• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.443-451
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• 2023

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.40-41
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• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.21-22
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• 2021

Early academic warning is considered as an inherent problem in education data mining. Early and timely concern and guidance can save a student's university career. It is widely assumed as a multi-class classification system in view of machine learning. Therefore, An accurate and precise methodical solution is a complicated task to accomplish. For this issue, we present a hybrid model employing rough set theory with a back-propagation neural network to ameliorate the predictive capability of the system with an illustrative example. The experimental results show that it is an effective early academic warning model with an escalating improvement in predictive accuracy.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.858-869
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• 2023

As one of the pivotal techniques of image restoration, single-channel blind source separation (SCBSS) is capable of converting a visual-only image into multi-source images. However, image degradation often results from multiple mixing methods. Therefore, this paper introduces an innovative SCBSS algorithm to effectively separate source images from a composite image in various mixed modes. The cornerstone of this approach is a novel triple generative adversarial network (TriGAN), designed based on dual learning principles. The TriGAN redefines the discriminator's function to optimize the separation process. Extensive experiments have demonstrated the algorithm's capability to distinctly separate source images from a composite image in diverse mixed modes and to facilitate effective image restoration. The effectiveness of the proposed method is quantitatively supported by achieving an average peak signal-to-noise ratio exceeding 30 dB, and the average structural similarity index surpassing 0.95 across multiple datasets.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.1
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• pp.1-21
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• 2024

This review comprehensively explores the evolution, theoretical underpinnings, variations, and applications of diffusion models. Originating as a generative framework, diffusion models have rapidly ascended to the forefront of machine learning research, owing to their exceptional capability, stability, and versatility. We dissect the core principles driving diffusion processes, elucidating their mathematical foundations and the mechanisms by which they iteratively refine noise into structured data. We highlight pivotal advancements and the integration of auxiliary techniques that have significantly enhanced their efficiency and stability. Variants such as bridges that broaden the applicability of diffusion models to wider domains are introduced. We put special emphasis on the ability of diffusion models as a crucial foundation model, with modalities ranging from image, 3D assets, and video. The role of diffusion models as a general foundation model leads to its versatility in many of the downstream tasks such as solving inverse problems and image editing. Through this review, we aim to provide a thorough and accessible compendium for both newcomers and seasoned researchers in the field.

• Smart Media Journal
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• v.13 no.2
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• pp.23-31
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• 2024

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Korean Journal of Cognitive Science
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• v.12 no.3
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• pp.1-18
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• 2001

The goal of this paper is the pedagogical application of a Korean Spell/Grammar Checker to the web-based language learning system for Korean writing. To maximize the efficient instruction of our learning system \\`Urimal Baeumteo\\` we have to improve our Korean Spell/Grammar Checker. Today the NLP system\\`s performance defends on its semantic processing capability. In our Korean Spell/Grammar Checker. the tasks accomplished in the semantic level are: the detection and correction of misused derived and compound nouns in a Korean spell-checking device and the detection and correction of syntactic and semantic errors in a Korean grammars-checking device. We describe a common approach to the partial parsing using collocation rules based on the dependency grammar. To provide more detailed semantic rules. we classified nouns according to their concepts. and subcategorized verbs referring to their syntactic and semantic features. Improving a Korean Spell/Gl-Grammar Checker makes our learning system active and intelligent in a web-based environment. We acknowledge the flaws in our system: the classification of nouns based on their meanings and concepts is a time consuming task. the analytic unit of this study is principally limited to the phrases in a sentence therefore the accurate parsing of embedded sentences remains a difficult problem to solve. Concerning the web-based language learning system. it is critically important to consider its interface design and structure of its contents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.141-150
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• 2017

This paper reviewed the awareness of the military personnel as to job training executed in the Korean army and to figure out how this job training affects the military morale and organizational performance. For this study, 220 volunteers were surveyed and analyzed using the SPSS statistical package program. As a result of the analysis, the following conclusion could be drawn. First, in the result of analysis on the status of-the-job education, the educational time marked 49.5% for 2-4 hours, the education of job capability development marked 77.7% for the content of education, and the charged instructor of job training marked 56.8% for the education department. Second, in the result of an analysis of job training on military morale, the recognition level of behavioral dimension increased with increasing participants' independence, and the satisfaction with the cognitive dimension increased with increasing participants' independence, continuous learning culture, and educational level. Moreover, the emotional score increased with increasing learning culture and educational level. Third, an analysis of the influences of job training on the organizational performance showed thatthe degree of job satisfaction increased with increasing learning motive and learning culture, and the organizational commitment increased with increasing instructor's quality. Fourth, regarding the influences of military morale upon the organizational performance, the higher job satisfaction and organizational commitment increased with increasing behavioral, cognitive, and emotional dimension. Therefore, military morale improves organizational performance and is an important factor in inducing individual development.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.149-162
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• 2020

The purpose of this study was to suggest implications for improving training performance by studying how the capacity of IPP workers and the characteristics of training programs affect the training performance through social support of employees. The study was conducted by distributing the online questionnaire to 270 IPP learning worker(of 9 university). As a result, it was found that the characteristics of the learning worker and the characteristics of training programs were positively related to the social support of the employees, and their social support was positively related to the training performance. The results of this study can contribute to the training performance when used as reference materials for selection of trainees and participating companies and development and operation of training courses. However, the limitation of this study is that the objectivity of the result is rather low by deriving the response centered on the recognition of the learning workers. In future studies, it is necessary to increase the objectivity of the results through three-dimensional cross-checks with training participants.