• Journal of Advanced Marine Engineering and Technology
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• v.34 no.2
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• pp.332-338
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• 2010

Annually, electrical fires caused by arcing phenomena in power system rapidly increase as the use of more electric appliances, but there is no established method for the prevention of the accidents. With this background, this paper dealt with the experimental results on a series arc detection technique and a device for air conditioners. Series arcing phenomena that is generated in incomplete connection of air conditioners was simulated, and the frequency spectrum was analyzed. The Fast Fourier Transform (FFT) of the arc pulse showed that the dominant frequency components exist in ranges of 190 kHz~250 kHz and 900 kHz~1.6 MHz. An arc detection circuit with low cut off frequency of 170 kHz to attenuate 60 Hz by 170 dB and a signal discriminator were designed. Also, an algorithm which separate series arc signal from unwanted noises produced by switching operation, inverter, and surge was proposed. Application experiment was carried out on several types of air-conditioners by using the arc generator specified in UL1699, and the results showed the over 99 % accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.499-504
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• 2002

In this paper, we analyzed the performance of FH/CPFSK system with differential detection in thermal noise, partial-band jamming noise and adjacent interference of all eight bit pattern. The parameters to analize performances of FH/CPFSK system have been used the bit rate, modulation index and performances of FH/CPFSK system with the differential detector have been presented with the optimum correlation function. And, we were compared with performance of FH/CPFSK and FH/BFSK system. In result, we could know that bit error probability of the approximation equation and exact equation nearly accorded in the high signal-to-noise ratio. And, we have been proved that FH/CPFSK system with differential detection according to jamming fraction ${\gamma}$ was worst to 3dB than FH/CPFSK system with limiter-discriminator. but was superior to 2dB than FH/BFSK.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3104-3109
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• 2009

The technology of direction finding is very important to find the direction of emitters for ELINT(electronic intelligence) system. The principle of TDOA(time difference of arrival) is to receive an emitter signal with two antennas, measure the time difference between two antennas, and converse the time difference to direction difference. This technology can be used in broadband frequency system and make the system very simple because a phase-discriminator and a voltage comparator are not needed. For fine DF accuracy, high time resolution receiver and long basis line antennas are needed. The DF accuracy of noise added signals is simulated with different time

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.68-73
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• 2013

In this paper, we proposed a readout circuit of pulse waveform and rate for a U-health system to monitor health condition. For long-time operation without replacing or charging a battery, either pulse waveform or pulse rate is selected as the output data of the proposed readout circuit according to health condition of a user. The proposed readout circuit consists of a simple digital logic discriminator and a dual-mode ADC which operates in the ADC mode or in the count mode. Firstly, the readout circuit counts pulse rate for 4 seconds in the count mode using the dual-mode ADC. Health condition is examined after the counted pulse rate is accumulated for 1 minute in the discriminator. If the pulse rate is out of the preset normal range, the dual-mode ADC operates in the ADC mode where pulse waveform is converted into 10-bit digital data with the sampling frequency of 1 kHz. These data are stored in a buffer and transmitted by 620 kbps to an external monitor through a RF transmitter. The data transmission period of the RF transmitter depends on the operation mode. It is generally 1 minute in the normal situation or 1 ms in the emergency situation. The proposed readout circuit was designed with $0.11{\mu}m$ process technology. The chip area is $460{\times}800{\mu}m^2$. According to measurement, the power consumption is $161.8{\mu}W$ in the count mode and $507.3{\mu}W$ in the ADC mode with the operating voltage of 1 V.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.2 s.105
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• pp.171-177
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• 2006

A low-cost, high-performance 40 Gb/s clock recovery module using a phase-locked loop(PLL) for a 40 Gb/s optical receiver with the clock-hold function has been designed and implemented. It consists of a clock extractor circuit, an RF mixer and a frequency discriminator for phase/frequency detection, a VC-DRO, a phase shifter, and a clock-hold circuit. The extracted 40 GHz clock is synchronized with a stable 10 GHz VC-DRO. The clock stability and jitter characteristics of the implemented PLL-based clock recovery module are significantly improved as compared with those of the conventional open-loop type clock recovery module with a DR filter. The measured peak-to-peak RMS jitter is about 230 fs. When an input signal is dropped, the 40 GHz clock is maintained continuously by the hold circuit.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.15-22
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• 2006

This paper presents a novel signal tracking algorithm for GNSS receivers using a MLE technique. In order to perform a robust signal tracking in severe signal environments, e.g., high dynamics for navigation vehicles or weak signals for indoor positioning, the MLE based signal tracking approach is adopted in the paper. With assuming white Gaussian additive noise, the cost function of MLE is expanded to the cost function of NLSE. Efficient and practical approach for Doppler frequency tracking by the MLE is derived based on the assumption of code-free signals, i.e., the cost function of the MLE for carrier Doppler tracking is used to derive a discriminator function to create error signals from incoming and reference signals. The use of the MLE method for carrier tracking makes it possible to generalize the MLE equation for arbitrary codes and modulation schemes. This is ideally suited for various GNSS signals with same structure of tracking module. This paper proposes two different types of MLE based tracking method, i.e., an iterative batch processing method and a non-iterative feed-forward processing method. The first method is derived without any limitation on time consumption, while the second method is proposed for a time limited case by using a 1st derivative of cost function, which is proportional to error signal from discriminators of conventional tracking methods. The second method can be implemented by a block diagram approach for tracking carrier phase, Doppler frequency and code phase with assuming no correlation of signal parameters. Finally, a state space form of FLL/PLL/DLL is adopted to the designed MLE based tracking algorithm for reducing noise on the estimated signal parameters.

• Korean Journal of Biological Psychiatry
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• v.12 no.2
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• pp.165-172
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• 2005

Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.21-29
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• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.