• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.7 s.361
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• pp.112-121
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• 2007

In this paper, we propose a new checkpointing strategy for dual modular redundancy real-time systems. For every checkpoints the execution results from two processors, and the result saved in the previous checkpoint are compared to detect faults. We devised an operation algorithm in chectpoints to recover from transient faults as well as permanent faults. We also develop a Markov model for the optimization of the proposed checkpointing strategy. The probability of successful task execution within its deadline is derived from the Markov model. The optimal number of checkpoints is the checkpoints which makes the successful probability maximum.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.21-30
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level, and based on the time the performances of items are investigated at the normal stress level. In this paper, when the mean of the prior of a failure rate is known in the exponential lifetime distribution with censored accelerated failure time data, we utilize the empirical Bayesian method by using the moment estimators in order to estimate the parameters of the prior distribution and obtain the empirical Bayesian predictive density and predictive intervals for a future observation under the normal stress level.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.202-207
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• 2016

Checkpoint placement is an effective fault tolerance technique against transient faults in which the task is re-executed from the latest checkpoint when a fault is detected. In this paper, we propose a new checkpoint placement strategy separating data saving and fault detection processes that are performed together in conventional checkpoints. Several fault detection processes are performed in one checkpoint interval in order to decrease the latency between the occurrence and detection of faults. We address the placement method of fault detection processes to maximize the probability of successful execution of a task within the given deadline. We develop the Markov chain model for a real-time task having the proposed checkpoints, and derive the optimal fault detection and checkpoint interval.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.437-444
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• 1999

We propose control charts for constant hazard rate by using the number of failures based on the non-placement(replacement) life test. Also we study the sensitivity of the control chart from the operating characteristic curve.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1004-1011
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• 2006

In this study, we have performed a systematic case study on troubles and diagnoses of passenger car's engine based on OBD. We have acquired 1,242 data in order to analysis accurate troubles' causes and apposite diagnoses. 128 data of them are got using OBD apparatus, and the rest of them are collected on related website. As results, distribution on trouble cases shows bad idling(32%), poor acceleration(21%), stop in running(19%), faulty start(11%), inferior fuel economy(9%), and insufficient power(8%) in order of magnitude. And in the systematic cases, it is not difficult to detect troubles in a single part. But we know that special apparatus such as multichannel scanner is needed in complicated troubles. Furthermore we think that the survey is continued in various ways for more systematic case study on troubles and diagnoses.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.9
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• pp.781-789
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• 2021

In this paper, we quantitatively analyzed the required UAV(Unmanned Aerial Vehicle) failure rate of small UAV (≤25kg) based on the harm to human caused by UAV crash to fly over the populated area. We compute the number of harm to human when UAV falls to the ground at certain descent point by using population density, car traffic, building to land ratio, number of floors of building data of urban area and UAV descent trajectory modeling. Based on this, the maximum allowable UAV failure rate is calculated to satisfy the Target Level of Safety(TLS) for each UAV descent point. Then we can generate the failure rate requirement in the form of map. Finally, we divide UAV failure rate into few categories and analyze the possible flight area for each failure rate categories. Considering the Youngwol area, it is analyzed that the UAV failure rate of at least 10^-4 (failure/flight hour) is required to access the residential area.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1413-1415
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• 1999

배전선로상에서는 상 불평, 고저항 지락사고나 선로탈락이 발생할 수 있다. 또한, 고장 감지기 정보의 불확실성 등으로 배전 SCADA 정보로부터 정확한 사고유형과 사고위치를 확인하는 작업은 매우 어렵다. 따라서 본 연구에서는 배전선로상에서 발생할 수 있는 다양한 사고들에 대해 사고유형과 사고발생 위치를 신속하고 정확하게 추론할 수 있는 전문가 시스템을 제안한다. 전문가 시스템은 배전 SCADA기능과 수집된 데이터를 종합적으로 활용하게 되는데, 특히, 정확한 사고유형 확인을 위해 절분점 감시 메카니즘이 새롭게 채택되며, 또한, 선로사고시 시스템 운영자들의 오류로부터 발생할 수 있는 파급효과를 최소화하기 위해 고장구간의 자동진단 전략이 개발된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.835-844
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• 2021

In this paper, a power facility communication control device was designed to autonomously determine and separate the fault section through communication between power facilities in the smart grid distribution system. In the power facility communication control device, the control module was designed as a DSP to measure three-phase voltage and current, and the communication module was designed as an embedded-based Raspberry Pi to determine the fault section and realize the fault section separation through communication between power facilities. Communication between DSP and Raspberry Pi was designed by SPI communication, and communication between Raspberry Pi was designed based on Wi-Fi. Finally, a performance evaluation system based on three power facility communication control devices was built, and simulation verification was conducted for various fault events that may occur on the distribution line. As a result of the test evaluation, it was possible to confirm the effectiveness of the design methodology of the communication control device by showing the required response of the communication control device to all test cases.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.859-870
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• 2011

The purpose of this paper is to develop reliability analysis procedures for repairable systems with interval failure time data and apply the procedures for assessing the storage reliability of a subsystem of a certain type of guided missile. In the procedures, the interval failure time data are converted to pseudo failure times using the uniform random generation method, mid-point method or equispaced intervals method. Then, such analytic trend tests as Laplace, Lewis-Robinson, Pair-wise Comparison Nonparametric tests are used to determine whether the failure process follows a renewal or non-renewal process. Monte Carlo simulation experiments are conducted to compare the three conversion methods in terms of the statistical performance for each trend test when the underlying process is homogeneous Poisson, renewal, or non-homogeneous Poisson. The simulation results show that the uniform random generation method is best among the three. These results are applied to actual field data collected for a subsystem of a certain type of guided missile to identify its failure process and to estimate its mean time to failure and annual mean repair cost.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.