• The KSFM Journal of Fluid Machinery
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• v.15 no.5
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• pp.27-31
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• 2012

Turbine inlet temperature is steadily increasing to achieve high specific thrust and efficiency of gas turbine engines. Turbine cooling technology is essential to increase turbine inlet temperature. For this study, a small or medium sized aircraft engine of 10,000 lbf class with the turbine inlet temperature of $1,400^{\circ}C$, the engine overall pressure ratio of 32.2, and the bypass ratio of 5 was set as the baseline model and its performance analysis was performed at the design point. The engine has the performance of 10,013 lbf thrust and the specific fuel consumption of 0.362 lbm/hr/lbf. The thrust and the specific fuel consumption of the baseline model were compared with those of similar class engines. Based on these results, the turbine design requirements were assigned. In addition, the parametric analysis of the engine, related to aerodynamic and cooling design of the high pressure turbine, was performed. Based on the baseline model engine, the influence of turbine inlet temperature, cooling flow ratio, and high pressure turbine efficiency variations on the engine performance was analyzed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.443-450
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• 2003

System identification techniques can be used to build a baseline modal model for a flawed structure that has no modal information on its as-built state. The accuracy of a system identification proposed by Stubbs and Kim is analyzed for plate-girder bridges and its impact on the accuracy of damage detection in those structures is also analyzed. A laboratory-scale model plate-girder is experimentally tested and the initial four bending modes are examined for certain damage scenarios. The performance of individual baseline modal models is assessed by detecting damage in the model structure.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.410-413
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• 2007

Water vapor in the atmosphere is an influential factor of the hydrosphere cycle, which exchanges heat through phase change and is essential to precipitation. Because of its significance in altering weather, the estimation of water vapor amount and distribution is crucial to determine the precision of the weather forecasting and the understanding of regional/local climate. It is shown that it is reliable to measure precipitable water (PW) using long baseline (500-2000km) GPS observations. However, it becomes infeasible to derive absolute PW from GPS observations in Taiwan due to geometric limitation of relatively short-baseline network. In this study, a method of deriving Near-Real-Time PW from short baseline GPS observations is proposed. This method uses a reference station to derive a regression model for wet delay, and to interpolate the difference of wet delay among stations. Then, the precipitable water is obtained by using a conversion factor derived from radiosondes. The method has been tested by using the reference station located on Mt. Ho-Hwan with eleven stations around Taiwan. The result indicates that short baseline GPS observations can be used to precisely estimate the precipitable water in near-real-time.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.14 no.2
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• pp.69-76
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• 2018

The paper presents preliminary investigation results for the effect of the baseline correction in the acceleration excitation method on finite element seismic analysis results (such as accumulated equivalent plastic strain, equivalent plastic strain considering cyclic plasticity, von Mises effective stress, etc) of nuclear safety Class I components. For investigation, finite element elastic-plastic time-history seismic analysis is performed for a surge line including a pressurizer lower head, a pressurizer surge nozzle, a surge piping, and a hot leg surge nozzle using the Chaboche hardening model. Analysis is performed for various seismic loading methods such as acceleration excitation methods with and without the baseline correction, and a displacement excitation method. Comparing finite element analysis results, the effect of the baseline correction is investigated. As a result of the investigation, it is identified that finite element analysis results using the three methods do not show significant difference.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.108-123
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• 2007

As H.264/AVC standard has proven to be a key technology of multimedia application, many researches to improve H.264/AVC standard are actively conducted. Those researches are conducted in various ways such as algorithm analysis and improvement or structure enhancement for reducing bottlenecks of performance. Even though targets and directions of those studies are not the same, performance of H.264/AVC standard is commonly analyzed in the early phase. In analysis phase, potential problems with H.264/AVC standard are identified and the most critical problem which has serious effects on performance is determined. Therefore, analysis phase is one of the important steps to decide overall directions and targets of the research. This research proposes a mathematical model which can be used in the early performance analysis phase to estimate performance in conducting research of improving the performance of H.264/AVC Baseline Profile decoder. The proposed model is designed by considering many variables of H.264/AVC decoder operation so that it is easy to predict its performance according to changes in each element.

• 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.

• Advances in Energy Research
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• v.4 no.2
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• pp.107-120
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• 2016

The aim of this research is to apply the eQuest model to investigate the energy conservation in a multifamily building located in Dayton, Ohio by using a Trombe wall and an ammonia ground source heat pump (R-717 GSHP). Integration of the Trombe wall into the building is the first retrofitting measure in this study. Trombe wall as a passive solar system, has a simple structure which may reduce the heating demand of buildings significantly. Utilization of ground source heat pump is an effective approach where conventional air source heat pump doesn't have an efficient performance, especially in cold climates. Furthermore, the type of refrigerant in the heat pumps has a substantial effect on energy efficiency. Natural refrigerant, ammonia (R-717), which has a high performance and no negative impacts on the environment, could be the best choice for using in heat pumps. After implementing the eQUEST model in the said multifamily building, the total annual energy consumption with a conventional R-717 air-source-heat-pump (ASHP) system was estimated as the baseline model. The baseline model results were compared to those of the following scenarios: using R-717 GSHP, R410a GSHP and integration of the Trombe wall into the building. The Results specified that, compared to the baseline model, applying the R-717 GSHP and Trombe wall, led to 20% and 9% of energy conservation in the building, respectively. In addition, it was noticed that by using R-410a instead of R-717 in the GSHP, the energy demand increased by 14%.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.782-791
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• 1991

In the fields of pattern recognition such as speech recognition, several new techniques using Artifical Neural network Models have been proposed and implemented. In particular, the Multilayer Perception Model has been shown to be effective in static speech pattern recognition. But speech has dynamic or temporal characteristics and the most important point in implementing speech recognition systems using Artificial Neural Network Models for continuous speech is the learning of dynamic characteristics and the distributed cues and contextual effects that result from temporal characteristics. But Recurrent Multilayer Perceptron Model is known to be able to learn sequence of pattern. In this paper, the results of applying the Recurrent Model which has possibilities of learning tedmporal characteristics of speech to phoneme recognition is presented. The test data consist of 144 Vowel+ Consonant + Vowel speech chains made up of 4 Korean monothongs and 9 Korean plosive consonants. The input parameters of Artificial Neural Network model used are the FFT coefficients, residual error and zero crossing rates. The Baseline model showed a recognition rate of 91% for volwels and 71% for plosive consonants of one male speaker. We obtained better recognition rates from various other experiments compared to the existing multilayer perceptron model, thus showed the recurrent model to be better suited to speech recognition. And the possibility of using Recurrent Models for speech recognition was experimented by changing the configuration of this baseline model.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2721-2731
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• 2018

This study purposes to derive the predictive model for the cold tolerance of Brassica napus, using the data collected in the Tree Breeding Lab of Gyeongsang National University during July and August of 2016. Three Brassica napus samples were treated at each of low temperatures from $4^{\circ}C$ to $-12^{\circ}C$ by decrement of $4^{\circ}C$, step by step, and electrolyte leakage levels were measured at each stage. Electrolyte leakages were observed tangibly from $-4^{\circ}C$. We tried to fit the six nonlinear regression models to the electrolyte leakage data of Brassica napus: 3-parameter logistic model, baseline logistic model, 4-parameter logistic model, (4-1)-parameter logistic model, 3-parameter Gompertz model, and (3-1)-parameter Gompertz model. The baseline levels of the electrolyte leakage estimated by these models were 4.81%, 4.07%, 4.19%, 4.07%, 4.55%, and 0%, respectively. The estimated median lethal temperature, LT50, were $-5.87^{\circ}C$, $-6.31^{\circ}C$, $-6.05^{\circ}C$, $-6.35^{\circ}C$, $-4.98^{\circ}C$, and $-5.15^{\circ}C$, respectively. We compared and discussed the measures of goodness of fit to select the appropriate nonlinear regression model.

• Annual Conference of KIPS
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• 2021.05a
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• pp.408-410
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• 2021

In the NLP field, the pre-training model BERT launched by the Google team in 2018 has shown amazing results in various tasks in the NLP field. Subsequently, many variant models have been derived based on the original BERT, such as RoBERTa, ERNIEBERT and so on. In this paper, the WWMBERT (Whole Word Masking BERT) model suitable for Chinese text tasks was used as the baseline model of our experiment. The experiment is mainly for "Text-level Chinese text classification tasks" are improved, which mainly combines Tapt (Task-Adaptive Pretraining) and "Multi-Sample Dropout method" to improve the model, and compare the experimental results, experimental data sets and model scoring standards Both are consistent with the official WWMBERT model using Accuracy as the scoring standard. The official WWMBERT model uses the maximum and average values of multiple experimental results as the experimental scores. The development set was 97.70% (97.50%) on the "text-level Chinese text classification task". and 97.70% (97.50%) of the test set. After comparing the results of the experiments in this paper, the development set increased by 0.35% (0.5%) and the test set increased by 0.31% (0.48%). The original baseline model has been significantly improved.