• Smart Structures and Systems
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• 제32권2호
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• 농업과학연구
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• 제47권4호
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Architectural research
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• 제14권4호
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• pp.153-161
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• 2012

This paper presents an integrated procedure to predict the temperature and moisture distributions in hardening concrete considering the effects of temperature and aging. The degree of hydration is employed as a fundamental parameter to evaluate hydro-thermal-mechanical properties of hardening concrete. The temperature history and temperature distribution in hardening concrete is evaluated by combining cement hydration model with three-dimensional finite element thermal analysis. On the other hand, the influences of both self-desiccation and moisture diffusion on variation of relative humidity are considered. The self-desiccation is evaluated by using a semi-empirical expression with desorption isotherm and degree of hydration. The moisture diffusivity is expressed as a function of degree of hydration and current relative humidity. The proposed procedure is verified with experimental results and can be used to evaluate the early-age crack of hardening concrete.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.133-152
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• 2022

Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5^th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2^nd largest crop being produced and 3^rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.

• 한국환경보건학회지
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• 제47권6호
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• pp.548-557
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• 2021

Background: Due to the emergence of new diseases such as COVID-19, an increasing number of people are struggling with stress and depression. Interest is growing in forest-based recreation for physical and mental relief. Objectives: A prediction model equation using meteorological factors and data was developed to predict the quantities of medicinal substances generated in forests (monoterpenes) in real-time. Methods: The concentration of phytoncide and meteorological factors in the forests near Chuncheon in South Korea were measured for nearly two years. Meteorological factors affecting the observation data were acquired through a multiple regression analysis. A model equation was developed by applying a linear regression equation with the main factors. Results: The linear regression analysis revealed a high explanatory power for the coefficients of determination of temperature and humidity in the coniferous forest (R²=0.7028 and R²=0.5859). With a temperature increase of 1℃, the phytoncide concentration increased by 31.7 ng/Sm³. A humidity increase of 1% led to an increase in the coniferous forest by 21.9 ng/Sm³. In the deciduous forest, the coefficients of determination of temperature and humidity had approximately 60% explanatory power (R²=0.6611 and R²=0.5893). A temperature increase of 1℃ led to an increase of approximately 9.6 ng/Sm³, and 1% humidity resulted in a change of approximately 6.9 ng/Sm³. A prediction model equation was suggested based on such meteorological factors and related equations that showed a 30% error with statistical verification. Conclusions: Follow-up research is required to reduce the prediction error. In addition, phytoncide data for each region can be acquired by applying actual regional phytoncide data and the prediction technique proposed in this study.

• 산업식품공학
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• 제21권4호
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• pp.312-317
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• 2017

감모율에 영향을 주는 인자를 파악하고 온습도 변화, 제상수, VPD방법을 이용해 감모율을 예측하였으며 실제 감모율과 비교분석을 진행하였다. 저장고 내부의 습도변화를 이용하여 예측한 결과 질소주입과정보다 유닛쿨러의 운전과정에서 발생하는 응축 결상 수분배출 과정이 지배적인 영향을준다. 또한 온습도를 이용한 감모율 예측방법이 실제 감모율과 가장 근사값을 나타냈다. 제상수를 이용해 예측한 결과 감모량은 유닛쿨러의 운전율이 높아질수록 많아졌으며 온습도를 이용한 예측방법보다 운전특성에 따른 감모율 변화가 더 뚜렷하게 나타났다. 이때 유닛쿨러의 운전율은 외기온도와 비례하였으며, 저장고 내부에서 응축된 수분량의 계측이 어렵기 때문에 실제 감모율과 오차가 발생한 것으로 보인다. VPD를 이용한 감모율 예측은 증산계수(K-value)의 영향이 지배적이며, 보고되어진 본 연구에서 이용한 후지사과의 증산계수값(42)에 대한 검증이 필요하다. 본 연구에서 후지사과의 K-value를 30으로 수정하였을 때 가장 근사한 예측값을 계산할 수 있었다.

• 설비공학논문집
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• 제13권3호
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• pp.167-173
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• 2001

This paper represents the numerical analysis for microenvironments various temperature and humidity in bed mattress. He purpose of this study is for healthful bed mattress by controling a bacteria with a prediction of the vapor and temperature distributions in the bed mattress. The numerical model is one dimensional unsteady state and the governing equations were discretized by fully implicit scheme. The numerical results were compared with experimental data, and showed a good agreement with them. Specially, the excess-relative humidity shows a lower distribution near the surface of mattress, meaning that the optimum living condition for bacteria will be caused.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 학회창립 10주년 기념 1999년도 가을 학술발표회 논문집
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• pp.767-770
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• 1999

The carbonation process is affected both by the concrete material properties such as W/C ratio, types of cement and aggregated, admixture characteristics and the environmental factors such as CO2 concentration, temperature, humidity. Based on results of preliminary research on carbonation, this study is to propose a carbonation prediction model by taking into account of prediction model by taking into account of CO2 concentration and W/C ratio among major factors affecting the carbonation process.

• 한국식품과학회지
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• 제19권2호
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• pp.176-180
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• 1987

건조마늘 플레이크를 상대습도 11%에서 84%까지 7단계의 상대습도별로$5^{\circ}C$, $20^{\circ}C$ 및 $35^{\circ}C$온도에 저장하면서 건조마늘 플레이크의 갈변 및 흡습특성을 조사하였다. 저장시간에 따른 건조마늘 플레이크의 흡습곡선은 RH 51% 이하에서는 단시간내에 평형에 도달하여 수분함량의 변화가 거의 없었으나, $20^{\circ}C$ 및 $35^{\circ}C$의 RH 67% 이상에서 $5^{\circ}C$의 RH 84%에서 평형수분함량이 급격히 증가하여 갈변현상이 나타났다. 건조마늘 플레이크의 단분자층 수분함량은 온도에 따라서 5.80%(DB)에서 6.20%(DB)로서 온도가 내려감에 따라 다소 증가하는 경향을 나타냈고 수분함량 및 저장온도가 낮으면 낮을수록 흡습력이 크기 때문에 건조마늘 플레이크의 장기저장에 방습포장재가 필요하였다. 상대습도 및 저장온도에 따라서 건조마늘 플레이크의 갈변도를 예측할 수 있는 회귀방정식을 도출하였다.

• Composites Research
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• 제32권6호
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• pp.382-387
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• 2019

상대습도와 온도가 PET 필름의 점탄성 특성에 미치는 영향을 조사하기 위해 RH-DMA를 이용하여 single frequency strain mode 시험, stress relaxation mode 시험, creep 시험을 수행하였다. 상대습도는 10%, 30%, 50%, 70%, 90%를 적용하고 온도는 single frequency strain mode 시험의 경우 30~95℃, stress relaxation mode 시험의 경우 30℃ 와 70℃, creep 시험의 경우 5~95℃를 고려하였다. 연구결과에 따르면 상대습도가 높아지면 저장탄성계수와 손실탄성계수는 낮아지며 손실탄성계수의 최대값은 상대습도의 변화에 큰 영향을 받지 않고 거의 일정해진다. 이완탄성계수는 초기에 급격히 감소하다가 일정한 값을 가지며 높은 온도에서는 상대습도의 변화에 민감해진다. 변형률 회복는 초기에 급격히 증가하며 온도가 높아지면 이완 탄성계수와 마찬가지로 상대습도에 민감하게 변한다. 크리프 컴플라이언스의 증가 정도는 온도가 높아지면 커지며 유리전이온도보다 온도가 높아지면 증가 정도는 더욱 커진다. 시간-온도 중첩법을 통해 구해지는 마스터 선도를 이용하면 상대습도와 온도 등의 운용 조건에서의 장기 성능을 예측할 수 있는 정보를 얻을 수 있다.