• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.5
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• pp.23-30
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• 2000

To compensate the nonlinearity of electroabsorption modulator(EAM) resulting from its near exponential transfer function, a semiconductor optical amplifier(SOA) that has a log transfer function is used. Since the transfer function of SOA is inverse to that of EAM, the intermodulation distortion(IMD) of EAM can be reduced by cascading SOA to EAM. Also, the RF gain can be increased by the optical gain of SOA. For these reasons, spurious free dynamic range(SFDR) of EAM is enhanced by connecting SOA to EAM in series and operating in gain salutation region. To improve the nonlinearity compensation of EAM, the increased gain of SOA is required and the slope of gain saturation, the ratio of gain to input SOA power, needs to be steep. However, signal spontaneous beat noise that is the dominant system noise increases in proportion to the gain such that the SFDR of EAM is reduced. The higher the gain of SOA is, the more ASE is increased. Thus the noise level of system is increased and the following SFDR of EAM is decreased. The slope of gain saturation region and ASE of have trade-off relation and the optimization is achieved at 8㏈ optical gain. 9㏈ enhancement of SFDR of EAM is obtained. This scheme is easy to embody the linear EAM and the integration with three components (DFB-LD, EAM and SOA) offers many merits, such as low insertion loss, low chirping and low polarization sensitivity.

• Journal of Korea Water Resources Association
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• v.38 no.1
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• pp.1-9
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• 2005

The study shows the possible use of the index flood frequency curves for an estimation of flood quantiles at ungauged locations. Flood frequency analysis were made for the annual maximum flood data series at 9 available stations in the Han river basin. From the flood frquency curve at each station the mean annual flood of 2.33-year return period was determined and the ratios of the flood magnitude of various return period to the mean annual flood at each station were averaged throughout the Han river basin, resulting mean flood ratios of different return periods. A correlation analysis was made between the mean annual flood and physiographic parameters of the watersheds i.e, the watershed area and mean river channel slope, resulting an empirical multiple linear regression equation over the whole Han river basin. For unguaged watershed the flood of a specified return period could be estimated by multiplying the mead flood ratio corresponding the return period with the mean annual flood computed by the empirical formula developed in terms of the watershed area and river channel slope. To verify the applicability of the methodology developed in the present study the floods of various return periods determined for the watershed in the river channel improvement plan formulation by the Ministry of Construction and Transportation(MOCT) were compared with those estimated by the present method. The result proved a resonable agreement up to the watershed area of approximately 2,000k $m^2$. It is suggested that the practice of design flood estimation based on the rainfall-runoff analysis might have to be reevaluated because it involves too much uncertainties in the hydrologic data and rainfall-runoff model calibration.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.353-360
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• 2015

The offshore oil production requires a huge amount of cost and time accompanied by multiple variables due to the peculiar nature of 'offshore'. And every process concerned is controlled by elaborate series of plans for reducing loss of lives, environment and property. This paper treats an optimization problem for offshore oil production and transportation. We present an offshore production and transportation network to define scope of the problem and construct a mixed integer linear programming model to tackle it. To demonstrate the validity of the optimization model presented, some computational experiments based on hypothetical offshore oil fields and demand markets are carried out by using MS Office Excel solver. The downstream of the offshore production and transportation network ends up with the maritime transportation problem distributing the crude oil produced from offshore fields to demand markets. We used MoDiSS(Model-based DSS in Ship Scheduling) which was built to resolve this maritime transportation problem. The paper concludes with the remark that the results of the study might be meaningfully applicable to the real world problems of offshore oil production and transportation.

• Journal of the Korean association of regional geographers
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• v.21 no.1
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• pp.98-113
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• 2015

In this study, spatial and temporal patterns and changes in seasonal multi-day cumulative extreme precipitation events defined by maximum 1~5 days cumulative extreme precipitation observed at 61 weather stations in the Republic of Korea for the recent 40 years(1973~2012) are examined. It is demonstrated that the magnitude of multi-day cumulative extreme precipitation events is greatest in summer, while their sensitivity relative to the variations of seasonal total precipitation is greatest in fall. According to analyses of linear trends in the time series data, the most noticeable increases in the magnitude of multi-day cumulative extreme precipitation events are observable in summer with coherences amongst 1~5 days cumulative extreme precipitation events. In particular, the regions with significant increases include Gyeonggi province, western Gangwon province and Chungcheong province, and as the period for the accumulation of extreme precipitation increases from 1 day to 5 days, the regions with significantly-increasing trends are extended to the Sobaek mountain ridge. It is notable that at several scattered stations, the increases of 1~2 days cumulative extreme precipitation events are observed even in winter. It is also observed that most distinct increasing tendency of the ratio of these multi-day cumulative extreme precipitation to seasonal total precipitation appears in winter. These results indicate that proactive actions are needed for spatial and temporal changes in not only summer but also other seasonal multi-day cumulative extreme precipitation events in Korea.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.699-712
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• 1996

Field scale experiments using an automated 144-channel TDR system were conducted which monitored the movement of solute through unsaturated loamy soils. The experiments were carried out on two different field plots of 0.54 ha to study the vertical movement of solute plume created by applying a square pulse of $CaCl_2$ as a tracer. The residence concentration was monitored at 24 locations on a transect and 5 depths per location by horizontally-positioning 50 cm long triple wire TDR probes to study the heterogeneity of solute travel times and the governing transport concept at field scale. This paper describes details of experimental methodology and calibration aspects of the TDR system. Three different calibration methods for estimation of solute concentration from TDR-measured bulk soil electrical conductivity were used for each field site. Data analysis of mean breakthrough curves (BTCs) and parameters estimated using the convection-dispersion model (CDE) and the convective-lognormal transfer function model (CLT) reveals that the automated TDR system is a viable technique to study the field scale solute transport providing a normal distribution of resident concentration in a high resolution of time series, and that calibration method does not significantly affect both the shape of BTC and the parameters related to the peak travel time. Among the calibration methods, the simple linear model (SLM), a modified version of Rhoades' model, appears to be promising in the calibration of horizontally-positioned TDR probes at field condition.

• Tunnel and Underground Space
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• v.16 no.3 s.62
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• pp.259-276
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• 2006

In rock mass subject to high in-situ stresses, the failure process of rock is dominated by the stress-induced fractures growing parallel to the excavation boundary. When the ratio of in situ stresses compared to rock strength is greater than a certain value, progressive brittle failure which is characterized by popping and spatting of rock debris occurs due to stress concentration. Traditional constitutive model like Mohr-Coulomb usually assume that the normal stress dependent frictional strength component and the cohesion strength component are constant, therefore modelling progressive brittle failure will be very difficult. In this study, a series of numerical analyses were conducted for surrounding rock mass near crude oil storage cavern using CW-FS model which was known to be efficient for modelling brittle failure and the results were compared with those of linear Mohr-Coulomb model. Further analyses were performed by varying plastic shear strain limits on cohesion and internal friction angle to find the proper values which yield the matching result with the observed failure in the oil storage caverns. The obtained results showed that CW-FS model could be a proper method to characterize essential behavior of progressive brittle failure in competent rock mass.

• Journal of Food Hygiene and Safety
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• v.32 no.2
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• pp.83-88
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• 2017

We introduced disposable amperometric immunosensors for the detection of Sildenafil and Vardenafil (SDF/VDF) based on screen printed carbon electrodes. The developed immunosensors were used as a non-competitive sandwich-type enzyme immunoassay with a horseradish peroxidase label. The sensors were constructed on screen printed carbon electrodes by the simple electrochemical deposition of a reduced graphene oxide and chitosan (ErGO-CS) composite. To evaluate the sensing chemistry and optimize the sensor characteristics, a series of electrochemical experiments were carried out including electrochemical impedance spectroscopy, cyclic voltammetry and amperometry. The sensors showed a linear response to SDF/VDF concentrations in a range from 100 pg/mL to 300 ng/mL. The lower detection limit was calculated to be 55 pg/mL, the sensitivity was calculated to be $1.02{\mu}Ang/mL/cm^2$, and the sensor performance exhibited good reproducibility with a relative standard deviation (RSD) of 7.1%. The proposed sensing chemistry strategy and the sensor format can be used as a simple, cost-effective, and feasible method for the in-field analysis of SDF/VDF in functional or health supplement food samples.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5164-5171
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• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.80-87
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• 2015

Memristor is a new kind of memory device whose resistance varies depending upon applied charge and whose previous resistance state is preserved even when its power is off. Ordinary memristor has a nonlinear programming characteristics about time when a constant voltage is applied. For the easiness of programming, it is desirable that resistance is programmed linearly about time. We had proposed previously a memristor bridge configuration with which weight can be programmed nicely in positive, negative or zero. In memristor bridge circuit, two memristors are connected in series with different polarity. Memristors are complementary each other and it follows that the memristance variation is linear with respect to time. In this paper, the linearization effect of weight programming of memristor bridge synapse is investigated and verified about both $TiO_2$ memristor from HP and a nonlinear memristor with a window function. Memristor bridge circuit would be helpful to conduct synaptic weight programming.