• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.341-345
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• 2009

We present an augmented reality (AR) application for cell phone where users put a virtual pet on their palms and play/interact with the pet by moving their hands and fingers naturally. The application is fundamentally based on hand/palm pose recognition and finger motion estimation, which is the main concern in this paper. We propose a fast and efficient hand/palm pose recognition method which uses natural features (e.g. direction, width, contour shape of hand region) extracted from a hand image with prior knowledge for hand shape or geometry (e.g. its approximated shape when a palm is open, length ratio between palm width and pal height). We also propose a natural interaction method which recognizes natural motion of fingers such as opening/closing palm based on fingertip tracking. Based on the proposed methods, we developed and tested the AR application on an ultra-mobile PC (UMPC).

• Journal of The Korean Association For Science Education
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• v.25 no.1
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• pp.41-56
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• 2005

Assessing students' knowledge can be a challenging endeavor, as researchers attempt to capture the full complexity and potential development of children's ideas. In this study, the Dynamic Science Assessment (DSA) method (Magnusson, Templin, and Boyle, 1997) was employed to investigate 9-12 year old students' understandings of light, while engaging in multiple tasks with a flashlight with various reflectors and mirrors. The results showed that DSA was effective in providing an opportunity to establish a Zone of Proximal Development, in addition to diagnosing a student's prior understanding. Throughout the interview, a student showed a conceptual model of light as being a solid single entity whose shape can be determined by the shape of the casing of a flashlight. However, as DSA provided phenomena that could not be explained by his unitary model, the student began to re-examine his original conceptual model, and attempted to revise it. This study addressed how Dynamic Science Assessment can help us better understand, not only students' current state of understanding, but also a potential development of understanding in their ZPD. In that sense, this study argues that we should pay more attention to the instructive role of classroom assessment that can promote and support further development of students' deeper understandings.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.8
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• pp.1051-1056
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• 2001

Data for teat number for Landrace (L), Yorkshire (Y), crossbred of Landrace and Yorkshire (LY), and crossbred of Landrace, Yorkshire and Chinese indigenous Min Pig (LYM) were analyzed using Gibbs sampling. In Bayesian inference, flat priors and some informative priors were used to examine their influence on posterior estimates. The posterior mean estimates of heritabilities with flat priors were $0.661{\pm}0.035$ for L, $0.540{\pm}0.072$ for Y, $0.789{\pm}0.074$ for LY, and $0.577{\pm}0.058$ for LYM, and they did not differ (p>0.05) from their corresponding estimates of REML. When inverse Gamma densities for variance components were used as priors with the shape parameter of 4, the posterior estimates were still corresponding (p>0.05) to REML estimates and mean estimates using Gibbs sampling with flat priors. However, when the inverse Gamma densities with the shape parameter of 10 were utilized, some posterior estimates differed (p<0.10) from REML estimates and/or from other Gibbs mean estimates. The use of moderate degree of belief was influential to the posterior estimates, especially for Y and for LY where data sizes were small. When the data size is small, REML estimates of variance components have unknown distributions. On the other hand, Bayesian approach gives exact posterior densities of variance components. However, when the data size is small and prior knowledge is lacked, researchers should be careful with even moderate priors.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.60-65
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• 2012

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.195-214
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• 2016

Monitoring of impact loads is a very important technique in the field of structural health monitoring (SHM). However, in most cases it is not possible to measure impact events directly, so they need to be reconstructed. Impact load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response function are usually known. Generally this leads to a so called ill-posed inverse problem. It is reasonable to use prior knowledge of the force in order to develop more suitable reconstruction strategies and to increase accuracy. An impact event is characterized by a short time duration and a spatial concentration. Moreover the force time history of an impact has a specific shape, which also can be taken into account. In this contribution these properties of the external force are employed to create a sample-force-dictionary and thus to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The reconstruction approach shown here is capable to estimate simultaneously the magnitude of the impact and the impact location, with a minimum number of accelerometers. The possibility of reconstructing the impact based on a noisy output signal is first demonstrated with simulated measurements of a simple beam structure. Then an experimental investigation of a real beam is performed.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.195-200
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• 2008

The accuracy of classifying pixels in HIRIS images is usually degraded by noisy bands since noisy bands may deform the typical shape of spectral reflectance. Proposed in this paper is a statistical method for noisy band removal which mainly makes use of the correlation coefficients between bands. Considering each band as a random variable, the correlation coefficient measures the strength and direction of a linear relationship between two random variables. While the correlation between two signal bands is high, existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The application of the correlation coefficient as a measure for detecting noisy bands is under a two-pass screening scheme. This method is independent of the prior knowledge of the sensor or the cause resulted in the noise. The classification in this experiment uses the unsupervised k-nearest neighbor algorithm in accordance with the well-accepted Euclidean distance measure and the spectral angle mapper measure. This paper also proposes a hierarchical combination of these measures for spectral matching. Finally, a separability assessment based on the between-class and within-class scatter matrices is followed to evaluate the performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• School Mathematics
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• v.9 no.1
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• pp.141-160
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• 2007

The purpose of this paper was to evaluate how students apply prior knowledge or experience in solving algebra word problems from the chain of signification-based perspective. Three middle school students were evaluated in this case study. The results showed that the subjects formed similarities in the process of applying knowledge needed for solving a problem. The student A and C used semi-open-end formulas and closed formulas as solutions. They then formed concrete shape for each solution using the chain of signification that was applied for solution by forming procedural similarity. At this time, the chain of signification could be the combination of numbers, words, and pictures (such as diagrams or graphs) or just numbers or words. On the other hand, the student C who recognized closed formulas and her own rule as a solution method could not formulate completely procedural similarity due to many errors arising from number information. Nonetheless, all of the subjects showed something in common in the process of coming up with a algorithm that was semi-open-end formula or closed formula.