• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1297-1301
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• 2007

The Conventional noise control attempts to simply reduce the level of product noise. But it is very straight forward way that we have consider human perception on noise. Since human listening is very sensitive to sound. Evaluation of the sound quality of a Vacuum Cleaner is studied base on human sensibility engineering. In this paper, we choose two Vacuum Cleaners that are sold in Korea and reduced noise control. Comparison Method is used to evaluate noise and preference of Vacuum Cleaner by steps. The sound quality of Vacuum Cleaner noise is analyzed by employing the subjective evaluation and by representing them in terms of the objective quantities. Semantic Differential Method is used to study sound quality Evaluation. To analyze the sound quality of Vacuum Cleaner noise, consider the coefficients of correlation between sound metrics and subjective rating. The linear regression models were obtained for the subjective evaluation and sound quality metrics.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.151-160
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• 2007

A datawarehouse which extracts and saves the massive analysis data is used for marketing and decision support of business. However, the datawarehouse has the problem of increasing the process time and cost as well as has a high risk of process errors because it integrates vast amount of data from distributed environments. Thus, we propose a metrics for measurement in the area of productivity, process quality and data quality. Also through the evaluation using the proposed metrics, we show that our proposal provides productivity enhancement and process improvement.

• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.143-157
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• 2001

There has been a need for predicting development efforts and costs of the system during the early stage of the software process and hundreds of metrics have been proposed for computer software, but not all provide practical support to the software engineer. Some demand measurement that is too complex, others are so esoteric that few real-world professionals have any hope of understanding them, and others violate the basic intuitive notions of what high-quality software really is. It is worthwhile that metrics should be tailored to best accommodate specific products and processes after grasping their good and no good point. This paper describes two size estimation techniques, the Karner technique and the Marchesi technique, and compares and analyzes them with proposed evaluation criteria. Both techniques are to estimate software size analyzed by use case that is mainly described during the object-oriented analysis phase. We also present an empirical comparison of them, both are applied in the Internet Medicine Prescription System. We also propose some guidance for experiments based on our analysis. We believe that it should be facilitating project management more effective by adjusting software metrics properly.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.6
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• pp.445-451
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• 2003

Booming sound is one of the most important interior sound of a passenger car. The conventional booming noise research was focused on the reduction of the A-weighted sound pressure level. However A-weighted sound pressure level cannot give the whole story about the booming sound of a passenger car. In this paper, we employed sound metrics, which are the subjective parameters, used in psycoacoustics. According to recent research results. the relation between sound metrics and subjective evaluation is very complex and has nonlinear characteristics. In order to estimate this nonlinear relationship, artificial neural network theory has been applied to derivation of sound quality index for booming sound of a passenger car.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.2
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• pp.5-20
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• 2017

The notion of object oriented analysis and design in software engineering has many rewards that aid the programmer to have an understanding of and improve the program efficaciously. Object oriented metrics helps rather a lot to a programmer or developer to comprehend and unravel the thing-oriented trouble readily and exactly. Object oriented metrics helps in examining the usefulness of object oriented applied sciences or in simple phrases Object-oriented metrics depict characteristics of object-oriented programming. The intention of this paper is to have an understanding of concerning the UML, Object oriented evaluation and design and the way it plays in UML.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.302-309
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• 2009

The effectiveness of Building Information Modeling (BIM) tools and processes has been recognized by the industry and owners are beginning to adopt Triple Bottom Line accounting practices, to enhance economic performance and environmental and social performance. However, the widespread and practical application of Green BIM remains largely unrealized. The authors identify that lack of understanding of the applicability of sustainability metrics to BIM design process is a significant barrier to this adoption. Through literature review this paper outlines the various sustainability metrics available to construction and elaborates on the potential of BIM for sustainable design. The paper maps and correlates applicable concepts of sustainability evaluation systems to BIM and describes the constraints in current BIM tools.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• Journal of Information Science Theory and Practice
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• v.10 no.4
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• pp.53-65
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• 2022

Altmetrics or alternative metrics gauge the digital attention received by scientific outputs from the web, which is treated as a supplement to traditional citation metrics. In this study, we performed a meta-analysis of correlations between classic citation metrics and altmetrics indicators of library and information science (LIS) articles. We followed the systematic review method to select the articles and Erasmus Rotterdam Institute of Management Guidelines for reporting the meta-analysis results. To select the articles, keyword searches were conducted on Google Scholar, Scopus, and ResearchGate during the last week of November 2021. Eleven articles were assessed, and eight were subjected to meta-analysis following the inclusion and exclusion criteria. The findings reported negative and positive associations between citations and altmetric indicators among the selected articles, with varying correlation coefficient values from -.189 to 0.93. The result of the meta-analysis reported a pooled correlation coefficient of 0.47 (95% confidence interval, 0.339 to 0.586) for the articles. Sub-group analysis based on the citation source revealed that articles indexed on the Web of Science showed a higher pooled correlation coefficient (0.41) than articles indexed in Google Scholar (0.30). The study concluded that the pooled correlation between citation metrics with altmetric indicators was positive, ranging from low to moderate. The result of the study gives more insights to the scientometrics community to propose and use altmetric indicators as a proxy for traditional citation indicators for quick research impact evaluation of LIS articles.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.183-196
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• 2015

In order to perform a successful outsourcing, we needs the SLA through improving the quality of IT services. In particular SLA metrics and evaluation criteria is an important factor as to substitute the IT viability of the company to promote IT Outsourcing. SLA metrics consist of technical, managerial, user perspective items, and has been managed to aim to provide reliable and continuous quality improvement of IT services. This study focuses on the HW availability metrics of SLA indicators of IT outsourcing. We propose the Infra availability criteria for the HW configuration level to meet the SLA contract and evaluation. We offer the Infra configuration standards of SLA contract, and propose criteria to determine the suitability of the target levels in IT operations audit environment. The proposed model was verified the necessity and effectiveness of the Infra configuration standards and operation audit check items through the surveys of experts and users.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.5 s.110
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• pp.457-469
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• 2006

In automotive engineering, the brand sound is one of the important advantage strategies in a car company. For the design of brand sound, the selection of descriptive word for a car sound is one of major works in automotive sound quality research. In this paper, booming and rumbling sound, which are professional words used by sound and vibration engineers are used for the design of brand sound. We employed sound quality metrics, which are used in the psychoacoustics. By most research results, the relationship between subjective evaluations and sound quality metrics has nonlinear characteristics. In order to correlate these subjective evaluations with sound quality metrics, the artificial neural network technology has been applied to two-dimensional sound quality index for a passenger car. These indexes are used for 46 passenger cars, which are samples of the famous cars around the world. Also a preference evaluation for car sound was carried out by sound and vibration engineers. We coupled this preference with booming and rumbling sounds by using artificial neural network. In future, the two dimensional sound and preference index will be very useful to develop brand sound in passenger cars.