• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.720-725
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• 2010

In AGPS method, user position can be obtained even in the shadow region by improving signal sensitivity. A hybrid long integration scheme employing both coherent and non-coherent integration method is commonly used in AGPS receivers. Because coherent loss increases as residual frequency become large, residual frequency should be minimized to maximize coherent integration gain. This paper presents performance analysis of residual frequency estimator using FFT in fine-time assistance AGPS method. Considering the hardware complexity and the estimation accuracy, optimal length of FFT is proposed for GPS L1 C/A signal. Signal sensitivity for estimating the residual frequency is also analysed. By field experimental results, it is found that the residual frequency can be successfully estimated using 1 second snap-shot data when GPS signal strength is larger than -150 dBm and its RMS error is 3Hz.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.10a
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• pp.61-63
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• 2001

Residual stress distribution in injection-molded short fiber composites was determined using layer-removal method. Polysterene with 3 vol% carbon fibers was injection-molded into the tensile specimen. With milling machine layer-removal process was conducted and the curvature data were acquired. Treuting and Read analysis which is assuming isotropic material, and White analysis considering anisotropy due to the fiber orientation were used to calculate residual stress of the flow direction through the thickness direction and compared with each other.

• Journal of IKEEE
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• v.24 no.4
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• pp.1011-1016
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• 2020

We propose a framework called Stacked Gated Recurrent Unit - Infrequent Residual Analysis (SG-IRA) that detects anomalies in time-series data that can be trained on streams of raw sensor data without any pre-labeled dataset. To enable such unsupervised learning, SG-IRA includes an estimation model that uses a stacked Gated Recurrent Unit (GRU) structure and an analysis method that detects anomalies based on the difference between the estimated value and the actual measurement (residual). SG-IRA's residual analysis method dynamically adapts the detection threshold from the population using frequency analysis, unlike the baseline model that relies on a constant threshold. In this paper, SG-IRA is evaluated using the industrial control systems (ICS) datasets. SG-IRA improves the detection performance (F1 score) by 5.9% compared to the baseline model.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.13-23
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• 2024

Music brings pleasure and relaxation to people. Therefore, it is necessary to classify musical genres based on scenes. Identifying favorite musical genres from massive music data is a time-consuming and laborious task. Recent studies have suggested that machine learning algorithms are effective in distinguishing between various musical genres. However, meeting the actual requirements in terms of accuracy or timeliness is challenging. In this study, a hybrid machine learning model that combines a deep residual auto-encoder (DRAE) and support vector machine (SVM) for musical genre recognition was proposed. Eight manually extracted features from the Mel-frequency cepstral coefficients (MFCC) were employed in the preprocessing stage as the hybrid music data source. During the training stage, DRAE was employed to extract feature maps, which were then used as input for the SVM classifier. The experimental results indicated that this method achieved a 91.54% F1-score and 91.58% top-1 accuracy, outperforming existing approaches. This novel approach leverages deep architecture and conventional machine learning algorithms and provides a new horizon for musical genre classification tasks.

• International Journal of Reliability and Applications
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• v.1 no.1
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• pp.39-47
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• 2000

In a resent paper, Na and Kim(2000) develop a family of test statistics for testing whether or not the mean residual life changes its trend based on complete data and show that the new tests perform better than previously known tests. In this paper, we extend their tests to the randomly censored data. The asymptotic normality of the test statistics is established. Monte Carlo simulations are conducted to compare our tests with a previously known test by the power of tests.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.1 s.244
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• pp.42-51
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• 2006

An indentation method to determine equi-biaxial residual stress is proposed by examining the data from the incremental plasticity theory based finite element analyses. We first select optimal normalized-parameters, which are minimally affected by indentation depth and material properties. Numerical linear regressions of obtained data exhibit that maximum load and contact area of imprint are the main parameters measuring the residual stress. The proposed indentation approach provides a substantial enhancement in accuracy compared with the prior methods.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.27-40
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• 2001

The purpose of this study is to test a multiple change point in the regression model with the passage of time, using the estimated residual variance figure suggested by Gasser, Sroka and Jennen - Steinmez (GSJS). As a result of the simulation, it is showed that there is a jump change of the estimated residual variance figure at that time of change point. The way to analyse a intuitive multiple change point through graphics is more effective and accurate than any other existing ways.

• Korean Journal of Metals and Materials
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• v.49 no.6
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• pp.468-473
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• 2011

Determining the residual stress during casting processes is important for evaluating the mechanical properties and strength of materials and to optimize manufacturing conditions. In this study, we propose a field data interface procedure between FDM and FEM in a 3-dimensional space for analyzing the casting process and structural analysis. The casting process was analyzed using FDM and the data of the temperature distribution were converted into a format suitable for FEM analysis to calculate the thermal stress and safety factor by tightening force. The results of the coupled analysis between FDM and FEM showed that casting residual stress is an important factor in predicting life time and evaluating durability.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.323-333
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• 2023

Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.713-723
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• 2003

In this study, a state of art review of existing residual stress analysis techniques and representative solutions is presented in order to develope the residual stress analysis procedure for fitness-for-service (FFS) assessment of welded structure. Critical issues associated with existing residual stress solutions and their treatments in performing FFS are discussed. It should be recognized that detailed residual stress evolution is an extremely complicated phenomenon that typically involves material-specific ther-momechanical/metallurgical response, welding process physics, and structural interactions within a component being welded. As a result, computational procedures can vary significantly from highly complicated numerical techniques intended only to elucidate a small part of the process physics to cost-effective procedures that are deemed adequate for capturing some of the important features in a final residual stress distribution. Residual stress analysis procedure for FFS purposes belongs to the latter category. With this in mind, both residual stress analysis techniques and their adequacy for FFS are assessed based on both literature data and analyses performed in this investigation.