• Korean Journal of Environment and Ecology
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• v.36 no.1
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• pp.56-71
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• 2022

This study was conducted to build basic terrestrial insect data for the management of the persimmon plantations in the Sangju dried persimmon agricultural area. The survey identified terrestrial insect species were 7 orders, 77 families, and 1,925 individuals of 177 species. And the number of species that appeared in each village was in the order of Seoman II Village in Naeseo Township > Seoman I Village in Naeseo Township > Soeun Village in Oenam Township. Hemiptera and Coleoptera predominated throughout the site due to the characteristics of the cultivated land. The special species were 13 Korean endemic species, 2 vulnerable (VU) species, 22 least concern (LC) species, 42 not-evaluated (NE) species on the national red list, and 2 species of ecosystem disturbance wildlife. A result of the cluster analysis identified the Ectmetopterus micantulusas the dominant species and Ceutorhynchus albosuturalisas the subdominant species. The species diversity (H') was 1.1636-1.6022, evenness (J') was 0.6748-0.7611, and dominance (D) was in the range of 0.2389-0.3252.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1447-1452
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• 2022

Seoul Special City, one of the world's top 10 cities and Metro City, has traditional urban manufacturing industries such as printing, sewing, and mechanical metals. Small business owners in these manufacturing clusters have developed in the form of mutual assistance. Due to the nature of the agglomeration site, each process is handled by an individual company. It is difficult for relatively small business owners to prepare order processing services that provide real-time logistics movement information between processes. This paper collects and analyzes existing logistics data for smooth order and delivery of small business owners in package manufacturing and special printing fields We design an artificial intelligence Fulfillment Service Platform system with CRNN, k-NN, and ID3 Decision Tree Algorithm. Through this study, it is expected that it will greatly contribute to increasing sales and improving capabilities by allowing small business owners in integrated areas to use individual orders and delivery customized services through the Cloud network.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.755-762
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• 2022

This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.

• Journal of Veterinary Science
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• v.21 no.1
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• pp.9.1-9.15
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• 2020

Regenerative therapy holds great promise in the development of cures of some untreatable diseases such as cardiovascular diseases, and pluripotent stem cells (PSCs) including induced PSCs (iPSCs) are the most important regenerative seed cells. Recently, differentiation of human PSCs into functional tissues and cells in vitro has been widely reported. However, although porcine reports are rare they are quite essential, as the pig is an important animal model for the in vitro generation of human organs. In this study, we reprogramed porcine embryonic fibroblasts into porcine iPSCs (piPSCs), and differentiated them into cluster of differentiation 31 (CD31)-positive endothelial cells (ECs) (piPSC-derived ECs, piPS-ECs) using an optimized single-layer culture method. During differentiation, we observed that a combination of GSK3β inhibitor (CHIR99021) and bone morphogenetic protein 4 (BMP4) promoted mesodermal differentiation, resulting in higher proportions of CD31-positive cells than those from separate CHIR99021 or BMP4 treatment. Importantly, the piPS-ECs showed comparable morphological and functional properties to immortalized porcine aortic ECs, which are capable of taking up low-density lipoprotein and forming network structures on Matrigel. Our study, which is the first trial on a species other than human and mouse, has provided an optimized single-layer culture method for obtaining ECs from porcine PSCs. Our approach can be beneficial when evaluating autologous EC transplantation in pig models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Korean Journal of Biological Psychiatry
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• v.29 no.1
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• pp.15-21
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• 2022

Objectives Maltreatment experiences can alter brain function related to emotion regulation, such as cognitive reappraisal. While dysregulation of emotion is an important risk factor to mental health problems in maltreated people, studies reported alterations in brain networks related to cognitive reappraisal are still lacking. Methods Twenty-seven healthy subjects were recruited in this study. The maltreatment experiences and positive reappraisal abilities were measured using the Childhood Trauma Questionnaire-Short Form and the Cognitive Emotion Regulation Questionnaire, respectively. Twelve subjects reported one or more moderate maltreatment experiences. Subjects were re-exposed to pictures after the cognitive reappraisal task using the International Affective Picture System during fMRI scan. Results The maltreatment group reported more negative feelings on negative pictures which tried cognitive reappraisal than the no-maltreatment group (p < 0.05). Activities in the right superior marginal gyrus and right middle temporal gyrus were higher in the maltreatment group (uncorrected p < 0.001, cluster size > 20). Conclusions We found that paradoxical activities in semantic networks were shown in the victims of maltreatment. Further study might be needed to clarify these aberrant functions in semantic networks related to maltreatment experiences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.891-898
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• 2022

Purpose: There is not much time left for mandatory BIM implementation for all sectors and stages of the construction industry. Therefore, it is necessary to find a way to secure technology to substantially improve the productivity of BIM work. In the research, we proposed a method to automatically verify related construction standards for major objects produced by BIM modeling procedures so that engineers can verify construction standards in the BIM-based design process. Method: We defined a modeling work procedure for BIM-based road design work and prepared a method for constructing related design standards in a database. In addition, a process map for developing a BIM-based design basis review automation system was also presented. Result: A BIM-based design standard review automation module was developed using Civil3D and Dynamo. And it was confirmed by the test application that it is possible to quickly judge whether the BIM object manufactured in the design process conforms to the construction design standard. Conclusion: BIM-based design standard review automation technology can improve the productivity of BIM model production work and secure the quality of the BIM model.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.87-95
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• 2023

On October 29, 2022, a very large number of people gathered in Itaewondong, Yongsan-gu, Seoul, Korea for a Halloween festival, and as crowds pushed through narrow alleys, 159 deaths and 195 injuries occurred, making it the largest crushing incident in Korea. There have been a number of stampede deaths where crowds gathered at large-scale festivals, event venues, and stadiums, both at home and abroad. When the density increases, the physical contact between bodies becomes very strong, and crowd turbulence occurs when the force of the crowd is suddenly added from one body to another; thus, the force is amplified and causes the crowd to behave like a mass of fluid. When crowd turbulence occurs, people cannot control themselves and are pushed into he crowd. To prevent a stampede accident, investigation and management of areas expected to be crowded and congested must be systematically conducted, and related ministries and local governments are planning to establish a crowd management system to prepare safety management measures to prevent accidents involving multiple crowds. In this study, based on national data, a continuous digital topographic map is modeled in 3D to analyze the risk of crowding and present a plan for selecting high-risk walking routes. Areas with a high risk of crowding are selected in advance based on various data (numerical data, floating population, and regional data) in a realistic and feasible way, and the analysis is based on the visible results from 3D modeling of the risk area. The study demonstrates that it is possible to prepare measures to prevent cluster accidents that can reflect the characteristics of the region.