• Journal of Environmental Science International
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• v.30 no.12
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• pp.1053-1065
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• 2021

In this study, the prediction technology of Hydrological Quantitative Precipitation Forecast (HQPF) was improved by optimizing the weather predictors used as input data for machine learning. Results comparison was conducted using bias and Root Mean Square Error (RMSE), which are predictive accuracy verification indicators, based on the heavy rain case on August 21, 2021. By comparing the rainfall simulated using the improved HQPF and the observed accumulated rainfall, it was revealed that all HQPFs (conventional HQPF and improved HQPF 1 and HQPF 2) showed a decrease in rainfall as the lead time increased for the entire grid region. Hence, the difference from the observed rainfall increased. In the accumulated rainfall evaluation due to the reduction of input factors, compared to the existing HQPF, improved HQPF 1 and 2 predicted a larger accumulated rainfall. Furthermore, HQPF 2 used the lowest number of input factors and simulated more accumulated rainfall than that projected by conventional HQPF and HQPF 1. By improving the performance of conventional machine learning despite using lesser variables, the preprocessing period and model execution time can be reduced, thereby contributing to model optimization. As an additional advanced method of HQPF 1 and 2 mentioned above, a simulated analysis of the Local ENsemble prediction System (LENS) ensemble member and low pressure, one of the observed meteorological factors, was analyzed. Based on the results of this study, if we select for the positively performing ensemble members based on the heavy rain characteristics of Korea or apply additional weights differently for each ensemble member, the prediction accuracy is expected to increase.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.2
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• pp.23-30
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• 2003

It is very important to detect and evaluate the surface or subsurface flaws because of their influences on mechanical properties of materials. Rayleigh wave and creeping wave are commonly used for the detection of surface and subsurface flaws. These techniques, however, have following problems. Each amplitudes are remarkably affected by the surface condition and evaluation of echo pattern is usually difficult because shear wave mode propagate in the material at the same time. On the other hand, surface SH-wave which is horizontally polarized shear wave traveling along near surface layer is an attractive technique for the surface or subsurface material characterization and this technique is useful to solve the problems mentioned above. In this paper, The stability and transmission coefficient of SH waves through a viscous fluid layer is theoretically studied and simulated. Its results agreed well with the theoretical expectation for the experimental verification. These experimental results show that viscosity of couplants, thickness of couplant and surface roughness are closely related to transfer efficiency in surface SH angle beam method.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.79-84
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• 2015

Transformer is widely used element on power system and industrial area. Especially the transformers installed at power system are exposed to an environment of arbitrary changed. Thus the prediction of degradation and the analysis of response to impulse are important. To conduct those works, the electrical characteristics of system should be analyzed, effectively. But the analysis of electrical characteristic in electric machine level such as pole and pad-mounted transformer is almost no, thus commercial VNA (Vector Network Analyzer) is used to getting the response in wide frequency range. However, the output power of VNA is usually under 10mW, so verification for effectiveness of measuring electrically large component should be conducted, firstly. Next, after getting total S-parameter of transformer, predicting impulse response can be performed in time-domain with circuit simulator. In this paper, it is introduced that verification effectiveness of VNA using transfer function from SFRA (Sweep Frequency Response Analyzer), firstly. Next, total S-parameter, six by six matix form, was built using measured 2 port S-parameter from vector network analyzer. To get the response to impulse which is defined by IEC 60060-1, time-domain simulation is conducted to ADS (Advenced Design System) circuit simulator.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.68-73
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• 2009

One of the traditional optical methods to monitor a tool is a CCD sensor-based vision system which captures an aspect of the tool in real time. In the case using the CCD sensor, specific lens-modules are necessary to monitor the tool with higher resolution than its pixel size, and a microprocessor is required to attain desired data from captured images. Thus theses additional devices make the entire measurement system complex. Another method is to use a pair of an optical source and a detector per measuring axis. Since the method is based on the intensity modulation, the structure of the measurement system is simper than the CCD sensor-based vision system. However, in the case measuring the three dimensional position of the tool, it is difficult to apply to micro machine-tools because there may not be space to integrate three pairs of an optical source and a detector. In this paper, in order to develop a tool-origin measurement system which is employed in micro machine-tools, the improved method to measure a tool origin in x, y and z axes is introduced. The method is based on the intensity modulation and employs one pair of an optical source radiating divergent beams and a quadrant photodiode to detect a three dimensional position of the tool. This paper presents the measurement models of the proposed tool-origin sensor. The models were verified experimentally The verification results show that the proposed method is possible and the induced models are available for design.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.137-144
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• 2000

In order to achieve systematical method for improving motion accuracy of hydrostatic table, an algorithm using finite element method is proposed in this paper. Quantification of averaging effect of oil film on motion error is performed theoretically by analysis on the relationship between spacial frequency of rail form error and motion error of table. Influences of film stiffness and pocket size on the motion error of table are also analyzed theoretically. Validity of the algorithm is verified experimentally from the test on the motion error of table with three types of rail which have different form profile. Experimental results show that the algorithm is very effective to analyze theoretically the motion error of hydrostatic table.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.4713-4718
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• 2013

Nowadays, three-dimensional computer added design(3D CAD) tool are widely and actively used for design of mechanical machine. Because using the tool is more effective to understand design concept and to collaborate with other operation than using two-dimensional design tool. In this study, the 3D CAD tool which is called I-DEAS was applied for three-dimensional modeling of main parts and assembling of modeled parts for identification the entire shape of a injection molding machine. In addition, a study was also performed regarding reduction for the weight of main plates for saving production cost and energy in the machine. A finite element method(FEM) program in I-DEAS tool was used for the improvement study. First, the current main plates were structural analysed and then the plate deformations, weak regions and stress distributions were graped. By the FEM results, the 2nd improved designing of the plates was conducted such as reinforcement or slimming of the plate wall thickness. The 2nd structural FEM was performed for verification of the redesigned plates and then the FEM results were compared with the 1st FEM's result. The weight of the main plates were averagely reduced approximately 3 - 7%. By these results, it was seemed that the improved plates have a useful availability.

• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.12-21
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• 2008

As the usage of computer systems is increasing in our lives, the reliability and safely of these systems need to be thoroughly checked through the verification techniques. As a basic formalism for several modeling methods, the finite state machine (FSM) is widely used in specification and verification of system models. And there is a technique for ing internal events of FSM in order to effectively analyze the system. However, this technique does not handle the state explosion problem since it can be applied after completely generating all the state space of the system. In this research, we provide a new approach for efficiently representing concurrent properties of FSM, the slice model and provide an efficient transition reduction method based on the slice model. Our approach is effective in time and space perspective since it is peformed by partially generating the needed system states while the existing abstraction technique can be applied to all the system states.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.41-55
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• 2021

To detect thunderstorms occurring in Korea, National Meteorological Satellite Center (NMSC) also introduced the rapid-development thunderstorm (RDT) algorithm developed by EUMETSAT. At NMCS, the H-RDT (HR) based on the Himawari-8 satellite and the K-RDT (KR) which combines the GK2A convection initiation output with the RDT were developed. In this study, we optimized the KR (KU) to improve the detection level of thunderstorms occurring in Korea. For this, we used all available data, such as GK2A/AMI, RADAR, lightning, and numerical model data from the recent two years (2019-2020). The machine learning of logistic regression and stepwise variable selection was used to optimize the KU algorithms. For considering the developing stages and duration time of thunderstorms, and data availability of GK2A/AMI, a total of 72 types of detection algorithms were developed. The level of detection of the KR, HR, and KU was evaluated qualitatively and quantitatively using lightning and RADAR data. Visual inspection using the lightning and RADAR data showed that all three algorithms detect thunderstorms that occurred in Korea well. However, the level of detection differs according to the lightning frequency and day/night, and the higher the frequency of lightning, the higher the detection level is. And the level of detection is generally higher at night than day. The quantitative verification of KU using lightning (RADAR) data showed that POD and FAR are 0.70 (0.34) and 0.57 (0.04), respectively. The verification results showed that the detection level of KU is slightly better than that of KR and HR.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.30-37
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• 2012

This paper presents the performance investigation and design technology of a Line Start-up Permanent Magnet Synchronous Motor (LSPMSM) with super premium efficiency, including a design consideration and evaluation for motor start-up, key performance, and advanced finite element analysis FEA) for the design, improvement and verification, prototype build and test, design and test data comparison with a $Premium^{(R)}$ Efficient Induction Motor (PEIM). To assess the design technology, the LSPMSM prototype was built amended from a PEIM with the same frame, stator punching and rated output. Based on the prototype test, two novel design improvements and analyses have been done to eliminate noise and vibration. Additionally, the comparisons with the PEIM on the power factor, efficiency, frame size and active material consumption indicated that a significant performance improvement and active material cost reduction can be achieved by the LSPMSM.