• Title/Summary/Keyword: neural cell

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A Manufacturing Cell Formantion Algorithm Using Neural Networks (신경망을 이용한 제조셀 형성 알고리듬)

  • 이준한;김양렬
    • Korean Management Science Review
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    • v.16 no.1
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    • pp.157-171
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    • 1999
  • In a increasingly competitive marketplace, the manufacturing companies have no choice but looking for ways to improve productivity to sustain their competitiveness and survive in the industry. Recently cellular manufacturing has been under discussion as an option to be easily implemented without burdensome capital investment. The objective of cellular manufacturing is to realize many aspects of efficiencies associated with mass production in the less repetitive job-shop production systems. The very first step for cellular manufacturing is to group the sets of parts having similar processing requirements into part families, and the equipment needed to process a particular part family into machine cells. The underlying problem to determine the part and machine assignments to each manufacturing cell is called the cell formation. The purpose of this study is to develop a clustering algorithm based on the neural network approach which overcomes the drawbacks of ART1 algorithm for cell formation problems. In this paper, a generalized learning vector quantization(GLVQ) algorithm was devised in order to transform a 0/1 part-machine assignment matrix into the matrix with diagonal blocks in such a way to increase clustering performance. Furthermore, an assignment problem model and a rearrangement procedure has been embedded to increase efficiency. The performance of the proposed algorithm has been evaluated using data sets adopted by prior studies on cell formation. The proposed algorithm dominates almost all the cell formation reported so far, based on the grouping index($\alpha$ = 0.2). Among 27 cell formation problems investigated, the result by the proposed algorithm was superior in 11, equal 15, and inferior only in 1.

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ASIC design of neural network CAM for connectionless serverof ATM network (ATM망의 비연결형 서버를 위한 신경망 연상메모리 ASIC 설계)

  • 최석준;박형근;김환용;백덕수
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.35C no.4
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    • pp.60-68
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    • 1998
  • In this paper, content addressable momory(CAM) using neural network algorithm is proposed to decrease cell loss and process the large amount of data in streaming mode connectionless server at high speed. To overcome problems of area and power dissipation in look-up table using conventional CAM, the proposed neural network CAM is designed to increase linearly address storage bit about increase of address input bit. Its design and imulation is performed by using VHDL and Compass Tool. Also, its layout is performed by using chip compiler, cell-base P&R tool of compass, in 0.8 .mu.m design rule environment.

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Neuron-on-a-Chip technology: Microelectrode Array System and Neuronal Patterning (뉴런온칩 기술: 미세전극칩시스템과 신경세포 패터닝 기술)

  • Nam, Yoon-Key
    • Journal of Biomedical Engineering Research
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    • v.30 no.2
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    • pp.103-112
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    • 2009
  • Neuron-on-a-Chip technology is based on advanced neuronal culture technique, surface micropatterning, microelectrode array technology, and multi-dimensional data analysis techniques. The combination of these techniques allowed us to design and analyze live biological neural networks in vitro using real neurons. In this review article, two underlying technologies are reviewed: Microelectrode array technology and Neuronal patterning technology. There are new opportunities in the fusion of these technologies to apply them in neurobiology, neuroscience, neural prostheses, and cell-based biosensor areas.

Design of an Intelligent Robot Control System Using Neural Network (신경회로망을 이용한 지능형 로봇 제어 시스템 설계)

  • 정동연;서운학;한성현
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.279-279
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    • 2000
  • In this paper, we have proposed a new approach to the design of robot vision system to develop the technology for the automatic test and assembling of precision mechanical and electronic parts fur the factory automation. In order to perform real time implementation of the automatic assembling tasks in the complex processes, we have developed an intelligent control algorithm based-on neural networks control theory to enhance the precise motion control. Implementing of the automatic test tasks has been performed by the real-time vision algorithm based-on TMS320C31 DSPs. It distinguishes correctly the difference between the acceptable and unacceptable defective item through pattern recognition of parts by the developed vision algorithm. Finally, the performance of proposed robot vision system has been illustrated by experiment for the similar model of fifth cell among the twelve cell fur automatic test and assembling in S company.

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Behavior Control of Autonomous Mobile Robots using ECANS1 (진화하는 셀룰라 오토마타를 이용한 자율이동로봇군의 행동제어)

  • Lee, Dong-Wook;Chung, Young-June;Sim, Kwee-Bo
    • Proceedings of the KIEE Conference
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    • 1998.07g
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    • pp.2183-2185
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    • 1998
  • In this paper, we propose a method of designing neural networks using biological inspired developmental and evolutionary concept. The living things are best information processing system in themselves. One individual is developed from a generative cell. And a species of this individual have adapted itself to the environment by evolution. Ontogeny of organism is embodied in cellular automata and phylogeny of species is realized by evolutionary algorithms. The connection among cells is determined by a rule of cellular automata. In order to obtain the best neural networks in the environment, we evolve the arrangement of initial cells. The cell, that is neuron of neural networks, is modeled on chaotic neuron with firing or rest state like biological neuron. A final output of network is measured by frequency of firing state. The effectiveness of the proposed scheme is verified by applying it to navigation problem of robot.

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Immunological Recognition by Artificial Neural Networks

  • Xu, Jin;Jo, Junghyo
    • Journal of the Korean Physical Society
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    • v.73 no.12
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    • pp.1908-1917
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    • 2018
  • The binding affinity between the T-cell receptors (TCRs) and antigenic peptides mainly determines immunological recognition. It is not a trivial task that T cells identify the digital sequences of peptide amino acids by simply relying on the integrated binding affinity between TCRs and antigenic peptides. To address this problem, we examine whether the affinity-based discrimination of peptide sequences is learnable and generalizable by artificial neural networks (ANNs) that process the digital experimental amino acid sequence information of receptors and peptides. A pair of TCR and peptide sequences correspond to the input for ANNs, while the success or failure of the immunological recognition correspond to the output. The output is obtained by both theoretical model and experimental data. In either case, we confirmed that ANNs could learn the immunological recognition. We also found that a homogenized encoding of amino acid sequence was more effective for the supervised learning task.

Analyzing the mechano-bactericidal effect of nano-patterned surfaces by finite element method and verification with artificial neural networks

  • Ecren Uzun Yaylaci;Murat Yaylaci;Mehmet Emin Ozdemir;Merve Terzi;Sevval Ozturk
    • Advances in nano research
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    • v.15 no.2
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    • pp.165-174
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    • 2023
  • The study investigated the effect of geometric structures of nano-patterned surfaces, such as peak sharpness, height, width, aspect ratio, and spacing, on mechano-bactericidal properties. Here, in silico models were developed to explain surface interactions with Escherichia coli. Numerical solutions were performed based on the finite element method and verified by the artificial neural network method. An E. coli cell adhered to the nano surface formed elastic and creep deformation models, and the cells' maximum deformation, maximum stress, and maximum strain were calculated. The results determined that the increase in peak sharpness, aspect ratio, and spacing values increased the maximum deformation, maximum stress, and maximum strain on E. coli cell. In addition, the results showed that FEM and ANN methods were in good agreement with each other. This study proved that the geometrical structures of nano-patterned surfaces have an important role in the mechano-bactericidal effect.

The origin-of-cell harboring cancer-driving mutations in human glioblastoma

  • Lee, Joo Ho;Lee, Jeong Ho
    • BMB Reports
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    • v.51 no.10
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    • pp.481-483
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    • 2018
  • Glioblastoma (GBM) is the most common and aggressive form of human adult brain malignancy. The identification of the cell of origin harboring cancer-driver mutations is the fundamental issue for understanding the nature of GBM and developing the effective therapeutic target. It has been a long-term hypothesis that neural stem cells in the subventricular zone (SVZ) might be the origin-of-cells in human glioblastoma since they are known to have life-long proliferative activity and acquire somatic mutations. However, the cell of origin for GBM remains controversial due to lack of direct evidence thereof in human GBM. Our recent study using various sequencing techniques in triple matched samples such as tumor-free SVZ, tumor, and normal tissues from human patients identified the clonal relationship of driver mutations between GBM and tumor-free SVZ harboring neural stem cells (NSCs). Tumor-free SVZ tissue away from the tumor contained low-level GBM driver mutations (as low as 1% allelic frequency) that were found in the dominant clones in its matching tumors. Moreover, via single-cell sequencing and microdissection, it was discovered that astrocyte-like NSCs accumulating driver mutations evolved into GBM with clonal expansion. Furthermore, mutagenesis of cancer-driving genes of NSCs in mice leads to migration of mutant cells from SVZ to distant brain and development of high-grade glioma through the aberrant growth of oligodendrocyte precursor lineage. Altogether, the present study provides the first direct evidence that NSCs in human SVZ is the cell of origin that develops the driver mutations of GBM.

Mechanisms of 5-azacytidine-induced damage and repair process in the fetal brain

  • Ueno, Masaki
    • Proceedings of the Korean Society of Toxicology Conference
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    • 2006.11a
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    • pp.55-64
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    • 2006
  • The fetal central nervous system (CNS) is sensitive to diverse environmental factors, such as alcohol, heavy metals, irradiation, mycotoxins, neurotransmitters, and DNA damage, because a large number of processes occur during an extended period of development. Fetal neural damage is an important issue affecting the completion of normal CNS development. As many concepts about the brain development have been recently revealed, it is necessary to compare the mechanism of developmental abnormalities induced by extrinsic factors with the normal brain development. To clarify the mechanism of fetal CNS damage, we used one experimental model in which 5-azacytidine (5AZC), a DNA damaging and demethylating agent, was injected to the dams of rodents to damage the fetal brain. 5AzC induced cell death (apoptosis)and cell cycle arrest in the fetal brain, and it lead to microencephaly in the neonatal brain. We investigated the mechanism of apoptosis and cell cycle arrest in the neural progenitor cells in detail, and demonstrated that various cell cycle regulators were changed in response to DNA damage. p53, the guardian of genome, played a main role in these processes. Further, using DNA microarray analysis, tile signal cascades of cell cycle regulation were clearly shown. Our results indicate that neural progenitor cells have the potential to repair the DNA damages via cell cyclearrest and to exclude highly affected cells through the apoptotic process. If the stimulus and subsequent DNA damage are high, brain development proceeds abnormally and results in malformation in the neonatal brain. Although the mechanisms of fetal brain injury and features of brain malformation afterbirth have been well studied, the process between those stages is largely unknown. We hypothesized that the fetal CNS has the ability to repair itself post-injuring, and investigated the repair process after 5AZC-induced damage. Wefound that the damages were repaired by 60 h after the treatment and developmental processes continued. During the repair process, amoeboid microglial cells infiltrated in the brain tissue, some of which ingested apoptotic cells. The expressions of genes categorized to glial cells, inflammation, extracellular matrix, glycolysis, and neurogenesis were upregulated in the DNA microarray analysis. We show here that the developing brain has a capacity to repair the damage induced by the extrinsic stresses, including changing the expression of numerous genes and the induction of microglia to aid the repair process.

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An Aminopropyl Carbazole Derivative Induces Neurogenesis by Increasing Final Cell Division in Neural Stem Cells

  • Shin, Jae-Yeon;Kong, Sun-Young;Yoon, Hye Jin;Ann, Jihyae;Lee, Jeewoo;Kim, Hyun-Jung
    • Biomolecules & Therapeutics
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    • v.23 no.4
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    • pp.313-319
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    • 2015
  • P7C3 and its derivatives, 1-(3,6-dibromo-9H-carbazol-9-yl)-3-(p-tolylamino)propan-2-ol (1) and N-(3-(3,6-dibromo-9H-carbazol-9-yl)-2-hydroxypropyl)-N-(3-methoxyphenyl)-4-methylbenzenesulfonamide (2), were previously reported to increase neurogenesis in rat neural stem cells (NSCs). Although P7C3 is known to increase neurogenesis by protecting newborn neurons, it is not known whether its derivatives also have protective effects to increase neurogenesis. In the current study, we examined how 1 induces neurogenesis. The treatment of 1 in NSCs increased numbers of cells in the absence of epidermal growth factor (EGF) and fibroblast growth factor 2 (FGF2), while not affecting those in the presence of growth factors. Compound 1 did not induce astrocytogenesis during NSC differentiation. 5-Bromo-2'-deoxyuridine (BrdU) pulsing experiments showed that 1 significantly enhanced BrdU-positive neurons. Taken together, our data suggest that 1 promotes neurogenesis by the induction of final cell division during NSC differentiation.