• 한국공간정보시스템학회 논문지
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• 제12권1호
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• pp.28-36
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• 2010

컴퓨터 환경에서 한 가지 사건으로 상황이 특징 지워지기도 하지만, 일반적 상황 인식은 공간과 시간을 포함하는 다양한 사건들에 의해서 결정되어 진다. 물리적 공간과 상호 작용하는 실시간 센서 네트워크 상황 인식 서비스는 시간적 특성을 포함한다. 상황 인식 서비스 관계 설정 방법으로 실시간 상황 인식을 시공간적으로 취급하는 것이 요구된다. 본 논문은 상황 인식 모델에 시공간적 특성이 포함 되도록 하는 방법을 제안하고, 이를 개선된 Petri-Net을 이용하여 효과를 확인한다. 실시간 센서 네트워크 상황 인식을 위해 기본 Petri-Net, 패턴화된 Petri-Net 및 시공간 Petri-Net 모델 특성을 연구한다. 이 방법을 이용하여 새만금 온도 변화 탐지에 적용예를 보였다. 본 연구를 통해 시공간 Petri-Net을 사용한 응용 개발 뿐 만 아니라 시공간 상황 인식 모델링에 기여 할 것이다.

• 천문학회보
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• 제46권1호
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• pp.49.5-50
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• 2021

We report the optical follow-up observations of three long γ-ray burst events, GRB 201020A, GRB 201103B and GRB 210104A by the network of telescopes in the SomangNet project. We show light curves, color evolution and SED evolution, and fit them to a single power law function to derive decay index and compare their properties with other long GRBs samples. Also, we show a good observational example that 0.4-1m class telescopes in SomangNet have potential to catch dim light from high red shift object (R>22 mag) by deep imaging. In conclusion, we found that three GRBs have optical afterglow properties of long GRB and our results are consistent with the reports of high energy analysis.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1447-1455
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• 2017

In a trawl-net simulation, it is very important to process the physical phenomenons resulting from real collisions between a net and fishes. However, because it is very difficult to reconstruct the surface with mass points, many researchers have generally detect the collision using an approximation model employing a sphere, a cube or a cylinder. These approaches occur often result in inaccurate movements of a fish due to the difference between a real-net and a designed-net. So, many systems have manually adjusted a net surface based on actual measurements of mass points. These methods are very inefficient because it needs much times in an adjustment and also causes more incorrect inputs according to a rapid increment in the number of points. Therefore, in this paper, we propose a reconstruction method that it semi-automatically reconstructed trawl-net surfaces using the equation of motion at each mass point in a mass-spring model. To get an easy start in a beginning step of the spread, it enables users to get interactive adjustment on each mass point. We had designed a trawl-net model using geometrical structures of trawl-net and then automatically reconstructed the trawl-net surface using scale-space meshing techniques. Last, we improve the accuracy of reconstructed result by correction user interaction.

• 호남수학학술지
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• 제37권4호
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• pp.549-557
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• 2015

We introduce strongly ${\kappa}$-$Fr{\acute{e}}chet$ and strongly ${\kappa}$-sequential spaces which are stronger than ${\kappa}$-$Fr{\acute{e}}chet$ and ${\kappa}$-net spaces respectively. For convenience, we use the terminology "${\kappa}$-sequential" instead of "${\kappa}$-net space", introduced by R.E. Hodel in [5]. And we study some properties and topological operations on such spaces. We also define strictly ${\kappa}$-$Fr{\acute{e}}chet$ and strictly ${\kappa}$-sequential spaces which are more stronger than strongly ${\kappa}$-$Fr{\acute{e}}chet$ and strongly ${\kappa}$-sequential spaces respectively.

• 센서학회지
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• 제30권5호
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• pp.290-294
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• 2021

Experts have designed popular and successful model architectures, which, however, were not the optimal option for different scenarios. Despite the remarkable performances achieved by deep neural networks, manually designed networks for classification tasks are the backbone of object detection. One major challenge is the ImageNet pre-training of the search space representation; moreover, the searched network incurs huge computational cost. Therefore, to overcome the obstacle of the pre-training process, we introduce a network adaptation technique using a pre-trained backbone model tested on ImageNet. The adaptation method can efficiently adapt the manually designed network on ImageNet to the new object-detection task. Neural architecture search (NAS) is adopted to adapt the architecture of the network. The adaptation is conducted on the MobileNetV2 network. The proposed NAS is tested using SSDLite detector. The results demonstrate increased performance compared to existing network architecture in terms of search cost, total number of adder arithmetics (Madds), and mean Average Precision(mAP). The total computational cost of the proposed NAS is much less than that of the State Of The Art (SOTA) NAS method.

• 호남수학학술지
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• 제36권3호
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• pp.669-678
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• 2014

We investigate various properties of ${\kappa}$-net convergence structures and define a ${\kappa}$-net-based continuous function on ${\kappa}$-net $\mathcal{L}^+$-convergence structures, and study relationships between continuity and ${\kappa}$-net-based continuity on ${\kappa}$-net $\mathcal{L}^+$-convergence structures. We also provide some characterizations of ${\kappa}$-net-based continuity.

• 천문학회보
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• 제42권2호
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• pp.60.2-61
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• 2017

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.77 for flare classification and 0.83 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.971-974
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• 2002

Petri net is an effective modeling tool for concurrent systems. Liveness problem is one of analysis problems in Petri net theory verifying whether the system is free from any local deadlocks. It is well known that computational complexity of liveness problem of general Petri net is deterministic exponential space. Some subclasses, such as marked graph and free choice net, are suggested where liveness problem is verified in less complexity. This paper studies liveness of siphon containing circuit (SCC) net. Liveness condition based on algebraic inequalities is shown. Then polynomial time decidability of liveness of SCC net is derived, if the given net is known to be an SCC net a priori.

• International Journal of Aeronautical and Space Sciences
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• 제8권2호
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• pp.126-131
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• 2007

In this paper, Monte Carlo Simulation (MCS) method was used as an uncertainty assessment tool for air flow, net thrust measurement. Uuncertainty sources of the net thrust measurement were analyzed, and the probability distribution characteristics of each source were discussed. Detailed MCS methodology was described including the effect of the number of simulation. Compared to the conventional sensitivity coefficient method, the MCS method has advantage in the uncertainty assessment. The MCS is comparatively simple, convenient and accurate, especially for complex or nonlinear measurement modeling equations. The uncertainty assessment result by MCS was compared with that of the conventional sensitivity coefficient method, and each method gave different result. The uncertainties in the net thrust measurement by the MCS and the conventional sensitivity coefficient method were 0.906% and 1.209%, respectively. It was concluded that the first order Taylor expansion in the conventional sensitivity coefficient method and the nonlinearity of model equation caused the difference. It was noted that the uncertainty assessment method should be selected carefully according to the mathematical characteristics of the model equation of the measurement.

• 한국항공우주학회지
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• 제38권3호
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• pp.207-212
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• 2010

본 논문에서는 고정익 무인항공기의 그물망 자동착륙을 위한 나선형 강하궤적과 종말 유도기법을 설계하였다. 시야가 확보된 좁은 지역에서 착륙하도록 고정익 항공기의 나선형 강하궤적을 설계하였고, 나선형 강하의 끝점에서 그물망 입사방향을 향하도록 비행경로각 명령을 생성하였다. 종말유도 단계에서는 그물망 중심으로의 정밀한 유도를 위하여 시선각 정보 기반 의사추적 유도기법을 설계하였다. 고정익 무인항공기의 시스템 인식 모델을 이용한 6자유도 수치 시뮬레이션을 수행하여 제안한 나선형 착륙비행의 성능을 검증하였다.