• Journal of information and communication convergence engineering
• /
• 제16권1호
• /
• pp.1-5
• /
• 2018

In radio-over-fiber (RoF) systems, nonlinear compensation is very important to meet the error vector magnitude (EVM) requirement of the mobile network standards. In this study, a nonlinear compensation technique based on an artificial neural network (ANN) is proposed for RoF systems. This technique is based on a backpropagation neural network (BPNN) with one hidden layer and three neuron units in this study. The BPNN obtains the inverse response of the system to compensate for nonlinearities. The EVM of the signal is measured by changing the number of neurons and the hidden layers in a RoF system modeled by a measured data. Based on our simulation results, it is concluded that one hidden layer and three neuron units are adequate for the RoF system. Our results showed that the EVMs were improved from 4.027% to 2.605% by using the proposed ANN compensator.

• 설비공학논문집
• /
• 제12권11호
• /
• pp.1038-1047
• /
• 2000

The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

• 대한치매학회지
• /
• 제17권3호
• /
• pp.83-89
• /
• 2018

Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron. The purpose of this review is to explain the fundamental concepts of artificial neural networks.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2007년도 춘계학술대회A
• /
• pp.136-141
• /
• 2007

This paper introduces an artificial neuron which is a nano composite continuous sensor. The continuous nano sensor is fabricated as a thin and narrow polymer film sensor that is made of carbon nanotubes composites with a PMMA or a silicone matrix. The sensor can be embedded onto a structure like a neuron in a human body and it can detect deteriorations of the structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensor can form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods. The artificial neuron is expected to effectively detect damage in large complex structures including composite helicopter blades and composite aircraft and vehicles.

• 한국멀티미디어학회논문지
• /
• 제20권8호
• /
• pp.1406-1420
• /
• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
• /
• pp.395-398
• /
• 1998

In this paper, a method for the improvement of learning speed and convergence rate was proposed applied it to physiological neural structure with the advantages of artificial neural networks and fuzzy theory to physiological neuron structure, To compare the proposed method with conventional the single layer perception algorithm, we applied these algorithms bit parity problem and pattern recognition containing noise. The simulation result indicated that our learning algorithm reduces the possibility of local minima more than the conventional single layer perception does. Furthermore we show that our learning algorithm guarantees the convergence.

• 한국포장학회지
• /
• 제29권3호
• /
• pp.163-174
• /
• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.

• 한국기계가공학회지
• /
• 제4권3호
• /
• pp.57-62
• /
• 2005

Neural networks, which have learning and self-organizing abilities, can be advantageously used in the pattern recognition. Neural network techniques have been widely used in monitoring and diagnosis, and compare favourable with traditional statistical pattern recognition algorithms, heuristic rule-based approaches, and fuzzy logic approaches. In this study the fault diagnosis of the face-milling using the artificial neural network was investigated. After training, the sample which measure load current was monitored by constant output results.

• 실천공학교육논문지
• /
• 제13권2호
• /
• pp.233-242
• /
• 2021

본 논문에서는 비전공자들을 위한 교양과정으로, 기초 인공신경망 과목 커리큘럼을 설계하기 위해, 지도학습 인공신경망 매개변수 최적화 방법과 활성화함수에 대한 기초 교육 방법을 제안하였다. 이를 위해, 프로그래밍 없이, 매개 변수 최적화 해를 스프레드시트로 찾는 방법을 적용하였다. 본 교육 방법을 통해, 인공신경망 동작 및 구현의 기초 원리 교육에 집중할 수 있다. 그리고, 스프레드시트의 시각화된 데이터를 통해 비전공자들의 관심과 교육 효과를 높일 수 있다. 제안한 내용은 인공뉴런과 Sigmoid, ReLU 활성화 함수, 지도학습데이터의 생성, 지도학습 인공신경망 구성과 매개변수 최적화, 스프레드시트를 이용한 지도학습 인공신경망 구현 및 성능 분석 그리고 교육 만족도 분석으로 구성되었다. 본 논문에서는 Sigmoid 뉴런 인공신경망과 ReLU 뉴런 인공신경망에 대해 음수허용 매개변수 최적화를 고려하여, 인공신경망 매개변수 최적화에 대한 네가지 성능분석결과를 교육하는 방법을 제안하고 교육 만족도 분석을 실시하였다.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 2019년도 추계학술대회
• /
• pp.136-137
• /
• 2019

본 논문에서는 ADALINE (Adaptive-Linear-Neuron) 구조의 ANN(Artificial Neural Network)을 이용한 3상 PWM 컨버터의 개방고장 진단 방법에 대해 제안한다. 3상 PMW 컨버터에서 스위치의 개방고장이 발생한 경우 보호회로에 의해 시스템이 중단되지 않으며, 개방고장으로 인한 상전류의 고조파와 직류 성분에 의해 주변 기기에 고장에 의한 파급효과가 나타날 수 있다. 이에 본 논문에서는 ADALINE을 이용하여 각 상의 THD(Total Harmonics Distortion)와 직류 성분 얻고 대소비교를 통해 개방고장이 발생한 스위치를 진단하는 방법에 대해 제안한다.