• Journal of Information Processing Systems
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• v.7 no.2
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• pp.287-298
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• 2011

Efficient service description and modeling methodologies are essential for dynamic service composition to provide autonomous services for users in ubiquitous computing environments. In our previous research, we proposed a 'u-service' ontology which is an abstract and structured concept for device operations in ubiquitous environments. One of the problems that we faced during the design process was that there are not enough standards to analyze the effectiveness of a u-service ontology. In this paper, we propose a quality evaluation model to facilitate the design process of a uservice ontology. We extract modeling goals and evaluation indicators based on the uservice description specification. We also present quality metrics to quantify each of the design properties. The experiment result of the proposed quality model shows that we can use it to analyze the design of u-service ontology from various angles. Also, it shows that the model can provide a guideline, and offer appropriate recommendations for improvements.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11b
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• pp.186-189
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• 2005

Since human listening is very sensitive to sound, a subjective index of a sound quality is required. Therefore, in the analysis for each situation, the sound evaluation is composed with sound quality factor. Many researchers spends their effort to make a more reliable and more accurate of sound in term of sound quality index for various system noise. In this study a reliable index is constructed and analyzed using correlation analysis, regression analysis and weighting factor for each sound quality factor. We have made the sound quality index that agrees with more than human subjective sensitivity which apply to various sound quality metrics. Also we applied a 'grade' metric to jury for sound evaluation, analyzed relation between sound duality index and sound quality grade. Then we will judge the sound quality level according to the sound quality grade scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2354-2376
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• 2020

SmartThings is one of the most popular open platforms for home automation IoT solutions that allows users to create their own applications called SmartApps for personal use or for public distribution. The nature of openness demands high standards on the quality of SmartApps, but there have been few studies that have evaluated this thoroughly yet. As part of software quality practice, code reviews are responsible for detecting violations of coding standards and ensuring that best practices are followed. The purpose of this research is to propose systematically designed quality metrics under the well-known Goal/Question/Metric methodology and to evaluate the quality of SmartApps through automatic code reviews using a static analysis. We first organize our static analysis rules by following the GQM methodology, and then we apply the rules to real-world SmartApps to analyze and evaluate them. A study of 105 officially published and 74 community-created real-world SmartApps found a high ratio of violations in both types of SmartApps, and of all violations, security violations were most common. Our static analysis tool can effectively inspect reliability, maintainability, and security violations. The results of the automatic code review indicate the common violations among SmartApps.

• Safety and Health at Work
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• v.8 no.2
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• pp.206-211
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• 2017

Background: Self-reported low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting nonchemical stressors and related adverse responses is more difficult. Stressor-response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g., Benchmark Dose methods), so different approaches were tried. Methods: This work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function. Results: Using maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found. Conclusion: A risk of LBP associated with CLI values > 1.5 existed in this worker population. The relevance for other populations requires further study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.847-856
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• 2013

This paper presents an AP placement technique considering indoor location-awareness and examines its performance. The proposed AP placement technique is addressed from three performance metrics: location-awareness and AP-based wireless network performance as well as its cost. The proposed AP placement technique consists of meta-heuristic algorithms that yield a near optimal AP configuration for given performance metrics, and deterministic algorithms that improve the fast convergence of the near optimal AP configuration. The performance of the AP placement technique presented in this paper is measured under the environments simulating indoor space, and numerical results obtained by experimental evaluation yield the fast convergence of a near-optimal solution to a given performance metric.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.11-19
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• 2007

This paper presents an objective video quality evaluation method for digital mobile video. The proposed method is to objectify subjective quality by extracting edge region feature metric and blockiness effect. To evaluate performance of proposed algorithm, we carried out subjective video qualify test with the DSCQS method and obtained mean opinion score(MOS) values for CIF/QCIF 140 video clips. We compared error of proposed method with that of existing. The experiment results show that the proposed method has 25% higher performance.

• ETRI Journal
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• v.37 no.3
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.531-541
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• 2018

Road freezing caused by snowfall during wintertime causes traffic congestion and many accidents. To prevent such problems, we developed, in this study, a system to predict road freezing based on weather forecast data and the freezing generation modules. The weather forecast data were obtained from a high-resolution model with 1 km resolution for Jeju Island from 00:00 KST on December 1, 2017, to 23:00 KST on February 28, 2018. The results of the weather forecast data show that index of agreement (IOA) temperature was higher than 0.85 at all points, and that for wind speed was higher than 0.7 except in Seogwipo city. In order to evaluate the results of the freezing predictions, we used model evaluation metrics obtained from a confusion matrix. These metrics revealed that, the Imacho module showed good performance in precision and accuracy and that the Karlsson module showed good performance in specificity and FP rate. In particular, Cohen's kappa value was shown to be excellent for both modules, demonstrating that the algorithm is reliable. The superiority of both the modules shows that the new system can prevent traffic problems related to road freezing in the Jeju area during wintertime.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2341-2347
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• 2021

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.