• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.531-536
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• 2007

This paper presents a power quality assessment model based on the Analytic hierarchy process. This model can assess unified power quality index which provide an overall performance of the distribution system. To obtain the unified power quality index, we propose the use of the AHP model which has three states: [Ideal]-[Actual]-[Worst]. The proposed method is especially useful and effective for planning. We have applied the proposed method to an actual relatively large system, and verified the usefulness.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.5
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• pp.255-265
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• 2013

3DTV is expected to be a promising next-generation broadcasting service. On the other hand, the visual discomfort/fatigue problems caused by viewing 3D videos have become an important issue. This paper proposes a perceptual quality assessment metric for a stereoscopic video (SV-PQAM). To model the SV-PQAM, this paper presents the following features: temporal variance, disparity variation in intra-frames, disparity variation in inter-frames and disparity distribution of frame boundary areas, which affect the human perception of depth and visual discomfort for stereoscopic views. The four features were combined into the SV-PQAM, which then becomes a no-reference stereoscopic video quality perception model, as an objective quality assessment metric. The proposed SV-PQAM does not require a depth map but instead uses the disparity information by a simple estimation. The model parameters were estimated based on linear regression from the mean score opinion values obtained from the subjective perception quality assessments. The experimental results showed that the proposed SV-PQAM exhibits high consistency with subjective perception quality assessment results in terms of the Pearson correlation coefficient value of 0.808, and the prediction performance exhibited good consistency with a zero outlier ratio value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2377-2389
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• 2022

At present people have higher and higher requirements for network video quality, but video quality will be impaired by various factors, so video quality assessment has become more and more important. This paper focuses on the video quality assessment method using different fuzzy neural networks. Firstly, the main factors that impair the video quality are introduced, such as unit time jamming times, average pause time, blur degree and block effect. Secondly, two fuzzy neural network models are used to build the objective assessment method. By adjusting the network structure to optimize the assessment model, the objective assessment value of video quality is obtained. Meanwhile the advantages and disadvantages of the two models are analysed. Lastly, the proposed method is compared with many recent related assessment methods. This paper will give the experimental results and the detail of assessment process.

• ETRI Journal
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• v.43 no.3
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• pp.538-548
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• 2021

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.558-573
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• 2024

Considering factors such as illumination, camera quality variations, and background-specific variations, identifying a face using a smartphone-based facial image capture application is challenging. Face Image Quality Assessment refers to the process of taking a face image as input and producing some form of "quality" estimate as an output. Typically, quality assessment techniques use deep learning methods to categorize images. The models used in deep learning are shown as black boxes. This raises the question of the trustworthiness of the models. Several explainability techniques have gained importance in building this trust. Explainability techniques provide visual evidence of the active regions within an image on which the deep learning model makes a prediction. Here, we developed a technique for reliable prediction of facial images before medical analysis and security operations. A combination of gradient-weighted class activation mapping and local interpretable model-agnostic explanations were used to explain the model. This approach has been implemented in the preselection of facial images for skin feature extraction, which is important in critical medical science applications. We demonstrate that the use of combined explanations provides better visual explanations for the model, where both the saliency map and perturbation-based explainability techniques verify predictions.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.197-209
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• 2009

Stereoscopic video delivers depth perception to users contrary to 2D video. Therefore, we need to develop a new video quality assessment model for stereoscopic video. In this paper, we propose a new method for objective assessment of stereoscopic video. The proposed method detects blocking artifacts and degradation in edge regions such as in conventional video quality assessment model. And it detects video quality difference between views using depth information for efficient quality prediction. We performed subjective assessment of stereoscopic video to check the performance of the proposed method, and we confirmed that the proposed algorithm is superior to the existing method in PSNR in respect to correlation with results of the subjective assessment.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.31-42
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• 2007

While Defense Agency fur Technology and Quality(DTaQ) has been utilized a risk assessment method as a quality assurance activity for defense goods since 1999, a risk assessment method is known to be ineffective to identify defective items. The objective of this study is to propose the new evaluation method, that adjusts a relative priority of evaluation elements using AHP(Analytic Hierarchy Process). Newly evaluated scores have been applied to the risk assessment result of 2005 defective items to test a validity of the new evaluation model. The new model is capable to identify more high and medium risk-level items than the current method. The company risk-level gets more scores than the item risk-level in the new model.

• Journal of Korean Society on Water Environment
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• v.31 no.3
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• pp.272-285
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• 2015

Accurate assessment of chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing is challenging due to the optical complexity of case 2 waters. and the inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of Chl-a concentrations. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. So we have applied and validated a recently developed Red-NIR three-band model and an IOPs Inversion Model for estimating Chl-a concentration and deriving inland water IOPs in Lake Uiam. Three band and IOPs based Chl-a estimation model accuracy was assessed with samples collected in different seasons. The results indicate that this models can be used to accurately retrieve Chl-a concentration and absorption coefficients. For all datasets the determination coefficients of the 3-band models versus Chl-a concentration ranged 0.65 and 0.88 and IOPs based model versus Chl-a concentration varied from 0.73 to 0.83 respectively. and Comparison between 3-band and IOPs based models showed significant performance with decrease of root mean square error from 18% to 33.6%. The results of this study provides the potential of effective methods for remote monitoring and water quality management in turbid inland water bodies using hyper-spectral remote sensing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.27-37
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• 2017

As the global uncertainty of manufacturing has increased and the quality problem has become global, the recall has become a fatal risk that determines the durability of the company. In addition, as the convergence of PSS (product-service system) product becomes common due to the development of IT convergence technology, if the function of any part of hardware or software does not operate normally, there will be a problem in the entire function of PSS product. In order to manage the quality of such PSS products in a stable manner, a new approaches is needed to analyze and manage the hardware and software parts at the same time. However, the Fishbone diagram, FTA, and FMEA, which are widely used to interpret the current quality problem, are not suitable for analyzing the quality problem by considering the hardware and software at the same time. In this paper, a quality risk assessment model combining FTA and FMEA based on defect rate to be assessed daily on site to manage quality and fishbone diagram used in group activity to solve defective problem. The proposed FTA-FMEA based risk assessment model considers the system structure characteristics of the defect factors in terms of the relationship between hardware and software, and further recognizes and manages them as risk. In order to evaluate the proposed model, we applied the functions of ITS (intelligent transportation system). It is expected that the proposed model will be more effective in assessing quality risks of PSS products because it evaluates the structural characteristics of products and causes of defects considering hardware and software together.

• KIEAE Journal
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• v.17 no.5
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• pp.33-41
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• 2017

Purpose: Indoor air quality in residential buildings needs to be evaluated over the long term. In previous research, there has been an attempt to perform the health risk assessment of pollutants by using numerical models as a method of long-term evaluation. However, the numerical model of this precedent study has limitations that do not reflect the actual concentration distribution. Therefore, this study introduces the CRPS index, constructs a numerical model that can reflect the concentration distribution, and then presents a more accurate health risk assessment method using it. At this time, the pollutants are toluene, which is a typical material released from building materials. Method: CRPS index was applied to existing numerical model to reflect concentration distribution. This was used to calculate concentrations at adult breathing area and to use them for exposure assessment in a health risk assessment. After that, we entered adult data and conducted a health risk assessment of toluene. Results: The non-carcinogenic risk of toluene was calculated to be 0.0060. This is 5% smaller than the existing numerical model, meaning that it is more accurate to predict the pollutant risks. This value is also lower than the US EPA reference value of 1. Therefore, under the conditions of this study, long-term exposure of adults to toluene has no impact on health.