• Structural Engineering and Mechanics
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• 제69권3호
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• pp.243-255
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• 2019

Utilization of fiber beam-column element has gained considerable attention in recent years due mainly to its ability to model distributed plasticity over the length of the element through a number of integration points. However, the relatively high sensitivity of the method to modeling parameters as well as material behavior models can pose a significant challenge. Residual drift is one of the seismic demands which is highly sensitive to modeling parameters and material behavior models. Permanent deformations play a prominent role in the post-earthquake evaluation of serviceability of bridges affected by a near-fault ground shaking. In this research, the influence of distributed plasticity modeling parameters using both force-based and displacement-based fiber elements in the prediction of internal forces obtained from the nonlinear static analysis is studied. Having chosen suitable type and size of elements and number of integration points, the authors take the next step by investigating the influence of material behavioral model employed for the prediction of permanent deformations in the nonlinear dynamic analysis. The result shows that the choice of element type and size, number of integration points, modification of cyclic concrete behavior model and reloading strain of concrete significantly influence the fidelity of fiber element method for the prediction of permanent deformations.

• 대한영양사협회학술지
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• 제25권1호
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• pp.44-58
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• 2019

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

• 상하수도학회지
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• 제35권6호
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• pp.507-516
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• 2021

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• 한국정보전자통신기술학회논문지
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• 제17권4호
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• pp.183-189
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• 2024

본 연구는 시계열 데이터를 효과적으로 예측하기 위해 데이터를 Seasonal-Trend Decomposition on Loess 을 통해 추세, 계절성, 잔차 성분으로 분해한 후 추세 성분에는 ARIMA, 계절성 성분에는 Fourier Series Regression, 잔차 성분에는 XGBoost를 적용하는 하이브리드 예측 모델을 제안하였다. 또한, ARIMA, XGBoost, LSTM, EMD-ARIMA, CEEMDAN-LSTM 모델을 포함한 성능 비교 실험을 수행하여 각 모델의 예측 성능을 평가하였다. 실험 결과, 제안된 하이브리드 모델은 MAPE, MAAPE, RMSE 지표에서 각각 3.8%, 3.5%, 0.35로 가장 좋은 평가 지표 값을 보이며 기존의 단일 모델보다 우수한 성능을 보였다.

• 대한치과교정학회지
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• 제27권6호
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• pp.943-954
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• 1997

교정적인 치아이동에 필요한 힘들은 orthodontic wire나 여러 가지 elastic rubber등으로 얻을 수 있다. 이중 교정용 elastic rubber는 환경 변화, 시간 경과, 신장(stretch) 정도에 따라 영구 변형과 힘의 소실(force decay)이 다양하게 나타나므로 적용된 힘을 예측하기 힘든 단점이 있다. 본 연구에서는 임상에서 널리 사용되는 3가지 종류 (Ormco : Generation II Power Chains ; brand A, RMO : Energy-Chain ; brand B, Unitek : AlastiK ; brand C)의 교정용 합성고무탄성재를 실험 환경, 초기 힘의 크기, 고무탄성재의 형태 그리고 신장속도를 달리한 뒤 시간에 따른 잔존 힘의 변화를 비교하였으며, 종류에 따른 특징적인 물리적 성질에 대하여 다음과 같은 결론을 얻었다. 1. 세 종류 모두에서 상온의 공기에 보관된 경우 잔존 힘의 비율이 가장 컸으며 물과 타액 간에는 차이가 없었다. 2. 세 종류 모두에서 24시간 이후로는 초기 힘의 크기에 따른 잔존 힘의 비율에 차이가 없었다. 3. A, B는 filament 유무에 따른 잔존 힘의 비율에 차이가 없었으나 C에서는 filament가 있는 경우 힘의 소실이 더 많았다. 4. 신장속도를 달리하여도 잔존 힘의 비율에는 큰 차이가 없었다. 5. B는 각각의 실험조건에서 A, C보다 상대적으로 잔존 힘의 비율이 높았다.

• 보건교육건강증진학회지
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• 제13권2호
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• pp.130-166
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• 1996

As compared with body height and body weight by ages and sexes, by means of the data reported under other researchers from 1967 to 1994 for 33 years, this study obtained the estimate value of body height and body weight by ages and sexes for the same period, and figured out prediction value of body height and body weight in the ages of between 6 and 14 from 1995 to 2000. These surveys and measurements took for one year from October 1st 1994 to September 30th. As shown in the 〈Table 1〉, in order to calculate the establishment, estimate value and prediction value of the chronological regression model of body height and body weight, by well-grounded 17 representative research papers, this research statistically tested propriety of liner regression model by the residual analysis in advance of being reconciled to simple liner regression model by the autonomous variable-year and the subordinate variable-body weight and measured prediction value, theoretical value from 1962 to 1994 by means of 2nd or 3rd polynomial regression model, with this redult did prediction value from 1995 to 2000. 1. Chronological Change of Body Height and Body Weight The analysis result from regression model of the chronological body height and body weight for the aged 6 - 16 in both sexes ranging from 1962 to 1994, corned from the 〈Table 2-20〉. On the one hand, the measurement value of respective researchers had a bit changes by ages with age growing, but the other hand, theoretical value, prediction value showed the regular increase by the stages and all values indicated a straight line on growth and development with age growing. That is, in case of the aged 6, males had 109.93cm in 1962 and females 108.93cm, but we found the increase that males had 1I8.0cm, females 1I3.9cm. In theoretical value, prediction value, males showed the increase from 109.88cm to 1I7.89cm and females from 109.27cm to 1I5.64cm respectively. There was the same inclination toward all ages. 2. Comparision to Measurement Value and Prediction Value of Body Height and Body Weight in 1994 As shown in the 〈Table 21〉, in case of body height, measurement value and prediction value of body height and body weight by ages and sexes almost showed the similiar inclination and poor grade, in case of body weight, prediction value in males had a bit low value by all ages, and prediction value in females had a high value in adolescence, to the contrary, a low value in adult. 3. Prediction Value of Body Height and Body Weight from 1995 to 2000 This research showed that body height and body weight remarkably increased in adolescence but slowly in adult. This study represented that Korean physique was on the increase and must be measured continually hereafter.

• 한국음향학회지
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• 제32권3호
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• pp.227-236
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• 2013

본 논문에서는 SAOC의 Karaoke 모드의 출력 신호 내에 존재하는 잔여 보컬 성분을 추정하여 억제시킴으로써 음질을 향상시킬 수 있는 알고리듬을 제안하였다. 잔여 보컬 성분은 Karaoke 모드 환경으로 합성된 신호와 Solo 모드로 새로 합성된 신호를 서로 교차 예측하여 추정될 수 있다. 그러나, 두 신호는 모두 같은 다운 믹스 신호로부터 합성되는 신호이므로, 두 신호간의 높은 상관성으로 인하여 가라오케 신호내의 잔여 보컬 성분뿐만 아니라 음악 성분도 함께 제거된다. 이러한 열화를 해결하기 위해, 본 논문에서는 교차 예측 과정에서 심리 음향적 특성을 고려한 예측 방해 신호를 적용하였으며, 이 신호의 크기는 심리음향모델의 마스킹 특성에 따라 음악적 음질의 열화가 최소화되도록 적응적으로 설정되었다. 실험은 보컬 객체가 포함된 음악 신호에 대해서 객관적 및 주관적 음질평가를 수행하였으며, 전체적으로 성능 향상이 있음을 확인하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제38권7호
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• pp.806-813
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• 2014

가솔린엔진에서 흡기계를 통하여 배기가스의 일부를 재연소시키는 EGR시스템은 NOx를 저감하기 위하여 양호한 배기배출특성을 나타내지만, 엔진으로 유입되는 배기가스량이 적절하지 않을 경우 불안정한 연소를 일으켜 엔진의 출력이 저하된다. 본 연구에서는 흡기관압력을 바탕으로 다양한 엔진운전조건에 따른 EGR율을 예측하는 방법을 검토하고, 이러한 예측자료를 실험적 방법을 통하여 확인하였다. 그리고 이러한 예측자료를 바탕으로 피드백 EGR제어 알고리즘을 구성한 후, 엔진운전조건에 대한 잔류 가스량을 계산한 데이터와 EGR 피드백 제어실험을 통해 얻어진 데이터를 비교한 결과를 통하여 정성적으로 유사한 결과치를 얻었다. 따라서 적용된 피드백 EGR제어 알고리즘 및 시스템은 실제 전자제어식 EGR기술에 응용될 실현 가능성을 보여주었다.

• 방송공학회논문지
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• 제16권5호
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• pp.793-796
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• 2011

H.264/AVC 동영상 압축 표준 기술은 인터/인트라 예측에 의한 잔여 영상을 주파수 변환하고 양자화하여 엔트로피 부호화한다. 이 논문에서는 인터 예측에 의한 잔여 영상을 주변 영상 블록의 영상 정보를 사용하는 기존의 방법을 향상시킨 DC 오프셋 보정 방법에 의해 잔여 영상의 정보량을 감소하는 것에 의해 압축 성능을 향상시키는 기술을 제안한다. DC 오프셋에 관련한 오버헤드 정보는 비트스트림에 포함하지 않고 인코더와 디코더가 같은 방법의 DC 오프셋을 수행한다. 실험결과는 H.264/AVC에 비해서는 BD-Rate으로 평균 0.25% 좋아지지만 기존의 DC 오프셋 방법과 비교해서는 영상 시퀀스에 따라 성능이 향상 혹은 저하되는 것을 보여 준다. 평균적으로는 0.09%의 압축 성능이 저하된다. 이 논문의 실험결과를 통해 기존의 DC 오프셋 방법과 제안하는 방법을 슬라이스 단위, 매크로블록 단위 등의 부호화단위에 따라 적응적으로 적용하는 방법에 의한 압축율 향상에 대한 가능성을 확인하였다.