• ETRI Journal
• /
• 제39권6호
• /
• pp.841-850
• /
• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• Computers and Concrete
• /
• 제24권6호
• /
• pp.555-560
• /
• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• 한국IT서비스학회지
• /
• 제18권4호
• /
• pp.83-95
• /
• 2019

It is becoming more and more difficult to cope with new knowledge and technology required by society by the efforts of one person or organization according to the development of science and technology. As a method to overcome this, collaborative research is becoming important. This tendency is increasing in the government R&D projects as well, and the 'A' test research institute, which is the subject of this paper, is also increasing a collaborative research. The purpose of this study is to analyze the network characteristics among the participating researchers in the government R&D project conducted by the institution A, and to ascertain how the network characters of the researchers actually affect the financial performance of the team. The results of the analysis show that 'closeness centrality' and 'degree of centrality' contribute positively to the financial performance of the team. On the other hand, 'betweenness centrality' and 'eigenvector centrality' have a negative effect on the financial performance of the team because they are not directly related to financial performance.

• The Journal of the Acoustical Society of Korea
• /
• 제17권1E호
• /
• pp.54-65
• /
• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• 한국정보통신학회논문지
• /
• 제15권5호
• /
• pp.1198-1206
• /
• 2011

본 논문에서 개발한 Z-Wave Mesh Network 방식을 이용한 홈네트워크 통합 디밍스위치는 현재 홈 네트워크 기술이 적용된 아파트를 대상으로 설치가 되고 있는 네트워크 방식의 스위치로써, 조명스위치 사용에 대한 시공자 설비의 편의성, 사용자의 조작 편의성 제공과 동체감지센서 및 광세기 검출센서를 이용한 조명의 효율적 제어를 통한 전력절감 효과가 기대되는 유무선 네트워크가 가능한 지능형 통합디밍 조명스위치이다. 본 논문에서 개발한 통합 디밍스위치의 유용성을 평가하기 위하여 실험시스템을 구현하였고, 부하성능 평가, 조명제어, 통신속도 면에서 우수한 성능을 보였다.

• Imaging Science in Dentistry
• /
• 제52권2호
• /
• pp.219-224
• /
• 2022

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2008년도 춘계학술대회 논문집
• /
• pp.1887-1891
• /
• 2008

In this paper, we dealt with a partial discharge (PD) measurement method that has been accepted as an effective and non-destructive technique to estimate insulation performance of low-voltage induction motors. The PD measurement system consists of a coupling network, a low noise amplifier, and associated electronics. A shielded box was used to reduce environmental noise. Frequency characteristic of the coupling network was estimated by a sinusoidal signal input, and the low cut-off frequency of the coupling network was 1 MHz (-3 dB). Also, we carried out a calibration test for the PD measurement system. Sensitivity of the system was of 84 m$V_{max}$/pC between stator winding and enclosure. In application test on a low-voltage three phase induction motor (5 HP), we could detect 88 pC at AC 800 $V_{max}$.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
• /
• pp.3040-3042
• /
• 2005

During the last several years, interest in broad-band power line communications (PLC) has been grown over medium voltage(MV) power distribution lines as well as low voltage lines. This paper introduces a medium voltage PLC test field that is set up in the suburbs of Euiwang city in Korea. This test field could be used not only for the measurement of communication channel environment but also for internet service. This paper shows the configuration of medium voltage test field with network devices like MV signal coupler and the results of channel environment like noise and impulse response. It also shows the service performance of PLC access network through network management system.

• 한국공작기계학회논문집
• /
• 제15권3호
• /
• pp.39-44
• /
• 2006

In this study, we nodestructive test based on ultrasonic test as inspection method and compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as pattern recognition algorithm of welding flasw. For this purpose, variables are applied the same to two algorithms. Where, feature variables are zooming flaw signals of reflected whole signals from welding flaws in time domain. Through this process, we confirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• 한국산업정보학회논문지
• /
• 제22권5호
• /
• pp.17-24
• /
• 2017

최근 딥러닝, 즉 거대 또는 깊은 인공신경망을 사용한 기술이 놀라운 성능을 보이고 있고, 점차로 그 네트워크의 규모가 커지고 있다. 하지만, 신경망 크기의 증가는 계산양의 증가로 이어져서 회로의 복잡성, 가격, 발열, 실시간성 제약 등의 문제를 야기한다. 또한, 신경망 연결에는 많은 중복성이 존재한다, 본 연구에서는 이 중복성을 효과적으로 제거하여 이용하여 원 신경망의 성능과 원하는 범위안의 차이를 보이면서, 네트워크 연결의 수를 줄이는 방법을 제안하고 실험하였다. 특히, 재학습에 의하여 성능을 향상시키고, 각 계층별 차이를 고려하기 위하여 계층별 오류율을 할당하여 원하는 성능을 보장할 수 있는 간단한 방법을 제안하였다. 대표적인 영상인식 신경망구조인 FCN (전연결) 구조와 CNN (컨벌루션 신경망) 구조에서 대하여 실험한 결과 약 1/10 정도의 연결만으로도 원 신경망과 유사한 성능을 보일 수 있음을 확인하였다.