• Annual Conference of KIPS
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• 2023.05a
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• pp.504-506
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• 2023

본 연구에서는 기존 여행지 추천의 플랫폼에 있어 개인의 취향에 맞는 여행지 추천이 어렵다는 점을 해결하고자, 비선형적 관계를 해결할 수 있는 NCF 심층신경망 추천시스템을 이용하여 개인의 성향에 따라 여행지를 추천해 주는 시스템을 제안하고 이를 평가한 결과를 보고한다.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1909-1916
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• 2017

Recently, deep learning algorithms have good performance in various fields, but they are not actively applied to sonar systems. In this study, we carried out experiments to classify active sonar returns into a metal object such as a mine and a rock using a convolutional neural network which is one of the deep learning algorithms. Data augmentation is applied on this paper to avoid overfitting and increase performance. And we analyzed performance variation depending on hyperparameter value and change of the number of training data through data augmentation. The experiments are performed with two training data; an aspect-angle independent and an aspect-angle dependent. As a result, the performances are 88.9% and 94.9% in aspect-angle independent and dependent, respectively. These are up to 4.5% point higher than the performance obtained by applying artificial neural network and support vector machine algorithm in the previous study.

• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.2
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• pp.71-82
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• 2022

This paper presents the effect on the inelastic behavior and structural performance of concrete and filled steel pipe through a numerical method for reliable judgment under various load conditions of the CJS composite structural system. Variable values optimized for the CJS synthetic structural system and the effects of multiple variables used for finite element analysis to present analytical modeling were compared and analyzed with experimental results. The Winfrith concrete model was used as a concrete material model that describes the confinement effect well, and the concrete structure was modeled with solid elements. Through geometric analysis of shell and solid elements, rectangular steel pipe columns and steel elements were modeled as shell elements. In addition, the slip behavior of the joint between the concrete column and the rectangular steel pipe was described using the Surface-to-Surface function. After finite element analysis modeling, simulation was performed for cyclic loading after assuming that the lower part of the foundation was a pin in the same way as in the experiment. The analysis model was verified by comparing the calculated analysis results with the experimental results, focusing on initial stiffness, maximum strength, and energy dissipation capability.

• Journal of IKEEE
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• v.22 no.2
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• pp.281-288
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• 2018

In this paper, we propose an intelligent face recognition and tracking system that distributes GPU resources using CUDA. The proposed system consists of five steps such as GPU allocation algorithm that distributes GPU resources in optimal state, face area detection and face recognition using deep learning, real time face tracking, and PTZ camera control. The GPU allocation algorithm that distributes multi-GPU resources optimally distributes the GPU resources flexibly according to the activation level of the GPU, unlike the method of allocating the GPU to the thread fixedly. Thus, there is a feature that enables stable and efficient use of multiple GPUs. In order to evaluate the performance of the proposed system, we compared the proposed system with the non - distributed system. As a result, the system which did not allocate the resource showed unstable operation, but the proposed system showed stable resource utilization because it was operated stably. Thus, the utility of the proposed system has been demonstrated.

• Annual Conference of KIPS
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• 2022.11a
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• pp.49-51
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• 2022

대규모 데이터를 처리하기 위해 머신러닝, 딥러닝과 같은 AI 활용 연구가 일반화되면서 시스템 환경 또한 병렬처리 연산에 강화된 가속기 기반의 이기종 아키텍처로 확산되고 있다. CPU 기반의 계산 환경과 달리 상대적으로 성능이 낮은 수천 개의 산술연산장치(ALU)를 활용해 쓰레드 방식으로 연산을 수행하며, I/O의 특성 또한 대규모의 데이터들이 수많은 연산장치에 전달되기 위한 Small I/O, High-throughput 처리 성능이 애플리케이션에 큰 영향을 끼친다. 본 논문에서는 병렬 컴퓨팅 환경에 AI 애플리케이션이 접목되면서 요구되는 스토리지, 파일시스템의 환경을 분석하고 나아가 성능 검증을 통해 I/O 특성을 파악하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.77-83
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• 2020

The heat treatment facility is in a situation where the scope of application of the remote IOT system is expanding due to the harsh environment caused by high heat and long working hours among the root industries. In this heat treatment process environment, the IOT middleware is required to play a pivotal role in interpreting, managing and controlling data information of IoT devices (sensors, etc.). Until now, the system controlled by the heat treatment remotely was operated with the command of the operator's batch system without overall monitoring of the site situation. However, for the safety and precise control of the heat treatment facility, it is necessary to control various sensors and recognize the surrounding work environment. As a solution to this, the heat treatment safety support system presented in this paper proposes a support system that can detect the access of the work manpower to the heat treatment furnace through thermal image detection and operate safely when ordering work from a remote location. In addition, an OPEN CV-based deterioration analysis system using DNN deep learning network was constructed for faster and more accurate recognition than general fixed hot spot monitoring-based thermal image analysis. Through this, we would like to propose a system that can be used universally in the heat treatment environment and support the safety management specialized in the heat treatment industry.

• Annual Conference of KIPS
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• 2018.10a
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• pp.924-927
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• 2018

본 논문에서는 거동이 불편한 환자나 장애인들을 위해 신체에 착용하는 부가적인 장비 없이 멀리 있는 가전을 직접 움직이지 않고 편리하게 제어할 수 있는 RGB-D 카메라를 활용한 손 인식과 딥러닝 기반 IoT 장치 컨트롤 시스템을 제안한다. 특히, 제어하고자 하는 장치의 위치를 알기 위하여 YOLO 알고리즘을 이용하여 장치를 인식한다. 또한 그와 동시에 RGB-D 카메라의 라이브러리를 이용하여 사용자의 손을 인식, 현재 사용자 손의 위치와 사용자가 취하는 손동작을 통하여 해당 위치의 장치를 제어한다.

• Annual Conference of KIPS
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• 2019.10a
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• pp.809-812
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• 2019

소프트웨어 크래시 분류를 개선하기 위해 호출 스택 정보를 기반한 많은 연구들이 있다. 본 연구에서는 크래시 직전 이미지를 수집하여, 기존 호출 스택 기반의 분류에서 발생하는 문제를 개선하고자 한다. 또한 이미지 자체의 직관성으로 개발자뿐만 아니라 개발 지식이 없는 실무자도 크래시 정보를 활용할 수 있고, 문제 해결을 위한 재현 루트 파악, 위변조 여부와 같은 추가 정보를 확인할 수 있을 것으로 기대한다. 비지도 학습 기반인 딥러닝 클러스터링 N2D 알고리즘을 통하여 이미지를 자동 분류하고 순위화하는 시스템을 구축하여, 특정 소프트웨어에 특화되지 않고 다양한 소프트웨어의 크래시 이미지 자동 분류에 기여할 수 있을 것으로 기대한다.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.