• Journal of Life Science
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• v.29 no.5
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• pp.580-587
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• 2019

Benthic macroinvertebrates were analyzed in March, June, September, and December 2018 to evaluate water quality in the Songji River in Sacheon-ci, Korea. The identified benthic macroinvertebrates included 447 individuals belonging to 20 species, 18 families, 12 orders, 5 classes, and 3 phyla. Various ecological parameters were estimated for evaluation of the river status. The total ecological score of benthic macroinvertebrate community (TESB) varied from 17 (Station D) to 41 (Station A). The saprobic index and ecological score of benthic macroinvertebrate community (ESB) for the evaluation of river status revealed a water quality evaluation at Station A of II (oligosaprobic), indicating some satisfactory water protection. The benthic macroinvertebrate index (BMI) varied from 25.207 (Site C) to 39.348 (Station A). The evaluation of the river status at Stations C and D was polysaprobic, and sensitive taxa were absent. The mean Shannon-Weaver index (H') of diversity varied from 1.288 (Station D) to 2.250 (Station A). The classification of saprobity based on H' was ${\beta}$-mesosaprobic at Station A and ${\alpha}$-mesosaprobic at the other stations. The value of geometric density was varied from 1.229 (Station A) to 2.071 (Station D), with a mean of 1.582. An artificial load is being added to this river. One of load is the rectal river construction which flows straight through the river physics. Thus, the environment of living organisms deteriorates due to insufficient water. In order to secure the quality of the Songji River and a good environmental habitat, several low-height stepped-beam structures are required.

• Journal of the Korean Wood Science and Technology
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• v.49 no.6
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• pp.643-657
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• 2021

In order to establish a strategy for revitalizing wood culture, a survey was conducted on the level of public awareness of wood culture and the experience of wood cultural resources by type. According to the survey, 31.4% of respondents had the images of cultural heritage such as palaces, temples, Hanoks, and cultural assets for wood cultural resources. The main reasons for having no image of wood cultural resources were the ambiguous concept and lack of interest in wood cultural resources. The importance of wood cultural resources classified into seven categories was in the order of cultural heritage, architecture of wood, cultural facilities, cultural festivals, wood products, cultural education, cultural contents. In the survey on the necessity and sufficiency of information on wood cultural resources, 46.7% of respondents needed more information to experience of wood cultural resources, while 64.8% of them had lacked information about wood cultural resources. More than half of the respondents wanted to experience of wood culture within next year, but about 20% of respondents participated in seven kinds of wood cultural resources, except wood products used in daily life. Based on these results, a systematic strategy should be developed to expand the opportunity for the public to experience of wood cultural resources and to promote them to public.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of Navigation and Port Research
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• v.45 no.1
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• pp.1-8
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• 2021

The purpose of this study was to explore the regulations and operational problems of on-board training, and to investigate and analyze the satisfaction level of consignment on-board training among students who completed their training at Korea Maritime & Ocean University. In terms of satisfaction differences by gender, female students generally had a higher relative satisfaction level than male students. The different types of satisfaction in the navigation and engineering categories, the overall practical effect and satisfaction level of the training records shows that the satisfaction level of the trainee is much lower than that of the trainee, so relevant educational institutions and shipping companies need to take measures to improve the satisfaction level of this part. The most important part of the comprehensive company evaluation completed by trainees asked whether they thought the training was equivalent in labor to the employees. Results suggest shipping companies must make a clear distinction between employees and trainees, while establishing relevant internal procedures so that the on-board training process can be carried out in accordance with the purpose of the on-board training consignment.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.4
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• pp.19-32
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• 2021

It is well known that any maintenance works for aircrafts must be carried out strictly in accordance with the specified maintenance manuals, especially for military airplanes. According to our previous studies, the largest portion of the maintenance jobs for military aircrafts is found to be related to the assembly/disassembly of various parts, which requires precise understanding of the work procedures as well as correlation between interconnected parts let alone grasping of the exact shapes of parts involved. However, the conventional manuals for aircraft maintenance have failed to provide enough information required for the efficient maintenance except for simple texts and vague pictures, which are far from being sufficient sets of information. On the contrary, unlike incomplete conventional manuals with poor contents, 3D modeling SW could provide us with not only powerful visualization tool even to see through inside any assembly but also freedom to watch parts under test from any angle we want. In addition, the maintenance personnels could learn the precise maintenance procedures through repeatedly watching 3D animated version of the maintenance work as if they were on the field. In this study, we have suggested the efficient procedures to develop 3D manual for aircraft maintenance using 3D modeling SW, Solidworks and implemented a 3D maintenance manual for Integrated Drive Generator(IDG) in Boeing 747. Characteristics of the developed 3D manual has been analyzed in comparison with the conventional ones as well. It is shown that the suggested method could be easily applied to develop a 3D maintenance manual for commercial airplanes since the maintenance works involving assembly/disassembly of major parts are very similar regardless of aircraft types.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.85-97
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• 2022

In this study, a method for an integrated flood risk mapping was proposed that simultaneously considers the flood inundation map indicating the degree of risk and the disaster vulnerability index. This method creates a new disaster map that can be used in actual situations by providing various and specific information on a single map. In order to consider the human, social and economic factors in the disaster map, the study area was divided into exposure, vulnerability, responsiveness, and recovery factors. Then, 7 indicators for each factor were extracted using the GIS tool. The data extracted by each indicator was classified into grades 1 to 5, and the data was selected as a disaster vulnerability index and used for integrated risk mapping by factor. The risk map for each factor, which overlaps the flood inundatoin map and the disaster vulnerability index factor, was used to establish an evacuation plan by considering regional conditions including population, assets, and buildings. In addition, an integrated risk analysis method that considers risks while converting to a single vulnerability through standardization of the disaster vulnerability index was proposed. This is expected to contribute to the establishment of preparedness, response and recovery plans for providing detailed and diverse information that simultaneously considers the flood risk including social, humanistic, and economic factors.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.71-84
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• 2022

The objective of this study is to develop a two-dimensional (2D) flood model that can perform accurate flood analysis with simple input data. The 2D flood inundation models currently used to create flood forecast maps require complex input data and grid generation tools. This sometimes requires a lot of time and effort for flood modeling, and there may be difficulties in constructing input data depending on the situation. In order to compensate for these shortcomings, in this study, a grid-based model that can derive accurate and rapid flood analysis by reflecting correct topography as simple input data was developed. The calculation efficiency was improved by extending the existing 2×2 sub-grid model to a 5×5. In order to examine the accuracy and applicability of the model, it was applied to the Gamcheon Basin where both urban and river flooding occurred due to Typhoon Rusa. For efficient flood analysis according to user's selection, flood wave propagation patterns, accuracy and execution time according to grid size and number of sub-grids were investigated. The developed model is expected to be highly useful for flood disaster mapping as it can present the results of flooding analysis for various situations, from the flood inundation map showing accurate flooding to the flood risk map showing only approximate flooding.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.45-51
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• 2022

Fear of contact exists due to the prevention of the spread of infectious diseases such as COVID-19. When using the common entrance door of an apartment, access is possible only if the resident enters a password or obtains the resident's permission. There is the inconvenience of having to manually enter the number and password for the common entrance door to enter. Also, contactless entry is required due to COVID-19. Due to the development of ICT, users can be easily identified through the development of face recognition and voice recognition technology. The proposed method detects a visitor's face through a CCTV or camera attached to the common entrance door, recognizes the face, and identifies it as a registered resident. Then, based on the registered information of the resident, it is possible to operate without contact by interworking with the elevator on the server. In particular, if face recognition fails with a hat or mask, the visitor is identified by voice or additional authentication of the visitor is performed based on the voice message. It is possible to block the spread of contagiousness without leaving any contactless function and fingerprint information when entering and exiting the front door of an apartment house, and without the inconvenience of access.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.149-156
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• 2022

In the current COVID-19 pandemic, fake news and misinformation related to COVID-19 have been causing serious confusion in our society. To accurately detect such fake news, social context-based methods have been widely studied in the literature. They detect fake news based on the social context that indicates how a news article is propagated over social media (e.g., Twitter). Most existing COVID-19 related datasets gathered for fake news detection, however, contain only the news content information, but not its social context information. In this case, the social context-based detection methods cannot be applied, which could be a big obstacle in the fake news detection research. To address this issue, in this work, we collect from Twitter the social context information based on CoAID, which is a COVID-19 news content dataset built for fake news detection, thereby building CoAID⁺ that includes both the news content information and its social context information. The CoAID⁺ dataset can be utilized in a variety of methods for social context-based fake news detection, thus would help revitalize the fake news detection research area. Finally, through a comprehensive analysis of the CoAID⁺ dataset in various perspectives, we present some interesting features capable of differentiating real and fake news.