• Transactions of the Korean hydrogen and new energy society
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• v.29 no.6
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• pp.668-679
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• 2018

Experimental and numerical data were compared through a counterflow burner for the characteristic of basic flame about SNG- C11. In order to use the numerical mechanism accurately, the validation was carried out at strain rate ($a_g=30$, $120s^{-1}$) and the UCSD model showed satisfactory results. The effective Lewis number of the extinction boundary, and the behavior of extinction for the symmetric flames of the SNG-C11, could be explained through the trend of $Le_V$, and the flame of the extinction condition was inspected by the major species, key radicals and the chemical reaction paths. The interactions phenomenon in the merged flames has chemical reaction path for producing $HO_2$ were generated at stagnation point. It can be expected the one of major factors in interaction phenomenon.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.111-122
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• 2021

Building footprint extraction is an active topic in the domain of remote sensing, since buildings are a fundamental unit of urban areas. Deep convolutional neural networks successfully perform footprint extraction from optical satellite images. However, semantic segmentation produces coarse results in the output, such as blurred and rounded boundaries, which are caused by the use of convolutional layers with large receptive fields and pooling layers. The objective of this study is to generate visually enhanced building objects by directly extracting the vertices of individual buildings by combining instance segmentation and keypoint detection. The target keypoints in building extraction are defined as points of interest based on the local image gradient direction, that is, the vertices of a building polygon. The proposed framework follows a two-stage, top-down approach that is divided into object detection and keypoint estimation. Keypoints between instances are distinguished by merging the rough segmentation masks and the local features of regions of interest. A building polygon is created by grouping the predicted keypoints through a simple geometric method. Our model achieved an F1-score of 0.650 with an mIoU of 62.6 for building footprint extraction using the OpenCitesAI dataset. The results demonstrated that the proposed framework using keypoint estimation exhibited better segmentation performance when compared with Mask R-CNN in terms of both qualitative and quantitative results.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.851-861
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• 2021

The main objective of this study is to examine the government ownership effect on the United Arab Emirates (UAE) firm's corporate social responsibility (CSR). Government ownership is assumed to affect the CSR either directly or indirectly. That is by moderating the association between corporate governance and CSR. Publicly listed companies on the UAE capital markets (Abu Dhabi and Dubai) from 2010-2013 constituted the study sample. Panel data regression analyses and random effect model is used to examine the effects of board size, board independence, and audit committee characteristics on CSR. Government ownership is used as a moderator variable. The result showed that the existence of government ownership has a moderator effect on the association between corporate governance mechanisms and the CSR. Precisely, the research revealed that the audit committee characteristics become more effective in improving the firm's CSR when the government owns shares in the organization. The main contribution of this study is to examine how firm ownership structure influences good corporate governance and CSR in the UAE. The study contributes to the CSR literature by merging between the existence of governmental ownership and the power to enforce the implementation of corporate governance in an emerging country.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.45-54
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• 2022

We analyzed the low wall-shear stress area in the intracranial aneurysm that occurred at an anterior communicating artery with a special emphasis on vortical structures close to the wall. We reconstructed the aneurysm model from patient CTA data. We assumed blood as an incompressible Newtonian fluid and treated the blood vessel as a solid wall. The pulsatile boundary condition was applied at the inlet of the anterior cerebral artery. From the instantaneous flow field, we computed the histogram of the wall-shear stress over the aneurysm wall and found the low wall-shear stress event (< 0.4 Pa). This extreme event was due to the low wall-shear stress area that occurred at the daughter sac. We found that the merging of two vortices induced the low wall-shear stress area; one arises from the morphological characteristics of the daughter sac, and the other is formed by a jet flow into the aneurysm sac. The latter approaches the daughter sac, which ultimately leads to the strong ejection event near the daughter sac.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.189-194
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• 2009

With the rapid development of information and telecommunication technologies, the adoption of ubiquitous technologies is increasing for the realization of new effective u-City services. u-City is defined as a next-generation informatization city that can innovate a city's various functions, such as improving the welfare of the citizenry, ensuring safety based on systematic urban management, improving the quality of lives, and increasing convenience in city life, by merging cuttingedge information and telecommunication infrastructures and ubiquitous information services with urban space. There is therefore a need to recognize that a successful u-City implementation strategy involves developing the previous UIS into a ubiquitous technology-based UIS and integrating UIS's various urban informations with effective u-City services. In this paper, for UIS-based u-City implementation, the intelligent integration model of urban spatial information based on interoperability is proposed.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.2
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• pp.123-134
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• 2000

This paper addresses a new triangulation method for constructing surface model from a set of wire-frame contours. The most important problem of contour triangulation is the branching problem, and we provide a new solution for the double branching problem, which occurs frequently in real data. The multiple branching problem is treated as a set of double branchings and an algorithm based on contour merging is developed. Our double branching algorithm is based on partitioning of root contour by Toussiant's polygon triangulation algorithml[14]. Our double branching algorithm produces quite natural surface model even if the branch contours are very complicate in shape. We treat the multiple branching problem as a problem of coarse section sampling in z-direction, and provide a new multiple branching algorithm which iteratively merge a pair of branch contours using imaginary interpolating contours. Our method is a natural and systematic solution for the general branching problem of contour triangulation. The result shows that our method works well even though there are many complicated branches in the object.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.37-44
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• 2006

In this paper, a new version of the ICM method(MICM, modified ICM) in which the contextual information is modelled by Markov random fields (MRF) is introduced. To extract the feature, a new local MRF model with a fitting block neighbourhood is proposed. This model selects contextual information not only from the relative intensity levels but also from the geometrically directional position of neighbouring cliques. Feature extraction depends on each block's contribution to the local variance. They discriminates it into several regions, for example context and background. Boundaries between these regions are also distinctive. The proposed algerian performs segmentation using directional block fitting procedure which confines merging to spatially adjacent elements and generates a partition such that pixels in unified cluster have a homogeneous intensity level. From experiment with ink rubbed copy images(Takbon, 拓本), this method is determined to be quite effective for feature identification. In particular, the new algorithm preserves the details of the images well without over- and under-smoothing problem occurring in general iterated conditional modes (ICM). And also, it may be noted that this method is applicable to the handwriting recognition.