• Journal of Korean Society for Quality Management
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• v.25 no.1
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• pp.44-55
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• 1997

In order to determine procedures for a, pp.opriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected according to data characteristics. Three different growth curve models were fitted onto data sets in an attempt to determine which growth curve models achieved the best forecasts for types of growth data. The analysis of the results gives rise to an a, pp.oach for selecting a, pp.opriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the goodness of fit test.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.355-362
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• 2022

Smart Farm, which combines information and communication technologies with agriculture is moving from simple monitoring of the growth environment toward discovering the optimal environment for crop growth and in the form of self-regulating agriculture. To this end, it is important to collect related data, but it is more important for farmers with cultivation know-how to analyze the collected data from various perspectives and derive useful information for regulating the crop growth environment. In this study, we developed a web service that allows farmers who want to obtain necessary information with data related to crop growth to easily analyze data. Web-based data analysis serivice developed uses R language for data analysis and Express web application framework for Node.js. As a result of applying the developed data analysis service together with the growth environment monitoring system in operation, we could perform data analysis what we want just by uploading a CSV file or by entering raw data directly. We confirmed that a service provider could provid various data analysis services easily and could add a new data analysis service by newly adding R script.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.230-236
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• 2024

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.

• Journal of Distribution Science
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• v.22 no.8
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• pp.17-27
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• 2024

Economic growth is influenced by various factors, including support from the banking world in channeling funds ownedthrough bank credit which will be a stimulus from economic activities as a source of economic growth. Purpose: Thisstudy aims to analyze the determinants of bank lending in supporting regional economic growth in South Sulawesi Province. Research Design, Data, and Methodology: This study uses secondary data taken from banking data and analyzed using path analysis Data analysis is carried out using the help of SPSS statistical analysis tools. Results: Non-Performance Loan, Three Partied Fund, Inflation, Exchange Rate directly affect economic growth. For the analysis of the indirect effect of Non-performance loans and Three Partied Funds have an indirect effect on economic growth through lending while the Loan to deposit Ratio, Inflation and exchange rate do not indirectly affect economic growththrough lending. Credit disbursement has a positive and significant effect on economic growth Conclusion: Economicgrowth of a region is influenced by many factors and these factors are influences from the banking world, the results ofthis study show that economic growth is strongly influenced by bank support through lending to support the economy by considering other factors such as interest rates and currency exchange rates

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Journal of the Korean Society of Safety
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• v.15 no.3
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• pp.1-6
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• 2000

In fatigue crack growth test, it is important not only to analyze characteristics of fatigue crack growth but also to determine the threshold stress intensity factor, ${\Delta}K_{th}$. which is the threshold value of fatigue crack growth. Linear regression analysis using fatigue test data near the threshold is suggested to determine the ${\Delta}K_{th}$ in the standard test method but the ${\Delta}K_{th}$ can be affected by a fitting method. And there are some limitations on the linear regression analysis in the case of small number of test data near the threshold. The objective of this study is to investigate differences of the ${\Delta}K_{th}$ due to regression analysis method and to evaluate the relative error range of the ${\Delta}K_{th}$ in same fatigue crack growth test data.

• The Journal of Economics, Marketing and Management
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• v.10 no.2
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• pp.15-22
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• 2022

Purpose: This study explores the effect of employee's growth needs strength on counterproductive work behaviors. Perceived job insecurity was also examined as a moderating variable on the relationship between the two variables. Research Design, data and methodology: This study collected 108 data samples from working individuals from South Korea. The Exploratory Factor Analysis (EFA) and the hierarchical regression analysis were used to analyze the data. Hierarchical regression analysis was performed using SPSS 24.0. Results: Our research results indicated that employee's growth needs strength has a negative effect on counterproductive work behaviors. Perceived job insecurity moderates the relationship between the two variables. Conclusions: Organizations should focus on creating growth opportunities for employees, since facilitating employee's growth need strength will counteract the desire to engage in behaviors that can be detrimental to the organization. and its members.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.52-59
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• 2018

Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier.

• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.51-68
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• 1994

Various growth models were each fitted onto the data sets in an attempt to determine which growth models achieved the best forecasts for differing types of growth data. Of six such models studied, some models do significantly better than others in predicting future levels of growth. It is recommened that Weibull and the Gompertz growth curve be considered along with Pearl model by those industries presently considering the implementation of substitution analysis in their life analysis. In the early stage of growth, linear estimation should suffice to give reasonable forecasts. In the latter stage, however, as more data become availavle, nonlinear estimation should be used.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.9-18
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• 2022

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.