• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.87-90
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• 1995

In this paper, we propose a new deinterlacing algorithm based on weighted wide vector correlations. This algorithm is developed mainly for the format conversion problem encountered in current HDTV system, but not limited to. By having wide vector correlations, visually annoying artifacts caused by interlacing, such as a serrate line, line crawling, a line flicker, and a large area flicker, can be remarkably reduced, since the use of wide vector correlation increases the detectability of edges in various orientations.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.44-54
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• 1996

In this paper, we propose a new 3-D deinterlacing algorithm based on wide sparse vector correlations and a vertical edge based motion detection algorithm. which is an extension of the deinterlacing algorithm proposed in [10. llJ by the authors. The prooised algorithm is developed mainly for the format conversion problem encountered in current HDTV system, but can also be aplicable to the double scan conversion problesm frequently encountered in ths NTSC systems. By exploiting the edge oriented spatial interpolation based on the wide vector correlations, visually annoying artifiacts caused by interlacing such as a serrate line. line crawling, a line flicker, and a large area flicker can be remarkably reduced since the use of the wide vectors increases the range of the edge orientations that can be detected, and by exploiting sparse vectors correlations the HjW complexity for realizing the algorithm in applications cam be significantly simplified. Simulations are provided indicating thet the proposed algorithm results in a high performance comparable to the performance of the deinterlacing algorithm. based on the wide vector correlations.

• Journal of the Chungcheong Mathematical Society
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• v.17 no.1
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• pp.47-56
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• 2004

The nonlinearity of a Boolean function f on $GF(2)^n$ is the minimum hamming distance between f and all affine functions on $GF(2)^n$ and it measures the ability of a cryptographic system using the functions to resist against being expressed as a set of linear equations. Finding out the exact value of the nonlinearity of given Boolean functions is not an easy problem therefore one wants to estimate the nonlinearity using extra information on given functions, or wants to find a lower bound or an upper bound on the nonlinearity. In this paper we extend the notion of auto-correlations of Boolean functions to vector Boolean functions and obtain upper bounds and a lower bound on the nonlinearity of vector Boolean functions in the context of their auto-correlations. Also we can describe avalanche characteristics of vector Boolean functions by examining the extended notion of auto-correlations.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1251-1260
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• 2015

The measurement and evaluation of an audience's visual attention are crucial processes for the scientific production of various contents and services. It has been alleged that a vector exists between objects on a screen, but this has not been explored empirically. This study tested whether or not there are any positive correlations between screen vectors and viewers' eye movements. Participants were exposed to four groups of pictures representing an independent vector, continuing vector, converging vector, and diverging vector. The results showed that vectors on screens induced viewers' eye movements to the vector object by the directivity of the visual stimuli.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.407-417
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• 2004

It is well known that the nonlinearity of vector Boolean functions F on n-dimensional vector space $GF(2)^n$ to $GF(2)^m$ is bounded above by $2^{n-1} - 2 ^{\frac{n}{2}-1}$. In this paper we derive upper bounds and a lower bound on the nonlinearity of vector Boolean functions in terms of auto-correlations. Strengths and weaknesses of each bounds are examined. Also, we modify the notions of the sum-of-square indicator and absolute indicator for Boolean functions to the case of vector Boolean functions to measure global avalanche characteristics of vector Boolean functions. Using those indicators we compare the global avalanche characteristics of DES (Data Encryption System) and Rijndael.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.747-752
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• 2011

The position detection of overlapping area in the interframe for image stitching using auto and cross correlation function (ACCF) and compounding one image with the stitching algorithm is presented in this paper. ACCF is used by autocorrelation to the featured area to extract the filter mask in the reference (previous) image and the comparing (current) image is used by crosscorrelation. The stitching is detected by the position of high correlation, and aligns and stitches the image in shifting the current image based on the moving vector. The ACCF technique results in a few computations and simplicity because the filter mask is given by the featuring block, and the position is enabled to detect a bit movement. Input image captured from CMOS is used to be compared with the performance between the ACCF and the window correlation. The results of ACCF show that there is no seam and distortion at the joint parts in the stitched image, and the detection performance of the moving vector is improved to 12% in comparison with the window correlation method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.88-96
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• 1994

This paper proposes a new vector quantization scheme which exploits high correlations among indexes in vector quantization. An optimal vector quantizer in the rate-distortion sense can be obtained, if it is designed so that the average distortion can be minimized under the constraint of the conditional entropy of indes, which is usually much smaller than the entropy of index due to the high correlations among indexes of neighboring vectors. The oprimization process is very similar to that in ECVQ(entropy-constrained vector quanization) except that in the proposed scheme the Viterbi algorithm is introduced to find the optimal index sequence. Simulations show that at the same bitrate the proposed method provides higher PSNR by 1.0~3.0 dB than the conventional ECVQ when applied to image coding.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.