• Journal of Appropriate Technology
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• v.6 no.2
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• pp.219-225
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• 2020

Importance of touch equipment and smart learning increases and public institutions and educational facilities are applying smart devices to their daily environments. However, users of public smart devices are at risk of being exposed to the direct and indirect spread of infectious diseases. This study develops an exo-finger that wraps the fingertips of smart device users and is intended to have a disease prevention effect when used on public equipment. An exoskeletal body was fabricated by inserting a secondary material which is a mixture of the activating material, carbon black (CB) and a macromolecular polymer (elastomer) into a mold. This device was confirmed to have a touch function when the CB content was 0.030 wt% or higher, and the content of the elastomer was varied so that it could have a friction force similar to that when a person touches a smart device (a friction coefficient of 2.5). Through experiments, it was concluded that the CB content had little effect on the friction coefficient. As a result of testing the completed prototype on a smart device, it was proven that the developed exoskeletal device can be useful in situations where it is impossible to touch due to wearing protective gears, or when equipment such as gloves is used to prevent the spread of infectious diseases.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1249-1258
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• 2022

Marine debris is defined as a substance that is intentionally or inadvertently left on the shore or is introduced or discharged into the ocean, which has or is likely to have a harmful effect on the marine environments. In this study, the detection of marine debris and the analysis of the amount of change on marine debris were performed using the object detection method for an efficient method of identifying the quantity of marine debris and analyzing the amount of change. The study area is Yuho Mongdol Beach in the northeastern part of Geoje Island, and the amount of change was analyzed through images collected at 15-minute intervals for 32 days from September 12 to October 14, 2022. Marine debris detection using YOLOv5x, a one-stage object detection model, derived the performance of plastic bottles mAP 0.869 and styrofoam buoys mAP 0.862. As a result, marine debris showed a large decrease at 8-day intervals, and it was found that the quantity of Styrofoam buoys was about three times larger and the range of change was also larger.

• KNOM Review
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• v.23 no.2
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• pp.29-41
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• 2020

Networks are growing faster than ever before causing a multi-domain complexity. The diversity, variety and dynamic nature of network traffic and services require enhanced orchestration and management approaches. While many standard orchestrators and network operators are resulting in an increase of complexity for handling E2E slice orchestration. Besides, there are multiple domains involved in E2E slice orchestration including access, edge, transport and core network each having their specific challenges. Hence, handling of multi-domain, multi-platform and multi-operator based networking environments manually requires specified experts and using this approach it is impossible to handle the dynamic changes in the network at runtime. Also, the manual approaches towards handling such complexity is always error-prone and tedious. Hence, this work proposes an automated and abstracted solution for handling E2E slice orchestration using an intent-based approach. It abstracts the domains from the operators and enable them to provide their orchestration intention in the form of high-level intents. Besides, it actively monitors the orchestrated resources and based on current monitoring stats using the machine learning it predicts future utilization of resources for updating the system states. Resulting in a closed-loop automated E2E network orchestration and management system.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.35-43
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• 2021

Recently, there has been a growing interest in zero-shot cross-lingual transfer, which leverages cross-lingual language models (CLLMs) to perform downstream tasks that are not trained in a specific language. In this paper, we point out the limitations of the data-centric aspect of quality estimation (QE), and perform zero-shot cross-lingual transfer even in environments where it is difficult to construct QE data. Few studies have dealt with zero-shots in QE, and after fine-tuning the English-German QE dataset, we perform zero-shot transfer leveraging CLLMs. We conduct comparative analysis between various CLLMs. We also perform zero-shot transfer on language pairs with different sized resources and analyze results based on the linguistic characteristics of each language. Experimental results showed the highest performance in multilingual BART and multillingual BERT, and we induced QE to be performed even when QE learning for a specific language pair was not performed at all.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.1
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• pp.22-30
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• 2023

Currently, almost all product development in the jewelry industry utilizes 3D CAD and 3D printing. In this situation, 3D CAD modeling and 3D printing ability units in colleges, Tomorrow Learning Card Education, and Course Evaluation-type jewelry design related education are conducted with developed curriculum based on the standards for training standards, training hours, training equipment, and practice materials presented by NCS. Accordingly, this study analyzes 3D CAD modeling and 3D printing training facilities, training hours, training equipment, etc into three categories of NCS precious metal processing and jewelry design, and studies the development of educational systems such as 3D CAD/3D printing curriculum and various environments that meet these standards. Education using this 3D CAD/3D printing education system will enable us to continuously supply professional talent with practical skills not only in the jewelry industry but also in the entire 3D CAD/3D printing manufacturing industry, which is called as one of the pillars of the 4th Industry. The quality of employment of trainees receiving education and the long-term retention rate after employed can also have a positive effect. In addition, excellent educational performance will help improve the recruitment rate of new students in jewelry jobs or manufacturing-related departments, which are difficult to recruit new students in recent years.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.271-281
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• 2019

The purpose of this study is to provide basic data for improving the adaptability of college life by examining the effects of individuality and relationship of university freshmen on college life adaptation. The study subjects were 383 freshmen enrolled in a university in Chungbuk C City, and a multiple regression analysis was conducted to examine the factors of impact. The first study found that boys were more individual in genders, depending on the general characteristics. Extroverted students were more relational. In the majors, students in the humanities and social sciences were more related, and students in the natural engineering department were more individual. Second, the lower factors affecting college students' adaptation to college life were found to be autonomous in individuality, and affinity and intimacy in relation. In particular, autonomy has the greatest impact on adaptation to college life, followed by affinity and intimacy. Based on these results, policy suggestions are needed first, it is necessary to balance and balance individuality and relationship. second, it is necessary to create activities and learning environments that you can choose for yourself. third, it is necessary to develop programs to promote affinity and intimacy such as department events and club activities. fourth, emotional and psychological program support through face-to-face contact should be activated to improve individuality and relationship.

• Journal of Korean Elementary Science Education
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• v.42 no.1
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• pp.149-160
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• 2023

A Korean-version science curiosity questionnaire (Science Curiosity in Learning Environments [SCILE(15)]) was developed after factor analysis of the Korean-version SCILE(30) questionnaire. Pre-service elementary school teachers were surveyed using the Korean-version SCILE(30), and a factor analysis based on their responses was performed. The factor analysis demonstrated that the Korean-version SCILE(15) consisted of three curiosity factors: a 'science practices' factor, a 'stretching' factor, and an 'embracing' factor. Confirmatory factor analysis of the factor structure revealed correlations between all the factors, thus confirming their commonality as a science curiosity factors. The Cronbach alpha for the reliability of all items in the Korean-version SCILE(15) and of items by factor was greater than 0.700. The Korean-version SCILE(15) was therefore evaluated to be reliable as a science curiosity questionnaire. Pre-service elementary teachers who participated in the survey for the development of the SCILE(15) were aware of the 'science practices', 'stretching', and 'embracing' science curiosity factors. Analysis in a general linear model of the degree of recognition accorded by pre-service elementary teachers to the three science curiosity factors demonstrated significant differences between the curiosity factors in terms of recognition. This cohort of pre-service elementary teachers showed the highest level of recognition of the 'stretching' curiosity factor and the lowest level of recognition of the 'embracing' curiosity factor.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.