춘란(Cymbidium goeringii)의 개화정도별 삭과 형성 및 종자 발아에 미치는 NaOCl의 영향

이대건,고재철 한국화훼산업육성협회 2010 화훼연구 Vol.18 No.4

본 연구는 춘란 야생 변이종의 육종을 위한 기초 자료 를 얻고자 실시하였으며, 야생종의 개화시기를 꽃봉오리, 반개화, 개화, 개화 후 10일, 개화 후 20일의 5단계로 나 누어 자가 수분시킨 후 삭과의 형태적 특성과 발아율을 조사한 결과이다. 착과율은 개화 후 20일에 교배한 것에 서 100%로 가장 높았고, 삭과의 무게는 반개화 때 교배 한 것이 가장 무거웠다. 5단계로 나누어 발아율을 조사한 결과 반개화에서 가장 높게 나타났으며, 교배 후 채종 일수를 150일, 165일, 180일의 3단계로 달리하여 발아율 을 조사한 결과에서는 교배 후 채종일수를 150일에 파종 한 종자에서 5%로 가장 높게 나타났다. NaOCl 처리를 통한 발아율을 조사한 결과에서는 2%로 처리한 것에서 53.3%의 발아율을 보여 가장 좋은 결과를 나타내었다. The study is aimed to obtain the basic data for developing new variations of wild spring orchid. The results was investigated the capsules’ formational characteristics and the germination ratio after having been self-pollinated by dividing the flowering period into the 5 stages into budding time, semi-flowering, full-flowering, 10 days after flowering, and 20 days after flowering. The fruit setting ratio was the highest as 100% in the plant which had been pollinated 20 days after the flowering, while the weight of the capsule was heaviest in the orchid which had been pollinated in semi-flowering period. As the result of investigating the germination ratio by dividing the period into 5 stages, it was the highest in the plant which had been pollinated during the semi-flowering period, and in the result of investigating the germination ratio by dividing the seeds harvesting days into the 3 stages, such as, 150 days, 165 days and 180 days after the pollination, it was highest as 5% in the orchid whose seeds had been harvested 150 days after the pollination. In the result of examining the germination ratio of the seeds treated with NaOCl, the those treated with 2% of NaOCl showed the highest as 67% in the germination ratio.

Strip Adjustment of Airborne Laser Scanner Data Using Area-based Surface Matching

이대건,유은진,염재홍,이동천 한국측량학회 2014 한국측량학회지 Vol.32 No.6

Multiple strips are required for large area mapping using ALS (Airborne Laser Scanner) system. LiDAR (LightDetection And Ranging) data collected from the ALS system has discrepancies between strips due to systematicerrors of on-board laser scanner and GPS/INS, inaccurate processing of the system calibration as well as boresightmisalignments. Such discrepancies deteriorate the overall geometric quality of the end products such asDEM (Digital Elevation Model), building models, and digital maps. Therefore, strip adjustment for minimizingdiscrepancies between overlapping strips is one of the most essential tasks to create seamless point clouddata. This study implemented area-based matching (ABM) to determine conjugate features for computing 3Dtransformation parameters. ABM is a well-known method and easily implemented for this purpose. It is obviousthat the exact same LiDAR points do not exist in the overlapping strips. Therefore, the term “conjugate point”means that the location of occurring maximum similarity within the overlapping strips. Coordinates of theconjugate locations were determined with sub-pixel accuracy. The major drawbacks of the ABM are sensitiveto scale change and rotation. However, there is almost no scale change and the rotation angles are quite smallbetween adjacent strips to apply AMB. Experimental results from this study using both simulated and realdatasets demonstrate validity of the proposed scheme.

BMS 에너지 효율 향상을 위한 리튬 배터리 SOC 계수 보정 알고리즘

이대건,정원재,임세미,채형일,박준석 한국정보통신설비학회 2016 한국정보통신설비학회 학술대회 Vol.2016 No.09

This paper describes a battery equivalent model’s SOC(state of charge) coefficient calibration method to improve the accuracy of the battery equivalent model. The battery SOC difference between each cells is a major factor of decreasing the battery life. BMS (battery management system) has been developed in order to reduce the inter-cell battery SOC deviation. however, since the general battery cell balancing techniques operate by measuring only the voltage of the battery cell, they can not keep up with the electrical characteristics due to variation of the internal resistance of the capacitor. In this paper, we propose the battery equivalent model’s SOC coefficient calibration algorithm : (1)choses the battery and battery model, (2)measure the battery charging and discharging characteristics, (3)extracting the resistance and the capacitor value on the simulation, following the actual battery charge and discharge characteristic curve. in this paper, we use a 3.7 V, 280 mAh and 650 mAh lithium battery. And a RC Tank based battery equivalent model is adopted. The maximum error of charging and discharging characteristic between measurement and modeling is approximately 2.13 %.

복합문화시설의 민간투자사업 사업기본계획에 관한 연구

이대건,박창배 대한건축학회지회연합회 2017 대한건축학회연합논문집 Vol.19 No.1

본 연구는 복합문화시설의 민간투자사업 계획과정을 분석하였다. 가동률이 낮게 나타나는 복합문화시설을 주요 사례로 각 단계의 문제점을 분석하였고, 각 단계별 대안 제시와 함께 전 단계를 포괄하는 문제 또한 도출하여 전체적인 개선 방향을 제시하였다. 논문에서 주요 분석자료로 삼은 것은 사업기본계획과 그 수립과정에 대한 것으로 앞 단계는 뒷 단계에 지속해서 영향을 주고 있었다. 각 사업단계를 살펴본 결과, 사용자에 대한 고려가 부족한 것과 발주처인 지방정부의 예산에 대한 권한이 적음, 시설수요추정의 어려움, 디자인 중심의 평가가 아님 등이 포괄적인 문제점으로 제시된다. 이에 대한 사업 전반에 대한 제안은 사업 전반에 걸친 주민의 참여가 가능하도록 사업추진 체계와 기간의 조정, 지방정부가 시설사업비 조정이 용이하도록 제도 개선, 다양한 사용자의 가치를 반영하고 통합적으로 계획의 품질을 평가할 수는 평가 방법의 도입 등이 요구된다. The objective of this study is to propose ways to improve the project planning process of public-private partnership(PPP) construction projects for cultural centers so that it is used to design public cultural centers which become a beloved and popular public space for cultural activities by residents of city. The study reviewed PPP project guidelines and previous projects in terms of structure of PPP project, procedure of establish a project plan, contents and meaning of preliminary feasibility study, and bid evaluation criteria. The study found following facts. The first, the preliminary feasibility study was too much focused on the political aspects of projects. The second, lack of flexibility in budget approved by national intensified difficulties to solve differences in interests of involved parties in a project and intensive studies and reviews on financial aspects of a project seemed to overlook characteristics of its regional environment and induce design proposals unrealistic or out of context. The third, evaluation criteria for the design and architectural design in detail criteria gained its relative importance every year. And each bid evaluation criteria gradually became similar to related field of specialties for juries and designer. However, it has a fundamental problem which makes the criteria hard to reflect user’s perspectives or symbolic value of the project. Therefore, this study proposes a few suggests. The first, citizen’s participation can be used from early stage of project to design a site-specific project. The second, the central government set up the more flexible plan and budget approval process for the projects. The third, types and scores of design evaluation criteria are need to be revised to accommodate user’s view on design. The hierarchy of the criteria also needs to be set by citizen’s participation using a systematic method such as the AHP.

대학병원 시설관리를 위한 BIM 도입 과정

이대건,박창배 대한건축학회지회연합회 2021 대한건축학회연합논문집 Vol.23 No.1

The successful cases of designing and constructing buildings with BIM (Building Information Modeling) are increasing, and institutions like a university hospital with many facilities are trying to gain the benefits of using BIM for facility planning and management. However, the complexity and the absence of BIM experience have made them to hesitant to adopt, so it is necessary to develop the method of BIM adoption suitable for large facility organizations. This study set up a BIM adoption process, as the hypothesis to be tested, for a university hospital for facility management and planning based on the previous case studies and interview with management personnel and examined qualitatively its sufficiency in detail based on observation on the process and response of management personnel. Its sufficiency was tested in three stages, such as adoption decision, implementation, and confirmation stage using 4 factors that influenced the decision-making process of BIM adoption. During the process, a few alternative methods such as 3D scanning of exiting a building and parametric algorithms were introduced and tested. This study confirms the initial courageous investment decision by the management group, personnel’s motivation to learn, and the visualization feature of BIM are important factors in BIM adoption when its previous cases for the reference are rare and not available. The visualization feature of BIM was not only played an important role in the adoption decision stage but also in the other stages. The lack of a unified data system between departments hindered the integration process of the equipment data and the space data and it was expected to be a serious challenge to utilize BIM for facility management even after the adoption.

합성곱 신경망 기반의 딥러닝에 의한 수치표면모델의 객체분류

이대건,이동천,조은지 한국측량학회 2019 한국측량학회지 Vol.37 No.6

Recently, DL (Deep Learning) has been rapidly applied in various fields. In particular, classification and object recognition from images are major tasks in computer vision. Most of the DL utilizing imagery is primarily based on the CNN (Convolutional Neural Network) and improving performance of the DL model is main issue. While most CNNs are involve with images for training data, this paper aims to classify and recognize objects using DSM (Digital Surface Model), and slope and aspect information derived from the DSM instead of images. The DSM data sets used in the experiment were established by DGPF (German Society for Photogrammetry, Remote Sensing and Geoinformatics) and provided by ISPRS (International Society for Photogrammetry and Remote Sensing). The CNN-based SegNet model, that is evaluated as having excellent efficiency and performance, was used to train the data sets. In addition, this paper proposed a scheme for training data generation efficiently from the limited number of data. The results demonstrated DSM and derived data could be feasible for semantic classification with desirable accuracy using DL. 최근 딥러닝(DL)은 여러 분야에서 급속도로 활용되고 있으며, 특히 영상으로부터 객체를 인식하여 분류하고 인식하기 위한 컴퓨터비전 분야에서 활발하게 연구가 진행되고 있다. 영상분야에서는 주로 합성곱 신경망(CNN)을 이용한 딥러닝 모델의 성능 향상에 주력하고 있다. 대부분의 합성곱 신경망은 영상을 학습시켜 영상분류 및 객체인식에 활용하고 있지만, 본 논문에서는 독일 사진측량, 원격탐사 및 공간정보학회(DGPF)가 구축하고 국제 사진측량 및 원격탐사학회(ISPRS)가 제공하는 데이터 셋 중에서 수치표면모델(DSM)과 이 데이터로부터 생성한 경사 및 주향 정보를 효율성과 성능이 우수하다고 평가받는 합성곱 신경망기반의 SegNet 모델에 적용하여 객체를 분류하고 분석하였다. 딥러닝은 고사양의 컴퓨터 시스템과 다량의 학습 데이터와 라벨 데이터가 필요하고, 다수의 시행착오에 의한 풍부한 경험이 요구된다. 또한 본 논문에서는 한정된 수량의 데이터로부터 효율적인 학습을 위한 데이터 생성 방법을 제시하고 수치표면모델을 분류하였다. 분석 결과 수치표면모델 데이터와 이로부터 도출한 부가적인 데이터를 딥러닝 모델에 적용해도 객체를 타당한 정확도로 분류할 수 있음을 확인하였다.

In vitro anti-skin-aging effects of dried pomegranate concentrated powder

이대건,최범락,구세광,강수진,박혜림,성미선,이영준,박기문 대한예방한의학회 2018 대한예방한의학회지 Vol.22 No.2

Purpose : In this study, we intended to observe the anti-wrinkle and moisturizing effects of dried pomegranate juice concentration powder (PCP) using in vitro test. Materials and methods : Antioxidant effects of PCP were determined by free radical scavenging capacity (DPPH assay) and the cytotoxicity of PCP was examined in human keratinocyte (HaCaT) and human primary dermal fibroblast-neonatal (HDF) cells. To investigate the moisturizing effect of PCP, hyaluronan synthesis was examined in HaCaT cells. Activity of procollagen production were assessed in HDF cells and elastase inhibition properties of PCP were evaluated in cell free condition, to determine their anti-wrinkle effects. Metalloproteinase 1 (MMP-1) activity was also assessed following UVB irradiation, in the current in vitro experiment. Results : No PCP treatment related significant cytotoxic effects were demonstrated against to the both HDF and HaCaT cells. PCP showed favorable free radical scavenging activities in dose-dependent manner. In PCP-treated HaCaT cells, hyaluronan synthesis was non-significantly but markedly increased, and pro-collagen productions were significantly increased in HDF cells, at all three different concentrations (0.25, 0.75 and 1 mg/ml), and elastase inhibitory activities were observed by PCP treatment. A significant decrease in UVB-induced MMP-1 activity was also observed in 1 mg/ml PCP-treated HDF cells as compared to those of UVB-exposed cells. Conclusions : Taken together, these results suggest that PCP has favorable antioxidant, anti-wrinkle and moisturizing effects without meaningful cytotoxicity on HDF and HaCaT cell lines.

딥러닝을 위한 영역기반 합성곱 신경망에 의한 항공영상에서 건물탐지 평가

이대건,조은지,이동천 한국측량학회 2018 한국측량학회지 Vol.36 No.6

딥러닝은 인간의 학습 및 인지능력을 닮은 인공지능을 실현하기 위해 여러 분야에서 활용하고 있으며, 높은 사양 의 컴퓨팅 파워가 요구되고 연산 시간이 많이 소요되는 복잡한 구조의 인공신경망에 의한 딥러닝은 컴퓨터 사양이 향상됨에 따라 성능이 개선된 다양한 딥러닝 모델이 개발되고 있다. 본 논문의 주요 목적은 상의 딥러닝을 위한 합성곱 신경망 중에서 최근에 FAIR (Facebook AI Research)에서 개발한 Mask R-CNN을 이용하여 항공상에서 건물을 탐지하고 성능을 평가하는 것이다. Mask R-CNN은 역기반의 합성곱 신경망으로서 픽셀 정확도까지 객 체를 의미적으로 분할하기 위한 딥러닝 모델로서 성능이 가장 우수한 것으로 평가받고 있다. 딥러닝 모델의 성능은 신경망 구조뿐 아니라 학습 능력에 의해 결정된다. 이를 위해 본 논문에서는 모델의 학습에 이용한 상에 다양한 변화를 주어 학습 능력을 분석하으며, 딥러닝의 궁극적 목표인 범용화의 가능성을 평가하다. 향후 연구방안으 로는 상에만 의존하지 않고 다양한 공간정보 데이터를 복합적으로 딥러닝 모델의 학습에 이용하여 딥러닝의 신 뢰성과 범용화가 향상될 것으로 판단된다. DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

다중 공간정보 데이터의 점진적 조합에 의한 의미적 분류 딥러닝 모델 학습 성능 분석

이대건,신영하,이동천 한국측량학회 2022 한국측량학회지 Vol.40 No.2

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach. 대부분의 경우 광학 RGB 영상을 딥러닝(DL: Deep learning)의 학습 데이터로 사용하여 객체탐지, 인식, 식별, 분류, 의미적 분할 및 객체 분할 등을 수행하지만, 실세계의 3차원 객체들을 2차원 영상으로 완전하게 파악하는 것 은 한계가 있다. 그러므로 대표적인 3차원 지형 공간정보인 수치표면모델(DSM: Digital Surface Model)과 더불어 DSM에 내재된 특성정보를 이용하여 3차원 지형지물을 분석하는 것이 효과적이다. 건물과 같이 기하학적으로 정 형화된 형태의 인공구조물은 3차원 공간데이터로부터 얻을 수 있는 기하학적 요소와 특성을 이용하여 객체의 분 류와 형상 묘사가 가능하다. 이 연구는 고차원 시각정보(high-level visual information) 시스템에서 중요한 역할을 하는 내재된 고유의 특성정보(intrinsic information)를 기반으로 하며, 이를 위하여 객체의 기하학적 요소인 경사 와 주향을 DSM으로부터 도출하고, 다방향에서 생성한 음영기복영상(SRI: Shaded Relief Image)과 함께 DL 모델 의 학습 수행에 사용하였다. 실험은 ISPRS (International Society for Photogrammetry and Remote Sensing)에서 제공하는 데이터 셋 중에서 DSM과 레이블 데이터를 객체의 의미적 분류를 위해 개발된 합성곱 기반의 SegNet 학 습에 사용하였다. 지형지물을 분류하고 분류 결과를 이용하여 건물을 추출하였다. 특히 DL 모델의 학습 성능 향상 을 위해 학습 데이터의 여러 조합에 따른 시너지 효과를 분석하는 것에 핵심이다. 제안한 방법은 건물 분류와 추출 에 효과적임을 보여주고 있다.

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이대건,최석원,이승호,박선경,주다영 한국감성과학회 2023 춘계학술대회 Vol.2023 No.-