• 제목/요약/키워드: Impact Prediction

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A Modeling of Impact Dynamics and its Application to Impact Force Prediction

  • Ahn Kil-Young;Ryu Bong-Jo
    • Journal of Mechanical Science and Technology
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    • 제19권spc1호
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    • pp.422-428
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    • 2005
  • In this paper, the contact force between two colliding bodies is modeled by using Hertz's force-displacement law and nonlinear damping function. In order to verify the appropriateness of the proposed contact force model, the drop type impact test is carried out for different impact velocities and different materials of the impacting body, such as rubber, plastic and steel. In the drop type impact experiment, six photo interrupters in series close to the collision location are installed to measure the velocity before impact more accurately. The characteristics of contact force model are investigated through experiments. The parameters of the contact force model are estimated using the optimization technique. Finally the estimated parameters are used to predict the impact force between two colliding bodies in opening action of the magnetic contactor, a kind of switch mechanism for switching electric circuits.

도로교통소음의 주요 예측인자 분석 및 예측지침 (Analysis of Major Factors and Guideline for Road Traffic Noise Prediction)

  • 강대준;이재원;구진회
    • 한국소음진동공학회논문집
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    • 제20권6호
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    • pp.515-520
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    • 2010
  • The noise map has been applied to predicting the effect of noise and establishing the noise abatement measure for several years overseas. However the introduction of the noise map in Korea is at the initial stage. Thus, we surveyed the several prediction models for road traffic noise used in EU, and the method of applying the noise map in noise impact assessment. In order to improve the noise impact assessment we have to apply the noise map, and propose the guideline of predicting the road traffic noise. We intend to obtain coherency and accuracy of prediction results. As a result of this study, we know that the prediction guideline is an essential prerequisite in order to predict the unified and accurate road traffic noise.

도로소음의 3차원 소음예측모델 적용방안 마련 (Preparation of Application Plan for 3-d Noise Prediction Model in Road Noise)

  • 선효성
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2014년도 추계학술대회 논문집
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    • pp.842-843
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    • 2014
  • When the environmental impact assessment (EIA) of an development project is performed, the noise prediction model is used to evaluate the noise impact and prepare the noise reduction measures according to the implementation of an development project. Especially, the application of a 3-d noise prediction model is increased to describe the complex noise environment including high-rise living accommodations. Therefore, this paper suggests the application plan of a 3-d noise predicton model for the road noise impact assessment of a development project.

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도로교통소음 환경영향평가 기법 개선 연구 I (Technical Improvement of Traffic Noise Environmental Impact Assessment I)

  • 박영민;최진권;장서일
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2005년도 추계학술대회논문집
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    • pp.55-58
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    • 2005
  • This study was Performed to grasp of the problem and improvement in traffic noise environmental impact assessment(EIA). National institute of environmental research(NIER) traffic noise prediction model is in general use in internal EIA. In this study, NIER noise prediction model need to improve in that the predicted results lower than the measured results. The other predict model(KLC KEI) is more accurate. Also the volume and speed of traffic is need to standardize in traffic noise prediction.

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부도예측 모형에서 뉴스 분류를 통한 효과적인 감성분석에 관한 연구 (A Study on Effective Sentiment Analysis through News Classification in Bankruptcy Prediction Model)

  • 김찬송;신민수
    • 한국IT서비스학회지
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    • 제18권1호
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    • pp.187-200
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    • 2019
  • Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

Impact parameter prediction of a simulated metallic loose part using convolutional neural network

  • Moon, Seongin;Han, Seongjin;Kang, To;Han, Soonwoo;Kim, Kyungmo;Yu, Yongkyun;Eom, Joseph
    • Nuclear Engineering and Technology
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    • 제53권4호
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    • pp.1199-1209
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    • 2021
  • The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

유압식 착암기 치즐의 타격 변형량 해석에 관한 연구 (A Study On the Analysis Of Impact Strain for Hydraulic Breaker Chisel)

  • 박종원;이기욱;김형의
    • 유공압시스템학회논문집
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    • 제4권4호
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    • pp.21-27
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    • 2007
  • A hydraulic breaker for construction machinery generally used for the destroying and disassembling of buildings, crashing road pavement, breaking rocks at quarry and so on. So the measurement of the impact energy of a hydraulic breaker is very important thing to prove its capability to manufacturers and customers. Therefore the prediction of impact energy in design process is very helpful to the most of breaker manufacturers. In this study, we carried on modeling and simulation of a hydraulic breaker to predict impact energy via commercial CAE software. The modeling and simulation of a hydraulic breaker was achieved with two parts. One is a hydraulic circuit analysis part via AMESim and the other is impact strain analysis part via ANSYS.

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KIAPS 전지구 수치예보모델 시스템에서 SAPHIR 자료동화 효과 (Impact of SAPHIR Data Assimilation in the KIAPS Global Numerical Weather Prediction System)

  • 이시혜;전형욱;송효종
    • 대기
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    • 제28권2호
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    • pp.141-151
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    • 2018
  • The KIAPS global model and data assimilation system were extended to assimilate brightness temperature from the Sondeur $Atmosph{\acute{e}}rique$ du Profil $d^{\prime}Humidit{\acute{e}}$ Intertropicale par $Radiom{\acute{e}}trie$ (SAPHIR) passive microwave water vapor sounder on board the Megha-Tropiques satellite. Quality control procedures were developed to assess the SAPHIR data quality for assimilating clear-sky observations over the ocean, and to characterize observation biases and errors. In the global cycle, additional assimilation of SAPHIR observation shows globally significant benefits for 1.5% reduction of the humidity root-mean-square difference (RMSD) against European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analysis. The positive forecast impacts for the humidity and temperature in the experiment assimilating SAPHIR were predominant at later lead times between 96- and 168-hour. Even though its spatial coverage is confined to lower latitudes of $30^{\circ}S-30^{\circ}N$ and the observable variable is humidity, the assimilation of SAPHIR has a positive impact on the other variables over the mid-latitude domain. Verification showed a 3% reduction of the humidity RMSD with assimilating SAPHIR, and moreover temperature, zonal wind and surface pressure RMSDs were reduced up to 3%, 5% and 7% near the tropical and mid-latitude regions, respectively.

USNCAP 정면충돌시험 결과를 이용한 HIC15 예측모델 개발 (A Development on the Prediction Model for the HIC15 using USNCAP Frontal Impact Test Results)

  • 임재문
    • 자동차안전학회지
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    • 제12권4호
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    • pp.31-38
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    • 2020
  • This study is to develop the prediction model for the HIC15 in frontal vehicle crash tests. The 28 frontal impact test results of the MY2019 and MY2020 USNCAP are utilized. The metrics for evaluating the crash pulse severity such as moving average acceleration, Restraint Quotient (RQ) and ride-down efficiency are reviewed to find out whether the metrics can predict the HIC15. It is observed that the R2 values based on the linear regression of all pairs between the existing metrics and the occupant injuries such as the HIC15, 3 ms chest g's and chest deflection are very low. In this study, using the vehicle crash pulses, the linear regression model for estimating the HIC15 is developed. The vehicle crash pulse is splitted seven 10 ms intervals in 70 ms after impact for extracting the average accelerations in each intervals. The prediction model can predict effectively not only the HIC15 but also the maximum head g's, chest deflection and 3 ms chest g's of 13 vehicles out of 28 vehicles.

터널 굴착시 발생하는 지하수의 유출량 예측에 관한 연구 (A Study on the Prediction of Outflow of Groundwater in Tunnel Construction Areas)

  • 박선환;장윤영;강형식;최준규;양근호
    • 환경영향평가
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    • 제16권6호
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    • pp.407-419
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    • 2007
  • This study investigated the predicted and abserved outflow of groundwater which occurred during tunnel constructions. Among the 586 road construction projects from 1986 to 2006, 4 route 25 tunnel construction areas and 26 waste water treatment facilities under construction were studied. Most of the tunnel outflow prediction in EIA (Environmental Impact Assessment) process have been classified into the 17 types of units depending on the assessor's options, which have not conformed to the request of the residents and non government organizations. The investigation results showed that the outflow of underground water in tunnel construction areas averaged about $0.133m^3/km{\cdot}min$ with the maximum $0.386m^3/km{\cdot}min$, and that the outflow mostly occurred in the early stage of tunnel excavation and diminished gradually. The prediction of outflow of underground water in the EIA process showed excessive results compared to observed outflow, the even 51.7 times. Consequently for more realistic prediction, current EIA method for prediction of outflow of underground water in tunnel construction areas has to adopt numerical methods coupled with hydraulics and geologic informations from unit methods of present time.