• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1997.06c
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• pp.89-98
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• 1997

This study was conducted to develop an automatic nutrient-solution control system for small-scale growers. The nutrient-solution control system consisted of a low-cost and precise metering device and a personal computer. The system controlled electric conductivity(EC) and pH of nutrient-solution based on the time-based feedback control method with the information about temperature, EC, and pH of the nutrient-soIution. The performance of the nutrient-solution control system was evaluated through the control of EC and pH while compared with those of commercial nutrient-solution control system. Also an experimental cultivation of tomato was conducted to verify and to improve the developed system. Results of this study were as follows. 1. An automatic nutrient-solution control system based on a low-cost and precise metering device was developed. 2. The developed system controlled EC and pH within $\pm$0.05 mS/cm and $\pm$0.2 pH full scale error respectively at $24^{/circ}C$. 3. When using the commercial system, the controlled values of EC and pH of the 500l of water were 1.29 mS/cm and 6.1 pH for the setting points of 1.4 mS/cm and 6.0 pH respectively at $22^{/circ}C$. 4. The developed nutrient-solution control system showed $\pm$0.05 mS/cm of deviation from the setting EC value over the experimental cultivation period. 5. The deviation from the average values of Ca and Mg mass content in the several nutrient-solution were 0.5% and 1.8% respectively.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.12
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• pp.70-78
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• 2000

A novel capacitance deviation-to-time interval converter based on ramp-integration is presented. It consists of two current mirrors, two schmitt triggers, and control digital circuits by the upper and lower sides, symmetrically. Total circuit has been with discrete components. The results show that the proposed converter has a linearity error of less than 1% at the time interval(pulse width) over a capacitance deviation from 295 pF to 375 pF. A capacitance deviation of 40pF and time interval of 0.2 ms was measured for sensor capacitance of 335 pF. Therefore, the high-resolution can be known by counting the fast and stable clock pulses gated into a counter for time interval. The application of a novel capacitance deviation-to time interval converter to a digital humidity controller is also presented. The presented circuit is insensitive to the capacitance difference in disregard of voltage source or temperature deviation. Besides the accuracy, it features the small MOS device count integrable onto a small chip area. The circuit is thus particularly suitable for the on-chip interface.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.277-285
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• 2014

The purpose of this study is to investigate experimentally the variation factors range having influence on the rheological properties of the combined self-compacting concrete according to materials quality, weighting error and site conditions. Two types cement (blast-furnace slag cement and belite cement), lime stone powder as binder and the optimum mix proportions in the preceded study are selected for this study. Also, variations for sensitivity test are as followings; (1) Concrete temperature 3 cases (2) Surface moisture of sand 5cases (3) Fineness modulus of sand 5cases (4) Specific surface of lime stone powder 3cases (5) Dosage of chemical admixture 5cases. Slump flow ($650{\pm}50mm$), 500 mm reaching time (($7{\pm}3sec$), V-type flowing time ($15{\pm}5sec$) and U-box height (min. 300 mm) are tested for sensitivity. As test results, the variations range for quality control are as followings. (1) Concrete temperature; $10{\sim}20^{\circ}C$(below $30^{\circ}C$) (2) Surface moisture of sand; $base{\pm}0.6%$ (3) Fineness modulus of sand; $2.6{\pm}0.2$ (4) Dosage of chemical admixture; $base{\pm}0.2%$ (5) Specific surface of lime stone powder $6000cm^2/g$. Compared with two types cement including based belite cement (binary type) and based slag cement (ternary type), the combined self-compacting concrete used belite cement type is most stable in the quality control because of high contents for lime stone powder and $C_2S$. It is to propose a control scheme of the combined self-compacting concrete in the actual construction work.

• Journal of ILASS-Korea
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• v.25 no.1
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• pp.8-14
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• 2020

Recently, 1-D model-based engine development using virtual engine system is getting more attention than experimental-based engine development due to the advantages in time and cost. Injection rate profile is the one of the main parameters that determine the start and end of combustion. Therefore, it is essential to set up a sophisticated model to accurately predict the injection rate as starting point of virtual engine system. In this research, procedure of 1-D model setup based on AMESim is introduced to predict the dynamic behavior and injection rate of diesel injector. As a first step, detailed 3D cross-sectional drawing of the injector was achieved, which can be done with help of precision measurement system. Then an approximate AMESim model was provided based on the 3D drawing, which is composed of three part such as solenoid part, control chamber part and needle and nozzle orifice part. However, validation results in terms of total injection quantity showed some errors over the acceptable level. Therefore, experimental work including needle movement visualization, solenoid part analysis and flow characteristics of injector part was performed together to provide more accuracy of 1-D model. Finally, 1-D model with the accuracy of less than 10% of error compared with experimental result in terms of injection quantity and injection rate shape under normal temperature and single injection condition was established. Further work considering fuel temperature and multiple injection will be performed.

• Journal of Biosystems Engineering
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• v.22 no.4
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• pp.469-478
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• 1997

This study was conducted to develop an automatic nutrient-solution mixing system for small-scale sewers. The nutrient-solution mixing system consisted of a low-cost and precise metering device and data acquisition ＆ control system with a personal computer. and, the metering device was composed of three parts those were supply pumps, metering cylinders and venturi tube. The system controlled electric conductivity(EC) and pH of nutrient-solution based on the time-based feedback control method with the information about temperature, EC, and pH of the nutrient-solution. The performance of the nutrient-solution mixing system was evaluated through the control of EC and pH while compared with those of commercial system. Also an experimental cultivation of tomato was conducted to verify and to improve the developed system. Results of this study were as follows. 1. The correlation coefficient of meteing device between the flow rate and operating time was 0.9999, and the linear reuession equation computed was y=21.759x, where y is the discharge($g$) and x is the operating time(s). 2. Calculated errors for the developed metering device and two commercial pump were $\pm$0.3% $\pm$2.45% and $\pm$1.38 % FS error respectively. 3. An automatic nutrient-solution mixing system based on a low-cost and precise metering device was developed. 4. The full scale errors of the developed system in controlling EC and pH at 23$\pm$1$^{\circ}C$ were $\pm$0.05mS/cm and $\pm$0.2, respectively 5. When using the commercial system, the controlled values of EC and pH of the 500 $\ell$ of water were 1.29 mS/cm and 6.1 pH for the setting points of 1.4 mS/cm and 6.0 pH respectively at 23$pm1^{\circ}C$. 6. The developed nutrient-solution control system showed $\pm$0.05 ms/cm of deviation from the setting EC value over the experimental cultivation period. 7. The deviation from the average values of Ca and Mg mass content in the several nutrient-solution were 0.5% and 1.8% respectively.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.3
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• pp.279-285
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• 2011

We perform a numerical study to determine the time of onset of natural convection in a transient hot wire (THW) device for measuring the thermal conductivity of nanofluids. The samples used in this simulation are water-based $Al_2O_3$ nanofluids with volume fractions of 1%, 4%, and 10%, and the properties are calculated by theoretical models and experimental correlations. The THW apparatus using coated wire is modeled by the control-volume-based finite difference method, and the start of natural convection is determined by observing the temperature rise of the wire under a gravity field. The onset time is 11.5 s for water and 41.6 s for water-based $Al_2O_3$ nanofluids predicted by Maxwell thermal conductivity model with a 10% volume fraction. We confirm that the onset time of natural convection of nanofluids in the cylinder increases with the nanoparticle volume fraction. We suggest a correlation for predicting the onset time on the basis of the numerical results. Finally, it is shown that the measurement error due to natural convection is negligible if the measurement using the transient hot wire method is completed before the onset of natural convection in the base fluid.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.821-832
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• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.9
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• pp.58-67
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• 2009

This paper presents a fully digital gain control system with a new high bandwidth and wide dynamic range power detector for DVB-S2 application. Because the peak-to-average power ratio (PAPR) of DVB-S2 system is so high and the settling time requirement is so stringent, the conventional closed-loop analog gain control scheme cannot be used. The digital gain control is necessary for the robust gain control and the direct digital interface with the baseband modem. Also, it has several advantages over the analog gain control in terms of the settling time and insensitivity to the process, voltage and temperature variation. In order to have a wide gain range with fine step resolution, a new AGC system is proposed. The system is composed of high-bandwidth digital VGAs, wide dynamic range power detectors with RMS detector, low power SAR type ADC, and a digital gain controller. To reduce the power consumption and chip area, only one SAR type ADC is used, and its input is time-interleaved based on four power detectors. Simulation and measurement results show that the new AGC system converges with gain error less than 0.25 dB to the desired level within $10{\mu}s$. It is implemented in a $0.18{\mu}m$ CMOS process. The measurement results of the proposed IF AGC system exhibit 80-dB gain range with 0.25-dB resolution, 8 nV/$\sqrt{Hz}$ input referred noise, and 5-dBm $IIP_3$ at 60-mW power consumption. The power detector shows the 35dB dynamic range for 100 MHz input.