• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.705-708
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• 2001

Nowadays there are being done many researches on chaos phenomenon among many kinds of people. Currently, already many applications has been developed, and they applied this phenomenon to engineering problem. now we have tried to make it possible to use secure communication through hyperchaotic synchronization system using 1-dimensional CNN(Cellular Neural Network). we focused on materializing secure communication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.899-902
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state variable in the SC-CNN.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.310-315
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• 2003

In this paper, We introduce an embedding driven synchronization method using SC-CNN(State-Controlled Cellular Neural Network) which has the purpose to secure communication method through the embedding driven synchronization method in the SC-CNN. we proposed new embedding driven synchronization that this method is only using one state variable compare to the general driven synchronization methods which is using all state variables. In this paper, We achieved the usage of embedding driven synchronization and we also applied it to secure communication.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.365-370
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• 2004

In this paper, complexity secure communication was presented. The complexity circuit is used to State-Controlled Cellular Neural Network(SC-CNN). We make a complexity circuit using SC-CNN with the N-double scroll. A complexity circuit is created by applying identical n-double scrolls with coupled method, to each cell. complexity synchronization was achieved using drive response synchronization between the transmitter and receiver about each state in the SC-CNN. From the result of the recovery signal through the demodulation method in the receiver. We shown that recovery quality in the receiver is the similar to other secure communication methods.

• Molecules and Cells
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• v.41 no.10
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• pp.881-888
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• 2018

During the cortical development, cells in the brain acquire somatic mutations that can be implicated in various neurodevelopmental disorders. There is increasing evidence that brain somatic mutations lead to sporadic form of epileptic disorders with previously unknown etiology. In particular, malformation of cortical developments (MCD), ganglioglioma (GG) associated with intractable epilepsy and non-lesional focal epilepsy (NLFE) are known to be attributable to brain somatic mutations in mTOR pathway genes and others. In order to identify such somatic mutations presenting as low-level in epileptic brain tissues, the mutated cells should be enriched and sequenced with high-depth coverage. Nevertheless, there are a lot of technical limitations to accurately detect low-level of somatic mutations. Also, it is important to validate whether identified somatic mutations are truly causative for epileptic seizures or not. Furthermore, it will be necessary to understand the molecular mechanism of how brain somatic mutations disturb neuronal circuitry since epilepsy is a typical example of neural network disorder. In this review, we overview current genetic techniques and experimental tools in neuroscience that can address the existence and significance of brain somatic mutations in epileptic disorders as well as their effect on neuronal circuitry.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.91-102
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• 2003

There are two goals in this paper. The one is development of existing CA(Cellular Automata) model to explain more realistic deceleration process to stop. The other is the application of the updated CA model to forecasting simulation to predict short term link travel time that takes a key rule in finding the shortest path of route guidance system of ITS. Car following theory of CA models don't makes not response to leading vehicle's velocity but gap or distance between leading vehicles and following vehicles. So a following vehicle running at free flow speed must meet steeply sudden deceleration to avoid back collision within unrealistic braking distance. To tackle above unrealistic deceleration rule, “Slow-to-stop” rule is integrated into NaSch model. For application to interrupted traffic flow, this paper applies “Slow-to-stop” rule to both normal traffic light and random traffic light. And vehicle packet method is used to simulate a large-scale network on the desktop. Generally, time series data analysis methods such as neural network, ARIMA, and Kalman filtering are used for short term link travel time prediction that is crucial to find an optimal dynamic shortest path. But those methods have time-lag problems and are hard to capture traffic flow mechanism such as spill over and spill back etc. To address above problems. the CA model built in this study is used for forecasting simulation to predict short term link travel time in Kangnam district network And it's turned out that short term prediction simulation method generates novel results, taking a crack of time lag problems and considering interrupted traffic flow mechanism.

• Molecules and Cells
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• v.43 no.6
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• pp.551-571
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• 2020

Nuclear receptor-related 1 (Nurr1) protein has been identified as an obligatory transcription factor in midbrain dopaminergic neurogenesis, but the global set of human NURR1 target genes remains unexplored. Here, we identified direct gene targets of NURR1 by analyzing genome-wide differential expression of NURR1 together with NURR1 consensus sites in three human neural stem cell (hNSC) lines. Microarray data were validated by quantitative PCR in hNSCs and mouse embryonic brains and through comparison to published human data, including genome-wide association study hits and the BioGPS gene expression atlas. Our analysis identified ~40 NURR1 direct target genes, many of them involved in essential protein modules such as synapse formation, neuronal cell migration during brain development, and cell cycle progression and DNA replication. Specifically, expression of genes related to synapse formation and neuronal cell migration correlated tightly with NURR1 expression, whereas cell cycle progression correlated negatively with it, precisely recapitulating midbrain dopaminergic development. Overall, this systematic examination of NURR1-controlled regulatory networks provides important insights into this protein's biological functions in dopamine-based neurogenesis.

• IMMUNE NETWORK
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• v.9 no.3
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• pp.90-97
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• 2009

Background: CD147, as a cellular receptor for cyclophilin A (CypA), is a multifunctional protein involved in tumor invasion, inflammation, tissue remodeling, neural function, and reproduction. Recent observations showing the expression of CD147 in leukocytes indicate that this molecule may have roles in inflammation. Methods: In order to investigate the role of CD147 and its ligand in the pathogenesis of atherosclerosis, human atherosclerotic plaques were analyzed for the expression pattern of CD147 and CypA. The cellular responses and signaling molecules activated by the stimulation of CD147 were then investigated in the human macrophage cell line, THP-1, which expresses high basal level of CD147 on the cell surface. Results: Staining of both CD147 and CypA was detected in endothelial cell layers facing the lumen and macrophage-rich areas. Stimulation of CD147 with its specific monoclonal antibody induced the expression of matrix metalloproteinase (MMP)-9 in THP-1 cells and it was suppressed by inhibitors of both ERK and NF-${\kappa}B$. Accordingly, the stimulation of CD147 was observed to induce phosphorylation of ERK, phosphorylation-associated degradation of $I{\kappa}B$, and nuclear translocation of NF-${\kappa}B$ p65 and p50 subunits. Conclusion: These results suggest that CD147 mediates the inflammatory activation of macrophages that leads to the induction of MMP-9 expression, which could play a role in the pathogenesis of inflammatory diseases such as atherosclerosis.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1143-1155
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• 1999

셀룰러 방식의 이동통신 시스템에서 전파의 유효신호 도달범위를 예측하기 위해서는 전파전파 모델을 이용한 예측기법이 주로 사용된다. 그러나, 전파과정에서 주변 지형지물에 의해 발생하는 전파손실은 매우 복잡한 비선형적인 특성을 가지며 수식으로는 정확한 표현이 불가능하다. 본 논문에서는 신경회로망의 함수 근사화 능력을 이용하여 전파손실 예측모델을 생성하는 방법을 제안한다. 즉, 전파손실을 송수신 안테나간의 거리, 송신안테나의 특성, 장애물 투과영향, 회절특성, 도로, 수면에 의한 영향 등과 같은 전파환경 변수들의 함수로 가정하고, 신경회로망 학습을 통하여 함수를 근사화한다. 전파환경 변수들이 신경회로망 입력으로 사용되기 위해서는 3차원 지형도와 벡터지도를 이용하여 전파의 반사, 회절, 산란 등의 물리적인 특성이 고려된 특징 추출을 통해 정량적인 수치들을 계산한다. 이와 같이 얻어진 훈련데이타를 이용한 신경회로망 학습을 통해 전파손실 모델을 완성한다. 이 모델을 이용하여 서울 도심 지역의 실제 서비스 환경에 대한 타 모델과의 비교실험결과를 통해 제안하는 모델의 우수성을 보인다.Abstract In cellular mobile communication systems, wave propagation models are used in most cases to predict cell coverage. The amount of propagation loss induced by the obstacles in the propagation path, however, is a highly non-linear function, which cannot be easily represented mathematically. In this paper, we introduce the method of producing propagation loss prediction models by function approximation using neural networks. In this method, we assume the propagation loss is a function of the relevant parameters such as the distance from the base station antenna, the specification of the transmitter antenna, obstacle profile, diffraction effect, road, and water effect. The values of these parameters are produced from the field measurement data, 3D digital terrain maps, and vector maps as its inputs by a feature extraction process, which takes into account the physical characteristics of electromagnetic waves such as reflection, diffraction and scattering. The values produced are used as the input to the neural network, which are then trained to become the propagation loss prediction model. In the experimental study, we obtain a considerable amount of improvement over COST-231 model in the prediction accuracy using this model.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.965-976
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• 2020

We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.