• Title/Summary/Keyword: Neural Circuit

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Development of the Power System Fault Diagnostic Algorithm for the Proton Accelerator Research Center of PEFP (양성자가속기 연구센터 전력계통 고장진단 알고리즘 개발)

  • Mun, Kyeong-Jun;Jeon, Gye-Po;Lee, Seok-Ki;Kim, Jun-Yeon;Jung, W.;Yoo, Suk-Tae
    • Proceedings of the KIEE Conference
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    • 2007.07a
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    • pp.685-686
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    • 2007
  • This paper presents an application of power system fault diagnostic algorithm for the PEFP Proton Accelerator Research Center using neural network. Proposed fault diagnostic system is constructed by the radial basis function (RBF) neural network because it has the capabilities of the pattern classification and function approximation of any nonlinear function. Proposed system identifies faulted section in the power system based on information about the operation of protection devices such as relays and circuit breakers. In this paper, parameters of the RBF neural networks are tuned by the GA-TS algorithm, which has the global optimal solution searching capabilities. To show the validity of the proposed method, proposed algorithm has been tested with a practical power system in Proton Accelerator Research Center of PEFP.

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A Spiking Neural Network for Autonomous Search and Contour Tracking Inspired by C. elegans Chemotaxis and the Lévy Walk

  • Chen, Mohan;Feng, Dazheng;Su, Hongtao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.9
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    • pp.2846-2866
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    • 2022
  • Caenorhabditis elegans exhibits sophisticated chemotaxis behavior through two parallel strategies, klinokinesis and klinotaxis, executed entirely by a small nervous circuit. It is therefore suitable for inspiring fast and energy-efficient solutions for autonomous navigation. As a random search strategy, the Lévy walk is optimal for diverse animals when foraging without external chemical cues. In this study, by combining these biological strategies for the first time, we propose a spiking neural network model for search and contour tracking of specific concentrations of environmental variables. Specifically, we first design a klinotaxis module using spiking neurons. This module works in conjunction with a klinokinesis module, allowing rapid searches for the concentration setpoint and subsequent contour tracking with small deviations. Second, we build a random exploration module. It generates a Lévy walk in the absence of concentration gradients, increasing the chance of encountering gradients. Third, considering local extrema traps, we develop a termination module combined with an escape module to initiate or terminate the escape in a timely manner. Experimental results demonstrate that the proposed model integrating these modules can switch strategies autonomously according to the information from a single sensor and control steering through output spikes, enabling the model worm to efficiently navigate across various scenarios.

The Synchronization and Secure Communication of Hyper-chaos circuit using SC-CNN (SC-CNN을 이용한 하이퍼카오스 회로에서의 동기화 및 비밀 통신)

  • 배영철;김주완
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.7
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    • pp.1064-1068
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    • 2002
  • In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state in the SC-CNN. From the result of the recovery signal through the demodulation method in the receiver, We shown that recovery quality of state variable $$\chi$_3$ is superior to that of ${$\chi$_2}, {$\chi$_1}$ in secure communication.

A Study on the Prediction of Welding Flaw Using Neural Network (인공 신경망을 이용한 실시간 용접품질 예측에 관한 연구)

  • Cho, Jae Hyung;Ko, Sang Hyun
    • Journal of Digital Convergence
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    • v.17 no.5
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    • pp.217-223
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    • 2019
  • A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

High Efficiency Drive of SRM with Genetic Algorithms and Neural Network (유전알고리즘과 신경회로망을 이용한 SRM의 고효율 구동)

  • Sohn Ick-Jin;Oh Seok-Gyu;Ahn Jin-Woo
    • Proceedings of the KIPE Conference
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    • 2002.07a
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    • pp.427-430
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    • 2002
  • The switched reluctance motor (SRM) drive system provides a good adjustable speed characteristics. But driving of SRM is nonlinear changed according to rotor position angle and phase current because of saturation in magnetic circuit, and it is difficult to drive the high efficiency. This paper proposes find point of high efficiency in variable load that are used to control switch-on/off angles and input voltage.

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Neural Hamming MAXNET Design for Binary Pattern Classification (2진 패턴분류를 위한 신경망 해밍 MAXNET설계)

  • 김대순;김환용
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.12
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    • pp.100-107
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    • 1994
  • This article describes the hardware design scheme of Hamming MAXNET algorithm which is appropriate for binary pattern classification with minimum HD measurement between stimulus vector and storage vector. Circuit integration is profitable to Hamming MAXNET because the structure of hamming network have a few connection nodes over the similar neuro-algorithms. Designed hardware is the two-layered structure composed of hamming network and MAXNET which enable the characteristics of low power consumption and fast operation with biline volgate sensing scheme. Proposed Hamming MAXNET hardware was designed as quantize-level converter for simulation, resulting in the expected binary pattern convergence property.

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Research Trend of the Remote Sensing Image Analysis Using Deep Learning (딥러닝을 이용한 원격탐사 영상분석 연구동향)

  • Kim, Hyungwoo;Kim, Minho;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.5_3
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    • pp.819-834
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    • 2022
  • Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

A High-Voltage Compliant Neural Stimulation IC for Implant Devices Using Standard CMOS Process (체내 이식 기기용 표준 CMOS 고전압 신경 자극 집적 회로)

  • Abdi, Alfian;Cha, Hyouk-Kyu
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.5
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    • pp.58-65
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    • 2015
  • This paper presents the design of an implantable stimulation IC intended for neural prosthetic devices using $0.18-{\mu}m$ standard CMOS technology. The proposed single-channel biphasic current stimulator prototype is designed to deliver up to 1 mA of current to the tissue-equivalent $10-k{\Omega}$ load using 12.8-V supply voltage. To utilize only low-voltage standard CMOS transistors in the design, transistor stacking with dynamic gate biasing technique is used for reliable operation at high-voltage. In addition, active charge balancing circuit is used to maintain zero net charge at the stimulation site over the complete stimulation cycle. The area of the total stimulator IC consisting of DAC, current stimulation output driver, level-shifters, digital logic, and active charge balancer is $0.13mm^2$ and is suitable to be applied for multi-channel neural prosthetic devices.

A 16-channel Neural Stimulator IC with DAC Sharing Scheme for Artificial Retinal Prostheses

  • Seok, Changho;Kim, Hyunho;Im, Seunghyun;Song, Haryong;Lim, Kyomook;Goo, Yong-Sook;Koo, Kyo-In;Cho, Dong-Il;Ko, Hyoungho
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.14 no.5
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    • pp.658-665
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    • 2014
  • The neural stimulators have been employed to the visual prostheses system based on the functional electrical stimulation (FES). Due to the size limitation of the implantable device, the smaller area of the unit current driver pixel is highly desired for higher resolution current stimulation system. This paper presents a 16-channel compact current-mode neural stimulator IC with digital to analog converter (DAC) sharing scheme for artificial retinal prostheses. The individual pixel circuits in the stimulator IC share a single 6 bit DAC using the sample-and-hold scheme. The DAC sharing scheme enables the simultaneous stimulation on multiple active pixels with a single DAC while maintaining small size and low power. The layout size of the stimulator circuit with the DAC sharing scheme is reduced to be 51.98 %, compared to the conventional scheme. The stimulator IC is designed using standard $0.18{\mu}m$ 1P6M process. The chip size except the I/O cells is $437{\mu}m{\times}501{\mu}m$.

Alterations of Cortical Folding Patterns in Patients with Bipolar I Disorder : Analysis of Local Gyrification Index (제1형 양극성장애 환자에서 대뇌피질 주름 패턴의 변형 : Local Gyrification Index 분석)

  • Lee, Junyong;Han, Kyu-Man;Won, Eunsoo;Lee, Min-Soo;Ham, Byung-Joo
    • Korean Journal of Biological Psychiatry
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    • v.24 no.4
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    • pp.225-234
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    • 2017
  • Objectives Local gyrification reflects the early neural development of cortical connectivity, and is regarded as a potential neural endophenotype in psychiatric disorders. Several studies have suggested altered local gyrification in patients with bipolar I disorder (BD-I). The purpose of the present study was to investigate the alterations in the cortical gyrification of whole brain cortices in patients with BD-I. Methods Twenty-two patients with BD-I and age and sex-matched 22 healthy controls (HC) were included in this study. All participants underwent T1-weighted structural magnetic resonance imaging (MRI). The local gyrification index (LGI) of 66 cortical regions were analyzed using the FreeSurfer (Athinoula A. Martinos Center for Biomedical Imaging). One-way analysis of covariance (ANCOVA) was used to analyze the difference of LGI values between two groups adjusting for age and sex as covariates. Results The patients with BD-I showed significant hypogyria in the left pars opercularis (uncorrected-p = 0.049), the left rostral anterior cingulate gyrus (uncorrected-p = 0.012), the left caudal anterior cingulate gyrus (uncorrected-p = 0.033). However, these findings were not significant after applying the multiple comparison correction. Severity or duration of illness were not significantly correlated with LGI in the patients with BD-I. Conclusions Our results of lower LGI in the anterior cingulate cortex and the ventrolateral prefrontal cortex in the BD-I group implicate that altered cortical gyrification in neural circuits involved in emotion-processing may contribute to pathophysiology of BD-I.