• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Life Science
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• v.27 no.2
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• pp.247-266
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• 2017

Cordyceps is a traditional Chinese medicinal herb well-known in China, Korea and Japan since B.C. 2,000. The original entomopathogenic fungus, Cordyceps sinensis belonging to the genus Cordyceps could not be found inside Korean peninsula due to the absence of the host insect for the corresponding entomogenous fungus. The development of artificial production methods of Korean type Cordyceps using the silkworm Bombyx mori as in vivo culture medium for the the entomopathogenic fungus Paecilomyces tenuipes is the first, and wonderful occasion in the research history of insect industry of this global world. The aim of this article is to review the historical research background, mass-production methods, and pharmacological effects of the silkworm-dongchunghacho (Paecilomyces tenuipes) which is a newly developed Korean medicinal insect-borne mushroom, and another non-insect-borne medicinal mushroom (Cordyceps militaris and Cordyceps pruinosa). Their biological actions include anti-tumor, immunostimulating, anti-fatigue, anti-stress, anti-oxidant, anti-aging, anti-diabetic, anti-inflammatory, anti-thrombosis, hypolipidaemic and insecticidal effects. The bioactive principles are protein-bound polysaccharides (hexose, hexosamin), cordycepin, D-manitol, acidic polysaccharide etc. Protein-bound polysaccharides and n-butanol fractions were demonstrated to show a significant anti-tumor activities but did not show a cytotoxicities. D-mannitol exhibited a significant prolongation of the life span in tumor bearing mice. Ergosterol did not show an efficient anti-tumor activity, but showed a significant phagocytosis enhancing activity. Anti-tumor activity of silkworm-dongchunghacho might be attributed to immuno-stimulating activities rather than cytotoxic effects [164]. Also this review comprises the breeding of Dongchunghacho varieties, optimization of culture conditions, improvement of learning and memory by Dongchunghacho, application of them as foods and chemical constituents.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.9
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• pp.1208-1214
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• 2011

Caffeine, a psychoactive stimulant, has been implicated in the modulation of learning and memory functions due to its action as a non-selective adenosine receptors antagonist. On the contrary, some side effects of caffeine have been reported, such as an increased energy loss and metabolic rate, decrease DNA synthesis in the spleen, and increased oxidative damage to exerted on LDL particles. Therefore, the aim of this study was to develop a safe stimulant from natural plants mixture (Aralia elata, Acori graminei Rhizoma, Chrysanthemum, Dandleion, Guarana, Shepherd's purse) that can be used as a substitute for caffeine. Thirty SD rats were divided into three groups; control group, caffeine group (15.0 mg/kg, i.p.), and natural plants mixture group (NP, 1 mL/kg, p.o.). The effect of NP extract on stimulant activity was evaluated with open-field test (OFT) and plus maze test for measurement of behavioral profiles. Plasma lipid profiles, lipid peroxidation in LDL (conjugated dienes), total antioxidant capacity (TRAP) and DNA damage in white blood, liver, and brain cells were measured. In the OFT, immobility time was increased significantly by acute (once) and chronic (3 weeks) supplementation of NP and showed a similar effect to caffeine treatment. Three weeks of caffeine treatment caused plasma lipid peroxidation and DNA damage in liver cells, whereas there were no changes in the NP group. NP group showed a higher plasma HDL cholesterol concentration compared to the caffeine group. The results indicate that the natural plants mixture had a stimulant effect without inducing oxidative stress.

• Journal of The Korean Association For Science Education
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• v.42 no.3
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• pp.311-324
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• 2022

This study analyzed the types of scientific emoticons made by science-gifted elementary school students and their perceptions on making scientific emoticons. To do this, 71 students from 4th to 6th graders of two gifted science education center in Seoul were selected. Scientific emoticons made by the students were analyzed according to the number and types. Their perceptions on making scientific emoticons were also analyzed through a questionnaire and group interviews. In the analyses for types of text in the scientific emoticons, 'word type' and 'sentence type' were made more than 'question and answer type'. And the majority of students made more 'pun using pronunciation type' and 'mixed type' than other types. They also made more 'graphic type' and 'animation type' than 'text type' in the images of the scientific emoticons. In the analyses for the information of the scientific emoticons, 'positive emotion type' and 'negative emotion type' of scientific emoticons were made evenly. The students made more 'new creation type' than 'partial correction type' and 'entire reconstruction type'. They also used scientific knowledge that preceded the knowledge of science curriculum in their grade level. The scientific knowledge of chemistry was used more than physics, biology, earth science, and combination field. 'Name utilization type' was more than 'characteristic utilization type' and 'principle utilization type'. Students had various positive perceptions in making scientific emoticons such as 'increase of scientific knowledge', 'increase of various higher-order thinking abilities', 'ease of explanation, use, memory, and understanding of scientific knowledge', 'increase of fun, enjoyment, and interest about science and science learning', and 'increase of opportunity to express emotions'. They were also aware of some limitations related to 'difficulties in the process of making scientific emoticons', 'lack of time', and 'limit that it may end just for fun'. Educational implications of these findings are discussed.

• Korean Journal of Food Science and Technology
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• v.47 no.3
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• pp.380-387
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• 2015

The anti-amnesic effect of Artemisia argyi H against trimethyltin (TMT)-induced learning and memory impairment and its neuroprotective effect against $H_2O_2$-inducedoxidative stress were investigated. Cognitive behavior was examined by Y-maze and passive avoidance test for 4 weeks, which showed improved cognitive functions in mice treated with the extract. In vitro neuroprotective effects against $H_2O_2$-induced oxidative stress were examined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium-bromide and lactate dehydrogenase (LDH) assays. A. argyi H. extract showed protective effects against $H_2O_2$-induced neurotoxicity; moreover, LDH release into the medium was inhibited. Finally, high-performance liquid chromatography (HPLC) analysis showed that eupatilin and jaceosidin were the major phenolic compounds in A. argyi H. extract. These results suggest that A. argyi H. could be a good source of functional substances to prevent neurodegenerative diseases.

• Korean Journal of Food Science and Technology
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• v.49 no.6
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• pp.649-658
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• 2017

The potential of the ethyl acetate fraction from Pteridium aquilinum (EFPA) to improve the cognitive function in high-fat diet (HFD)-induced diabetic mice was investigated. EFPA-treatment resulted in a significant improvement in the spatial, learning, and memory abilities compared to the HFD group in behavioral tests, including the Y-maze, passive avoidance, and Morris water maze. The diabetic symptoms of the EFPA-treated groups, such as fasting glucose and glucose tolerance, were alleviated. The administration of EFPA reduced the acetylcholinesterase (AChE) activity and malondialdehyde (MDA) content in mice brains, but increased the acetylcholine (ACh) and superoxide dismutase (SOD) levels. Finally, kaempferol-3-o-glucoside, a major physiological component of EFPA, was identified by using high-performance liquid chromatography coupled with a hybrid triple quadrupole-linear ion trap mass spectrometer (QTRAP LC-MS/MS).

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.4
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• pp.466-475
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• 2005

Docosahexaenoic acid (22:6n-3, DHA) is highly enriched in membrane of brain and retina, and plays an important role in maintaining an optimal function of the central nervous system. We investigated the effect of n-3 fatty acid deficiency on rat brain, retina and liver fatty acyl composition at two different ages (3 wks and 15 wks) under DHA deficient condition. Rat pups born to dams fed a diet with $3.1\%$ of total fatty acids as $\alpha-linolenic$ acid (LNA) were fed using an artificial rearing system either an n-3 deficient (n-3 Def) or n-3 adequate (n-3 Adq) diet. Both diets contained $17.1\%$ linoleic acid (LA) but the n-3 Adq diet also contained $3.1\%$ LNA. Rats consuming the n-3 Def diet showed a lower brain $(50\%\;in\;13\;wks\;and\;70\%\;in\;15\;wks,\;p<0.05)$ and retinal $(50\%\;in\;13\;wks\;and\;63\%\;in\;15\;wks,\;p<0.05)$ DHA than those on the n-3 Adq diet, which was largely compensated for by an increase in docosapentaenoic acid (22:5n-6, DPAn-6). In the liver of the n-3 Def group, the percentage of DHA decreased by $97\%$ at 3 wks of age with an apparent increase in DPAn-6 relative to the n-3 Adq group (p<0.05), while there was a $65\%$ lower liver DHA in n-3 Def group at 15 wks of age than the n-3 Adq group (p<0.05). Liver arachidonic acid (20:4n-6, AA) was increased at 3 wks of age but decreased at 15 wks of age in the n-3 Def group compared with n-3 Adq group (p<0.05). In conclusion, the replacement of DHA by DPAn-6 in brain and retina fatty acid composition may be related to the suboptimal function in spatial learning, memory and visual acuity. This artificial rearing method presents a first generation model for n-3 deficiency that is similar to the case of human nutrition that commonly employed two generation model.

• Korean Journal of Psychosomatic Medicine
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• v.13 no.2
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• pp.102-111
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• 2005

Objective : The purpose of this study was to investigate the quality of life and it's related factors in caregivers of attention deficit hyperactivity disorder patients. Methods : The subjects were 38 attention deficit hyperactivity disorder patients' caregivers(mean age : $37.5{\pm}6.5$, 38 women). Patients were diagnosed with DSM-IV-TR ADHD criteria. Korean version of WHOQOL-BREF(World Health Organization Quality of Life assessment instrument Abbreviated Version) was used for assessment. Results : 1) No significant differences were found in the score of WHOQOL-BREF, overall QOL, physical health domain, psychological domain, social relationships domain and environmental domain between caregiver and control group. 2) The score of Activity of daily living facet$(3.0{\pm}0.7\;vs.\;3.6{\pm}0.7)(p=0.008)$ and self-esteem facet $(2.8{\pm}0.7\;vs.\;3.3{\pm}0.7)(p=0.049)$ were significantly decreased in caregivers of ADHD. 3) Total score of WHOQOL-BREF(r=0.437, p=0.007) and physical health domain(r=0.370, p=0.024) were correlated with caregiver's educational age. 4) In the psychological domain, the score of self-esteem facet(r=-0.337, p=0.039) and thinking, learning, memory & concentration facet(r=-.341, p=0.036) were decreased with caregiver's age. 5) The score of environmental domain were significantly increased with caregiver's educational age (r=0.482, p=0.003), but decreased with patient's age(r=0.328, p=0.044). Conclusion : Although the quality of life in caregivers of ADHD patient had not significantly decreased than control, the quality of lift were positively correlated with educational age of caregives, and negatively correlated with chronological age of caregivers and children. Above results suggest that physicians should consider integrated approaches for caregiver's subjective quality of life in the management of ADHD.

• Journal of Gifted/Talented Education
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• v.12 no.2
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• pp.1-15
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• 2002

The purpose of this study was to investigate the characteristics of preschoolers' giftedness by their parents' perception. Total 3 groups of 148 subjects from age 30 months to 6 years 10 months old young gifted children's parents participated. The major findings were as follows : (1) There were critical characteristics of preschoolers' giftedness by parents' perception, which were 'good memory', 'high curiosity', 'read and understand of math', 'enjoy of learning and high motivation', 'high concentration', reading books', 'verbal ability', 'creativity', 'questions', and 'independency', (2) These characteristics of preschoolers' giftedness showed more strong and intense as they got older, and (3) Some characteristics revealed more, but the other characteristics revealed less as they got older. These findings suggested the consideration of child's age as the reliable identification process of young gifted children.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.