• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.2
• /
• pp.188-193
• /
• 2005

Surface texturing of tribological application is another attractive technology of friction reducing. Also, reduction of friction is therefore considered to be a necessary requirement for improved efficiency of machine. In this paper attempts to investigate the effect of density for micro-scale dimple pattern on bearing steel flat mated with pin-on-disk. We demonstrated the lubrication mechanism for a Stribeck curve, which has a relationship between the friction coefficient and a dimensionless parameter for lubrication condition. It is found that friction coefficient is depended on the density of surface pattern. It was thus verified that micro-scale dimple could affect the friction reduction considerably under mixed and hydrodynamic lubrication conditions from based on friction map. Lubrication condition regime has an influence on the friction coefficient induced the density of micro dimple.

• Tribology and Lubricants
• /
• v.2 no.2
• /
• pp.65-66
• /
• 1986

중공이 성립된것이 1949년 10월이며, 그후 1952년부터 5개년계획의 공업건설이 시작되어, 소련으로 부터 돌아온 연구자와 기술자가 중심이 되어 과학원소층의 연구소 (장춘광학정밀기계연구소 등), 대학((청화대학 등)및 각공업부의 연구소에서 본격적인 연구가 시작되었다. 그후 1962년에 전국 Tribology 회의를 처음으로 난주에서 열고, 100편의 논문이 발표되었다. 그후 중소관계악화, 1965년말에 발표된 문화혁명의 영향을 받아 사실상 연구는 완전히 스톱된 상태가 되고 말았다. 그후 천안문사건, 그리고 사인조실각의 이듬해인 1977년 연구지도등의 명을 받고, 마찰, 마모, 윤활의 연구를 재개하게 되었다.

• Tunnel and Underground Space
• /
• v.23 no.3
• /
• pp.219-227
• /
• 2013

Although Mohr-Coulomb failure criterion has limitations in that it is a linear criterion and the effect of the intermediate principal stress on failure is ignored, this criterion has been widely accepted in rock mechanics design. In order to overcome these shortcomings, the Hoek-Brown failure criterion was introduced and recently a number of 3-D failure criteria incorporating the effect of the intermediate principal stress on failure have been proposed. However, in many rock mechanics designs, the possible failure of rock mass is still evaluated based on Mohr-Coulomb criterion and most of practitioners are accustomed to understanding the strength of rock mass in terms of the internal friction angle and cohesion. Therefore, if the equivalent Mohr-Coulomb strength parameters of the advanced failure criteria are calculated, it is possible to take advantage of the advanced failure criteria in the framework of the Mohr-Coulomb criterion. In this study, a method expressing the tangential Mohr-Coulomb strength parameters in terms of the stress invariant is proposed and it is applied to the generalized Hoek-Brown criterion and the HB-WW criterion. In addition, a new approach describing the geometric meaning of the ${\sigma}_2$-dependency of failure criteria in 3-D principal stress space is proposed. Implementation examples of the proposed method show that the influence of the intermediate principal stress on the tangential friction angle and cohesion of the HB-WW criterion is considerable, which is not the case for the 2-D failure criterion.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.5
• /
• pp.468-472
• /
• 2013

This paper deals with the indoor localization problem for a SSMR (Skid Steering Mobile Robot) subjected to wheel-ground friction and with one LRF (Laser Range Finder). In order to compensate for some friction effect, a friction related coefficient is estimated by the recursive least square algorithm and appended to the maneuvering command. Also to reduce odometric information based localization errors, the lines are extracted with scan points of LRF and matched with the ones of the corresponding map built in advance. The present friction compensation and scan map matching schemes have been applied to a laboratory SSMR, and experimental results are given to validate the localization performance along an indoor corridor.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1993.06a
• /
• pp.233-237
• /
• 1993

본 논문에서는 패턴 매칭 방법에 근거하여 인식 단위가 음소인 음소 기반 인식 시스템을 구성하였다. 선택한 신경망 구조는 생물학적 신경망인 코호넨(T. Kohonen)의 SOFM(Self-Organizing Feature Map)으로 패턴 매칭 과정 중 cluster로 사용하였다. SOFM 신경망은 신호 공간에 대해서 최적의 국소(局所) 해부적 사사에 의한 자기 조직화 과정을 수행하며, 그 결과 인식 문제에 있어서 상당히 높은 정확도를 나타낸다. 따라서 SOFM 신경망은 음소 인식에도 효과적으로 응용될 수 있다. 또한 음소 인식 시스템의 성능 향상을 위해 K-means 클러스터링 알고리즘이 결합된 학습 알고리즘을 제안하였다. 제안된 음소 인식 시스템의 성능을 평가하기 위해, 먼저, 우리말 음소들을 모음, 파열음, 마찰음, 파찰음, 유음 및 비음, 종성의 6개 음소군으로 분류하고 각 음소군에 대한 특징 지도를 구성하여 labeler의 기능을 수행하게 하였다. 화자 종속 인식실험 결과 87.2%의 인식률을 보였으며 제안한 학습법의 빠른 수렴성과 인식률 향상을 확인하였다.

• Journal of Bio-Environment Control
• /
• v.16 no.4
• /
• pp.297-302
• /
• 2007

Discharge variations of perforated hose and drip irrigation systems were examined to evaluate irrigation uniformity at different pressures and length of branch line. Evaluation using statistical uniformity indicated that button drippers performed at excellent level but drip tapes and drip hoses were a little lower level. Nominal discharge of drip irrigation systems showed at the high side within the range of regulating pressure provided by the manufacturer. It is desirable that the length of branch line for drip hose, drip tape, and button dripper should be limited to 50 m, 70 m, and 100 m, respectively. Irrigation uniformity of perforated hoses showed very low level. So it is recommended that the length of branch line for perforated hoses should be limited to $30{\sim}35m$.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.1
• /
• pp.49-55
• /
• 2010

This study describes a development of GIS-based program called Debris Flow Analyzer for simulating the hazard extent of debris flow on the assumption that is uniform continuous, incompressible, unsteady. The Debris Flow Analyzer was designed to process debris flow numerical simulation with Finite Difference Formulation; smoothed DEM, slope, debris flow directions, extract valley, debris volume, water volume, debris flow moving speed, effective viscosity, dynamic friction coefficient. Also, it is expected that we can be improved the inform of debris flow hazard map by Google Earth.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.1
• /
• pp.101-113
• /
• 2010

Recently actual rainfall pattern is decreasing rainy days and increasing in rainfall intensity and the frequency of flood occurrence is also increased. To consider recent situation, Engineers use deterministic methods like a PMP(Probable Maximum Precipitation). If design storm wouldn't occur, increasing of design criteria is extravagant. In addition, the biggest structure cause trouble with residents and environmental problem. And then it is necessary to study considering probability of rainfall parameter in each sub-basin for design of water structure. In this study, stochastic rainfall patterns are generated by using log-ratio method, Johnson system and multivariate Monte Carlo simulation. Using the stochastic rainfall patterns, hydrological analysis, hydraulic analysis and 2nd flooding analysis were performed based on GIS for their applicability. The results of simulations are similar to the actual damage area so the methodology of this study should be used about making a flood risk map or regidental shunting rout map against the region.

• Journal of Korea Water Resources Association
• /
• v.39 no.1 s.162
• /
• pp.23-33
• /
• 2006

In this study, application potential of SINMAP, a GIS-based landslide hazard mapping tool, is evaluated through a case study. Through the application to the severe landslide events occurred during a heavy storm in 1991 on the Mt. Dalbong area about 78 kilometers south from Seoul, SINMAP successfully spotted most landslide sites. The effects and proper ranges of three calibration parameters of SINMAP, i.e. the soil internal friction angle, the combined cohesion of tree roots and soil, and T/R, were examined through comparison of predicted landslides with the landslide inventory data. From the findings of this study, it seems that SINMAP could be used as an effective screening tool for landslide hazard mapping especially for mountain areas with fairly steep slopes and relatively thin soil layers.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.23-31
• /
• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.