• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.169-176
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• 2014

This research discusses the characteristics and the implementation strategies for two types of quality metrics to analyze innovation effects in six sigma projects: fixed specification type and moving specification type. $Z_{st}$, $P_{pk}$ are quality metrics of fixed specification type that are influenced by predetermined specification. In contrast, the quality metrics of moving specification type such as Strictly Standardized Mean Difference(SSMD), Z-Score, F-Statistic and t-Statistic are independent from predetermined specification. $Z_{st}$ sigma level obtains defective rates of Parts Per Million(PPM) and Defects Per Million Opportunities(DPMO). However, the defective rates between different industrial sectors are incomparable due to their own technological inherence. In order to explore relative method to compare defective rates between different industrial sectors, the ratio of specification and natural tolerance called, $P_{pk}$, is used. The drawback of this $P_{pk}$ metric is that it is highly dependent on the specification. The metrics of F-Statistic and t-Statistic identify innovation effect by comparing before-and-after of accuracy and precision. These statistics are not affected by specification, but affected by type of statistical distribution models and sample size. Hence, statistical significance determined by above two statistics cannot give a same conclusion as practical significance. In conclusion, SSMD and Z-Score are the best quality metrics that are uninfluenced by fixed specification, theoretical distribution model and arbitrary sample size. Those metrics also identify the innovation effects for before-and-after of accuracy and precision. It is beneficial to use SSMD and Z-Score methods along with popular methods of $Z_{st}$ sigma level and $P_{pk}$ that are commonly employed in six sigma projects. The case studies from national six sigma contest from 2011 to 2012 are proposed and analyzed to provide the guidelines for the usage of quality metrics for quality practitioners.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.283-296
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• 2013

Various video services with networks are increasingly available as smart phones, computers and IPTV are widely used. However, transmission over networks may experience transmission errors due to traffic increases and noise. As a result, video quality may suffer. Therefore, quality monitoring emerges as an important issue. In this paper, we propose a video quality assessment method using bit stream information. The video quality metric is calculated using header information and ES (elementary stream) information. To assess performance of the proposed algorithm, subjective quality assessment tests are conducted (VGA resolution). It is shown high correlation between subjective result and the proposed method.

• Journal of KIISE
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• v.42 no.11
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• pp.1361-1371
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• 2015

Depending on the increase amongst various cloud services, the technology of the Cloud Service Broker (CSB) to find the most appropriate services to meet the needs of cloud service consumers has emerged. In order to advance for cloud services to be used through the CSB, it is important to ensure the quality level that meets the demands of consumers through a negotiation process based on the Service Level Agreement (SLA). However, quality metrics of cloud services are different from each other based on the measurement scale, which represents the quality level, and the calculation for each type of cloud services. Therefore, it is necessary to analyze the variability of the quality of cloud services and establish a SLA model for ensuring and improving the level of quality. In this paper, we analyze the quality metrics for the specific type of cloud services by applying the cross-cutting concept and propose a Virtual SLA (VSLA) meta-model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2451-2461
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• 2014

Mobile application is software executing on smart devices regardless of the time and place. Many individuals and companies have provided a lot of mobile applications services. However, there is not certain standard in terms of application's quality evaluation because study is deficient compared with increase amount of development of mobile application. Moreover, mobile application basically has many special characteristics. For these reasons mobile application is required special standard of quality different from general software. To satisfy these needs, I design and propose mobile application evaluation model. Evaluation model is mapped by characteristics of mobile application based on ISO/IEC 25000's quality characteristics and propose each quality characteristics and metrics. For verification, scenario-based studies were applied to quality model and carried out.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.34-42
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• 2004

In the traditional use of conjoint analysis, in order to evaluate the relative importance of several elements composing a service, interviewed customers are asked to express their judgement about different scenarios (specific combinations of elements). In order to reduce the number of possible scenarios, design of experiments methodology is usually exploited. Previous experiences show that, even a limited number of proposed scenarios cause difficulty in answering for the interviewed customer if the scenarios differ for elements of very low interest to him/her. Consequently, a high rate of abandon of the interview has been observed. In this study it is assumed that a service can be decomposed in several improvable elements and/or enriched with new "optionals". In both cases, what under study is assumed to be a set of dichotomous attributes. For each of these attributes, its marginal contribution to customer satisfaction has to be modelled and estimated. To obtain the required information, an opportune questionnaire is proposed to a sample of interviewed customers. An interviewing procedure consisting in a customer driven design of scenarios is followed, starting from the full-optional scenario and eliminating one by one the less satisfying elements. For each interviewed customer, a ranking of attributes is so obtained. Then, by asking the interviewed customer to evaluate on a metric scale the scenarios he previously selected, a rating of attributes can also be obtained. A case study conducted in collaboration with a public transportation company is presented. Contrarily to previous experiences, the abandon rate proved extremely reduced.y reduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.257-271
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• 2016

The Structural SIMilarity (SSIM) index is one of the most widely-used methods for perceptual image quality assessment (IQA). It is based on the principle that the human visual system (HVS) is sensitive to the overall structure of an image. However, it has been reported that indices predicted by SSIM tend to be biased depending on the type of distortion, which increases the deviation from the main regression curve. Consequently, SSIM can result in serious performance degradation. In this study, we investigate the aforementioned phenomenon from a new perspective and review a constant that plays a big role within the SSIM metric but has been overlooked thus far. Through an experimental study on the influence of this constant in evaluating images with SSIM, we are able to propose a new solution that resolves this issue. In the proposed IQA method, we first design a system to classify different types of distortion, and then match an optimal constant to each type. In addition, we supplement the proposed method by adding color perception-based structural information. For a comprehensive assessment, we compare the proposed method with 15 existing IQA methods. The experimental results show that the proposed method is more consistent with the HVS than the other methods.

• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.240-245
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• 2016

Scalable video streaming over wireless networks has many challenges. The most significant challenge is related to packet loss. To overcome this problem, in this paper, we propose an unequal loss protection (ULP) method using a new forward error correction (FEC) mechanism for robust scalable video streaming over wireless networks. For an efficient FEC assignment considering video quality, we first introduce a simple and efficient performance metric, the layer-based recovery rate (LRR), for quantifying the unequal error propagation effects of the temporal and quality layers on the basis of packet losses. LRR is based on the unequal importance in both the temporal and the quality layers of a hierarchical scalable video coding structure. Then, the proposed ULP-LRR method assigns an appropriate number of FEC packets on the basis of the LRR to protect the video layers against packet lossy network environments. Compared with conventional ULP algorithms, the proposed ULP-LRR algorithm demonstrates a higher performance for various error-prone wireless channel statuses.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.80-90
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• 2009

This paper presents objective methods which predict perceptual noise levels caused by refrigerators. Eight home refrigerators are chosen and their noises are recorded in an anechoic-chamber and a real-life apartment. In order to obtain perceptual noise levels of the refrigerators, subjective quality assessment tests were performed by 100 evaluators Then, we compute 5 sound quality metrics (SQM) which reflect psychoacoustics characteristics. Finally, objective assessment model for refrigerator noises is developed by linear combination of SQMs.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.781-792
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• 2003

We medical software is becoming important means to secure competitive power of medical service by explosive increase of medical device based on software, and the quality of medical service is affected to quality of medical software as well as medical devices. But domestic related study and development is not sufficient for quality of medical software, and a falling-off in medical software quality can induce a falling-off in quality of medical service. Therefore, it is necessary to raise qualitative level of medical service by progress and quality improvement of medical devices. Therefore, in this paper, we developed test module and qualify inspection table which can test medical software and produce result based on ISO/1EC 12119.