• 한국전자통신학회논문지
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• 제13권4호
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• pp.897-902
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• 2018

본 연구는 D-F-M(Diagnose Fure Funktionelle Medizine) 이론을 기반으로 하여 신체 7부위에 13Hz의 양과 음의 임펄스전압을 인가함으로서 생성된 출력 파형을 이용하여 환자의 건강상태를 진단하는 생체신호 측정기의 개발에 관한 것이다. 측정기에서 취득한 데이터는 인공지능을 기반으로 한 귀납적 추론을 통하여 간엽조직 뿐만 아니라 신체기관의 상태를 진단하는 기기로서의 역할을 수행할 것으로 기대된다. 본 논문에서는 생체신호를 취득하고 관리하는 시스템 하드웨어에 주제를 한정할 것이다.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1146-1155
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• 2017

Normally, the artificial intelligence algorithms are widely applied to the optimal controller design. Then, it is expected that the best output performance is achieved. Unfortunately, when resulting controller parameters are implemented by using the practical devices, the output performance cannot be the best as expected. Therefore, the paper presents the optimal controller design using the combination between the state-space averaging model and the adaptive Tabu search algorithm with the new criteria as two penalty conditions to handle the mentioned problem. The buck-boost converter regulated by the cascade PI controllers is used as the example power system. The results show that the output performance is better than those from the conventional design method for both input and load variations. Moreover, it is confirmed that the reported controllers can be implemented using the realistic devices without the limitation and the stable operation is also guaranteed. The results are also validated by the simulation using the topology model of MATLAB and also experimentally verified by the testing rig.

• Nuclear Engineering and Technology
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• 제49권3호
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• pp.495-503
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• 2017

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

• 대한임베디드공학회논문지
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• 제14권1호
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• pp.1-9
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• 2019

In this paper, we propose a method of generating a 2D gray image from LiDAR 3D reflection intensity. The proposed method uses the Fully Convolutional Network (FCN) to generate the gray image from 2D reflection intensity which is projected from LiDAR 3D intensity. Both encoder and decoder of FCN are configured with several convolution blocks in the symmetric fashion. Each convolution block consists of a convolution layer with $3{\times}3$ filter, batch normalization layer and activation function. The performance of the proposed method architecture is empirically evaluated by varying depths of convolution blocks. The well-known KITTI data set for various scenarios is used for training and performance evaluation. The simulation results show that the proposed method produces the improvements of 8.56 dB in peak signal-to-noise ratio and 0.33 in structural similarity index measure compared with conventional interpolation methods such as inverse distance weighted and nearest neighbor. The proposed method can be possibly used as an assistance tool in the night-time driving system for autonomous vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1929-1943
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• 2021

Millimeter wave (mmWave) communication based on the wide bandwidth of >28 GHz is one of the key technologies for cellular-connected unmanned aerial vehicles (UAVs). The selection of mmWave beams in such cellular-connected UAVs is challenging and critical, especially when downlink transmissions toward aerial user equipment (UE) suffer from poor signal-to-interference-plus-noise ratio (SINR) more often than their terrestrial counterparts. This study proposed a coordinated mmWave beam selection scheme using fingerprint for cellular-connected UAV. The scheme comprises fingerprint database configuration and coordinated beam selection. In the fingerprint database configuration, the best beam index from the serving cell and interference beam indexes from neighboring cells are stored. In the coordinated beam selection, the best and interference beams are determined using the fingerprint database information instead of performing an exhaustive search, and the coordinated beam transmission improves the SINR for aerial UEs. System-level simulations assess the UAV effect based on the third-generation partnership project-new radio mmWave and UAV channel models. Simulation results show that the proposed scheme can reduce the overhead of exhaustive search and improve the SINR and spectral efficiency.

• 한국항해항만학회지
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• 제47권2호
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• pp.85-92
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• 2023

The hardware of an LED lighting control system for coastal lighting at coastal pier entrance consists of a power supply unit, an AVR control unit, a CLCD output unit, an LED control unit, a scenario selection switch unit, and an operation speed display unit. It is made of an 8-channel. The CPU used ATmega128 and the FET was used to control the current signal. To operate the CPU, DC 12V was converted to DC 5V using a regulator 7805. A heat sink was used to remove heat generated in the FET. By connecting the load LED module to the manufactured 8-channel LED lighting control system, the operation was confirmed through various production scenarios. In addition, a control system was designed to show the most suitable color for the atmosphere of the coastal pier according to the input value of temperature and illumination using a fuzzy control system. Computer simulation was then conducted. Results confirmed that fuzzy control did not need to store many data inputs due to characteristics of artificial intelligence and that it could efficiently represent many output values with simple fuzzy rules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.280-294
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• 2023

Unmanned aerial vehicles (UAVs) and millimeter-wave frequencies play key roles in supporting 5G wireless communication systems. They expand the field of wireless communication by increasing the data capacities of communication systems and supporting high data rates. However, short wavelengths, owing to the high millimeter-wave frequencies can cause problems, such as signal attenuation and path loss. To address these limitations, research on high directional beamforming technologies continue to garner interest. Furthermore, owing to the mobility of the UAVs, it is essential to track the beam angle accurately to obtain full beamforming gain. This study presents a beam tracking method based on the unscented Kalman filter using hybrid beamforming. The simulation results reveal that the proposed beam tracking scheme improves the overall performance in terms of the mean-squared error and spectral efficiency. In addition, by expanding analog beamforming to hybrid beamforming, the proposed algorithm can be used even in multi-user and multi-stream environments to increase data capacity, thereby increasing utilization in new-radio multiple-input multiple-output orthogonal frequency-division multiplexing systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.521-523
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• 2021

자동차의 발명과 차량용 도로의 건설이 시작되면서 교통사고의 발생이 늘어나기 시작하였다. 이에 도로 건설 방법의 변경 및 신호등의 신호체계들을 이용하여 교통사고를 방지하기 위한 노력이 있었으나, 현재까지도 기상악화로 인한 도로의 결빙이 원인이 된 교통사고로 매년 수많은 인명과 재산피해가 발생하고 있었다. 본 논문에서는 도로의 결빙으로 인한 교통사고 경감을 위해서 적외선 카메라를 사용하여 얻은 적외선 파장 데이터를 딥러닝 학습을 시도하여 얻어낸 결빙감지 정보를 차량의 내비게이션으로 전달하는 방법을 제안한다.

• ETRI Journal
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• 제46권1호
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• pp.22-34
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• 2024

Exposure to varied noisy environments impairs the recognition performance of artificial intelligence-based speech recognition technologies. Degraded-performance services can be utilized as limited systems that assure good performance in certain environments, but impair the general quality of speech recognition services. This study introduces an audiovisual speech recognition (AVSR) model robust to various noise settings, mimicking human dialogue recognition elements. The model converts word embeddings and log-Mel spectrograms into feature vectors for audio recognition. A dense spatial-temporal convolutional neural network model extracts features from log-Mel spectrograms, transformed for visual-based recognition. This approach exhibits improved aural and visual recognition capabilities. We assess the signal-to-noise ratio in nine synthesized noise environments, with the proposed model exhibiting lower average error rates. The error rate for the AVSR model using a three-feature multi-fusion method is 1.711%, compared to the general 3.939% rate. This model is applicable in noise-affected environments owing to its enhanced stability and recognition rate.