• The Journal of the Acoustical Society of Korea
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• v.11 no.2
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• pp.73-84
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• 1992

This paper studies noise subtraction methods and distance measures for speech recognition in a noisy environment, and investigates noise robustness of the distance measures applied to the problem of isolated word recognition in white Gaussian and colored noise (vehicle noise) environments. Noise subtraction methods which can be used as a pre-processor for the speech recognition system, such as the spectral subtraction method, autocorrelation subtraction method, adaptive noise cancellation and acoustic beamforming are studied, and distance measures such and Log Likelihood Ratio ($d_{LLR}$), cepstral distance measure ($d_{CEP}$), weighted cepstral distance measure ($d_{WCEP}$), spectral slope distance measure ($d_{RPS}$) and cepstral projection distance measure ($d_{CP},\;d_{BCP},\;d_{WCP},\;d_{BWCP}$) are also investigated. Testing of the distance measures for speaker-dependent isolated word recognition in a noisy environment indicate that $d_{RPS}\;and\;d_{WCEP}$ which weigh higher order cepstral coefficients more heavily give considerable performance improvement over $d_{CEP}and\;d_{LLR}$. In addition, when no pre-emphasis is performed, the recognizer can maintain higher performance under high noise conditions.

• Journal of the Microelectronics and Packaging Society
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• v.15 no.2
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• pp.29-36
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• 2008

In this paper, mechanical reliability issues of copper through-wafer interconnections are investigated numerically and experimentally. A hermetic wafer level packaging for MEMS devices is developed. Au-Sn eutectic bonding technology is used to achieve hermetic sealing, and the vertical through-hole via filled with electroplated copper for the electrical connection is also used. The MEMS package has the size of $1mm{\times}1mm{\times}700{\mu}m$. The robustness of the package is confirmed by several reliability tests. Several factors which could induce via hole cracking failure are investigated such as thermal expansion mismatch, via etch profile, and copper diffusion phenomenon. Alternative electroplating process is suggested for preventing Cu diffusion and increasing the adhesion performance of the electroplating process. After implementing several improvements, reliability tests were performed, and via hole cracking as well as significant changes in the shear strength were not observed. Helium leak testing indicated that the leak rate of the package meets the requirements of MIL-STD-883F specification.

• Composites Research
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• v.31 no.5
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• pp.227-237
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• 2018

In this paper, the design application for the lightweight and insulation of the manipulator of the mobile welding robot for the closed/narrow space is presented. A variety of robotic platforms have been developed for weld-worker using a welding robot outside a workpiece for welding work in a complex and narrow space such as a ship or an offshore plant. Normally, The development process of robots consists of machine development, electronic device development, control algorithm development and integration verification considering application environment and requirements. In order to develop the robustness of the welding robot, the lightweight design of the robot manipulator considering the environmental conditions was performed in the basic design of the robot platform. Also, The results of the robot selection and validation, analysis and testing for the insulation performance and cooling performance and the results of the research are shown.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.170-176
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• 2005

Recently the velocity stack inversion is having many attentions as an useful way to perform various seismic data processing. In order to be used in various seismic data processing, the inversion method used should have properties such as robustness to noise and parsimony of the velocity stack result. The IRLS (Iteratively Reweighted Least-Squares) method that minimizes ${L_1}-norm$ is the one used mostly. This paper introduce another method, CGG (Conjugate Guided Gradient) method, which can be used to achieve the same goal as the IRLS method does. The CGG method is a modified CG (Conjugate Gradient) method that minimizes ${L_1}-norm$. This paper explains the CGG method and compares the result of it with the one of IRSL methods. Testing on synthetic and real data demonstrates that CGG method can be used as an inversion method f3r minimizing various residual/model norms like IRLS methods.

• The Journal of the Acoustical Society of Korea
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• v.35 no.6
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• pp.501-509
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• 2016

We propose a method to improve the robustness of speaker verification on short test utterances. The accuracy of the state-of-the-art i-vector/probabilistic linear discriminant analysis systems can be degraded when testing utterance durations are short. The proposed method compensates for utterance variations of short test feature vectors using deep neural networks. We design three different types of DNN (Deep Neural Network) structures which are trained with different target output vectors. Each DNN is trained to minimize the discrepancy between the feed-forwarded output of a given short utterance feature and its original long utterance feature. We use short 2-10 s condition of the NIST (National Institute of Standards Technology, U.S.) 2008 SRE (Speaker Recognition Evaluation) corpus to evaluate the method. The experimental results show that the proposed method reduces the minimum detection cost relative to the baseline system.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.391-405
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• 2019

Compared to the ambient vibration test mainly identifying the structural modal parameters, such as frequency, damping and mode shapes, the impact testing, which benefits from measuring both impacting forces and structural responses, has the merit to identify not only the structural modal parameters but also more detailed structural parameters, in particular flexibility. However, in traditional impact tests, an impacting hammer or artificial excitation device is employed, which restricts the efficiency of tests on various bridge structures. To resolve this problem, we propose a new method whereby a moving vehicle is taken as a continuous exciter and develop a corresponding flexibility identification theory, in which the continuous wheel forces induced by the moving vehicle is considered as structural input and the acceleration response of the bridge as the output, thus a structural flexibility matrix can be identified and then structural deflections of the bridge under arbitrary static loads can be predicted. The proposed method is more convenient, time-saving and cost-effective compared with traditional impact tests. However, because the proposed test produces a spatially continuous force while classical impact forces are spatially discrete, a new flexibility identification theory is required, and a novel structural identification method involving with equivalent load distribution, the enhanced Frequency Response Function (eFRFs) construction and modal scaling factor identification is proposed to make use of the continuous excitation force to identify the basic modal parameters as well as the structural flexibility. Laboratory and numerical examples are given, which validate the effectiveness of the proposed method. Furthermore, parametric analysis including road roughness, vehicle speed, vehicle weight, vehicle's stiffness and damping are conducted and the results obtained demonstrate that the developed method has strong robustness except that the relative error increases with the increase of measurement noise.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.3
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• pp.157-163
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• 2022

An automatic mooring system for a ship consists of a vacuum suction pad and a mechanical part, enabling quick and safe mooring of a ship. In the development of a mooring system, the design of a vacuum suction pad is a key to secure enough mooring forces and achieve stable operation of a mooring system. In the vacuum suction pad, properly designing its rubber seal determines the performance of the suction pad. Therefore, it is necessary to appropriately design the rubber seal for maintaining a high-vacuum condition inside the pad as well as achieving its mechanical robustness for long-time use. Finite element analysis for the design of the rubber seal requires the use of an appropriate strain energy function model to accurately simulate mechanical behavior of the rubber seal material. In this study, we conducted simple uniaxial tensile testing of Chloroprene Rubber (CR) to explore the strain energy function model best-fitted to its experimentally measured engineering strain-stress curves depending on various temperature environments. This study elucidates the temperature-dependent mechanical behaviors of CR and will be foundational to design rubber seal for an automatic mooring system under various temperature conditions.

• Journal of Space Technology and Applications
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• v.3 no.4
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• pp.355-372
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• 2023

As the need for low-cost, high-efficiency cubesats develops in the new space age, commercial paradigms are shifting in the private sector. This paper examines the challenges of launching and operating both domestic and foreign cubesats, and proposes practical solutions to ensure the robustness and reliability of the satellites from a practical perspective. In particular, the paper deals with checkpoints that are easy to miss, focusing on key events that can occur from the satellite deployment process through normal mode to mission mode in the operation scenario. Although the contents presented in this paper may not be technically applicable to all cubesat systems due to the different nature of each satellite bus system, they will be of some help during satellite assembly, integration and testing.

• Steel and Composite Structures
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• v.52 no.3
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.