• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1025-1033
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• 2010

This paper proposes a method to improve and analysis error cause of FI(Fault Indicator) information to be used for detecting fault section in distribution automation system. FI error cause is made by consideration fault current magnitude and time. So, a new method to prevent FI error is proposed to include fault current magnitude, time and direction. Therefore, it's considered network environments that grounded and ungrounded network in distribution automation system. The proposed method is proved by Matlap Simulink. By the result in this research, it's possible to quickly restoration, supplying stability and reliability power to customer.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.271-280
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• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.684-690
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• 2015

This paper presents a modified residual-based EKF (Extended Kalman Filter) for performance improvement of indoor positioning using WiFi RSSI (Received Signal Strength Indicator) measurement. Radio signal strength in indoor environments may have irregular attenuation characteristics due to obstacles such as walls, furniture, etc. Therefore, the performance of the RSSI-based positioning with the conventional trilateration method or Kalman filter is insufficient to provide location-based accurate information services. In order to enhance the performance of indoor positioning, in this paper, error analysis of the distance calculated by using the WiFi RSSI measurement is performed based on the radio propagation model. Then, an IARM (Irregularly Attenuated RSSI Measurement) error is defined. Also, it shows that the IARM error is included in the residual of the positioning filter. The IARM error is always positive. So, it is presented that the IARM error can be estimated by taking the absolute value of the residual. Consequently, accurate positioning can be achieved based on the IEM (IARM Error Mitigated) EKF with the residual modified by using the estimated IARM error. The performance of the presented IEM EKF is verified experimentally.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.107-116
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• 2016

Premature babies of less than 37 weeks gestation might require oxygen therapy as an integral part of treatment and respiratory support. Because of their under-developed lungs, these so-called "preemies" might contract respiratory distress syndrome (RDS). To treat RDS, neonatal oxygen therapy is administered, where controlled oxygen gas is measured as a fraction of inspired oxygen ($FiO_2$). However, exposure to high oxygen content during long treatment could cause oxygen intoxication, which might cause permanent blindness due to retinopathy of prematurity (ROP), whereas insufficient oxygen exposure could cause severe hypoxia. A doctor would use oxygen saturation ($SpO_2$) data and prescribe a dose of $FiO_2$ to maintain $SpO_2$ within a suitable range. One objective is to maintain $SpO_2$ within the acceptable range using $FiO_2$ that is as low as possible. Adjustment of $FiO_2$ would normally be done by nurses every 15 to 30 minutes, which might not be safe in many situations. An error in $FiO_2$ adjustment during a manual procedure could be as large as +/- 2.5%. This paper presents a system that can determine an $FiO_2$ value suitable to the current $SpO_2$ and that automatically adjusts $FiO_2$ with an error clearance of +/- 0.25%.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.175-179
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• 2021

Enhancing the reliability of the system is important for recent system-on-chip (SoC) designs. This importance has led to studies on fault diagnosis and tolerance. Fault-injection (FI) techniques are widely used to measure the fault-tolerance capabilities of resilient systems. FI techniques suffer from limitations in relation to environmental conditions and system features. Moreover, a hardware-based FI can cause permanent damage to the target system, because the actual circuit cannot be restored. Accordingly, we propose a simulation-based FI framework based on the Verilog Procedural Interface for measuring the failure rates of SoCs caused by soft errors. We execute five benchmark programs using an ARM Cortex M0 processor and inject soft errors using the proposed framework. The experiment has a 95% confidence level with a ±2.53% error, and confirms the reliability and feasibility of using proposed framework for fault analysis in SoCs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.105-111
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• 2017

In this paper, the analysis results are shown about several weights of Weighted K-Nearest Neighbor method, Recently, it is employed for the indoor positioning technologies using WiFi fingerprint which has been actively studied. In spite of the simplest feature, the W-KNN method shows comparable performance to another methods using WiFi fingerprint technology. So W-KNN method has employed in the existing indoor positioning system. It shows positioning error performance according to data preprocessing and weight factor, and the analysis on the weight is very important. In this paper, based on the real measured WiFi fingerprint data, the estimation error is analyzed and the performances are compared, for the case of data processing methods, of the weight of average, variance, and distance, and of the averaging several position of number K. These results could be practically useful to construct the real indoor positioning system.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.323-334
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• 2014

In this paper, we proposed indoor positioning system with improved accuracy. The proposed indoor location measurement system is based pedestrian location measurement method that use the embedded sensor of smartphone. So, we do not need wireless external resources, such as GPS or WiFi signals. The conventional methods measure indoor location by generating a movement route of pedestrian by step and direction recognition. In this paper, to correct the direction sensor error, we use the common feature of the normal indoor floor map that the indoor path is lattice-structured. And we quantize moving directions depending on the direction of indoor path. In addition, we propose moving direction measuring method using geomagnetic sensor and gyro sensor to improve the accuracy. Also, the proposed step detection method uses angle and accelerometer sensors. The proposed step detection method is not affected by the posture of the smartphone. Direction errors caused by direction sensor error is corrected due to proposed moving direction measuring method. The proposed location error correction method corrects location error caused by step detection error without the need for external wireless signal resources.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.298-300
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• 2015

Beacon is a signal device, which mainly used to support the safety of the locate and operation of a watercraft and aircraft. Recently, the IT sector BLE (Bluetooth Low Energy) also made possible to operate with less energy over several months using a beacon standards are applied. In addition, the location-based services and technologies using BLE Beacon has attracted attention. However, there is the problem that by using only the position location Beacon devices when high error rate can be measured accurate position. Therefore, in this paper, combines WiFi and Beacon based on Smartphone. Also propose an indoor positioning algorithm reduces the error rate of the position location value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.