• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.160.2-160
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• 2001

This paper presents an individual cylinder spark advance control strategy based upon the location of peak pressure (LPP) in spark ignition engines using artificial neural networks. The LPP is estimated using a feedforward multi-layer perceptron network (MLPN), which needs only five samples of output voltage from the cylinder pressure sensor. The cyclic variation of LPP restricts the gain of the feedback controller, and results in poor regulation performance during the transient operation of the engine. The transient performance of the spark advance controller is improved by adding a feedforward controller which reflects the abrupt changes of the engine operating conditions such as engine speed and manifold absolute pressure (MAP)...

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1704-1709
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• 2000

This paper proposes a spherical probe allowing acoustic intensity measurements in three dimensions to be made, which creates a diffracted field that is well-defined, thanks to analytic solution of diffraction phenomena. Six microphones are distributed on the surface of the sphere along three rectangular axes. Its measurement technique is not based on finite difference approximation, as is the case for the ID probe but on the analytic solution of diffraction phenomena. In fact, the success of sound source identification depends on the inverse models used to estimate inverse diffraction phenomena, which has non-linear properties. In this paper, we introduce the concept of nonlinear inverse diffraction modeling using a neural network and the idea of 3 dimensional sound source identification with several tests.

• IE interfaces
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• v.11 no.1
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• pp.191-197
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• 1998

This paper describes a machine vision algorithm for inspecting a crimpled terminal. The crimpled terminal is one of wire harness assemblies which transmit current or signals between a pair of electrical or electronic assemblies. The major defect considered is wire exposure on wire barrels. To detect the wire exposure, we develope a multi-layer perceptron in which three features extracted from the image of the crimpled terminal are used as input data. The three features are edginess, variance, and total number of valley points(TVP). The multi-layer neural network has been successfully tested on a number of real specimens collected from a wire-harness factory.

• Annual Conference on Human and Language Technology
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• 1993.10a
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• pp.317-328
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• 1993

본 논문은 신경망과 퍼지 이론을 결합한 한국어 철자 교정기 KSCNN(Korean Spelling Corrector using Neural Network)에 대하여 기술한다. KSCNN은 퍼셉트론(perceptron) 학습을 이용한 연상 메모리(associative memory)로 구성되며 자판 배열 특성을 고려한 퍼지 멤버쉽 함수에 의해 신경망의 입력값을 정한다. 본 철자 교정기의 장점은 인지적인 방법으로 철자를 교정하기 때문에 기존의 VA나 BNA와는 달리 오류의 종류에 영향을 받지 않으며 교정된 철자나 후보자들에 대한 견인값(attraction value)을 측정하여 시스템의 신뢰도를 높일 수 있다는 데 있다. 또한, 본 논문은 실험을 통해서 퍼지 멤버쉽 함수에 의한 입력 노드의 활성화가 자판 배열특성을 고려할 수 있기 때문에 시스템의 성능을 향상시킨다는 사실을 보여준다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.377-380
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• 2002

신경회로망은 문자인식, 자동제어 등의 여러 분야에 널리 쓰이는 방식이다. 그러나 신경회로망을 구현하는데는 연산량이 많아서 실시간으로 구현하기에 어려움이 많이 따른다. 본 논문은 신경회로망을 구현하는데 필요한 연산을 살펴보고 그 연산을 구현하는 방법을 비교 분석하였다. 신경회로망을 구현하기 위해 DSP(Digital Signal Processor), PC의 FPU(Floating Point Unit), Intel사의 Pentium 계열 프로세서에서 지원하는 SIMD(Single Instruction Multiple Data) 기술을 사용하여 결과를 비교 분석 하였다. 신경회로망의 핵심인 MLP(Multi Layer Perceptron) 연산에 대해 실험한 결과 SIMD 기술을 이용하는 방법이 다른 방법에 비해 2배이상 좋은 결과를 나타내었다.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.65-68
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• 1996

Neural networks are increasingly being used to enhance the classification and recognition powers of data collected from sensor array. This papers reports the effectiveness of multilayer perceptron network based on back-propagation algorithm combined with the outputs from "Electronic Nose" using electrically conducting polymers as sensor materials. Robust performance and classification results are produced with preprocessing method.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.36-40
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• 2007

miRNA 유전체학의 중요한 이슈로 miRNA가 조절하는 목표 유전자를 예측하는 작업과 miRNA가 목표 유전자를 조절하는 메커니즘이 무엇인지 규명하는 것을 들 수 있다. 본 논문에서는 생물학적 특징들과 다층 퍼셉트론 신경망을 이용하여 miRNA의 목표 유전자를 예측하고 해당 miRNA 조절 메커니즘 타입을 분별해주는 시스템을 제안하고 실제 데이터를 사용하여 그 성능을 평가한다. 실험적으로 검증된 데이터를 사용하여 제안 시스템을 평가해본 결과, 다층 퍼셉트론 신경망을 사용할 경우 84.63%의 정확도로 miRNA의 목표 유전자를 예측할 수 있었고, 87.90%의 정확도로 miRNA가 목표 유전자를 조절하는 메커니즘을 분별할 수 있었다. 학습 데이터가 충분히 많아진다면 제안 시스템의 예측 성능은 더욱 높아질 것으로 예상된다.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.131-135
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• 2021

Numbers of studies related to optimization of design of organic light emitting diodes(OLED) through machine learning are increasing. We propose the generative method of the image to assess the performance of the device combining with machine learning technique. Principle parameter regarding dark spot growth mechanism of the OLED can be the key factor to determine the long-time performance. Captured images from actual device and randomly generated images at specific time and initial pinhole state are fed into the deep neural network system. The simulation reinforced by the machine learning technique can predict the device parameters accurately and faster. Similarly, the inverse design using multiple layer perceptron(MLP) system can infer the initial degradation factors at manufacturing with given device parameter to feedback the design of manufacturing process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.705-708
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• 2009

This research describes a neural network-based phoneme segmenter for recognizing spontaneous speech. The input of the phoneme segmenter for spontaneous speech is 16th order mel-scaled FFT, normalized frame energy, ratio of energy among 0~3[KHz] band and more than 3[KHz] band. All the features are differences of two consecutive 10 [msec] frame. The main body of the segmenter is single-hidden layer MLP(Multi-Layer Perceptron) with 72 inputs, 20 hidden nodes, and one output node. The segmentation accuracy is 78% with 7.8% insertion.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.848-857
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• 2021

This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.