• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.3
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• pp.373-378
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• 2013

The speed change performance of transmissions has become a serious issue because of the increase in the inertia moment that has accompanied increases in engine output and transmission size. Therefore, it is necessary to develop better wear resistant friction materials. In this study, an appropriate sintered friction component for the synchronizer ring of a diesel manual transmission was developed, and its bonding characteristics were analyzed. That is, a process for bonding an Fe-based base material and Cu-based sintered friction material was developed. BSE and EDX analyses of this bonding layer were conducted, along with a shear strength test, to determine the bonding characteristics.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.773-781
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• 2018

In recent years, a study of power-line inspection using an unmanned aerial vehicle (UAV) has been actively conducted. However, relevant studies have been conducting power-line inspection with an UAV operated by manual control, and they have developed just power-line detection algorithm on aerial images. To overcome limitations of existing research, we propose a drone-based power-line tracking system in this paper. The main contributions of this paper are to operate developed system under configured environment and to develop a power-line detection algorithm in real-time. Developed system is composed of the power-line detection and the image-based tracking control. To detect a power-line in real-time, a region of interest (ROI) image is extracted. Furthermore, clustering algorithm is used in order to discriminate the power-line from background. Finally, the power-line is detected by using the Hough transform, and a center position and a tilt angle are estimated by using the Kalman filter to control a drone smoothly. We design a position controller and an attitude controller for image-based tracking control, and both controllers are designed based on the proportional-derivative (PD) control method. The interaction between the position controller and the attitude controller makes the drone track the power-line. Several experiments were carried out in environments where conditions are similar to actual environments, which demonstrates the superiority of the developed system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.688-693
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• 2016

This paper describes the parachute dynamics modeling and simulation results for the development of training simulator of a HALO (High Altitude Low Opening) parachute, which is currently in use for military purposes. The target parachute is a rectangular shaped parafoil and its dynamic model is derived based on the real geometry data as the 9-DOF nonlinear equations of dynamics. The simulation was conducted through the moment of inertia and its aerodynamic derivatives to reflect the real characteristics based on the MATLAB/Simulink. In particular, its modeling includes the typical characteristics of the added mass and moment of inertia, which is shown in the strong effects in Lighter-Than-Air(LTA) flight vehicle. The proposed dynamic modeling was evaluated through the simulation under the spiral turning flight conditions of the asymmetric control inputs and compared with the performance index in the target parachute manual.

• Journal of the Korean Society for Railway
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• v.18 no.6
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• pp.567-577
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• 2015

This paper presents an assessment method of the ground vibration level with a combination of measured data and an analytic method. The basic concept of the method is similar to that in FRA(Federal Railway Administration) manual for detailed vibration analyses. However, going into detail, the assessment method was modified for a feasible evaluation of the vibration reduction effects of diverse types of wave barriers. The force density was evaluated in a vehicle-track interaction analysis and the transfer mobility of vibration was analyzed through a 2-D ground vibration analysis. The calculated 2-D transfer mobility was corrected to incorporate transfer characteristics of actual ground vibration by comparing the previously measured data and analysis results. Nine types of vibration reduction effects of wave barriers were analyzed on a shallow tunnel section of an urban railway where numerous civil complaints had actually been filed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.575-584
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• 2012

A established military security tast based on existing manual and off-line needs the change and development to support effective and systematic task performance according to environment change of informational and scientific project in the military. Therefore this study suggests to set up the standard model of the defense integrated security system to automate and informationize major defense security task based on actual and problem in the area of major defense of security task and case analysis of these in America, England and other countries. The standard model consist of unit systems were made up integrated security system, security management system, man entrance system, vehicle entrance system, high-tech guard system, terror prevention system and the security accident analysis system, and this suggested model based on possible technology and system. If this model is apply to each real military unit, we will expect the development of defense security.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.69-74
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• 2022

Even after the drunk driving law was revised through the Yoon Chang-ho Act in 2019, the proportion of habitual offenders among all drunk drivers in 2021 was 4.7%, up 0.5% from 2018. In addition, drunk driving is not easily stopped due to the addiction of alcohol, and there is a high probability of recidivism in accidents as it is often driven again. Therefore, in this paper, to prevent this, when alcohol is measured using its own sensor rather than a manual police measure, the vehicle stops and related data such as the current location and time are automatically saved. Since it is not possible to develop directly on the car, this system was developed by converging various technologies and sensors such as Arduino board, Firebase, and GPS based on the IoT environment in consideration of the simulation environment.

• Laboraroty Animal Research
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• v.34 no.4
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• pp.317-328
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• 2018

Cognitive impairment responses are important research topics in the study of degenerative brain diseases as well as in understanding of human mental activities. To compare response to scopolamine (SPL)-induced cognitive impairment, we measured altered parameters for learning and memory ability, inflammatory response, oxidative stress, cholinergic dysfunction and neuronal cell damages, in Korl:ICR stock and two commercial breeder stocks (A:ICR and B:ICR) after relevant SPL exposure. In the water maze test, Korl:ICR showed no significant difference in SPL-induced learning and memory impairment compared to the two different ICRs, although escape latency was increased after SPL exposure. Although behavioral assessment using the manual avoidance test revealed reduced latency in all ICR mice after SPL treatment as compared to Vehicle, no differences were observed between the three ICR stocks. To determine cholinergic dysfunction induction by SPL exposure, activity of acetylcholinesterase (AChE) assessed in the three ICR stocks revealed no difference of acetylcholinesterase activity. Furthermore, low levels of superoxide dismutase (SOD) activity and high levels of inflammatory cytokines in SPL-treated group were maintained in all three ICR stocks, although some variations were observed between the SPL-treated groups. Neuronal cell damages induced by SPL showed similar response in all three ICR stocks, as assessed by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, Nissl staining analysis and expression analyses of apoptosis-related proteins. Thus, the results of this study provide strong evidence that Korl:ICR is similar to the other two ICR. Stocks in response to learning and memory capacity.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.