• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.119-125
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• 2008

This work involves analyzing partial discharge (PD), which has estimated the detected signal accumulation based on Labview, and analyzing by Fuzzy algorithm. In algorithm, we developed system configuration that detected accumulating PD signal. With practical PD logic implementation of theoretical detected system and hardware implementation, the device for 50kV setup has generated and then has applied with 15k~17kV with 1:1 time probe. Our new class of PD detected algorithm has also compared with PRPDA or Fuzzy algorithm, which has diagnose more conveniently by adding numerical values.

• Journal of Semiconductor Engineering
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• v.1 no.3
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• pp.88-98
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• 2020

As biosensors are widely used in the medical field, flexible devices compatible with live animals have aroused great interest. Especially, significant research has been carried out to develop implantable or skin-attachable devices for real-time bio-signal sensing. From the device point of view, various biosensor types such as field-effect transistors (FETs) and multi-electrode arrays (MEAs) have been reported as diverse sensing strategies. In particular, the flexible FETs and MEAs allow semiconductor engineering to expand its application, which had been impossible with stiff devices and materials. This review summarizes the state-of-the-art research on flexible FET and MEA biosensors focusing on their materials, structures, sensing targets, and methods.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.611-615
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• 2004

A neural prostheses can be designed to permit stimulation of specific sites in the nervous system to restore their functions, lost due to disease or trauma. This study focuses on the feasibility of optoelecronic stimulation into nervous system. Optoelectronic stimulation supplies, power and signal into the implanted optical detector inside the body by optics. It can be effective strategy especially on the retinal prosthesis, because it enables the non-invasive connection between the external source and internal detector through natural optical window 'eye'. Therefore, we designed an effective neural stimulating setup by optically based stimulation. Stimulating on the sciatic nerve of a rat with proper depth probe through optical stimulation needs higher ratio of current spreading through the neural surface, because of high impedance of neural interface. To increase the insertion current spreading into the neuron, we used a parallel low resistance compared to load resistance organic interface and calculated the optimized outer parallel resistance for maximum insertion current with the assumption of limited current by photodiode. Optimized outer parallel resistance was at a range of 500Ω-700Ω and a current was at a level between 580uA and 650uA. Stimulating current efficiency from initial photodiode induced current was between 47.5 and 59.7%. Various amplitude and frequency of the optical stimulation on the sciatic nerve showed the reliable visual tremble, and the action potential was also recorded near the stimulating area. These result demonstrate that optoelectronic stimulation with no bias can be applied to the retinal prosthesis and other neuroprosthetic area.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.239-248
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• 2020

Exact rock classification helps suitable support patterns to be installed. Face mapping is usually conducted to classify the rock mass using RMR (Rock Mass Ration) or Q values. There have been several attempts to predict the grade of rock mass using mechanical data of jumbo drills or probe drills and photographs of excavation surfaces by using deep learning. However, they took long time, or had a limitation that it is impossible to grasp the rock grade in ahead of the tunnel surface. In this study, a method to predict the Q value ahead of excavation surface is developed using recurrent neural network (RNN) technique and it is compared with the Q values from face mapping for verification. Among Q values from over 4,600 tunnel faces, 70% of data was used for learning, and the rests were used for verification. Repeated learnings were performed in different number of learning and number of previous excavation surfaces utilized for learning. The coincidence between the predicted and actual Q values was compared with the root mean square error (RMSE). RMSE value from 600 times repeated learning with 2 prior excavation faces gives a lowest values. The results from this study can vary with the input data sets, the results can help to understand how the past ground conditions affect the future ground conditions and to predict the Q value ahead of the tunnel excavation face.

• The Korean Journal of Nuclear Medicine
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• v.37 no.1
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• pp.43-52
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• 2003

Microarray technology allows the simultaneous analysis of gene expression patterns of thousands of genes, in a systematic fashion, under a similar set of experimental conditions, thus making the data highly comparable. In some cases arrays are used simply as a primary screen loading to downstream molecular characterization of individual gene candidates. In other cases, the goal of expression profiling is to begin to identify complex regulatory networks underlying developmental processes and disease states. Microarrays were originally used with ceil lines or other simple model systems. More recently, microarrays have been used in the analysis of more complex biological tissues including neural systems and the brain. The application of cDNA arrays in neuropsychiatry has lagged behind other fields for a number of reasons. These include a requirement for a large amount of input probe RNA In fluorescent-glass based array systems and the cellular complexity introduced by multicellular brain and neural tissues. An additional factor that impacts the general use of microarrays in neuropsychiatry is the lack of availability of sequenced clone sets from model systems. While human cDNA clones have been widely available, high qualify rat, mouse, and drosophilae, among others are just becoming widely available. A final factor in the application of cDNA microarrays in neuropsychiatry is cost of commercial arrays. As academic microarray facilitates become more commonplace custom made arrays will become more widely available at a lower cost allowing more widespread applications. in summary, microarray technology is rapidly having an impact on many areas of biomedical research. Radioisotope-nylon based microarrays offer alternatives that may in some cases be more sensitive, flexible, inexpensive, and universal as compared to other array formats, such as fluorescent-glass arrays. In some situations of limited RNA or exotic species, radioactive membrane microarrays may be the most practical experimental approach in studying psychiatric and neurodegenerative disorders, and other complex questions in the brain.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Experimental Neurobiology
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• v.26 no.5
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• pp.241-251
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• 2017

Saturation mutagenesis was performed on a single position in the voltage-sensing domain (VSD) of a genetically encoded voltage indicator (GEVI). The VSD consists of four transmembrane helixes designated S1-S4. The V220 position located near the plasma membrane/extracellular interface had previously been shown to affect the voltage range of the optical signal. Introduction of polar amino acids at this position reduced the voltage-dependent optical signal of the GEVI. Negatively charged amino acids slightly reduced the optical signal by 33 percent while positively charge amino acids at this position reduced the optical signal by 80%. Surprisingly, the range of V220D was similar to that of V220K with shifted optical responses towards negative potentials. In contrast, the V220E mutant mirrored the responses of the V220R mutation suggesting that the length of the side chain plays in role in determining the voltage range of the GEVI. Charged mutations at the 219 position all behaved similarly slightly shifting the optical response to more negative potentials. Charged mutations to the 221 position behaved erratically suggesting interactions with the plasma membrane and/or other amino acids in the VSD. Introduction of bulky amino acids at the V220 position increased the range of the optical response to include hyperpolarizing signals. Combining The V220W mutant with the R217Q mutation resulted in a probe that reduced the depolarizing signal and enhanced the hyperpolarizing signal which may lead to GEVIs that only report neuronal inhibition.

• KSBB Journal
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• v.20 no.5 s.94
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• pp.363-370
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• 2005

Neural stem cells (NSCs) of the central nervous system (CNS) have raised a great interest not only for their importance in basic neural development but also for their therapeutic potentials in neurologically degenerative diseases such as Parkinson's, Alzheimer and stroke. During the CNS development, two molecular cascades determine specification of midbrain dopamine system. In one pathway, FGF-8, sonic hedgehog and transcription factor Nurr1 specify dopamine neurotransmitter phenotype. In the other, transcription factors $Lm{\times}lb\;and\;Pt{\times}3$ are required for induction of dopaminergic neurons. In Nurr1 knockout mouse, tyrosine hydroxylase (TH) positive cells fail to appear in substantia nigra, indicating that Nurr1 is essential in specification of dopaminergic cell phenotype. In this study, we used the immortalized human NSCs retrovirally transduced with Nurr1 gene to probe the Nurr1 mediated mechanism to induce dopamine phenotype. While Nurr1 over-expression alone did not generate dopamine phenotype in NSCs, applications of retinoid and forskolin induced expression of TH and AADC mRNAs. In addition, co-cultures of Nurr1 expressing NSCs with human astrocytes induced a marked increase of TH expression. In this co-culture system, the addition of retinoid and forskolin dramatically increased expression of TH. These results indicate that the immortalized human NSCs with Nurr1 gene could have a clinical utility for cell replacement for the Parkinson patients.

• The Korean Journal of Nuclear Medicine
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• v.32 no.3
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• pp.238-249
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• 1998

Purpose: To localize and compare the neural basis of verbal and visual human working memory, we performed functional activation study using $H_2^{15}O$ PET. Materials and Methods: Repeated $H_2^{15}O$ PET scans with one control and three different activation tasks were performed on six right-handed normal volunteers. Each activation task was composed of 13 match-ing trials. On each trial, four targets, a fixation dot and a probe were presented sequentially and subject's task was to press a response button to indicate whether or not the probe was one of the previous targets. Short meaningful Korean words, simple drawings and monochromic pictures of human faces were used as matching objects for verbal or visual memory. All the images were spatially normalized and the differences between control and activation states were statistically analyzed using SPM96. Results: Statistical analysis of verbal memory activation with short words showed activation in the left Broca's area, promoter cortex, cerebellum and right cingulate gyrus. In verbal memory with simple drawings, activation was shown in the larger regions including where activated with short words and left superior temporal cortex, basal ganglia, thalamus, prefrontal cortex, anterior portion of right superior temporal gyrus and right infero-lateral frontal cortex. On the other hand, the visual memory task activated predominantly right-sided structures, especially inferior frontal cortex, supplementary motor cortex and superior parietal cortex. Conclusion: The results are consistent with the hypothesis of the laterality and dissociation of the verbal and visual working memory from the invasive electrophysiological studies and emphasize the pivotal role of frontal cortex and cingulate gyrus in working memory system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.