• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.106-113
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• 2019

In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.125-129
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• 2019

This paper describes the jamming effect of phase-coded pulse compression(PCPC) radar. Barker code radar, a typical PCPC radar, separates transmission pulses into 13 or 31 small pulses and phase modulates and transmits each pulse signal to increase radar detection efficiency and reduce the influence of jamming. Generally, when the radar is subjected to jamming, the detection distance becomes shorter and the detection error rate becomes higher. In the case of noise jamming or carrier jamming on the PCPC radar, the jamming effect is very small for no phase-coded convergence. However, the jamming effect is large in the case of synchronous jamming using the pulse-coded signal as a jamming signal with DRFM. It can be seen that the jamming effect increases when the storage time of the pulse-coded signal is prolonged. This study is considered to be useful for PCPC radar and EW jamming system design.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.65-72
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• 2021

We consider the joint optimization problem of transmit power level, MMSE receiver filter and access point(AP) selection for multi access points environment. In the previous work, transmit power and MMSE receiver filter were jointly optimized[1] and transmit power and best access point were optimized jointly[2]. For each case, the algorithm was proposed and its convergence which guarantees the minimum total transmit power was proved. In this paper, we further improve the algorithm by jointly optimizing three parameters. More specifically, 1) we propose the algorithm by considering transmit power, MMSE receiver filter and access point selection jointly. 2) we prove that the proposed algorithm guarantees convergence with minimum transmit power consumption. In the simulation results, it is shown that proposed algorithm outperforms two other algorithms, i.e., 1) algorithm with transmit power and MMSE receiver filter, and 2) algorithm with transmit power and best access point selection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1364-1370
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• 2019

Tilt sensors are mainly used to measure the collapse of structures such as buildings, bridges and tunnels. Recently, due to the ease of use and low price, many tilt sensors using MEMS sensors have been used, but the measurement angle range is limited, and thus, they do not have high precision for 360 degree. This is due to the inherent offset and scale errors of MEMS sensors. In this paper, we proposed an algorithm for the calculation of precision angles to reduce the mechanical error of MEMS sensors, and produced a MEMS sensor module and a transmission module to compare the angle accuracy of sensor modules before calibration and the angle measurement accuracy after calibration. Experimental results show that the proposed technique has a precision of ± 0.015 degrees for all 360-degree.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.63-68
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• 2021

Recently there is sharply increasing interest in watching and creating video contents such as YouTube. However, creating such video contents without privacy protection technique can expose other people in the background in public, which is consequently violating their privacy rights. This paper seeks to remedy these problems and proposes a technique that identifies faces and protecting portrait rights by blurring the face. The key contribution of this paper lies on our deep-learning technique with low detection error and high computation that allow to protect portrait rights in real-time videos. To reduce errors, an efficient tracking algorithm was used in this system with face detection and face recognition algorithm. This paper compares the performance of the proposed system with and without the tracking algorithm. We believe this system can be used wherever the video is used.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1391-1396
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• 2019

With the recent increase in the importance of software, software education is required in elementary schools in Korea. At this point, coding education combining play and education using smart toys is widely used. One of the most widely used smart toys is a block type smart toy. In this study, usability evaluation was performed to improve user's usability for block type smart toy with smartphone application. In order to achieve the purpose of this study, a heuristic technique with experts, one of various usability evaluation techniques, was performed. As a result of the analysis, it was found that the buttons and the switch of the menu were not well distinguished, and the cause of the error was not provided to the user. The results of this study can be the basis for improving the usability of applications for smart toy in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.102-107
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• 2021

This paper discusses the containment control problem for multi-agent systems with input saturations. The goal of the containment control is to obtain swarming behavior by driving follower agents into the convex hull which is spanned by multiple leader agents. This paper considers multiple leader agents moving at the same constant speed. Then, to solve the containment problem for moving leaders, we propose a PI-based distributed control algorithm. We next analyze the convergence of follower agents to the desired positions. Specifically, we apply the integral-type Lyapunov function to take into account the saturation nonlinearity. Then, based on Lasalle's Invariance Principle, we show that the asymptotic convergence of error states to zero for any positive constant gains. Finally, numerical examples with the static and moving leaders are provided to validate the theoretical results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1280-1287
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• 2020

This paper proposes a vision-based food recognition method for automated production of custom cakes. A small camera module mounted on a food art printer recognizes objects' shape and estimates their center points through image processing. Through the perspective transformation, the top-view image is obtained from the original image taken at an oblique position. The line and circular hough transformations are applied to recognize square and circular shapes respectively. In addition, the center of gravity of each figure are accurately detected in units of pixels. The test results show that the shape recognition rate is more than 98.75% under 180 ~ 250 lux of light and the positioning error rate is less than 0.87% under 50 ~ 120 lux. These values sufficiently meet the needs of the corresponding market. In addition, the processing delay is also less than 0.5 seconds per frame, so the proposed algorithm is suitable for commercial purpose.