• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.581-592
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• 2019

This study investigates the factors that stimulate or suppress the use of food delivery applications. As potential antecedent factors, the present research examined the review diagnosticity, descriptive norms, and multidimensional risk perception. Based on this, users' data were collected from major metropolitan cities where the food delivery application business is most active. The results of structural equation modeling confirmed that users' approach to food delivery apps becomes more favorable when the review diagnosticity and descriptive norms were improved and when the perceived multidimensional risk expected to be associated with app use is mitigated. Additionally, we found that the positive influence of these attitudes on the actual intention to accept delivery applications became weaker at higher levels of perceived risk. These empirical results may contribute to the formation of strategic and systematic guidelines for promoting the expansion of the recently emerging O2O service platform across diverse sectors. Namely, the significance of this study lies in that it has raised awareness regarding the strategic considerations that such new O2O service providers should take into account for their market positions, in addition to discovering factors that could aid the prompt expansion of the applications' user base.

• Journal of Navigation and Port Research
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• v.45 no.4
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• pp.186-193
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• 2021

To enhance the competitiveness of the Busan Port in accordance with changes in global shipping and port industry trends, the Busan New Port is promoting step-by-step integration and developing a port resource-sharing platform. However, inefficient resource-sharing can cause unnecessary additional costs or impede port productivity, so accurate supply and demand matching of shared resources is required. In this study, the supply and demand of port resources were investigated for employees of Busan New Port and North Port, and port resources that could be ideally shared through IPA(Importance Performance Analysis) were analyzed. As a result of analyzing the equipment in the port, Yard Tractor, Reach Stacker, and Top Handler were the top considerations, and for facilities in the port, berths and aprons, empty container yards, and refrigerated container yards were the most important considerations. As for the data in the port, gate status, equipment specifications, and berth and apron conditions were the top considerations.

• Korean Journal of Construction Engineering and Management
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• v.22 no.5
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• pp.86-98
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• 2021

In this study, we developed a simulation model for supply chain management of Precast Concrete (PC) based construction. To this end, information on the Factory Production/Site Construction system was collected through literature review and field research, and based on this information, a simulation model was defined by describing the supply chain, entities, resources, and processes. Next, using the Arena simulation software, a simulation model for the PC supply chain was developed by setting model frameworks, data modules, flowchart modules, and animation modules. Finally, verification and validation were performed using five review methodologies such as model check, animation check, extreme value test, average value test, and actual case test to the developed model. As a result, it was found that the model adequately represented the flows and characteristics of the PC supply chain without any logical errors and provided accurate performance evaluation values for the target supply chains. It is expected that the proposed simulation model will faithfully play a role as a performance evaluation platform in the future for developing management techniques in order to optimally operate the PC supply chain.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.1-12
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• 2019

In this paper, it was found that most of the machinery facilities problems generated by clients could be prevented in advance by systematically managing the mechanical equipment production process of small and medium enterprises (SMEs) that produce machinery facilities. Major point of this process is to establish an operating system that corresponds to reality of facility manufacturers as it represents 63% of machinery facilities problems that occur in customers and is a task that needs to be solved most intensively. Technical issues account for 23% of machinery facilities problems occurring at the client's companies and should be approached from a long-term perspective as they are directly related to the technical capabilities of the manufacturers. Organizational problems account for 14% of machinery facilities problems occurring in customer companies, and can change depending on the relationship of members and the nature of the human being, such as morality and motivation. In addition, we propose the establishment of an Internet-based production process management platform for smooth and efficient transfer of information between customers and machinery facilities manufacturers.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1203-1213
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• 2018

In Arctic and Antarctic ocean, remote sensing is the most effective observation for environmental changes due to the inaccessibility of the regions. Even though satellite, UAV (Unmanned Aerial Vehical) are well known remote sensing platforms, and research vessel also used for automatic measurement on the regions, varied environment of Polar regions require time series and wide coverage of data. Especially, in high latitude, apply an optical satellite remote sensing is not easy due to low sun altitude. In this paper, we introduce an operation of hyper-spectrometer (HyperSAS/Satlantic inc.) which is mounted on Ice Breaker Research Vessel ARAON of Korea Polar Research Institute since 2010, to acquire an above water reflectance atomatically through every research cruise on Arctic and Antarctic ocean and transit both regions. In addition to, auxiliary data for the remotely acquired data, in situ water sampling were also obtained. The above water reflectance and in situ water sampling data are continuously acquired since 2010 will contribute to improve an Ocean Color algorithm in the high latitude and help to understand ocean reflectances over from high latitude through low latitude. Preliminary result from above water reflectance showed characteristics of Arctic ocean and Antarctic Ocean and used to develop algorithms for estimating various ocean factors such as chlorophyll and suspended sediment.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.3
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• pp.223-245
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• 2019

As the foundation for the Fourth Industrial Revolution, blockchain is becoming an essential core infrastructure and technology that creates new growth engines in various industries and is rapidly spreading to the environment of businesses and institutions worldwide. In this study, the characteristics and trends of blockchain technology were investigated and arranged, its application to the records management section of public institutions was required, and the procedures and methods of construction in the records management field of public institutions were studied in literature. Finally, blockchain technology was applied to the records management to propose an archive chain model and describe possible expectations. When the transactions that record the records management process of electronic documents are loaded into the blockchain, all the step information can be checked at once in the activity of processing the records management standard tasks that were fragmentarily nonlinked. If a blockchain function is installed in the electronic records management system, the person who produces the document by acquiring and registering the document enters the metadata and information, as well as stores and classifies all contents. This would simplify the process of reporting the production status and provide real-time information through the original text information disclosure service. Archivechain is a model that applies a cloud infrastructure as a backend as a service (BaaS) by applying a hyperledger platform based on the assumption that an electronic document production system and a records management system are integrated. Creating a smart, electronic system of the records management is the solution to bringing scattered information together by placing all life cycles of public records management in a blockchain.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.