• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.328-342
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• 2022

The Internet of Things (IoT) is a technology that offers lucrative services in various industries to facilitate human communities. Important information on people and their surroundings has been gathered to ensure the availability of these services. This data is vulnerable to cybersecurity since it is sent over the internet and kept in third-party databases. Implementation of data encryption is an integral approach for IoT device designers to protect IoT data. For a variety of reasons, IoT device designers have been unable to discover appropriate encryption to use. The static support provided by research and concerned organizations to assist designers in picking appropriate encryption costs a significant amount of time and effort. IoTES is a web app that uses machine language to address a lack of support from researchers and organizations, as ML has been shown to improve data-driven human decision-making. IoTES still has some weaknesses, which are highlighted in this research. To improve the support, these shortcomings must be addressed. This study proposes the "IoTES with Security" model by adding support for the security level provided by the encryption algorithm to the traditional IoTES model. We evaluated our technique for encryption algorithms with available security levels and compared the accuracy of our model with traditional IoTES. Our model improves IoTES by helping users make security-oriented decisions while choosing the appropriate algorithm for their IoT data.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.732-749
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• 2011

In this study, 27-year (1979-2005) regional climate over the CORDEX East Asia domain was reproduced using a regional climate model, RegCM4, driven by HadGEM2-AO output, and the model's simulation skill was evaluated in terms of surface air temperature and precipitation. The RegCM4 reasonably simulated the spatial distribution and interannual variability and seasonal variability of surface air temperature, while it had systematic biases in the simulation of precipitation. In particular, simulated rainband of East Asian summer monsoon was southward shifted below $30^{\circ}N$ as compared with the observation, thereby, summer mean precipitation over South Korea was significantly underestimated. Simulated temperature from the RegCM4 driven by the HadGEM2-AO output was comparable to that driven by the reanalysis. However, the RegCM4 driven by the HadGEM2-AO had prominently poor skill in the simulation of precipitation. This can be associated with the distorted monsoon circulations in the driving data (i.e., HadGEM2-AO) such as southward shifted low-level southwesterly, which resulted in the erroneous evolution of East Asian summer monsoon simulated by RegCM4.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.657-670
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• 2020

The knowledge-information-processing competency is the most essential competency in a knowledge-information-based society and is the most fundamental competency in the new problem-solving ability. Data-driven science inquiry, which emphasizes how to find and solve problems using vast amounts of data and information, is a way to cultivate the problem-solving ability in a knowledge-information-based society. Therefore, this study aims to develop a teaching-learning model and strategy for data-driven science inquiry and to verify the validity of the model in terms of knowledge information processing competency. This study is developmental research. Based on literature, the initial model and strategy were developed, and the final model and teaching strategy were completed by securing external validity through on-site application and internal validity through expert advice. The development principle of the inquiry model is the literature study on science inquiry, data science, and a statistical problem-solving model based on resource-based learning theory, which is known to be effective for the knowledge-information-processing competency and critical thinking. This model is titled "Exploratory Scientific Data Analysis" The model consisted of selecting tools, collecting and analyzing data, finding problems and exploring problems. The teaching strategy is composed of seven principles necessary for each stage of the model, and is divided into instructional strategies and guidelines for environment composition. The development of the ESDA inquiry model and teaching strategy is not easy to generalize to the whole school level because the sample was not large, and research was qualitative. While this study has a limitation that a quantitative study over large number of students could not be carried out, it has significance that practical model and strategy was developed by approaching the knowledge-information-processing competency with respect of science inquiry.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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• pp.250-257
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• 2013

This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.1
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• pp.66-73
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• 2010

This work was focused on the material parameter extraction for the isothermal cyclic deformation analysis for which Chaboche(Combined Nonlinear Isotropic and Kinematic Hardening) and Overlay(Multi Linear Hardening) models are normally used. In this study all the parameters were driven especially based on Overlay theories. A simple method is suggested to find out best material parameters for the cyclic deformation analysis prior to the isothermal LCF(Low Cycle Fatigue) analysis. The parameter extraction was done using 400 series stainless steel data which were published in the reference papers. For simple and quick review of the parameters extracted by suggested method, 1D FORTRAN program was developed, and this program could reduce the time for checking the material data tremendously. For the application to FE code ABAQUS user subroutine for the material models was developed by means of UMAT(User Material Subroutine), and the stabilized hysteresis loops obtained by the numerical analysis were in good harmony with test results.

• Journal of Distribution Science
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• v.15 no.4
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• pp.25-32
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• 2017

Purpose - The introduction and expansion of the railway network since the 19th century brought revolutionary changes in economic activities, performance, and structure. The purpose of this study is estimating the impact of railroads on the local agricultural and manufacturing structures in the 19th century USA. Research design, data, and methodology - To identify the impact of railroads on local economic structure, county-level panel data from the U.S. census were analyzed using a panel fixed-effect differences-in-differences regression. The empirical investigation focuses on whether railroads changed the overall volume and sectoral composition of the local agricultural sector, and whether they contributed to the growth of the local manufacturing industry and its productivity. Results - The railroad introduction led to the relative decline of the agricultural sector, while encouraging the growth of market-oriented gardening. As such, manufacturing productivity increased by the introduction of railroads, although manufacturing inputs and home manufactures were unaffected. Conclusions - The findings imply that railroads contributed to the growth of market-oriented farming in rural areas, and the rise of productivity in the local manufacturing sector. Meanwhile, evidence of railroad-driven growth for the entire agricultural sector or a massive reallocation of resources from agriculture to manufacturing were not found.

• English Language & Literature Teaching
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• v.16 no.4
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• pp.239-265
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• 2010

This study investigates the effects of corpus-based vocabulary tasks on the acquisition of English vocabulary in an attempt to explore the influence of corpus use on EFL pedagogy. For this to be realized, a total of 40 Korean high school students participated in the study over a 4-week period. An experimental group used a set of corpus-based tasks for vocabulary learning, whereas a control group carried out a traditional task (i.e., the L1-L2 translation) for vocabulary learning. To assess learning gains, the students were asked to complete the pre- and post-treatment tests measuring the word form, meaning, and use aspects of target lexical items. Results of the study indicate that in the experimental group the corpus-based vocabulary tasks were beneficial for the learning of word forms and use. In particular, corpus-based benefits were greatest in the low-proficiency EFL learners' collocational aspects of vocabulary use. On the other hand, in the control group, the traditional vocabulary tasks benefited the meaning aspects of target vocabulary items the most. In addition, survey results revealed that most students were positive about the corpus-based learning experience although some expressed reservations about the heavy cognitive load and the time-consuming nature of the analysis of corpus data primarily due to learners' lack of language proficiency.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1003-1008
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• 2016

Breast cancer is now the leading cause of cancer death in women worldwide. Cancer progression is driven not only by cancer cell intrinsic alterations and interactions with tumor microenvironment, but also by systemic effects. Integration of multiple profiling data may provide insights into the underlying molecular mechanisms of complex systemic processes. We performed a bioinformatic analysis of two public available microarray datasets for breast tumor stroma and peripheral blood mononuclear cells, featuring integrated transcriptomics data, protein-protein interactions (PPIs) and protein subcellular localization, to identify genes and biological pathways that contribute to dialogue between tumor stroma and the peripheral circulation. Genes of the integrin family as well as CXCR4 proved to be hub nodes of the crosstalk network and may play an important role in response to stroma-derived chemoattractants. This study pointed to potential for development of therapeutic strategies that target systemic signals travelling through the circulation and interdict tumor cell recruitment.