• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.377-386
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• 2020

Mobile cloud computing involves mobile or embedded devices as clients, and features small devices with constrained resource and low availability. Due to the fast expansion of smart phones and smart peripheral devices, researches on mobile cloud computing attract academia's interest more than ever. Computation offloading, or code offloading, enhances the performance of computation by migrating a part of computation of a mobile system to nearby cloud servers with more computational resources through wired or wireless networks. Code offloading is considered as one of the best approaches overcoming the limited resources of mobile systems. In this paper, we analyze the factors and the performance of code offloading, especially focusing on static program partitioning and data protection. We survey state-of-the-art researches on analyzed topics. We also describe directions for future research.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.41-50
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• 2008

Recent progress in state-of-the-art geospatial information technologies such as digital mapping, LiDAR(Light Detection And Ranging), and high-resolution satellite imagery provides various data sources fer Digital Elevation Model(DEM). DEMs are major source to extract elements of the hydrological terrain property that are necessary for efficient watershed management. Especially, watersheds extracted from DEM are important geospatial database to identify physical boundaries that are utilized in water resource management plan including water environmental survey, pollutant investigation, polluted/wasteload/pollution load allocation estimation, and water quality modeling. Most of the previous studies related with watershed extraction using DEM are mainly focused on the hydrological elements analysis and preprocessing without considering grid size of the DEMs. This study aims to analyze accuracy of the watersheds extracted from DEMs with various grid sizes generated by LiDAR data and digital map, and appropriate preprocessing methods.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.163-173
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• 2021

The purpose of this study was to explore a support model for domestic ICT companies to advance into overseas markets suitable for the post-corona era along with the rapidly changing global economic situation. As a result of the analysis, first, by analyzing the internationalization cases of domestic ICT companies through the realization theory, in the COVID-19 situation, domestic ICT companies organically respond to crisis situations through ad hoc responses, use of international networks, use of external organizations, and resource-based practices. Through qualitative analysis, we were able to confirm that we are growing overseas while achieving our goals. In addition, strategies for domestic ICT companies to grow through internationalization were derived. Ultimately, it was interpreted as a part that required advancement of government policy according to the government's overseas expansion support system.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.285-292
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• 2019

This study was done to evaluate the effects of a mixture of essential oils and organic acid supplementation on growth performance, blood profiles, leg bone length and intestinal morphology in Ross broilers. A total of 40 Ross 308 broilers ($1140{\pm}80g$) were randomly allocated to 2 groups, a basal diet (CON) and a basal diet + 0.05% $Avi-protect^{(R)}$ (AVI, Mixture of 25% citric, 16.7 sorbic, 1.7% thymol, and 1.0% vanillin), with 20 replicates for every group and 1 chicken per replicate per cage. The broilers were raised in a temperature-controlled room maintained at $24{\pm}1^{\circ}C$ and $50{\pm}5%$ humidity. The body weight (p < 0.05) and weight gain (p < 0.05) of the broilers were increased in the AVI group compared with the CON group. The triglyceride (p < 0.05) and low density lipoprotein (LDL) (p < 0.05) contents were significantly decreased in the AVI group compared with the CON group. There was no significant difference in the leg bone length between the AVI and CON groups (p > 0.05). The villi height (p < 0.05) and goblet cell count (p < 0.05) were significantly increased in the AVI group compared with the CON group. In conclusion, $Avi-protect^{(R)}$ as a feed additive improved the growth performance and lipid metabolism and promoted the development of the intestinal morphology of broilers.

• Journal of Convergence for Information Technology
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• v.9 no.4
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• pp.14-20
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• 2019

The purpose of this paper is to present a smart QR code recording system and a method of non­storage data delivery that transforms the original data into a form of URL information by encrypting it and encoding the URL information into a QR code so that the QR code can be written and decrypted in a medium without storing the original data. The method of the study was presented by the prior art study and the literature research. Analysis results show that the system is built on the online administration server. The data input signal matching secret code is stored in DB, the QR code generation command converts input data from the password DB to the password information combined into the subordinate locator of the admin server's domain name, URL code. Therefore, the smart QR method of data management (recording and providing) indicates that there are no limitations in the ease and space of use or obstacles to capacity use.

• Journal of Communication Design
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• v.55
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• pp.20-30
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• 2016

This study was to verify the effect of art-education-oriented integrated human resource education (STEAM) on scientific competence and job competence of middle school students. The theme of the program developed for this study is "food design" using a 3-D printer. The 5 mockup lectures were developed for 237 students in ninth and tenth grades of Y Middle School in Gyeonggi-do Province, and the pre-/post-results were analyzed. As a result of analyzing the competence of STEAM education before and after the program, the job competence of general, science, and food science of students was improved after the program in general. Especially among three sub-areas of scientific competence, the change in component of research shows the most significant change in test statistics. The result of test statistics of food science job competence showed the most significant change among three sub-areas: general, scientific, and food science. This program was developed for on-site to be used for a school lecture, and the program may be reformed or used for different themes.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.137-142
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• 2021

The Internet of Things (IoT) paradigm is at the forefront of present and future research activities. The huge amount of sensing data from IoT devices needing to be processed is increasing dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing jobs. The fog computing technology is a model that is used to support cloud computing by implementing pre-processing jobs close to the end-user for realizing low latency, less power consumption in the cloud side, and high scalability. However, it may be that some resources in fog computing networks are not suitable for some kind of jobs, or the number of requests increases outside capacity. So, it is more efficient to decrease sending jobs to the cloud. Hence some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud server. Obviously, this issue affects the performance of the fog environment when dealing with big data applications or applications that are sensitive to time processing. This research aims to build a fog topology job scheduling (FTJS) to schedule the incoming jobs which are generated from the IoT devices and discover all available fog nodes with their capabilities. Also, the fog topology job placement algorithm is introduced to deploy jobs into appropriate resources in the network effectively. Finally, by comparing our result with the state-of-art first come first serve (FCFS) scheduling technique, the overall execution time is reduced significantly by approximately 20%, the energy consumption in the cloud side is reduced by 18%.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.711-716
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• 2023

A large amount of stone is required as a high-end strategy for building new construction and remodeling work, and aggregates have to be collected from many stone mountains due to the lack of river sand and gravel required for concrete production. This study examines the case of development of stone materials for construction, conduct and analyze residents' attitudes, find out residents' thoughts on stone mountain development, develops the area as an alternative to the devastation caused by stone mountain development, and recycles it as an efficient resource. We intend to conduct basic research to propose a plan. As a result, Local residents have a lot of antipathy against the use of quarry, so it is necessary to use the area that is far from the residents' areas as a quarry. It was concluded that it was desirable to restore and develop in the right direction.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.571-578
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• 2018

The deteriorated pipline often causes the unexpected leakage and crack. Negligence and late maintenance leads the enormous damage for gas and water resource. This paper proposes early fault detection and diagnosis algorithm for pipeline using acoustic emission (AE) signals. Early fault detection method for pipeline compares the frequency amplitude of the spectrum to that of the spectrum in normal condition. Larger amplitude of the spectrum indicates abnormal condition. Early fault diagnosis algorithm uses support vector machines (SVM), which is trained for normal and abnormal conditions to diagnose the measured AE signal from the target pipeline. In the experiment, a pipeline testbed is constructed similarly to real industrial pipeline. Normal, 5mm cracked, 10mm holed pipelines are installed and tested in this study. The proposed fault detection and diagnosis technique is validated as an efficient approach to detect early faulty condition of pipeline.