• 한국생산제조학회지
• /
• 제7권5호
• /
• pp.92-97
• /
• 1998

The morpholgies of the wear particles are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the machine driving system. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different patter characteristic and recognized the friction condition and materials very well by artificial neural network. We discussed how the network determines differencee in wear debris feature, and this approach can be applied to foreseeability and decisio for moving condition of the Machine driving system.

• Journal of Information Processing Systems
• /
• 제18권1호
• /
• pp.12-25
• /
• 2022

In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct highresolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.

• Journal of electromagnetic engineering and science
• /
• 제15권4호
• /
• pp.258-265
• /
• 2015

A de-embedding method for multiport networks, especially for coupled odd interconnection lines, is presented in this paper. This method does not require a conversion from S-parameters to T-parameters, which is widely used in the de-embedding technique of multiport networks based on cascaded simple two-port relations, whereas here, we apply an operation to the S-matrix to generate all the uncoupled and coupled coefficients. The derivation of the method is based on the relations of incident and reflected waves between the input of the entire network and the input of the intrinsic device under test (DUT). The characteristics of the intrinsic DUT are eventually achieved and expressed as a function of the S-parameters of the whole network, which are easily obtained. The derived coefficients constitute ABCD-parameters for a convenient implementation of the method into cascaded multiport networks. A validation was performed based on a spice-like circuit simulator, and this verified the proposed method for both uncoupled and coupled cases.

• 전자공학회논문지B
• /
• 제32B권3호
• /
• pp.130-138
• /
• 1995

In this paper, a Novel Approach is Proposed which Identifies linear system Parameters Using a multilayer feedforward neural network trained with backpropagation algorithm. The parameters of linear system can be represented by x9t)/x(t) and x(t)/u(t). Thud, its parameters can be represented in terms of the derivative of output with respect to input of parameters can be represented in terms of the derivative of output with respect to input of trained neural network which is a function of weights and output of neurons. Mathematical representation of the proposed approach is derived, and its validity is shown by simulation results on 2-layer and 3-layer neural network.

• 한국전자파학회논문지
• /
• 제25권8호
• /
• pp.808-820
• /
• 2014

본 논문에서는 10-dB 감쇠기 및 상용 패키지 된 MMIC 능동소자를 이용하여 구성된 증폭기, 2가지의 온-웨이퍼(on-wafer)형 DUT(Device-Under Test)를 구성하고, 이들의 잡음파라미터를 8-port 회로망을 이용하여 추출하는 방법을 제시하였다. 제작된 10-dB 감쇠기의 경우 수동소자이기 때문에, 이것의 S-파라미터를 측정하여 얻을 경우, 이것의 잡음파라미터를 알 수 있고, 또한 증폭기의 경우 이것의 잡음파라미터가 datasheet에 있다. 따라서 제안한 방법을 이용한 잡음파라미터 측정 결과에 대한 평가를 용이하게 할 수 있다. 기존 저자들에 의하여 발표된 6-포트회로망을 확장한 8-포트회로망을 이용한 잡음파라미터 측정은 사용된 8-포트회로망의 S-파라미터를 필요로 하는데, 동축형 DUT에 국한된다. 온-웨이퍼 프로브가 8-포트회로망에 삽입될 경우, 8-포트회로망의 S-파라미터 측정은 이종 형태의 커넥터를 갖는 8-포트회로망이 된다. 본 논문에서는 회로망 분석기(Network analyzer)의 Smart-cal 기능을 이용하여 8-포트회로망의 S-파라미터를 추출하였다. 측정된 잡음파라미터는 최소잡음지수, $NF_{min}$ 경우, 예상된 결과에 대하여 약 ${\pm}0.2dB$의 오차를 보인다. 다른 잡음파라미터는 주파수에 따라 예상된 결과와 근접하게 일치하는 결과를 보여주고 있다.

• Journal of Biosystems Engineering
• /
• 제37권4호
• /
• pp.271-278
• /
• 2012

Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.

• 정보처리학회논문지B
• /
• 제14B권5호
• /
• pp.377-382
• /
• 2007

본 논문에서는 방사 기저함수 네트워크의 파라미터를 전 영역에서 최적화하는 학습 알고리즘을 제안한다. 기존의 학습 알고리즘들은 지역 최적화만을 수행하기 때문에 성능의 한계가 있고 최종 결과가 초기 네트워크 파라미터 값에 크게 의존하는 단점이 있다. 본 논문에서 제안하는 하이브리드 모의 담금질 기법은 모의 담금질 기법의 전 영역 탐색 능력과 경사 기반 학습 알고리즘의 지역 최적화 능력을 조합하여 전 파라미터 영역에서 해를 찾을 수 있도록 한다. 제안하는 기법을 함수 근사화 문제에 적용하여 기존의 학습 알고리즘에 비해 더 좋은 학습 및 일반화 성능을 보이는 네트워크 파라미터를 찾을 수 있으며, 초기 파라미터 값의 영향을 크게 줄일 수 있음을 보인다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1998년도 하계종합학술대회논문집
• /
• pp.86-89
• /
• 1998

Multiple types of information in a multimedia service are delivered though multiple virtual connections on ATM network, while each virtual connection may be controlled independently. A multimedia service requires an associative relationship among multiple information streams to provide required harmonization. There may be required additional traffic descriptors to guarantee the required harmonization among multiple information streams in a multimedia service. For buffering of large bandwidth information stream(e.g., video), extremely large buffer size is necessary, but this approach should not be efficient way to compensate a severely delayed cells/blocks experienced at network. The best way to solve this problem will be minimization of relative-delayed-transfer of cells/blocks to application processes through ATM network control. To minimize a delayed transfer the mapping between relative delay parameter(i.e., associative Group QoS parameters) and per-VC traffic descriptor will be necessary. This paper is present additional functions and parameters to interpret the mapping between relative delay parameters(i.e., associative Group QoS parameters) and per-VC traffic descriptors in ATM API for multimedia application services.

• 한국정밀공학회지
• /
• 제15권5호
• /
• pp.43-49
• /
• 1998

Adaptive neural network approach is presented for determining Electrical Discharge Machining (EDM) parameters. Electrical Discharge Machining has been widely used with its capability of machining hard metals and tough shapes. In the past few years, EDM has been established in tool-room and large-scale production. However. in spite of it's wide application, an universal selection method of EDM parameters has not been established yet. No attempt has been tried before to suggest a logical method in determining essential machine parameters considering the machining rate and resulting surface roughness integrity. The paper presents a method, which is focusing on determining appropriate machining parameters. Depending on the electrode wear and surface roughness, an adaptive neural network is designed for providing suitable machining guideline.

• 소성∙가공
• /
• 제31권2호
• /
• pp.81-88
• /
• 2022

Numerical analysis and dynamic material properties are required to analyze the behavior of workpiece during an electrohydraulic forming (EHF) process. In this study, EHF experiments were conducted under three conditions (6, 7, 8 kV). Dynamic material properties of Al 5052-H34 were inversely estimated through an ANN (Artificial Neural Network) model constructed based on LS-Dyna analysis results. Parameters of Cowper-Symonds constitutive equation, C and p, were used to implement dynamic material properties. By comparing experimental results of three conditions with ANN model results, optimized parameters were obtained. To determine the reliability of the derived parameters, experimental results, LS-Dyna analysis results, and ANN results of three conditions were compared using MSE and SMAPE. Valid parameters were obtained because values of indicators were within confidence intervals.