• 화약ㆍ발파
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• 제33권2호
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• pp.25-39
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• 2015

수중 암반 제거 방법은 화약을 사용한 수중발파와 크레인에 장착된 쇄암봉 낙하 충격을 이용하는 방법 등이 널리 이용된다. 이와 같은 암반 제거 방법은 환경적인 요인에서 지반 진동과 수중 소음을 유발하게 된다. 본 연구 대상 현장은 하역 부두의 접안능력을 향상시키기 위해 기 설치된 잔교식 돌핀 구조물에 근접한 지역의 수중 기반암을 쇄암봉 낙하에 의해 제거하도록 설계되어 있다. 시험시공을 통하여 쇄암봉 낙하 충격으로 유발되는 진동에 대한 계측, 평가를 거쳐 진동 추정식을 획득하였고, 이를 본 공사에 반영하여 구조물에 대한 안전성을 확보하였다.

• 대한산업공학회지
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• 제38권1호
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• pp.46-56
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• 2012

The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer's metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.

• 사회복지연구
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• 제42권2호
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• pp.493-523
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• 2011

본 연구는 사회복지사의 개인적 동기(심리적 동기, 직무 동기)가 이직의도에 미치는 영향을 살펴보되, 다중몰입(직무몰입, 경력몰입, 조직몰입)의 매개효과에 초점을 둔 연구로, 자기결정이론과 계획된 행동이론을 토대로 사회복지사의 이직의도 예측 모형을 제안하고 검증하고자 하였다. 연구목적을 달성하기 위해 전국 15개 시·도의 이용시설, 생활시설, 보건의료 기관, 기타 사회복지 관련 재단 및 협회, 각종 센터, 기관에 근무 중인 사회복지사를 대상으로 우편 설문조사를 실시하였다. 배포된 총 1,918부의 설문지 중 회수된 1,671부를 검토하여 이직의도가 있는 것으로 확인된 979부를 최종 분석하였다. 분석결과, 사회복지사의 심리적 동기와 직무 특성은 이직의도에 직접적 영향을 미치는 것으로 나타났다. 그러나 사회복지사의 역할 스트레스는 이직의도에 직접적 영향을 미치지 않는 것으로 나타나, 사회복지사의 이직의도에 대한 충동적 경로 모형이 심리적 동기와 직무 특성에 한해 부분적으로 지지됨을 알 수 있었다. 둘째, 사회복지사의 심리적 동기와 직무 동기는 다중몰입을 통하여 이직의도에 간접적 영향을 미치는 것으로 나타나, 사회복지사의 이직의도에 대한 반영적 경로 모형이 경력몰입, 직무몰입, 조직몰입 모두에서 지지됨을 알 수 있었다. 다중몰입 요인 중 이직의도에 가장 큰 영향력을 발휘하는 변수는 경력몰입이며 그 다음이 직무몰입, 조직몰입 순으로 나타나, 향후 경력몰입에 대한 학계의 관심이 증대되어야 함을 보여 주었다. 이상을 토대로 사회복지사의 경력관리 방안과 사회복지조직의 인적자원개발 방안을 제시하였다.

• Asian-Australasian Journal of Animal Sciences
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• 제17권2호
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• pp.228-236
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• 2004

Two varieties of alfalfa (Medicago sativa L cv. Pioneer and Beaver) and timothy (Phleum pratense L cv. Climax and Joliette), grown at different locations in Saskatchewan (Canada), were cut at three stages [1=one week before commercial cut (early bud for alfalfa; joint for timothy); 2=at commercial cut (late bud for alfalfa; pre-bloom head for timothy); 3=one week after commercial cut (early bloom for alfalfa; full head for timothy)]. The energy values of forages were determined using three approaches, including chemical (NRC 2001 formula) and biological approaches (standard in vitro and in situ assay). The objectives of this study were to determine the effects of forage variety and stage of maturity on energy values under the climate conditions of western Canada, and to investigate relationship between chemical (NRC 2001 formula) approach and biological approaches (in vitro and in situ assay) on prediction of energy values. The results showed that, in general, forage species (alfalfa vs. timothy) and cutting stage had profound impacts, but the varieties within each species (Pioneer vs. Beaver in alfalfa; Climax vs. Joliette in timothy) had minimal effects on energy values. As forage maturity increased, the energy contents behaved in a quadratic fashion, increasing at stage 2 and then significantly decreasing at stage 3. However, the prediction methods-chemical approach (NRC 2001 formula) and biological approaches (in vitro and in situ assay) had great influences on energy values. The highest predicted energy values were found by using the in situ approach, the lowest prediction value by using the NRC 2001 formula, and the intermediate values by the in vitro approach. The in situ results may be most accurate because it is closest to simulate animal condition. The energy values measured by biological approaches are not predictable by the chemical approach in this study, indicating that a refinement is needed in accurately predicting energy values.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• 한국컴퓨터정보학회논문지
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• 제28권8호
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• pp.49-58
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• 2023

학생들의 중도 탈락은 대학의 재정적 손실 뿐 아니라, 학생 개개인 및 사회적으로도 부정적인 영향을 끼친다. 이러한 문제를 해결하기 위해 기계 학습을 이용하여 대학생들의 중도 탈락 여부를 예측하고자 하는 다양한 시도가 이루어지고 있다. 본 논문에서는 대학생들의 중도 탈락 여부를 예측하기 위해 DNN(Deep Neural Network)과 LGBM(Light Gradient Boosting Machine)을 이용한 모델을 구현하고 성능을 비교하였다. 학습 데이터로는 서울 소재 중소규모 4년제 대학인 A 대학의 20,050명의 학생을 대상으로 수집된 학적 및 성적 데이터를 학습에 이용하였다. 원본 데이터의 140여개의 속성 중 중도 탈락 여부를 나타내는 속성과의 상관계수가 0.1 이상인 속성들만 추출하여 학습하였다. 두 모델의 성능 실험 결과, DNN과 LGBM의 F1-스코어는 0.798과 0.826이었으며, LGBM이 DNN에 비해 2.5% 나은 예측 성능을 보였다.

• 한국물환경학회지
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• 제28권1호
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• pp.1-9
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• 2012

It has been known that Golf course could impose negative impacts on water-ecosystem if pollutant-laden runoff is not treated well. It is important to control non-point source and re-use treated wastewater from the golf course to secure water quality of receiving waterbodies. At golf courses, the rainfall-runoff is affected by various practices to manage grasses. In many hydrological modelings, especially in simple rainfall-runoff modeling, effects on runoff of plant growth and cutting are not considered. In the study, the water erosion prediction project (WEPP), capable of simulating plant growth and various management, was evaluated for its runoff prediction from golf course under grass cutting and irrigation. The %Difference, $R^2$ and the NSE for runoff comparisons were 1.15%, 0.93 and 0.92 for calibration, and 18.12%, 0.82 and 0.88 for validation period, respectively. In grass cutting scenario, grass height was managed to be 18~25 mm. The estimated runoff was decreased by 27%. The difference in estimated total runoff was 11.8% depending on irrigation. As shown in this study, if grass management and irrigation are well-controlled, water quality of downstream areas could be obtained.

• Computers and Concrete
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• 제31권2호
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.