• 전기산업
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• v.15 no.12
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• pp.57-68
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• 2004

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.175-182
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• 1995

Despite the simplicity of processing, a conventional autocorrelation function (ACF) method for the precise determination of fetal heart rate (FHR) has many problems. In case of weak or noise corrupted Doppler ultrasound signal, the ACF method is very sensitive to the threshold level and data window length. It is very troublesome to extract FHR when there is a data loss. To overcome these problems, the high resolution pitch detection algorithm was adopted to estimate the FHR. This method is more accurate, robust and reliable than the ACF method. With a lot of calculation, however, it is impossible to process real time FHR estimation. This paper is presented a new FHR estimation algorithm for real time processing and studied the real time FHR monitoring system by high resolution pitch detection algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.4
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• pp.167-174
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• 2002

This study describes a fetal heart rate(FHR) estimation algorithm using phonogram. Using a phonogram amplifier, various fetal heart sounds are collected in a university hospital. The FHR estimation algorithms consists of a lowpass filter, decimation, envelop detection, pitch detection, and post-processing. The post-processing is the FHR decision procedure using all informations of fetal heart rates. Using the algorithm and other parameters of fetal heart sound, a fetal monitoring software was developed. This can display the original signals, the FFT spectra, FHR and its trajectory. Even though the fetal phonogram amplifier detects the fetal heart sounds well, the sound quality is not so good as the ultrasonography. In case of very week fetal heart sound, autocorrelation of it showed clear periodicity. But two main peaks in one period is an obstacle in pitch detection and peaks are not so vivid. The proposed FHR estimation algorithm showed very accurate and stable results. Since the developed software displays multiple parameters in real time and has convenient functions, it will be useful for the phonogram-style fetal monitoring device.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.48-56
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• 1998

In this paper, a moving object detection and face region extraction algorithm which can be used in video monitoring systems is presented. The proposed algorithm is composed of two stages. In the first stage, each frame of an input video sequence is analyzed using three measures which are based on image pixel difference. If the current frame contains moving objects, their skin regions are extracted using color and frame difference information in the second stage. Since the proposed algorithm does not rely on computationally expensive features like optical flow, it is well suited for real-time applications. Experimental results tested on various sequences have shown the robustness of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.334-343
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• 2014

In this paper, we suggest a method to improve the fusion of an accelerometer and gyro sensor by using a Kalman filter to produce a more high-quality respiration signal to supplement the weakness of using a single accelerometer. To evaluate our proposed algorithm's performance, we developed a chest belt-type module. We performed experiments consisting of aerobic exercise and muscular exercises with 10 subjects. We compared the derived respiration signal from the accelerometer with that from our algorithm using the standard respiration signal from the piezoelectric sensor in the time and frequency domains during the aerobic and muscular exercises. We also analyzed the time delay to verify the synchronization between the output and standard signals. We confirmed that our algorithm improved the respiratory rate's detection accuracy by 4.6% and 9.54% for the treadmill and leg press, respectively, which are dynamic. We also confirmed a small time delay of about 0.638 s on average. We determined that real-time monitoring of the respiration signal is possible. In conclusion, our suggested algorithm can acquire a more high-quality respiration signal in a dynamic exercise environment away from a limited static environment to provide safer and more effective exercises and improve exercise sustainability.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.198-205
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• 2005

In this paper, we proposed a new algorithm for the separation of fetal ECG from single channel abdominal ECG. The algorithm consists of a stage of demixing vector calculation for initial signal and a stage of fetal beat detection for the rest of signal. The demixing vector was obtained by applying independent component analysis technique to projected signals into time-frequency domain. For the test of this algorithm, simulation signals, De Lathauwer's data and some measured data, which was acquired from 8 healthy volunteers whose pregnant periods ranged from 22 weeks to 35 weeks and whose ages from 27 to 37, were used. For each data, the accuracy of fetal beat detection was $100\%$ and with the location of fetal beats, fetal heart rate variability and morphology could be offered. In conclusion, this proposed algorithm showed the possibility of fetal beat separation with a single channel abdominal ECG and it might be adopted to a fetal health monitoring system, by which a single channel abdominal ECG is acquired.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.710-715
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• 2006

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, a design of pervasive monitoring and control system has been made possible. In this paper, we present a new algorithm for one of the most likely applications for sensor networks; tracking moving targets. The proposed algorithm uses a cooperations between the sensor nodes which detect moving objects. Therefore, the proposed scheme is robust against prediction failures which may result in temporary loss of the target. Using simulations we show that tile proposed moving object tracking algorithm is capable of accurately tracking targets with random movement patterns.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.62-65
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• 2019

Wireless power transfer systems require an algorithm to determine the presence of the target object for mitigating standby power and safety issues. Although many schemes that sense various external objects have been actively proposed for inductive power transfer systems, not many studies on capacitive power transfer systems have been conducted compared with those on inductive power transfer systems. This study proposes a target object detection algorithm by monitoring the capacitance in transmitter-side electrodes without additional pressure sensors or distance sensors. The proposed algorithm determines the presence of a target object by monitoring the change in capacitance in transmitter-side electrodes using the step pulse of the microcontroller unit. The algorithm is verified by two step processes. First, the performance in capacitance measurement is compared with that of an LCR meter. Then, the verification is conducted in a 5-W capacitive power transfer hardware. Experimental result shows that the interelectrode capacitance increases by 6 times when the target object is fully aligned. Thus, the proposed scheme can successfully detect the presence of the target object.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.5
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• pp.519-526
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• 2018

In this study, an algorithm to predict evacuation routes in support of shipboard lifesaving activities is presented. As the first step of algorithm development, the feasibility and necessity of an evacuation route prediction algorithm are shown numerically. The proposed algorithm can be explained in brief as follows. This system continuously obtains and analyzes passenger movement data from the ship's monitoring system during non-disaster conditions. In case of a disaster, evacuation route prediction information is derived using the previously acquired data and a prediction tool, with the results provided to rescuers to minimize casualties. In this study, evacuation-related data obtained through fire evacuation trials was filtered and analyzed using a statistical method. In a simulation using the conventional evacuation prediction tool, it was found that reliable prediction results were obtained only in the SN1 trial because of the conceptual and structural nature of the tool itself. In order to verify the validity of the algorithm proposed in this study, an industrial engineering tool was adapted for evacuation characteristics prediction. When the proposed algorithm was implemented, the predicted values for average evacuation time and route were very similar to the measured values with error ranges of 0.6-6.9 % and 0.6-3.6 %, respectively. In the future, development of a high-performance evacuation route prediction algorithm is planned based on shipboard data monitoring and analysis.