• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.467-472
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• 2008

In this paper, we analyzed the error rate Performance of convolution coded 16 QAM signal with Optimum Threshold Detection with selective combining diversity in Rician Fading Environments. The performance of 16-QAM signal with CTD (conventional threshold detection) which employs convolution coding technique was analyzed and the performance improvement of convolution coded 16-QAM signal with OTD (optimum threshold detection) which is varied according to fading parameter "K" and AWGN in Rician Fading channel was simulated. As a result of analysis, it was shown the effect of performance improvement to overcome the environment of mobile radio data communication channel.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.33-40
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• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.380-388
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• 2004

This study proposed an improved spike detection algorithm which automatically detects suitable spike threshold on the amplitude of surface electromyography(SEMG) signal during isometric contraction. The EMG data from the low back muscles was obtained in six channels and the proposed signal processing algorithm is compared with the median frequency and Gabriel's spike parameter. As a result, the reliability of spike parameter was inferior to the median frequency. This fact indicates that a spike parameter is inadequate for analysis of multi-channel EMG signal. Because of uncertainty of fixed spike threshold, the improved spike detection algorithm was proposed. It automatically detects suitable spike threshold depending on the amplitude of the EMG signal, and the proposed algorithm was able to detect optimal threshold based on mCFAR(modified Constant False Alarm Rate) in the every EMG channel. In conclusion, from the reliability points of view, neither median frequency nor existing spike detection algorithm was superior to the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.402-403
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• 2014

Micro-magnetic detection system is used to detect small particles in an automatic transmission valve body, which signal noise and time-delay may occurs in process of signal transmitting and filtering. In this paper, we present the design and implement of a micro-magnetic detection system based on wireless sensor networks. Micro-magnetic detection system consists of five modules which are magnetic sensor detector, signal processing unit, wireless sensor networks, system control unit and system monitoring unit. The experimental results show that signal noise and time-delay decreased.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.119-122
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• 2016

Feature detection is recognized as an accurate spectrum sensing approach when the information of the desired signal is partly known at the receiver. This type of detection was proposed to overcome large noise environment. Cyclostationary detection is an example of feature detection in spectrum sensing technique in cognitive radio. However, the cyclostationary process calculation requires a lot of processing time and information about the designed signals. On the other hand, energy detection spectrum sensing is widely known as a simple and compact spectrum sensing technique. However, energy detection is highly affected by large noise and lead to high detection error probability. In this paper, the combination of energy detection and cyclostationary is proposed in order to increase the accuracy and decrease the calculation and processing time. The two-layer threshold is utilized in order to reduce the complexity of computation and processing time in cyclostationary which can lead to the improved throughput of the system. The simulation result shows that the implementation of energy-based cyclostationary detector can help to improve the performance of the system while it can considerably reduce the required time for signal detection.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1382-1383
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• 2008

In this paper, we have studied for analysis of the partial discharge(PD) signal based on fuzzy algorism. Partial discharge signal detector is difficult because of partial discharge signal is very non-linear. Also, it is very difficult work that separate partial discharge signal from noise. We constructed partial discharge accumulation detection system that use Labview for detection of non-linear partial discharge signal. And analyzed Partial discharge signal that is detected by Labview system utilizing Fuzzy model.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.53-59
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• 2011

This paper proposes a new method of traffic signal detection and recognition in an RGB color model. The proposed method firstly processes RGB-filtering in order to detect traffic signal candidates. Secondly, it performs adaptive threshold processing and then analyzes connected components of the binary image. The connected component of a traffic signal has to be satisfied with both a bounding box rate and an area rate that are defined in this paper. The traffic signal recognition system is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithms are quite successful.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.48-51
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• 2024

Recently, high-performance BLDC(Brushless DC) motors are being applied to various fields such as industrial and personal mobility devices and drones. To achieve the best performance of BLDC, sensors such as hall sensors, encoders, and resolvers are used to determine the position of the rotor, and various speed control technologies are being developed. However, due to problems with high-speed control due to external environmental factors and frequency bandwidth of semiconductor sensing devices, research on BLDC motors without semiconductor sensing devices is in progress. Therefore, in this study, a signal brush was added to the end of the rotor of a BLDC motor and the rotor position of the BLDC motor was detected by analyzing the signal output through the signal brush.