• Advances in environmental research
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• v.1 no.4
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• pp.305-321
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• 2012

This study entails establishing reference standard sand in Nigeria for engineering and geoenvironmental research work. Sands from four geographical locations in southwestern Nigeria were examined for baseline geotechnical and mineralogical properties. A total of sixteen sand samples were collected. The samples were air dried and subjected to tests in accordance with standard specifications. The tests carried out were: specific gravity, grain size analysis, moisture content, bulk density, porosity, void ratio, chemical analysis, X-ray diffraction and Differential Thermal Analysis. The properties of the samples were compared with a standard (Ottawa sand in Illinois of the United States) in order to find out which of the four samples selected from southwestern Nigeria could serve as standard baseline sand. The results show that Igbokoda sand has geotechnical and mineralogical characteristics closest to Ottawa sand. It was therefore concluded that Igbokoda sand could be used as a standard baseline sand for research work in southwestern Nigeria and other parts of Nigeria since it needs little processing to bring it to the same level as standard baseline sand, like the Ottawa sand.

• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.265-269
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• 2005

Cox's proportional hazards model (PHM) has been widely applied in the analysis of lifetime data, and it can be characterized by the baseline hazard function and covariates influencing systems' lifetime, where the covariates describe operating environments (e.g. temperature, pressure, humidity). In this article, we consider the constant baseline hazard function and a discrete random variable of a covariate. The estimation procedure is developed in a parametric framework when there are not only complete data but also incomplete one. The Expectation-Maximization (EM) algorithm is employed to handle the incomplete data problem. Simulation results are presented to illustrate the accuracy and some properties of the estimation results.

• Smart Structures and Systems
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• v.26 no.3
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• pp.291-309
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• 2020

To study oceanic and meteorological problems related to climate change, Korea has been operating several ocean research stations (ORSs). In 2011, the Gageocho ORS was attacked by Typhoon Muifa, and its structural members and several observation devices were severely damaged. After this event, the Gageocho ORS was rehabilitated with 5 m height to account for 100-yr extreme wave height, and the vibration measurement system was equipped to monitor the structural vibrational characteristics including natural frequencies and modal damping ratios. In this study, a mass reallocation method is presented for structural model updating of the Gageocho ORS based on the experimentally identified natural frequencies. A preliminary finite element (FE) model was constructed based on design drawings, and several of the candidate baseline FE models were manually built, taking into account the different structural conditions such as corroded thickness. Among these candidate baseline FE models, the most reasonable baseline FE model was selected by comparing the differences between the identified and calculated natural frequencies; the most suitable baseline FE model was updated based on the identified modal properties, and by using the pattern search method, which is one of direct search optimization methods. The mass reallocation method is newly proposed as a means to determine the equivalent mass quantities along the height and in a floor. It was found that the natural frequencies calculated based on the updated FE model was very close to the identified natural frequencies. In conclusion, it is expected that these results, which were obtained by updating a baseline FE model, can be useful for establishing the reference database for jacket-type offshore structures, and assessing the structural integrity of the Gageocho ORS.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.382-387
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• 2003

In this paper, a neural networks-based damage detection method using the modal properties is presented, which can effectively reduce the effect of the modeling errors in the baseline finite element model from which the training patterns for the networks are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Results of laboratory test on a simply supported bridge model and field test on a bridge with multiple girders confirm the applicability of the present method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.567-573
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• 2015

This paper aims to evaluate the variation in the mechanical properties of carbon/epoxy composites under in situ low- and high-temperature environments. In situ low- and high-temperature environments were simulated with temperature ranging from $-40^{\circ}C$ to $220^{\circ}C$ using an environmental chamber and furnace. The variation in the mechanical properties of the composites was measured for longitudinal and transverse tensile properties, in-plane shear properties and interlaminar shear strength. Under the low temperature of $-40^{\circ}C$, all mechanical properties increased moderately compared to the baseline properties measured at room temperature. The changes in the longitudinal tensile properties decreased moderately with increasing temperature. However, transverse tensile properties, in-plane shear properties and interlaminar shear strength each showed a significant drop due to the glass transition behavior of the matrix after $140^{\circ}C$. Notably, the tensile property value near $100^{\circ}C$ increased compared to baseline property value, which was an unusual occurrence. This behavior was a direct result of post-curing of the epoxy resin due to its exposure to high temperature.

• Journal of ILASS-Korea
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• v.6 no.3
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• pp.38-44
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• 2001

The effect of four diesel fuels with oxygenated agents fuels on spray properties from plain-orifice atomizer was investigated. The oxygenates evaluated were diglyme, MTBE, DEE and DMM and were blended in weights of 5, 10, 15, 20 and 30% in a baseline diesel fuel. The physical properties such as surface tension, density and viscosity are also measured for each blended oxygenated fuels. It was found that changes in physical properties of fuels considered are enough to influence spray properties, i.e. spray angle, spray tip penetration and mean drop size. Spray properties were measured by PMAS(particle motion analysis system) which is employing a point measurement technology. Spray angle increased with increase in oxygenate content. The effect, however, was not great in the higher blend level. The oxygenated fuels produced more shorter spray tip penetration than diesel fuels. SMD was decreased with the increase in blending percent. SMD for DMM and DEE are represented 10.33 and 3.41% decreasing rates respectively. It was found that changes in spray characteristics of oxygenated fuel were easily large enough to impact pollutant emissions. It was clear from this study that spray characteristics of oxygenated fuel is one of possible cause of reducing pollutant emissions. It was clear from this study that spray characteristics of oxygenated fuel is one of possible cause of reducing pollutant emissions from diesel engines when oxygenated fuels is applied.

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.103-113
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• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.15-22
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• 1989

This paper presents a distributed routing scheme for allowing network control to be distributed through the switching elements of a multistage baseline network. The routing technique we use here is the destination tag scheme which uses the binary representation of a destination address as the reuting tag. With the topological properties of the network, we introduce a distributed algorithm which allows any connection from a source to arbitrary number of destinations. Also, the performance of the proposed method is evaluated by computer simulation, and the results of the method is compared to other schemes in terms of time complexity.

• Wind and Structures
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• v.32 no.3
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• pp.205-217
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• 2021

Cold regions with high air density and wind speed attract wind energy producers across the globe exhibiting its potential for wind exploitation. However, exposure of wind turbine blades to such cold conditions bring about devastating impacts like aerodynamic degradation, production loss and blade failures etc. A series of wind tunnel tests were performed to investigate the effect of icing on the aerodynamic properties of wind turbine blades. A baseline clean wing configuration along with four different ice accretion geometries were considered in this study. Aerodynamic force coefficients were obtained from the surface pressure measurements made over the test model using MPS4264 Simultaneous pressure scanner. 3D printed Ice templates featuring different ice geometries based on Icing Research Tunnel data is utilized. Aerodynamic characteristics of both the clean wing configuration and Ice accreted geometries were analysed over a wide range of angles of attack (α) ranging from 0° to 24° with an increment of 3° for three different Reynolds number in the order of 105. Results show a decrease in aerodynamic characteristics of the iced aerofoil when compared against the baseline clean wing configuration. The key flow field features such as point of separation, reattachment and formation of Laminar Separation Bubble (LSB) for different icing geometries and its influence on the aerodynamic characteristics are addressed. Additionally, attempts were made to understand the influence of Reynolds number on the iced-aerofoil aerodynamics.