• Journal of Advanced Marine Engineering and Technology
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• v.36 no.4
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• pp.512-519
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• 2012

This paper proposes a 3-D pose (positions and orientations) estimation system based on the recognition of circular ring patterns. To deal with monocular vision-based pose estimation problem, we specially design a circular ring pattern that has a simplicity merit in view of object recognition. A pose estimation procedure is described in detail, which utilizes the geometric transformation of a circular ring pattern in 2-D perspective projection space. The proposed method is evaluated through the analysis of accuracy and precision with respect to 3-D pose estimation of a quadrotor-type vehicle in 3-D space.

• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.704-711
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• 2010

Modal testing requires measuring the vibration of many points, for which an accelerometer, a gab sensor and laser vibrometer are generally used. Conventional modal testing requires mounting of these sensors to all measurement points in order to acquire the signals. However, this can be disadvantageous because it requires considerable measurement time and effort when there are many measurement points. In this paper, we propose a method for modal testing using a camera image. A camera can measure the vibration of many points at the same time. However, this task requires that the measurement points be classified frame by frame. While it is possible to classify the measurement points one by one, this also requires much time. Therefore, we try to classify multiple points using pattern recognition. The feasibility of the proposed method is verified by a beam experiment. The experimental results demonstrate that we can obtain good results.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.25-37
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• 2002

The purpose of the study was to survey the distribution patterns of inorganic elements of PM-10 in the various indoor environments and analyze the pollution patterns of aerosol in various places of indoor environment using a pattern recognition method based on cluster analysis and disjoint principal component analysis. A total of 40 samples in the indoor had been collected using mini-vol portable samplers. These samples were analyzed for their 19 bulk inorganic compounds such as B, Na, Mg, Al, K, Ca, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, and Pb by using an ICP-MS. By applying a disjoint principal component analysis, four patterns of the indoor air pollutions were distinguished. The first pattern was identified as a group with high concentrations of PM-10, Na, Mg, and Ca. The second pattern was identified as a group with high concentrations B, Mg, At, Ca, Fe, Cu, and Ba. The third pattern was a group of sites with high concentrations of K, Zn. Cd. The fourth pattern was a group with low concentrations PM-10 and all inorganic elements. This methodology was found to be helpful enough to set the criteria standard of indoor air quality, corresponding pollutants, and classification of indoor environment categories when making an indoor air quality law.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.99-107
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• 2009

This paper presents an enhanced hybrid model based on Independent Component Analysis(ICA) in order to features of numeric characters in license plates. ICA which is used only in high dimensional statistical features doesn't consider statistical features in low dimension and correlation between numeric characters. To overcome the drawbacks of ICA, we propose an improved ICA with the hybrid model using both Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA). Experiment results show that the proposed model has a superior performance in feature extraction and recognition compared with ICA only as well as other hybrid models.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1228-1233
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• 2019

Recently, a variety of algorithms using encryption technology have been adopted as methods of unlocking smartphone. It is advancing toward the direction to solve the unlocking problem through human biometrics technology, which has already succeeded in commercializing. These include finger print recognition, face recognition, and iris recognition. In this study, the evaluation items are five algorithms, including finger print recognition, face recognition, iris recognition, pattern recognition, and password input method. Based on the algorithms adopted, the AHP (analytic hierarchy process) technique was used to calculate the preferred priorities for smartphone users. Finger print recognition ( .400) was the top priority for smartphone users. Next, pattern recognition ( .237) was placed in the second priority for smartphone users. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.754-759
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• 2007

In this paper, we proposed the Bi-Modal Sensor Fusion Algorithm which is the emotional recognition method that be able to classify 4 emotions (Happy, Sad, Angry, Surprise) by using facial image and speech signal together. We extract the feature vectors from speech signal using acoustic feature without language feature and classify emotional pattern using Neural-Network. We also make the feature selection of mouth, eyes and eyebrows from facial image. and extracted feature vectors that apply to Principal Component Analysis(PCA) remakes low dimension feature vector. So we proposed method to fused into result value of emotion recognition by using facial image and speech.

• Proceedings of the Optical Society of Korea Conference
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• 2007.02a
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• pp.177-178
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• 2007

• Proceedings of the PSK Conference
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• 2000.10a
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• pp.254.1-254.1
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• 2000

• Korean Journal of Pharmacognosy
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• v.34 no.4 s.135
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• pp.278-281
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• 2003

Cynanchi Wilfordii Radix has been used for the treatment of prematurely grey hair, bald and constipation and Polygoni Multiflori Radix has been used for the treatment of weakness, knee pain, premature greying, elevated serum cholesterol, coronary heart disease, naurasthenia and insominia. Pattern recognition for the analysis of Cynanchi Wilfordii Radix and Polygoni Multiflori Radix was conducted using HPLC method. Pattern of Cynanchi Wilfordii Radix and Polygoni Multiflori Radix was different and we distinguished two medicinal plants by HPLC.