• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.18-35
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• 2020

Many human, roadway, and vehicle factors affect vehicle-pedestrian crashes. Especially, the roadway factors are easily defined and suitable for suggesting countermeasures. The characteristics of the road network are one of the roadway factors. The road network significantly influences behaviors and conflicts of drivers and pedestrians. A metropolitan city such as Seoul contains various types of road networks, and crash prevention strategy considering characteristics of the road network is required. In this study, we analyze the effects of road networks on vehicle-pedestrian crashes. In the study, high order road ratio, intersection ratio, high-low intersection ratio are considered as road network variables. Using Geographically Weighted Poisson Regression, crash frequencies in Dongs of Seoul are analyzed based on the road network variable as well as socioeconomic variables. As a result, Dongs are grouped by coefficient signs, and each group is suggested about improvement directions considering conflict situations.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.433-440
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• 2018

I investigated whether network structure of legislators affects legislative performance in a cosponsorship network. I presented the theoretical basis with network closure and structural holes, and analyzed the network in the 19th National Assembly of Republic of Korea. In the directed and weighted network, each tie means that cosponsors support the bill sponsors proposed. The performance was measured by the number of initiatives and the ratio of reflected legislation, and the network structure was measured by size, density, hierarchy, and constraint. I found that the legislators with brokerage structure have a lot of initiatives in making connection with many legislators in various groups and the legislators with hierarchical structure have the higher ratio of reflected legislation with the continuous and strong support from the members of their group. I also found that the network of ruling party lawmaker is more hierarchical than the network of opposition lawmaker.

• Annals of Clinical Neurophysiology
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• v.25 no.2
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• pp.93-102
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• 2023

Background: We aimed to identify any differences in the structural covariance network based on structural volume and those in the functional network based on cerebral blood flow between the ipsilateral and contralateral hemispheres of pain in patients with episodic migraine without aura. Methods: We prospectively enrolled 27 patients with migraine without aura, all of whom had unilateral migraine pain. We defined the ipsilateral hemisphere as the side of migraine pain. We measured structural volumes on three-dimensional T1-weighted images and cerebral blood flow using arterial spin labeling magnetic resonance imaging. We then analyzed the structural covariance network based on structural volume and the functional network based on cerebral blood flow using graph theory. Results: There were no significant differences in structural volume or cerebral blood flow between the ipsilateral and contralateral hemispheres. However, there were significant differences between the hemispheres in the structural covariance network and the functional network. In the structural covariance network, the betweenness centrality of the thalamus was lower in the ipsilateral hemisphere than in the contralateral hemisphere. In the functional network, the betweenness centrality of the anterior cingulate and paracingulate gyrus was lower in the ipsilateral hemisphere than in the contralateral hemisphere, while that of the opercular part of the inferior frontal gyrus was higher in the former hemisphere. Conclusions: The present findings indicate that there are significant differences in the structural covariance network and the functional network between the ipsilateral and contralateral hemispheres of pain in patients with episodic migraine without aura.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.579-584
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• 1998

In this work, we present a method to deal with the interval valued decision making systems. First, we propose a new type of equality measure based on the Ordered Weighted Averaging (OWA) operator. The proposed equality measure has a structure to render the extreme values of the measure by choosing a suitable weighting vector of the OWA operator. From this property, we derive a bidirectional fuzzy inference network which can be applied for the decisionmaking systems requiring the inverval valued decisions.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.202-202
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• 2006

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.143-146
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• 1996

In this paper, we propose a hopfield model for solving the part-matching which is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and net total path of part-connection. Therefore, this kind of problem is referred to as a combinatorial optimization problem. First of all, we review the theoretical basis for hopfield model to optimization and present two method of part-matching; Traveling Salesman Problem (TSP) and Weighted Matching Problem (WMP). Finally, we show demonstration through computer simulation and analyzes the stability and feasibility of the generated solutions for the proposed connection methods.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.475-476
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• 2007

The stress induced leakage currents will affect data retention in synapse transistors and the stress current, transient current is used to estimate to fundamental limitations on oxide thicknesses. The synapse transistor made by thin silicon oxides has represented the neural states and the manipulation which gaves unipolar weights. The weight value of synapse transistor was caused by the bias conditions. Excitatory state and inhibitory state according to weighted values affected the channel current. The stress induced leakage currents affected excitatory state and inhibitory state.

• Proceedings of the Korean Society for Information Management Conference
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• 2013.08a
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• pp.19-22
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• 2013

네트워크 분석이 확산되면서 여러 분야에서 다양한 중심성 척도가 개발되어 활용되고 있으나 가중 네트워크에서 지역중심성을 측정할 수 있는 척도로는 최근접이웃중심성 이외에는 거의 알려져 있지 않다. 최근접이웃중심성 척도는 동률값이 흔히 나타나므로 변별력이 낮다는 단점을 가지고 있다. 이 연구에서는 최근접이웃중심성 척도를 일반화한 이웃중심성 척도를 제안하고 가상 자료 및 실제 자료에 대해 적용하여 검증해보았다.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1042-1045
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• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.3-10
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• 2018

Depth map has been utilized in a varity of fields. Recently research on generating depth map by artificial neural network (ANN) has gained much interest. This paper validates the feasibility of implementing the ready-made depth map generation by convolutional neural network (CNN). First, for a given image, a depth map is generated by the weighted average of a saliency map as well as a motion history image. Then CNN network is trained by test images and depth maps. The objective and subjective experiments are performed on the CNN and showed that the CNN can replace the ready-made depth generation method.