• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.48 no.2
• /
• pp.55-60
• /
• 2011

Most of neural network chips use an analog-type maximum selector circuit (MS). As the increase of integration level, the analog MS has difficulties in achieving sufficient resolution. Contrary, the digital-type MS is easy to get high resolution but slower than its analog counterparts. A new high-speed digital MS circuit called MSIT (Maximum Selector with Internal Trigger-signal) is presented in this paper. The MSIT has been designed to achieves both the high reliability by using trigger-signals and high speed by removing the unnecessary waiting times. The response time of MSIT is 3.4ns for 32 data with 10-bit resolution in the simulation with 1.2V, $0.13{\mu}m$-process model parameters, which is much faster than its analog counterparts. It shows that digital MS circuits like MSIT can achieve higher speed as well as higher resolution than analog MS circuits.

• Annals of Clinical Neurophysiology
• /
• v.3 no.2
• /
• pp.128-135
• /
• 2001

Background : Tremor is uncommon manifestation of stroke. Therefore a few cases have been reported until now. There is still uncertainty about the characteristics of post-stroke tremor. Furthermore the pathogenesis and responsible structures of post-stroke tremor are not precisely known. We have recently experienced 34 cases of post-stroke tremor for the past 6 years. We analysed the clinical features and electrophysiologic findings of post-stroke tremor to evaluate the general characteristics and to analogize the possible pathogenetic mechanisms of post-stroke tremor. Methods : The clinical characteristics of post-stroke tremor were summarized in according to the onset time, involved body parts, types, tremor frequencies, neuroradiologic findings, and associated symptoms. The tremor frequencies were recorded by using a gyroscope. The spectral analysis of tremor frequencies were done automatically with Motus I soft ware. Results : Tremor onset were remarkably varied. Some patients showed a tremor appearing at the onset of a stroke and other patients showed delayed-onset tremor 10 years after a stroke. Tremor frequencies were also much varied. The range of hand tremor frequencies were from 1.5 to 12 Hz. Lesions were found in 31 cases(infarction 27, hemorrhage 4) on neuroimaging. In the cases of cerebral infarctions, 7 cases showed multiple small vessel diseases and 20 cases showed cerebral vessel lesions. The most commonly involved cerebral vessel lesion was the middle cerebral artery territory Several different clinical patterns of post-stroke tremor were identified. Conclusions : There are some evidences from the data summarized here to suggest that several pathogenetic mechanisms including central oscillators could be involved for the development of tremors and that tremor generating neural circuits could be more complex than previously suggested neural circuits.

• Korean Journal of Construction Engineering and Management
• /
• v.20 no.6
• /
• pp.34-43
• /
• 2019

The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.

• Development and Reproduction
• /
• v.14 no.1
• /
• pp.35-41
• /
• 2010

Mammalian reproduction is regulated by a feedback circuit of the key reproductive hormones such as GnRH, gonadotropin and sex steroids on the hypothalamic-pituitary-gonadal axis. In particular, the onset of female puberty is triggered by gain of a pulsatile pattern and increment of GnRH secretion from hypothalamus. Previous studies including our own clearly demonstrated that genistein (GS), a phytoestrogenic isoflavone, altered the timing of puberty onset in female rats. However, the brain-specific actions of GS in female rats has not been explored yet. The present study was performed to examine the changes in the activities of GnRH neurons and their neural circuits by GS in female rats. Concerning the drug delivery route, intracerebroventricular (ICV) injection technique was employed to eliminate the unwanted actions on the extrabrain tissues which can be occurred if the testing drug is systemically administered. Adult female rats (PND 100, 210-230 g BW) were anaesthetized, treated with single dose of GS ($3.4{\mu}g$/animal), and sacrificed at 3 hrs post-injection. To determine the transcriptional changes of reproductive hormone-related genes in hypothalamus, total RNAs were extracted and applied to the semi-quantitative reverse transcription polymerase chain reaction (RT-PCR). ICV infusion of GS significantly raised the transcriptional activities of enhanced at puberty1 (EAP-1, p<0.05), glutamic acid decarboxylase (GAD67, p<0.01) which are known to modulate GnRH secretion in the hypothalamus. However, GS infusion could not change the mRNA level of nitric oxide synthase 2 (NOS-2). GS administration significantly increased the mRNA levels of KiSS-1 (p<0.001), GPR54 (p<0.001), and GnRH (p<0.01) in the hypothalami, but decreased the mRNA levels of LH-$\beta$ (p<0.01) and FSH-$\beta$ (p<0.05) in the pituitaries. Taken together, the present study indicated that the acute exposure to GS could directly activate the hypothalamic GnRH modulating system, suggesting the GS's disrupting effects such as the early onset of puberty in immature female rats might be derived from premature activation of key reproduction related genes in hypothalamus-pituitary neuroendocrine circuit.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.14 no.5
• /
• pp.658-665
• /
• 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 $437{\mu}m{\times}501{\mu}m$.

• Korean Journal of Biological Psychiatry
• /
• v.24 no.4
• /
• pp.225-234
• /
• 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.

• Journal of Ocean Engineering and Technology
• /
• v.35 no.4
• /
• pp.287-295
• /
• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• 전자공학회논문지 IE
• /
• v.44 no.3
• /
• pp.12-22
• /
• 2007

In this paper, we designed of the high speed digital neuron processor in order to digital neural networks. we designed of the MAC(Multiplier and Accumulator) operation unit used residue number system without carry propagation for the high speed operation. and we implemented sigmoid active function which make it difficult to design neuron processor. The Designed circuits are descripted by VHDL and synthesized by Compass tools. we designed of MAC operation unit and sigmoid processing unit are proved that it could run time 19.6 nsec on the simulation and decreased to hardware size about 50%, each order. Designed digital neuron processor can be implementation in parallel distributed processing system with desired real time processing, In this paper.

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

• Journal of information and communication convergence engineering
• /
• v.4 no.1
• /
• pp.18-22
• /
• 2006

A small-sized gas identification system has been fabricated and characterized using an integrated gas sensor array and artificial neural-network. The sensor array consists of four thick-film oxide semiconductor gas sensors whose sensing layers are $In_{2}O_{3}-Sb_{2}O_{5}-Pd-doped\;SnO_2$ + Pd-coated layer, $La_{2}O_{5}-PdCl_{2}-doped\;SnO_2,\;WO_{3}-doped\;SnO_{2}$ + Pt-coated layer and $ThO_{2}-V_{2}O_{5}-PdCl_{2}\;doped\;SnO_{2}$. The small-sized gas identification instrument is composed of a GMS 81504 containing an internal ROM (4k bytes), a RAM (128 bytes) and four-channel AD converter as MPU, LEDs for displaying alarm conditions for three gases (liquefied petroleum gas: LPG, liquefied natural gas: LNG and carbon monoxide: CO) and interface circuits for them. The instrument has been used to identify alarm conditions for three gases among the real circumstances and the identification has been successfully demonstrated.