• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.274-280
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• 2007

An ultra-wideband signal is characterized by a radiated spectrum with wide bandwidth around a relatively low center frequency. In this paper, the bit error rate (BER), packet error rate (PER), and data throughput performance for an ultra-wideband system with M-ary correlation receiver are analyze in additive white Gaussian noise (AWGN) and co-channel interference channel. To evaluate the performance of UWB system, a set of UWB communication waveform as pulse position modulated (PPM) signals consisting of more than one UWB pulse is used. The M-ary PPM signals are defined to be equally correlated in order to simplify the system performance analysis. The analysis for system performance shows that the wireless channel error significantly degrades throughput performance and can be effectively increased by hybrid ARQ scheme. Also, an attempt for comparing the data throughput of ultra-wideband system on different performance improvement schemes and parameters has been made. From the performance evaluation process, it is shown that the effects of wireless channel and hybrid ARQ scheme for ultra wideband M-ary PPM system can be evaluated by means of a suitable combination of the PER, throughput vs. signal-to-noise power ratio per bit.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.173-182
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• 2013

The early Sea-viewing Wide Field-of-view Sensor(SeaWiFS) atmospheric correction algorithm which is the basis of the atmospheric correction algorithm for Geostationary Ocean Color Imager(GOCI) assumes that water-leaving radiances is negligible at near-infrared(NIR) wavelengths. For this reason, all of the satellite measured radiances at the NIR wavelengths are assigned to aerosol radiances. However that assumption would cause underestimation of water-leaving radiances if it were applied to turbid Case-2 waters. To overcome this problem, Management Unit of the North Sea Mathematical Models(MUMM) atmospheric correction algorithm has been developed for turbid waters. This MUMM algorithm introduces new parameter ${\alpha}$, representing the ratio of water-leaving reflectance at the NIR wavelengths. ${\alpha}$ is calculated by statistical method and is assumed to be constant throughout the study area. Using this algorithm, we can obtain comparatively accurate water-leaving radiances in the moderately turbid waters where the NIR water-leaving reflectance is less than approximately 0.01. However, this algorithm still underestimates the water-leaving radiances at the extremely turbid water since the ratio of water-leaving radiance at two NIR wavelengths, ${\alpha}$ is changed with concentration of suspended particles. In this study, we modified the MUMM algorithm to calculate appropriate value for ${\alpha}$ using an iterative technique. As a result, the accuracy of water-leaving reflectance has been significantly improved. Specifically, the results show that the Root Mean Square Error(RMSE) of the modified MUMM algorithm was 0.002 while that of the MUMM algorithm was 0.0048.

• Journal of Astronomy and Space Sciences
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• v.21 no.4
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• pp.417-428
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• 2004

We have investigated the optical property of the KAO(Korea Astronomy Observatory) wide field telescope (named NEOPAT-3; Near Earth Object and Satellite Patrol-3) and aligned optical system. The NEOPAT-3 is restricted to V,R,I-filters because of the refractive property of the correcting lens system. Because of the fast focal ratio, the optical performance of the NEOPAT-3 is very sensitive to its alignment factors of the optical system. To make the spot radius smaller than $8{\mu}m$ in rms over 2degree${\times}2$degree field, the optical system must satisfy the following conditions: 1) The tilt error between detector plane and focal plane should be less than 0.05degree. 2) The decenter error between the primary mirror and the correcting lens system should be less than 1mm. 3) The distance error between the primary mirror and the correcting lens system should be less than 2.3mm. In order to align the optical system accurately, we measured the aberrations of the telescope quantitatively by means of curvature sensing technique. NEOPAT-3 is installed temporary on the roof of the TRAO(Taeduk Radio Astronomy Observatory) main building to normalize system performance and to develop automatic observation.

• Genomics & Informatics
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• v.10 no.2
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• pp.117-122
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• 2012

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1153-1166
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• 2011

Detecting misbehavior in vehicular ad-hoc networks is very important problem with wide range of implications including safety related and congestion avoidance applications. Most misbehavior detection schemes are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners. Because of rational behavior, it is more important to detect false information than to identify misbehaving nodes. In this paper, we propose the variable precision rough sets based misbehavior detection scheme which detects false alert message and misbehaving nodes by observing their action after sending out the alert messages. In the proposed scheme, the alert information system, alert profile is constructed from valid actions of moving nodes in vehicular ad-hoc networks. Once a moving vehicle receives an alert message from another vehicle, it finds out the alert type from the alert message. When the vehicle later receives a beacon from alert raised vehicle after an elapse of time, then it computes the relative classification error by using variable precision rough sets from the alert information system. If the relative classification error is lager than the maximum allowable relative classification error of the alert type, the vehicle decides the message as false alert message. Th performance of the proposed scheme is evaluated as two metrics: correct ratio and incorrect ratio through a simulation.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.24-35
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• 2022

For water quality management, it is necessary to continuously improve the forecasting by analyzing the past water quality, and a Data-driven model is emerging as an alternative. Because the Data-driven model is built based on a wide range of data, it is essential to apply the correlation analysis method for the combination of input variables to obtain more reliable results. In this study, the Gamma Test was applied as a preceding step to build a faster and more accurate data-driven water quality prediction model. First, a physical-based model (HSPF, EFDC) was operated to produce daily water quality reflecting the complexity of the watershed according to various hydrological conditions for Paldang Dam. The Gamma Test was performed on the water quality at the water quality prediction site (Paldangdam2) and major rivers flowing into the Paldang Dam, and the method of selecting the optimal input data combination was presented through the analysis results (Gamma, Gradient, Standar Error, V-Ratio). As a result of the study, the selection criteria for a more efficient combination of input data that can save time by omitting trial and error when building a data-driven model are presented.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.350-355
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• 2017

UWB (Ultra Wide Band) refers to a system with a bandwidth of over 500 MHz or a bandwidth of 20% of the center frequency. It is robust against channel fading and has a wide signal bandwidth. Using the IR-UWB based ranging system, it is possible to obtain decimeter-level ranging accuracy. Furthermore, IR-UWB system enables acquisition over glass or cement with high resolution. In recent years, IR-UWB-based ranging chipsets have become cheap and popular, and it has become possible to implement positioning systems of several tens of centimeters. The system can be configured as one-way ranging (OWR) positioning system for fast ranging and TWR (two-way ranging) positioning system for cheap and robust ranging. On the other hand, the ranging based positioning system has a limitation on the number of terminals for localization because it takes time to perform a communication procedure to perform ranging. To overcome this problem, code multiplexing and channel multiplexing are performed. However, errors occur in measurement due to interference between channels and code, multipath, and so on. The measurement filtering is used to reduce the measurement error, but more fundamentally, techniques for removing these measurements should be studied. First, the TWR based positioning was analyzed from a stochastic point of view and the effects of outlier measurements were summarized. The positioning algorithm for analytically identifying and removing single outlier is summarized and extended to three dimensions. Through the simulation, we have verified the algorithm to detect and remove single outliers.

• 한국연소학회:학술대회논문집
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• 2000.12a
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• pp.168-174
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• 2000

A diode laser sensor system based on absorption spectroscopy techniques has been developed to measure $CO_2$ concentration and temperature non-intrusively in high temperature combustion environments using a 2.0 ${\mu}m$ DFB(Distributed Feedback) laser. Two optics was fabricated in pig-tail fashion and all optical components were implemented in a single box. The evolution of measurement sensitivity was done using test cell by changing sweep frequency and $CO_2$ concentration. Gas temperature was determined from the ratio of integrated line strengths. Species concentration was determined from the integrated line intensity and the measured temperature. The result show that the system has 2% error in wide operation frequency range and accuracy of $CO_2$ concentration was about 3%. The system was applied to measure temperature and concentration in the combustion region of a premixed $CH_4$ +air triangular flame. The measurement results of gas temperature agreed well with thermocouple results. Many considerations were taken into account to reduce optical noise, etalon effect, beam steering and base line matching problem. The evaluations results and actual combustion measurement demonstrate the practical and applicability for in-situ and real time combustion monitoring in a practical system.

• Smart Structures and Systems
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• v.26 no.6
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• pp.735-751
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• 2020

Identification of structure parameters is crucial in Structural Health Monitoring (SHM) context for activities such as model validation, damage assessment and signal processing of structure response. In this paper, guided waves generated by piezoelectric transducers are used for in-situ and non-destructive structural parameter estimation based on Bayesian approach. As Bayesian approach needs iterative process, which is computationally expensive, this paper proposes a method in which an analytical model is selected and developed in order to decrease computational time and complexity of modeling. An experimental set-up is implemented to estimate three target elastic and geometrical parameters: Young's modulus, Poisson ratio and thickness of aluminum and steel plates. Experimental and simulated data are combined in a Bayesian framework for parameter identification. A significant accuracy is achieved regarding estimation of target parameters with maximum error of 8, 11 and 17 percent respectively. Moreover, the limitation of analytical model concerning boundary reflections is addressed and managed experimentally. Pulse excitation is selected as it can excite the structure in a wide frequency range contrary to conventional tone burst excitation. The results show that the proposed non-destructive method can be used in service for estimation of material and geometrical properties of structure in industrial applications.