A study for improvement of Recognition velocity of Korean Character using Neural Oscillator

권용범(Yong-Bum Kwon),이준탁(Joon-Tark Lee) 한국지능시스템학회 2004 한국지능시스템학회 학술발표 논문집 Vol.14 No.1

Neural Oscillator can be applied to oscillatory systems such as the image recognition, the voice recognition, estimate of the weather fluctuation and analysis of geological fluctuation etc in nature and principally, it is used often to pattern recognition of image information. Conventional BPL(Back-Propagation Learning) and MLNN(Multi Layer Neural Network) are not proper for oscillatory systems because these algorithm complicate Learning structure, have tedious procedures and sluggish convergence problem. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(phase-Locked Loop) function and by using a simple Hebbian learning rule. And also, Recognition velocity of Korean Character can be improved by using a Neural Oscillator's learning accelerator factor ŋij.

신경 진동자와 IPMC 구동기를 결합한 생체 모방형 시스템

양우성(Woosung Yang),최수호(Suho Choi),이승엽(Seung-Yop Lee) 대한기계학회 2009 대한기계학회 춘추학술대회 Vol.2009 No.11

We propose a control scheme of the IPMC actuator exploiting neural oscillators to achieve biologically inspired motion generation and control. In general, humans or animals show novel adaptive behaviors regardless of their kinematic configurations against unexpected disturbances or environmental changes. This is because that the entrainment property of the neural oscillator plays a key role to adapt their nervous system to the natural frequency of the interacted environments. Thus we apply the biomimetic approach to a novel control of the IPMC actuator, since the IPMC has many difficulties in control such as nonlinearities, flexibility, and unexpected motion, etc. In order to demonstrate the excellence of its entrainment, we implement experimentally the proposed control approach to the IPMC actuator. The coupled IPMC actuator successfully exhibits the motion excited by the neural oscillator. Experimental results confirm biologically inspired, selfadaptive behaviors that enable the IPMC actuator to make adaptive changes corresponding to unexpected disturbances in phase.

Classification of impinging jet flames using convolutional neural network with transfer learning

Minwoo Lee,Sangwoong Yoon,Ju Han Kim,Yuangang Wang,Kee-Man Lee,Frank Chongwoo Park,Chae Hoon Sohn 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.3

Depending on the equivalence ratio and the Reynolds number, impinging jet flames exhibit several modes of thermoacoustic oscillation. In this study, we present a machine-learning-based method for classifying the regimes of thermoacoustic oscillation. We perform transfer learning to train the convolutional neural network model designed to classify flame images. We show that an accurate classification of impinging jet flames is achieved with an accuracy of 93.6 % by using just a single snapshot image. This study constitutes the first demonstration of transfer learning in classifying fluid images, opening up new possibilities for robust image-based diagnostics of various fluid and combustion systems.

M-type potassium conductance controls the emergence of neural phase codes: a combined experimental and neuron modelling study

Kwag, Jeehyun,Jang, Hyun Jae,Kim, Mincheol,Lee, Sujeong Royal Society 2014 Journal of the Royal Society, Interface Vol.11 No.99

<P>Rate and phase codes are believed to be important in neural information processing. Hippocampal place cells provide a good example where both coding schemes coexist during spatial information processing. Spike rate increases in the place field, whereas spike phase precesses relative to the ongoing theta oscillation. However, what intrinsic mechanism allows for a single neuron to generate spike output patterns that contain both neural codes is unknown. Using dynamic clamp, we simulate an <I>in vivo</I>-like subthreshold dynamics of place cells to <I>in vitro</I> CA1 pyramidal neurons to establish an <I>in vitro</I> model of spike phase precession. Using this <I>in vitro</I> model, we show that membrane potential oscillation (MPO) dynamics is important in the emergence of spike phase codes: blocking the slowly activating, non-inactivating K<SUP>+</SUP> current (<I>I</I><SUB>M</SUB>), which is known to control subthreshold MPO, disrupts MPO and abolishes spike phase precession. We verify the importance of adaptive <I>I</I><SUB>M</SUB> in the generation of phase codes using both an adaptive integrate-and-fire and a Hodgkin–Huxley (HH) neuron model. Especially, using the HH model, we further show that it is the perisomatically located <I>I</I><SUB>M</SUB> with slow activation kinetics that is crucial for the generation of phase codes. These results suggest an important functional role of <I>I</I><SUB>M</SUB> in single neuron computation, where <I>I</I><SUB>M</SUB> serves as an intrinsic mechanism allowing for dual rate and phase coding in single neurons.</P>

적응형 오실레이터기반의 이족보행 제어

유재관(J. K. Ryu),엄태웅(T. W. Um),백주현(J. H. Baek) 한국정밀공학회 2011 한국정밀공학회 학술발표대회 논문집 Vol.2011 No.6월

Recognition of the Korean Alphabet using Phase Synchronization of Neural Oscillator

Joon-Tark Lee(李浚柝),Kwon-Yong Bum(權容凡) 한국지능시스템학회 2004 한국지능시스템학회논문지 Vol.14 No.1

Neural oscillator can be applied to oscillatory systems such as analyses of image information, voice recognition and etc. Conventional EBPA (Error back Propagation Algorithm) is not proper for oscillatory systems with the complicate input’s patterns because of its tedious training procedures and sluggish convergence problems. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(Phase Locked Loop) function and by using a simple Hebbian learning rule. Therefore, in this paper, a technique for Recognition of the Korean Alphabet using Phase Synchronized Neural Oscillator was introduced.

Recognition of the Korean Character Using Phase Synchronization Neural Oscillator

Lee, Joon-Tark,Kwon, Yang-Bum The Korean Society of Marine Engineering 2004 한국마린엔지니어링학회지 Vol.28 No.2

Neural oscillator can be applied to oscillator systems such as analysis of image information, voice recognition and etc, Conventional learning algorithms(Neural Network or EBPA(Error Back Propagation Algorithm)) are not proper for oscillatory systems with the complicate input patterns because of its too much complex structure. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(phase locked loop) function and a simple Hebbian learning rule, Therefore, in this paper, it will introduce an technique for Recognition of the Korean Character using Phase Synchronization Neural Oscillator and will show the result of simulation.

가변 부성저항을 이용한 새로운 CMOS 뉴럴 오실레이터의 집적회로 설계 및 구현

송한정 대한전자공학회 2003 電子工學會論文誌-SC (System and control) Vol.40 No.4

0.5㎛ 2중 폴리 CMOS 공정을 이용하여 새로운 뉴럴 오실레이터를 설계, 제작하였다. 제안하는 뉴럴 오실레이터는 트랜스콘덕터 및 캐패시터와 비선형 가변 부성저항으로 이루어진다. 뉴럴 오실레이터의 입력단으로 사용되는 비선형 가변 부성저항은 정귀환의 트랜스콘덕터와 가우시안 분포의 전류전압 특성을 지니는 범프 회로를 이용하여 구현하였다. 또한 SPICE 모의실험을 통하여 제안한 오실레이터의 특성분석 후 집적회로 설계를 실시하였다. 한편 흥분성 및 억제성 시냅스로 연결된 4개의 뉴럴 오실레이터로 간단한 신경회로망을 구성하여 그 특성을 확인하였다. 집적회로로 제작된 뉴럴 오실레이터에 대하여 ± 2.5 V 전원 조건하에서 측정된 결과를 분석하고 모의실험 결과와 비교한다. A new neural oscillator has been designed and fabricated in an 0.5 ${\mu}{\textrm}{m}$ double poly CMOS technology. The proposed neural oscillator consists of a nonlinear variable resistor with negative resistance as well as simple transconductors and capacitors. The variable negative resistor which is used as a input stage of the oscillator consists of a positive feedback transconductors and a bump circuit with Gaussian-like I-V curve. The proposed neural oscillator has designed in integrated circuit with SPICE simulations. Simulations of a network of 4 oscillators which are connected with excitatory and inhibitory synapses demonstrate cooperative computation. Measurements of the fabricated oscillator chip with a $\pm$ 2.5 V power supply is shown and compared with the simulated results.

Recognition of the Korean Alphabet using Phase Synchronization of Neural Oscillator

Lee, Joon-Tark,Bum, Kwon-Yong Korean Institute of Intelligent Systems 2004 한국지능시스템학회논문지 Vol.14 No.1

Neural oscillator can be applied to oscillatory systems such as analyses of image information, voice recognition and etc. Conventional EBPA (Error back Propagation Algorithm) is not proper for oscillatory systems with the complicate input`s patterns because of its tedious training procedures and sluggish convergence problems. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(Phase Locked Loop) function and by using a simple Hebbian learning rule. Therefore, in this paper, a technique for Recognition of the Korean Alphabet using Phase Synchronized Neural Oscillator was introduced.

Stochastic Oscillator Death in Globally Coupled Neural Systems

Woochang Lim,Sang-Yoon Kim 한국물리학회 2008 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.52 No.6

We consider an ensemble of globally coupled subthreshold Morris-Lecar neurons. As the coupling strength passes a lower threshold, the coupling stimulates coherence between noise-induced spikings. This coherent transition is well described in terms of an order parameter. However, for sufficiently large J, ``stochastic oscillator death'' ( i.e.}, quenching of noise-induced spikings), leading to breakup of collective spiking coherence, is found to occur. Using the techniques of nonlinear dynamics, we investigate the dynamical origin of stochastic oscillator death. Thus, we show that stochastic oscillator death occurs because each local neuron is attracted to a noisy equilibrium state via an infinite-period bifurcation. Furthermore, we introduce a new ``statistical-mechanical'' parameter, called the average firing probability Pf and quantitatively characterize a transition from firing to non-firing states which results from stochastic oscillator death. For a firing (non-firing) state, Pf tends to be non-zero (zero) in the thermodynamic limit. We note that the role of P_f for the firing-nonfiring transition is similar to that of the order parameter used for the coherence-incoherence transition.