• Title/Summary/Keyword: neural network.

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Prediction of Undrained Shear Strength of Normally Consolidated Clay with Varying Consolidation Pressure Ratios Using Artificial Neural Networks (인공신경회로망을 이용한 압밀응력비에 따른 정규압밀점토의 비배수전단강도 예측)

  • 이윤규;윤여원;강병희
    • Journal of the Korean Geotechnical Society
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    • v.16 no.1
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    • pp.75-81
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    • 2000
  • The anisotropy of soils has an important effect on stress-strain behavior. In this study, an attempt has been made to implement artificial neural network model for modeling the stress-strain relationship and predicting the undrained shear strength of normally consolidated clay with varying consolidation pressure ratios. The multi-layer neural network model, adopted in this study, utilizes the error back-propagation loaming algorithm. The artificial neural networks use the results of undrained triaxial test with various consolidation pressure ratios and different effective vertical consolidation pressure fur learning and testing data. After learning from a set of actual laboratory testing data, the neural network model predictions of the undrained shear strength of the normally consolidated clay are found to agree well with actual measurements. The predicted values by the artificial neural network model have a determination coefficient$(r^2)$ above 0.973 compared with the measured data. Therefore, this results show a positive potential for the applications of well-trained neural network model in predicting the undrained shear strength of cohesive soils.

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Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

  • Farhadian, Maryam;Salemi, Fatemeh;Saati, Samira;Nafisi, Nika
    • Imaging Science in Dentistry
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    • v.49 no.1
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    • pp.19-26
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    • 2019
  • Purpose: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. Materials and Methods: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. Results: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. Conclusion: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

A Efficient Rule Extraction Method Using Hidden Unit Clarification in Trained Neural Network (인공 신경망에서 은닉 유닛 명확화를 이용한 효율적인 규칙추출 방법)

  • Lee, Hurn-joo;Kim, Hyeoncheol
    • The Journal of Korean Association of Computer Education
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    • v.21 no.1
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    • pp.51-58
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    • 2018
  • Recently artificial neural networks have shown excellent performance in various fields. However, there is a problem that it is difficult for a person to understand what is the knowledge that artificial neural network trained. One of the methods to solve these problems is an algorithm for extracting rules from trained neural network. In this paper, we extracted rules from artificial neural networks using ordered-attribute search(OAS) algorithm, which is one of the methods of extracting rules, and analyzed result to improve extracted rules. As a result, we have found that the distribution of output values of the hidden layer unit affects the accuracy of rules extracted by using OAS algorithm, and it is suggested that efficient rules can be extracted by binarizing hidden layer output values using hidden unit clarification.

Neural-based Blind Modeling of Mini-mill ASC Crown

  • Lee, Gang-Hwa;Lee, Dong-Il;Lee, Seung-Joon;Lee, Suk-Gyu;Kim, Shin-Il;Park, Hae-Doo;Park, Seung-Gap
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.6
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    • pp.577-582
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    • 2002
  • Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

A study on the control chart pattern for detecting shifts using neural network in start-up process (초기공정에서 공정변화에 대한 신경망을 이용한 관리도 형태 연구)

  • 이희춘
    • Journal of Korea Society of Industrial Information Systems
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    • v.6 no.3
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    • pp.65-70
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    • 2001
  • This Paper Propose the control chart Pattern to provide a more comprehensive scheme for detecting process shifts using individual observations in start-up process. In this paper, which uses the backpropagation algorithm two samples are fed into the trained neural network to provide outputs ranging from 0 to 1. The main advantage of using neural networks approach with a control chart is that the neural network has almost no delay in detecting small shift. This paper illustrates how neural networks can provide a useful method for optimizing parameter(connection weights) that affect process control. Simulation results show that the performance of the proposed control chart using the neural network (NNCC) is quite promising.

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On-line Parameter Estimation of IPMSM Drive using Neural Network (신경회로망을 이용한 IPMSM 드라이브의 온라인 파라미터 추정)

  • Park, Ki-Tae;Choi, Jung-Sik;Ko, Jae-Sub;Lee, Jung-Ho;Kim, Jong-Kwan;Park, Byung-Sang;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2006.07b
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    • pp.761-762
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    • 2006
  • A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

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Design of a Neural Chip for Classifying Iris Flowers based on CMOS Analog Neurons

  • Choi, Yoon-Jin;Lee, Eun-Min;Jeong, Hang-Geun
    • Journal of Sensor Science and Technology
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    • v.28 no.5
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    • pp.284-288
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    • 2019
  • A calibration-free analog neuron circuit is proposed as a viable alternative to the power hungry digital neuron in implementing a deep neural network. The conventional analog neuron requires calibrations because a voltage-mode link is used between the soma and the synapse, which results in significant uncertainty in terms of current mapping. In this work, a current-mode link is used to establish a robust link between the soma and the synapse against the variations in the process and interconnection impedances. The increased hardware owing to the adoption of the current-mode link is estimated to be manageable because the number of neurons in each layer of the neural network is typically bounded. To demonstrate the utility of the proposed analog neuron, a simple neural network with $4{\times}7{\times}3$ architecture has been designed for classifying iris flowers. The chip is now under fabrication in 0.35 mm CMOS technology. Thus, the proposed true current-mode analog neuron can be a practical option in realizing power-efficient neural networks for edge computing.

Recognition of Characters Printed on PCB Components Using Deep Neural Networks (심층신경망을 이용한 PCB 부품의 인쇄문자 인식)

  • Cho, Tai-Hoon
    • Journal of the Semiconductor & Display Technology
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    • v.20 no.3
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    • pp.6-10
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    • 2021
  • Recognition of characters printed or marked on the PCB components from images captured using cameras is an important task in PCB components inspection systems. Previous optical character recognition (OCR) of PCB components typically consists of two stages: character segmentation and classification of each segmented character. However, character segmentation often fails due to corrupted characters, low image contrast, etc. Thus, OCR without character segmentation is desirable and increasingly used via deep neural networks. Typical implementation based on deep neural nets without character segmentation includes convolutional neural network followed by recurrent neural network (RNN). However, one disadvantage of this approach is slow execution due to RNN layers. LPRNet is a segmentation-free character recognition network with excellent accuracy proved in license plate recognition. LPRNet uses a wide convolution instead of RNN, thus enabling fast inference. In this paper, LPRNet was adapted for recognizing characters printed on PCB components with fast execution and high accuracy. Initial training with synthetic images followed by fine-tuning on real text images yielded accurate recognition. This net can be further optimized on Intel CPU using OpenVINO tool kit. The optimized version of the network can be run in real-time faster than even GPU.

A Novel Spiking Neural Network for ECG signal Classification

  • Rana, Amrita;Kim, Kyung Ki
    • Journal of Sensor Science and Technology
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    • v.30 no.1
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    • pp.20-24
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    • 2021
  • The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

A Binary Classifier Using Fully Connected Neural Network for Alzheimer's Disease Classification

  • Prajapati, Rukesh;Kwon, Goo-Rak
    • Journal of Multimedia Information System
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    • v.9 no.1
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    • pp.21-32
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    • 2022
  • Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.