• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.157-164
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• 2002

Based on the results of prototype air-shaft construction, penetration capacity of RBM(Raise Boring Machine) was analyzed and compared with TBM(Tunnel Boring Machine) performance in this study. Utilization, down time, net penetration rate and advance rate were evaluated and compared. By conducting the laboratory tests for rock properties with the analysis of penetration capacity, relation of penetration capacity and geotechnical parameters was studied. The results showed that much more higher value of utilization, however lower value of net penetration rate for RBM was obtained compared to those of TBM. In addition, as the strength of rock penetrated increased, higher value of net penetration rate was obtained contrarily to the results of TBM performance. Finally, new relationship between total hardness and net penetration rate for weak and weathered rock was derived from these results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.561-565
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• 2005

net-VE 환경에서 다중 사용자들이 정보를 공유하는 경우 교환되는 이벤트 트래픽을 줄이기 위하여 확장 칼만필터를 이용해 객체 이벤트의 가변적 경로예측을 한다. 다중 사용자를 지원하는 3차원 공간 정보공유는 가상환경에 대한 상태정보를 중앙 서버에서 관리하므로 일관성 유지가 용이하다는 장점이 있으나 네트워크에 과중한 부담을 주며, 메시지 병목현상, 확장성이 부족하다는 문제점이 있다. 본 논문에서는 이동되어져온 궤적의 유클리디 실즉치와 칼만 예측치와의 오차 정보인 이노베이션을 사용하여 가변적 경로예측을 하고, net-VE 공유 및 이벤트 필터링 과정을 제안한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1064-1074
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• 1997

In this paper we propose an every-other-row-connecting bilayered ShuffkeNet for optical WDM(wavelength division multiplexing) multihop networks. We calculate the diameter and the average number of hops of the proposed every-other-row-connecting bilayered ShuffleNet. Using the result, we also calcuate throughputs and delays of the proposed topology, which show higher efficiencies compared to the conventional ShuffleNet, the bilayered ShuffleNet and asymmetric bilayered ShuffleNet.

• The Journal of Engineering Research
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• v.5 no.1
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• pp.5-16
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• 2004

In the era which computer communication changes from auxiliary means of communication to main means of communication, the messenger system becomes the leader of communication. The multi messenger implemented in this paper used .NET and strong MFC as a language of next generation to put n higher competitiveness than current another messenger systems. In addition, it included face to face chatting function as well as messenger function.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.569-576
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• 2004

In this paper, to detect external sound frequencies on the latticed structure, fiber optic sensor net using Sagnac interferometer was fabricated and tested. The latticed structure was fabricated with a dimension of 50 cm in width and 50 cm in height. The optical fiber of 50m in length was distributed and fixed on the surface of the latticed structure. Single mode fiber, a laser with 1,550 nm in wavelength, 2 ${\times}$ 2 coupler were used. External sound signal, 240 Hz, 495 Hz, 1.445 kHz, 2k Hz, applied to the fiber optic sensor net and the detected optical signals were compared to the detected microphone signals against time and frequency domains. Based on the experimental results, fiber optic sensor net using Sagnac interferometer detected external sound frequency, effectively. This system can be expanded to the structural health monitoring system.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.221-236
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• 2015

Application software is delivered to customers as a form of service at cloud environment. A cloud service provider is a marketplace between supply side (application providers) and demand side (customers). Cloud service providers have to validate applications to be included in their service portfolio. Not only performance, security, networking, compliances should be checked but also business contract, authentication should be provided. Organization customers are more sensitive to these validation criteria and process. We study the Internet2 NET+, which is a successful cloud marketplace of applications for research and education organizations. This case study shows us three things : (i) a cloud marketplace's application management process : selection, validation, transition to service, customization of applications (ii) what a cloud marketplace has for its infrastructure like authentication, security, access control etc. (iii) what a cloud marketplace has as its governance structure. This case study will provide informative analysis of Internet2 NET, a profit-making vertical and buyer's marketplace (education industry). And we will get some strategic implications for planning and implementing cloud marketplaces.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1133-1141
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• 2019

Most of the previous studies for bark recognition have focused on the extraction of LBP-like statistical features. Deep learning approach was not well studied because of the difficulty of acquiring large volume of bark image dataset. To overcome the bark dataset problem, this study utilizes the MobileNet which was trained with the ImageNet dataset. This study proposes two approaches. One is to extract features by the pixel-wise convolution and classify the features with SVM. The other is to tune the weights of the MobileNet by flexibly freezing layers. The experimental results with two public bark datasets, BarkTex and Trunk12, show that the proposed methods are effective in bark recognition. Especially the results of the flexible tunning method outperform state-of-the-art methods. In addition, it can be applied to mobile devices because the MobileNet is compact compared to other deep learning models.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.187-193
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• 2021

Our final goal is to implement nursing robots that can recognize patient's faces and their medicine on prescription. They can help patients to take medicine on time and prevent its abuse for recovering their health soon. As the first step, we proposed a medicine classifier with a low computational network that is able to run on embedded PCs without GPU in order to be applied to universal nursing robots. We confirm that our proposed model called MedicineNet achieves an 99.99% accuracy performance for classifying 15 kinds of medicines and background images. Moreover, we realize that the calculation time of our MedicineNet is about 8 times faster than EfficientNet-B0 which is well known as ImageNet classification with the high performance and the best computational efficiency.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.392-400
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• 2021

Unlike optical equipment, SAR(Synthetic Aperture Radar) has the advantage of obtaining images in all weather, and object detection in SAR images is an important issue. Generally, deep learning-based object detection was mainly performed in real-valued network using only amplitude of SAR image. Since the SAR image is complex data consist of amplitude and phase data, a complex-valued network is required. In this paper, a complex-valued ResNet network is proposed. SAR image object detection was performed by combining the ROI transformer detector specialized for aerial image detection and the proposed complex-valued ResNet. It was confirmed that higher accuracy was obtained in complex-valued network than in existing real-valued network.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.136-146
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• 2022

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.