• Korean Management Science Review
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• v.28 no.1
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• pp.1-10
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• 2011

One of the fundamental roles of web object navigation is to support what the user wants precisely and efficiently from the enormous web database to the web browser. As long as the web search results are a set of individual lists, it is all right to display each and every web result for the web browser to display a web object one by one. However, in case the search results are a collection of multiple interrelated web objects, then there is a need to represent for a new mechanism for linked web objects at a time. We define a unit of web objects derived from a Steiner tree where the web objects include a set of specific keywords calculated by the weight from which the solutions are extracted. Even if a web object does not include all the keywords, then the related hypertext linked web objects are derived and displayed onto the mobile web browser with meta data in one shot. In this paper, it is applied for the mobile browser that the web contents can dynamically be displayed with Steiner trees until each renewal of the navigation request may be issued. In this paper, a new synchronized mobile browsing method is developed so that the navigating time can drastically be reduced and the web navigating efficiency can be dramatically enhanced without sacrificing memory consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.8-18
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• 2020

An Exponential Bandwidth Harmony Search with Centralized Global Search (EBHS-CGS) was developed to enhance the performance of a Harmony Search (HS). EBHS-CGS added two methods to improve the performance of HS. The first method is an improvement of bandwidth (bw) that enhances the local search. This method replaces the existing bw with an exponential bw and reduces the bw value as the iteration proceeds. This form of bw allows for an accurate local search, which enables the algorithm to obtain more accurate values. The second method is to reduce the search range for an efficient global search. This method reduces the search space by considering the best decision variable in Harmony Memory (HM). This process is carried out separately from the global search of the HS by the new parameter, Centralized Global Search Rate (CGSR). The reduced search space enables an effective global search, which improves the performance of the algorithm. The proposed algorithm was applied to a representative optimization problem (math and engineering), and the results of the application were compared with the HS and better Improved Harmony Search (IHS).

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.629-635
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• 2019

In term of the factors affecting psychosocial adjustment of breast cancer patients, their quality of life after surgical operation, radiation, and chemotherapy were systematically meta-analyzed. As a result, their qualities of life of the patients that had radiation therapy was the lowest right after the therapy, and gradually increased after the end of the therapy. However, after six months, their quality of life failed to reach the same level before the therapy. They had depression and side effects the most right after the therapy, and somewhat reduced them after the end of the therapy. In case of surgical operation, the more they were educated, the more they had psychosocial adjustment, and the more they had a medical examination and took out an insurance policy, the more they had psychosocial adjustment. In case of chemotherapy, their cognitive function is influenced so that they have impairments in memory, learning, and thinking stages. Since subjective cognitive impairment has a relationship with depression, it is necessary to monitor depression of chemotherapy patients. Given the results of this systematic meta-analysis, when three types of therapies (surgical operation, radiation therapy, and chemotherapy) are applied to patients with breast cancer, it is necessary to recognize their psychosocial adjustment, depression, anxiety, and quality of life in the nursing and radiation therapy fields and thereby to introduce an intervention program for a holistic approach.

• Journal of Korean Dental Science
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• v.16 no.2
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• pp.128-148
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• 2023

Dementia is an umbrella term that describes the loss of thinking, memory, attention, logical reasoning, and other mental abilities to the extent that it interferes with the activities of daily living. More than 50 million individuals worldwide live with dementia, which is expected to increase to 131 million by 2050. Recent research has shown that poor oral health increases the risk of dementia, while oral health declines with cognitive decline. In this narrative review, the literature was based on the "hypothesis" that dementia and oral health have a close relationship, and appropriate oral health and occlusal rehabilitation treatment can improve the quality of life of patients with dementia and prevent progression. We conducted a literature search in PubMed and Google Scholar databases, using the search terms "dementia," "major neurocognitive disorder," "dentition," "occlusion," "tooth loss," "dental prosthesis," "dental implant," and "occlusal rehabilitation" in the title field over the past 30 years. A total of 131 studies that scientifically addressed dementia, oral health, and/or oral rehabilitation were included. In a meta-analysis, the random effect model demonstrated significant tooth loss increasing the dementia risk 3.64-fold (pooled odds ratio=3.64, 95% confidence interval [2.50~5.32], P-value=0.0348). Tooth loss can be an important indicator of cognitive function decline. As the number of missing teeth increases, the risk of dementia increases. Loss of teeth can lead to a decrease in the ascending information to the brain and reduced masticatory ability, cerebral blood flow, and psychological atrophy. Oral microbiome dysbiosis and migration of key bacterial species to the brain can also cause dementia. Additionally, inflammation in the oral cavity affects the inflammatory response of the brain and the complete body. Conversely, proper oral hygiene management, the placement of dental implants or prostheses to replace lost teeth, and the restoration of masticatory function can inhibit symptom progression in patients with dementia. Therefore, improving oral health can prevent dementia progression and improve the quality of life of patients.

• Journal of the Korean Ceramic Society
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• v.40 no.1
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• pp.52-61
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• 2003

$SrBi_2Ta_2O_9(SBT)$and$ZrO_2$thin films for MFIS structure(Metal-Ferroelectric-Insulator-Semiconductor) were deposited by RF magnetron sputtering method. In order to investigate the effect of heat treatment of insulator layers and MFIS structure, the insulator layers were heat treated from $550^{circ}C;to; 850^{\circ}C$in conventional furnace or RTA furnace under$O_2$and Ar ambient, respectively. After then, C-V characteristics and leakage current were measured. The capacitor with 20 nm thick $ZrO_2$layer treated at RTA$750^{circ}C;in;O_2$ atmosphere had the largest memory window. The C-V and leakage current characteristics of the$Pt/SBT(260nm)/ZrO_2(20nm)/Si$structure were better than those of$Pt/SBT(260nm)/Si$ structure. These results showed that$ZrO_2$films took a role of buffer layer effectively.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.7
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• pp.57-64
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• 2011

To adjust the difference of bandwidth between host interface and NAND flash memory, DRAM is adopted as the buffer management in SSD (Solid-state Disk). In this paper, we propose cost-effective techniques to enhance SSD performance instead of using expensive high bandwidth DRAM. The SSD data can be classified into three groups such as user data, meta data for handling user data, and FEC(Forward Error Correction) parity/ CRC(Cyclic Redundancy Check) for error control. In order to improve the performance by considering the features of each data, we devise a flexible burst control method through monitoring system and a page based FEC parity/CRC application. Experimental results show that proposed methods enhance the SSD performance up to 25.9% with a negligible 0.07% increase in chip size.

• Journal of KIISE
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• v.43 no.4
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• pp.470-479
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• 2016

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.