• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1363-1372
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• 2014

In the previous study, the structural performance of proposed precast concrete arch with reinforced joint was evaluated by structural experiment. In this paper, finite element analysis considering both material and contact nonlinearity was carried out on the specimens of the previous study. Based on the result of analysis and experiment, friction coefficient between concrete blocks was determined. To evaluate the strength of sectional member, elastic analysis was carried out on the arch using linear elastic analysis program. The section force was compared with the nominal strength of arch section. It was concluded that the maximum load of all the specimens exceed the nominal strength of arch section. Those results of the strength evaluation were similar to the results of structural experiments. Therefore, it is concluded that the elastic analysis and ultimate strength model can effectively evaluate the strength for the proposed precast concrete arch composed of concrete blocks and reinforced joint in design.

• Journal of Sensor Science and Technology
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• v.9 no.3
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• pp.196-202
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• 2000

In this paper, we proposed the new bridge combination detection method for three dimensional piezoresistive silicon accelerometer, and the accelerometer with SOI structures was fabricated by bulk micromachining technology for using higher temperature than $200^{\circ}C$. The sensitivities of fabricated accelerometer for X, Y and Z-axis acceleration were about 8mV/V G, 8mV/V G and 40mV/V G. The nonlinearity of the output voltage was 1.6%FS and cross-axis sensitivity was within 4.6%. We confirmed that the three bridges detection method is very simple and the output characteristics of this accelerometer were similar to arithmetic circuit method accelerometer. The temperature characteristics of SOI structure accelerometer showed high operating temperature and good stability. And the temperature coefficient of offset voltage and sensitivity were $1033ppm^{\circ}C^{-1}$ and $1145ppm^{\circ}C$ respectively.

• Computers and Concrete
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• v.19 no.1
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• pp.59-68
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• 2017

This study focused on modeling the behavior of the compressive stress using the average strain and ultrasonic test results in concrete. Feed-forward backpropagation artificial neural network (ANN) models were used to compare four types of concrete mixtures with varying water cement ratio (WC), ordinary concrete (ORC) and concrete with short steel fiber-reinforcement (FRC). Sixteen (16) $150mm{\times}150mm{\times}150mm$ concrete cubes were used; each contained eighteen (18) data sets. Ultrasonic test with pitch-catch configuration was conducted at each loading state to record linear and nonlinear test response with multiple step loads. Statistical Spearman's rank correlation was used to reduce the input parameters. Different types of concrete produced similar top five input parameters that had high correlation to compressive stress: average strain (${\varepsilon}$), fundamental harmonic amplitude (A1), $2^{nd}$ harmonic amplitude (A2), $3^{rd}$ harmonic amplitude (A3), and peak to peak amplitude (PPA). Twenty-eight ANN models were trained, validated and tested. A model was chosen for each WC with the highest Pearson correlation coefficient (R) in testing, and the soundness of the behavior for the input parameters in relation to the compressive stress. The ANN model showed increasing WC produced delayed response to stress at initial stages, abruptly responding after 40%. This was due to the presence of more voids for high water cement ratio that activated Contact Acoustic Nonlinearity (CAN) at the latter stage of the loading path. FRC showed slow response to stress than ORC, indicating the resistance of short steel fiber that delayed stress increase against the loading path.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1539-1544
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• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.573-586
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• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.1
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• pp.22-28
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• 2009

We investigated the sintering and electric properties of ZnO-1.0 at% $TeO_2$ (ZT1) and 1.0 at% Mn-doped ZT1(ZT1M1) system. $TeO_2$ itself melts at $732^{\circ}C$ in air but forms the $ZnTeO_3$ or $Zn_2Te_3O_8$ phase with ZnO as increasing temperature and therefore retards the densification of ZnO to $1000^{\circ}C$. In ZT1M1 system, also, the densification of ZnO was retarded up to $1000^{\circ}C$ and then reached > 90% of theoretical density above $1100^{\circ}C$. It was found that a good varistor characteristics(nonlinear coefficient $a{\sim}60$) were developed in ZT1M1 system sintered at $1100^{\circ}C$ due to Mn which known as improving the nonlinearity of ZnO varistors. The results of C-V characteristics such as barrier height (${\Phi}_b$), donor density ($N_D$), depletion layer (W), and interface state density ($N_t$) in ZT1M1 ceramics were $1.8{\times}10^{17}cm^{-3}$, 1.6 V, 93 nm, and $1.7{\times}10^{12}cm^{-2}$, respectively. Also we measured the resistance and capacitance of grain boundaries with temperature using impedance and electric modulus spectroscopy. It will be discussed about the stability and homogeneity of grain boundaries using distribution parameter ($\alpha$) simulated with the Z(T)"-logf plots.

• The Korean Journal of Pesticide Science
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• v.10 no.4
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• pp.296-305
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• 2006

The adsorption and persistence of pencycuron {1-(4-chlorobenzyl) cyclopentyl-3-phenylurea} in soils were investigated under laboratory and field conditions to in order to assess the safety use and environmental impact. In the adsorption rate experiments, a significant power function of relation was found between the adsorbed amount of pencycuron and the shaking time. Within one hour following the shaking, the adsorption amounts in the SCL and the SiCL were 60 and 65% of the maximum adsorption amounts, respectively. The adsorption reached a quasi-equilibrium 12 hours after shaking. The adsorption isotherms followed the Freundlich equation. The coefficient (1/n) indicating adsorption strength and degree of nonlinearity was 1.45 for SCL and 1.68 to SiCL. The adsorption coefficients ($K_d$) were 2.31 for SCL and 2.92 to SiCL, and the organic carbon partition coefficient, $K_{oc}$, was 292.9 in SCL and 200.5 inSiCL. In the laboratory study, the degradation rate of pencycuron in soils followed a first-order kinetic model. The degradation rate was greatly affected by soil temperature. As soil incubation temperature was increased from 12 to $28^{\circ}C$, the residual half life was decreased from 95 to 20 days. Arrhenius activation energy was 57.8 kJ $mol^{-1}$. Furthermore, the soil moisture content affected the degradation rate. The half life in soil with 30 to 70% of field moisture capacity was ranged from 21 to 38 days. The moisture dependence coefficient, B value in the empirical equation was 0.65. In field experiments, the half-life were 26 and 23 days, respectively. The duration for period of 90% degradation was 57 days. The difference between SCL and SiCL soils varied to pencycuron degradation rates were very limited, particularly under the field conditions, even though the characteristics of both soils are varied.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.9
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• pp.735-744
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• 2000

The electrical procerties and stability of ZnO-Pr$_{6}$O$_{11}$-CoO-Cr$_2$O$_3$-Er$_2$O$_3$ (ZPCCE) based varistors were investigated in the Er$_2$O$_3$content range of 0.0 to 4.0 mol%. ZPCCE ceramics containing 2.0 mol% Er$_2$O$_3$ exhibited the highest density of 5.74 g/㎤ corresponding to 99.3% of theoretical density. The varistors with 0.5 mol% and 2.0 mol% Er$_2$O$_3$exhibited a relatively satisfying nonlinearity, which the nonlinear exponent is 40.50 and 47.15, respectively and the leakage current is 2.66 $mutextrm{A}$, respectively. Under more severe d.c. stress, such as (0.80 V$_{1mA}$/9$0^{\circ}C$/12h)+(0.85 V$_{1mA}$115$^{\circ}C$/12h)+(0.90 V$_{1mA}$12$0^{\circ}C$/12h)+(0.95 V$_{1mA}$1$25^{\circ}C$12h), they showed a very excellent stability, which the variation rate of the variator voltage is -0.89% and -0.15%, the variation rate of the nonlinear coefficient is -4.67% and -3.56%, and the variation rate of leakage current is -6.02% and -19.56%, respectively. It is surely bellived that ZnO-0.5 mol% Pr$_{6}$O$_{11}$-1.0 mol% CoO-0.5 mol% Cr$_2$O$_3$-x mol% Er$_2$O$_3$(x=0.5, 2.0) based varistors will be greatly contributed to develop the advanced Pr$_{6}$O$_{11}$-based ZnO varistors in future.uture. future.uture.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1061-1069
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• 2021

Recently, the magnitude and frequency of extreme heavy rains and localized heavy rains have increased due to abnormal climate, which caused increased flood damage in river basin. As a result, the nonlinearity of the hydrological system of rivers or basins is increasing, and there is a limitation in that the lead time is insufficient to predict the water level using the existing physical-based hydrological model. This study predicted the water level at Ulsan (Taehwagyo) with a lead time of 0, 1, 2, 3, 6, 12 hours by applying deep learning techniques based on Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and evaluated the prediction accuracy. As a result, DNN model using the sliding window concept showed the highest accuracy with a correlation coefficient of 0.97 and RMSE of 0.82 m. If deep learning-based water level prediction using a DNN model is performed in the future, high prediction accuracy and sufficient lead time can be secured than water level prediction using existing physical-based hydrological models.