다. 이러한 부분을 챗봇이 의사에게 Deep Neural Network(DNN)으로 예측된 정보를 제공해 도움을 준다.

• 인터넷정보학회논문지
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• 제22권3호
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• 대한의용생체공학회:의공학회지
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• 제40권2호
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• ETRI Journal
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• 제45권6호
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• Journal of Multimedia Information System
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• 제8권3호
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• pp.175-182
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• 2021

Oral cancer is ranked second most diagnosed cancer among Indian population and ranked sixth all around the world. Oral cancer is one of the deadliest cancers with high mortality rate and very less 5-year survival rates even after treatment. It becomes necessary to detect oral malignancies as early as possible so that timely treatment may be given to patient and increase the survival chances. In recent years deep learning based frameworks have been proposed by many researchers that can detect malignancies from medical images. In this paper we have proposed a deep learning-based framework which detects oral cancer from histopathology images very efficiently. We have designed our model to split the color channels and extract deep features from these individual channels rather than single combined channel with the help of Efficient NET B3. These features from different channels are fused by using feature fusion module designed as a layer and placed before dense layers of Efficient NET. The experiments were performed on our own dataset collected from hospitals. We also performed experiments of BreakHis, and ICML datasets to evaluate our model. The results produced by our model are very good as compared to previously reported results.

• 로봇학회논문지
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• 제14권1호
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.