• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.41-53
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• 2006

The exact cause of the system's failure is often unknown in the masked system lifetime data. In such type of data, there are two observable quantities, namely (i) the systems time to failure and (ii) the set of systems components that contains the component, which might cause the system to fail. Our objective in this paper is to use the maximum likelihood procedure in the presence of masked data to make inference for the reliability of the system's components. We assume a multi-component series system where each component has a constant failure rate. Different cases that permit for closed form solutions of point estimates are considered. The results obtained in this paper generalize other published results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.7-11
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• 2002

This paper estimates defect and outbreak probabilities of each individual component from some subset of masked data where the exact component causing system failure might be unknown. A system consists of k components that fails whenever there is a defect in at least one of the components. Due to cost and time constraints it is not feasible to learn exactly which components are defective. Because, test procedures ascertain that the defective components belong to some subset of the k components. This phenomenon is termed masking. We describe a, b, c type in which a sample of masked subsets is subjected to intensive failure analysis. This recorded data of a, b, c type enables maximum likelihood estimation of defect probability of each individual component and leads to outbreak of the defective components in future masked failures.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.647-650
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• 2021

The most powerful and modern face recognition techniques are using deep learning methods that have provided impressive performance. The outbreak of COVID-19 pneumonia has spread worldwide, and people have begun to wear a face mask to prevent the spread of the virus, which has led existing face recognition methods to fail to identify people. Mainly, it pushes masked face recognition has become one of the most challenging problems in the face recognition domain. However, deep learning methods require numerous data samples, and it is challenging to find benchmarks of masked face datasets available to the public. In this work, we develop a new simulated masked face dataset that we can use for masked face recognition tasks. To evaluate the usability of the proposed dataset, we also retrained the dataset with ArcFace based system, which is one the most popular state-of-the-art face recognition methods.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.944-947
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• 2022

Due to the coronavirus pandemic, the wearing of a mask has been increasing worldwide; thus, the importance of image analysis on masked face images has become essential. Although head pose estimation can be applied to various face-related applications including driver attention, face frontalization, and gaze detection, few studies have been conducted to address the performance degradation caused by masked faces. This study proposes a new data augmentation that synthesizes the masked face, depending on the face image size and poses, which shows robust performance on BIWI benchmark dataset regardless of mask-wearing. Since the proposed scheme is not limited to the specific model, it can be utilized in various head pose estimation models.

• International Journal of Reliability and Applications
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• v.18 no.2
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• pp.45-63
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• 2017

In this paper, we have formulated optimum Accelerated Life Test (ALT) plan for a parallel system with two independent components using masked data with ramp-stress loading scheme and Type-I censoring. Consider a system of two independent and non-identical components connected in parallel. Such a system fails whenever all of its components has failed. The exact component that causes the system to fail is often unknown due to cost and time constraint. For each parallel system at test, we observe its system's failure time and a set of component that includes the component actually causing the system to fail. The stress-life relationship is modelled using inverse power law, and cumulative exposure model is assumed to model the effect of changing stress. The optimal plan consists in finding out the optimum stress rate using D-optimality criterion. The method developed has been explained using a numerical example and sensitivity analysis carried out.

• International Journal of Reliability and Applications
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• v.6 no.2
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• pp.117-133
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• 2005

Estimations of the parameters included in a two-component system are derived based on masked system life test data, when the probability of masking depends upon the exact cause of system failure. Also estimations of reliability for the individual components at a specified mission time are derived. Maximum likelihood and Bayes methods are used to derive these estimators. The problem is explained on a series system consisting of two independent components each of which has a Pareto distributed lifetime. Further we present numerical studies using simulation.

• The Journal of the Acoustical Society of Korea
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• v.8 no.5
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• pp.7-22
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• 1989

This paper presents basic data on loudness level and loudness along with data obtained by the authors, and describes an application of the idea of masked loudness to perception of music in the presence of noise. It is shown that timbre or sound quality of music is well explained by masked loudness vs frequency characteristic.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.353-362
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• 2000

다양한 컴포넌트들로 구성된 시스템의 수명 데이터는 시스템 컴포넌트들의 신뢰성을 추정하는데 많이 사용된다. 하지만 비용이나 고장진단의 기술적 문제 때문에 시스템 고장의 정확한 원인을 밝혀내기는 어렵다. 시스템이나 컴포넌트의 수명 데이터 중 정확한 고장원인을 알 수 없는 데이터를 마스크 데이터라 한다. 본 연구는 마스크데이터와 베이지안 추정의 연구방향을 살펴보고, 그리고 고장률의 비정보 사전분포를 이용하여, 컴포넌트가 직렬로 구성된 시스템의 수명 데이터가 마스크 데이터를 갖는 지수분포의 시스템 컴포넌트 고장률을 추정한다.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.53-62
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• 2000

다양한 컴포넌트들로 구성된 시스템의 수명 데이터는 시스템 컴포넌트들의 신뢰성을 추정하는데 많이 사용된다. 하지만 비용이나 고장진단의 기술적 문제 때문에 시스템 고장의 정확한 원인을 밝혀내기는 어렵다. 시스템이나 컴포넌트의 수명 데이터 중 정확한 고장원인을 알 수 없는 데이터를 마스크 데이터라 한다. 본 연구는 마스크데이터와 베이지안 추정의 연구방향을 살펴보고, 그리고 고장률의 비정보 사전분포를 이용하여, 컴포넌트가 직렬로 구성된 시스템의 수명 데이터가 마스크 데이터를 갖는 지수분포의 시스템 컴포넌트 고장률을 추정 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1136-1141
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• 2022

Researches of face recognition on masked faces have been increasingly important due to the COVID-19 pandemic. To realize a stable and practical recognition performance, large amount of facial image data should be acquired for the purpose of training. However, it is difficult for the researchers to obtain masked face images for each human subject. This paper proposes a novel method to synthesize a face image and a virtual mask pattern. In this method, a pair of masked face image and unmasked face image, that are from a single human subject, is fed into a convolutional autoencoder as training data. This allows learning the geometric relationship between face and mask. In the inference step, for a unseen face image, the learned convolutional autoencoder generates a synthetic face image with a mask pattern. The proposed method is able to rapidly generate realistic masked face images. Also, it could be practical when compared to methods which rely on facial feature point detection.