• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.209-214
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• 2021

Panoptic segmentation, which is now widely used in computer vision such as medical image analysis, and autonomous driving, helps understanding an image with holistic view. It identifies each pixel by assigning a unique class ID, and an instance ID. Specifically, it can classify 'thing' from 'stuff', and provide pixel-wise results of semantic prediction and object detection. As a result, it can solve both semantic segmentation and instance segmentation tasks through a unified single model, producing two different contexts for two segmentation tasks. Semantic segmentation task focuses on how to obtain multi-scale features from large receptive field, without losing low-level features. On the other hand, instance segmentation task focuses on how to separate 'thing' from 'stuff' and how to produce the representation of detected objects. With the advances of both segmentation techniques, several panoptic segmentation models have been proposed. Many researchers try to solve discrepancy problems between results of two segmentation branches that can be caused on the boundary of the object. In this survey paper, we will introduce the concept of panoptic segmentation, categorize the existing method into two representative methods and explain how it is operated on two methods: top-down method and bottom-up method. Then, we will analyze the performance of various methods with experimental results.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• Restorative Dentistry and Endodontics
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• v.27 no.6
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• pp.587-599
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• 2002

Extracellular single unit recordings were made from the ventral posteromedial thalamic (VPM) nociceptive neurons to determine mechanoreceptive field (RF) and response properties. A total of 44 VPM thalamic nociceptive neurons were isolated from rats anesthetized with urethane-chloralose. Based on responses to various mechanical stimuli including touch, pressure and pinch applied to the RF, 32 of 44 neurons were classified as nociceptive specific (NS) neuron. The other 12 neurons, classified as wide dynamic range (WDR), showed a graded response to increasingly intense stimuli, with a maximum discharge to noxious pinch. The VPM nociceptive neurons showed various spontaneous activity ranged from 0-6 Hz. They were located throughout the VPM, and had an contralateral RF including mainly intraoral (and perioral) regions. The RF size was relatively small, and very few neurons had a receptive field involving 3 trigeminal divisions. The NS neurons activated only by pressure and pinch stimuli had high mechanical thresholds compared to WDR neurons activated also by touch stimuli. The VPM nociceptive neurons were tested with suprathershold graded mechanical stimuli. Most of 21 NS and 8 WDR neurons showed a progressive increase in number of spikes as mechanical stimulus intensity was increased. In some neurons, the responses reached a peak before the highest intensity was given. Application of 5 mM $CoCl_2{\;}(10{\;}{\mu}\ell)$ solution to the trigeminal subnucleus caudalis did not produce any significant changes in the spontaneous activity, RF size, mechanical threshold, and response to suprathreshold mechanical stimuli of 9 VPM nociceptive neurons tested. 17 of 33 VPM nociceptive neurons responded to noxious heat as well as noxious mechanical stimuli applied to their RF. Application of the mustard oil, a small-fiber excitant and inflammatory irritant, to the right maxillary first molar tooth pulp induced an immediate but short-lasting neuronal discharges upto approximately 4 min in 16 of 42 VPM nociceptive neurons. These results suggest that VPM thalamic nucleus may contribute to the sensory discriminative aspect of orofacial nociception.

• Restorative Dentistry and Endodontics
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• v.24 no.4
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• pp.614-622
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• 1999

Recent studies have implicated that more rostral components of the trigeminal spinal nucleus including subnucleus oralis (Vo) in orofacial nociceptive mechanisms. Since there is only limited electrophysiological evidence, the present study was initiated to characterize the receptive field and response properties of malls nociceptive neurons in chloralose/urethan-anesthetized rats. Single neuronal activity was recorded in right subnucleus oralis, and types of nociceptive neurons classified wide dynamic range (WDR), NS (nociceptive specific) and deep nociceptive (D) and the mechanoreceptive field (RF) and response properties were determined. A total of 34 nociceptive neurons could be subdivided into 17WDR neurons, 12NS neurons and 5D neurons. Vo nociceptive neurons had RF involving maxillary and/or mandibular divisions mainly located in the intraoral and/or perioral regions. Majority of Vo nociceptive neurons showed spontaneous activity less than 1Hz. The NS and D neurons activated only by heavy pressure and/or pinch stimuli had high mechanical thresholds compared to WDR neurons activated also by tactile stimuli. Vo nociceptive neurons showed a progressive increase of response to the graded mechanical stimuli. 39% of Vo nociceptive neurons received C-fiber electrical input as well as A-fiber electrical input from their RF, and 45% of them responded to electrical stimulation of the right maxillary first molar. 41% of Vo nociceptive neurons responded to noxious heat applied to their RF, and 18% of them showed an immediate burst of discharges following MO application to the right maxillary first molar pulp. These results indicate that Vo is involved in the transmission of nociceptive information mainly coming from intraoral or perioral region including tooth pulp.

• Progress in Medical Physics
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• v.9 no.2
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• pp.95-104
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• 1998

The dynamic properties of the 3rd-order neuron of the retina was investigated by using conventional intracellular recording techniques. Experiments were performed in the superfused retina-eyecup preparation of the channel catfish, Ictalurus punctatus. The cornea, iris, lens, and vitreous were removed by absorption with Kimwipe tissue under the dissection microscope thereby exposing the retina in a hemi -eyecup. The electrical signal was amplified by electrometer, viewed on oscilloscope. Regular signals from the cells were recorded on a penwriter and stored by data recorder and computer. Full-field, spot or annular light stimuli were generated on a computer monitor and focused onto the retina. Baclofen hyperpolarized the dark membrane potential, suppressed sustained component and enhanced transient component of the ON-sustained cell with a large transient component, but did not affect the surround antagonism of the cell. Baclofen selectively suppressed responses evoked by moving bar light stimuli on the ON-OFF transient cell. The results suggest that transient cells have directional selectivity in the inner retina. These dynamic properties of amacrine and ganglion cells were modulated by baclofen. Therefore, it is presumed that there is baclofen-induced directional selectivity in ON-OFF transient cells in the catfish retina.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Korean Neurosurgical Society
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• v.29 no.8
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• pp.985-994
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• 2000

Objective : There are some advantages of trigeminal evoked potential(TEP) recording compared to other somatosensory evoked potential(SSEP) recordings. The trigeminal sensory pathway has a pure sensory nerve branch, a broader receptive field in cerebral cortex, and a shorter pathway. Despite these advantages, there is little agreement as to what constitutes a normal response and what wave forms truly characterize the intraoperative TEP. This study presents the normative data of TEP recorded on the epidural surface of the rat with a platinum ball electrode. Materials & Methods : Under general anesthesia with urethane, the adult Sprague-Dawley male rats(300-350g) were given electrical stimulation with two stainless steel electrodes which were inserted into the subcutaneous layer of the area around whiskers. A reference electrode was positioned in the temporalis muscle ipsilateral to the recording site. Results : TEPs were recorded in the Par I area of somatosensory cortex and recorded most apparently on the point of 2mm posterior from the bregma and 6mm lateral from the midline. The typical wave form consisted of 5 peaks (N1-P1-N2-P2-N3 according to emerging order, upward negativity). Each latency to corresponding peaks was not influenced by the different intensities of stimulation, especially from 1 to 5mA. Average latencies of 5 peaks were in the following order ; 7.7, 11.1, 15, 22.3, 29.4ms. There was also no significant difference between latencies before and after administration of muscle relaxant(pancuronium). For the electrophysiological localization of recorded waves, the action potential of a single unit was recorded with glass microelectrode(filled with 2M NaCl, $3-5M{\Omega}$) in the thalamus of rat. A sharp wave was recorded in the VPM nucleus, in which the latency was shorter than that of N1. This suggests that all 5 peaks were generated by neural activities in the suprathalamic pathway. Conclusion : In terms of recording near-field potentials, our data also suggests that TEP in the rat may be superior to other SSEPs. In overall, these results may afford normative data for the studies of supratentorial lesions such as hydrocephalus or cerebral ischemia which can have an influence on near-field potentials.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.183-185
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• 2009

As imaging technology develops, magnetic resonance imaging (MRI) techniques have contributed to the understanding of brain function by providing anatomical structure of the brain and functional imaging related to information processing. Manganese-enhanced MRI (MEMRI) techniques can provide useful information about functions of the nervous system. However, systematic studies regarding information processing of pain have not been conducted. The purpose of this study was to detect brain activation during painful electrical stimulation using MEMRI with high spatial resolution. Male Sprague-Dawley rats (250-300 g) were divided into 3 groups: normal control, sham stimulation, and electric stimulation. Rats were anesthetized with 2.5% isoflurane for surgery. Polyethylene catheter (PE-10) was placed in the external carotid artery to administrate mannitol and MnCl2. The blood brain barrier (BBB) was broken by 20% D-mannitol under anesthesia mixed with urethane and a-chloralose. The hind limb was electrically stimulated with a 2Hz (10V) frequency while MnCl2 was infused. Brain activation induced by electrical stimulation was detected using a 4.7 T MRI. Remarkable signal enhancement was observed in the primary sensory that corresponds to sensory tactile stimulation at the hind limb region. These results suggest that signal enhancement is related to functional activation following electrical stimulation of the peripheral receptive field.

• The Korean Journal of Physiology and Pharmacology
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• v.2 no.1
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• pp.9-19
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• 1998

The present study was primarily carried out to characterize the properties of the spinomesencephalic tract (SMT) neurons that project from the upper cervical spinal segments to the midbrain. It was also investigated whether these neurons received convergent afferent inputs from other sources in addition to cervical inputs. Extracellular single unit recordings were made from neurons antidromically activated by stimulation of midbrain. Recording sites were located in lamina $I{\sim}VIII\;of\;C1{\sim}C3$ segments of spinal cord. Receptive field (RF) and response properties to mechanical stimulation were studied in 71 SMT neurons. Response profiles were classified into six groups: complex (Comp, n=9), wide dynamic range (WDR, n=16), low threshold (LT, n=5), high threshold (HT, n=6), deep/tap (Deep, n=10), and non- responsive (NR, n=25). Distributions of stimulation and recording sites were not significantly different between SMT groups classified upon their locations and/or response profiles. Mean conduction velocity of SMT neurons was $16.7{\pm}1.28\;m/sec$. Conduction velocities of SMTs recorded in superficial dorsal horn (SDH, n=15) were significantly slower than those of SMTs recorded in deep dorsal horn (DDH, n=18), lateral reticulated area (LRA, n=21), and intermediate zone and ventral horn (IZ/VH, n=15). Somatic RFs for SMTs in LRA and IZ/VH were significantly larger than those in SDH and DDH. Five SMT units (4 Comps and 1 HT) had inhibitory somatic RFs. About half (25/46) of SMT units have their RFs over trigeminal dermatome. Excitabilities of 5/12 cells and 9/13 cells were modulated by stimulation of ipsilateral phrenic nerve and vagus nerve, respectively. These results suggest that upper cervical SMT neurons are heterogenous in their function by showing a wide range of variety in location within the spinal gray matter, in response profile, and in convergent afferent input.

• The Korean Journal of Physiology and Pharmacology
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• v.3 no.4
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• pp.365-373
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• 1999

Somatostatin (SOM) is one of the major neuropeptides in dorsal root ganglion cells, but its role in spinal nociceptive process has not been well known. In present study we aimed to investigate the effect of SOM on the response of dorsal horn neurons to the various types of peripheral nociceptive stimuli in anesthetized cats. Using carbon-filament microelectrode, the single cell activities of wide dynamic range neurons were recorded from the lumbosacral enlargement after noxious mechanical (squeeze), thermal (radiant heat lamp) and cold (dry ice) stimulation to the receptive field. Sciatic nerve was stimulated electrically to evoke $A\;{\delta}-$ and C-nociceptive responses. SOM analogue, octreotide $(10\;{\mu}g/kg),$ was applied intravenously and the results were compared with those of morphine (2 mg/kg, MOR). Systemic SOM decreased the cellular responses to the noxious heat and the mechanical stimulation, but increased those to the cold stimulation. In the responses to the electric stimuli of sciatic nerve, $A\;{\delta}-nociceptive$ response was increased by SOM, while C-nociceptive response was decreased. On the other hand, MOR inhibited the dorsal horn cell responses to all the noxious stimuli. From the above results, it is concluded that SOM suppresses the transmission of nociceptive heat and mechanical stimuli, especially via C-fiber, while it facilitates those of nociceptive cold stimuli via $A\;{\delta}-fiber$.