• Journal of Distribution Science
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• v.21 no.6
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• pp.69-81
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• 2023

Purpose: The study investigates the moderating impact of interpersonal mindfulness (IM) on the link between perceived similarity (OPS), physical appearance (OPA), and suitable behavior (OSB) - three key factors of other consumer perception (OCP) and brand experience (BE) in distribution of OCP and brand. Research design, data, and methodology: This study collected data from 612 consumers at shopping malls. SmartPLS 3.3.9 software were used to assess the measurement model and structural model. Results: According to the study's findings, IM has a negative modality in the impact between BE and OPS, OPA, and OSB. That also demonstrates how distribution of OCP and brand can affect a person's brand experience. Conclusions: The distribution of OCP and IM interactions have a significant influence on the brand experience in brand distribution. The study's results show that IM including mindfulness will function as a moderator between perceived similarity, physical appearance, suitable behavior regarded proper by other consumers, and brand experiences; therefore, they impact to brand distribution. The findings give a foundation for further IM research and add to the brand distribution theory that already exists. The findings also have some managerial implications in brand distribution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6810-6814
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• 2014

The concept of IFS (Iterated Function System) was applied to compress and transmit image data efficiently. To compress the image data with IFS, self-similarity was used to search a similar block. To improve the coding performance for the iterated function system with natural images, the image will be formed of properly transformed parts of itself to minimize the coding error. The simulation results using the proposed IFS represent high PSNR performance and improved compression efficiency with the coefficient of a recursive function.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.45-52
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• 2004

According tothe development of digital media technologies various algorithms for video sequence matching have been proposed to match the video sequences efficiently. A large number of video sequence matching methods have focused on frame-wise query, whereas a relatively few algorithms have been presented for video sequence matching or video shot matching. In this paper, we propose an efficientalgorithm to index the video sequences and to retrieve the sequences for video sequence query. To improve the accuracy and performance of video sequence matching, we employ the Cauchy function as a similarity measure between histograms of consecutive frames, which yields a high performance compared with conventional measures. The key frames extracted from segmented video shots can be used not only for video shot clustering but also for video sequence matching or browsing, where the key frame is defined by the frame that is significantly different from the previous fames. Several key frame extraction algorithms have been proposed, in which similar methods used for shot boundary detection were employed with proper similarity measures. In this paper, we propose the efficient algorithm to extract key frames using the cumulative Cauchy function measure and. compare its performance with that of conventional algorithms. Video sequence matching can be performed by evaluating the similarity between data sets of key frames. To improve the matching efficiency with the set of extracted key frames we employ the Cauchy function and the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed method yields the high matching performance and accuracy with a low computational load compared with conventional algorithms.

• Journal of Applied Reliability
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• v.14 no.3
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• pp.162-168
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• 2014

This paper discusses the application of Bayesian techniques with test data on similar products for performing the Economical Reliability Test of new one-shot device. Using the test data on similar products, reliability test required lower sample size currently being spent in order to demonstrate a target reliability with a specified confidence level. Furthermore, lower sample size reduces cost, time and various resources on reliability test. In this paper, we use similarity as calculating weight of similar products and analyze similarity between new and similar product for comparison of the essential function.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.21-27
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• 2017

In this paper, we propose a pupil detection method using PCA(principal component analysis) and Hough transform. To reduce error to detect eyebrows as pupil, eyebrows are detected using projection function in eye region and eye region is set to not include the eyebrows. In the eye region, pupil candidates are detected using rank order filter. False candidates are removed by using symmetry. The pupil candidates are grouped into pairs based on geometric constraints. A similarity measure is obtained for two eye of each pair using PCA and hough transform, we select a pair with the smallest similarity measure as final two pupils. The experiments have been performed for 1000 images of the BioID face database. The results show that it achieves the higher detection rate than existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.6
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• pp.1195-1209
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• 1997

This paper presents a new morphological spatio-temporal segmentation algorithm. The algorithm incorporates luminance and motion information simultaneously, and uses morphological tools such as morphological filtersand watershed algorithm. The procedure toward complete segmentation consists of three steps:joint marker extraction, boundary decision, and motion-based region fusion. First, the joint marker extraction identifies the presence of homogeneours regions in both motion and luminance, where a simple joint marker extraction technique is proposed. Second, the spatio-temporal boundaries are decided by the watershed algorithm. For this purposek, a new joint similarity measure is proposed. Finally, an elimination ofredundant regions is done using motion-based region function. By incorporating spatial and temporal information simultaneously, we can obtain visually meaningful segmentation results. Simulation results demonstratesthe efficiency of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1859-1864
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• 2007

Existing barcode signal restoration algorithms are not robust to unmodeled outliers that may exist in scanned barcode images due to scratches, dirts, etc. In this paper, we describe a robust barcode signal restoration algorithm that uses the hybrid $L_1-L_2$ norm as a similarity measure. To optimze the similarity measure, we propose a modified iterative reweighted least squares algorithm based on the one step minimization of a quadratic surrogate function. In the simulations and experiments with barcode images, the proposed method showed better robustness than the conventional MSE based method. In addition, the proposed method converged quickly during optimization process.

• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.40-44
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• 2012

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.971-980
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• 2003

The tree method can be extended to multivariate responses, such as repeated measure and longitudinal data, by modifying the split function so as to accommodate multiple responses. Recently, some decision trees for multiple responses have been constructed by Segal (1992) and Zhang (1998). Segal suggested a tree can analyze continuous longitudinal response using Mahalanobis distance for within node homogeneity measures and Zhang suggested a tree can analyze multiple binary responses using generalized entropy criterion which is proportional to maximum likelihood of joint distribution of multiple binary responses. In this paper, we will modify CART procedure and suggest a new tree-based method that can analyze multiple binary responses using similarity measures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.81-86
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• 2001

In this paper, we investigated the modeling of Bank Asset Management System(BAME) based on intelligent agent. To achieve this goal, we introduced several kinds of agents that show intelligent features. BAMS is a user friendly system and adopts fuzzy converting system and fuzzy matching system that returns reasonable similarity matching results. Generation function of the proximity degree is suggested. Fuzzification of investment type categories and feature values are defined, and generation of proximity degree is also derived. An example of bank asset management system is introduced and simulated. Investment type matching utilizing fuzzy measure is tested and it showed quite reasonable similarity matching results.