• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.1
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• pp.50-56
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• 2014

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.189-199
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• 2019

The objectives of this study are to propose the Integrated Waqf Environmental Protection Model (IWEP) and investigate the farmers' intention to adopt it. In developing the IWEP model and investigating farmers' willingness to adopt it, this study surveyed 400 farmers in Kedah. The intention of the farmers to adopt the proposed model was analysed by adding perceived barriers and socio-economic variables into the theory of reasoned action (TRA) model. The collected data were processed using structural equation modelling (SEM). The SEM results show that the subjective norm is positive and has a significant impact on the intentions of low-income farmers to accept the IWEP model. This indicates that the decision of the low-income farmers to accept the IWEP model is significantly influenced by their family members, neighbours and friends. Furthermore, awareness and perceived barriers have a greater impact on the elderly, highly educated and wealthy farmers. The findings indicate that the elderly, highly educated and wealthy farmers are aware of climate change and they perceive higher risks or barriers to climate change. As a result, they are more likely to have an adaptation intention. If we encourage people to create waqf fund, we can increase the value of the farmer and the country's total GDP.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.615-616
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• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.1087-1098
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• 2019

In this study, we developed an integrated urban river data platform that collects, cleans, and provides data for urban river management. The urban river integrated data platform has the function of collecting data provided by various institutions using the Open API service. The collected data is purified through pre-processing and loaded into a database. The collected data can be reviewed and analyzed using a visualization system and provided through the Open API, so that it can be used as individual input data by combining them in the urban river model. In addition, the development system for real-time data was developed to apply real-time data to urban river models. Through this, users will be able to reduce the time and effort required for data collection, pre-processing and input data construction, thereby increasing efficiency and scalability in the development of urban river models and systems.

• International Journal of Contents
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• v.8 no.3
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• pp.12-19
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• 2012

This study investigated what factors affect consumers' decision making concerning the adoption of smart TVs. For this purpose, the integrated adoption model that consists of six major constructs from the diffusion of innovation theory (DIT), the technology acceptance model (TAM), and the model of innovation resistance(MIR) was employed. To collect data, an online survey was used. Data collected were analyzed with the structural equation model (SEM). Findings showed that the innovativeness has a positive influence on the both of perceived usefulness and perceived ease of use. It was also shown that both of perceived usefulness and perceived ease of use affect the intention to use smart TVs in a positive way. The innovation resistance has a negative influence on the intention to use. The mediating role of the innovation resistance was also found. The implications of these results are discussed.

• Journal of the Korean Solar Energy Society
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• v.33 no.6
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• Journal of Environmental Impact Assessment
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• v.12 no.2
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• pp.87-98
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• 2003

The main purpose of this study is to propose the integrated assessment model for the rational and effective selection of proposed sites in National Trust (NT) and conserve the ruined natural environment by excessive land development. The results of this study are as follows; 1) The specialists thought that rare and endangered species were very important in plant and animal, in case of landscape and environment, naturality and water quality were too important. 2) In the result of the correlation measure on the indicator of assessment, 'erosion of soil'and 'air pollutant'was highly correlative. Secondly, 'suspended solids' and 'erosion of soil'was high correlation. 3) In the result of forming the factors into the integrated indicators, they were classified into conditional, stable, valuable and potential factors and the purpose of this formation is to evaluate proposed sites in NT objectively and rationally with organic assessment. 4) In the integrated assessment model, the degree of explanation was observed approximately 36.4% and the important factor was the conditional factor, but we have to consider all factors for the effective and objective assessment. Therefore we organically have to apply and use them for the assessment of proposed sites in NT. It turns out to offer raw data on the land conservation and carry out the role of the instrument of measurement. As for future directions, the follow are proposed: 1) adaptation of real proposed site, 2) verification of effect and problem, 3) practical survey for diverse types as mountain, coast and inland.