• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.43.3-43
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• 2001

In conventional RBF network, the activation functions of hidden layers generally are symmetric functions like gaussian function. This has been considered to be one of the limiting factors for the network to speed up learning of actuately describing a given function. To avoid this criticism, we propose a pseudo gaussian function (PGF) whose deviation is changed according to the direction of incoming pattern. This property helps to estimate the given function more effectively with a minimal number of centers because of its flexibility of functional representation. A level set method is used to describe the asymmetric shape of deviation of the pseudo gaussian function. To demonstrate the performance of the proposed network ...

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.81-84
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• 2002

Activation energy value is different according to cement, admixture and water-cement ratio also the relation of age-temperature is as non-linear as activation energy value is large. So to make accurate explanation for the effect of temperature on concrete strength development property, it is necessary to investigation for activation energy value. This study compares activation energy value recommended by Freiesleben and ASTM with activation energy value obtained by consequence of mortar examination according to ASTM C 1074-93. As the result of this study, activation energy value obtained by the study is 37.19KJ/mol, and in case of activation energy value obtained by the study explain temperature's influence about concrete strength development more accurate than activation energy value recommend by Freiesleben and ASTM.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.10
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• pp.303-308
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• 2020

The ReLU(Rectified Linear Unit) function has been dominantly used as a standard activation function in most deep artificial neural network models since it was proposed. Later, Leaky ReLU, Swish, and Mish activation functions were presented to replace ReLU, which showed improved performance over existing ReLU function in image classification task. Therefore, we recognized the need to experiment with whether performance improvements could be achieved by replacing the RELU with other activation functions in the super resolution task. In this paper, the performance was compared by changing the activation functions in EDSR model, which showed stable performance in the super resolution task. As a result, in experiments conducted with changing the activation function of EDSR, when the resolution was converted to double, the existing activation function, ReLU, showed similar or higher performance than the other activation functions used in the experiment. When the resolution was converted to four times, Leaky ReLU and Swish function showed slightly improved performance over ReLU. PSNR and SSIM, which can quantitatively evaluate the quality of images, were able to identify average performance improvements of 0.06%, 0.05% when using Leaky ReLU, and average performance improvements of 0.06% and 0.03% when using Swish. When the resolution is converted to eight times, the Mish function shows a slight average performance improvement over the ReLU. Using Mish, PSNR and SSIM were able to identify an average of 0.06% and 0.02% performance improvement over the RELU. In conclusion, Leaky ReLU and Swish showed improved performance compared to ReLU for super resolution that converts resolution four times and Mish showed improved performance compared to ReLU for super resolution that converts resolution eight times. In future study, we should conduct comparative experiments to replace activation functions with Leaky ReLU, Swish and Mish to improve performance in other super resolution models.

• Research in Community and Public Health Nursing
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• v.26 no.3
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• pp.303-311
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• 2015

Purpose: The purpose of this study was to survey patient activation for self-management and to identify factors associated with patient activation for self-management among community residents with osteoarthritis in Korea. Methods: Cross-sectional study design was used. Survey data were collected from 270 community residents with osteoarthritis through direct interviews. Studied factors included patient activation, joint pain, physical function, depression, and general characteristics. Data were analyzed using chi-squared test, t-test and multivariate logistic regression analysis. Results: The participants' mean score of patient activation was $56.0{\pm}16.61$. The mean score of each factor was $10.6{\pm}5.89$ for joint pain, $5.5{\pm}3.56$ for physical function, and $19.3{\pm}10.01$ for depression. The patient activation level was significantly associated with depression and general characteristics such as education, religion, comorbid hypertension, and use of medical clinics (p<.05). Conclusion: The findings suggest that depression, education, religion, comorbid hypertension, and use of medical clinics may be important factors to be considered when developing programs of patient activation for self-management. This is the first study that measured patient activation, and further studies are suggested to find factors associated with patient activation for self-management among community residents with other chronic diseases.

• BMB Reports
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• v.48 no.7
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• pp.369-370
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• 2015

Actin dynamics is critical for the formation and sustainment of the immunological synapse (IS) during T cell interaction with antigen-presenting cells (APC). Thus, many actin regulating proteins are involved in spatial and temporal actin remodeling at the IS. However, little is known whether or how actin stabilizing protein controls IS and the consequent T cell functions. TAGLN2 − an actin-binding protein predominantly expressed in T cells − displays a novel function to stabilize cortical F-actin, thereby augmenting F-actin contents at the IS, and acquiring leukocyte function-associated antigen-1 activation following T cell activation. TAGLN2 also competes with cofilin to protect F-actin in vitro and in vivo. During cytotoxic T cell interaction with cancer cells, the expression level of TAGLN2 at the IS correlates with the T cell adhesion to target cancer cells and production of lytic granules such as granzyme B and perforin, thus expressing cytotoxic T cell function. These findings identify a novel function for TAGLN2 as an actin stabilizing protein that is essential for stable immunological synapse formation, thereby regulating T cell immunity. [BMB Reports 2015; 48(7): 369-370]

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.97-105
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• 2009

This paper presents a combination of the generalized Cascade Correlation and generalized Recurrent Cascade Correlation learning algorithms. The new network will be able to grow with vertical or horizontal direction and with recurrent or without recurrent units for the quick solution of the pattern classification problem. The proposed algorithm was tested learning capability with the sigmoidal activation function and hyperbolic tangent activation function on the contact lens and balance scale standard benchmark problems. And results are compared with those obtained with Cascade Correlation and Recurrent Cascade Correlation algorithms. By the learning the new network was composed with the minimal number of the created hidden units and shows quick learning speed. Consequently it will be able to improve a learning capability.

• Journal of IKEEE
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• v.22 no.1
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• pp.197-200
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• 2018

In this paper, sigmoid function, which is difficult to implement at hardware level and has a slow speed, is approximated by using PLAN. We use this as an activation function of MLP structure to reduce resource consumption and speed up. In this paper, we show that the proposed method maintains 95% accuracy in $5{\times}5$ size recognition and 1.83 times faster than GPGPU. We have found that even with similar resources as MLPA accelerators, we use more neurons and converge at higher accuracy and higher speed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.168-175
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• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• Molecules and Cells
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• v.22 no.1
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• pp.1-7
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• 2006

The NFAT family of transcription factors plays pivotal roles in the development and function of the immune system. Their activation process is tightly regulated by calcium-dependent phosphatase calcineurin and has been a target of the immunosuppressive drugs cyclosporin A and FK-506. Although the clinical use of these drugs has dramatically increased the success of organ transplantation, their therapeutic use is limited by severe side effects. Recent studies for the calcineurin/NFAT signaling pathway have identified a number of cellular proteins that inhibit calcineurin function. Specific peptide sequences that interfere with the interaction between calcineurin and NFAT have also been characterized. Moreover, diverse approaches to identify small organic molecules that modulate NFAT function have been performed. This review focuses on the recent advances in our understanding of the inhibitory modulation of NFAT function, which may open up the additional avenues for immunosuppressive therapy.

• Smart Structures and Systems
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• v.33 no.2
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• pp.133-143
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• 2024

Piezoelectric (PZT) actuators have been widely used in precision positioning fields for their excellent displacement resolution. However, due to the inherent characteristics of piezoelectric actuators, hysteresis has been proven to greatly reduce positioning performance. In this paper, five mathematical hysteretic models based on activation function are proposed to characterize the nonlinear hysteresis characteristics of piezoelectric actuators. Then the performance of the proposed models is verified by particle swarm optimization (PSO) algorithm and the experiment data. Thirdly, the fitting performance of the proposed models is compared with the classical Bouc-Wen model. Finally, the performance of the five proposed models in modelling hysteresis nonlinearity of piezoelectric drivers is compared, in terms of RMSE, MAPE, SAPE and operation efficiency, and relevant suggestions are given.