• Informatization Policy
• /
• v.29 no.4
• /
• pp.43-66
• /
• 2022

With the development of artificial intelligence technology, competition for artificial intelligence technology patents around the world is intensifying. During the period 2000 ~ 2021, artificial intelligence technology patent applications at the US Patent and Trademark Office have been steadily increasing, and the growth rate has been steeper since the 2010s. As a result of analyzing Korea's artificial intelligence technology competitiveness through patent indices, it is evaluated that patent activity, impact, and marketability are superior in areas such as auditory intelligence and visual intelligence. However, compared to other countries, overall Korea's artificial intelligence technology patents are good in terms of activity and marketability, but somewhat inferior in technological impact. While noise canceling and voice recognition have recently decreased as topics for artificial intelligence, growth is expected in areas such as model learning optimization, smart sensors, and autonomous driving. In the case of Korea, efforts are required as there is a slight lack of patent applications in areas such as fraud detection/security and medical vision learning.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.99-108
• /
• 2021

Recently, unexpected and more advanced cyber medical treat attacks are on the rise. However, in responding to various patterns of cyber medical threat attack, rule-based security methodologies such as physical blocking and replacement of medical devices have the limitations such as lack of the man-power and high cost. As a way to solve the problems, the medical community is also paying attention to artificial intelligence technology that enables security threat detection and prediction by self-learning the past abnormal behaviors. In this study, there has collecting and learning the medical information data from integrated Medical-Information-Systems of the medical center and introduce the research methodology which is to develop the AI-based Net-Working Behavior Adaptive Information data. By doing this study, we will introduce all technological matters of rule-based security programs and discuss strategies to activate artificial intelligence technology in the medical information business with the various restrictions.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.53-63
• /
• 2023

The utilization of department information in bibliometric analysis using scientific and technological literature is highly advantageous. In this paper, the department information dataset was built through the screening, data refinement, and classification processing of authors' department type belonging to university institutions appearing in academic journals in the field of science and technology published in Korea, and the automatic classification model based on deep learning was developed using the department information dataset as learning data and verification data. In addition, we analyzed the co-authorship structure and network in the field of computer science using the department information dataset and affiliation information of authors from domestic academic journals. The research resulted in a 98.6% accuracy rate for the automatic classification model using Korean department information. Moreover, the co-authorship patterns of Korean researchers in the computer science and engineering field, along with the characteristics and centralities of the co-author network based on institution type, region, institution, and department type, were identified in detail and visually presented on a map.

• Computers and Concrete
• /
• v.33 no.1
• /
• pp.77-90
• /
• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• Journal of The Korean Institute of Defense Technology
• /
• v.5 no.4
• /
• pp.16-21
• /
• 2023

Although the global number of landmines is on a declining trend, the damages caused by previously buried landmines persist. In light of this, the present study contemplates solutions to issues and constraints that may arise due to the improvement of mine detection equipment and the reduction in the number of future soldiers. Current mine detectors lack data storage capabilities, posing limitations on data collection for research purposes. Additionally, practical data collection in real-world environments demands substantial time and manpower. Therefore, in this study, gprMax simulation was utilized to generate data. The lightweight CNN-based model, MobileNet, was trained and validated with real data, achieving a high identification rate of 97.35%. Consequently, the potential integration of technologies such as deep learning and simulation into geographical detection equipment is highlighted, offering a pathway to address potential future challenges. The study aims to somewhat alleviate these issues and anticipates contributing to the development of our military capabilities in becoming a future scientific and technological force.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.509-521
• /
• 2016

A novel Samples Balanced Genetic Algorithm combined with Extreme Learning Machine (SBGA-ELM) for Parkinson's Disease diagnosis and detecting bio-markers is presented in this paper. Proposed approach uses genes' expression data of 22,283 genes from open source ParkDB data base for accurate PD diagnosis and detecting bio-markers. Proposed SBGA-ELM includes two major steps: feature (genes) selection and classification. Feature selection procedure is based on proposed Samples Balanced Genetic Algorithm designed specifically for genes expression data from ParkDB. Proposed SBGA searches a robust subset of genes among 22,283 genes available in ParkDB for further analysis. In the "classification" step chosen set of genes is used to train an Extreme Learning Machine (ELM) classifier for an accurate PD diagnosis. Discovered robust subset of genes creates ELM classifier with stable generalization performance for PD diagnosis. In this research the robust subset of genes is also used to discover 24 bio-markers probably responsible for Parkinson's Disease. Discovered robust subset of genes was verified by using existing PD diagnosis approaches such as SVM and PBL-McRBFN. Both tested methods caused maximum generalization performance.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.565-579
• /
• 2016

In this paper a novel cancer subtype's classifier based on Hybrid Samples Balanced Genetic Algorithm with Extreme Learning Machine (hSBGA-ELM) is presented. Proposed cancer subtype's classifier uses genes' expression data of 16063 genes from open Global Cancer Map (GCM) data base for accurate cancer subtype's classification. Proposed method efficiently classifies 14 subtypes of cancer (breast, prostate, lung, colorectal, lymphoma, bladder, melanoma, uterus, leukemia, renal, pancreas, ovary, mesothelioma and CNS). Proposed hSBGA-ELM unifies genes' selection procedure and cancer subtype's classification into one framework. Proposed Hybrid Samples Balanced Genetic Algorithm searches a reduced robust set of genes responsible for cancer subtype's classification from 16063 genes available in GCM data base. Selected reduced set of genes is used to build cancer subtype's classifier using Extreme Learning Machine (ELM). As a result, reduced set of robust genes guarantees stable generalization performance of the proposed cancer subtype's classifier. Proposed hSBGA-ELM discovers 95 genes probably responsible for cancer. Comparison with existing cancer subtype's classifiers clear indicates efficiency of the proposed method.

• Journal of Korean Library and Information Science Society
• /
• v.40 no.3
• /
• pp.203-223
• /
• 2009

The standards for school library in America affected school library policy in US as well as worlds. The America standards for school library established by NEA for the first time in 1920. After the first standard established, America standards revised about 10 times to accommodate educational and technological changes. The Americas new school library standards 'empowering learners: guidelines for school library media programs' established by AASL in 2009. The guidelines are relevant to the 'standards for the 21st-century learner', 'standards for the 21st-century learner in action'. The guidelines expected to affect US as well as school library policy of worlds. Thus, this study aimed to analyze the 'standards for the 21st-century learner' and 'standards for the 21st-century learner in action'. The standard for 21st-century learner offer vision for teaching and learning to both guide and beckon our profession as education leaders. They will both shape the library program and serve as a tool for library media specialists to use to shape the learning of students in the school. The standards for the 21st-century learner in action offer how are AASL's new learning standards, the standards for the 21st-century learner, incorporated into the school library.

• The Mathematical Education
• /
• v.61 no.4
• /
• pp.521-537
• /
• 2022

The unprecedented COVID-19 pandemic has disrupted, interrupted, and changed the way we normally prepare our teacher candidates in teacher preparation programs. In this paper, we, two mathematics teacher educators (MTEs), reflect our own experiences in appropriating, transforming, reconstructing, and modifying our pedagogies of teacher education in making a transition from face-to-face to online environment during the COVID-19 pandemic. Using a collaborative self-study, we discussed issues, challenges, changes, opportunities, and innovations of teaching an elementary mathematics methods course in the online environment. Using a constant comparison method, we explored the following three themes: (1) using virtual manipulatives; (2) creating collaborative, interactive, and shared learning experiences for preservice teachers; and (3) making preservice teachers engaged in student thinking. These findings indicated that online teaching requires transformative knowledge for teacher educators. Transferring face-to-face to online is not a simple matter of putting the existing content to online; it should focus on pedagogical improvement in teaching mathematics rather than technology's sake or how it can be repurposed in a new online environment in a way that students' learning is optimized. The findings of this study provide implications for unpacking MTEs' technological pedagogical content knowledge (TPACK), creating collaborative learning experiences for preservice teachers, and designing a collaborative self-study between MTEs engaged in the community of professional learning.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.1
• /
• pp.53-62
• /
• 2021

After the first to present projected moving pictures to audiences, the film industry has been reshaping along with technological advancements. Through the full-scale introduction of visual effects-oriented post-production and digital technologies in the film-making process, the film industry has not only undergone significant changes in the production, but is also embracing the cutting edge technologies broadly and expanding the scope of industry. Not long after the change to digital cinema, the concept of artificial intelligence, first known at the Dartmouth summer research project in 1956, before the digitalization of film, is expected to bring about a big transformation in the film industry once again. Large volume of clear digital data from digital film-making makes easy to apply recent artificial intelligence technologies represented by machine learning and deep learning. The use of artificial intelligence techniques is prominent around major visual effects studios due to automate many laborious, time-consuming tasks currently performed by artists. This study aims to predict how artificial intelligence technology will change the film industry in the future through analysis of visual effects production cases using artificial intelligence technology as a production tool and to discuss the industrial potential of artificial intelligence as visual effects technology.