• Journal of Aerospace System Engineering
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• v.10 no.3
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• pp.15-22
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• 2016

The on-board appendages of satellites with mechanical moving parts such as the fly-wheel, the control-moment gyro, the cryocooler, and the gimbal-type directional antenna can generate an undesirable micro-vibration disturbance, which is one of the main causes of the image-quality degradation that affects high-resolution observation satellites. Consequently, the isolation of the micro-vibration issue has always been considered as salient, and the micro-vibration is therefore the focus of this study wherein a complex system that can provide the dual functions of a guaranteed vibration-isolation performance and electrical energy harvesting is proposed. The vibration-isolation and energy-harvesting performances of the complex system are predicted through a numerical analysis based on the characteristics that are obtained from component-level tests. In addition, the effectiveness of the complex system that is proposed in this study is verified through an assembly-level functional-performance test.

• ETRI Journal
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• v.37 no.3
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• pp.491-501
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• 2015

Spectrum sensing is an elementary function in cognitive radio designed to monitor the existence of a primary user (PU). To achieve a high rate of detection, most techniques rely on knowledge of prior spectrum patterns, with a trade-off between high computational complexity and long sensing time. On the other hand, blind techniques ignore pattern matching processes to reduce processing time, but their accuracy degrades greatly at low signal-to-noise ratios. To achieve both a high rate of detection and short sensing time, we propose fast spectrum sensing with coordinate system (FSC) - a novel technique that decomposes a spectrum with high complexity into a new coordinate system of salient features and that uses these features in its PU detection process. Not only is the space of a buffer that is used to store information about a PU reduced, but also the sensing process is fast. The performance of FSC is evaluated according to its accuracy and sensing time against six other well-known conventional techniques through a wireless microphone signal based on the IEEE 802.22 standard. FSC gives the best performance overall.

• Journal of The Korean Association For Science Education
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• v.32 no.1
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• pp.160-181
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• 2012

The purpose of this study is to understand raters' errors in rating performance assessments of science inquiry. For this, 60 middle school students performed scientific inquiry about sound propagation and 4 trained raters rated their activity sheets. Variance components estimation for the result of the generalizability analysis for the person, task, rater design, the variance components for rater, rater by person and rater by task are about 25%. Among 4 raters, 2 raters' severity is higher than the other two raters and their severities were stabilized. Four raters' rating agreed with each other in 51 cases among the 240 cases. Through the raters' conferences, the rater error types for 189 disagreed cases were identified as one of three types; different salience, severity, and overlooking. The error type 1, different salience, showed 38% of the disagreed cases. Salient task and salient assessment components are different among the raters. The error type 2, severity, showed 25% and the error type 3, overlooking showed 31%. The error type 2 seemed to have happened when the students responses were on the borders of two levels. Error type 3 seemed to have happened when raters overlooked some important part of students' responses because she or he immersed her or himself in one's own salience. To reduce the above rater errors, raters' conference in salience of task and assesment components are needed before performing the holistic scoring of complex tasks. Also raters need to recognize her/his severity and efforts to keep one's own severity. Multiple raters are needed to prevent the errors from being overlooked. The further studies in raters' tendencies and sources of different interpretations on the rubric are suggested.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.123-130
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• 2008

Automatic lipreading is to recognize speech by observing the movement of a speaker's lips. It has received attention recently as a method of complementing performance degradation of acoustic speech recognition in acoustically noisy environments. One of the important issues in automatic lipreading is to define and extract salient features from the recorded images. In this paper, we propose a feature extraction method by using a new filtering technique for obtaining improved recognition performance. The proposed method eliminates frequency components which are too slow or too fast compared to the relevant speech information by applying a band-pass filter to the temporal trajectory of each pixel in the images containing the lip region and, then, features are extracted by principal component analysis. We show that the proposed method produces improved performance in both clean and visually noisy conditions via speaker-independent recognition experiments.

• Tunnel and Underground Space
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• v.19 no.2
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• pp.158-166
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• 2009

As a basic study for investigating the development of the stress-induced crack in Hoek-Brown rock, a homogenization technique of elastic cracks is proposed. The onset of crack is monitored by Hoek-Brown empirical criterion, while the orientation of the crack is determined by the critical plane approach. The concept of volume averaging in stress and strain component was invoked to homogenize the representative rock volume which consists of intact rock and cracks. The formulation results in the constitutive relations for the homogenized equivalent anisotropic material. The homogenization model was implemented in the standard FEM code COSMOSM. The numerical uniaxial tests were performed under plane strain condition to check the validity of the propose numerical model. The effect of friction between the loading plate and the rock sample on the mode of deformation and fracturing was examined by assuming two different contact conditions. The numerical simulation revealed that the homogenized model is able to capture the salient features of deformation and fracturing which are observed commonly in the uniaxial compression test.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1067-1076
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• 2020

In this paper, an unsupervised saliency guided change detection method using UAV and aerial imagery is proposed. Regions that are more different from other areas are salient, which make them more distinct. The existence of the substantial difference between two images makes saliency proper for guiding the change detection process. Change Vector Analysis (CVA), which has the capability of extracting of overall magnitude and direction of change from multi-spectral and temporal remote sensing data, is used for generating an initial difference image. Combined with an unsupervised CVA and the saliency, Principal Component Analysis(PCA), which is possible to implemented as the guide for change detection method, is proposed for UAV and aerial images. By implementing the saliency generation on the difference map extracted via the CVA, potentially changed areas obtained, and by thresholding the saliency map, most of the interest areas correctly extracted. Finally, the PCA method is implemented to extract features, and K-means clustering is applied to detect changed and unchanged map on the extracted areas. This proposed method is applied to the image sets over the flooded and typhoon-damaged area and is resulted in 95 percent better than the PCA approach compared with manually extracted ground truth for all the data sets. Finally, we compared our approach with the PCA K-means method to show the effectiveness of the method.

• Earthquakes and Structures
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• v.17 no.4
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• pp.373-385
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• 2019

In this article, different frequently adopted modeling aspects of linear and nonlinear dynamic soil-structure interaction (SSI) are studied on a pile-supported integral abutment bridge structure using the open-source platform OpenSees (McKenna et al. 2000, Mazzoni et al. 2007, McKenna and Fenves 2008) for a 2D domain. Analyzed approaches are as follows: (i) free field input at the base of fixed base bridge; (ii) SSI input at the base of fixed base bridge; (iii) SSI model with two dimensional quadrilateral soil elements interacting with bridge and incident input motion propagating upwards at model bottom boundary (with and without considering the effect of abutment backfill response); (iv) simplified SSI model by idealizing the interaction between structural and soil elements through nonlinear springs (with and without considering the effect of abutment backfill response). Salient conclusions of this paper include: (i) free-field motions may differ significantly from those computed at the base of the bridge foundations, thus put a significant bias on the inertial component of SSI; (ii) conventional modeling of SSI through series of soil springs and dashpot system seems to stay on the safer side under dynamic conditions when one considers the seismic actions on the structure by considering a fully coupled SSI model; (iii) consideration of abutment-backfill in the SSI model positively affects the general response of the bridge, as a result of large passive resistance that may develop behind the abutments.

• Sleep Medicine and Psychophysiology
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• v.5 no.2
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• pp.119-133
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• 1998

Attentional processes facilitate cognitive and behavioral performance in several ways. Attention serves to reduce the amount of information to receive. Attention enables humans to direct themselves to appropriate aspects of external environmental events and internal operations. Attention facilitates the selection of salient information and the allocation of cognitive processing appropriate to that information. Attention is not a unitary process that can be localized to a single neuroanatomical region. Before the cortical registration of sensory information, activation of important subcortical structures occurs, which is called as an orienting response. Once sensory information reaches the sensory cortex, a large number of perceptual processes occur, which provide various levels of perceptual resolution of the critical features of the stimuli. After this preattentional processing, information is integrated within higher cortical(heteromodal) systems in inferior parietal and temporal lobes. At this stage, the processing characteristics can be modified, and the biases of the system have a direct impact on attentional selection. Information flow has been traced through sensory analysis to a processing stage that enables the new information to be focused and modified in relation to preexisting biases. The limbic and paralimbic system play significant roles in modulating attentional response. It is labeled with affective salience and is integrated according to ongoing pressures from the motivational drive system of the hypothalamus. The salience of information greatly influences the allocation of attention. The frontal lobe operate response selection system with a reciprocal interaction with both the attention system of the parietal lobe and the limbic system. In this attentional process, the search with the spatial field is organized and a sequence of attentional responses is generated. Affective, motivational and appectitive impulses from limbic system and hypothalamus trigger response intention, preparation, planning, initiation and control of frontal lobe on this process. The reticular system, which produces ascending activation, catalyzes the overall system and increases attentional capacity. Also additional energetic pressures are created by the hypothalamus. As psychophysiological measurement, skin conductance, pupil diameter, muscle tension, heart rate, alpha wave of EEG can be used. Event related potentials also provide physiological evidence of attention during information process. NI component appears to be an electrophysiological index of selective attention. P3 response is developed during the attention related to stimulus discrimination, evaluation and response.

• Korean Journal of Cognitive Science
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• v.33 no.2
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• pp.95-107
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• 2022

Facial processing of different racial origin has been investigated at various levels including perceptual, emotional, and socio-cultural processing. Particularly, a good deal of studies have been conducted to show "other race effect (ORE)" to indicate that subtle facial information such as identity or emotional expressions are often under-processed in racial out-group members. However, few studies have investigated whether attentional modulation toward racial out-group faces could explain ORE. We investigated whether novelty-driven attentional mechanism is involved in face perception using event-related potential (ERP). Twenty-two Korean (KR) and nine Caucasian-American (AM) participants were presented with emotional faces from the two racial origins while they performed a gender categorization task. KRs showed significantly greater P3 amplitudes to AM than to KR faces indicating that the early attentional processing underlies differential perception of racial out-group faces. Interestingly, P3 was not up-regulated in the AM subjects when they were presented with KR faces, perhaps due to massive habituation to KR faces during everyday social interaction. These results indicate that racial out-group faces are highly salient stimuli which automatically occupy attentional resources, but easily habituated with repeated exposure to the racial-out group.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.