• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.29-34
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• 2017

A CMOS-based temperature sensor is proposed for low-voltage and low-power on-chip thermal monitoring applications. The proposed temperature sensor converts a proportional to absolute temperature (PTAT) current to a PTAT frequency using an integrator and hysteresis comparator. In addition, it operates in the subthreshold region, allowing reduced power consumption. The proposed temperature sensor was fabricated in a standard 90 nm CMOS technology. Measurement results of the proposed temperature sensor show a temperature error of between -0.81 and $+0.94^{\circ}C$ in the temperature range of 0 to $70^{\circ}C$ after one-point calibration at $30^{\circ}C$, with a temperature coefficient of $218Hz/^{\circ}C$. Moreover, the measured energy of the proposed temperature sensor is 36 pJ per conversion, the lowest compared to prior works.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.413-422
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• 2000

Samples of banana were dried in a two-stage heat pump dryer capable of producing stepwise control of the inlet drying air temperature while keeping absolute humidity constant. Two stepwise air temperature profiles were tested. The incremental temperature step change in temperature of the drying air about the mean air temperature of 30 $^{\circ}C$ was 5 $^{\circ}C$. The total drying time for each temperature-time profile was about 300 minutes. The drying kinetics and color change of the products dried under these stepwise variation of the inlet air temperature were measured and compared with constant air temperature drying. The stepwise air temperature variation was found to yield better quality product in terms of color of the dried product. Further, it was found that by employing a step-down temperature profile, it was possible to reduce the drying time to reach the desired moisture content.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.457-459
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• 2018

Solar power generation system is a energy generation technology that produces electricity from solar power, and it is growing fastest among renewable energy technologies. It is of utmost importance that the solar power system supply energy to the load stably. However, due to unstable energy production due to weather and weather conditions, accurate prediction of energy production is needed. In this paper, an Artificial Neural Network(ANN) that predicts solar energy using 15 kinds of meteorological data such as precipitation, long and short wave radiation averages and temperature is implemented and its performance is evaluated. The ANN is constructed by adjusting hidden parameters and parameters such as penalty for preventing overfitting. In order to verify the accuracy and validity of the prediction model, we use Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) as performance indices. The experimental results show that MAPE = 19.54 and MAE = 2155345.10776 when Hidden Layer $Sizes=^{\prime}16{\times}10^{\prime}$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.626-628
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• 2018

In this paper, we compare the performances of SVM (Support Vector Machine) and ANN (Artificial Neural Network) machine learning models for predicting solar energy by using meteorological data. Two machine learning models were built by using fifteen kinds of weather data such as long and short wave radiation average, precipitation and temperature. Then the RBF (Radial Basis Function) parameters in the SVM model and the number of hidden layers/nodes and the regularization parameter in the ANN model were found by experimental studies. MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) were considered as metrics for evaluating the performances of the SVM and ANN models. Sjoem Simulation results showed that the SVM model achieved the performances of MAPE=21.11 and MAE=2281417.65, and the ANN model did the performances of MAPE=19.54 and MAE=2155345.10776.

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.919-925
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• 2006

The regression models for the water temperatures of Ban Byeon Stream and Yong Jeon stream were developed using multi-regression method. It was also investigated that the applicability of the stream temperature prediction to two-dimensional numerical simulation to predict the vertical water temperature in Imha Reservoir. Air temperature and dew point as independent variables were selected to be applicable to cases with the different variation of flow rates. The data division of water temperature using a cutoff flow rate of $20m^3/s$ was found to reduce the prediction error of the stream temperature. The mean absolute percent error of the numerical simulation results of the vertical water temperature in Imha Reservoir using the regression models was 11%, which was only 4.3% lager than the simulation result using the measured stream temperature. Therefore, the regression models of the stream temperatures using multi-regression method applied in this study could be applied to predict the vertical water temperature in Imha Reservoir with a good accuracy.

• Journal of the Korean Society of Safety
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• v.28 no.2
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• pp.49-54
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• 2013

The autoignition temperature(AIT) is important index for the safe handling of flammable liquids which constitute the solvent mixtures. This study measured the AITs and ignition delay time for n-Decane and Acetic acid system by using ASTM E659 apparatus. The AITs of n-Decane and Acetic acid which constituted binary system were $212^{\circ}C$ and $512^{\circ}C$, respectively. The experimental AITs of n-Decane and Acetic acid system were a good agreement with the calculated AITs by the proposed equations with a few A.A.D.(average absolute deviation). And n-Decane and Acetic acid system was shown the minimum autoignition temperature behavior(MAITB).

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.655-658
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• 1998

CMOS Reference Voltage Generator(RVG) is designed to possible CMOS process without additional process steps. It is possible to compensate the temperature of RVG by using PTAT(proportional to the absolute temperature). Temperature compensation is profitable because $\mun$ (electron mobility) is used. When VDD sweeps from 3V to 7V, variation ratio of Vref is 0.3125mV/V. Also temperature variation ratio of Vref is $047.1ppm/^{\circ}C$ during sweeping from $0^{\circ}C$ to $100^{\circ}C.$ Power Consumption is $50.3\muW.$

• Journal of the Korean Society of Safety
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• v.29 no.6
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• pp.55-61
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• 2014

The autoignition temperature (AIT) of a material is the lowest temperature at which the substance will spontaneously ignite in the absence of an external ignition source such as a spark or flame. The AIT may be used as combustion property to specify operating, storage, and materials handling procedures for processs safety. This study measured the AITs of n-Propanol+n-Decane system from ignition delay time(time lag) by using ASTM E659 apparatus. The AITs of n-Propanol and n-Decane which constituted binary system were $435^{\circ}C$ and $212^{\circ}C$, respectively. The experimental AITs of n-Propanol+n-Decane system were a good agreement with the calculated AITs by the proposed equations with a few A.A.D(average absolute deviation).

• Korean journal of food and cookery science
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• v.29 no.6
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• pp.647-652
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• 2013

Solubility profiles of zein and carotenoid in aqueous ethanol were studied. Zein showed minimum turbidity at the aqueous ethanol concentration of 87-92%, indicating least aggregations between protein molecules. Solubilities of zein and carotenoid increased linearly with the content of yellow zein up to 20% in the aqueous ethanol range of 60-95% tested. At room temperature of $20^{\circ}C$, zein showed maximum solubility in broad ethanol concentration ranges of 60-95%, while that for carotenoid was somewhat narrower with ethanol concentration range of 85-95%. However, at incubation temperature of $-20^{\circ}C$, solubilities of both carotenoid and zein were lowered, with dramatic reduction being exhibited at aqueous ethanol concentration of 60% for both compounds, while substantial reduction in solubility was shown at 95% ethanol by zein only. Zein was practically insoluble in absolute ethanol, regardless of temperature range tested, while carotenoid remained largely soluble, though there was pronounced decrease in solubility at the subfreezing temperature.