• Journal of Korea Multimedia Society
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• v.19 no.9
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• pp.1659-1668
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• 2016

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1209-1221
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• 2010

Three dimensional (3D) video is expected to be an important application for broadcast and IP streaming services. One of the main limitations for the transmission of 3D video over IP networks is network bandwidth mismatch due to the large size of 3D data, which causes fatal decoding errors and mosaic-like damage. This paper presents a novel selective frame discard method to address the problem. The main idea of the proposed method is the symmetrical discard of the two dimensional (2D) video frame and the depth map frame. Also, the frames to be discarded are selected after additional consideration of the playback deadline, the network bandwidth, and the inter-frame dependency relationship within a group of pictures (GOP). It enables the efficient utilization of the network bandwidth and high quality 3D IPTV service. The simulation results demonstrate that the proposed method enhances the media quality of 3D video streaming even in the case of bad network conditions.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.462-464
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• 1992

In this paper, an extraction of depth intonation in pattern using neural network is presented. All the 3D images represent the depth information in grey pixels. This pixels which have analog values translated digital values. Because of the noise and distortion in pattern, we use the normalization in learning and recalling the patterns. Our method has eight direction vectors and slopes for pattern. Also, we use potential to obtain the mean slope and direction vectors of given 3D patches. The higher level of deduction finding the global depth information is also carried out by using neural network.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.454-467
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• 2002

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neuro-fuzzy-evolutionary technique, that is, Adaptive-Network-based Fuzzy Inference System (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving sir ale objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

• Journal of the Korean GEO-environmental Society
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• v.14 no.5
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• pp.15-22
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• 2013

The submarine pipe, which is one of the most important coastal structures, is widely used in the development of coastal and ocean engineering. The scour of the submarine pipe occurs due to the wave and the current according to the state of the sea bed. The scour affects the submarine pipe and causes it to undergo settlement and fatigue. It is difficult to predict the local scour under complicated and various conditions of the coastal environment, even though many researches on the scour of the submarine pipe have been studied in recent years. This study analyzed the scour depth around a submarine pipe by using the Neural Network technique. The back-propagation algorithms was used to train the Neural Network. The 58 simulating experimental data for the performance and validation of the Neural Network technique were analyzed in this study. Then, the regression analysis for the same data was performed in this study to predict and compare with the Neural Network technique for the scour depth.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.926-937
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• 1996

A graph traversal technique is a certain pattern of visiting nodes of a graph. Many special traversal techniques have been applied to solve graph related problems. For example, the depth first search technique has been used for finding strongly onnected components of a directed graph or biconnected components of a general graph. The distributed protocol to implement his depth first search technique on the distributed network can be divided into a fixed topology problem where there is no topological change and a dynamic topology problem which has some topological changes. Therefore, in this paper, we present a more efficient distributed depth first search protocol with fixed topology and a resilient distributed depth first search protocol where there are topological changes for the distributed network. Also, we analysed the message and time complexity of the presented protocols and showed the improved results than the complexities of the other distributed depth first search protocols.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.52-62
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• 1995

In the laser surface hardening process the geometrical parameters, especially the depth, of the hardened layer are utilized to assess the integrity of the hardening layer quality. Monitoring of this geometrical parameter ofr on-line process control as well as for on-line quality evaluation, however, is an extremely difficult problem because the hardening layer is formed beneath a material surface. Moreover, the uncertainties in monitoring the depth can be raised by the inevitable use of a surface coating to enhance the processing efficiency and the insufficient knowledge on the effects of coating materials and its thicknesses. The paper describes the extimation results using neural network to estimate the hardening layer depth from measured surface temperanture and process variables (laser beam power and feeding velocity) under various situations. To evaluate the effec- tiveness of the measured temperature in estimating the harding layer depth, estimation was performed with or without temperature informations. Also to investigate the effects of coating thickness variations in the real industry situations, in which the coating thickness cannot be controlled uniform with good precision, estimation was done over only uniformly coated specimen or various thickness-coated specimens. A series of hardening experiments were performed to find the relationships between the hardening layer depth, temperature and process variables. The estimation results show the temperature informations greatly improve the estimation accuracy over various thickness-coated specimens.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.113-121
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• 2007

Commonly, in the Sensor Network that composed with multiple nodes uses Ad-hoc protocol to communicate each other. Each sensed data packets are collected by base node and processed by Host PC. But the Ad-hoc protocol is too vulnerable to Sinkhole attack, where the intruder attracts surrounding nodes with unfaithful routing information, and then performs selective forwarding or changes the data passing through it. The Sinkhole attack increases overhead over the network and boosts energy consumption speed to decrease network's life time. Since the other attacks can be easily adopted through sinkhole attack, the countermeasure must be considered carefully. In this paper, we proposed the Hop-depth algorithm that detects intruder in Sinkhole attack and colluded nodes. First, the proposed algorithm makes list of suspected nodes and identifies the real intruder in the suspected node list through the Hop-depth count value. And recalculates colluder's path information to find the real intruder. We evaluated the performance of the proposed algorithm using NS2. We compared and analyzed the success ratio of finding real intruder, false positive ratio, false negative ratio, and energy consumption.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.182-189
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• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.

• East Asian Journal of Business Economics (EAJBE)
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• v.6 no.4
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• pp.1-12
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• 2018

Purpose - This paper tests the effect of the structure of manufacturing and marketing subsidiary network on FX exposure of Korean MNEs. Furthermore, the moderating effect of pass-through ability on the relationship between the subsidiary network and FX exposure is explored. Research design and methodology - This study utilizes a sample of 309 Korean MNEs constructed from database offered by KOTRA and KIS-VALUE. Results - As operational flexibility arising from having operations in multiple locations provides an option for firms to tackle FX exposure, greater breadth of manufacturing subsidiary network reduces FX exposure, and greater depth increases FX exposure. However, both the breadth and depth of marketing subsidiary network decrease FX exposure due to the firm's higher level of market presence and knowledge to devise an appropriate marketing strategy that can buffer adverse exchange rate movement. Such an effect is intensified when MNE's have FX exposure pass-through ability stemming from differentiated good. Conclusions - Empirical findings suggest that types and structure of Korean MNEs' foreign subsidiary network are closely related to the level of FX exposure they are experiencing. Also, they can utilize marketing subsidiary network more efficiently when having a higher R&D intensity.