• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Health Policy and Management
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• v.10 no.3
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• pp.50-67
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• 2000

This study aims to estimate the consumption of antibiotics in Korea and to suggest the further studies. To measure the amount of antibiotics consumption, we referred to the statistic of NFHI(National Federation of Health Insurance) and a private institute of pharmaceutical information(Korea Intercontinental Medical Statistics; IMS Korea). There were 1,563 antibiotics produced in Korea in 1997. The total amount of antibiotics production was 1,197 billion won in 1997. Antibiotics accounted for 17.6% of the total pharmaceutical productions in 1997. Cephalosporins have taken the largest part of antibiotics production since 1992. The estimation using NFHI data showed that the total expenditure of antibiotics used in health facilities was 268 billion won, 608 billion won, 911 billion won in 1990, 1994, 1997 respectively. Tertiary hospitals spent 246 billion won, general hospitals 287 billion won, hospitals 78 billion won, clinics 300 billion won in 1997. The amount of expenditure and the intensity of antibiotics consumption in hospitals have increased more steeply than any other health facilities. The total expenditure of antibiotics consumption in health facilities and pharmacies was 778 billion won when estimated using the data from IMS Korea, and 999 billion won from NFHI. Cephalosporins was the fast growing antibiotics group in all of the market- hospitals, clinics, pharmacies since 1991. To measure the amount and patterns of antibiotics consumption more precisely, a pharmaceutical monitoring or surveillance system is needed.

• International Journal of Human Ecology
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• v.15 no.2
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• pp.1-12
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• 2014

This study focuses on apparel production and consumption trends in major Asian economies in order to understand a paradigm change in the Asian fashion industry. A comparison of trade among ASEAN, NICs, and developed countries shows the changes that have occurred in terms of production and consumption of fashion products before and after 2000 in Korea, Hong Kong, and China. The flow of imports and exports in the apparel industry was analyzed using UN trade statistics data. The results found a change of industry structures in Asian NICs and ASEAN countries. Garment production bases have moved to lower cost regions like China and ASEAN; in addition, NICs sent a part of their export business in the fashion industry to ASEAN countries. The Asian fashion industry has transformed from a production base for developed countries into a consumption market with the emergence of newly industrializing economies.

• Asian Journal of Business Environment
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• v.4 no.2
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• pp.17-21
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• 2014

Purpose - The paper analyzes basic indicators characterizing the volume of energy sector activity in the Russian Federation, Privolzhsky Federal district, Republic of Tatarstan. Research design, data, and methodology - The study analyzed data from the Privolzhsky Federal district, specifically, industrial production volume, electricity production, energy consumption, energy-balance data, capital investments, and capital investment structure. An array of data has been investigated in recent years. The dataset's dynamics were analyzed in 1998. Fixed capital investment dynamics were studied in 1946 the figures were converted to a comparable form using the index method. Trends were analyzed using multivariate statistics methods and the Statgraphics software package. Results - Hypothesis 1. There are sectoral disproportions in energy flows,taking into account the volume of electricity production and consumption. Trends in electricity production in general coincide with industrial production volume trends. Energy flows have disparities in individual territorial units, and in general. Hypothesis 2. The degree of sectoral economic stability decreases with insufficient levels of investment in fixed capital energy organizations. Conclusions - Because totalelectricity production is largely determined by fixed capital investments, the study of their trends and patterns will coordinate efforts on investment operations in this area.

• Environmental and Resource Economics Review
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• v.26 no.1
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• pp.37-56
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• 2017

This study analyzed the mutual causal relationship between energy consumption, production, and export for manufacturing industry in Korea. The Korean manufacturing industry was divided into nine industries and panel data was constructed from 1991 to 2013. The panel Granger causality test method developed by Demitrescu and Hurlin (2012) was used along with the Vector Error Correction Model. This analysis showed that there was Granger Causality from production to energy consumption, from exports to energy consumption. However, Granger Causality was not established in the opposite direction. Therefore, this result supports the conservation hypothesis of Qzturk (2010) that energy-saving policies in the manufacturing sector can be implemented without adverse effects on production or exports in short-run. There is a long-run cointegrating relationship between production, energy consumption, exports, labor, and capital in the Korean manufacturing sector. Furthermore, the energy consumption contributes to the increasing of production in long-run equilibrium relationship.

• Journal of Digital Convergence
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• v.6 no.2
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• pp.97-105
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• 2008

This paper tries to measure the spill-over effect of the production and investment of telecommunication service industry (hereafter telecommunication industry), using the most recent data of 2003 input-output tables. The results are summarized as follows. First, the industries which have the biggest spill-over effect from the production of telecommunication industry is miscellaneous business service (including the sale commission of telecommunication service), other engineering services (including royalty), and business consumption. Second, the production of telecommunication industry induces more value-added, and less production, less import, and less employment than related industries such as radio and television equipment, communications and broadcasting equipment, and computer and peripheral equipment. Third, while the investment of telecommunication service amounts to 15% of its production, the effect of the investment on production, value-added, consumption, and employment reaches 70% of that of its production. The policy implication of this paper is that the telecommunication industry contributes to overall economy mainly through its investment.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.1
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• pp.17-21
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• 2013

Purpose: Forecasting of petroleum consumption is useful in planning and management of petroleum production and control of air pollution. Research Design, Data and Methodology: ARMA models, sometimes called Box-Jenkins models after the iterative Box-Jenkins methodology usually used to estimate them, are typically applied to auto correlated time series data. Results: Petroleum consumption modeling plays a role key in big urban air pollution planning and management. In this study three models as, MLFF, MLFF with GARCH (1,1) and ARMA(1,1), have been investigated to model the petroleum consumption forecasts. Certain standard statistical parameters were used to evaluate the performance of the models developed in this study. Based upon the results obtained in this study and the consequent comparative analysis, it has been found that the MLFF with GARCH (1,1) have better forecasting results.. Conclusions: Survey of data reveals that deposit of government policies in recent yeas, petroleum consumption rises in Tehran and unfortunately more petroleum use causes to air pollution and bad environmental problems.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.10-16
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• 2023

The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.1
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• pp.1-11
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• 2004

Cultural value is widely accepted as the crucial concept in understanding consumer behavior: cultural values influence consumption values, which determine choices of consuming everyday products and services. The objectives of this study were to (a) identify the difference between cultural and consumption values in the Korean fashion industry and (b) to explain the difference using the existing theories introducing fashion system, which are Solomon(2002)'s cultural production model in marketing area and Hamilton(1997)'s fashion system arbiter in clothing area. The qualitative data used to identify cultural values were 160 apparel advertisements listed in a fashion magazine issued in 2002. Utilizing the convenient sampling method, the quantitative data used to identify consumption values were gathered by surveying female university students aged over 20 living in the Seoul metropolitan area. Of 369 returned questionnaires, 255 were used in factor analysis and paired t-test. Cultural value ignored functional aspect of apparel while it was one of the most salient factors for consumption value. With respect to success and fashion orientation factors, cultural value highly appreciated them while they were the least considered factors for consumption value. These implicate that the Korean fashion industry can be explained by Hamilton's macro-level cultural and fashion system arbiters and cultural production model as well. Introducing macro-level perspectives about fashion system, this study encourages researchers to expand their research spectrum from micro-level consumers to macro-level fashion industry, which has long been neglected by the fashion marketing researchers in Korea.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.54-63
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• 2021

Since the recent COVID-19 pandemic, countries have been strengthening trade protection for their security, and the importance of securing strategic materials, such as food, is drawing attention. In addition to the cultural aspects, the global preference for food produced in Korea is increasing because of the Korean Wave. Thus, the Korean food industry can be developed into a high-value-added export food industry. Currently, Korea has a low self-sufficiency rate for foodstuffs apart from rice. Korea also suffers from problems arising from population decline, aging, rapid climate change, and various animal and plant diseases. It is necessary to develop technologies that can overcome the production structures highly dependent on the outside world of food and foster them into export-type system industries. The global agricultural industry-related technologies are actively being modified via data accumulation, e.g., environmental data, production information, and distribution and consumption information in climate and production facilities, and by actively expanding the introduction of the latest information and communication technologies such as big data and artificial intelligence. However, long-term research and investment should precede the field of living organisms. Compared to other industries, it is necessary to overcome poor production and labor environment investment efficiency in the food industry with respect to the production cost, equipment postmanagement, development tailored to the eye level of field workers, and service models suitable for production facilities of various sizes. This paper discusses the flow of domestic and international technologies that form the core issues of the site centered on the 4th Industrial Revolution in the field of agriculture, livestock, and fisheries. It also explains the environmental awareness production technologies centered on sustainable intelligence platforms that link climate change responses, optimization of energy costs, and mass production for unmanned production, distribution, and consumption using the unstructured data obtained based on detection and growth measurement data.