• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.960-962
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• 1999

The main idea of the proposed neuro-fuzzy system is conditional clustering whose main objective is to develop clusters preserving homogeneity of the clustered patterns with regard to their similarity in the input space as well as their respective values assumed in the output space. In the proposed neuro-fuzzy system, the structure identification is used with conditional fuzzy clustering, the parameter identification carried out by the hybrid learning scheme using back-propagation and total least squares.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.293-296
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• 2004

This paper proposes a new protective relaying algorithm using ACI(Advanced Computational Intelligence) and wavelet transform. To organize the advanced neuro-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of D1 coefficient and RSM value within half cycle after fault occurrence. Subsequently, the advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within half cycle after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.175-180
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• 2001

This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set\ulcorner Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.

• Journal of Power Electronics
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• v.4 no.1
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• pp.28-38
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• 2004

In this paper, the position control of a detuned indirect field oriented control (IFOC) induction motor drive is studied. A proposed Simple-Neuro-Controllers (SNCs) are designed and analyzed to achieve high-dynamic performance both in the position command tracking and load regulation characteristics for robotic applications. The proposed SNCs are trained on-line based on the back propagation algorithm with a modified error function. Four SNCs are developed for position, speed and ｄ-ｑ axes stator currents respectively. Also, a synchronous proportional plus integral-derivative (PI-D) two-degree-of-freedom (2DOF) position controller and PI-D speed controller are designed for an ideal IFOC induction motor drive with the desired dynamic response. The performance of the proposed SNCs and synchronous PI-D 2DOF position controllers for detuned field oriented induction motor servo drive is investigated. Simulation results show that the proposed SNCs controllers provide high-performance dynamic characteristics which are robust with regard to motor parameter variations and external load disturbance. Furthermore, comparing the SNC position controller with the synchronous PI-D 2DOF position controller demonstrates the superiority of the proposed SNCs controllers due to attain a robust control performance for IFOC induction motor servo drive system.

• Journal of Korean Academy of Nursing
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• v.9 no.2
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• pp.27-38
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• 1979

This study was undertaken to determine the subjective symptoms of fatigue among the house-wives groups. Inquiries into subjective symptoms of fatigue were made by the form designied by the Industrial Fatigue Research Committee of the Japan Society of Industrial Health (1961), Comprising 30 items. These items are classified into 3 groups of 10 items, namely, A) Physical Symptoms, B) Mental Symptoms, C) Neuro-Sensory Symptoms (Figure 1 ). The results of the investigation can be summerized as follows: 1. Within the total items (T), the physical symptoms (A) were the strongest in the effect on the feelings of fatigue, and were followed by (B), and (C). 2. There was a significant difference shown in the distribution of responses by height (X$^2$=236.29, d.f. = 145, p < 0.00001). In the mental category (F = 2.22, d.f. = 4, p = 0.05) and neuro-sensory category (F = 2.64, d.f. = 4, p < 0.001), there was a difference in the responses’com-plaints by weight. 3. As for the ages, housewives at the age of 50 presented a higher rate than those 30 or 20. 4. Regarding the number of children, respondents have more children showed higher frequency rate of complaints. 5. In the investigation sample, complaints were related to education level (f = 18.34, d.f. = 3, p<0.0001) pentruation (t = 2.31, p< 0.022), and sleeping hours (F = 6.04, d.f. = 6, p< 0.0001).

• Structural Engineering and Mechanics
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• v.62 no.2
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• pp.237-246
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• 2017

In the present paper a new Neuro-Wavelet control algorithm is proposed based on a cost function to actively control the vibrations of structures under earthquake loads. A wavelet neural network (WNN) was developed to train the control algorithm. This algorithm is designed to control multi-degree-of-freedom (MDOF) structures which consider the geometric and material non-linearity, structural irregularity, and the incident direction of an earthquake load. The training process of the algorithm was performed by using the El-Centro 1940 earthquake record. A numerical model of a three dimensional (3D) three story building was used to accredit the control algorithm under three different seismic loads. Displacement responses and hysteretic behavior of the structure before and after the application of the controller showed that the proposed strategy can be applied effectively to suppress the structural vibrations.

• Advances in nano research
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• v.13 no.2
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• pp.121-134
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• 2022

Emerge of nanotechnology impacts all aspects of humans' life. One of important aspects of the nanotechnology and nanoparticles (NPs) is in the food production industry. The safety of such foods is not well recognized and producing safe foods using nanoparticles involves delicate experiments. In this study, we aim to incorporate intelligent computer simulation in predicting safety degree of nanofoods. In this regard, the safety concerns on the nano-foods are addressed considering cytotoxicity levels in metal oxides nanoparticles using adaptive neuro-fuzzy inference system (ANFIS) and response surface method (RSM). Three descriptors including chemical bond length, lattice energy and enthalpy of formation gaseous cation of 15 selected NPs are examined to find their influence on the cytotoxicity of NPs. The most effective descriptor is selected using RSM method and dependency of the toxicity of these NPs on the descriptors are presented in 2D and 3D graphs obtained using ANFIS technique. A comprehensive parameters study is conducted to observe effects of different descriptors on cytotoxicity of NPs. The results indicated that combinations of descriptors have the most effects on the cytotoxicity.

• Investigative Magnetic Resonance Imaging
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• v.24 no.2
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• pp.76-84
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• 2020

Background: Determination of inter-method differences between clinically available volumetry methods are essential for the clinical application of brain volumetry in a wider context. Purpose: The purpose of this study was to examine the inter-method reliability and differences between the Siemens morphometry (SM) software and the NeuroQuant (NQ) software. Materials and Methods: MR images of 86 subjects with subjective or objective cognitive impairment were included in this retrospective study. For this study, 3D T1 volume images were obtained in all subjects using a 3T MR scanner (Skyra 3T, Siemens). Volumetric analysis of the 3D T1 volume images was performed using SM and NQ. To analyze the inter-method difference, correlation, and reliability, we used the paired t-test, Bland-Altman plot, Pearson's correlation coefficient, intraclass correlation coefficient (ICC), and effect size (ES) using the MedCalc and SPSS software. Results: SM and NQ showed excellent reliability for cortical gray matter, cerebral white matter, and cerebrospinal fluid; and good reliability for intracranial volume, whole brain volume, both thalami, and both hippocampi. In contrast, poor reliability was observed for both basal ganglia including the caudate nucleus, putamen, and pallidum. Paired comparison revealed that while the mean volume of the right hippocampus was not different between the two software, the mean difference in the left hippocampus volume between the two methods was 0.17 ml (P < 0.001). The other brain regions showed significant differences in terms of measured volumes between the two software. Conclusion: SM and NQ provided good-to-excellent reliability in evaluating most brain structures, except for the basal ganglia in patients with cognitive impairment. Researchers and clinicians should be aware of the potential differences in the measured volumes when using these two different software interchangeably.

• BioChip Journal
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• v.12 no.4
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• pp.332-339
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• 2018

The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to $80{\mu}V$ while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of $2{\mu}W$ under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves $60G{\Omega}$ of input impedance and input referred noise of $8.5nv/{\sqrt{Hz}}$ over the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists.