• International journal of advanced smart convergence
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• v.10 no.2
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• pp.73-78
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• 2021

Recently, the number of people who visit the hospital due to diabetes is increasing. According to the Korean Diabetes Association, it is statistically indicated that one in seven adults aged 30 years or older in Korea suffers from diabetes, and it is expected to be more if the pre-diabetes, fasting blood sugar disorders, are combined. In the last study, the validity of Triglyceride and Cholesterol associated with diabetes was confirmed and analyzed using Random Forest. Random Forest has a disadvantage that as the amount of data increases, it uses more memory and slows down the speed. Therefore, in this paper, we compared and analyzed Random Forest and XGBoost, focusing on improvement of learning speed and prevention of memory waste, which are mainly dealt with in machine learning. Using XGBoost, the problem of slowing down and wasting memory was solved, and the accuracy of the diabetes recognition factor was further increased.

• ETRI Journal
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• v.46 no.1
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• pp.137-153
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• 2024

This study introduces a new approach in Korean morphological analysis combining dictionary-based techniques with Transformer-based deep learning models. The key innovation is the use of a BERT-based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first-stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary-based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.

• Korean Journal of Artificial Intelligence
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• v.12 no.3
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• pp.33-39
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• 2024

In this paper, we explore the application of Kodaly hand signs in enhancing children's music education, performances, and auditory assistance technologies. This research focuses on improving the recognition rate of Multilayer Perceptron (MLP) models in identifying Kodaly hand sign scales through the integration of Artificial Neural Networks (ANN). We developed an enhanced MLP model by augmenting it with additional parameters and optimizing the number of hidden layers, aiming to substantially increase the model's accuracy and efficiency. The augmented model demonstrated a significant improvement in recognizing complex hand sign sequences, achieving a higher accuracy compared to previous methods. These advancements suggest that our approach can greatly benefit music education and the development of auditory assistance technologies by providing more reliable and precise recognition of Kodaly hand signs. This study confirms the potential of parameter augmentation and hidden layers optimization in refining the capabilities of neural network models for practical applications.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.135-140
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• 2007

Due to the expensiveness of IKONOS Pro and Precision Products, it is attractive to use the low-cost IKONOS Geo Product with vendor-provided RPCs to produce highly accurate mapping products. The imaging geometry of IKONOS high-resolution imagery is described by RFs instead of rigorous sensor models. This paper presents four different models defined respectively in object space and image space to improve the accuracies of the RF-derived ground coordinates. The four models include the offset model, the scale & offset model, the affine model and the 2nd-order polynomial model. Different configurations of ground control points (GCPs) are carefully examined to evaluate the effect of the GCPs arrangement on the accuracy of ground coordinates. The experiment also evaluates the effect of different cartographic parameters such as the number location, and accuracy of GCPs on the accuracy of geopositioning.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.27-30
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• 2007

The objective of this study is to improve an accuracy of PWV estimates using GPS in Korea. We determined a weighted mean temperature equation by a linear regression method based on 6 radiosonde meteorological observations, for a total 17,129 profiles, from 2003 to 2005. Weighted mean temperature, Tm, is a key parameter in the retrieval of atmospheric PWV from ground-based GPS measurements of zenith path delay. The accuracy of the GPS-derived PWV is proportional to the accuracy of Tm. And we applied the reduction of air Pressure to GPS station altitude. The reduction value of air pressure from mean sea level to GPS stations altitude is adopted a reverse sea level correction.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2382-2386
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• 2003

The Global Positioning System(GPS) is a satellite based precise positioning system avaliable worldwide. The GPS have many error sources. The earth's ionosphere and atmosphere cause delays in the GPS signal that translate into position errors. Some errors can be factored out using mathematics and modeling. The configuration of the satellites in the sky can magnify other errors. The problem of accuracy on GPS measurement data can be meaningful. In this study, we propose the method for GPS positioning accuracy improvement. The FUZZY set theory on PDOP(Position Dilution of Precision) and SNR(Signal to Noise Ratio) provide improved for measured positioning data. The accuracy of positioning has been improved by selecting data from original using the FUZZY set theory on PDOP and SNR.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.399-404
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• 2002

Improvement of measuring accuracy is an essential part of quality control manufacturing process. The OMM is less than the CMM in measure accuracy but the OMM system is more efficient, easier to use than other measurement system. About 40~70% of the machine tool errors are induced by the thermal errors. Therefore, a key requirement for improving the measuring accuracy is to reduce the geometric and thermal errors. Thermal errors are measured by a ball bar system and predicted by the thermal error modeling. Furthermore, using the pre-defined thermal error map approach compensates the geometric accuracy of the OMM. Appropriate experiments are performed using ball-bar system, temperature measuring devices and touch-type probe. Compensated results are compared with those obtained using CMM to verify the proposed methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.546-550
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• 2002

This paper proposes an error compensation method that improves accuracy with geometry information of injection molding parts. Geometric information can give an improved accuracy in reverse engineering. Measuring data can not lead to get accurate geometric model, including errors of physical parts and measuring machines. Measuring data include errors which can be classified into two types. One is molding error in product, the other is measuring error. Measuring error includes optical error of laser scanner, deformation by probe forces of CMM and machine error. It is important to compensate these in reverse engineering. Least square method(LSM) provides the cloud data with a geometry compensation, improving accuracy of geometry. Also, the functional shape of a part and design concept can be reconstructed by error compensation using geometry information.

• Transactions of Materials Processing
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• v.23 no.6
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• pp.380-385
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• 2014

An analysis procedure is proposed to improve the prediction accuracy of springback as well as to evaluate the structural stability of the tooling used for fabricating a side sill part from UHSS. The analysis couples the stamping analysis and the subsequent analysis of the tool structural. The deformation and stress results for the tool structure are obtained from the proposed analysis procedure. The results show that the amount of deformation and stresses are so high that the tool structure must be reinforced and the tooling design must consider structural stability. Springback is predicted with CAE in order to compare the prediction accuracy between the given tool geometry and the geometry from the structural analysis. The simulation results with the deformed tool can predict the experimental springback tendency accurately.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.39-43
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• 2013

The current representative temperature selection method with five cities cannot reflect the sufficient regional climate characteristics. In this paper, the new representative temperature selection method is proposed with the consideration of eight representative cities. The proposed method considered the recent trend of power sales, the climate characteristics and population distribution to improve the accuracy of short-term load forecasting. Case study results for the accuracy of short-term load forecasting are compared for the traditional temperature weights of five cities and the proposed temperature weights of eight cities. The simulation results show that the proposed method provides more accurate results than the traditional method.