• Journal of Medicine and Life Science
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• v.15 no.2
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• pp.62-66
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• 2018

A-type $K^+$ ($I_A$) channels are transiently activated in the suprathreshold membrane potential and then rapidly inactivated. These channels play roles to control the neuronal excitability in pyramidal neurons in hippocampi. We here electrophysiologically tested if regulatory functions of $I_A$ channels might be targeted by acute activation of glutamatergic synaptic transmission in cultured hippocampal neurons(DIV 6~8). The application of high KCl in recording solutions(10 mM, 2 min) to increase presynaptic glutamate release, significantly reduced the peak of somatic $I_A$ without changes of gating kinetics. This indicates that neuronal excitation induced by the enhancement of synaptic transmission may process with distinctive signaling cascades to affect voltage-dependent ion channels in hippocampal neurons. Therefore, it is possible that short-lasting enhancement of synaptic transmission is functionally restricted in local synapses without effects on intracellular signaling cascades affecting a whole neuron, efficiently and rapidly enhancing synaptic functions in hippocampal network.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.465-472
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• 2020

Recently, various deep neural networks based methods have been proposed for audio event detection. In this study, we improved the performance of audio event detection by adopting an attention approach to a baseline CRNN. We applied context gating at the input of the baseline CRNN and added an attention layer at the output. We improved the performance of the attention based CRNN by using the audio data of strong labels in frame units as well as the data of weak labels in clip levels. In the audio event detection experiments using the audio data from the Task 4 of the DCASE 2018/2019 Challenge, we could obtain maximally a 66% relative increase in the F-score in the proposed attention based CRNN compared with the baseline CRNN.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.395-399
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• 2001

This paper describes modular fuzzy inference systems(MFIS) with adaptive capability to extract fuzzy inference modules from observation data through the learning process. The proposed MFIS is based on the structural similarity to Tagaki-Sugeno fuzzy models and a modular neural architecture. The learning of MFIS is done by assigning new fuzzy inference modules and by updating the parameters of existing modules. The fuzzy inference modules consist of local model network and fuzzy gating network. The parameters of the MFIS are updated by the standard LMS algorithm. The performance of the MFIS is illustrated with adaptive control of a nonlinear dynamic system.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.456-459
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• 1999

In this paper, we propose a method of unconstrained handwritten numeral recognition using image dithering and multiple modular MLPs. The set of sample numeral patterns is subdivided into clusters which are extended by their radius. On each extended cluster, we constructed MLPs network as the expert recognizer of corresponding cluster. The gating network is also trained by an MLPs to weigh the outputs of expert MLPs. In training and test phase of the recognizer, we utilize the multiple dithered numeral images and the combination of the outputs for corresponding dithered images. Experimental results show that our recognition method works very well.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1834-1843
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• 2000

In this paper, we propose a modular neural network based classification method for handwritten numerals utilizing rotated and translated images of an input image. The whole numeral pattern space is divided into smaller spaces which overlap each other and form multiple clusters. On these multiple clusters, multiple multilayer perceptrons (MLP) neural networks, specialized in those clusters, are constructed. Thus, each MLP acts as an expert network on the corresponding cluster. An MLP is also used as a gating network functioning as a mediator among the multiple MLPs. In the learning phase, an input numeral image is dithered by tow geometric operations of translation and rotation so that new numeral images similar to original one are generated. In the recognition phase, we utilize not only input numeral image, but also nearly generated images through the rotation and the translation of the original image. Thus, multiple output values for those generated images were combined to make class decision by various combination methods. The experimental results confirm the validity of the proposed method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.11
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• pp.1217-1227
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• 2012

There are several antenna calibration methods, so-called 3-Antenna Method, Standard Site Method, and Standard Antenna Method which measure the antenna gains or antenna factors. These methods yield the free space or quasi free space antenna gains in only the frequency domain. In this paper, an antenna calibration method using the time domain in the open area test site is discussed. The reflected waves due to the ground are traced in the time domain. After they are removed by the time gating function of network analyzer, the free space transmission coefficient $S_{21}$ is extracted. Such a way is applied to the broad band horn antenna ranging 1 GHz to 18 GHz, and the free space gains are obtained by Friis transmission equation. The method is checked by Standard Site Method in open area test site. The results show comparatively good agreement except for 18 GHz.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.24-31
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• 2020

In this paper, the deep convolutional neural network for sound event detection is studied. Especially, the end-to-end neural network, which generates the detection results from the input audio waveform, is studied for weakly supervised problem that includes weakly-labeled and unlabeled dataset. The proposed system is based on the network structure that consists of deeply-stacked 1-dimensional convolutional neural networks, and enhanced by the skip connection and gating mechanism. Additionally, the proposed system is enhanced by the sound event detection and post processings, and the training step using the mean-teacher model is added to deal with the weakly supervised data. The proposed system was evaluated by the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Task 4 dataset, and the result shows that the proposed system has F₁-scores of 54 % (segment-based) and 32 % (event-based).

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.1 s.24
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• pp.109-116
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• 2006

This paper presents a real time distance measuring device using a W-band linear frequency modulated continuous wave(FMCW) radar and TMS320C6701 digital signal processor(DSP). We used FFT operation for measuring distance with the beat signals and the results of FFT could be converted to distance with ease. We presented how to implement a real time miniaturized hardware system including network protocols using a single DSP core. Also how to control the modulation signal of FMCW system to compensate the VCO nonlinearity using the Time Gating control of DSP is presented. We have shown that the proposed system has good performances for measuring distance in real time via outdoor environment experiments.

• BMB Reports
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• v.40 no.3
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• pp.302-314
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• 2007

N type calcium channels (CaV2.2) play a key role in the gating of transmitter release at presynaptic nerve terminals. These channels are generally regarded as parts of a multimolecular complex that can modulate their open probability and ensure their location near the vesicle docking and fusion sites. However, the proteins that comprise this component remain poorly characterized. We have carried out the first open screen of presynaptic CaV2.2 complex members by an antibody-mediated capture of the channel from purified rat brain synaptosome lysate followed by mass spectroscopy. 589 unique peptides resulted in a high confidence match of 104 total proteins and 40 synaptosome proteome proteins. This screen identified several known CaV2.2 interacting proteins including syntaxin 1, VAMP, protein phosphatase 2A, $G_{o\alpha}$, G$\beta$ and spectrin and also a number of novel proteins, including clathrin, adaptin, dynamin, dynein, NSF and actin. The unexpected proteins were classified within a number of functional classes that include exocytosis, endocytosis, cytoplasmic matrix, modulators, chaperones, and cell-signaling molecules and this list was contrasted to previous reports that catalogue the synaptosome proteome. The failure to detect any postsynaptic density proteins suggests that the channel itself does not exhibit stable trans-synaptic attachments. Our results suggest that the channel is anchored to a cytoplasmic matrix related to the previously described particle web.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.68-73
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• 2020

This paper proposes an asynchronous circuit design methodology using a new Single Gate Sleep Convention Logic (SG-SCL) with advantages such as low area overhead, low power consumption compared with the conventional null convention logic (NCL) methodologies. The delay-insensitive NCL asynchronous circuits consist of dual-rail structures using {DATA0, DATA1, NULL} encoding which carry a significant area overhead by comparison with single-rail structures. The area overhead can lead to high power consumption. In this paper, the proposed single gate SCL deploys a power gating structure for a new {DATA, SLEEP} encoding to achieve low area overhead and low power consumption maintaining high performance during DATA cycle. In this paper, the proposed methodology has been evaluated by a liquid state machine (LSM) for pattern and digit recognition using FPGA and a 0.18 ㎛ CMOS technology with a supply voltage of 1.8 V. the LSM is a neural network (NN) algorithm similar to a spiking neural network (SNN). The experimental results show that the proposed SG-SCL LSM reduced power consumption by 10% compared to the conventional LSM.