• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.7 s.100
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• pp.821-828
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• 2005

In our life, we have used many digital appliances. They help us to improve the quality of life but sometimes give us unsatisfactory result. Because they produce specific noise. Especially vacuum cleaner produce much noise that is very annoying. So we need to study what sound metrics affect human sensibility. In this paper, we develop sound quality index for vacuum cleaner using the sound quality metrics defined in psychoacoustics. First, we carry out the subjective evaluation of vacuum cleaner sound to verify what vacuum sound feels good to human. And then artificial neural network estimated the complexity and the nonlinear characteristics of the relations between subjective evaluation and sound metrics. Finally the ANN is trained repeatedly to have a good performance for sound qualify index of the vacuum cleaner. As a result, the sound quality index of vacuum cleaner has a correlation of $93.5\%$ between the subjective evaluation and ANN. So, there exist three factors that Is loudness, sharpness, roughness which affect the sound quality of vacuum cleaner.

• Journal of the Korea Institute of Building Construction
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• v.12 no.1
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• pp.1-9
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• 2012

This study aims to propose a performance metric for the application of a horizontal air-exhausting system to be used for the reduction of vapor and/or moisture that exists in the waterproof layer, by evaluating the physical properties. For this reason, tests in accordance with current standards were carried out, and the results were examined. Finally, a proposal was established for a general performance metric that could be applied as fundamental data based on the user's judgment. This has some limitations, in that the object is existing merchandise, however it should be useful for application in the construction field. In the future, analysis of a wider area, including workability, should be added in the phase of field application.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.5
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• pp.37-44
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• 2010

Impact noise is induced in a car when it is driven on a harsh road or over some bumps. This noise occurs with the very high level of sound, which affects passengers in some way or other. Although it is impossible to clearly remove such noise, it is necessary to research an improvement in sound quality for impact noise. A new sound metric for impact sound is presented. This metric is verified by comparison between mean subjective ratings and several sound metrics. In this paper, more objective attributes are considered, which the attributes are expressing the level and modulation of sound. Three sound metrics are employed to get impact sound indexes for each course by the method of multiple linear regressions. The indexes are verified by considering the correlation between the estimated values from the multiple linear regressions and the mean subjective ratings by evaluators. Also, the subjective ratings on the indexes are estimated for the case in which some parts of suspension system are changed. The estimated ratings represent more reasonable or acceptable ratings. Thus, such indexes can be used for modification of the parts of suspension system under considering a good sound quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1234-1241
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• 2008

Cost effectiveness is greatly related with the degree of reducing the testing cost by the technical reviews. In this paper, we present a new metric My for evaluating the cost effectiveness of technical reviews during software development. First, we estimate and compare My with conventional measure using data collected during practical software development procedure, then we show the validity and usefulness of the proposed measure My. Also by formulating the relationship between the data collected during the reviews and the test, we present a method to estimate the value of the metric My using only the data collected during review phase.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.136-142
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• 2016

The FMEA is a widely used technique to pre-evaluate and avoid risks due to potential failures for developing an improved design. The conventional FMEA does not consider the possible time gap between occurrence and detection of failure cause. When a failure cause is detected and corrected before the failure itself occurs, there will be no other effect except the correction cost. But, if its cause is detected after the failure actually occurs, its effects will become more severe depending on the duration of the uncorrected failure. Taking this situation into account, a risk metric is developed as an alternative to the RPN of the conventional FMEA. The severity of a failure effect is first modeled as linear and quadratic severity functions of undetected failure time duration. Assuming exponential probability distribution for occurrence and detection time of failures and causes, the expected severity is derived for each failure cause. A new risk metric REM is defined as the product of a failure cause occurrence rate and the expected severity of its corresponding failure. A numerical example and some discussions are provided for illustration.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.201-208
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• 2010

Mobile Ad Hoc Network(MANET) has highly dynamic topology, hence presents a great challenge on the network performance evaluation and network protocol design. We proposed total path break up time, $\sum_{i}T_i$, as a metric to measure the performance of the total system as well as an individual connection. In this paper, we evaluate and analyze the performance of three mobility models(Random Waypoint, Manhattan, Blocked Manhattan) by applying the total path break up metric, investigate why network parameters such as packet delivery ratio, end-to-end delay, etc. vary by mobility models. We also present analysis result how much AODV Buffer improve packet delivery ratio and increase the end-to-end delay in spite of the path break up.

• Korean Management Science Review
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• v.30 no.1
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• pp.103-124
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• 2013

This study aims to develop a framework to measure MOT competitiveness of enterprises while proposing a concept called management of technology competitiveness (MOTC). The framework of MOTC based on both resource-based view and competence-based view is consisted of technology competitiveness and management competitiveness. A variety of metrics to measure MOTC are extracted through substantial literature review. As technology competitiveness metric, this study examines R&D investment, R&D workforce, R&D facilities, intellectual property assets, and utilization of information and communication technology; as metric of management competitiveness, leadership competitiveness, maturity of the R&D systems, collaboration and partnership, learning and innovation, and commercialization are considered. We then confirm and derive the multi-dimensions of MOTC through its reliability and validity analysis. The study is expected to provide useful guidelines and references for enterprises' self-evaluation of technology and management competitiveness that is equally applicable to small, medium, and large enterprises that must compete in the global marketplace.

• Korean Journal of Ecology and Environment
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• v.54 no.3
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• pp.221-228
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• 2021

Recently, environmental DNA (eDNA)-based metabarcoding approaches have been proposed to evaluate the status of freshwater ecosystems owing to various advantages, including fast and easy sampling and minimal habitat disruption from sampling. Therefore, as a case study, we applied eDNA metabarcoding techniques to evaluate the effects of an abandoned mine land located near a headwater stream of Nakdonggang River, South Korea, by examining benthic macroinvertebrate diversity and compared the results with those obtained using the traditional Surber-net sampling method. The number of genera was higher in Surber-net sampling (29) than in the eDNA analysis (20). The genus richness tended to decrease from headwater to downstream in eDNA analysis, whereas richness tended to decrease at sites with acid-sulfated sediment areas using Surber-net sampling. Through cluster analysis and non-metric multidimensional scaling, the sampling sites were differentiated into two parts: acid-sulfated and other sites using Surber-net sampling, whereas they were grouped into the two lowest downstream and other sites using eDNA sampling. To evaluate freshwater ecosystems using eDNA analysis in practical applications, it is necessary to constantly upgrade the methodologies and compare the data with field survey methods.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.1-12
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• 2018

Mixed-criticality systems consist of components with different criticality. Recently, components are categorized depending on criticality by ISO 26262 standard and DO-178B standard in automotive and avionic domain. Existing mixed-criticality system research achieved efficient and safe scheduling through system-level criticality mode. The drawback of these approaches is performance degradation of low-criticality tasks on high-criticality mode. Task-level criticality mode is one method to address the problem and improve the performance of low-critical tasks. In this paper, we propose probabilistic performance metric for the approach. In simulation results with probabilistic performance metric, we showed that our approach has better performance than the existing approaches.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1673-1681
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• 2021

Item cold start is a well studied problem in the research field of recommender systems. Still, many existing collaborative filters cannot recommend items accurately when only a few user-item interaction data are available for newly introduced items (Cold items). We propose a interaction feature prediction method to mitigate item cold start problem. The proposed method predicts the interaction features that collaborative filters can calculate for the cold items. For prediction, in addition to content features of the cold-items used by state-of-the-art methods, our method exploits the interaction features of k-nearest content neighbors of the cold-items. An attention network is adopted to extract appropriate information from the interaction features of the neighbors by examining the contents feature similarity between the cold-item and its neighbors. Our evaluation on a real dataset CiteULike shows that the proposed method outperforms state-of-the-art methods 0.027 in Recall@20 metric and 0.023 in NDCG@20 metric.