• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.385-396
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• 2024

To enhance the accuracy of sinkhole detection using GPR, this study derived a convolutional neural network that can optimally extract sinkhole characteristics from GPR B-scan grayscale images. The pre-trained convolutional neural network is evaluated to be more than twice as effective as the vanilla convolutional neural network. In pre-trained convolutional neural networks, fast feature extraction is found to cause less overfitting than feature extraction. It is analyzed that the top-1 verification accuracy and computation time are different depending on the type of architecture and simulation conditions. Among the pre-trained convolutional neural networks, InceptionV3 are evaluated as most robust for sinkhole detection in GPR B-scan grayscale images. When considering both top-1 verification accuracy and architecture efficiency index, VGG19 and VGG16 are analyzed to have high efficiency as the backbone for extracting sinkhole feature from GPR B-scan grayscale images. MobileNetV3-Large backbone is found to be suitable when mounted on GPR equipment to extract sinkhole feature in real time.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.1
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• pp.58-68
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• 2010

In this paper, we propose an efficient link protection switching scheme for provider backbone bridge systems with a spanning tree for backup links exclusively, and evaluate its performance. The proposed scheme offers guaranteed QoS flows even when a link fault occurrs in the primary link by flooding the flows over the profiled spanning tree. The flooding mechanism over the spanning tree can also provide low latency and remove the loopback flows. We also derive the efficiency of bandwidth usage for the normal flows and the number of lost frames during the link restoration. For evaluating its feasibility, we implement a prototype of PBB-TE systems based on the Linux bridge codes, which can support both link protection switching capability with CCM and MAC-in-MAC encapsulation. A related protocol analyzer is also developed. One can see that the proposed scheme and the prototype can be useful for developing carrier class Ethernet systems based on PBB-TE.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.32-39
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• 2015

The voice communications with wireless backbone based networks are evolving into a packet switching VoIP systems. In those networks, a call processing scheme is required for management of subscribers and connection between them. A VoIP service scheme for those systems requires reliable subscriber management and connection establishment schemes, but the conventional call processing schemes based on the centralized server has lack of reliability. Thus, the mutual-backup architecture of SIP-servers is required to ensure efficient subscriber management and reliable VoIP call processing capability, and the synchronization and call processing schemes should be changed as the architecture is changed. In this paper, a mutual-backup architecture of SIP-servers is proposed for wireless backbone based networks. A message format for synchronization and information exchange between SIP servers is also proposed in the paper. This paper also proposes a FSM scheme for the fast call processing in unreliable networks to detect multiple servers at a time. The performance analysis results show that the mutual backup server architecture increases the call processing success rates than conventional centralized server architecture. Also, the FSM scheme provides the smaller call processing times than conventional SIP, and the time is not increased although the number of SIP servers in the networks is increased.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.119-128
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• 2010

In the shipbuilding industry, due to the global trends where the number of IT (Information Technology) devices of a smart ship have been increased rapidly, the need to develop a new shipboard backbone network has recently emerged for integrating and managing the IT devices of a smart ship efficiently. A shipboard backbone network requires high survivability because it is constructed in automatic and unmanned smart ships where a failure of the backbone network can cause critical problems. The purpose of this paper thus is to study SAN (Ship Area Network) as a efficient shipboard backbone network, considering particularity of shipboard environment and requirement of high survivability. In order to do so, we designed a dual network topology that all network nodes, including the IT devices installed in a smart ship, are connected each other through dual paths, and reuding tht IT devices pnstalles supporices network survivability as well as t Iffic efficiency for the dual network topology. And then, we verified the performance of the suggested SAN by theoretical and practical analysis including the graph theory, the probability theory, implemental specifications, and computer simulations.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.362-362
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• 2010

Today, vast attention has been paid to periodic arrays of nanostructures due to their potential for applications such as memory with huge storage density. Such application requires large-scale fabrication of well ordered nano-sized structures. One of the most widely used methods for the ordered nanostructures is lithography. This top-down process, however, has the limit to reduce size. Here the promising alternative is the self-organization of ordered nano-sized structures such as large scale 2d carbon-induced reconstructions on W(110). In the present study, we report on the first well-resolved atomic resolution STM studies of the well-known R($15{\times}3$) and R($15{\times}12$) carbon induced reconstruction of the W(110). From the atomic image of R($15{\times}3$) for different values of tunneling gap resistance, we can tell there are no missing atoms in unit cells of R($15{\times}3$) and some atomic displacements are substantial from the clean W(110), even though not all the imaged position of atoms correspond to tungsten, but may include those of carbon. We are considering two cases; First case is related to lattice deformation, or top layer of W(110) is deformed in the process of relief of strain caused by random inserting of carbon atoms possibly in the interstitial position. In the second case, R($15{\times}3$) unit cell results from a coincidence lattice between clean W(110) substrate and tungsten carbide overlayer which has rectangular atomic arrangement and giving R($15{\times}3$) coincidence lattice. beta-W2C showing rectangular unit cell should be a candidate. Further, we report on new reconstructions. Unlike the well-known R($15{\times}12$) consisting of two parts, two inner structures between two "Backbone" structures. The new reconstruction, which we found for the first time, contains more parts between the "Backbone"s. Sometimes we can observe the reconstruction consists of only inner parts without "Backbone" parts. Thus, the observed reconstruction can be built by constructing of two types of "Lego"-like block. Moreover, the rectangle shape of "Backbone" transform to parallelogram-like shape over time, the so-called wavy-R($15{\times}12$). Adsorption of hydrogen can be the reason for this transformation.

• Journal of KIISE
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• v.42 no.8
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• pp.1057-1064
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• 2015

The tactical network is system based on wireless networking technologies that ties together surveillance reconnaissance systems, precision strike systems and command and control systems. Several alternative paths exist in the network because it is connected as a grid to improve its survivability. In addition, the network topology changes frequently as forces and combatants change their network access points while conducting operations. However, most Internet routing standards have been designed for use in stable backbone networks. Therefore, tactical networks may exhibit a deterioration in performance when these standards are implemented. In this paper, we propose Priority based Multi-Path routing with Local Optimization(PMPLO) for a tactical backbone network. The PMPLO separately manages the global and local metrics. The global metric propagates to other routers through the use of a routing protocol, and it is used for a multi-path configuration that is guaranteed to be loop free. The local metric reflects the link utilization that is used to find an alternate path when congestion occurs, and it is managed internally only within each router. It also produces traffic that has a high priority privilege when choosing the optimal path. Finally, we conducted a simulation to verify that the PMPLO can effectively distribute the user traffic among available routers.

• Journal of the Korean Chemical Society
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• v.65 no.3
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• pp.179-184
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• 2021

In this study, the molecular dynamics simulation of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) single molecule was conducted by changing the solvent properties in order to investigate the change in the glycerol backbone structure in phospholipids according to the solvent properties. DOPC has three different conformations according to glycerol C1-C2 bond: A(θ3 = trans, θ4 = gauche), B(θ3 = gauche, θ4 = gauche-), C(θ3 = gauche-, θ4 = trans). Changes in the glycerol backbone structure of the DOPC were examined using the solvent's dielectric constant and surface tension constant as variables. As a result, the population of the B structure increased as the dielectric constant increased. The reason is that the solvation energy of the B structure is larger than that of A. In addition, as the surface tension constant increased, the population of the B structure increased because the surface area of B was smaller than that of A. The results of these studies are expected to be used in the study of phospholipid structure in the future.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.10a
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• pp.453-453
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• 2009

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.727-727
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• 2006