• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.2
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• pp.118-125
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• 2017

In this paper, we propose a smart transformer for a smart transformer miniature model, which can replace a 60 [Hz] single-phase transformer installed in an electric vehicle. The proposed smart transformer is lighter than a conventional transformer, can control instantaneous voltage, and can be expected to improve power quality through harmonic compensation. The proposed intelligent transformer consists of an incoming part, an AC/DC converter, and a dual active bridge. Only the incoming part and the AC/DC converter are described in this paper. The proposed intelligent transformer has 75 kVA 3.3 kV input and 750 V DC output, which are verified by simulation and experiment.

• Journal of the Optical Society of Korea
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• v.9 no.3
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• pp.111-118
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• 2005

In this paper, a new optical parallel binary arithmetic processor (OPBAP) capable of computing arbitrary n-bit look-ahead carry full-addition is proposed and implemented. The conventional Boolean algebra is considered to implement OPBAP by using two schemes of optical logic processor. One is space-variant optical logic gate processor (SVOLGP), the other is shadow-casting optical logic array processor (SCOLAP). SVOLGP can process logical AND and OR operations different in space simultaneously by using free-space interconnection logic filters, while SCOLAP can perform any possible 16 Boolean logic function by using spatial instruction-control filter. A dual-rail encoding method is adopted because the complement of an input is needed in arithmetic process. Experiment on OPBAP for an 8-bit look-ahead carry full addition is performed. The experimental results have shown that the proposed OPBAP has a capability of optical look-ahead carry full-addition with high computing speed regardless of the data length.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.543-546
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• 2006

최근 여러 연구에서 다양한 종류의 멀티 모드 하이브리드 동력 전달계가 제안되고 있다. 멀티모드 동력전달계는 두 개 이상의 다른 유성기어식 하이브리드 시스템으로 이루어져 있으며 클러치를 사용하여 각 상황에 유리한 유성기어 시스템을 사용하여 주행한다. 각 유성기어 시스템의 단점들을 보완할 수 있기 때문에 단일 모드를 사용하여 주행했을 때보다 여러 면에서 높은 성능을 보인다. 일반적으로 유성기어식 하이브리드 시스템은 크게 입력, 출력, 복합 분기식의 세 가지 종류로 나눌 수 있는데 이 논문에서는 입력 및 복합 분기식 구조의 특징을 분석해 보았다. 또한 시뮬레이션을 통해 입력, 복합 분기식 구조를 사용하여 듀얼 모드를 구성하였을 때 단일 모드와 비교하여 어느정도의 성능을 보이는지 알아보았다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.97-104
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• 2008

Most of engine control systems for helicopter turboshaft engines are equipped with dual sensors. For the system with dual redundancy, analytic methods are used to detect faults based on the system dynamical model. Helicopter engine dynamics are affected by aerodynamic torque induced from the dynamics of the main rotor. In this paper an engine model including the rotor dynamics is constructed for the T700-GE-700 turboshaft engine powering UH-60 helicopter. The singular value decomposition(SVD) method is applied to the developed model in order to detect sensor faults. The SVD method which do not need an additional computation to generate residual uses the characteristics that the system outputs in direction of the left singular vector if an input is applied in direction of the right singular vector. Simulations show that the SVD method works well in detecting and isolating the sensor faults.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.423-430
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• 2021

Aiming Graph convolutional networks (GCNs) have achieved outstanding performances on skeleton-based action recognition. However, several problems remain in existing GCN-based methods, and the problem of low recognition rate caused by single input data information has not been effectively solved. In this article, we propose a Dual-stream fusion method that combines video data and skeleton data. The two networks respectively identify skeleton data and video data and fuse the probabilities of the two outputs to achieve the effect of information fusion. Experiments on two large dataset, Kinetics and NTU-RGBC+D Human Action Dataset, illustrate that our proposed method achieves state-of-the-art. Compared with the traditional method, the recognition accuracy is improved better.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1170-1178
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• 2021

This paper proposed a versatile algorithm based on a dual-tree complex wavelet transform for intensifying the visual aspect of medical images. First, the decomposition of the input image into a high sub-band and low-sub-band image is done. Further, to improve the resolution of the resulting image, the high sub-band image is interpolated using Lanczos interpolation. Also, contrast enhancement is performed by singular value decomposition (SVD). Finally, the image reconstruction is achieved by using an inverse wavelet transform. Then, the Gaussian filter will improve the visual quality of the image. We have collected images from the hospital and the internet for quantitative and qualitative analysis. These images act as a reference image for comparing the effectiveness of the proposed algorithm with the existing state-of-the-art. We have divided the proposed algorithm into several stages: preprocessing, contrast enhancement, resolution enhancement, and visual quality enhancement. Both analyses show the proposed algorithm's effectiveness compared to existing methods.

• Journal of Semiconductor Engineering
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• v.3 no.1
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• pp.154-160
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• 2022

In this paper, we design a DC-DC boost converter with RF noise immunity to supply a stable positive output voltage for OLED displays. For RF noise immunity, an input voltage variation reduction circuit (IVVRC) is adopted to ensure display quality by reducing the undershoot and overshoot of output voltage. The boost converter for a positive voltage Vpos operates in the SPWM-PWM dual mode and has a dead-time controller using a dead-time detector, resulting in increased power efficiency. A chip was fabricated using a 0.18 um BCDMOS process. Measurement results show that power efficiency is 30% ~ 76% for load current range from 1 mA to 100 mA. The boost converter with the IVVRC has an overshoot of 6 mV and undershoot of 4 mV compared to a boost converter without that circuit with 18 mV and 20 mV, respectively.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.480-488
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• 2022

Images of tobacco retail licenses have complex unstructured characteristics, which is an urgent technical problem in the robot process automation of tobacco marketing. In this paper, a novel recognition approach using a double attention mechanism is presented to realize the automatic recognition and information extraction from such images. First, we utilized a DenseNet network to extract the license information from the input tobacco retail license data. Second, bi-directional long short-term memory was used for coding and decoding using a continuous decoder integrating dual attention to realize the recognition and information extraction of tobacco retail license images without segmentation. Finally, several performance experiments were conducted using a largescale dataset of tobacco retail licenses. The experimental results show that the proposed approach achieves a correction accuracy of 98.36% on the ZY-LQ dataset, outperforming most existing methods.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.196-200
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• 1987

The following methods are proposed for implementing digital dual tone multi-frequency (DTMF) receiver: using infinite impulse response(IIR) digital filters, period-counting algorithm, discrete Fourier transform(DFT), and fast Fourier transform(FFT)[2]. The PARCOR(Partical Correlation) analysis method which has been widly used in the speech signal processing area is applied to the dual tone multi-frequency(DTMF) signal detection. This method is easy to implement digitally and stronger to digit simulation of speech than any other methods proposed up to date. Since sampling rate of 4KHz is used in the DTMF receiver for the detection of input DTMF signal originally sampled at 8KHz, it effects two times higher multiplexing efficiency.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.