• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4419-4441
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• 2016

A cause of vehicle accidents is the reduced visibility due to bad weather conditions such as fog. Therefore, an onboard vision system should take visibility detection into account. In this paper, we propose a simple and effective approach for measuring the visibility distance using a single camera placed onboard a moving vehicle. The proposed algorithm is controlled by a few parameters and mainly includes camera parameter estimation, region of interest (ROI) estimation and visibility computation. Thanks to the ROI extraction, the position of the inflection point may be measured in practice. Thus, combined with the estimated camera parameters, the visibility distance of the input foggy image can be computed with a single camera and just the presence of road and sky in the scene. To assess the accuracy of the proposed approach, a reference target based visibility detection method is also introduced. The comparative study and quantitative evaluation show that the proposed method can obtain good visibility detection results with relatively fast speed.

• Journal of IKEEE
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• v.24 no.4
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• pp.1081-1085
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• 2020

Instantaneous frequencies (IF) of the AM-FM signal is estimated based on the inflection point detection (IPD) method. Local maxima/minima are detected using the IPD, and they are exploited to find the IF of AM and FM components, respectively. The envelope of the maxima/minima is obtained to estimate the IF of the AM part. And the distance between neighboring maxima (or minima) is used to estimate the IF of the FM component. Computer simulation shows that the proposed method properly estimates the IF of the AM and FM when the signal has fixed frequencies for both parts. In the case of the time-varying IF of the FM part, the estimated IF shows some deviation from the true IF due to the rough sampling effect of the maximum/minimum points. Thus, the post-processing such as the lowpass filtering of the estimated IF is required to refine the resulting IF estimation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1916-1917
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• 2007

Aortic AIx(augmentation index) has been used to measure aortic stiffness and evaluate ventricular load quantitatively. Algorithm for the detection of augmentation point gradually increases the differential order to detect inflection point rather than detects the distinctive point that appears after a specific time. Developed algorithm for AIx is proved to provide more accurate results than the ones developed by previous studies with the deviation from $-11.5{\pm}14.34$ points to $-3.75{\pm}1.26$ points. Results could provide the basis for the measurement of aortic stiffness using easily-measurable radial artery pulse waves, and could be extended to develop a system for early diagnosis of various vascular diseases.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.28-32
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• 2014

For a signal such as speech showing piece-wise linear shape in a very short time period, a nonuniform sampling method based on the inflection point detection (IPD) is proposed to reduce data rate. The method exploits the geometrical characteristics of signal further than the existing local maxima/minima detection (MMD) based sampling method. As results, the reconstructed signal by the interpolation of the IPD based sampled data resembles the original speech more. Computer simulation shows that the proposed IPD based method produces about 9~23 dB improvement over the existing MMD method. To show the usefulness of the IPD technique, it is applied to speech coding, and compared to the continuously variable slope delta modulation (CVSD). The nonuniformly sampled data is binary coded with one bit flag set "1". Noninflection samples are not sent, but only flag bits set 0 are sent. The method shows 0.3 ~ 9 dB SNR and 0.5 ~ 1.3 mean opinion score (MOS) improvements over the CVSD.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2205-2210
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• 2009

In this paper, the proposed method is a denoising technology by tangent curve interpolation of zero points. The problem of the hard threshold method is improved by the proposed method. The quantity of time fluctuation of the electromagnetic signal as the quantity of electric fluctuation of the natural world or the curve of motion waveform of the fast movement of human extracted using virtual reality is, in fact, complex. Therefore it is important to decide exactly the signal properties as the inflection point for observation signal. In particular, it is necessary to extract the properties after denoising, since the measurement signal of the natural world include some noises. It shows that the noise of the inflection point signal with noise II, noise factor 5, is eliminated by the proposed method, and the result of SNR for the signal is improved 3.4dB than that by the conventional hard threshold.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.279-292
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• 2011

Movie is a representative media that can transmit stories to audiences. Basically, a story is described by characters in the movie. Different from other simple videos, movies deploy narrative structures for explaining various conflicts or collaborations between characters. These narrative structures consist of 3 main acts, which are beginning, middle, and ending. The beginning act includes 1) introduction to main characters and backgrounds, and 2) conflicts implication and clues for incidents. The middle act describes the events developed by both inside and outside factors and the story dramatic tension heighten. Finally, in the end act, the events are developed are resolved, and the topic of story and message of writer are transmitted. When story information is extracted from movie, it is needed to consider that it has different weights by narrative structure. Namely, when some information is extracted, it has a different influence to story deployment depending on where it locates at the beginning, middle and end acts. The beginning act is the part that exposes to audiences for story set-up various information such as setting of characters and depiction of backgrounds. And thus, it is necessary to extract much kind information from the beginning act in order to abstract a movie or retrieve character information. Thereby, this paper proposes a novel method for extracting the beginning boundaries. It is the method that detects a boundary scene between the beginning act and middle using the accumulation graph of characters. The beginning act consists of the scenes that introduce important characters, imply the conflict relationship between them, and suggest clues to resolve troubles. First, a scene that the new important characters don't appear any more should be detected in order to extract a scene completed the introduction of them. The important characters mean the major and minor characters, which can be dealt as important characters since they lead story progression. Extra should be excluded in order to extract a scene completed the introduction of important characters in the accumulation graph of characters. Extra means the characters that appear only several scenes. Second, the inflection point is detected in the accumulation graph of characters. It is the point that the increasing line changes to horizontal line. Namely, when the slope of line keeps zero during long scenes, starting point of this line with zero slope becomes the inflection point. Inflection point will be detected in the accumulation graph of characters without extra. Third, several scenes are considered as additional story progression such as conflicts implication and clues suggestion. Actually, movie story can arrive at a scene located between beginning act and middle when additional several scenes are elapsed after the introduction of important characters. We will decide the ratio of additional scenes for total scenes by experiment in order to detect this scene. The ratio of additional scenes is gained as 7.67% by experiment. It is the story inflection point to change from beginning to middle act when this ratio is added to the inflection point of graph. Our proposed method consists of these three steps. We selected 10 movies for experiment and evaluation. These movies consisted of various genres. By measuring the accuracy of boundary detection experiment, we have shown that the proposed method is more efficient.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.3
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• pp.68-79
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• 2008

Aortic AIx(augmentation index) has been used to measure aortic stiffness quantitatively and even to evaluate ventricular load. However, in order to calculate aortic AIx catheters should be inserted to the subjects' artery, which hampers its clinical usage. To overcome such limitation, aortic AIx has been indirectly calculated by estimating aortic pressure wave from the peripheral arterial pulse by applying transfer functions. In this study, central aortic pressure waves using Millar catheter and radial artery pulse waves using tonometry pressure sensor were measured to establish transfer functions for an estimation of central aortic pressure waves from radial artery pulse waves. Also, an algorithm which detects augmentation point for the calculation of AIx were developed. Developed algorithm for the detection of augmentation point gradually increases the differential order to detect inflection point rather than detects the distinctive point that appears after a specific time. Transfer functions were established using 10th order ARX model and were verified for the stability of the transfer function through residual analysis. Evaluation of an algorithm for the detection of augmentation point were performed by comparing the augmentation points obtained from developed algorithm with the known augmentation points synthesized in various conditions. In addition, developed algorithm for the AIx is proved to provide more accurate results than the ones developed by previous studies. The significance of the study was in two folds. Firstly, the results could provide the basis for the measurement of aortic stiffness using easily-measurable radial artery pulse waves, and secondly, extension of the study may enable the early diagnosis of various vascular diseases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.67-73
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• 2020

The clustering algorithm is the grouping of similar objects. In radar system, it is mainly used to group adjacent hits using the CFAR algorithm results. However it is difficult to separate adjacent targets by a general clustering method. In this paper, we describe how to separate adjacent targets using double clustering method. First, we execute a range direction clustering. And we find the inflection point and separate it. Next, we execute a doppler direction clustering using range clustering results. This method makes the computation time less change even if the target increases by range-doppler clustering respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.152-157
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• 2014

Digital still camera generally uses optical low-pass filter(OLPF) to enhance its image quality because it removes high spatial frequencies causing aliasing. However, the use of OLPF causes some loss of detail. On the other hand, when image are captured by using no OLPF, the moir$\acute{e}$ is generally existed in high spatial frequency region of an image. Therefore, in this paper, moir$\acute{e}$ reduction method in case of using no OLPF is suggested. To detect the moir$\acute{e}$, spatial frequency response(SFR) of camera was firstly analyzed by using ISO 12233 resolution chart. Then, moir$\acute{e}$ region is detected by using the patterns that are related to the SFR of camera. next, this region is analysed in the frequency domain. Then, the moir$\acute{e}$ is reduced by removing its frequency component, which represents inflection point between high frequency and DC components. Through the experimental results, it is shown that the proposed method can achieve moir$\acute{e}$ reduction with preserving the detail.