• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.599-606
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• 2018

Failure data of systems in many field can be erroneous, which influences the reliability analysis of the systems. The general form of failure data is right censored data with accurate time information. But due to its nature of data collection in the military field, failure time of one-shot weapon systems can have errors which are related to the maintenance period. So this paper suggests a model that can reduce the error by utilizing interval censored data as an alternative to right censored data in weibull distribution.

• Journal of Environmental Health Sciences
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• v.26 no.3
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• pp.50-54
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• 2000

Ventilation rates can be measured directly by a tracer decay method, although little is known of the effects of different sampling intervals on decay rte calculations. This study determined variations in decay rates calculated by three techniques using residential ozone decay data. The calculation techniques were a regression technique, decay techniques using half-life and average-life, and finite difference techniques using two different time intervals. Variation associated with regression technique calculations for residential ozone decay rates based on data from both sample intervals were within 10% (2.81$\pm$1.88 hr-1). However, both half-life and finite difference technique calculations using a shorter-time interval were significantly different from those obtained with the regression technique(p<0.05). Therefore, the use of short sampling intervals in tracer decay may cause significant error in decay rate calculations.

• Current Optics and Photonics
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• v.8 no.5
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• pp.508-514
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• 2024

This paper presents a deep learning-based weight sensor, using optical speckle patterns of multimode fiber, designed for real-time intrusion detection. The weight sensor has been trained to identify 11 distinct speckle patterns, ranging in weight from 0.0 kg to 2.0 kg, with an interval of 200 g between each pattern. The estimation for untrained weights is based on the generalization capability of deep learning. This results in an average weight error of 243.8 g. Although this margin of error precludes accurate weight measurement, the system's ability to detect abrupt weight changes makes it a suitable choice for intrusion detection applications. The weight sensor is integrated with the Google Teachable Machine, and real-time intrusion notifications are facilitated by the ThingSpeak^TM cloud platform, an open-source Internet of Things (IoT) application developed by MathWorks.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.21-29
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• 2016

Indoor location system has been used Wi-Fi to get a location. After the development of BLE(Bluetooth Low Energy), the interest in the method of a indoor positioning had been move on. It has more advantages than using Wi-Fi. Easy installation, low power consumption, low signal interference and changeable setting(Advertising interval, tx power, etc.). These things can improve efficiency or accuracy in a indoor positioning system. For this reason, recent indoor positioning system uses BLE rather than Wi-Fi. Accordingly, error factors of BLE beacon based indoor positioning should be studying for high accuracy of indoor positioning. In this research, set up few experiment scenarios and keep a close watch on how technological, environmental factor is affecting positioning accuracy. When a application uses largest signal strength to get the indoor location, the mean error of experimental results was decreased compare to using received signal strength in real-time. The result was same when the application applied average and standard deviation to get the indoor location. Changing advertising interval had an effect on the mean error of indoor positioning. Short advertising interval makes the lower mean error than large advertising interval.

• Journal of Information Technology Applications and Management
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• v.11 no.2
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• pp.29-43
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• 2004

As the development of an information technique, gradually, mobile device is going to be miniaturized and operates at high speed. By such the requirements, the devices using a flash memory as a storage media are increasing. The flash memory consumes low power, is a small size, and has a fast access time like the main memory. But the flash memory must erase for recording and the erase cycle is limited. JFFS is a representative filesystem which reflects the characteristics of the flash memory. JFFS to be consisted of LSF structure, writes new data to the flash memory in sequential, which is not related to a file size. Mounting a filesystem or an error recovery is achieved through the sequential approach. Therefore, the mounting delay time is happened according to the file system size. This paper proposes a MJFFS to use a multi-checkpoint information to manage a mass flash file system efficiently. A MJFFS, which improves JFFS, divides a flash memory into the block for suitable to the block device, and stores file information of a checkpoint structure at fixed interval. Therefore mounting and error recovery processing reduce efficiently a number of filesystem access by collecting a smaller checkpoint information than capacity of actual files. A MJFFS will be suitable to a mobile device owing to accomplish fast mounting and error recovery using advantage of log foundation filesystem and overcoming defect of JFFS.

• 전기의세계
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• v.27 no.4
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• pp.41-44
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• 1978

Intermittent failures exercise their effects only part of the time but constitute a dominant factor for the field failures. We consider the data raliability of the partially self-checking network with which a single intermittent failure will be recovered by a rollback method. Even if the self-testingness of partially self-checking network is guranteed for a set of permanent failures, it sometimes may not be so for intermittent failures. We introduce the notion of error residual and provide the basis for calculating the data reliability. Both the duration of each intermittent failure and the occurrence interval of successive ones are assumed to be negative exponentially distributed; the convolution of the intervals is distributed according to an Erlangen distribution.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.403-413
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• 2012

The modifications suggested in Uhm et al. (2011) are studied using a partly parametric version of Aalen's additive risk model. A follow-up time period is partitioned into intervals, and hazard functions are estimated as a piecewise constant in each interval. A maximum likelihood estimator by iteratively reweighted least squares and variance estimates are suggested based on the model as well as evaluated by simulations using mean square error and a coverage probability, respectively. In conclusion the modifications are needed when there are a small number of uncensored deaths in an interval to estimate the piecewise constant hazard function.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4223-4239
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• 2016

In this paper, we consider an amplify-and-forward (AF) full-duplex relay network (FDRN) using simultaneous wireless information and power transfer, where a battery-free relay node harvests energy from the received radio frequency (RF) signals from a source node and uses the harvested energy to forward the source information to destination node. The time-switching relaying (TSR) protocol is studied, with the assumption that the channel state information (CSI) at the relay node is imperfect. We deliver a rigorous analysis of the outage probability of the proposed system. Based on the outage probability expressions, the optimal time switching factor are obtained via the numerical search method. The simulation and numerical results provide practical insights into the effect of various system parameters, such as the time switching factor, the noise power, the energy harvesting efficiency, and the channel estimation error on the performance of this network. It is also observed that for the imperfect CSI case, the proposed scheme still can provide acceptable outage performance given that the channel estimation error is bounded in a permissible interval.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.25-32
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• 2017

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.