• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.592-606
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• 2011

Currently, the development of multimedia communication has progressed so rapidly that the video program service has become a requirement for ordinary customers. The quality of experience (QoE) for the visual signal is of the fundamental importance for numerous image and video processing applications, where the goal of video quality assessment (VQA) is to automatically measure the quality of the visual signal in agreement with the human judgment of the video quality. Considering the codec effect to the video quality, in this paper an efficient non-reference (NR) VQA algorithm is proposed which estimates the video quality (VQ) only by utilizing the distorted video signal at the destination. The VQA feature vectors (FVs) which have high relationships with the subjective quality of the distorted video are investigated, and a hybrid NR VQA (HNRVQA) function is established by considering the multiple FVs. The simulation results, testing on the SDTV programming provided by VCEG Phase I, show that the proposed algorithm can represent the VQ accurately, and it can be used to replace the subjective VQA to measure the quality of the video signal automatically at the destinations.

• The Journal of Korean Association of Computer Education
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• v.11 no.4
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• pp.85-93
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• 2008

This paper proposes a method for generating low-level geometric models with retaining salient features during decimation. Our method employs feature extraction technique for extracting feature lines defined via curvature derivatives on the model (we divide features into ridges and valleys). We add the extraction method to simplification technique (Feature Quadric Error Metric) for making coarse model with features. This paper clearly shows that experimental results have better quality and smaller geometric error than previous methods.

• Architectural research
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• v.21 no.4
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• pp.91-98
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• 2019

In modern times, numerous cities are competing to create the unique skyline adjacent to the water. Tall buildings located across the river have a great contribution to the skyline of a riverfront city and can be a precious asset for the city. Moreover, in several cities, tall buildings and their impact on the urban skyline are a matter that should be considered and regulated in urban design. Therefore, as a prominent element in a larger visual setting of the city, tall buildings should improve the visual quality of the skyline rather than diminish that quality. This research attempts to provide an objective method to analyze the visual quality of the skyline made by a group of tall buildings through their feature of heights and spatial arrangement from riverfront views. The analysis is determined by the design variables of building heights variation, heights transition, density, and spacing of a group of tall buildings. A comparative case study of tall buildings in Yeouido and Lujiazui was conducted to prove the effectiveness of the analysis. The proposed method can be used in a simple way in the quantitative approach to quantify the visual quality of the skyline. In conclusion, Yeuido's skyline is not quite interesting from the riverfront view in terms of height variation and continuity of the skyline view because they are dispersed. Conversely, Lujiazui's skyline from the riverfront vantage points has a good quality in all aspects of the feature of height and spatial arrangements of tall buildings cluster. These factors can be used for the urban designer on how proposed tall buildings within the cluster should appropriately respond to adding image on the skyline.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.537-552
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• 2019

Purpose: The development of information technology makes it easy to utilize high-dimensional categorical data. In this regard, the purpose of this study is to propose a novel method to select the proper categorical variables in high-dimensional categorical data. Methods: The proposed feature selection method consists of three steps: (1) The first step defines the goodness-to-pick measure. In this paper, a categorical variable is relevant if it has relationships among other variables. According to the above definition of relevant variables, the goodness-to-pick measure calculates the normalized conditional entropy with other variables. (2) The second step finds the relevant feature subset from the original variables set. This step decides whether a variable is relevant or not. (3) The third step eliminates redundancy variables from the relevant feature subset. Results: Our experimental results showed that the proposed feature selection method generally yielded better classification performance than without feature selection in high-dimensional categorical data, especially as the number of irrelevant categorical variables increase. Besides, as the number of irrelevant categorical variables that have imbalanced categorical values is increasing, the difference in accuracy between the proposed method and the existing methods being compared increases. Conclusion: According to experimental results, we confirmed that the proposed method makes it possible to consistently produce high classification accuracy rates in high-dimensional categorical data. Therefore, the proposed method is promising to be used effectively in high-dimensional situation.

• Laser Solutions
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• v.3 no.2
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• pp.53-61
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• 2000

On the laser weld production line, a slight alteration of the welding condition produces many defects. The defects are monitored in real time, in order to prevent continuous occurrence of defects, reduce the loss of material, and guarantee good quality. The measurement system is produced by using three photo-diodes for detection of the plasma and spatter signal in CO$_2$ laser welding. For high speed CO$_2$ laser welding, laser tailored welded blanks for example, on-line weld quality monitoring system was developed by using fuzzy multi-feature pattern recognition. Weld qualities were classified optimal heat input, a little low heat input, low heat input, and focus misalignment, and final weld quality were classified good and bad.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.333-339
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• 2010

Data mining is the process of finding and analyzing data from a big database and summarizing it into useful information for a decision-making. A variety of data mining techniques have been being used for wide range of industries. One application of those is especially so for gathering meaningful information from process data in manufacturing factories for quality improvement. The purpose of this paper is to provide a methodology to improve manufacturing quality of fuel tanks which are auto-parts. The methodology is to analyse influential attributes and establish a model for optimal manufacturing condition of fuel tanks to improve the quality using decision tree, association rule, and feature selection.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.468-472
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• 2004

This paper presents an intelligent iris recognition system which consists of quality check, iris localization, feature extraction, and verification. For the quality check, the local statistics on the pupil boundary is used. Gaussian mixture model is used to segment and localized the iris region. The feature extraction method is based on an optimal waveform simplification. For the verification, we use an intelligent variable threshold.

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.291-309
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• 2010

Customer satisfaction is an ever-growing concern of management throughout the world. To find the way to increase customer satisfaction, we must understand customer requirements. Kano distinguishes between three types of product requirements(must-be, one-dimensional, attractive requirement) which influence customer satisfaction in different ways when met. Timko has developed customer satisfaction(CS) coefficient based on Kano model. The CS coefficient is indicative of how strongly a product feature may influence satisfaction. In this paper, potential customer satisfaction improvement(PCSI) index was developed using Kano model and CS coefficient. The PCSI index represents how much a product feature can increase the degree of customer satisfaction when the product feature is fully fulfilled. In order to explain the meaning of PCSI index, a case study for cellular phones is done. It is also discussed how to use the index strategically.

• Journal of Korean Society for Quality Management
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• v.38 no.2
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• pp.248-260
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• 2010

Customer satisfaction is an ever-growing concern of management throughout the world. To find the way to increase customer satisfaction, we must understand customer requirements. Kano distinguishes between three types of product requirements (;must-be, one-dimensional, attractive requirement) which influence customer satisfaction in different ways when met. Timko has developed customer satisfaction(CS) coefficient based on Kano model. The CS coefficient is indicative of how strongly a product feature may influence satisfaction. In this paper, potential customer satisfaction improvement(PCSI) index was proposed using Kano model and CS coefficient. The PCSI index represents how much a product feature can increase the degree of customer satisfaction when the product feature is fully fulfilled. In order to explain the meaning of PCSI index, a case study for cellular phones is done. It is also discussed how to use the index strategically.

• IE interfaces
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• v.16 no.spc
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• pp.39-44
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• 2003

As one of many design variables, the role of dimension tolerances is to restrict the amount of size variation in a manufactured feature while ensuring functionality. In this study, a nonlinear integer model has been modeled to allocate the optimal tolerance to each individual feature at a minimum manufacturing cost. While a normal distribution determines statistically worst tolerances with its symmetrical property in many previous tolerance allocation studies, a asymmetrical distribution is more realistic because its mean is not always coincident with a process center. A nonlinear integer model is modeled to allocate the optimal tolerance to a feature based on a beta distribution at a minimum total cost. The total cost as a function of tolerances is defined by machining cost and quality loss. After the convexity of manufacturing cost is checked by the Hessian matrix, the model is solved by the Complex Method. Finally, a numerical example is presented demonstrating successful model implementation for a nonlinear design case.