• Journal of IKEEE
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• v.25 no.4
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• pp.650-663
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• 2021

Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.5
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• pp.48-55
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• 2002

An Internet-based software called the Repair Advisory Service (RAS) was developed to assist aircraft composite repair. The RAS takes advantage of the web user interface and provides estimation of the failure loads of repaired composite laminates and Structural Repair Manual (SRM) with search capability. In this paper, a failure model of lap repair is discussed as an example of the modules in the RAS. The model takes into account anisotropy of each ply in the laminate and in the repair ply, and non-elastic behavior of the interlayer between the laminate and the repair patch. Failure loads calculated by the model were compared with test data, and a good agreement was found between the results of the model and the test.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2007.02a
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• pp.13-17
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• 2007

스케일러블 비디오 코딩(SVC, Scalable Video Coding)은 MPEG(Moving Picture Expert Group)과 VCEG (Video Coding Expert Group)의 JVT(Joint VIdeo Team)에 의해 현재 표준화 되고 있는 새로운 압축 표준 기술이며 시간, 공간 및 화질의 스케일러빌리티를 지원하기 위해 계층 구조를 가지고 있다. 공간적 스케일러빌리티를 위해 기본 계층으로부터 텍스처, 움직임 그리고 잔차신호 정보를 예측하여 사용한다. 그러나 고효율의 압축효과를 얻기 위해 기존의 방식에서는 기본계층에서 얻은 세가지 정보이외에 현재 향상 계층에서 자체적으로 얻은 부호화 정보를 비교하여 최소의 RD(Rate Distortion) 비용을 가지는 정보를 이용하여 부호화 하도록 되어 있다. 하지만 이러한 방식은 향상 계층에서 인터 모드 결정 시 $16\times16,\;16\times8,\;8\times16,\;8\times8,\;4\times4,\;4\times8,\;4\times4$ 블록 모드에 대한 움직임 벡터 예측 및 보상 과정을 거쳐야 하기 때문에 향상 계층에서의 부호화 복잡도는 기본 계층에 비해 상당히 증가하게 된다. 본 논문에서는 기본계층에서 예측한 움직임 벡터 정보를 이용하여 항상 계층에서 모드 결정을 고속화하는 방법에 대해 소개한다. 제안된 방법은 기본 계층에서 예측한 블록모드 중에서 큰 블록인 $16\times16$ 블록에서 움직임 벡터가 (0, 0) 일 경우에 대하여 향상 계층에서는 $16\times16$매크로 블록에 대해서만 움직임 예측 및 보상을 수행함으로써 향상 계층에서 움직임 모드 결정을 조기에 완료하게 된다. 이것은 하위 공간 계층에서 예측한 움직임 벡터 정보가 아주 작을 때는 큰 블록 크기로 모드로 결정되는 일반적인 원리를 이용한 것이고 이 제안 방법을 이용하였을 경우 향상계층에의 모드 결정과정을 고속화함으로써 전체 스케일러빌 비디오 부호하기의 연산량 및 복잡도를 최대 70%까지 감소 시켰다. 그러나 연산량 감소에 따른 비트율의 증가와 화질 열화는 각각 최대 1.32%와 최대 0.11dB로 무시할 수 있을 정도로 작음을 확인 하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.111-116
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• 2022

The causes of motor burnout include overload, phase loss, restraint, interlayer short circuit, winding ground fault, instantaneous overvoltage, and the rotor contacting the stator, leading to insulation breakdown, leading to breakdown or electrical accidents. Therefore, equipment failure causes not only loss due to cost required for equipment maintenance/repair, but also huge economic loss due to productivity decrease due to process stop because the process itself including the motor is stopped. The current level of technology for diagnosing motor failures uses vibration, heat, and power analysis methods, but there is a limit to analyzing the problems only after a considerable amount of time has passed according to the failure. Therefore, in this paper, a device and algorithm for measuring insulation resistance using DC AMP signal was applied to an industrial motor to solve this problem. And by following the insulation resistance state value, we propose a diagnosis of deterioration and failure of the motor that cannot be solved by the existing method.