• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.259-268
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• 2020

The purpose of this research is to investigate the determinants of life satisfaction and social support among the elderly. For this purpose, a set of characteristic factors that are supposed to affect elderly life satisfaction were suggested and empirically analyzed. To achieve this study used final analysis with 470 questionnaires which did a questionnaire survey after gathering purposeful sampling method of 500 person who were visiting on Senior Club and Senior Welfare Center. The collected data were analyzed by using the SPSS statistical program. The methods of analysis were frequency, factor analysis, and regression analysis. The results of this study are as follows. Firstly, characteristic factors such as the family relation, health and economic of the elderly have significant impacts on their life satisfaction. Secondly, it was seen that social support regulated the effects of family relation and economic on characteristic factors. The implications of this study are, above all, to build a human network for training, education and social support for career development while participating in the labor market. Such activities could lead to social support for economic independence in old age.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.3
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• pp.7-15
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• 2019

Cloud computing markets are rapidly growing as cost savings and business innovation are being carried out through ICT worldwide. In line with this paradigm, the nation is striving to introduce cloud computing in various areas, including the public sector and defense sector, through various research. In the defense sector, DIDC was established in 2015 by integrating military, naval, air and military computing centers, and it provides cloud services in the form of IaaS to some systems in the center. In DIDC and various future cloud defense systems, It is an important issue to ensure availability in cloud defense systems in the defense sector because system failures such as network delays and system resource failures are directly linked to the results of battlefields. However, ensuring the highest levels of availability for all systems in the defense cloud can be inefficient, and the efficiency that can be gained from deploying a cloud system can be reduced. In this paper, we classify and define the level of availability of defense cloud systems step by step, and propose the strategy of introducing Erasure coding and failure acceptance systems, and disaster recovery system technology according to each level of availability acquisition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.29-43
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• 2019

The calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. This method can be extended to other optimization tasks using GA in transportation studies.

• IMMUNE NETWORK
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• v.20 no.5
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• pp.43.1-43.11
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• 2020

Capicua (CIC) is a transcriptional repressor that regulates several developmental processes. CIC deficiency results in lymphoproliferative autoimmunity accompanied by expansion of CD44^hiCD62L^lo effector/memory and follicular Th cell populations. Deletion of Cic alleles in hematopoietic stem cells (Vav1-Cre-mediated knockout of Cic) causes more severe autoimmunity than that caused by the knockout of Cic in CD4⁺CD8⁺ double positive thymocytes (Cd4-Cre-mediated knockout of Cic). In this study, we compared splenic CD4⁺ T cell activation and proliferation between whole immune cell-specific Cic-null (Cic^f/f;Vav1-Cre) and T cell-specific Cic-null (Cic^f/f;Cd4-Cre) mice. Hyperactivation and hyperproliferation of CD4⁺ T cells were more apparent in Cic^f/f;Vav1-Cre mice than in Cic^f/f;Cd4-Cre mice. Cic^f/f;Vav1-Cre CD4⁺ T cells more rapidly proliferated and secreted larger amounts of IL-2 upon TCR stimulation than did Cic^f/f;Cd4-Cre CD4⁺ T cells, while the TCR stimulation-induced activation of the TCR signaling cascade and calcium flux were comparable between them. Mixed wild-type and Cic^f/f;Vav1-Cre bone marrow chimeras also exhibited more apparent hyperactivation and hyperproliferation of Cic-deficient CD4⁺ T cells than did mixed wild-type and Cic^f/f;Cd4-Cre bone marrow chimeras. Taken together, our data demonstrate that CIC deficiency at the beginning of T cell development endows peripheral CD4⁺ T cells with enhanced T cell activation and proliferative capability.

• IMMUNE NETWORK
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• v.20 no.5
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• pp.41.1-41.11
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• 2020

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is a positive-sense single-stranded RNA (+ssRNA) that causes coronavirus disease 2019 (COVID-19). The viral genome encodes twelve genes for viral replication and infection. The third open reading frame is the spike (S) gene that encodes for the spike glycoprotein interacting with specific cell surface receptor - angiotensin converting enzyme 2 (ACE2) - on the host cell membrane. Most recent studies identified a single point mutation in S gene. A single point mutation in S gene leading to an amino acid substitution at codon 614 from an aspartic acid 614 into glycine (D614G) resulted in greater infectivity compared to the wild type SARS-CoV2. We were interested in investigating the mutation region of S gene of SARS-CoV2 from Korean COVID-19 patients. New mutation sites were found in the critical receptor binding domain (RBD) of S gene, which is adjacent to the aforementioned D614G mutation residue. This specific sequence data demonstrated the active progression of SARS-CoV2 by mutations in the RBD of S gene. The sequence information of new mutations is critical to the development of recombinant SARS-CoV2 spike antigens, which may be required to improve and advance the strategy against a wide range of possible SARS-CoV2 mutations.

• International Journal of Stem Cells
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• v.15 no.3
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• pp.258-269
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• 2022

Background and Objectives: Currently, safety pharmacological tests for the central nervous system depend on animal behavioral analysis. However, due to the subjectivity of behavioral analysis and differences between species, there is a limit to appropriate nervous system toxicity assessment, therefore a new neurotoxicity assessment that can simulate the human central nervous system is required. Methods and Results: In our study, we developed an in vitro neurotoxicity assessment focusing on neuronal function. To minimize the differences between species and fast screening, hiPSC-derived neurons and a microelectrode array (MEA) that could simultaneously measure the action potentials of the neuronal networks were used. After analyzing the molecular and electrophysiological characters of our neuronal network, we conducted a neurotoxicity assessment on neurotransmitters, neurotoxicants, illicit drugs, and new psychoactive substances (NPS). We found that most substances used in our experiments responded more sensitively to our MEA-based neurotoxicity assessment than to the conventional neurotoxicity assessment. Also, this is the first paper that evaluates various illicit drugs and NPS using MEA-based neurotoxicity assessment using hiPSC-derived neurons. Conclusions: Our study expanded the scope of application of neurotoxicity assessment using hiPSC-derived neurons to NPS, and accumulated evaluation data of various toxic substances for hiPSC-derived neurons.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.143-155
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• 2011

Lately electronic tagging policy for the sexual offenders was introduced in order to reduce and prevent sexual offences. However, most sexual offences against children happening these days are committed by the tagged offenders whose identities have been released. So, for the crime prevention, we need measures with which we could minimize the suffers more promptly and actively. This paper suggests a new system to relieve the sexual abuse related anxiety of the children and solve the problems that electronic bracelet has. Existing bracelets are only worn by serious criminals, and it's only for risk management and positioning, there is no way to protect the children who are the potential victims of sexual abuse and there actually happened some cases. So we suggest also letting the students(children) wear the LBS(Location Based Service) and USN(Ubiquitous Sensor Network) technology based electronic bracelets to monitor and figure out dangerous situations intelligently, so that we could prevent sexual offences against children beforehand, and while a crime is happening, we could judge the situation of the crime intelligently and take swift action to minimize the suffer. And by checking students' attendance and position, guardians could know where their children are in real time and could protect the children from not only sexual offences but also violent crimes against children like kidnapping. The overall system is like follows : RFID Tag for children monitors the approach of offenders. While an offender's RFID tag is approaching, it will transmit the situation and position as the first warning message to the control center and the guardians. When the offender is going far away, it turns to monitoring mode, and if the tag of the child or the offender is taken off or the child and offender stay at one position for 3~5 minutes or longer, then it will consider this as a dangerous situation, then transmit the emergency situations and position as the second warning message to the control center and the guardians, and ask for the dispatch of police to prevent the crime at the initial stage. The RFID module of criminals' electronic bracelets is RFID TAG, and the RFID module for the children is RFID receiver(reader), so wherever the offenders are, if an offender is at a place within 20m from a child, RFID module for children will transmit the situation every certain periods to the control center by the automatic response of the receiver. As for the positioning module, outdoors GPS or mobile communications module(CELL module)is used and UWB, WI-FI based module is used indoors. The sensor is set under the purpose of making it possible to measure the position coordinates even indoors, so that one could send his real time situation and position to the server of central control center. By using the RFID electronic roll book system of educational institutions and safety system installed at home, children's position and situation can be checked. When the child leaves for school, attendance can be checked through the electronic roll book, and when school is over the information is sent to the guardians. And using RFID access control turnstiles installed at the apartment or entrance of the house, the arrival of the children could be checked and the information is transmitted to the guardians. If the student is absent or didn't arrive at home, the information of the child is sent to the central control center from the electronic roll book or access control turnstiles, and look for the position of the child's electronic bracelet using GPS or mobile communications module, then send the information to the guardians and teacher so that they could report to the police immediately if necessary. Central management and control system is built under the purpose of monitoring dangerous situations and guardians' checking. It saves the warning and pattern data to figure out the areas with dangerous situation, and could help introduce crime prevention systems like CCTV with the highest priority. And by DB establishment personal data could be saved, the frequency of first and second warnings made, the terminal ID of the specific child and offender, warning made position, situation (like approaching, taken off of the electronic bracelet, same position for a certain time) and so on could be recorded, and the data is going to be used for preventing crimes. Even though we've already introduced electronic tagging to prevent recurrence of child sexual offences, but the crimes continuously occur. So I suggest this system to prevent crimes beforehand concerning the children's safety. If we make electronic bracelets easy to use and carry, and set the price reasonably so that many children can use, then lots of criminals could be prevented and we can protect the children easily. By preventing criminals before happening, it is going to be a helpful system for our safe life.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.