• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.634-636
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• 2021

In this study, we studied the macro-modeling of an feedback field-effect transistor (FBFET) using SPICE simulation. The previously presented macro-model of the FBFET is consisting of two circuits. one is charge integration circuit, and the other is current generation circuit. The previous current generation circuit has problem that can't predict performance accurately of the circuits, due to implementing only I_DS-V_GS characteristics. To solve this problem, we presents a model that can implement not only I_DS-V_GS characteristics but alos I_DS-V_DS characteristics by adding the diode in the current generation circuit.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.55-61
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• 2017

The V-world, korean type spatial information open platform, provides various services to easily utilize 2D, 3D map and administrative information of the country. Among them, V-world 3D map service, modeled in individual building unit, require requests for each building model file and the draw calls for drawing models on the screen by the request. This causes a large number of model requests and draw calls to occur that increase the latency occurring during the transmission and conversion process between the central processing unit(CPU) and the graphic processing unit(GPU), which lead to the performance degradation of the 3D map service. In this paper, we propose a performance improvement plan to reduce the performance degradation of 3D map service caused by multiple model requests and draw calls. Therefore, we tried to reduce the number of requests and draw calls for the model file by applying a 3D tile model that combined multiple building models to single tile. In addition, we applied the quadtree algorithm to reduce the time required to load the model file by shortening the retrieval time of the model. This is expected to contribute to improving the performance of 3D map service of V-world.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.61-67
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• 2017

In this paper we introduce two experimental results from classification of Instagram images and some valuable lessons from them. We have tried some experiments for evaluating the competitive power of Convolutional Neural Network(CNN) in classification of real social network images such as Instagram images. We used AlexNet and ResNet, which showed the most outstanding capabilities in ImageNet Large Scale Visual Recognition Challenge(ILSVRC) 2012 and 2015, respectively. And we used 240 Instagram images and 12 pre-defined categories for classifying social network images. Also, we performed fine-tuning using Inception V3 model, and compared those results. In the results of four cases of AlexNet, ResNet, Inception V3 and fine-tuned Inception V3, the Top-1 error rates were 49.58%, 40.42%, 30.42%, and 5.00%. And the Top-5 error rates were 35.42%, 25.00%, 20.83%, and 0.00% respectively.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.105-111
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• 2019

Core technologies that lead the Fourth Industrial Revolution era, such as artificial intelligence, big data, and autonomous driving, are implemented and serviced through the rapid development of computing power and hyper-connected networks based on the Internet of Things. In this paper, we implement two different models for drivable area segmentation in various environment, and propose a better model by comparing the results. The models for drivable area segmentation are using DeepLab V3+ and Mask R-CNN, which have great performances in the field of image segmentation and are used in many studies in autonomous driving technology. For driving information in various environment, we use BDD dataset which provides driving videos and images in various weather conditions and day&night time. The result of two different models shows that Mask R-CNN has higher performance with 68.33% IoU than DeepLab V3+ with 48.97% IoU. In addition, the result of visual inspection of drivable area segmentation on driving image, the accuracy of Mask R-CNN is 83% and DeepLab V3+ is 69%. It indicates Mask R-CNN is more efficient than DeepLab V3+ in drivable area segmentation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.251-254
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• 2006

2000 년대 초반부터 글로벌화에 따라 화상회의 시스템이 정부와 기업에 중요한 업무지원도구로 확산이 되고 있다. 그 동안은 화상회의는 기업이라는 제한된 울타리에서 1:1 또는 다자간의 회의를 위한 도구로 만족을 하였지만 점차 화상회의를 상업용, 가정용으로 적극적인 활용을 모색하는 추세이다. 또한 기존의 인터넷, 모바일 등을 이용한 비즈니스 모델은 발전의 한계점에 도달하였으며, 고객들의 새로운 요구에 부응하기 위하여는 화상회의를 이용한 비즈니스 모델의 출현이 요구되는 시점이다. 본 연구에서는 화상회의를 이용한 비즈니스의 확산을 위하여, 화상회의 비즈니스 모델의 구조를 설계하였다. 또한 설계를 적용할 수 있는 환경연구로서, e-커머스에 대응한 화상회의 상거래, 즉 v-커머스를 지향한 기술 및 서비스 구성, 비즈니스 모델의 유형과 고객가치속성, 모델의 구성요소, 적용사례 등을 제시하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.5B
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• pp.677-684
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• 2001

본 논문에서는 계단근사법이 아닌 다중 box분할기법을 이용하여 증가형 MOSFET의 강반전조건하에서의 I-V 모델링을 제안한다. 즉, 이온주입된 MOSFET의 강반전층의 깊이를 다중box분할기법에 의하여 구하고, 이 깊이에서의 이동전하농도 및 수직전계의존 LMS이동도 모델에 의한 이동도를 구하였다. 그리도 이들 파라메터들을 바탕으로 드레인전압에 대한 드레인 전류식을 유도하였다. 제안 드레인전류식의 타당성을 검증하기 위하여 게이트 전압을 변화시켜 가면서, 제안된 I-V 모델링에 대해 모의 실험을 수행하고 Charge-sheet 모델에 의해서 구한 드레인 전류치와 비교하였다. 모의실험수행결과 유사한 I-V 특성을 나타냄을 확인하였다.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.57-66
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• 2007

Organizations of Software development are very important issue on enhancement of a software quality as rapid progress of software industry. Especially there are diverse attempts for enhancement of test maturity of the software organization through some kinds of the test maturity model. But the current test maturity models based on CMM(Capability Maturity Model) lack part of actual testing measurement and only measure level of test maturity. To solve these problems, we suggest 'double V-model' to execute both software development process and test process simultaneously, and also 'test attributes to Maturity Levels Correlation Matrix' for evaluating level of test maturity included with definitions of test attribute and level. That is, we enhance TMM(Test Maturity Model) adopted with 'Improvement Suggestion' of TPI(Test Process Improvement) which is easy the evaluation of test maturity of organization and gives the direction of improvement to level up the test maturity for the measured organization. As a result, we will contribute to level up the test maturity of the organization.

• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.169-177
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• 2019

LVC(Live-Virtual-Constructive) training system is drawing attention due to changes in battlefield situation and the development of advanced information and communication technologies. The ROKAF(Republic of Korea Air Force) plans to construct LVC training system capable of scientific training. This paper analyzes the results of V-C interoperability test with three fighter simulators as virtual systems and a theater-level wargame model as a constructive system. The F-15K, KF-16, and FA-50 fighter simulators, which have different interoperable methods, were converted into a standard for simulation interoperability. Using the integrated field environment simulator, the fighter simulators established a mutually interoperable environment. In addition, the Changgong model, which is the representative training model of the Air Force, was converted to the standard for simulation interoperability, and the integrated model was implemented with optimized interoperability performance. Throughput experiments, It was confirmed that the fighter simulators and the war game model of the ROKAF could be interoperable with each other. The results of this study are expected to be a good reference for the future study of the ROKAF LVC training system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.157-165
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• 2021

Recently, as the spatial resolution of satellite and aerial images has improved, various studies using remotely sensed data with high spatial resolution have been conducted. In particular, since the building extraction is essential for creating digital thematic maps, high accuracy of building extraction result is required. In this manuscript, building extraction models were generated using SegNet, U-Net, FC-DenseNet, and HRNetV2, which are representative semantic segmentation models in deep learning techniques, and then the evaluation of building extraction results was performed. Training dataset for building extraction were generated by using aerial orthophotos including various buildings, and evaluation was conducted in three areas. First, the model performance was evaluated through the region adjacent to the training dataset. In addition, the applicability of the model was evaluated through the region different from the training dataset. As a result, the f1-score of HRNetV2 represented the best values in terms of model performance and applicability. Through this study, the possibility of creating and modifying the building layer in the digital map was confirmed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.890-895
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• 2021

Recently, the "Act of Serious Disasters, etc" was enacted and institutional and social interest in safety accidents is increasing. In this paper, we analyze statistical data published by government agency on safety accidents that occur in manufacturing sites, and compare various object detection models based on deep learning to build a model to determine dangerous situations to reduce the occurrence of safety accidents. The data-set was directly constructed by collecting images from CCTVs at the manufacturing site, and the YOLO-v4, SSD, CenterNet models were used as training data and evaluation data for learning. As a result, the YOLO-v4 model obtained a value of 81% of mAP. It is meaningful to select a class in an industrial field and directly build a dataset to learn a model, and it is thought that it can be used as an initial research data for a system that determines a risk situation and infers it.