• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.3
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• pp.83-95
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• 2022

This study aims to present primary data for habitat restoration and artificial breeding conditions of L. unmunsana by identifying the habitat conditions and the larvae's food sources. In order to investigate the habitat characteristics of the adult L. unmunsana and land snails, which are the primary food sources for the larvae, field surveys were conducted on a total of 10 habitats in south-central parts of Korea including Sanseongcheon, Jeonju. The results revealed that the L. unmunsana habitat in the Sanseongcheon area had a broadleaf forest with a multi-layered vegetation structure, adjacent water features, and the north/northeast/northwest slopes with little effect of artificial lighting. The adult L. unmunsana in the Sanseongcheon area appeared from the end of May to the end of June, and was especially intensively observed around the middle of June. The most active time was from 23:30 to 00:30 with a temperature range of 19~22℃ and higher than 80% humidity. The peak count of the observed adults L. unmunsana was a total of 774 on June 11, 2021. In the case of land snails, 11 families and 23 species were observed in 10 habitats of L. unmunsana, and Euphaedusa fusaniana was the most extensive and the most observed in the five survey areas. The land snails of L. unmunsana habitats are mostly found under the organic layers of leaves and a fallen tree branch in broadleaf forests, where a thick organic material layer buffers temperature changes and provides high humidity for various snails. These habitat conditions are suitable for the larva of L. unmunsana and land snails to inhabit, feed, hide and hibernate.

• Journal of Fashion Business
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• v.25 no.5
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• pp.131-148
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• 2021

Upcycling is a sustainable way to recycle waste resources and solve the global problem of environmental pollution. Upcycling is now attracting attention as fiberization and the disposal of waste clothing have become a serious issue. However, the customer's willingness to purchase upcycled products should be increased by propagating that the product value of a reborn commodity is of high value; these products are meant for new purposes and prepared with recycled materials. In this study, we created 11 designer items by applying an eco-friendly concept in the design process of upcycled textiles and products. From 2020 to 2021, a PBL(Problem Based Learning) curriculum was taught in design planning classes. The final 11 design items were derived after developing an eco-friendly product design for upcycled textiles. These final items were as follows: 5 fashion bags, 3 dog products, 1 clothing, 1 fashion accessory, and 1 sanitary mask design. In order to develop only one aesthetic design idea for upcycling, we considered the following features: user-centered convenience, functionality, and practicality. Then, tie-dye, drawing, patchwork, and embroidery were used to create innovative design items. The product design of recycled materials is based on high functionality, waterproofing, and the use of organic natural materials. The results of this study indicate that the creative product design of upcycling has contributed to a sustainable and eco-friendly environment. Related research studies must be conducted for innovating the continuous design process of the future.

• Journal of People, Plants, and Environment
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• v.24 no.1
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• pp.39-51
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• 2021

Background and objective: Urban topology can be characterized as impervious, which changes the hydrologic features of an area, increasing surface water flow during local heavy rain events. The pluvial flooding is also influenced by the vertical structures of the urban area. This study suggested a modified digital elevation model (DEM) to identify changes in urban hydrological conditions and segmentalized urban micro catchment areas using a geographical information system (GIS). Methods: This study suggests using a modified DEM creation process based on Rolling Ball Method concepts along with a GIS program. This method proposes adding realized urban vertical data to normal DEM data and simulating hydrological analyses based on RBM concepts. The most important aspect is the combination of the DEM with polygon data, which includes urban vertical data in three datasets: the contour polyline, the locations of buildings and roads, and the elevation point data from the DEM. DEM without vertical data (DCA) were compared with the DEM including vertical data (VCA) to analyze catchment areas in Shin-wol district, Seoul, Korea. Results: The DCA had 136 catchments, and the area of each catchment ranged from 3,406 m² to 423,449 m². The VCA had 2,963 catchments, with the area of each ranging from 50 m² to 16,209 m². The most important finding is that in the overlapped VCA; the boundary of areas directly affected by flooding and the direction of surface water flow could be identified. Flooding data from September 21, 2010 and July 27, 2011 in the Shin-wol district were applied as ground reference data. The finding is that in the overlapped VCA; the boundary of areas directly affected by flooding and the direction of surface water flow could be identified. Conclusion: The analysis of the area vulnerable to surface water flooding (SWF) was more accurately determined using the VCA than using the DCA.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.696-702
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• 2021

Informatization of early childhood education in the EU occurs in the context of the use of ICT as a means of sharing experiences, practices in the education and training of preschool children, communication, both at the national level and locally - within educational institutions, as a means of document management, search, data processing and information for the management of early childhood educational institutions, and planning activities for these institutions. This article aims to identify the features of the informatization of early childhood education in EU countries. Results. The countries of the EU have different levels of workload on the staff of early childhood education institutions, which is caused by different numbers of preschoolers and workforce. The greatest load on the staff in France due to a large number of preschoolers, which, despite the reduction, remained the highest among all the countries. By comparison, Poland's significant workload is mitigated by the size of its workforce. With almost equal numbers of staff in Poland and Germany, the countries differ significantly in the number of preschoolers. The countries also have different funding mechanisms for early childhood education, which determines the potential for digitalization. In France, total spending on early childhood education has grown the least (by 11 % between 2012 and 2018), in Poland by 51 %, in the Czech Republic by 44 %, and in Germany by 49%. In France, 100 % is funded by the government, in Poland 78 % is funded by the government, in the Czech Republic and Germany 87 % and 85 % respectively is funded by the government. The results of the survey of teachers' training in the use of ICTs and the level of specialists' readiness to use them in their studies indicate a mismatch between education and the practice of using technology. At the same time, given the high level of professional training of teachers in the use of technology in education, a low level of practice of ICT use in teaching preschool children was revealed. Teachers require professional development of ICT skills.

• Journal of the Korean Geotechnical Society
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• v.38 no.12
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• pp.29-43
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• 2022

Various underground facilities are being constructed to improve the urban environment. Therefore, it is more necessary than ever to reasonably evaluate the seismic response of underground utility tunnels, playing a significant part in urban infrastructure. In this study, the major features and differences of two types of existing pseudo-static analysis methods are reviewed. Each method uses a simplified 2D frame model to represent the seismic behavior of underground structures. Applying each method to a one-barrel rectangular utility tunnel in Korea, the suitability in predicting seismic responses, especially the racking deformation of the tunnel, is examined. In addition, several precautions and suggestions are provided in this study against the inattentive application of the methods to seismic evaluation of underground structures.

• Journal of Korean Association for Spatial Structures
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• v.22 no.3
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• pp.15-24
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• 2022

In this research, the case of modern wooden structures since 1950 with span of 30m or more was investigated and analyzed the construction status and structural planning characteristics of wooden large space architecture. As a result, wooden large space buildings have built around Asia, North America, and Europe, in which cases of ice skating stadiums with span of 30m to 60m were concentrated. In the case of baseball parks and football stadiums, even a span of about 165m was built in a wooden structure. In addition, it was found that the structural systems used in wooden large space structures were a funicular arch and truss structure, in that cases, funicular arch system consisting of radial arrangements was used in the examples exceeded 150m and the two way truss system was also used in long span wooden structures exceeding 100m. As the truss structure with a tie-rod or the flexure+tension structure was partially investigated, it can be seen that various timber structural systems need to be devised and researched. Also, It was investigated that a technique in which some members of the truss are made of steel or a composite member of steel and timber is also possible to develop

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.51-58
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• 2022

The article emphasizes the features of the modern education system in Poland, reveals the peculiarities of improving the professional competence of a specialist in Poland through the implementation of multimedia technologies. Various forms of innovations implemented in improving the professional competence of a specialist are listed: improvement (rationalization), modernization, innovation. The forms of professional improvement through the introduction of computer technologies in general and multimedia technologies, in particular, primarily include various professional courses, qualification, preparatory, methodological conferences, seminars, postgraduate studies, foreign and state internships. At the same time, the main direction is self-education. The subject of professional improvement in the application of computer technologies by specialists is the updating of existing knowledge, exchange of professional experience, planning, as well as discussion of innovative works in which specialists participate. Professional growth of specialists can occur both during work and in higher education institutions during their studies. Modernization of computer technologies, especially multimedia ones, is a necessary condition for the functioning of specialists in modern society, since specialists are at the center of the educational process, during the improvement of professional competence. The main functions of the educational process necessary for improving the professional competence of specialists through the implementation of multimedia technologies are revealed. These functions not only contribute to the professional improvement of specialists, but also affect their solutions and optimize the maintenance of contacts between specialists. The importance of creating conditions that are consistent with the modern needs of innovative education is emphasized.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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• pp.1-18
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• 2023

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.