• 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.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1612-1612
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• 2001

The production of grain for export and domestic use is one of Australia's most important agricultural industries, and the NIR technique has been used extensively over many years for the routine monitoring of grain quality, particularly moisture and protein content. Because most Australian grain is intended for human food production, the determinants of grain quality for livestock feed, apart from protein, have been largely ignored. However the increasing use of grain for feeding to pigs, poultry, beef cattle and dairy cows has led to an important national research project entitled “Premium Grains for Livestock”. Two of the objectives of this project are to determine the compositional and functional characteristics of grains which influence their nutritional quality for the various classes of livestock, and to adopt rapid and objective analytical tests for these quality criteria. NIR has been used in this project firstly to identify a set of grain samples from a large population of breeders' lines which showed a wide spectral variation, and hence a potentially wide variation in nutritional value. The selected samples were not only subjected to an extensive array of chemical, physical and in vitro analyses, but also were grown out to produce sufficient quantities of grain to feed to animals in vivo studies. Additional grains were also strategically selected from farms in order to include the effect of weather damage, such as rain, drought and frost. In this study to date, NIR calibrations have been derived or attempted, on both ground and whole grains, for in vivo dry matter digestibility (DMD), pepsin-cellulase dry matter disappearance, protein, fat, acid detergent fibre, neutral detergent fibre, starch, in sacco DMD and in vitro assays to simulate starch digestion in the lumen and small intestine. Results so far indicate high calibration accuracy for chemical components (SECV 0.3 to 2.6%) and very promising statistics for in vivo DMD (SECV 1.8, $R^2$ 0.93, SD 7.0, range 61.9 to 92.3, n=60). There appears to be some potential for NIR to estimate some in vitro properties, depending upon the accuracy of reference methods and appropriate sample populations. Current work is in progress to extend the range of grains with in vivo DMD values (a very laborious and expensive process) and to increase the robustness of the various NIR calibrations, with the aim of implementing uniform testing procedures for nutritional value of grains throughout Australia.