• Journal of Institute of Control, Robotics and Systems
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• v.8 no.3
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• pp.275-280
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• 2002

A new algorithm for gradual scene change detection in MPEG based frame sequences is proposed in this paper. The proposed algorithm is based on the fact that most of gradual curves can be characterized by variance distributions of edge information in the frame sequences. Average edge frame sequences are obtained by performing "sober" edge detection. Features are extracted by comparing variances with those of local blocks in the average edge frames. Those features are further processed by the opening operation to obtain smoothing variance curves. The lowest variance in the local frame sequences is chosen as a gradual detection point. Experimental results show that the proposed method provides 85% precision and 86% recall rate fur gradual scene changes.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.439-454
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• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.771-780
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• 2012

In the current analysis, we examine news coverage data widely used in media studies. News coverage data is usually time series data to capture the volume or the tone of the news media's coverage of a topic. We first describe the distributional properties of autoregressive conditionally heteroscadestic(ARCH) effects and compare two major American newspaper's coverage of U.S.-North Korea relations. Subsequently, we propose a change point detection model and apply it to the detection of major change points in the tone of American newspaper coverage of U.S.-North Korea relations.

• Journal of Power System Engineering
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• v.19 no.3
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• pp.11-15
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• 2015

In this paper, the multi-point hydrogen detection system based on the combination of the hetero-core optical fiber SPR hydrogen tip sensor and interrogator by pseudorandom noise (PN) code correlation reflectometry has been developed. In a light intensity-based experiment with an LED operating at 850 nm, it has been presented that a transmitted loss change of 0.32dB was induced with a response time of 25 s for 4% $H_2$ in $N_2$ in the case of the 25-nm Au, 60-nm $Ta_2O_5$, and 5-nm Pd multi-layers film. The proposed sensor characteristic shows excellent reproducibility in terms of loss level and time response for the in- and out- $H_2$ action. In addition, in the experiment for multi-point hydrogen detection, all sensors show the real-time response for 4% hydrogen adding with reproducible working. As a result, the real-time multi-point hydrogen detection could be realized by means of the combination of interrogating system and hetero-core optical fiber SPR hydrogen tip sensors.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.687-695
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• 2009

The Bayesian multiple change-point estimation has been applied to the daily means of ozone and PM10 data in Seoul for the period 1999. We focus on the detection of multiple change-points in the ozone and PM10 bivariate vectors by evaluating the posterior probabilities and Bayesian information criterion(BIC) using the stochastic approximation Monte Carlo(SAMC) algorithm. The result gives 5 change-points of mean vectors of ozone and PM10, which are related with the seasonal characteristics.

• Journal of the Korea Computer Industry Society
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• v.9 no.4
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• pp.149-156
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• 2008

There is the temporal correlation of a animation sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the scene change detection algorithm for block matching using the temporal correlation of the animation sequence and the center-biased property of motion vectors. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate point on each search region. Simulation results show that the proposed algorithm has better detection performance, such as recall rate, then the existing method. The algorithm has the advantage of speed, simplicity and accuracy. In addition, it requires less amount of storage.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.638-642
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• 2002

User in video data utilization of high-capacity can grasp whole video data at a look. Offer frame list that summarize information of video data to do so that can remake video from branch that want when need. Need index process of video data for effective video retrieval. This treatise wishes to propose effective method about scene change point detection of video that is been based on contents base index. Proposed method video data so that can grasp whole structure of video detection color value of schedule pixel for diagonal line direction in image sampling do. Data that get into sampling could grasp scene change point on one eye. Color value of pixel that detection in each frame is i frame number by i$\times$j procession to procession A, j stores to reflex height of frame. Introduce MSE and calculate mean error of each frame. If exceed mean error and schedule critical value, wish to detect the frame for scene change point.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.71-77
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• 2016

In this paper, we proposed a verification of speaker change utilizing the KL distance based on GMM-UBM to improve the performance of conventional BIC based Speaker Change Detection(SCD). We have verified Conventional BIC-based SCD using KL-distance based SCD which is robust against difference of information volume than BIC-based SCD. And we have applied GMM-UBM to compensate asymmetric information volume. Conventional BIC-based SCD was composed of two steps. Step 1, to detect the Speaker Change Candidate Point(SCCP). SCCP is positive local maximum point of dissimilarity d. Step 2, to determine the Speaker Change Point(SCP). If ${\Delta}BIC$ of SCCP is positive, it decides to SCP. We examined verification of SCP using GMM-UBM based KL distance D. If the value of D on each SCP is higher than threshold, we accepted that point to the final SCP. In the experimental condition MDR(Missed Detection Rate) is 0, FAR(False Alarm Rate) when the threshold value of 0.028 has been improved to 60.7%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.951-957
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• 2002

When retrieving voluminous capacity of video image data, it is necessary to provide synopsized frame lists of video image data for indexing and replaying at the exact point where the user want to retrieve. We apply Mean Squared Error method to extract certain pixel value from diagonal direction of a frame. The RGB value of a pixel extracted from each frame is saved in a matrix form, and this frame is retrievedas a scene change point if the compared value of two points met the certain condition. Also implement the algorithm and provide a way to seize entire structure of video image and the point of scene changes. finally, we analyze and prove that our method has better performance compared with the others.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.154-163
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• 2010

Process monitoring of output variables affecting final performance have been mainly executed in semiconductor manufacturing process. However, even earlier detection of causes of output variation cannot completely prevent yield loss because a number of wafers after detecting them must be re-processed or cast away. Semiconductor manufacturers have put more attention toward monitoring process inputs to prevent yield loss by early detecting change-point of the process. In the paper, we propose the method to efficiently monitor functional input variables in multi-phase semiconductor manufacturing process. Measured input variables in the multi-phase process tend to be of functional structured form. After data pre-processing for these functional input data, change-point analysis is practiced to the pre-processed data set. If process variation occurs, key variables affecting process variation are selected using contribution plot for monitoring efficiency. To evaluate the propriety of proposed monitoring method, we used real data set in semiconductor manufacturing process. The experiment shows that the proposed method has better performance than previous output monitoring method in terms of fault detection and process monitoring.