• Smart Media Journal
• /
• v.12 no.10
• /
• pp.9-18
• /
• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.2
• /
• pp.21-25
• /
• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.16 no.1
• /
• pp.88-95
• /
• 2011

In this paper, a new standby-mode control method for multiple output switching-mode power-supply is suggested, which uses the control signal of the feedback compensator of the inner loop in the linear voltage regulator located at the transformer secondary side, as the load current information. Conventional method has a problem that standby mode occurs depending only on the load condition of the main controller output, which makes the other secondary side output very inaccurate by burst mode operation. The proposed method detects all the load current information and operates in burst mode only when the all of them are light load condition. Minimum of the additional components are required for the implementation of the proposed method because the load information is obtained from the existing feedback circuit of the post-stage linear regulator. In this paper, the operating principles of the proposed standby-mode circuit are presented with an numerical analysis, and are verified by 25W hardware prototype implementation.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.26 no.1
• /
• pp.39-58
• /
• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2006.05a
• /
• pp.410-414
• /
• 2006

In the present, Orthogonal Frequency Division Multiple Access (OFDMA) that wireless access scheme for high speed data transmission is noticed in mobile communication market and OFDMA/TDD scheme will be used combining Time Division Duplex (TDD) scheme based on OFDMA. The Base Station's receiver synchronizes the symbol timing to anyone user's symbol and the other user's symbols have some Symbol Timing Offset (STO). Linear phase shift is occurred by each user's STO in an OFDMA symbol and the Multiple Access Interference (MAI) caused by the summation of each user's linear phase shift degrades the performance of ranging code detection. In this paper, we analyze the ranging code detection performance for each users STO in OFDMA/TDD system. Simulation results show that the more users access and mobile speed increase, the more ranging code detection performance degrades.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.286-289
• /
• 1997

In the design of structure the forces acting on the structure are important parameter for noise and vibration control. However, in the complex structure, the forces at the injection pomt on the structure cannot be measured directly. Thus it is necessary to find out indirect force evaluation method. In thls paper forces have been measured with in-situ vibration responses and system information. Three existing techniques of indirect force measurement, viz. direct inverse, principal component analysis and regularization have been compared. It has been shown that multi-vibration responses are essential for the precise estimation of the forces. To satisfy those cond~tions, Rotary compressor is adopted as test sample, because it is very difficult to measurc the injection forces from internal excitat~on to shell. It has also been obtained that relatively higher force IS transmitted through three welding paths to the compressor shell. It shows a good agreement between direct and indirect force evaluation wlth curvature shell and plate and is investigated the possibility of force evaluation of rotary compressor as a complex structure.

• Korean Journal of Agricultural Science
• /
• v.39 no.3
• /
• pp.421-425
• /
• 2012

The objective of this study was to improve the detection limit of rapid detection kit for Salmonella Typhimurium by image analysis system. The rapid detection kit was comprised of four elements: sample pad, conjugate pad, nitrocellulose pad and absorbent pad. Gold nanoparticle and Salmonella antibody were used as a tag and a receptor. Salmonella antibody and goat rabbit IgG antibody were used as test and control lines on nitrocellulose membrane. The color intensity of test line began to increase from $10^5CFU/mL$ of Salmonella sample. A multiple linear regression analysis was employed to explain the relationship between predicted and measured number of Salmonella cells. The developed model could successfully predict the cell number of Salmonella with validation against extra-experimental result.

• The Journal of the Korea Contents Association
• /
• v.16 no.7
• /
• pp.484-491
• /
• 2016

This study is to investigate the correlation of the period of child care support agency and child language cognitive development and to lead the development of the child in a positive side. For this purpose, Korea Children's Panel's 2012 data by the Korea Institute of Child Care and Education (KICCE) were used, selected 913 children of total 1703 parts. The result was derived from the results of frequency analysis, t-test, one-way ANOVA and multiple linear regression analysis. Analysis result, there was significant correlation between the period of child care support agency and child language cognitive development, and the longer the period of child care supper agency was the better child language cognitive development. Applying this results in health policy to expand the 'Free Childcare Policy', it will be higher for young child language and cognitive development.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.406-411
• /
• 2013

Big data issue has been considered in diverse fields. Also, big data learning has been required in all areas such as engineering and social science. Statistics and machine learning algorithms are representative tools for big data learning. In this paper, we study learning tools for big data and propose an efficient methodology for big data learning via legacy data to practical application. We apply our big data learning to patent analysis, because patent is one of big data. Also, we use patent analysis result for technology forecasting. To illustrate how the proposed methodology could be applied in real domain, we will retrieve patents related to big data from patent databases in the world. Using searched patent data, we perform a case study by text mining preprocessing and multiple linear regression of statistics.

• Journal of Korean Academy of Nursing
• /
• v.50 no.4
• /
• pp.631-640
• /
• 2020

Purpose: This study aimed to examine premenstrual symptoms (PMS) of shift nurses and identify the association between PMS, sleep, and occupational stress. Methods: This study was conducted with a secondary data analysis that used data from the Shift Work Nurse's Health and Turnover study. The participants were 258 nurses who were working in shifts including night shifts. PMS, sleep patterns (sleep time and sleep time variability), sleep quality, and the occupational stress of each participant were measured using the Moos Menstrual Distress Questionnaire, a sleep diary, an actigraph, the Insomnia Severity Index, and the Korean Occupational Stress Scale, respectively. Data were analyzed using SPSS 23 and STATA 15.1 to obtain descriptive statistics, Pearson's correlation coefficients, multiple linear regression with generalized estimating equations (GEE) and Baron and Kenny's mediating analysis. Results: The average PMS score, average sleep time, average sleep time variability, average sleep quality score, and average occupational stress score of the participants was 53.95 ± 40.45, 7.52 ± 0.89 hours, 32.84 ± 8.43%, 12.34 ± 5.95, and 49.89 ± 8.98, respectively. A multiple linear regression analysis with GEE indicated that sleep time variability (B = 0.86, p = .001), and sleep quality (B = 2.36, p < .001) had negative effects on nurses' PMS. We also found that sleep quality had a complete mediating effect in the relationship between occupational stress and PMS. Conclusion: These findings indicate that both sleep time variability and sleep quality are important factors associated with PMS among shift work nurses. To improve shift nurses' PMS status, strategies are urgently needed to decrease sleep time variability and increase sleep quality.