• 제목/요약/키워드: Input predictor

검색결과 83건 처리시간 0.023초

Predicting blast-induced ground vibrations at limestone quarry from artificial neural network optimized by randomized and grid search cross-validation, and comparative analyses with blast vibration predictor models

  • Salman Ihsan;Shahab Saqib;Hafiz Muhammad Awais Rashid;Fawad S. Niazi;Mohsin Usman Qureshi
    • Geomechanics and Engineering
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    • 제35권2호
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    • pp.121-133
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    • 2023
  • The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

분류 트리 기법을 이용한 국내 일괄사육 양돈장의 차단방역 수준에 영향을 미치는 기여 요인 평가 (Classification Tree Analysis to Assess Contributing Factors Influencing Biosecurity Level on Farrow-to-Finish Pig Farms in Korea)

  • 김규욱;박선일
    • 한국임상수의학회지
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    • 제33권2호
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    • pp.107-112
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    • 2016
  • The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

유아 영재의 판별과 역동적 평가 (Early Identification of Gifted Young Children and Dynamic assessment)

  • 장영숙
    • 영재교육연구
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    • 제11권3호
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    • pp.131-153
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    • 2001
  • 유아기에 영재를 판별하여 유아 영재의 흥미와 능력에 적합한 교육 프로그램을 제공하면, 유아영재의 잠재력은 극대화될 수 있다. 유아기에는 다른 연령에 비해 특히 실제의 발달과 잠재적 발달간의 차이가 특히 많기 때문에, 혼자 해결하도록 할 때보다 적절한 교육적 개입을 해주었을 때 더 많은 잠재적 능력을 발휘할 수 있다. 유아가 가진 영재성을 사장시키지 않기 위해서는 유아의 영재성을 조기에 판별하여 그들에게 적절한 교육적 프로그램을 제공하는 것이 필요하다. 본 연구의 목적은 역동적 평가방법을 적용한 유아 영재 판별방법을 제시하고자 하는 것이다. 이를 위해. 본 연구에서는 기존의 유아 영재 판별 방법들을 검토하고 이에 대한 문제점을 살펴보았다. 그런 후에 역동적 평가의 특징과 유형을 살펴보고, 마지막으로, 역동적 평가가 유아 영재를 판별하고자 할 때 어떻게 적용될 수 있는지를 탐색해 보았다. 유아기에 역동적 평가방법을 이용해 영재를 판별하면 다음과 같은 점에서 유용성이 있다. 첫째, 유아 영재의 판별에 역동적 평가방법을 사용하면 표준화 검사에 능력을 보이지 않는 잠재력이 있는 유아를 판별할 수 있다. 둘째, 역동적 평가방법은 학습에 대한 산출물보다 학습에 대한 과정을 평가할 수 있다. 셋째, 역동적 검사는 유아 영재를 조기에 발견하고 이에 적절한 교육적 처치를 할 수 있는 정보를 제공받을 수 있어 개별화교육이 가능하도록 해준다. 넷째, 역동적 평가는 진단과 교수를 연결시킴으로써 유아의 잠재력을 더욱 정확하게 측정할 수 있도록 해주며, 유아 영재들이 계속적인 성장을 할 수 있도록 교육적인 환경을 제공해 줄 수 있다.ject-orientation. For the convenience of input, output analysis, GUI(Graphic User Interface) of menu, window, dialog box, etc. are provided to the user, For the execution of DADSim, Silicon Graphic IRIX 6.3 or high version is required. DADSim can be used for the effectiveness analysis of­defence systems. Some illustrative examples will be shown in this paper.s, namely resources of military force planing requirement for 15 years, is given already for the accomplishment of military strategy. The purpose of this study is to seek a direction of the ROK′s military build-up policy in a viewpoint of capability-based military build-up.group, no difference was found in its fragrance. And, no difference was found in brightness and viscosity between samples. As a result of conducting the palatability test,

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