• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.293-301
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• 2013

The purpose of this study is to introduce the limit of previously used six sigma quality process evaluation metrics, $Z_{st}$ and $P_{pk}$, and a solution to overcome this drawback by using a metric based on performance evaluation of Z-factor quality innovation. Case analysis on projects from national six sigma contest from 2011 to 2012 is performed and literature review on new drug development HTS (High Throughput Screening) is used to propose innovative performance evaluation metrics. This research shows that experimental study on six sigma evaluation metric, $Z_{st}$ and $P_{pk}$, have no significance difference between industrial type (Manufacturing, Semi-Public Institute, Public Institute) and CTQ type (Product Technology Type CTQ, Process Technology Type CTQ). Following discovery characterize this quality improvement as fixed target type project. As newly developed moving target type of quality innovation performance metric Z-Factor is used for evaluating experimental study, hypothetical analysis suggests that $Z_{st}$ and $P_{pk}$ share different relationship or even show reciprocal relationship. Constraints of the study are relatively small sample size of only 37 projects from past 2 years and conflict on having interview and communication with six sigma quality practitioner for qualitative experimental study. Both moving target type six sigma innovation project and fixed target type improvement project or quality circle enables efficient ways for a better understanding and quality practitioner use by applying quality innovation performance metric. Downside of fixed target type quality performance evaluation metric, $Z_{st}$ and $P_{pk}$, is presented through experimental study. In contrast, advantage of this study is that high throughput requiring product technology, process technology and quantum leap typed innovation effect is evaluated based on precision and accuracy and Z-Factor that enables relative comparison between enterprises is proposed and implemented.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.987-999
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• 2017

Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.87-92
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• 2022

In this study, we aimed to illustrate that the thresholding method gives different results when tested on the original and the refined histograms. We use the global thresholding method, the well-known image segmentation method for separating objects and background from the image, and the refined histogram is created by the neighborhood distinction metric. If the original histogram of an image has some large bins which occupy the most density of whole intensity distribution, it is a problem for global methods such as segmentation and contrast enhancement. We refined the histogram to overcome the big bin problem in which sub-bins are created from big bins based on distinction metric. We suggest the refined histogram for preprocessing of thresholding in order to reduce the big bin problem. In the test, we use Otsu and median-based thresholding techniques and experimental results prove that their results on the refined histograms are more effective compared with the original ones.

• Bulletin of the Korean Mathematical Society
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• v.34 no.1
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• pp.115-125
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• 1997

In [3,4,5], G. Godini introduced a normed almost linear space(nals), generalizing the concept of a normed linear space. In contrast with the case of a normed linear space, tha norm of a nals $(X, $\mid$$\mid$$\mid$ \cdot $\mid$$\mid$$\mid$)$ does not generate a metric on X $(for x \in X \backslash V_X we have $\mid$$\mid$$\mid$ x - x $\mid$$\mid$$\mid$ \neq 0)$.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.43-55
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• 2016

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.108-117
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• 2015

If typical contrast enhancement algorithms for natural images are applied to X-ray images, they may cause artifacts such as overshooting or produce unnatural visual quality because they do not consider inherent characteristics of X-ray images. In order to overcome such problems, we propose a locally adaptive block-based contrast enhancement algorithm for X-ray images. After we derive a weighted cumulative distribution function for each block, we apply it to each block for contrast enhancement. Then, we obtain images that are removed from block effect by adopting block-based overlapping. In post-processing, we obtain the final image by emphasizing high frequency components. Experimental results show that the proposed block-based contrast enhancement algorithm provides at maximum 5-times higher visual quality than the exiting algorithm in terms of quantitative contrast metric.

• KIEAE Journal
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• v.8 no.6
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• pp.21-26
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• 2008

According to the tendency of energy efficiency and environment-friendly chracteristics, demend of High-efficiency lighting using LED(Light Emitting Diode)are being increased actively and applied in various fields. However, In order to adequate application of LED light sources, it is necessary to lighting environment and luminous characteristics of LED light sources. This Study aims to characterize the work-plane lighting environment by LED light sources comparing with fluorescent light sources which are widely used. For the sake of this study, a fluorescent light source and 5 LED light sources were introduced and luminance and correlated color temperature were measured to evaluate luminance contrast. The experimental model is Mock-up which is $4.9m{\times}7.2m$ with a height of 2.9m. The test room was set up partition and desks. Luminance and correlated color temperature were measured work-plane on the desk which was set up local lighting by the Radiant Imaging ProMetric 1400. The optical characteristics data of LED can give a lot of advantages to design LED lighting appliances. Hereafter, the object of research will be conducted to evaluate effects of LED light sources on working performance, survey of visual performance, preference and physiology of subjects.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1205-1211
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• 2012

Recently, the evaluating method of the bone mineral density (BMD) in X-ray absorptiometry image has been studied for the early diagnosis of osteoporosis which is known as a metabolic disease. The BMD, in general, is evaluated by calculating pixel intensity in the bone segmented regions. Accurate bone region extraction is extremely crucial for the BMD evaluation. So, a X-Ray image enhancement is needed to get precise bone segmentation. In this paper, we propose an image enhancement method of X-ray image having multiple noise based sparse representation. To evaluate the performance of proposed method, we employ the contrast to noise ratio (CNR) metric and cut-view graphs visualizing image enhancement performance. Experimental results show that the proposed method outperforms the BayesShrink noise reduction methods and the previous noise reduction method in sparse representation with general noise model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1904-1926
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• 2016

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.691-700
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• 2015

Here, we present a new framework for histogram equalization in which both local and global contrasts are enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Filters are chosen depending on image properties, such as noise removal and smoothing. Our experimental results confirmed that this does not increase the computational cost because the filtering process is done by our proposed arrangement of making the histogram while checking neighborhood metrics simultaneously. If the two methods, i.e., histogram equalization and filtering, are performed sequentially, the first method uses the original image data and next method uses the data altered by the first. With combined histogram equalization and filtering, the original data can be used for both methods. The proposed method is fully automated and any spatial neighborhood filter type and size can be used. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.