• Journal of Environmental Science International
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• v.31 no.7
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• pp.555-567
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• 2022

With the introduction of the tele-monitoring system (TMS) in South Korea, monitoring of the concentration of pollutants discharged from nationwide water quality TMS attachments is possible. In addition, the Ministry of Environment is implementing a smart sewage system program that combines ICT technology with wastewater treatment plants. Thus, many institutions are adopting the automatic operation technique which uses process operation factors and TMS data of sewage treatment plants. As a part of the preliminary study, a multilayer perceptron (MLP) analysis method was applied to TMS data to identify predictability degree. TMS data were designated as independent variables, and each pollutant was considered as an independent variables. To verify the validity of the prediction, root mean square error analysis was conducted. TMS data from two public sewage treatment plants in Chungnam were used. The values of RMSE in SS, T-N, and COD predictions (excluding T-P) in treatment plant A showed an error range of 10%, and in the case of treatment plant B, all items showed an error exceeding 20%. If the total amount of data used MLP analysis increases, the predictability of MLP analysis is expected to increase further.

• Smart Media Journal
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• v.12 no.10
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• pp.102-109
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• 2023

This study aimed to investigate the growth parameters and antioxidant activity of Cnidium officinale under controlled temperature and carbon dioxide levels during the cultivation period. The plants were cultivated for four months, each group being set at the average temperature of the cultivation area +1.8℃/445ppm(SSP1), +3.6℃/872ppm(SSP3), and +4.4℃/1,142ppm(SSP5), respectively. During the cultivation period, the growth, Top/Root ratio, and leaf weight ratio(LWR) of C. officinale slightly decreased in SSP3 and SSP5 compared to SSP1, while the root weight ratio(RWR) increased. The antioxidant activity and related phenolic compound content in the aerial parts of C. officinale increased proportionally with temperature and CO₂ concentration. However, an adverse effect was observed in the high-concentration SSP5 group. Conversely, in the roots, the SSP5 group exhibited the highest antioxidant activity. This study suggests that it can be utilized as fundamental data necessary for understanding the correlation between environmental conditions and the growth as well as physiological activities of medicinal plants.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Journal of Drive and Control
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• v.20 no.4
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• pp.100-106
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• 2023

We developed a crawler-based driving platform that can perform harvesting, transportation, pest control, and rotary operation by equipping it with various implements, and analyzed its performance. This single platform was developed to perform as pepper harvester, peanut harvester, and transporter with a 46-kW engine. A simulation model was developed to study the specifications of the platform, and the accuracy was also analyzed. The absolute percentage error ranged from 0.2 to 5.9%, which made it possible to predict the platform performance using simulation model. In T-test, both torque and speed on field and asphalt showed a significant difference (1%). Driving torque required differed depending on the nature of the field, and the speeds also changed based on soil load. The developed platform has the advantage of being equipped with a variety of working tools, expected to be used to harvest root crops in the future.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.105-109
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• 2020

This study was conducted to the improvement of solar light-based artificial light supply system and effect of lettuce cultivation. The artificial light supply system was consisted of units such as light source, power, system measurement and controller. The light source supply was composed of a solar transmitter and an LED lamp. The power supply consisted of an leakage breaker, SMPS, LED controller and relay. The solar transmitter was made of a quartz optical fiber with optimal light transmission. Artificial light used white lamp among LEDs. System measurement and control consisted of touch screen, Zigbee communication module and light quantity sensor. The results of test confirmed that the LED light is automatically activated when the intensity measured by the light intensity sensor is 200 μmolm^-2s^-1 or less. Moreover, the leaf length, root length, chlorophyll content and root fresh weight of optical fiber treatment was hight than LED lamp treatment. Therefore, it can be inferred that the energy-saving solar light collector device can be effective in the indoor lettuce production. However, the use of LED lamp is also recommended to assure the availability of sufficient sunlight in cloudy and rainy days.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.568-574
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• 2020

In this paper, we propose a method to reduce the square root computation having high computation complexity in Canny edge detection algorithm using image processing. The proposed method is to reduce the number of operation calculating gradient magnitude using pixel's continuity using make a specific pattern instead of square root computation in gradient magnitude calculating operation. Using various test images and changing number of hole pixels, we can check for calculate match rate about 97% for one hole, and 94%, 90%, 88% when the number of hole is increased and measure decreasing computation time about 0.2ms for one hole, and 0.398ms, 0.6ms, 0.8ms when the number of hole is increased. Through this method, we expect to implement low power embedded vision system through high accuracy and a reduced operation number using two-hole pixels.

• Journal of Drive and Control
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• v.17 no.4
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• pp.141-151
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• 2020

The aim of this study was to analyze the safety factor of range-shift gear pairs on multi-purpose agricultural implement machinery for an optimal design of a transmission system. Gear-strengths such as bending and contact stress and safety factors were analyzed under three load conditions: an equivalent engine torque at plow tillage, a rated engine torque, and the maximum engine torque. Root and contact safety factor were calculated to be 3.88, 5.14, 2.24, 2.11, 2.21, 0.99 and 0.78, 0.94, 0.65, 0.68, 0.84, 0.85, respectively, under equivalent engine torque condition at the plow tillage. The root and contact safety factor were calculated to be 1.91, 2.53, 1.10, 1.04, 1.07, 0.48 and 0.55, 0.66, 0.46, 0.48, 0.59, 0.59, respectively, under rated engine torque condition. The root and contact safety factor were calculated to be 1.60, 2.11, 0.92, 0.87, 0.90, 0.40 and 0.51, 0.61, 0.42, 0.44, 0.54, 0.54, respectively, under the maximum engine torque condition. The multi-purpose agricultural implement machinery could be conducted under plow tillage operation. However, gear specifications for tooth surface need modification because the gear surface would be broken at all driving conditions as safety factors are lower than 1.

• Korean Journal of Environmental Agriculture
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• v.42 no.1
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• pp.14-20
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• 2023

Light is an important factor that influences the growth and development of flowering plants. The present study investigated the effects of in vitro acclimatization to different light colors (white light (WL; control), blue light (BL; 447 nm), green light (GL; 519 nm), and red light (RL; 667 nm)) on the growth of petunia (Petunia hybrida) and of hardening cultivation of plant transferred form in vitro to a greenhouse under sunlight. Compared to the control, the shoot length and leaf width of Petunia increased by 42% and 11.7%, respectively, after acclimatization to BL and the shoot growth increased by 29.3% after acclimatization to RL. The chlorophyll and carotenoid contents after acclimatization to BL and GL were 16.7% and 11.3% higher, respectively, and 14.4% and 11.9% higher, respectively, than those in the control. During greenhouse cultivation, the shoot length increased by 16.7% and 11.3%, respectively, after acclimatization to BL and RL, respectively, and the leaf length and leaf width increased by 14.4% and 11.9%, respectively, after acclimatization to GL. While dry weight of root of GL and BL was not significant difference in vitro, increased by 59.0% and 22.9% ex vitro than that of WL. Thus, acclimatization to BL increased the shoot growth and leaf chlorophyll contents, and acclimatization to GL and RL enhanced shoot and root growth, in petunia.