• Journal of the Korean Society for Precision Engineering
• /
• v.32 no.2
• /
• pp.185-190
• /
• 2015

This paper presents a new method to determine sensitivity coefficients of temperature and humidity of torque transducers by using a natural and seasonal variation of ambient conditions at the laboratory. We had measured the sensitivities of the torque transducers over almost one year using the KRISS 2 kN m torque standard machine. The sensitivity data acquired at various ambient conditions were processed using our measurement model to extract the sensitivity coefficients of temperature and humidity simultaneously with high precision. A comparison with a previous method using an environmental control chamber was carried out to test the feasibility of using our new method. Two results agreed within the uncertainty. We revealed that the torque measuring errors could be 8 times higher than the measurement and calibration capability of KRISS torque standard machine if the sensitivity changes due to the temperature and humidity are not properly corrected during a calibration.

• International Journal of Highway Engineering
• /
• v.20 no.2
• /
• pp.127-135
• /
• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Journal of Energy Engineering
• /
• v.25 no.3
• /
• pp.27-33
• /
• 2016

The heat recovery system that was applied in this study, is the energy-saving type that can produce the maximum cooling capacity less power in use. In order to have a more precise control function the temperature and humidity of the constant temperature and humidity machine, control algorithm is applied to designed a fuzzy PID controller, and the outside air compensation device (air-cooled) demonstrated excellent ability to dehumidify the moisture, $-20^{\circ}C$ in winter. High efficiency and the low-noise type sirocco fan operate quitely and designed to fit the bottom-up and top-down in accordance with the characteristics of equipment. as a result of experiment data, the conversion efficiency is 95% or more, power recovery time is within 5sec, stop delay time is within 30sec, pump down time is 10sec, pump delay time is 5sec, heating delay time is 5sec, temperature deviation is ${\pm}2^{\circ}C$ (cooling deviation: $2^{\circ}C$, Heating deviation : $2^{\circ}C$), humidity deviation is a ${\pm}5%$ (humidification deviation 3.0%, dehumidification deviation 3.0%). Recently, ubiquitous technology is important. so, the constant temperature and humidity machine designed to be able to remotely control to via the mobile phone, and more scalable to support MMI software and automatic interface. Further, the life of the parts and equipment is extended by the failure.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.11
• /
• pp.167-173
• /
• 2002

The look up table method conventionally applied to control the inner temperature and humidity of a laver drying machine has repeatedly occurred not only laver's damage but also inferior goods since the reaching time at the optimum state takes a long time. In this paper, a fuzzy control theory instead of the look up table was proposed to reduce the reaching time at the optimum state. The proposed method used six input variables and four output variables for the fuzzy control, and a triangle rule for a fuzzifier, The Mandani's min-max method was applied to a fuzzy inference. Also, the mean method of maximum was applied to a defuzzifier. The method applied to the fuzzy controller contributed to reduce the reaching time at the optimum state, and to minimize not only laver's damage but also inferior goods.

• Journal of Korean Association for Spatial Structures
• /
• v.21 no.2
• /
• pp.99-110
• /
• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

• Proceedings of the KSR Conference
• /
• 2008.06a
• /
• pp.948-951
• /
• 2008

Ubiquitous sensor network (USN) based on ZigBee communication protocol has been used in various application fields, such as home-network, intelligent building and machine, logistics, environmental monitoring, military field, security field and etc. The ZigBee is targeted at radio-frequency application that require a low data rate, long battery life and secure network. Especially, the USN system can be applied efficiently to building-indoor where the complex geometry is adopted. In this study, all 90 points of railway cabin indoor were monitored for temperature and humidity using USN technology. All sensors were pre/post-calibrated and the temperature/humidity change were analyzed in a railway cabin in real-time. The results would be useful to develop the cabin heating, ventilating and air conditing (HVAC) system to meet all passengers' thermal comfort regardless of their seat position.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.22 no.5
• /
• pp.824-830
• /
• 2013

In ultra-precision machining, the inside temperature should be controlled precisely. The important factors are environmental conditions (outside temperature, humidity) and temperature conditions (cutting heat, spindle heat). Thus, in this study, an environmental control cell for the ultra-precision machine that could control the inside temperature and minimize effects of the surrounding environment to achieve a thermal deformation of less than 400nm of a specimen was designed and verified through C.F.D. Further, a method that could control the temperature precisely by using a blower, heat exchanger and heater was evaluated. As a result, this study established a C.F.D technic for the environmental control cell, and the specimen temperature was controlled to be within $17.465{\pm}0.055^{\circ}C$.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.19 no.3
• /
• pp.86-92
• /
• 2017

In this study, a model to classify frost occurrence and frost free day was developed using the digital weather forecast data provided by Korea Meteorological Administration (KMA). The minimum temperature, average wind speed, relative humidity, and dew point temperature were identified as the meteorological variables useful for classification frost occurrence and frost-free days. It was found that frost-occurrence date tended to have relatively low values of the minimum temperature, dew point temperature, and average wind speed. On the other hand, relatively humidity on frost-free days was higher than on frost-occurrence dates. Models based on machine learning methods including Artificial Neural Network (ANN), Random Forest(RF), Support Vector Machine(SVM) with those meteorological factors had >70% of accuracy. This results suggested that these models would be useful to predict the occurrence of frost using a digital weather forecast data.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.42 no.4
• /
• pp.473-482
• /
• 1997

Temperature, humidity and wetness duration were monitored for fully developed paddy rice canopies with 3 different structures induced by the seeding method(puddled-soil drill seeding, DS ; hand broadcasting, HB ; machine broadcasting, MB). Within-canopy air temperature averaged over "clear sky" hours during the study period(maximum tillering through heading) was lower than the screen temperature at a nearby standard weather station, especially in the night. The same trend was true for "overcast sky" hours except the diurnal distinction. Vapor pressure within the canopy was high during the daytime and low in the night, making the daytime deviation from outside the canopy more significant on clear days. Under the overcast sky, the canopy maintained a steady 5 to 10％ higher vapor pressure than the outside regardless of day or night. Daily maximum temperature was observed to be higher within the canopies with more leaf mass, making MB the highest, HB the lowest, and DS in between. Relative humidity was over 90％ in the night and dropped to 70％ in the mid-afternoon, but vapor pressure within the canopy was highest at around 13:00 LST. Dew point depression was lowest and, combined with the temperature, the relative humidity was highest in HB. Mean period of wetting duration was in the order of DS＞HB＞MB, while the dew point depression was greatest in DS.

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.155-165
• /
• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.