• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.243-253
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• 2012

An opinion mining technique which was developed from document classification in area of data mining now becomes a common interest in domestic as well as international industries. The core of opinion mining is to decide precisely whether an opinion document is a positive or negative one. Although many related approaches have been previously proposed, a classification accuracy was not satisfiable enough to applying them in practical applications. A opinion documents written in Korean are not easy to determine a polarity automatically because they often include various and ungrammatical words in expressing subjective opinions. Proposed in this paper is a new approach of classification of opinion documents, which considers only a frequency of word patterns and excludes the grammatical factors as much as possible. In proposed method, we express a document into a bag of words and then apply a learning algorithm using a frequency of word patterns, and finally decide the polarity of the document using a score function. Additionally, we also present the experiment results for evaluating the accuracy of the proposed method.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.549-557
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• 2015

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platformand the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4Gbyte of storage size (87%of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.79-82
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• 2019

This study aims to design and implement automated SEO tools that has applied the artificial intelligence techniques for search engine optimization (SEO; Search Engine Optimization). Traditional Search Engine Optimization (SEO) on-page optimization show limitations that rely only on knowledge of webpage administrators. Thereby, this paper proposes the metadata generation system. It introduces three approaches for recommending metadata; i) Downloading the metadata which is the top of webpage ii) Generating terms which is high relevance by using bi-directional Long Short Term Memory (LSTM) based on attention; iii) Learning through the Generative Adversarial Network (GAN) to enhance overall performance. It is expected to be useful as an optimizing tool that can be evaluated and improve the online marketing processes.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.1
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• pp.1-6
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• 2022

Vocal fold injection (VFI) is widely accepted as a first line treatment in treating unilateral vocal fold paralysis and other vocal fold diseases. Although VFI is advantageous for its minimal invasiveness and efficiency, the invisibility of the needle tip remains an essential handicap in precise localization. Real-time light-guided vocal fold injection (RL-VFI) is a novel technique that was developed under the concept of performing simultaneous injection with precise placement of the needle tip under light guidance. RL-VFI has confirmed its possibility of technical implementation and the feasibility in injecting the needle from various directions through ex vivo animal studies. Further in vivo animal study has approved the safety and feasibility of the procedure when various transcutaneous approaches were applied. Currently, RL-VFI device is authorized for clinical use by the Ministry of Food and Drug Safety in South Korea and is clinically applied to patients with safe and favorable outcome. Several clinical studies are currently under process to approve the safety and the efficiency of RL-VFI. RL-VFI is expected to improve the complication rate and the functional outcome of voice. Furthermore, it will support laryngologists in overcoming the steep learning curve by its intuitive guidance.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.147-156
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• 2024

Science practice is a process of establishing new relationships with 'foreign things' such as learning objects or tools for observation and measurement. Since the practice of science in major subjects has been increasingly emphasized, we sought to understand the meaning co-created by students and numerous materials who have experienced astronomical observation as a Deleuzian experience of "becoming". We collected activity logs and photographic data written by 17 students participating in astronomical observation activities at "A" High School, and conducted in-depth interviews with the students. We assessed the collected data by reconstructing a situation analysis. The main research results include the students' existential-epistemological 'becoming' process: 1) discovering newness through repetition, 2) becoming an 'explanation machine' to convey the affect of astronomical observation activities, 3) breaking out of a stabilized territory, and crossing a threshold. Based on the results, we suggested the need for follow-up research on the practices and new experimental approaches of teachers in earth science education.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1089-1101
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• 2022

Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid development of immunotherapeutic agents, from immune checkpoint inhibitors to chimeric antigen receptor-T cells, with which many radiologists may not be familiar, and the atypical responses to therapy, such as pseudoprogression and hyperprogression. Therefore, new response assessment methods such as immune response assessment, functional/molecular imaging biomarkers, and artificial intelligence (including radiomics and machine learning approaches) have been developed and investigated. Radiologists should be aware of recent trends in immunotherapy development and new response assessment methods.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.325-335
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• 2024

In this study, I investigated cases of operating Sustainable Development Goals (SDGs) sustainable design courses based on interdisciplinary education for diverse design major students in the fashion design department. Through literature review, we examined the necessity of this course operation and analyzed the course through class design, execution, and operational results. Sustainable design courses were organized for 2nd to 4th-year students, promoting integrated learning for fashion design and various design majors to enhance interdisciplinary skills based on the in-depth study of SDGs issues. The educational content in the classes focused on the sustainable development goals achieved through upcycling design of waste PET bottle fibers developed by local industries, aiming to pursue sustainable values of designers through problem discovery and resolution. Students developed various upcycled products, evaluated metrics, and assessed satisfaction levels. Through this process, students gained an understanding of the practical value of SDGs, recognized the importance of sustainable development through design approaches for solving local issues, and acknowledged the significance of interdisciplinary education with various design majors.

• Journal of Gifted/Talented Education
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• v.24 no.1
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• pp.63-80
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• 2014

The open inquiry is one of the representative learning approaches for gifted students. However, a gifted student should develop various complicated competencies to succeed in the open inquiry because of its complexity. This study develops a question list in accordance with each stage of inquiry so that the list could provide scaffolding in the process of open inquiry and students develop near-professional competencies and produce distinguished outcomes. For the purpose, we have reviewed various literatures related to research methodologies, academic writings, and learning of inquiry. Based on the review, we identified the open inquiry as cognitive, metacognitive, and sociocultural processes and set up the direction of the development of the question list. We also have elaborated the goals of the open inquiry, provided a model of the stage of inquiry, and developed the guiding question list belonging to each stage. As a discussion, we provided several noteworthy issues in the situation of when the list is used in the teaching of the open inquiry for the gifted.