• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.275-288
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• 2004

This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.49-56
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• 2011

This paper deals with a methodology for searching optimal route of hazard material (hazmat) vehicles. When we make a decision of hazmat optimal paths, there is a conflict between the public aspect which wants to minimize risk and the private aspect which has a goal of minimizing travel time. This paper presents Efficient Vector Labeling algorithm as a methodology for searching optimal path of hazmat transportation, which is intrinsically one of the multi-criteria decision making problems. The output of the presented algorithm is a set of Pareto optimal paths considering both risk and travel time at a time. Also, the proposed algorithm is able to identify non-dominated paths which are significantly different from each other in terms of links used. The proposed Efficient Vector Labeling algorithm are applied to test bed network and compared with the existing k-shortest path algorithm. Analysis of result shows that the proposed algorithm is more efficient and advantageous in searching reasonable alternative routes than the existing one.

• Journal of applied mathematics & informatics
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• v.41 no.3
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• pp.511-524
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• 2023

An L(p₁, p₂, p₃, . . . , p_m)-labeling of a graph G is an assignment of non-negative integers, called as labels, to the vertices such that the vertices at distance i should have at least pi as their label difference. If p₁ = 4, p₂ = 3, p₃ = 2, p₄ = 1, then it is called a L(4, 3, 2, 1)-labeling which is widely studied in the literature. A L(4, 3, 2, 1)-path coloring of graphs, is a labeling g : V (G) → Z⁺ such that there exists at least one path P between every pair of vertices in which the labeling restricted to this path is a L(4, 3, 2, 1)-labeling. This concept was defined and results for some simple graphs were obtained by the same authors in an earlier article. In this article, we study the concept of L(4, 3, 2, 1)-path coloring for complete bipartite graphs, 2-edge connected split graph, Cartesian product and join of two graphs and prove an existence theorem for the same.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.25-29
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• 2010

In this paper, we implement a multi-touch system using FIDR. The implementation consists of hardware manufacture and development of image processing system. In the hardware system, touch screen, infrared LED placements and infrared camera are made. The image processing procedure is to extract each pointer's coordinates from image data and includes binary-coding, noise-elimination, labeling and calculation of mass center. From the implementation, we are able to make a multi-touch system with considerably lower cost than the existing ones.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• Reproductive and Developmental Biology
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• v.35 no.4
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• pp.503-510
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• 2011

Cellular uptake of nanoparticles for stem cell labeling and tracking is a critical technique for biomedical therapeutic applications. However, current techniques suffer from low intracellular labeling efficiency and cytotoxic effects, which has led to great interest in the development of a new labeling strategy. Using silica-coated nanoparticles conjugated with rhodamine B isothiocyanate (RITC) (SR), we tested the cellular uptake efficiency, biocompatibility, proliferation or differentiation ability with murine bone marrow derived hematopoietic stem/progenitor cells. The bone marrow hematopoietic cells showed efficient uptake with SR with dose or time dependent manner and also provided a higher uptake on hematopoietic stem/progenitor cells. Biocompatibility tests revealed that the SR had no deleterious effects on cell cytotoxicity, proliferation, or multi-differentiation capacities in vitro and in vivo. SR nanoparticles are advantageous over traditional labeling techniques as they possess a high level of cellular internalization without limiting the biofunctionality of the cells. Therefore, SR provides a useful alternative for gene or drug delivery into hematopoietic stem/progenitor cells for basic research and clinical applications.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.23-29
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• 2009

Blob detection is an essential ingredient process in some computer applications such as intelligent visual surveillance. However, previous blob detection algorithms are still computationally heavy so that supporting real-time multi-channel intelligent visual surveillance in a workstation or even one-channel real-time visual surveillance in a embedded system using them turns out prohibitively difficult. In this paper, we propose a fast and precise blob detection algorithm for visual surveillance. Blob detection in visual surveillance goes through several processing steps: foreground mask extraction, foreground mask correction, and connected component labeling. Foreground mask correction necessary for a precise detection is usually accomplished using morphological operations like opening and closing. Morphological operations are computationally expensive and moreover, they are difficult to run in parallel with connected component labeling routine since they need much different processing from what connected component labeling does. In this paper, we first develop a fast and precise foreground mask correction method utilizing on neighbor pixel checking which is also employed in connected component labeling so that the developed foreground mask correction method can be incorporated into connected component labeling routine. Through experiments, it is verified that our proposed blob detection algorithm based on the foreground mask correction method developed in this paper shows better processing speed and more precise blob detection.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.960-965
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• 2006

This research aims to develop the multiple channel electrochemical DNA chip using microfabrication technology. At first, we fabricated a high integration type DNA chip array by lithography technology. Several probe DNAs consisting of thiol group at their 5-end were immobilized on the gold electrodes. Then target DNAs were hybridized and reacted. Cyclic voltammetry showed a difference between target DNA and control DNA in the anodic peak current values. Therefore, it is able to detect a plural genes electrochemically after immobilization of a plural probe DNA and hybridization of non-labeling target DNA on the electrodes simultaneously. It suggested that this DNA chip could recognize the sequence specific genes.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.56-59
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• 2017

CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1780-1784
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• 2015

Traditional IP routing, see only the Destination Address When Forwarding Layer 3 routing and exchange information and Destination-Based Routing Lookup is required for all Hop. Thus, all routers Full Internet routing information, the route information of more than about 120,000 may require. Therefore, the router configuration, which can be dispersed in the environment, the traffic load is required in accordance with this congestion. In this study, a unique characteristic of the Internet in the environment of an existing network Best Effect for QoS guarantee and hardware high speed switching of large multimedia data transmitted using a Labeling for forwarding a packet environment configuration is required. Video Stream Broadcast Transport Labeling rather than in much of the higher performance of the multi-step policy to most of the Video Stream Packet deulim was fixed to Labeling Header Format proposes a method of applying an effective QoS policy to a more simplified policy.