• Cross-Cultural Studies
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• v.53
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• pp.219-249
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• 2018

This essay attempts a historical examination of educational function of the novel. It pays attention to the eighteenth century sentimentalism, and its historical vicissitudes up to early twenties century. The eighteenth century is the period in which debates on the nature of emotion and its moral and aesthetic role have passionately taken place and the modern paradigm of thought on affect has been formed. This is why "affect revival phenomenon" in the late twenties century goes back to this period. This essay finds in Adam Smith the most sophisticated arguments on sympathy in their relation to the development of the novel; it examines the relationship of Smith's argument with modern novel in the tradition of sentimentalism, and its revision in modernist novel. Through this examination, it discusses how cognitive and non-cognitive approaches, the two representative positions in contemporary thinking on emotion/affect, have revised and transformed the eighteenth century sentimentalism.

• Journal of English Language & Literature
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• v.58 no.2
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• pp.189-215
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• 2012

This paper reads Mary Shelley's Frankenstein (1818) in light of the 18th-century understanding of 'sympathy' including those of Hume and Smith and also in light of what Michael Hardt in our century has called "affective labor." I argue that the imaginative capacity and "seeing" are crucial in understanding Smith's idea of 'sympathy.' By showing how the monster's ugliness precludes any human character from sympathizing with him, Mary Shelley exposes that Smith's idea of sympathy fails to maintain social harmony. Mary Shelley revises Smith's 'sympathy' and makes it more radical by suggesting that the active affective labor could bridge the epistemological distance lying between the agent concerned and the impartial spectator. I first read Smith's idea of sympathy as an imaginative capacity which is inevitably influenced by 'seeing' and visual perception. Then I analyze the scenes in which the creature in Frankenstein fails to acquire any human sympathy due to his ugliness, and show how the specular nature of 'sympathy' is disrupted when one party is visually ugly and deformed. I conclude that affective labor and active moral reflection on the part of the spectator need to be provided when the agent concerned is 'ugly' and thus challenges our habitual epistemological boundary. Shelley's re-evaluation of Smith's sympathy, thus, suggests that affective labor may not be something that women alone have to perform, but an ethical practice that concerns all human beings and that can transform the otherwise flawed human capacity for sympathy.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.73-84
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• 2020

As the Internet began to become popular, there have been hacking and attacks on networks including systems, and as the techniques evolved day by day, it put risks and burdens on companies and society. In order to alleviate that risk and burden, it is necessary to detect hacking and attacks early and respond appropriately. Prior to that, it is necessary to increase the reliability in detecting network intrusion. This study was conducted on applying weight initialization and weight optimization to the KDD'99 dataset to improve the accuracy of detecting network intrusion. As for the weight initialization, it was found through experiments that the initialization method related to the weight learning structure, like Xavier and He method, affects the accuracy. In addition, the weight optimization was confirmed through the experiment of the network intrusion detection dataset that the Adam algorithm, which combines the advantages of the Momentum reflecting the previous change and RMSProp, which allows the current weight to be reflected in the learning rate, stands out in terms of accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.529-536
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• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.267-275
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• 2022

This paper proposed a Deep Neural Network Model system utilizing Keras for predicting air pollution risk of the cardiovascular disease through the effect of each component of air on the harmful virus using past air information, with analyzing 18,000 data sets of the Seoul Open Data Plaza. By experiments, the model performed tasks with higher accuracy when using methods of sigmoid, binary_crossentropy, adam, and accuracy through 3 hidden layers with each 8 nodes, resulting in 88.92% accuracy. It is meaningful in that any respiratory disease can utilize the risk prediction system if there are data on the effects of each component of air pollution and fine dust on oil-borne diseases. It can be further developed to provide useful information to companies that produce masks and air purification products.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.227-237
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• 2022

Amyloid beta (Aβ) is produced from an amyloid precursor protein by the activation of the amyloidogenic pathway, and it is widely known to cause Alzheimer's disease (AD). In this study, we investigated the neuroprotective effects of three flavonoids, quercitrin, isoquercitrin, and afzelin, from Acer okamotoanum against Aβ-induced neurotoxicity in SH-SY5Y neuronal cells. Aβ_25-35 treatments resulted in decreased cell viability and increased levels of nuclei condensation and fragmentation. However, an isoquercitrin treatment dose-dependently increased cell viability and decreased nuclei condensation and fragmentation levels. SH-SY5Y cells treated with Aβ_25-35 showed increased reactive oxygen species (ROS) production compared to that from cells not treated with Aβ_25-35. However, treatment with the three flavonoids significantly inhibited ROS production compared to an Aβ_25-35-treated control group, indicating that the three flavonoids blocked neuronal oxidative stress. For a closer examination of the neuroprotective mechanisms, we measured the expressions of the non-amyloidogenic pathway-related proteins of a disintegrin and metalloprotease 10 (ADAM10) and the tumor necrosis factor-α converting enzyme (TACE). An isoquercitrin treatment enhanced the expressions of ADAM10 compared to the control group. In addition, the three flavonoids activated the non-amyloidogenic pathway via the upregulation of TACE. In conclusion, we demonstrated neuroprotective effects of three flavonoids from A. okamotoanum, in particular isoquercitrin, on neurotoxicity by the regulation of the non-amyloidogenic pathway in Aβ_25-35-treated SH-SY5Y cells. Therefore, we suggest that flavonoids from A. okamotoanum may have some potential as therapeutics of AD.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.9-16
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• 2023

Technological advancement is being advanced in sports such as electronic protection of taekwondo competition and VAR of soccer. However, a person judges and guides the posture by looking at the posture, so sometimes a judgment dispute occurs at the site of the competition in Taekwondo Poomsae. This study proposes an artificial intelligence model that can more accurately judge and evaluate Taekwondo movements using artificial intelligence. In this study, after pre-processing the photographed and collected data, it is separated into train, test, and validation sets. The separated data is trained by applying each model and conditions, and then compared to present the best-performing model. The models under each condition compared the values of loss, accuracy, learning time, and top-n error, and as a result, the performance of the model trained under the conditions using ResNet50 and Adam was found to be the best. It is expected that the model presented in this study can be utilized in various fields such as education sites and competitions.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• Tuberculosis and Respiratory Diseases
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• v.55 no.1
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• pp.21-30
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• 2003

Background : Mucin synthesis in airways has been reported to be regulated by the epidermal growth factor receptor (EGFR) system. Epidermal growth factor receptor transactivation was identified as a critical element in G-protein-coupled receptors (GPCRs)-induced mitogenic signaling. EGF receptor transactivation by G-protein-coupled receptors requires metalloproteinase cleavage of proHB-EGF. This study was hypothesized that lipopolysaccharide (LPS)-induced mucin production associates with epidermal growth factor receptor transactivation, and MUC5AC production associates with epidermal growth factor receptor transactivation by G-protein-coupled receptors that regulates by metalloproteinase. Method : MUC5AC mucin production was examined in NCI-H292 cells and MUC5AC protein synthesis was assessed using ELISA. For the evaluation of mechanism of LPS-induced MUC5AC production, $TNF{\alpha}$ was measured using ELISA with or without pretreatment of heterotrimeric G-protein inhibitor, mastoparan. MUC5AC protein was measure with pretreatment of polyclonal $TNF{\alpha}$ antibody or mastoparan on LPS-induced MUC5AC production. For the evaluation of relation of G-protein and MUC5AC production, G-protein stimulant, mastopara-7, or matrix metalloproteinase, ADAM10, was added to NCI-H292 cells. MUC5AC protein was measure with pretreatment of polyclonal EGF antibody on mastoparan-7-induced MUC5AC production. Results : LPS alone did not increase significantly MUC5AC production. LPS with $TNF{\alpha}$ induced dose-dependently MUC5AC production in NCI-H292 cells. LPS increased dose-dependently $TNF{\alpha}$ secretion, which was inhibited by mastoparan. LPS with $TNF{\alpha}$-induced MUC5AC production was inhibited by neutralizing polyclonal $TNF{\alpha}$ antibody, mastoparan or AG 1472. Mastoparan-7 or ADAM10 increased dose-dependently MUC5AC production, which was inhibited by polyclonal neutralizing EGF antibody. Conclusion : In LPS-induced MUC5AC synthesis, LPS causes $TNF{\alpha}$ secretion, which induces EGFR expression. EGFR tyrosine kinase phosphorylation result in MUC5AC production. EGF-R transactivation by G-protein-coupled receptors requires matrix metalloproteinase cleavage of proHB-EGF.