• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2001.04b
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• pp.75-76
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• 2001

순환식 펄라이트재배에서 배액 재사용을 위한 양분흡수 모델링을 작성하고자 EC 처리(1.5, 1.8, 2.1, 2.4, 2.7 dSㆍm-1)를 수행하였다. 생육 중기까지 EC 수준에 따른 양액흡수량은 차이가 없었지만 중기 이후 EC가 높을수록 흡수량이 감소되는 경항을 보였다(Fig. 1). NO$_3$-N, P 및 K의 흡수량은 생육기간 동안 처리간 차이를 유지하였는데 N과 K는 생육 중기 이후 일정 수준을 유지하였으나 P는 생육기간 동안 다소 증가되는 경향을 보였다. S의 흡수량은 생육 중기 이후 모든 처리에서 급격한 감소를 보였으며 생육 후기에는 처리간에 차이가 없었다(Fig. 2). 오이의 무기이온 흡수율에서와 같이 흡수량에서도 EC간 차이를 보여 EC를 무기이온 흡수량을 추정하는 요소로 이용할 수 있을 것으로 생각되었다. 무기이온 흡수량은 모든 EC 처리간에 생육 초기에는 차이를 보이지 않았으나 생육중기 이후에는 뚜렷한 차이를 보인 후 생육 후기의 높은 농도에서 그 차이가 다소 감소되는 경향을 보였다. 단위일사량에 따른 양액흡수량과 EC를 주된 변수로 한 오이의 이온 흡수량 예측 회귀식을 작성하였는데 모든 무기이온 흡수량 추정식의 상관계수는 S를 제외한 모든 이온에서 높게 나타났는데 특히 N, P, K 및 Ca에서 높았다. S이온에서의 상관계수는 0.47로 낮게 나타났으나 각 이온들의 회귀식에 대한 상관계수는 모두 1% 수준에서 유의성을 보여 위의 모델식을 순환식 양액재배에서 무기이온 추정식으로 사용이 가능할 것으로 생각되었다(Table 1). 이를 이용한 실측치와의 비교는 신뢰구간 1%내에서 높은 정의상관을 보여 실제적인 적용이 가능할 것으로 생각되었다(Fig 3)..ble 3D)를 바탕으로 MPEG-4 시스템의 특징들을 수용하여 구성되고 BIFS와 일대일로 대응된다. 반면에 XMT-0는 멀티미디어 문서를 웹문서로 표현하는 SMIL 2.0 을 그 기반으로 하였기에 MPEG-4 시스템의 특징보다는 컨텐츠를 저작하는 제작자의 초점에 맞추어 개발된 형태이다. XMT를 이용하여 컨텐츠를 저작하기 위해서는 사용자 인터페이스를 통해 입력되는 저작 정보들을 손쉽게 저장하고 조작할 수 있으며, 또한 XMT 파일 형태로 출력하기 위한 API 가 필요하다. 이에, 본 논문에서는 XMT 형태의 중간 자료형으로의 저장 및 조작을 위하여 XML 에서 표준 인터페이스로 사용하고 있는 DOM(Document Object Model)을 기반으로 하여 XMT 문법에 적합하게 API를 정의하였으며, 또한, XMT 파일을 생성하기 위한 API를 구현하였다. 본 논문에서 제공된 API는 객체기반 제작/편집 도구에 응용되어 다양한 멀티미디어 컨텐츠 제작에 사용되었다.x factorization (NMF), generative topographic mapping (GTM)의 구조와 학습 및 추론알고리즘을소개하고 이를 DNA칩 데이터 분석 평가 대회인 CAMDA-2000과 CAMDA-2001에서 사용된cancer diagnosis 문제와 gene-drug dependency analysis 문제에 적용한 결과를 살펴본다.0$\mu$M이 적당하며, 초기배발달을 유기할 때의 효과적인 cysteamine의 농도는 25~50$\mu$M인 것으로 판단된다.N)A(N)/N을 제시하였다(A(N)=N에 대한 A값). 위의 실험식을 사용하여 헝가리산 Zempleni 시료(15%

• Journal of KIISE:Software and Applications
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• v.28 no.3
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• pp.211-223
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• 2001

본 논문에서 제안하는 시나리오기반 검증기법의 목적은 UML로 작성된 객체지향 분석모델의 완전성 및 일관성을 진단하는 것이다. 검증기법의 전체 절차는 요구분석을 위한 Use Case 모델링 과정에서 생성되는 Use Case 시나리오와 UML 분석모델로부터 역공학적 방법으로 도출된 객체행위 시나리오와의 상호참조과정 및 시나리오 정보트리 추적과정을 이용하여 단계적으로 수행된다. 본 검증절차를 위하여 우선, UML로 작성된 객체지향 분석모델들은 우선 정형명세언어를 사용하여 Use Case 정형명세로 변환하다. 그 다음에, Use Case 정형명세로부터 해당 Use Case 내의 객체의 정적구조를 표현하는 시나리오 정보트리를 구축하고, Use Case 정형명세 내에 포함되어 있는 객체 동적행위 정보인 메시지 순차에 따라 개별 시나리오흐름을 시나리오 정보트리에 표현한다. 마지막으로 시나리오 정보트리 추적과 시나리오 정보 테이블 참조과정을 중심으로 완전성 및 일관성 검증작업을 수행한다. 즉, 검증하고자 하는 해당 Use Case의 시나리오 정보트리를 이용한 시나리오 추적과정을 통해 생성되는 객체행위 시나리오와 요구분석 과정에서 도출되는 Use Case 시나리오와의 일치여부를 조사하여 분석모델과 사용자 요구사양과의 완전성을 검사한다. 그리고, 시나리오 추적과정을 통해 수집되는 시나리오 관련종보들을 가지고 시나리오 정보 테이블을 작성한 후, 분석과정에서 작성된 클래스 관련정보들의 시나리오 포함 여부를 확인하여 분석모델의 일관성을 검사한다. 한편, 본 논문에서 제안하는 검증기법의 효용성을 증명하기 위해 대학의 수강등록시스템 개발을 위해 UML을 이용해 작성된 분석모델을 특정한 사례로써 적용하여 보았다. 프로세싱 오버헤드 및 메모리와 대역폭 요구량 측면에서 MARS 모델보다 유리함을 알 수 있었다.과는 본 논문에서 제안된 프리페칭 기법이 효율적으로 peak bandwidth를 줄일 수 있다는 것을 나타낸다.ore complicate such a prediction. Although these overestimation sources have been attacked in many existing analysis techniques, we cannot find in the literature any description about questions like which one is most important. Thus, in this paper, we quantitatively analyze the impacts of overestimation sources on the accuracy of the worst case timing analysis. Using the results, we can identify dominant overestimation sources that should be analyzed more accurately to get tighter WCET estimations. To make our method independent of any existing analysis techniques, we use simulation based methodology. We have implemented a MIPS R3000 simulator equipped with several switches, each of which determines the accuracy level of the

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.363-373
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• 2021

The purpose of this research is to analyze the Spill-over economic effect of the cultural and creative industries(CCI) in Henan Province, China. The research object is the CCI of Henan Province, which is mainly based on five sectors out of 42 industries in the industrial association table of the Statistical Bureau of Henan Province, China in 2017 (culture, sports; recreation and research sector; experimental development and integrated technical services sector; information transmission, computer services and software sector; education sector, etc), and is analyzed through secondary integration and redefinition of the CCI of Henan Province. Through the analysis of Henan Province Industry Association Table, this paper provides some enlightenment to the future direction of the cultural and creative industries. The main analysis results are as follows. The total production inducement of the CCI in Henan province is 48,848 billion yuan, and in particular, the production inducement coefficient of the industry in Henan province is 2.72809, 2.23909 (total of columns and rows), Index of the power of dispersion is 0.26325, and the index of the sensitivity of dispersion is 0.87535. Income induction coefficient is 0.55211, production tax induction coefficient is 0.09291. Because CCI of Henan Province has full development potential, the government needs to provide active support and policy support, in addition to the need for legal provisions and supervision of market management. In order to improve the innovative development of the CCI, it is necessary to develop a new model of "CCI+X".

• Wind and Structures
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• v.28 no.2
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• pp.71-87
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• 2019

The yaw and interference effects of blades affect aerodynamic performance of large wind turbine system significantly, thus influencing wind-induced response and stability performance of the tower-blade system. In this study, the 5MW wind turbine which was developed by Nanjing University of Aeronautics and Astronautics (NUAA) was chosen as the research object. Large eddy simulation on flow field and aerodynamics of its wind turbine system with different yaw angles($0^{\circ}$, $5^{\circ}$, $10^{\circ}$, $20^{\circ}$, $30^{\circ}$ and $45^{\circ}$) under the most unfavorable blade position was carried out. Results were compared with codes and measurement results at home and abroad, which verified validity of large eddy simulation. On this basis, effects of yaw angle on average wind pressure, fluctuating wind pressure, lift coefficient, resistance coefficient,streaming and wake characteristics on different interference zone of tower of wind turbine were analyzed. Next, the blade-cabin-tower-foundation integrated coupling model of the large wind turbine was constructed based on finite element method. Dynamic characteristics, wind-induced response and stability performance of the wind turbine structural system under different yaw angle were analyzed systematically. Research results demonstrate that with the increase of yaw angle, the maximum negative pressure and extreme negative pressure of the significant interference zone of the tower present a V-shaped variation trend, whereas the layer resistance coefficient increases gradually. By contrast, the maximum negative pressure, extreme negative pressure and layer resistance coefficient of the non-interference zone remain basically same. Effects of streaming and wake weaken gradually. When the yaw angle increases to $45^{\circ}$, aerodynamic force of the tower is close with that when there's no blade yaw and interference. As the height of significant interference zone increases, layer resistance coefficient decreases firstly and then increases under different yaw angles. Maximum means and mean square error (MSE) of radial displacement under different yaw angles all occur at circumferential $0^{\circ}$ and $180^{\circ}$ of the tower. The maximum bending moment at tower bottom is at circumferential $20^{\circ}$. When the yaw angle is $0^{\circ}$, the maximum downwind displacement responses of different blades are higher than 2.7 m. With the increase of yaw angle, MSEs of radial displacement at tower top, downwind displacement of blades, internal force at blade roots all decrease gradually, while the critical wind speed decreases firstly and then increases and finally decreases. The comprehensive analysis shows that the worst aerodynamic performance and wind-induced response of the wind turbine system are achieved when the yaw angle is $0^{\circ}$, whereas the worst stability performance and ultimate bearing capacity are achieved when the yaw angle is $45^{\circ}$.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.855-865
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• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.794-807
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• 2022

Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.