• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.463-468
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• 2020

Artificial intelligence researches are being applied and developed in various fields. In this paper, we build a neural network by using the method of implementing artificial intelligence in the form of spiking natural networks (SNN), the next-generation of artificial intelligence research, and analyze how the number of neurons in that neural networks affect the performance of the neural networks. We also analyze how the performance of neural networks changes while increasing the amount of neural network learning. The findings will help optimize SNN-based neural networks used in each field.

• Journal of the Korean Society of Tobacco Science
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• v.29 no.2
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• pp.118-124
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• 2007

GRLs(Guidance Residue Levels) of agricultural chemicals for tobacco are recommended by the CORESTA Agro-Chemical Advisory Committee guide. In the GRLs list, organochlorine group is one of pesticides commonly used on tobacco cultivation. In this model study, the quantitative correlation in the transfer rate of pesticide residue into tobacco smoke by spiking of organochlorine pesticides to cigarette and pyrolysates were investigated. The spiking concentration referred to the range of GRLs list and the organochlorine pesticides in mainstream smoke were analyzed by GC-MS. For the understanding of the composition variation versus temperature, the behavior of pesticides was investigated by pyrolysis-gas chromatography-mass spectrometry(Py-GC-MS). In this study, the transfer rate of pesticide residue into tobacco smoke at four different spiking concentration and the composition of pyrolysates were analyzed differently. At $10\;{\mu}g/cig$ spiking concentrations, the organochlorine pesticides were transferred into tobacco smoke in $0.02\;{\sim}\;10.19\;%$ each of component and the most of pesticides were pyrolyzed during smoking. It was found that the decomposition compounds from organochlorine pesticides were mainly composed of oxygenated and nitrogenous compounds. This study could estimate that the transfer rate of pesticides into tobacco smoke is very small amount.

• Tuberculosis and Respiratory Diseases
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• v.41 no.3
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• pp.302-306
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• 1994

A 33-year old male was admitted due to continuous high spiking fever for 2 months via local clinic. He had been diagnosed pulmonary tuberculosis at local clinic. However, spiking fever had not been controlled by anti-tuberculous medications. Chest PA showed confluent consolidation on right upper & mid-lung field. 5 anti-tuberculous regimens(Streptomycin, Isoniazid, Rifampin, Ethambutol, Pyrazinamaide) were administered initially and steroid therapy was followed for relieving toxic symptoms Very slowly resolved chest X-ray lesion and continuous fever suggested the possibility of misdiagnosis. After 60th hospital day, the chest X-ray lesion was resolved gradually and fever subsided almost completely. He was discharged on 76th hospital day with anti-tuberculous drugs and steroid(prednisolon), without any other problems except sustained mild fever.

• 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 Korean Journal of Physiology and Pharmacology
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• v.7 no.6
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• pp.317-323
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• 2003

Interneuron diversity is one of the key factors to hinder understanding the mechanism of cortical neural network functions even with their important roles. We characterized inhibitory interneurons in layer II/III of the rat primary visual cortex, using patch-clamp recording and confocal reconstruction, and classified inhibitory interneurons into fast spiking (FS), late spiking (LS), burst spiking (BS), and regular spiking non-pyramidal (RSNP) neurons according to their electrophysiological characteristics. Global parameters to identify inhibitory interneurons were resting membrane potential (＞-70 mV) and action potential (AP) width (＜0.9 msec at half amplitude). FS could be differentiated from LS, based on smaller amplitude of the AP (＜∼50 mV) and shorter peak-to-trough time (P-T time) of the afterhyperpolarization (＜4 msec). In addition to the shorter AP width, RSNP had the higher input resistance (＞200 $M{Omega}$) and the shorter P-T time (＜20 msec) than those of regular spiking pyramidal neurons. Confocal reconstruction of recorded cells revealed characteristic morphology of each subtype of inhibitory interneurons. Thus, our results provide at least four subtypes of inhibitory interneurons in layer II/III of the rat primary visual cortex and a classification scheme of inhibitory interneurons.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1992-1995
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• 2002

This paper presents a novel 4-D chaotic spiking oscillator. The oscillator can generate hyperchaos characterized by two positive Lyapunov exponents. Us-ing a simple test circuit, typical phenomena can be verified in the laboratory.

• Annals of Clinical Neurophysiology
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• v.2 no.2
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• pp.119-124
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• 2000

Background : We studied EEG changes during pilocarpine-induced status epilepticus(SE), a widely used model whose EEG characteristics have not been fully described previously. Methods : Male Sprague-Dawley rats weighing 250-350 grams were used as subjects. SE was induced 5-7 days after placement of chronic epidural electrodes, using 360-380 mg/Kg pilocarpine IP. Rats were observed with continuous EEG recording following pilocarpine injection until end of the SE episode. Results : SE occurred in 10/12 rats studied. SE began with a series of discrete seizures $11.1{\pm}3.93$ minutes after pilocarpine injection. $5.2{\pm}2.71$ seizures occurred over $10.9{\pm}4.62$ minutes, until the EEG converted to a waxing and waning pattern, during which the amplitude and frequency of epileptiform activity increased. After $1.4{\pm}1.82$ minutes, a pattern of continuous high amplitude rapid spiking was established. Continuous spiking continued for $3.4{\pm}0.48$ hours with a very gradual decline in amplitude and frequency, until periodic epileptiform discharges(PEDs) began to occur. The EEG consisted primarily of PEDs for another $7.4{\pm}3.09$ hours, until electrographic generalized seizures began to occur. These continued for $5.8{\pm}4.82$ hours until death. Duration of SE was $17.0{\pm}5.88$ hours. Flat periods were a prominent feature during all EEG patterns in this model. Conclusion : EEG features distinctive in pilocarpine SE(but not unique to it) include flat periods during all patterns and resumption of continuous spiking episodes after the onset of PEDs. The sequence of discrete seizures to waxing and waning to continuous spiking to PEDs was identical to that which has been described in humans and other animal models.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.613-618
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• 2022

Research on artificial intelligence based on SNN (Spiking Neural Networks) is drawing attention as a next-generation artificial intelligence that can overcome the limitations of artificial intelligence based on DNN (Deep Neural Networks) that is currently popular. In this paper, we describe the structure of the SNN compiler, a system SW that generate code from SNN description for neuromorphic computing systems. We also introduce the algorithms used for compiler implementation and present experimental results on how the execution time varies in neuromorphic computing systems depending on the the mapping algorithm. The mapping algorithm proposed in the text showed a performance improvement of up to 3.96 times over a random mapping. The results of this study will allow SNNs to be applied in various neuromorphic hardware.

• Proceedings of the KWS Conference
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• 2010.05a
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• pp.93-93
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• 2010

환경 규제 및 배출가스 규제에 의하여 차량 경량화를 위해 점차적으로 Al합금의 차체 및 부품적용 비율이 점차 확대되고 있다. 이에 따라 알루미늄의 레이저 용접 시 출력, 초점거리, 용접 속도 등 공정 변수의 상관관계와 용접 결함 현상에 의한 관심이 집중된 연구가 많이 발표되었으며, 알루미늄 5000계열의 경우 박판 용접 시 기공, 균열 등 과 같은 결함 현상을 방지하기 위하여, Twin spot laser, Laser-TIG hybrid 등과 같은 공법 적용을 제안되었다. 본 연구에서는 Yb:YAG laser welding 시 Mg 함량이 높은 AA5J32을 소재를 이용하여 박판 겹치기 용접 시 Back side spiking 결함 방지를 위한 레이저 빔 출력 파형을 설계하여 실험을 수행하였다. 또한 파형의 특성에 따라 나타나는 겹치기 용접부의 기계적 특성과 기공에 대해 알아 보고자 하였다.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.20-24
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• 2021

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.