• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1117-1125
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• 2012

The occurrence of food poisoning is usually modeled by meteorological variables like the temperature and the humidity. In this paper, we investigate the relationship between food poisoning occurrence and climate variables in Korea and compare Poisson regression and autoregressive moving average model to select the forecast model. We confirm that lagged climate variables affect the food poisoning occurrences. However, it turns out that, from the viewpoint of the prediction, the number of previous occurrences is more influential to the current occurrence than meteorological variables and Poisson regression model is less reliable.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.7-13
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• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.766-773
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• 2021

This paper presents an implementation result and performance evaluation of pavilion management services that does not only provide real-time status of the pavilion in the forest but also prediction services through machine learning. The developed hardware prototype detects whether the pavilion is occupied using a motion detection sensor and then sends it to a cloud database along with location information, date and time, temperature, and humidity data. The real-time usage status of the collected data is provided to the user's mobile application. The performance evaluation confirms that the average response time from the hardware module to the applications was 1.9 seconds. The accuracy was 99%. In addition, we implemented a pavilion availability prediction service that applied a machine learning-based SVM (Support Vector Model) model to collected data and provided it through mobile and web applications.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.671-674
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• 2007

With increasing concerns of indoor air quality, $CO_2$ concentration in the public transportation, such as train, bus, and subway, draws big interests. The $CO_2$ concentration in the indoor air is regarded as index of ventilation status rather than that of adverse health effect. In this study, we measured the time-series of $CO_2$ concentrations in the subway saloon at the Subway line 1 (Suwon-station to Cheongyangri-station) with the number of passengers on board. At the same time, the concentration of particulate matter (PM), temperature, and humidity were monitored. It was found that the $CO_2$ concentration was correlated linearly with number of passengers and the relation function is suggested for the prediction of $CO_2$ conecntration by the number of passengers.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.262-264
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• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.11
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• pp.1041-1050
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• 2009

The ambient temperature and concentration of carbon dioxide in Gwangju and the reducing method of temperature, air pollutants were investigated using the atmospheric data in Gwangju. Average ambient temperature ($T_{a-ave}$ was $13.5^{\circ}C$ during 1961 to 2008. The temperature was predicted as increasing of about $2.7^{\circ}C$ in 2108 after 100 years using the trend line of regression equation. Carbon dioxide was 370.7 and 391.4 ppm at Anmyundo, in 1999 and 2008, respectively, showing proportionally increased as ambient temperature. The temperature at Gwangju, $14.2^{\circ}C$ during 1997 to 2008, was a little higher than at neighboring counties as Naju, Damyang, Hwasoon, and Jangsung. In Gwangju, Spring will start in mid-January of 2108, Summer in mid-May, Autumn in mid-October, and Winter in last-December. The average relative humidity in the air ($RH_{a-ave}$) was gradually decreased as the temperature inversely increased. The average $CO_2$ was 457 ppm, which is 65.6 ppm higher than that in Anmyundo, korean background area of $CO_2$ in 2008. Carbon dioxide showed positive correlation, both of them, with carbon monoxide (0.87) and relative humidity (0.48).

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.130-131
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• 2018

The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were learned using the Deep Learning Algorithm to derive an estimated function model. The purpose of this study is to investigate the effect of the number of hidden layers on the prediction performance in the process of estimating the compressive strength for an arbitrary combination.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.834-838
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• 2004

This paper proposes an algorithm for EEG pattern classification using the Multi-template method, which is a kind of speaker adaptation method for speech signal processing. 10-channel EEG signals are collected in various environments. The linear prediction coefficients of the EEGs are extracted as the feature parameter of human sensibility. The human sensibility classification algorithm is developed using neural networks. Using EEGs of comfortable or uncomfortable seats, the proposed algorithm showed about 75% of classification performance in subject-independent test. In the tests using EEG signals according to room temperature and humidity variations, the proposed algorithm showed good performance in tracking of pleasantness changes and the subject-independent tests produced similar performances with subject-dependent ones.

• Advances in environmental research
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• v.2 no.3
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• pp.203-227
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• 2013

Global demand for dwelling energy and implications of changing climatic conditions on buildings confront the built environment to build sustainable dwellings. This study investigates the variability of future climatic conditions on newly built detached dwellings in the UK. Series of energy modelling and simulations are performed on ten detached houses to evaluate and predict the impact of varying future climatic patterns on five building performance indicators. The study identifies and quantifies a consistent declining trend of building performance which is in consonance with current scientific knowledge of annual temperature change prediction in relations to long term climatic variation. The average percentage decrease for the annual energy consumption was predicted to be 2.80, 6.60 and 10.56 for 2020s, 2050s and 2080s time lines respectively. A similar declining trend in the case of annual natural gas consumption was 4.24, 9.98 and 16.1, and that for building emission rate and heating demand were 2.27, 5.49 and 8.72 and 7.82, 18.43 and 29.46 respectively. The study further analyse future heating and cooling demands of the three warmest months of the year and ascertain future variance in relative humidity and indoor temperature which might necessitate the use of room cooling systems to provide thermal comfort.

• Architectural research
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• v.25 no.4
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• pp.73-84
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• 2023

Indoor thermal conditions greatly affect the health and comfort of humans who occupy the space in it. The purpose of this research is to analyze the influence of water and vegetation elements as a microclimate modifier in buildings to obtain thermal comfort through the study of thermal environment models. This research covers two objects, namely public buildings and housing in Makassar City, South Sulawesi Prov-ince - Indonesia. Quantitative methods through field surveys and measurements based on thermal and personal variables. Data analysis based on ASHRAE 55 2020 standard. The data was processed with a parametric statistical approach and then simulated with the Computational Fluid Dynamics (CFD) simulation method to find a thermal prediction model. The model was made by increasing the ventilation area by 2.0 m2, adding 10% vegetation with shade plant characteristics, moving water features in the form of fountains and increasing the pool area by 15% to obtain PMV + 0.23, PPD + 8%, TSV-1 - +0, Ta_25.7℃, and relative humidity 63.5 - 66%. The evaluation shows that the operating temperature can analyze the visitor's comfort temperature range of >80% and comply with the ASHRAE 55-2020 standard. It is concluded that water elements and indoor vegetation can be microclimate modifiers in buildings to create desired comfort conditions and adaptive con-trols in buildings such as the arrangement of water elements and vegetation and ventilation systems to provide passive cooling effects in buildings.