• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.93-98
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• 2023

Recently, as the size of display devices tends to increase and taking pictures with smart phones has become commonplace, the need for taking high-resolution pictures with smart phones is increasing. However, when the lens size of a camera is limited, such as in a smartphone, there is a physical limit to increasing the resolution of a photo. This paper is about a technique for increasing the resolution of a picture even when using a small-sized lens like a smartphone camera. It is to take multiple pictures while moving the smartphone, and to increase the resolution by combining these pictures into one picture. First of all, two pictures were taken while moving the smartphone horizontally for the 2D picture. Processes such as camera matrix estimation, and homograph inverse transformation were performed using OpenCV, and the resolution was improved by synthesizing one picture. It was confirmed that the resolution was improved in parts such as oblique lines or arcs on several test pictures.

• Mycobiology
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• v.50 no.4
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• pp.254-257
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• 2022

Wolfiporia cocos is a wood-decay brown rot fungus belonging to the family Polyporaceae. While the fungus grows, the sclerotium body of the strain, dubbed Bokryeong in Korean, is formed around the roots of conifer trees. The dried sclerotium has been widely used as a key component of many medicinal recipes in East Asia. Wolfiporia cocos strain KMCC03342 is the reference strain registered and maintained by the Korea Seed and Variety Service for commercial uses. Here, we present the first draft genome sequence of W. cocos KMCC03342 using a hybrid assembly technique combining both short- and long-read sequences. The genome has a total length of 55.5 Mb comprised of 343 contigs with N50 of 332 kb and 95.8% BUSCO completeness. The GC ratio was 52.2%. We predicted 14,296 protein-coding gene models based on ab initio gene prediction and evidence-based annotation procedure using RNAseq data. The annotated genome was predicted to have 19 terpene biosynthesis gene clusters, which was the same number as the previously sequenced W. cocos strain MD-104 genome but higher than Chinese W. cocos strains. The genome sequence and the predicted gene clusters allow us to study biosynthetic pathways for the active ingredients of W. cocos.

• Applied Microscopy
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• v.50
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• pp.2.1-2.7
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• 2020

Electron energy loss spectroscopy (EELS) is an analytical technique that can provide the structural, physical and chemical information of materials. The EELS spectra can be obtained by combining with TEM at sub-nanometer spatial resolution. However, EELS spectral information can't be obtained easily because in order to interpret EELS spectra, we need to refer to and/or compare many reference data with each other. And in addition to that, we should consider the different experimental variables used to produce each data. Therefore, reliable and easily interpretable EELS standard reference data are needed. Our Electron Energy Loss Data Center (EELDC) has been designated as National Standard Electron Energy Loss Data Center No. 34 to develop EELS standard reference (SR) data and to play a role in dissemination and diffusion of the SR data to users. EELDC has developed and collected EEL SR data for the materials required by major industries and has a total of 82 EEL SR data. Also, we have created an online platform that provides a one-stop-place to help users interpret quickly EELS spectra and get various spectral information. In this paper, we introduce EEL SR data, the homepage of EELDC and how to use them.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.291-298
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• 2022

A generative adversarial network (GAN) is a network in which two internal neural networks (generative network and discriminant network) learn while competing with each other. The generator creates an image close to reality, and the delimiter is programmed to better discriminate the image of the constructor. This technology is being used in various ways to create, transform, and restore the entire image X into another image Y. This paper describes a method that can be forged into another object naturally, after extracting only a partial image from the original image. First, a new image is created through the previously trained DCGAN model, after extracting only a partial image from the original image. The original image goes through a process of naturally combining with, after re-styling it to match the texture and size of the original image using the overall style transfer technique. Through this study, the user can naturally add/transform the desired object image to a specific part of the original image, so it can be used as another field of application for creating fake images.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.3
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• pp.291-300
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• 2016

The emergence of smart devices and IoT leads to the appearance of O2O service to blur the difference between online and offline. As online services' merits were added to the offline market, it caused a change in the dynamics of the offline industry, which means the offline-space's digitization. Unlike these changing aspects of the offline market, exhibition industry grows steadily in the industry, however it is also possible to create a new value added by combining O2O service. We conducted a survey targeting 20 spectators in '2015 Seoul Design Festival' at COEX. The survey was used to analysis of the spatial structure and generate the dataset for machine learning. We identified problems with the analysis study of the existing spatial structure, and based on this investigation we propose a new method for analyzing a spatial structure. Also by processing a machine learning technique based on the generated dataset, we propose a novel evaluation model of exhibition-space cells for O2O service layout.

• The Journal of Bigdata
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• v.6 no.2
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• pp.169-179
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• 2021

Consumers' food consumption behavior is likely to be affected not only by structured data such as consumer panel data but also by unstructured data such as mass media and social media. In this study, a deep learning-based consumption prediction model is generated and verified for the fusion data set linking structured data and unstructured data related to food consumption. The results of the study showed that model accuracy was improved when combining structured data and unstructured data. In addition, unstructured data were found to improve model predictability. As a result of using the SHAP technique to identify the importance of variables, it was found that variables related to blog and video data were on the top list and had a positive correlation with the amount of paprika purchased. In addition, according to the experimental results, it was confirmed that the machine learning model showed higher accuracy than the deep learning model and could be an efficient alternative to the existing time series analysis modeling.

• The Journal of Bigdata
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• v.7 no.1
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• pp.139-171
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• 2022

Big Data and the fourth industrial revolution are the first revolution that has not spawned a new form of energy but has triggered a new technological phenomenon called digitization. Digital transformation has caused disruptive innovation, and each country and major corporations need to respond to it. Despite this importance, empirical studies at home and abroad are insufficient. Therefore, in this study, factors affecting the promotion of corporate digital transformation were discovered through literature review, and a research model was developed and empirically analyzed by modifying and supplementing it through a Delphi study. The research model was composed of the main standards such as technology, innovation, organization, and environment and 17 sub-standards by combining the IDT and TOE models. In order to empirically analyze this, the AHP decision-making technique was used for experts in domestic digital transformation promotion companies and business partners. Companies that promote digital transformation will be able to increase the chances of achieving successful digital transformation if they take into account the factors that influence the digital transformation promotion according to the characteristics of the type of industry and company size of the group to which the company belongs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2C
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• pp.125-131
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• 2008

Ground stiffness(shear wave velocity) is one of the key parameters in geotechnical earthquake engineering. An In-situ seismic technique has its own advantages and disadvantages over the others in stiffness measurements. By combining the crosshole and seismic cone techniques and utilizing favourable features of bender elements, a new hybrid probe has been developed in order to enhance data quality and easiness of testing. The basic structure of the probe, called "MudFork" is a fork composed of two blades, on each of which source and receiver bender elements were mounted respectively. To evaluate the disturbance caused by the penetration of the probe, shear wave velocity measurements were carried out in the Kaolinite slurry in the laboratory. Finally, the probe was penetrated in coastal mud near Incheon, Korea, using SPT(standard penetration test)rods pushed with a routine boring machine and shear wave velocity measurements were carried out. The results were verified with data from laboratory and cone testing. The performance of the probe turns out to be excellent in terms of data quality and testing convenience.

• Asia Marketing Journal
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• v.15 no.3
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• pp.1-14
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• 2013

Our research brings managerial insights for developing new digital convergence of devices and services. To explain the phenomenon of device and service convergence, we combine two different approaches from separate research fields: a perceptual mapping technique generally used for segmentation in marketing and associative network analysis mobilized to understanding network structure of core and peripheral as well as the information mediating role of nodes in network science. By combining these two approaches, we provide an in-depth analysis of the associations among devices and services by assessing the centrality of device and service nodes in an associative network. This is done by examining the connections between these services and devices as well as investigating the role of mediation in the combined device-service associative network. Our results based on bi-partite network analysis of survey responses from 250 Internet Protocol (IP) television viewers show which device and which service will play the major role in future device and service convergence as well as which characteristics and functionalities have to be incorporated into future convergence. Among the devices, the mobile handset with the betweenness centrality of 0.26 appears to be the device that would lead future device convergence. Among the services, wireless broadband with the betweenness centrality of 0.276 appears to be the service on which future service convergence needs to be developed. This result is quite unexpected, since wireless broadband has a lower penetration rate than other services, such as fixed broadband and cable TV. In addition, we indicate the possibility of converging devices, such as personal digital assistant (PDA) and mobile handset, and services, such as IPTV and mobile Internet, into wireless broadband services in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.