• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.59-65
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• 2017

In order to realize the operation of the creative modular toy, it is required to record the motion and to read and repeat the motion. At this time, a control potentiometer is used to read the absolute angle of rotation of the toy motion output shaft. However, the unstable part of the sensing area of the potentiometer is present in a certain region, which may lead to instability of the motor control. In this paper, we propose an algorithm to find the absolute angle of one rotation by reading two stable potentiometers on one axis and reading each stable region. We also describe the correction algorithm that is needed to perform multiple rotations. The proposed method is applied to Topobo modular toys to record the operation and perform iterative operation. In addition, multi-turn operation is recorded and operated to suggest the usefulness of the proposed method. In the future, we will expand the functions of recording and playback through various actions.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.5
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• pp.49-58
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• 2018

This paper introduces a research on the control method design for the subsonic intake flow of a dual-combustion ramjet engine. To design the control method, the intake flow dynamic response characteristics, based on a designated flow condition and intake geometry, are investigated, and a control method concept considering the intake flow characteristics is established. Using a dynamic simulation model of a dual-combustion ramjet, control input/output linearized models are obtained such that a control loop design based on linearized models can be accomplished. Finally, from various control loop simulations, the performance of the control method, including its control loop stability, is evaluated.

• Translational and Clinical Pharmacology
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• v.26 no.4
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• pp.166-171
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• 2018

Although there are many commercially available training software programs for pharmacokinetics, they lack flexibility and convenience. In this study, we develop simulation software to facilitate pharmacokinetics education. General formulas for time courses of drug concentrations after single and multiple dosing were used to build source code that allows users to simulate situations tailored to their learning objectives. A mathematical relationship for a 1-compartment model was implemented in the form of differential equations. The concept of population pharmacokinetics was also taken into consideration for further applications. The source code was written using R. For the convenience of users, two types of software were developed: a web-based simulator and a standalone-type application. The application was built in the JAVA language. We used the JAVA/R Interface library and the 'eval()' method from JAVA for the R/JAVA interface. The final product has an input window that includes fields for parameter values, dosing regimen, and population pharmacokinetics options. When a simulation is performed, the resulting drug concentration time course is shown in the output window. The simulation results are obtained within 1 minute even if the population pharmacokinetics option is selected and many parameters are considered, and the user can therefore quickly learn a variety of situations. Such software is an excellent candidate for development as an open tool intended for wide use in Korea. Pharmacokinetics experts will be able to use this tool to teach various audiences, including undergraduates.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.426-439
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• 2018

Purpose: To continuously monitor soil and climatic properties, a data acquisition system (DAQ) was developed and tested in plum farms (Gyewol-ri and Haechang-ri, Suncheon, Korea). Methods: The DAQ consisted of a Raspberry-Pi processor, a modem, and an ADC board with multiple sensors (soil moisture content (SEN0193), soil temperature (DS18B20), climatic temperature and humidity (DHT22), and rainfall gauge (TR-525M)). In the laboratory, various tests were conducted to calibrate SEN0193 at different soil moistures, soil temperatures, depths, and bulk densities. For performance comparison of the SEN0193 sensor, two commercial moisture sensors (SMS-BTA and WT-1000B) were tested in the field. The collected field data in Raspberry-Pi were transmitted and stored on a web server database through a commercial communications wireless network. Results: In laboratory tests, it was found that the SEN0193 sensor voltage reading increased significantly with an increase in soil bulk density. A linear calibration equation was developed between voltage and soil moisture content depending on the farm soil bulk density. In field tests, the SEN0193 sensor showed linearity (R = 0.76 and 0.73) between output voltage and moisture content; however, the other two sensors showed no linearity, indicating that site-specific calibration is important for accurate sensing. In the long-term monitoring results, it was observed that the measured climate temperature was almost the same as website information. Soil temperature information was higher than the values measured by DS18B20 during spring and summer. However, the local rainfall measured using TR 525M was significantly different from the values on the website. Conclusion: Based on the test results obtained using the developed monitoring system, it is thought that the measurement of various parameters using one device would be helpful in monitoring plum growth. Field data from the local farm monitoring system can be coupled with website information from the weather station and used more efficiently.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.9
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• pp.1389-1396
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• 2019

Objective: The current study analysed the relationships between methane ($CH_4$) output from animal and dietary factors. Methods: The dataset was obtained from 159 Dorper${\times}$thin-tailed Han lambs from our seven studies, and $CH_4$ production and energy metabolism data were measured in vivo by an opencircuit respiratory method. All lambs were confined indoors and fed pelleted diet during the whole experimental period in all studies. Data from two-thirds of lambs were used to develop linear and multiple regressions to describe the relationship between $CH_4$ emission and dietary variables, and data from the remaining one third of lambs were used to validate the established models. Results: $CH_4$ emission (g/d) was positively related to dry matter intake (DMI) and gross energy intake (GEI) (p<0.001). $CH_4$ energy/GEI was negatively related to metabolizable energy/gross energy and metabolizable energy/digestible energy (p<0.001). Using DMI to predict $CH_4$ emission (g/d) resulted in a coefficient of determination ($R^2$) of 0.80. Using GEI, digestible energy intake, and metabolizable energy intake predict $CH_4$ energy/GEI resulted in a $R^2$ of 0.92. Conclusion: the prediction equations established in the current study are useful to develop appropriate feeding and management strategies to mitigate $CH_4$ emissions from sheep.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.57-62
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• 2019

This paper evaluates the QE-MMA (Quantized Error-MMA) adaptive equalization algorithm by the number of quantizer in order to compensates the intersymbol interference due to channel in the transmission of high spectral efficient nonconstant modulus signal. In the adaptive equalizer, the error signal is needed for the updating the tap coefficient, the QE-MMA uses the polarity of error signal and correlation multiplier that condered nonlinear finite bit power-of-two quantizing component in order to convinience of H/W implementation. The different adaptive equalization performance were obtained by the number of quantizer, these performance were evaluated by the computer simulation. For this, the equalizer output signal constellation, residual isi, maximum distortion, MSE, SER were applied as a performance index. As a result of computer simulation, it improved equalization performance and reduced equalization noise were obtained in the steady state by using large quantizer bit numbers, but gives slow in convergence speed for reaching steady state.

• Earthquakes and Structures
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• v.20 no.1
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.47-53
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• 2021

In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1226-1274
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• 2022

In this paper, we dealt with a safety helmet for detecting dangerous situations that focuses on falling accidents and gas leaks, which are the main causes of industrial accidents. the fall situation range was set through gravity acceleration measurement using an acceleration sensor, and as a result, a fall detection rate of 80% could be confirmed. .In addition, the dangerous gas concentration was measured through a gas sensor, and when a digital value of 188 or more was output through a serial monitor, it was determined as a gas dangerous situation, and a fall warning message and a gas warning message could be checked through a smart-phone application produced based on the app inventor program.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.