• Journal of Wetlands Research
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• v.25 no.4
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• pp.267-273
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• 2023

Algae are an important component of the ecosystem. However, the excessive growth of cyanobacteria has various harmful effects on river environments, and diatoms affect the management of water supply processes. Algal monitoring is essential for sustainable and efficient algae management. In this study, an object detection model was developed that detects and classifies images of four types of harmful cyanobacteria used for the criteria of the algae alert system, and one diatom, Synedra sp.. You Only Look Once(YOLO) v8, the latest version of the YOLO model, was used for the development of the model. The mean average precision (mAP) of the base model was analyzed as 64.4. Five models were created to increase the diversity of the input images used for model training by performing rotation, magnification, and reduction of original images. Changes in model performance were compared according to the composition of the input images. As a result of the analysis, the model that applied rotation, magnification, and reduction showed the best performance with mAP 86.5. The mAP of the model that only used image rotation, combined rotation and magnification, and combined image rotation and reduction were analyzed as 85.3, 82.3, and 83.8, respectively.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.543-553
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• 2022

A collaborative robot(Cobot) is one of the production systems presented in the 4th industrial revolution and are systems that can maximize efficiency by combining the exquisite hand skills of workers and the ability of simple repetitive tasks of robots. Also, research on the development of an efficient interface method between the worker and the robot is continuously progressing along with the solution to the safety problem arising from the sharing of the workspace. In this study, a method for controlling the robot by recognizing the worker's hand signal was presented to enhance the convenience and concentration of the worker, and the safety of the worker was secured by introducing the concept of a safety zone. Various technologies such as robot control, PLC, image processing, machine learning, and ROS were used to implement this. In addition, the roles and interface methods of the proposed technologies were defined and presented for using educational media. Students can build and adjust the educational media system by linking the introduced various technologies. Therefore, there is an excellent advantage in recognizing the necessity of the technology required in the field and inducing in-depth learning about it. In addition, presenting a problem and then seeking a way to solve it on their own can lead to self-directed learning. Through this, students can learn key technologies of the 4th industrial revolution and improve their ability to solve various problems.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.893-901
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• 2023

The International Association Marine Aids to Navigation and Lighthouse Authorities (IALA) proposed guidelines for VTS manual operation in 2016 for safe and efficient operation of ship. The Korea Coast Guard (KCG) established and operated 19 VTS centers in ports and coastal waters across the country by 2022 based on the IALA VTS manual and VTS operator's education and training guidelines. In addition, IALA proposed the Inter-VTS Exchange Format (IVEF) Service recommendation (V-145), a standard for data exchange between VTS, in 2011 for efficient e-Navigation system services and safe and efficient VTS service support by VTS authorities. The IVEF service in a common framework for ship information exchange, and it presents seven basic IVEF service (BISs) models. VTS service providers can provide safer and more efficient VTS services by sharing VTS information on joint area using IVEF standards. Based on the BIS data, interaction, and interfacing models, this paper introduced the development of the cloud-based VTS integration services performed by the KCG and the results of the VTS integration platform test-bed for IVEF service implementation. In addition, the results of establishing a cloud VTS integrated platform test-bed for the implementation of IVEF service and implementing the main functions of IVEF service were presented.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.930-938
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• 2023

Maritime English is a specialized language system designed for ship operations, maritime safety, and external and internal communication onboard. According to the International Maritime Organization's (IMO) International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), it is imperative that navigational officers engaged in international voyages have a thorough understanding of Maritime English including the use of Standard Marine Communication Phrases (SMCP). This study measured students' proficiency in Maritime English using a learning and testing platform that includes voice recognition, translation, and word entry tasks to evaluate the resulting improvement in Maritime English exam scores. Furthermore, the study aimed to investigate the level of platform use needed for cadets to qualify as junior navigators. The experiment began by examining the correlation between students' overall English skills and their proficiency in SMCP through an initial test, followed by the evaluation of improvements in their scores and changes in exam duration during the mid-term and final exams. The initial test revealed a significant dif erence in Maritime English test scores among groups based on individual factors, such as TOEIC scores and self-assessment of English ability, and both the mid-term and final tests confirmed substantial score improvements for the group using the platform. This study confirmed the efficacy of a learning platform that could be extensively applied in maritime education and potentially expanded beyond the scope of Maritime English education in the future.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.965-975
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• 2023

Education in radiology involves a large portion of practical training, and it is difficult to conduct it through non-face-to-face online education. In this study, we utilized the Metaverse platform, which can replace hands-on education, to implement a practice room that is difficult to access through non-face-to-face classes into a virtual world, and then evaluated the satisfaction of learners after the practice class, the practicality of the Metaverse platform class, and the future orientation of the curriculum. I wanted to find out. Using the metaverse platforms ZEPETO (Build It) and ZEP (ZEP), the S University radiology department lab was implemented into a virtual world and used for students' classes. A total of 50 students were surveyed twice, divided into pre- and post-surveys, and all questions used a 5-point Likert scale. As a result of the study, satisfaction was low at 2.32 for education without using Metaverse virtual space, while education using virtual space was very high at 4.16. As a result of the analysis, the satisfaction level of the new education system and the practicality of the Metaverse platform classes are very high, and it is believed that it will be a more effective education platform when conducting additional education such as app explanations in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.147-153
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• 2023

As it spreads to all industries of artificial intelligence technology, AIaaS equipped with artificial intelligence services is emerging. In particular, non-IT companies are suffering from the absence of software experts, difficulties in training big data models, and difficulties in collecting and analyzing various types of data. AIaaS makes it easier and more economical for users to build a system by providing various IT resources necessary for artificial intelligence software development as well as functions necessary for artificial intelligence software in the form of a service. Therefore, the supply and demand for such cloud-based AIaaS services will increase rapidly. However, the quality of services provided by AIaaS becomes an important factor in what is required as the supply and demand for AIaaS increases. However, research on a comprehensive and practical quality evaluation metric to measure this is currently insufficient. Therefore, in this paper, we develop and propose a usability, replacement, scalability, and publicity metric, which are the four metrics necessary for measuring reusability, based on implementation, convenience, efficiency, and accessibility, which are characteristics of AIaaS, for reusability evaluation among the service quality measurement factors of AIaaS. The proposed metrics can be used as a tool to predict how much services provided by AIaaS can be reused for potential users in the future.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.142-148
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• 2023

The National Agency for Quality Assurance in Higher Education plays a crucial role in education in Ukraine, as an independent entity creates and ensures quality standards of higher education, which allow to properly implement the educational policy of the state, develop the economy and society as a whole.The purpose of the article: to reveal the crucial role of the National Agency for Quality Assurance in Higher Education to create quality management of higher education institutions, to show its mechanism as an independent entity that creates and ensures quality standards of higher education. and society as a whole. The mission of the National Agency for Quality Assurance in Higher Education is to become a catalyst for positive changes in higher education and the formation of a culture of its quality. The strategic goals of the National Agency are implemented in three main areas: the quality of educational services, recognition of the quality of scientific results, ensuring the systemic impact of the National Agency. The National Agency for Quality Assurance in Higher Education exercises various powers, which can be divided into: regulatory, analytical, accreditation, control, communication.The effectiveness of the work of the National Agency for Quality Assurance in Higher Education for 2020 has been proved. The results of a survey conducted by 183 higher education institutions of Ukraine conducted by the National Agency for Quality Assurance in Higher Education are shown. Emphasis was placed on the development of "Recommendations of the National Agency for Quality Assurance in Higher Education regarding the introduction of an internal quality assurance system." The international activity and international recognition of the National Agency for Quality Assurance in Higher Education are shown.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.