• Title/Summary/Keyword: 천공 정확성

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Analytic Hierarchy Process Analysis on Correlation Between Drilling Error and Blasting Accuracy (발파공의 천공오차와 발파정확도의 상관성에 관한 현장조사 및 계층분석기법 연구)

  • Lee, Deok-Hwan;Choi, Sung-Oong;Kim, Chang-Oh
    • Tunnel and Underground Space
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    • v.24 no.5
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    • pp.386-394
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    • 2014
  • Drilling accuracy is known to be one of the most important factors determining blasting efficiency in mining by blast operation. Therefore analysing the causes of drilling error and preparing a countermeasure for minimizing drilling error are very important for blasting efficiency and safety. In this study, causes of drilling error are analyzed with dividing them into controllable factors and uncontrollable factors, and relationship between each cause is also comprehended through field measurement and AHP analysis. Finally, effective measures to help lower the drilling error are proposed with the results from weighting analysis for each factor.

Hole Navigation System Development in Drilling Rig (천공기의 천공위치 안내 시스템 개발)

  • Kim, Je-Dong;Kang, Byung-Soo;Hahm, Young-Gook;Yoo, Jae-Sung;Lee, Duck-Hwan;Cho, Jae-Sang
    • Proceedings of the Korea Information Processing Society Conference
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    • 2016.10a
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    • pp.783-784
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    • 2016
  • 천공위치 안내 시스템은 천공작업 Hole의 정확한 위치를 추적하는 시스템이다. 천공작업의 정확성과 안정성, 그리고 장비 조작의 용이성을 위하여 범지구 위성 항법 시스템(Global navigation satellite system, GNSS)을 이용하여 지표면의 천공위치를 찾아주는 안내 시스템을 연구 개발하였다.

PERFORATION OF THE TEMPOROMANDIBULAR JOINT MENISCUS: DIAGNOSED BY MAGNETIC RESONANCE IMAGING (MAGNETIC RESONANCE IMAGING을 이용한 악관절 원판 천공 진단)

  • Kim, Houng-Gon;Dolan, Eward;Vogler, James B.;Nokes, Steven R.
    • Maxillofacial Plastic and Reconstructive Surgery
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    • v.11 no.2
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    • pp.11-18
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    • 1989
  • Advancement of surgical techniques has made it necessary to accurately diagnose internal derangements. Arthrography and computerized tomography have been used to diagnose the majority of temporomandibular joint disorders, however, these methods have had their disadvantages. Magnetic resonance imaging utilizing surface coils has greatly improved the ability to diagnose meniscus abnormalities without using intrarticular injections or ionizing radiation. Ninety-two patients (184 joints) were evaluated by means of magnetic resonance imaging(MRI). Thirty-one patients (39 joints) were diagnosed as having meniscus perforation. Retrospective review of fifteen patients (20 joints) with a perforated meniscus diagnosed by magnetic resonance imaging pre-operatively demonstrated a sixty-five percent correlation between the radiographic diagnosis and the surgical findings.

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Effective Arrangement of Non-explosive Demolition Agents and Empty Holes for Improving Fragmentation of Square Concrete Structures (정사각형 콘크리트 구조물의 파쇄도 향상을 위한 비폭성 파쇄제와 천공 홀의 효과적인 배치)

  • Cho, Hwangki;Nam, Yunmin;Kim, Kyeongjin;Lee, Jaeha;Sohn, Dongwoo
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.30 no.2
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    • pp.145-151
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    • 2017
  • As an alternative to conventional explosive methods for demolition of concrete structures and rocks, the use of non-explosive demolition agents can be considered to reduce noise, vibration, and dust emissions during the demolition process. In this study, we conduct finite element analysis for crack initiation and propagation caused by the expansion of non-explosive demolition agents in square concrete structures. The predicted crack patterns are compared with the experimental results in the literature. The minimum values of the required expansion pressure of non-explosive demolition agents are also estimated, which depend upon the arrangement of non-explosive demolition agents and empty holes. Furthermore, we investigate the effect of empty holes on the fragmentation of concrete structures, and discuss the effective arrangement of non-explosive demolition agents and empty holes for fragmentation improvement.

A Study on the Characteristics of Daylight Distributions by Different Sky Conditions and Controlled Roller Shade Heights (천공종류 및 롤러쉐이드의 제어된 높이에 따른 주광분포 특성에 관한 연구)

  • Park, Byoung-Chul;Lim, Ji-Sun;Kim, Yu-Sin;Lee, Jeong-Ho;Choi, An-Seop
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.23 no.2
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    • pp.18-26
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    • 2009
  • Daylight responsive dimming systems are one of lighting control systems which are to control artificial lightings using available daylight for energy savings. This system is not popular because useful daylight is usually blocked by uncontrolled passive shading systems in buildings. It is necessary to integrate daylight responsive dimming systems and automata! roller shading systems. In this research, mock-up test is performed to analyze the daylight distributions in three different rooms for integrated systems. Roller shades are installed in two rooms. One is fully downed and the other is controlled by sun profile angle. The other room has no shading system as a reference room.

Deep Learning based Estimation of Depth to Bearing Layer from In-situ Data (딥러닝 기반 국내 지반의 지지층 깊이 예측)

  • Jang, Young-Eun;Jung, Jaeho;Han, Jin-Tae;Yu, Yonggyun
    • Journal of the Korean Geotechnical Society
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    • v.38 no.3
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    • pp.35-42
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    • 2022
  • The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.