• Journal of Family Resource Management and Policy Review
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• v.8 no.3
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• pp.113-125
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• 2004

This study aims to provide philosophical frameworks and approaches, and practical guidelines to interview research. While there are some studies which focus on the practical guidelines, there is a lack of studies which address the philosophical and conceptual issues integral to qualitative interview research. By discussing three different but related philosophical aspects of qualitative interviewing--phenomenology, hermeneutics, and dialectics--consistency among research purpose, research method, research methodology is pursued. Based on this discussion, four stages of interviewing--designing, conducting, analyzing, and writing of qualitative interview research--are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1738-1764
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• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.1-13
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• 2018

Deep learning (DL) models have been widely applied to AI applications such image recognition and language translation with big data. Recently, DL models have becomes larger and more complicated, and have merged together. For the accelerated training of a large-scale deep learning model, model parallelism that partitions the model parameters for non-shared parallel access and updates across multiple machines was provided by a few distributed deep learning frameworks. Model parallelism as a training acceleration method, however, is not as commonly used as data parallelism owing to the difficulty of efficient model parallelism. This paper provides a comprehensive survey of the state of the art in model parallelism by comparing the implementation technologies in several deep learning frameworks that support model parallelism, and suggests a future research directions for improving model parallelism technology.

• Membrane Journal
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• v.28 no.2
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• pp.129-135
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• 2018

ZIFs (Zeolitic imdazolate frameworks) have attracted great attention as membrane materials in recent years due to their high chemical and thermal stability, high specific surface area and adjustable pore structure. In this study, ZIF-8 membranes were synthesized by in-situ growth method on two different support materials (${\alpha}$-alumina and YSZ) and their $H_2/CO_2$ gas permeation characteristics were investigated. In order to synthesize defect-free ZIF-8 layer, YSZ support required less synthesis time than ${\alpha}$-alumina support due to smaller pore size. After in-situ growth for 3 h, ZIF-8 membranes prepared on both YSZ and ${\alpha}$-alumina supports showed $H_2/CO_2$ selectivity of about 10.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.601-613
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• 2012

It is required to design and implement secure web applications to provide safe web services. For this reason, there are several scoring frameworks to measure vulnerabilities in web applications. However, these frameworks do not classify according to seriousness of vulnerability because these frameworks simply accumulate score of individual factors in a vulnerability. We rate and score vulnerabilities according to probability of privilege acquisition so that we can prioritize vulnerabilities found in web applications. Also, our proposed framework provides a method to score all web applications provided by an organization so that which web applications is the worst secure and should be treated first. Our scoring framework is applied to the data which lists vulnerabilities in web applications found by a web scanner based on crawling, and we show the importance of categorizing vulnerabilities according to privilege acquisition.

• International Journal of Contents
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• v.13 no.3
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• pp.25-31
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• 2017

With tremendous advancement of information and communication technologies, mobile learning systems have been widely adopted in language learning contexts, and several frameworks have been developed for identifying and categorizing different factors of mobile-assisted language learning (MALL). However, pre-existing frameworks have limitations when evaluating the importance level of criteria. The purpose of this study is to develop a comprehensive hierarchical framework for identifying and categorizing success factors of MALL and prioritizing them according to the importance level. To do that, AHP method is used to quantitatively estimate weight values of MALL criteria. Results reveal that the priority of MALL criteria is ordered as follows: content, system, learner, language learning. Local weights of each criterion are also analyzed; for example, usefulness, accuracy, and authenticity are critical factors for improving MALL contents. Ease of use and mobility of MALL systems are also considered more critical than other systematic factors. In addition, availability of immediate feedback and self-directness has the highest weight values of importance. The findings of the study are discussed regarding hierarchical orders of MALL criteria and conclude that successful MALL implementation may be achieved if related elements are diversely measured and evaluated. Pedagogical implications and suggestions for further research are also presented.

• Safety and Health at Work
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• v.9 no.1
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• pp.63-70
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• 2018

Background: Psychosocial risk management [Psychosocial Risk Management Approach (PRIMA)] has, through the years, been applied in several organizations in various industries and countries globally. PRIMA principles have also been translated into international frameworks, such as PRIMA-EF (European framework) and the World Health Organization Healthy Workplace Framework. Over the past 10 years, an oil and gas company has put efforts into adopting and implementing international frameworks and standards for psychosocial risk management. More specifically, the company uses a PRIMA. Methods: This study explores available quantitative and qualitative risk data collected through the PRIMA method over the past 8 years in order to explore specific and common psychosocial risks in the petroleum industry. Results: The analyses showed a significant correlation between job resources and symptoms of work-related stress, there was a significant correlation between job demands and symptoms of work-related stress, and there were differences in psychosocial risk factors and symptoms of work-related stress onshore and offshore. The study also offers recommendations on how the results can further be utilized in building a robust system for managing psychosocial risks in the industry. Conclusion: The results from the analyses have provided meaningful and important information about the company-specific psychosocial risk factors and their impact on health and well-being.

• Korean Journal of Materials Research
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• v.33 no.10
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• pp.377-384
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• 2023

Exploring earth-abundant, highly effective and stable electrocatalysts for electrochemical water splitting is urgent and essential to the development of hydrogen (H₂) energy technology. Iron-cobalt layered double hydroxide (FeCo-LDH) has been widely used as an electrocatalystfor OER due to its facile synthesis, tunable components, and low cost. However, LDH synthesized by the traditional hydrothermal method tends to easily agglomerate, resulting in an unstable structure that can change or dissolve in an alkaline solution. Therefore, studying the real active phase is highly significant in the design of electrochemical electrode materials. Here, metal-organic frameworks (MOFs) are used as template precursors to derive FeCo-LDH from different iron sources. Iron salts with different anions have a significant impact on the morphology and charge transfer properties of the resulting materials. FeCo-LDH synthesized from iron sulfate solution (FeCo-LDH-SO₄) exhibits a hybrid structure of nanosheets and nanowires, quite different from other electrocatalysts that were synthesized from iron chloride and iron nitrate solutions. The final FeCo-LDH-SO₄ had an overpotential of 247 mV with a low Tafel-slope of 60.6 mV dec^-1 at a current density of 10 mA cm^-2 and delivered a long-term stability of 40 h for the OER. This work provides an innovative and feasible strategy to construct efficient electrocatalysts.

• Journal of radiological science and technology
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• v.46 no.3
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• pp.207-217
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• 2023

Image noise reduction algorithm performs important functions in light microscopy. This study aims to systematically review the research trend of types and performance evaluation methods of noise reduction algorithm in light microscopic images. A systematic literature search of three databases of publications from January 1985 to May 2020 was conducted; of the 139 publications reviewed, 16 were included in this study. For each research result, the subjects were categorized into four major frameworks-1. noise reduction method, 2. imaging technique, 3. imaging type, and 4. evaluation method-and analyzed. Since 2003, related studies have been conducted and published, and the number of papers has increased over the years and begun to decrease since 2016. The most commonly used method of noise reduction algorithm for light microscopy images was wavelet-transform-based technology, which was mostly applied in basic systems. In addition, research on the real experimental image was performed more actively than on the simulation condition, with the main case being to use the comparison parameter as an evaluation method. This systematic review is expected to be extremely useful in the future method of numerically analyzing the noise reduction efficiency of light microscopy images.

• Journal of Sensor Science and Technology
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• v.29 no.6
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• pp.374-381
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• 2020

Hydrogen gas has attracted considerable attention as a promising candidate for future energy resources because of its eco-friendly characteristics; however, its highly combustible characteristics should be thoroughly examined to preclude potential disasters. In this regard, a highly sensitive method for the selective detection of H₂ is extremely important. To achieve excellent H₂ selectivity, the utilization of a metal-organic framework (MOF) membrane can physically screen interfering gas molecules by restricting the size of kinetic diameters that can penetrate its nanopores. This paper summarizes the various endeavors of researchers to utilize the MOF molecular sieving layer for the development of highly selective H₂ sensors. Further, the review affords useful insights into the development of highly reliable H₂ sensors.