• Progress in Medical Physics
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• v.24 no.2
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• pp.108-118
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• 2013

Currently, an arterial spin labeling (ASL) magnetic resonance imaging (MRI) technique does not routinely used in clinical studies to measure perfusion in brain because optimization of imaging protocol is required to obtain optimal perfusion signals. Therefore, the objective of this study was to investigate changes of perfusion-weighed signal intensities with varying several parameters on a pulsed arterial spin labeling MRI technique obtained from a 3T MRI system. We especially evaluated alternations of ASL-MRI signal intensities on special brain areas, including in brain tissues and lobes. The signal targeting with alternating radiofrequency (STAR) pulsed ASL method was scanned on five normal subjects (mean age: 36 years, range: 29~41 years) on a 3T MRI system. Four parameters were evaluated with varying: 1) the labeling gap, 2) the labeling delay time, 3) the labeling thickness, and 4) the slice scan order. Signal intensities were obtained from the perfusion-weighted imaging on the gray and white matters and brain lobes of the frontal, parietal, temporal, and occipital areas. The results of this study were summarized: 1) Perfusion-weighted signal intensities were decreased with increasing the labeling gap in the bilateral gray matter areas and were least affected on the parietal lobe, but most affected on the occipital lobe. 2) Perfusion-weighted signal intensities were decreased with increasing the labeling delay time until 400 ms, but increased up to 1,000 ms in the bilateral gray matter areas. 3) Perfusion-weighted signal intensities were increased with increasing the labeling thickness until 120 mm in both the gray and white matter. 4) Perfusion-weighted signal intensities were higher descending scans than asending scans in both the gray and white matter. We investigated changes of perfusion-weighted signal intensities with varying several parameters in the STAR ASL method. It should require having protocol optimization processing before applying in patients. It has limitations to apply the ASL method in the white matter on a 3T MRI system.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.261-264
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• 2000

본 논문에서는 이동 통신용 단말기에 주로 사용되는 RF-chip inductor의 자동 외관검사를 위한 시스템 개발에 필요한 알고리즘을 제안하였다. 본 논문에서 제안한 방법은 영상취득 후 처리과정에서 동적 이진화 방법, 가산투영 등 영상처리에 관련된 방법을 이용해 코일 부분과 코어부분을 분리한 후 세선화 방법, 라벨링 방법 등을 적용하여 분리된 코일부분에 대해 코일의 감긴 회수와 피치간격의 불균일 검사를 수행하고 기준값 이상의 오차를 갖는 소자를 불량으로 처리하는 보다 개선된 처리방법을 제안하였으며 모의실험을 통해 성능을 검증하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.89-96
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• 2000

In this paper, we propose a vision algorithm for the inspection of very small RF-chip inductor which is used in mobile-communication terminal. The proposed method divides coil part from the inductor body by local adaptive thresholding and integral projection method. After dividing work, the coil components are extracted by thinning and labelling techniques. The test items are the number of turns, the intervals in coil, and the measure of uniformity between the extracted lines. If the values of these are more than the specific value a tested product is decided bad one. In the simulation, the proposed method has a good performance.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.