• 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.

• Korean Journal of Ecology and Environment
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• v.51 no.1
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• pp.86-95
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• 2018

The objective of this study was to describe the development and testing of an initial ecological health assessment model, based on the index of biological integrity (IBI) using fish assemblages, before establishing the final and currently used model for ecological health assessment, conservation and management of freshwater fish in Korea. The initial fish IBI model was developed during 2004~2006 and included 10 metrics, and in 2007 the final IBI 8-metric model was established for application to streams and rivers in four major Korean watersheds. In this paper, we describe how we developed fish sampling methods, determined metric attributes and categorized tolerance guilds and trophic guilds during the development of the multi-metric model. Two of the initial metrics were removed and the initial evaluation categories were reduced from six to four (excellent, good, fair, poor) before establishing the final national fish model. In the development phase, IBI values were compared with chemical parameters (BOD and COD as indicators of organic matter pollution) and physical habitat parameters to identify differences in IBI model values between chemical and physical habitat conditions. These processes undertaken during the development of the IBI model may be helpful in understanding the modifications made and contribute to creating efficient conservation and management strategies for stream environments to be used by limnologists and fish ecologists as well as stream/watershed managers.

• 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.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.571-576
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• 2015

Image dehazing has been extensively studied, but the performance evaluation method for dehazing techniques has not attracted significant interest. This paper surveys many existing performance evaluation methods of image dehazing. In order to analyze the reliability of the evaluation methods, synthetic hazy images are first reconstructed using the ground-truth color and depth image pairs, and the dehazed images are then compared with the original haze-free images. Meanwhile we also evaluate dehazing algorithms not by the dehazed images' quality but by the performance of computer vision algorithms before/after applying image dehazing. All the aforementioned evaluation methods are analyzed and compared, and research direction for improving the existing methods is discussed.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.815-818
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• 2015

AODV routing protocol, one of the most studied routing protocols for the Mobile Ad hoc Network (MANET), uses the number of hops as the metric to choose a path from a source node to a destination node. If the path is deteriorated, it will cause many problems to the communication. In order to improve the performance of the network, we propose AuM-AODV routing protocol that contains an auxiliary metric besides the number of hops. Nodes using AuM-AODV use control packets such as Route Request (RREQ), Route Reply (RREP), and HELLO to exchange information about network topology like AODV routing protocol. AuM-AODV routing protocol is implemented in NS-3 for performance evaluation. We use three performance metrics, that is to say, throughput, packet delivery ratio, and average end-to-end delay. According to numerical results, the new AuM-AODV routing protocol has better performance over three performance metrics than AODV routing protocol.

• Convergence Security Journal
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• v.11 no.1
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• pp.11-18
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• 2011

Wireless mesh network technology with transmission speeds similar to wired and wireless technology means to build, compared with wired networks, building a more efficient network to provide convenience and flexibility. The wireless mesh network router nodes in the energy impact of the mobility is less constrained and has fewer features entail. However, the characteristics of various kinds due to network configuration settings and the choice of multiple paths that can occur when the system overhead and there are many details that must be considered. Therefore, according to the characteristics of these network routing technology that is reflected in the design and optimization of the network is worth noting. In this paper, a multi-path setting can be raised in order to respond effectively to the problem of the router node data loss and bandwidth according to traffic conditions and links to elements of the hop count evaluation by using a genetic algorithm as a workaround for dynamic routing the routing metric for wireless mesh network scheme is proposed.

• 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.