• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.4
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• pp.65-75
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• 1993

Presented is a method for nonstationary response analysis of an offshore guyed tower subjected to strong earthquake motions under moderate random waves and current loadings. By taking the time varying envelope function and the auto-correlation function of the ground acceleration in terms of complex exponential functions, an analytical procedure is developed for computing time varying variances of the tower response. The stationary responses due to small random waves are obtained by using frequency domain method, and the results are combined with the nonstationary results due to earthquakes. Finally, the expected maximum responses are estimated. Through the example analyses, the nonstationary method developed in this study is verified, and the contributions of the earthquake, wave and current loadings to the total maximum response are investigated.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.1
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.2
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• pp.229-237
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• 1992

Series system reliability analysis of non-reactive contaminant transport is performed in a two dimensional horizontal domain with two different limit state functions: (1) concentration threshold and (2) exposure time threshold. The transient source transport model is combined with the system reliability model to evaluate the probability that a specified maximum concentration at a node of interest would be exceeded or that a moderate concentration would exceed some exposure limit over a given period of time. The results give probabilities of exceedence greater than probability of each component and they tend to be dominanted by the component with larger probability. Transverse dispersivity turns out to be an important parameter in addition to hydraulic conductivity in a two-dimensional contaminant transport model with transient source. System sensitivity is found to reflect the corresponding sensitivity of both components, with the component with larger probability having a greater influence.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.71-83
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• 1994

This paper deals with the improvement of the synthesized speech quality and naturality in the Korean TTS(Text-to-Speech) system. We had extracted the parameters(table2) such as its amplitude, duration and pitch period in a syllable through the analysis of speech waveforms(table1) in the time domain and synthesized syllables using them. To the frequencies of the Korean pronunciation large vocabulary dictionary we had synthesized speeches selected 229 syllables such as V types are 19, CV types are 80. VC types are 30 and CVC types are 100. According to the 4 Korean syllable types from the data format dictionary(table3) we had tested each 15 syllables with the objective MOS(Mean Opinion Score) evaluation method about the 4 items i.e., intelligibility, clearness, loudness, and naturality after selecting random group without the knowledge of them. As the results of experiments the qualities of them are very clear and we can control the prosodic elements such as durations, accents and pitch periods (fig9, 10, 11, 12).

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.59-67
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• 2012

It was reported that the latest sensor technology allow an 65536 conductive polymer sensor array to be made with broad but overlapping selectivity to different families of chemicals emulating the characteristics found in biological olfaction. However, the supernumerary redundancy always accompanies great error and risk as well as an inordinate amount of computation time and local minima in signal processing, e.g. neural networks. In this paper, we propose a new method to reduce the number of sensor for analysis by reducing redundancy between sensors and by removing unstable sensors using the cosine similarity method and to decide on representative sensor using FCM(Fuzzy C-Means) algorithm. The representative sensors can be just used in analyzing. And, we introduce DWT(Discrete Wavelet Transform) for data compression in the time domain as preprocessing. Throughout experimental trials, we have done a comparative analysis between gas sensor data with and without reduced redundancy. The possibility and superiority of the proposed methods are confirmed through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1234-1249
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• 2017

One of the difficulties developers encounter in maintaining tasks of a large-scale software system is the updating of suitable libraries on time. Developers tend to miss or make mistakes when searching for and choosing libraries during the development process, or there may not be a stable library for the developers to use. We present a novel approach for helping developers modify software easily and on time and avoid software failures. Using a tool previously built by us called GPES, we collected information of projects, such as abstract syntax trees, tokens, software metrics, relations, and evolutions, for our experiments. We analyzed the contexts of source codes in existing projects to predict changes automatically and to recommend suitable libraries for the projects. The collected data show that researchers can reduce the overall cost of data analysis by transforming the extracted data into the required input formats with a simple query-based implementation. Also, we manually evaluated how the extracted contexts are similar to the description and we found that a sufficient number of the words in the contexts is similar and it might help developers grasp the domain of the source codes easily.

• International Journal of Fluid Machinery and Systems
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• v.8 no.4
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• pp.264-273
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• 2015

This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.9
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• pp.1047-1056
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• 2007

In this paper, we analyze the performance of the encoding and the modulation processes in the downlink and uplink of the EPCglobal Gen-2 system through the analysis and simulation. Furthermore, the synchronization issues on time and frequency domain and the preamble architecture are evaluated. By incorporating the encoding and the modulation technigue in the downlink and uplink, we assess the performance of the EPCglobal Gen-2 system. We also introduce the encoding and backscatter modulation process as well as the BER performance of FM0 code. In addition, the importance and the role of the frequency and time synchronization, such as the preamble and frame synchronization are explained. Through the simulation in the uplink on the detection probability through preamble, we find that the detection probability approaches 1 for 13 dB $E_b/N_0$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.513-516
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• 2013

In this paper, we present an information collecting system in medical information management domain. Our proposed system performs a systemized process, consisting of collection, transmission, and management, to develop intelligent medical information system and medical big data processing system. Our information collecting system consists of low-power biomedical sensors, biomedical information collecting devices, and storage systems. Currently, almost biomedical information of patients is collected manually by employees like nurses and medical doctors. Therefore, collected biometric data can be error-pronoun data. Since there is a lack to make big data of medical information, it is difficult to enhance the quality of medical services and researches. Accordingly, through our proposed system, we can overcome the problems like error-pronoun biometric data. In addition, we can extremely extend the area of collectable biometric data. Furthermore, using this system, we are able to make a real-time biomedical analysis system, like a real-time patient diagnosis system, and establish a strategy to against future medical markets changing rapidly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.48-55
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• 2020

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.