• Korean Journal of Food Science and Technology
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• v.22 no.3
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• pp.325-331
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• 1990

The most important factors in the cooking process which is a main process in the sausage manufacture are cooking temperature and relative humidity. In order to design energy efficient processes in cooking, accurate data for the process parameters are necessary. Therefore, texture profiles were analysed and weight losses were measured at different process conditions of the forementioned factors and at different sizes of sausage, The prediction model for the sausage cooking time was then developed by the SPSS computer program The models were developed as a function of cooking temperature, relative humidity and the diameter of sausage by analyszing the scattergram. Then the model obtained could predict the values within 2.5% error. The higher temperature and relative humidity are the less changes of weight during sausage cooking. As the results of measuring physical properties, the values of hardness and cohesiveness at different temperatures and humidities were so much changed, while the values of elasticity and chewiness had little differences.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.44-53
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• 1999

The statistical characteristics of the factors related to the daily rainfall prediction model are analyzed . Records of daily precipitation, mean air temperature, relative humidity , dew-point temperature and air pressure from 1973∼1998 at 8 meteorological sttions in south-western part of Korea were used. 1. Serial correlatino of daily precipitaiton was significant with the lag less than 1 day. But , that of other variables were large enough until 10 day lag. 2. Crosscorrelation of air temperature, relative humidity , dew-point temperature showed similar distribution wiht the basin contrours and the others were different. 3. There were significant correlation between the meteorological variables and precipitation preceded more than 2 days. 4. Daily preciption of each station were treated as a truncated continuous random variable and the annual periodic components, mean and standard deviation were estimated for each day. 5. All of the results could be considered to select the input variables of regression model or neural network model for the prediction of daily precipitation and to construct the stochastic model of daily precipitation.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.135-135
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• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.374-379
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• 2004

This paper proposes an algorithm for human sensibility evaluation using 4-channel EEG signals of the prefrontal and the parietal lobes. The algorithm uses an artificial neural network and the multiple templates. The linear prediction coefficients are used as the feature parameters of human sensibility. Comfortableness for chairs and temperature/humidity are evaluated. Many conventional researches have emphasized that a wave of left prefrontal lobe is activated in case of positive sensibility and that of right prefrontal lobe is activated in case of negative sensibility. So the power ratio of a wave is obtained from FFT computation and the results are compared. The results of the comfortableness evaluation for temperature and humidity showed that the outputs of the proposed algorithm coincided with corresponding sensibilities depending on the task of the temperature and the humidity. The . conventional method using a wave is hardly related with comfortableness. And it is also observed that the uncomfortable state due to the high temperature and humidity can be easily changed to the comfortable state by small drop of the temperature and the humidity. It seems to be good results to get 66.7％ of evaluation performance in spite of using EEG and the subject independent approach.

• Journal of the Microelectronics and Packaging Society
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• v.20 no.3
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• pp.7-10
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• 2013

This study presents an approach to predict insulation resistance failure of multilayer ceramic capacitors (MLCCs) using non-linear modeling. A capacitance aging model created by non-linear modeling allowed for the prediction of insulation resistance failure. The MLCC data tested under temperature-humidity-bias testing conditions showed that a change in capacitance, when measured against a capacitance aging model, was able to provide a prediction of insulation resistance failure.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.42-48
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• 2011

This paper presents a bridge road surface frost prediction and monitoring system. The node sensing hardware comprises microprocessor, temperature sensors, humidity sensors and Zigbee wireless communication. A software interface is implemented the control center to monitor and acquire the temperature and humidity data of bridge road surface. A bridge road surface frost occurs when the bridge deck temperature drops below the dew point and the freezing point. Measurement data was used for prediction of road surface frost occurrences. The actual alert is performed at least 30 minutes in advance the road surface frost. The road surface frost occurrences data are sent to nearby drivers for traffic accidents prevention purposes.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.158-159
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• 2021

When the porous concrete is exposed to the external environment, the internal relative humidity changes from time to time due to the inflow and outflow of moisture. This change in moisture is affected by temperature. The temperature and humidity of concrete is dominant in the carbonation rate, the largest cause of deterioration of concrete. In this study, actual weather data were used as boundary conditions. A carbonization model of concrete temperature and humidity and calcium hydroxide was constructed to perform long-term analysis. There is a slight error in the carbonation formula of the Japanese Academy of Architecture applying the Kishtani coefficient, a representative experimental formula related to carbonization, and the analysis result values. However, considering that it behaves very similarly, it is thought that a fairly reliable numerical analysis model has been established. A slight error is believed to be due to the fact that the amount of residual calcium hydroxide in the carbonated site has not yet been clearly identified.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.3
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• pp.177-190
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• 1998

The concentrations of air pollutants In large cities such as Pusan area have been increased every year due to the increasing of fuels consumption at factories and by vehicles as well as the gravitation of the population. In addition to the pollution sources, time and spatial variation of air pollutants concentration and meteorological factors have a great influence on the air pollution problem. Especially , its concentration is governed by wind direction, wind speed, precipitation, solar radiation, temperature, humidity and cloud amounts, etc. In this study, we have analyzed various data of meteorological factors using typical patterns of the air pressure to investigate how the concentration of air pollutants is varied with meteorological condition. Using the relationship between meteorological factors (air temperature, relative humidity, wind speed and solar radiation) and the concentration of air pollutants (SO2, O3) , experimental prediction formulas for their concentration were obtained. Therefore, these prediction formulas at each meteorological factor in a pressure pattern may be roughly used to predict the air pollutants concentration and contributed to estimate the variation of its value according to the weather condition in Pusan city.

• Atmosphere
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• v.27 no.2
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.4 no.1
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• pp.23-32
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• 1997

The change in moisture content of moisture sensitive products in moisture-semipermeable packages was investigated for the purpose of predicting the shelf life of a product-package combination. A mathematical model, and a computer program based on the physiochemical properties of the product and the moisture permeability of the package was developed. The moisture content for products in moisture-semipermeable packages was determined under various environmental conditions and the results were compared with the predicted values by means of the simulation model. These experimental studies demonstrated that the prediction of the change in moisture content of packaged products over time by the simulation model is accurate, within a practical range of temperature and relative humidity values. The developed semi-empirical model is considered to have applications in industry, since it provides product shelf life information for a range of temperature and relative humidity conditions, with a limited number of experimentally obtained data points.