• The Korea Journal of Herbology
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• v.36 no.6
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• pp.1-8
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• 2021

Objectives : Atractylodis rhizoma Alba has been traditionally used as a medicinal resource that is used for enhancing Qi (氣) in traditional medicine in Korea, China, and Japan. This study investigated the protective effects of Atractylodis rhizoma Alba extract (ARE) against trimethyltin (TMT), a neurotoxin that causes selective hippocampal injury, using both in vitro and in vivo models. Methods : We investigated the effects of ARE on TMT- (5mM) induced cytotoxicity in primary cultures of mouse hippocampal cells (7 days in vitro ) and on hippocampal injury in C57BL/6 mice injected with TMT (2.6 mg/kg). Results : We observed that ARE treatment (0 - 50 ㎍/mL) significantly reduced TMT-induced cytotoxicity in cultured hippocampal neurons in a dose-dependent manner, based on results of lactate dehydrogenase and 3-4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide assays. Additionally, this study showed that orally administered ARE (5 mg/kg; between -6 and 0 days before TMT injection) significantly attenuated seizures in adult mice. Furthermore, quantitative analysis of allograft inflammatory factor-1 (Iba-1)- and glial fibrillary acidic protein (GFAP)- positive cells showed significantly reduced levels of Iba-1- and GFAP-positive cell bodies in the dentate gyrus of mice treated with ARE prior to TMT injection. These findings indicate the significant protective effects of ARE against the TMT-induced massive activation of microglia and astrocytes in the hippocampus. Conclusions : We conclude that ARE minimizes the detrimental effects of TMT-induced hippocampal neurotoxicity, both in vitro and in vivo . Our findings may serve as useful guidelines to support ARE administration as a promising pharmacotherapeutic approach to hippocampal degeneration.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.67-75
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• 2022

The purpose of this study is to present an appropriate management plan as a supplement to the scientific evidence of the currently operated distancing system for preventing COVID-19. The currently being used mathematical models are expressed as simultaneous ordinary differential equations, there is a problem in that it is difficult to use them for the management of entry and exit of small business owners. In order to supplement this point, in this paper, a method for quantitatively expressing the risk of infection by people who gather is presented in consideration of the allowable risk given to the gathering space, the basic infection reproduction index, and the risk reduction rate due to vaccination. A simple quantitative model was developed that manages the probability of infection in a probabilistic level according to a set of visitors by considering both the degree of infection risk according to the vaccination status (non-vaccinated, primary inoculation, and complete vaccination) and the epidemic status of the virus. In a given example using the model, the risk was reduced to 55% when 20% of non-vaccinated people were converted to full vaccination. It was suggested that management in terms of quarantine can obtain a greater effect than medical treatment. Based on this, a generalized model that can be applied to various situations in consideration of the type of vaccination and the degree of occurrence of confirmed cases was also presented. This model can be used to manage the total risk of people gathered at a certain space in a real time, by calculating individual risk according to the type of vaccine, the degree of inoculation, and the lapse of time after inoculation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.25-33
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• 2023

Noise generated during the acquisition and transmission of CT images acts as a factor that degrades image quality. Therefore, noise removal to solve this problem is an important preprocessing process in image processing. In this paper, we remove noise by using a deformable convolutional autoencoder (DeCAE) model in which deformable convolution operation is applied instead of the existing convolution operation in the convolutional autoencoder (CAE) model of deep learning. Here, the deformable convolution operation can extract features of an image in a more flexible area than the conventional convolution operation. The proposed DeCAE model has the same encoder-decoder structure as the existing CAE model, but the encoder is composed of deformable convolutional layers and the decoder is composed of conventional convolutional layers for efficient noise removal. To evaluate the performance of the DeCAE model proposed in this paper, experiments were conducted on CT images corrupted by various noises, that is, Gaussian noise, impulse noise, and Poisson noise. As a result of the performance experiment, the DeCAE model has more qualitative and quantitative measures than the traditional filters, that is, the Mean filter, Median filter, Bilateral filter and NL-means method, as well as the existing CAE models, that is, MAE (Mean Absolute Error), PSNR (Peak Signal-to-Noise Ratio) and SSIM. (Structural Similarity Index Measure) showed excellent results.

• Journal of Nutrition and Health
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• v.55 no.2
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• pp.263-277
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• 2022

Purpose: The present study examined the associations of Korean Food-based Index of Dietary Inflammatory Potential (FBDI) scores with the prevalence of diabetes and hemoglobin A1c (HbA1c) level of diabetes patients in Korean adults. Methods: The Korean Genome and Epidemiology Study (KoGES) Health Examinee baseline data, collected between 2004 and 2013 and followed up between 2012 and 2016, were used in our study. A total 56,391 participants including diabetes (n = 5,733) and non-diabetes (n = 50,658) were analyzed. The subjects were classified into quartiles of FBDI scores using the semi-quantitative food-frequency questionnaire developed for KoGES. The prevalence rate of diabetes under FBDI scores was assessed by Cox proportional risk models and the severity of the diabetes was analyzed by multiple regression analysis. Results: There were 775 incident cases of diabetes after a mean follow-up of 3.97 years. There was no statistically significant association between FBDI scores and incidence of diabetes. Among diabetes patients at baseline, FBDI scores were related to the risk of progression of diabetes which was represented by greater than 9% HbA1c (Q1 vs. Q4; odds ratio, 1.562 [95% confidence intervals, 1.13-2.15]; p for trend = 0.007). The stratified analysis showed a stronger association in females, irregular exercise group, and higher body mass index group. Conclusion: These results suggest that a pro-inflammatory diet is not associated with the incidence of diabetes but is related to the HbA1c level of diabetes patients. Thus, further longitudinal studies with longer periods are required to determine a relationship between dietary inflammatory index and diabetes in Korea.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.95-117
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• 2022

With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.3
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• pp.303-314
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• 2009

Image analyses for sheared joint specimens are performed to study asperity degradation characteristics with respect to the roughness mobilization of rock joints. Four different types of joint specimens, which are made of high-strength gypsum materials, are prepared by replicating the three-dimensional roughness of rock joints. About twenty jointed rock shear tests are performed at various normal stress levels. The characteristic and scale of asperity degradation on the sheared joint specimens are analyzed using the digital image analysis technique. The results show that the asperity degradation characteristic mainly depends on the normal stress level and can be defined by asperity failure and wear. The asperity degradation develops significantly around the peak shear displacement and the average amount of degraded asperities remains constant with further displacement because of new degradation of small scale asperities. The shear strength results using high-strength gypsum materials can not fully represent physical properties of each mineral particles of asperities on the natural rock joint surface. However the results of this quantitative estimation for the relationship between the peak shear displacement and the asperity degradation suggest that the characterization of asperity degradation provides an important insight into mechanical characteristics and shear models of rock joints.

• The Korean Journal of Franchise Management
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• v.3 no.2
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• pp.1-19
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• 2012

This study intends to suggest the cooperative, win-win collaboration, as methods for settling disputes with the existing self-employed people over back-street business areas and disputes and conflicts between a franchiser and franchisees. In addition, it intends to analyze the Dunkin' Donuts purchasing cooperative in the US, where the franchising industry has been well developed; and to find the implications of cooperation strategies between Dunkin' Donuts and its franchisees that may be helpful for the South Korea's franchising industry. This study tries to discover a new model of the Korean-style franchise cooperative out of the basic principles and practice guidelines of cooperatives ranging from an early American franchise cooperative in 1955 to ARCOP, KFC, and Dunkin' Doughnuts in the late 1970s. Further, it looks into successful programs of a purchasing cooperative at Dunkin' Donuts such as TDP (Total Distribution Program), SFP (Shortening Futures Program) and DCP (Distribution Commitment Program). The case of the US Dunkin' Donuts, which operates the purchasing cooperative, suggests the following for the improvement of franchisees' profitability. First, relations of cooperation rather than of power are necessary between a franchiser and franchisees. Second, mutual solidarity of franchisees is necessary. Third, problems proper to the Korean franchise system should be improved. Fourth, an entrepreneurial spirit of going together rather than going fast is required. Fifth, complete satisfaction management is required. Considering different system environments between the two countries such as quantitative expansion within a short franchising history of 30 years or so and franchise profit models, there is a limit to generalizing down to a successful model of the win-win partnership cooperative. It is hoped that the sustainable management of the domestic franchising industry will be promoted in the future through the in-depth analysis of successful cooperatives.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.