• 제목/요약/키워드: multi regression analysis

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The Decomposition of Leaf Litters of Some Tree Species in Temperate Deciduous Forest in Korea I. Losses in Dry Weight of Leaf Litter

  • Yang, Keum-Chul;Shim, Jae-Kuk
    • The Korean Journal of Ecology
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    • 제26권4호
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    • pp.203-208
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    • 2003
  • Losses in the dry weight of leaf litter from six tree species were studied during 16 months on the forest floor in temperate deciduous forest of Mt. Cheonma in the vicinity of Seoul in Korea by using litter bag method. The decomposition rate of each leaf litter varies with each species. After 16 months elapsed, the leaf litter of Acer pseudo-sieboidianum showed the highest decomposition constant (0.82) as Olson´s decomposition constant, while that of Pinus densiflora showed the lowest decomposition constant (0.33). The decomposition constant of Quercus mongolica, Q. serrata, Betula ermani and Carpinus laxiflora showed 0.43, 0.37, 0.66 and 0.75, respectively. The decomposition constant of leaf litter was considered with temperature and precipitation which accumulated daily during each term of litter bag collection. The decomposition constant of leaf litter showed closely positive correlation with daily accumulative temperature and precipitation. The relationships between decomposition constant and the daily accumulative temperature and precipitation at each period of litter bag collection were analyzed through multi-regression analysis. The correlation coefficients as a result of multi-regression analysis in Q. mongolica, Q. serrata, P densiflora, B. ermani, C. laxiflorais and A. pseudo-sieboldianum were 0.83, 0.81, 0.69, 0.77, 0.77 and 0.62, respectively. The precipitation showed higher effect, about 10 times, on the leaf litter decomposition than the daily accumulative temperature.

차 대 보행자 충돌 시 사고해석 모델 개발 (Development of Accident Analysis Model in Car to Pedestrian Accident)

  • 강대민;안승모
    • 동력기계공학회지
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    • 제13권5호
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    • pp.76-81
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    • 2009
  • The fatalities of pedestrian account for about 21.2% of all fatalities at 2007 year in Korea. To reconstruct exactly the accident, it is important to calculate the throw distance of pedestrian in car to pedestrian accident. The frontal shape of SUV vehicle is dissimilar to passenger car and bus, so the trajectory and throw distance of pedestrian by SUV vehicle is not the same of passenger car and bus. The influencing on it can be classified into the factors of vehicle and pedestrian, and road factor. It was analyzed by PC-CRASH for simulation, and SPSS s/w was used for regression analysis. From the simulation results, the maximum impact energy of multi-body of pedestrian was occurred to that of torso body at the same time. And the throw distance increased with the increasing of impact velocity, and decreased with the increasing of impact offset. Also it decreased with the increasing of velocity of pedestrian at accident, and the throw distance of wet road was longer than that of dry road. Finally, the regression analysis model of SUV(Nissan Pathfinder type)vehicle in car to pedestrian accident was as follows; $$disti_i=-0.87-0.11offseti_i+0.69speed_i-4.27height_i+0.004walk_i+0.63wet_i+{\epsilon}_i$$.

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다수준분석을 활용한 개인특성 및 지역환경에 따른 우울증 관련 영향요인 분석 (Related Factors of Depression according to Individual Attributes and Regional Environment: Using Multi-Level Analysis)

  • 문석준;이가람;남은우
    • 보건행정학회지
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    • 제30권3호
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    • pp.355-365
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    • 2020
  • Background: This study is aimed to verify individual and regional-level factors affecting the depression of Koreans and to develop social programs for improving the depressive status. Methods: This study used individual-level variables from the Korean Community Health Survey (2018) and used the e-regional index of the Korean Statistical Information Service as the regional-level variable. A multi-level logistic regression was executed to identify individual and regional-level variables that were expected to affect the extent of depressive symptoms and to draw the receiver operating characteristic curve to compare the volume of impact between variables from both levels. Results: The results of the multi-level logistic regression analysis in regards to individual-level factors showed that older age, female gender, a lower income level, a lower education level, not having a spouse, the practice of walking, the consumption of breakfast higher levels of stress, and having high blood pressure or diabetes were associated with a greater increase in depressive symptoms. In terms of regional factors, areas with fewer cultural facilities and fewer car registration had higher levels of depressive symptoms. The comparison of area under the curve showed that individual factors had a greater influence than regional factors. Conclusion: This study showed that while both, individual and regional-level factors affect depression, the influence of the latter was relatively weaker as compared to the first. In this sense, it is necessary to develop programs focused on the individual, such as social prescribing at the local or community-level, rather than the city and nation-level approach that are currently prevalent.

The Effect of the Cutting Parameters on Performance of WEDM

  • Tosun, Nihat
    • Journal of Mechanical Science and Technology
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    • 제17권6호
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    • pp.816-824
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    • 2003
  • In this study, variations of cutting performance with pulse time, open circuit voltage, wire speed and dielectric fluid pressure were experimentally investigated in Wire Electrical Discharge Machining (WEDM) process. Brass wire with 0.25 mm diameter and AISI 4140 steel with 10 mm thickness were used as tool and work materials in the experiments. The cutting performance outputs considered in this study were surface roughness and cutting speed. It is found experimentally that increasing pulse time, open circuit voltage, wire speed and dielectric fluid pressure increase the surface roughness and cutting speed. The variation of cutting speed and surface roughness with cutting parameters is modeled by using a regression analysis method. Then, for WEDM with multi-cutting performance outputs, an optimization work is performed using this mathematical models. In addition, the importance of the cutting parameters on the cutting performance outputs is determined by using the variance analysis (ANOVA).

A correlation-based analysis on wind-induced interference effects between two tall buildings

  • Xie, Z.N.;Gu, M.
    • Wind and Structures
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    • 제8권3호
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    • pp.163-178
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    • 2005
  • Wind-induced mean and dynamic interference effects of tall buildings are studied in detail by a series of wind tunnel tests in this paper. Interference excitations of several types of upwind structures of different sizes in different upwind terrains are considered. Comprehensive interference characteristics are investigated by artificial neural networks and correlation analysis. Mechanism of the wakes vortex-induced resonance is discussed, too. Measured results show significant correlations exist in the distributions of the interference factors of different configurations and upwind terrains and, therefore, a series of relevant regression equations are proposed to simplify the complexity of the multi-parameter wind induced interference effects between two tall buildings.

An AutoML-driven Antenna Performance Prediction Model in the Autonomous Driving Radar Manufacturing Process

  • So-Hyang Bak;Kwanghoon Pio Kim
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권12호
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    • pp.3330-3344
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    • 2023
  • This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

생성 AI기반 뉴스 감성 분석과 부동산 가격 예측: LSTM과 VAR모델의 적용 (Sentiment Analysis of News Based on Generative AI and Real Estate Price Prediction: Application of LSTM and VAR Models)

  • 김수아;권미주;김현희
    • 정보처리학회 논문지
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    • 제13권5호
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    • pp.209-216
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    • 2024
  • 부동산 시장은 다양한 요인에 의해 가격이 결정되며 거시경제 변수뿐 만 아니라 뉴스 기사, SNS 등 다양한 텍스트 데이터의 영향을 받는다. 특히 뉴스 기사는 국민들이 느끼는 경제 심리를 반영하고 있으므로 부동산 매매 가격 예측에 있어 중요한 요인이다. 본 연구에서는 뉴스 기사를 감성 분석하여 그 결과를 뉴스 감성 지수로 점수화 한 후 부동산 가격 예측 모델에 적용하였다. 먼저 기사 본문을 요약 후 요약된 내용을 바탕으로 생성 AI를 활용하여 긍정, 부정, 중립으로 분류한 다음 총 점수를 산출하였고 이를 부동산 가격 예측 모델에 적용하였다. 부동산 가격 예측 모델로는 Multi-head attention LSTM 모델과 Vector Auto Regression 모델을 사용하였다. 제안하는 뉴스 감성 지수를 적용하지 않은 LSTM 예측 모델은 1개월, 2개월, 3개월 예측에서 각각 0.60, 0.872, 1.117의 Root Mean Square Error (RMSE)을 보였으며, 뉴스 감성 지수를 적용한 LSTM 예측 모델은 각각 0.40, 0.724, 1.03의 RMSE값을 나타낸다. 또한 뉴스 감성 지수를 적용하지 않은 Vector Auto Regression 예측 모델은 1개월, 2개월, 3개월 예측에서 각각 1.6484, 0.6254, 0.9220, 뉴스 감성 지수를 적용한 Vector Auto Regression 예측 모델은 각각 1.1315, 0.3413, 1.6227의 RMSE 값을 나타낸다. 앞선 아파트 매매가격지수 예측 모델을 통해 사회/경제적 동향을 반영한 부동산 시장 가격 변동을 예측할 수 있을 것으로 보인다.

Multi-Layer Perceptron과 Random Forest를 이용한 실린더 판재의 성형 조건 예측 (Application of Multi-Layer Perceptron and Random Forest Method for Cylinder Plate Forming)

  • 김성겸;황세윤;이장현
    • 대한조선학회논문집
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    • 제57권5호
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    • pp.297-304
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    • 2020
  • In this study, the prediction method was reviewed to process a cylindrical plate forming using machine learning as a data-driven approach by roll bending equipment. The calculation of the forming variables was based on the analysis using the mechanical relationship between the material properties and the roll bending machine in the bending process. Then, by applying the finite element analysis method, the accuracy of the deformation prediction model was reviewed, and a large number data set was created to apply to machine learning using the finite element analysis model for deformation prediction. As a result of the application of the machine learning model, it was confirmed that the calculation is slightly higher than the linear regression method. Applicable results were confirmed through the machine learning method.

다수준 프레일티모형 변수선택법을 이용한 다기관 방광암 생존자료분석 (Analysis of multi-center bladder cancer survival data using variable-selection method of multi-level frailty models)

  • 김보현;하일도;이동환
    • Journal of the Korean Data and Information Science Society
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    • 제27권2호
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    • pp.499-510
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    • 2016
  • 생존분석 회귀모형에서 적절한 변수를 선택하는 것은 매우 중요하다. 본 논문에서는 "frailtyHL" R 패키지 (Ha 등, 2012)를 기반으로 하여 다수준 프레일티 모형 (multi-level frailty models)에서 벌점화 변수선택 방법 (penalized variable-selection method)의 절차를 소개한다. 여기서 모형 추정은 벌점화 다단계 가능도에 기초하며, 세 가지 벌점 함수 (LASSO, SCAD 및 HL)가 고려된다. 개발된 방법의 예증을 위해 벨기에 EORTC (European Organization for Research and Treatment of Cancer; 유럽 암 치료기구)에서 수행된 다국가/다기관 임상시험 자료를 이용하여 세 가지 변수 선택 방법의 결과를 비교하고, 그 결과들의 상대적 장 단점에 대해 토론한다. 특히, 자료 분석 결과에 의하면 SCAD와 HL방법이 LASSO보다 중요한 변수를 잘 선택하는 것으로 나타났다.

패션상품 구매의사 결정과정에서의 상품유형별 채널평가가 멀티채널 이용도에 미치는 영향 (Influences of channel assessment on the usage levels of multi-channels by product category in decision making process for purchasing fashion products)

  • 박성렬;김미숙
    • 복식문화연구
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    • 제24권6호
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    • pp.803-816
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    • 2016
  • The purposes of this study were to investigate the influences of channel assessments on the usage of multi-channels by product types, and the differences in the usage of multi-channels among product types in buying decision making process for fashion products. Data were collected from 510 consumers in their 20s to 50s with purchasing experiences through multi-channel distribution system and living in Seoul and Kyunggi province; 491 were analyzed after deleting incomplete questionnaires. Factor analysis, multiple regression analysis and one-way ANOVA were used for statistical analysis by using SPSS 18.0. The results were as follows: 5 factors were extracted for channel assessment: utility, accuracy, risk, price benefit and sharing information. Price benefits, utility and sharing information for online channel tended to influence positively on the usage of online channel and online+offline channels. Accuracy and low perceived risk of offline influenced positively on offline and on+offline channel usages. The usage levels of on-line and off-line channels for cosmetics were significantly lower than the usage levels for clothes and accessories on information search, evaluation of alternatives, and purchase stages. Significant differences were also found in the usage levels of multi-channels (on+off-line) on information search and evaluation of alternatives stages. The usage levels of the multi-channels for clothes were the highest followed by those of accessories and cosmetics in order.