• 한국정보통신학회논문지
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• 제25권3호
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• pp.486-489
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• 2021

Recently, the demand for the use of personal mobility vehicles, such as an electric kickboard, is increasing explosively because of its high portability and usability. However, the number of traffic accidents caused by personal mobility vehicles has also increased rapidly in recent years. To address the issues regarding the driver's safety, we propose a novel approach that can monitor context information around personal mobility vehicles using deep learning-based object detection and smartphone captured videos. In the proposed framework, a smartphone is attached to a personal mobility device and a front or rear view is recorded to detect an approaching object that may affect the driver's safety. Through the detection results using YOLOv5 model, we report the preliminary results and validated the feasibility of the proposed approach.

• 인터넷정보학회논문지
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• 제23권5호
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• 천문학회보
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• 제44권2호
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• pp.70.4-70.4
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• 2019

Image super-resolution (SR) is a technique that enhances the resolution of a low resolution image. In this study, we use three SR models (RCAN, ProSRGAN and Bicubic) for enhancing solar SDO/HMI magnetograms using deep learning. Each model generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). The pixel resolution of HMI is about 0.504 arcsec. Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained three models with HMI images in 2014 and test them with HMI images in 2015. We find that the RCAN model achieves higher quality results than the other two methods in view of both visual aspects and metrics: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is also much better than the conventional bi-cubic interpolation. We apply this model to a full-resolution SDO/HMI image and compare the generated image with the corresponding Hinode NFI magnetogram. As a result, we get a very high correlation (0.92) between the generated SR magnetogram and the Hinode one.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
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• pp.84.1-84.1
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• 2015

Recent years in particular in Korea see intensive interests in a deep geothermal engineering and its application in different uses as far as from direct uses to power generation sectors, that are achieved by harnessing hot energy sources from the earth. For instance widespread interest has been generated because the geothermal energy is the source that one extracts it for more than 20 hours per day and for about 30 years of an operation of the plant, which enables to give base load as for heating as well as an electric generation. In retrospect, shallow geothermal energy using heat pumps is commonplace in Korea while the deep geothermal is in the early stage of the development. Geothermal energies in view of the way of extracting heat are mainly categorized into several types such as a single well system, a hydrothermal system, an enhanced geothermal system (EGS) etc. In this talk, this speaker focuses on the thermo-fluid engineering of the single well system by introducing the modeling in order to harness hot fluid that is thermally balanced with the fluid of an injection well, which provides a challenge to assess the life time of the well. To avoid the loss of the temperature in producing the hot fluid, a specialized pipe or a borehole heat exchanger has been designed, and its concept is introduced. On the other hand, a binary system or an organic Rankine cycle, which provides the methodology to convert the heat into an electricity, is briefly introduced. Some experimental results of the binary system which has been constructed in our lab will be presented. Lastly as for the future direction, some comments for the industrialization of the deep geothermal energy in this country will be discussed.

• 정보처리학회논문지C
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• 제16C권4호
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• pp.417-422
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• 2009

특화된 하드웨어의 도움 없이 범용 다중 프로세서 플랫폼에서 DPI(Deep Packet Inspection) 시스템을 구현하는 방법은 비용 측면에서 매력적이다. 문제는 성능인데, 일반적으로 다중 프로세서 시스템에서는 작업들을 여러 프로세서에 적절하게 배분하는 로드밸런싱 방법과 DPI 프로세싱 전용 개별 프로세서를 지정하여 시스템의 성능을 향상 시킨다. 그러나, 우리는 DPI 시스템의 경우 위와 같은 단순한 프로세서 통제 방안이 반드시 최선책이 아니라고 생각한다. 본 논문에서는 작업의 종류에 따라 정해진 프로세서에 할당한 후, 프로세서 상태에 따라 역할을 변경하는 방식을 제안한다. 우리는 제안하는 방식을 리눅스 기반 듀얼 프로세서 시스템에 구현하고 실험을 통해 그 성능을 기존의 로드밸런싱 방식과 비교하였다. 제안된 방식에서는 하나의 프로세서는 인터럽트 처리를 포함한 일반적 패킷 프로세싱 역할만을 담당토록 하고 다른 프로세서는 DPI엔진을 전담하도록 역할로 분리시켜 캐시접근실패 (cache miss) 과 스핀락(spin lock) 발생빈도를 낮추었으며, DPI 전담 프로세서가 처리한계에 이르렀을 경우에는 두 프로세서 모두 DPI를 위해 자원을 사용토록 하여, 기존의 리눅스 로드 밸런싱 방식 DPI 시스템 대비 약 60%의 성능향상을 달성하였다.

• 한국콘텐츠학회논문지
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• 제20권8호
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• pp.10-18
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• 2020

본 연구에서는 콘텐츠 산업 중 음악 분야 2차 산업데이터를 활용하여 딥러닝 기법을 이용한 흥행 예측모델 구축 가능성을 살펴보았다. 본 연구를 통해 구축한 딥러닝 예측 모델은 17개 독립변인 -가수 파워, 가수 영향력, 피처링 가수 파워, 피처링 가수 영향력, 참여 가수 수, 참여 가수의 성별, 작사가 역량, 작곡가 역량, 편곡가 역량, 제작사 역량, 유통사 역량, 앨범의 타이틀 여부, 음원 스트리밍 플랫폼 좋아요 수, 음원 스트리밍 플랫폼 코멘트 수, 사전 홍보 기사 수, 티저 영상 조회 수, 초기 흥행성과를 기반으로 음원 흥행성과 -음원이 차트내 상주하는 기간을 예측하는 구조다. 추가적으로 본 연구가 딥러닝 기법을 콘텐츠 분야에 접목시킨 초기단계 연구임을 고려하여, 콘텐츠 흥행예측 선행연구에서 요인 추출을 위해 활용하는 선형회귀분석을 통해 변인 소거 후 구축한 DNN 예측모델과 예측률 비교를 진행하였다.

• 대한관절경학회지
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• 제17권1호
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• pp.84-87
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• 2013

원위 대퇴부 후방은 주요 신경과 혈관이 위치하고 있고 여러개의 힘줄들과 근육들이 위치하여, 심부 근막 내에 농양이 발생시 근막, 힘줄과 근육을 따라 근위 대퇴부, 슬와부, 근위 하퇴부 후방 및 무릎 관절의 주변으로 광범위하게 퍼질 수 있다. 이런 경우에 단순히 항생제 투여만으로는 치료가 어렵고, 수술적인 배농과 변연 절제술이 필요하다. 하지만 고식적인 방법으로는 충분한 시야를 확보하기 어려울 수 있으며, 슬와부의 주요 신경과 혈관 등에 치명적인 손상을 줄 수도 있다. 저자들은 64세 남자 환자에서 발생한 좌측 원위 대퇴부의 광범위 심부 농양에 대하여 내시경적 치료를 시행하여 주요 신경, 혈관 등 정상 조직에 대한 손상이 없이 성공적인 결과를 얻었기에 문헌 고찰과 함께 보고하는 바이다.

• 제어로봇시스템학회논문지
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• 제21권1호
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.

• 산업경영시스템학회지
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• 제44권3호
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• 한국산업융합학회 논문집
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• 제7권3호
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• pp.249-257
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• 2004

Forms provide meaning and deep correlation with human life All religion has symbolity through form expressions. The symbols of christanity is architectural interpreation to express the symbol of glories Form expression was Investigator from architectural design. Expression is regarded as important from the view of forms Form expressions are the motivation of expressing religion as an architectural factor. Form expression is one of proassing from architectural position Also it is the basic data of expressing glory from the architectural designer. So planning of church should be the harmony between city view and acceptance of love.