• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.15-15
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• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• KSCI Review
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• v.1 no.2
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• pp.73-94
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• 1995

In this paper, design of efficient NDB(Network Data Base) for the activation plan of chain information network. The DB structure build up, logical structure, store structure, physical structure, the data express for one's record, and the express using linked in the releation of data. Also express as hierarchical model on the DSD(Data Structure Diagram) from the database with logical structure. Each node has express on record type, the linked in course of connective this type, the infuence have efficent of access or search of data, in the design for connection mutually a device of physical, design for database, and construction a form of store for logical. Also activation of chain information network of efficent, using POS(Point Of Sale) system in OSI(Open Systems Interconnection) environment for network standardization, and build up network a design for system.

• BMB Reports
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• v.35 no.4
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• pp.371-376
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• 2002

The receptor activator of nuclear factor kappa B (RANK) is a member of the tumor necrosis factor (TNF) receptor superfamily. It plays a critical role in osteoclast differentiaion, lymph node organogenesis, and mammary gland development. The stimulation of RANK causes the activation of transcription factors NF-${\kappa}B$ and activator protein 1 (AP1), and the mitogen activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). In the signal transduction of RANK, the recruitment of the adaptor molecules, TNF receptor-associated factors (TRAFs), is and initial cytoplasmic event. Recently, the association of the MAPK kinase kinase, transforming growth factor-$\beta$-activated kinase 1 (TAK1), with TRAF6 was shown to mediate the IL-1 signaling to NF-${\kappa}B$ and JNK. We investigated whether or not TAK1 plays a role in RANK signaling. A dominant-negative form of TAK1 was discovered to abolish the RANK-induced activation of AP1 and JNK. The AP1 activation by TRAF2, TRAF5, and TRAF6 was also greatly suppressed by the dominant-negative TAK1. the inhibitory effect of the TAK1 mutant on RANK-and TRAF-induced NF-${\kappa}B$ activation was also observed, but less efficiently. Our findings indicate that TAK1 is involved in the MAPK cascade and NF-${\kappa}B$ pathway that is activated by RANK.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.129-132
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• 2022

Super-resolution is the process of converting a low-quality image into a high-quality image. This study was conducted using ESPCN. In a super-resolution deep neural network, different quality images can be output even when receiving the same input data according to the activation function that determines the weight when passing through each node. Therefore, the purpose of this study is to find the most suitable activation function for super-resolution by applying the activation functions ReLU, ELU, and Swish and compare the quality of the output image for the same input images. The CelebaA Dataset was used as the dataset. Images were cut into a square during the pre-processing process then the image quality was lowered. The degraded image was used as the input image and the original image was used for evaluation. As a result, ELU and swish took a long time to train compared to ReLU, which is mainly used for machine learning but showed better performance.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.79-85
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• 2001

We obtain a solution of inverse kinematic of 3 axis manipulator by using a self-organizing neral network(SONN) with a fuzzy compensator. The self-organizing neural network using the gaussian potential function as the activation function has one hidden layer in the first learning time. The network obtains the optimal number of node by increasing the number of hidden layer node through the learning, and the fuzzy compensator has the optimal loaming rate of neutral network. In this results, we can confirmed that the learning rate is improved and the rapid convergence to the steady-state.

• Nutrition Research and Practice
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• v.1 no.2
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• pp.94-99
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• 2007

The immunostimulating activities of mucilage fraction from yam were investigated. The proliferation of BSA-primed lymph node cells was enhanced between 4.1- to 10.9-fold compare to control, when cultured with 1 to $25{\mu}g/mL$ of yam-mucilage fraction. It showed strong immunostimulating activity than ginseng extract and as remarkable as Bifidobacterium adolescentis M101-4 known as a positive immunostimulator. Mitogenicity to lymph node cells was fully induced by concanavalin A and lipopolysaccharide. The proliferation of splenocytes and Peyer's patch cells was enhanced between 5.0- to 14.1-fold and 2.4- to 6.4-fold, respectively, when cultured with 1 to $25{\mu}g/mL$ of yam-mucilage fraction. It enhanced the production of cytokines such as tumor necrosis $factor-{\alpha}$ and IL-6 in the culture of RAW 264.7 macrophage cells. In the culture of lipopolysaccharide-stimulated RAW 264.7 cells, production of cytokines was as similar as compared to controls. In unstimulated RAW 264.7 cells, both tumor necrosis $factor-{\alpha}$ and IL-6 production were enhanced between 15.6- to 60.1-fold and 2.3- to 9.1-fold, respectively. Mucilage fraction from yam is expected to be a safe immunopotentiator to maintain the host immunity and develop a physiologically functional food.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.123-145
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• 2017

Due to the increasing demand for spectrum resources, cognitive radio networks and dynamic spectrum access draw a lot of research into efficiently utilizing limited spectrum resources. To set up cluster-based CR ad-hoc common channels, conventional methods require a relatively long time to successfully exchange the initialization messages. In this paper, we propose a fast and reliable common channel initialization protocol for CR ad-hoc networks. In the proposed method, the cluster head sequentially broadcasts a system activation signal through its available channels with a predetermined correlation pattern. To detect the cluster head's broadcasting channels and to join the cluster, each member node implements fast Fourier transform (FFT) and computes autocorrelation of an FFT bin sequence for each available channel of the member node. This is compared to the predetermined reference pattern. The join request and channel decision procedures are also presented in this paper. In a simulation study, the performance of the proposed method is evaluated.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.783-790
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• 2022

The development of SNS and video platforms provided an opportunity to explode the activation of content production and consumption. However, in the legacy system, due to the host-based location-oriented data transmission, there are inherent limitations in efficient operation and management. As an alternative to this, a Contents Centric Network (CCN) was studied. In this paper, when intermediate nodes located between the information provider and the information requester between the real-time streaming services in the CCN environment move or restrict their use, failure through monitoring of wireless reception strength to solve problems like disconnection of transmission quality at the information consumer. We propose a stable intermediate node management mechanism through active response before occurrence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2513-2533
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• 2018

In cognitive radio technology, the overall efficiency of communications systems can be improved without allocating additional bands by allowing a secondary system to utilize the licensed band when the primary system, which has the right to use the band, does not use it. In this paper, we propose a fast and reliable common channel initialization protocol without any exchange of initialization messages between the cluster head and the member nodes in cognitive ad-hoc networks. In the proposed method, the cluster and member nodes perform channel-based spectrum sensing. After sensing, the cluster head transmits a system activation signal through its available channels with a predetermined angle difference pattern. To detect the cluster head's transmission channels and to join the cluster, each member node implements fast Fourier transform (FFT) and computes autocorrelation for the angle difference sequence of the received signal patterns. This is compared to the predetermined reference angle difference pattern. The join-request and channel-decision procedures are presented in this paper. Performance evaluation of the proposed method is presented in the simulation results.