• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1720-1728
• /
• 2022

The industrial 4.0 era has been opened with the development of artificial intelligence technology, and the realization of smart farms incorporating ICT technology is receiving great attention in the livestock industry. Among them, the quality management technology of livestock products and livestock operations incorporating computer vision-based artificial intelligence technology represent key technologies. However, the insufficient number of livestock image data for artificial intelligence model training and the severely unbalanced ratio of labels for recognizing a specific defective state are major obstacles to the related research and technology development. To overcome these problems, in this study, combining oversampling and adversarial case generation techniques is proposed as a method necessary to effectively utilizing small data labels for successful defect detection. In addition, experiments comparing performance and time cost of the applicable techniques were conducted. Through experiments, we confirm the validity of the proposed methods and draw utilization strategies from the study results.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1571-1576
• /
• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• Journal of IKEEE
• /
• v.26 no.1
• /
• pp.10-18
• /
• 2022

Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.1
• /
• pp.177-186
• /
• 2017

Recently, Autonomous vehicles are getting hot attention. Amazon, the biggest online shopping service provider is developing a delivery system that uses drones. This kinds of platforms are need accurate attitude information for navigation. In this paper, a structure design of AHRS using low-cost inertia sensor is proposed. To estimate attitudes a Kalman filter which uses a quaternion based dynamic model, bias-removed measurements from MEMS Gyro, raw measurements from MEMS accelerometer and magnetometer, is designed. To remove bias from MEMS Gyro, an additional Kalman filter which uses raw Gyro measurements and attitude estimates, is designed. The performance of implemented AHRS is compared with high price off-the-shelf 3DM-GX3-25 AHRS from Microstrain. The Gyro bias was estimated within 0.0001[deg/s]. And from the estimated attitude, roll and pitch angle error is smaller than 0.2 and 0.3 degree. Yaw angle error is smaller than 6 degree.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.3
• /
• pp.279-288
• /
• 2018

In this paper, we present the results of educational innovation case study using U-Learning Box and Ubiquitous-based Test(UBT) system for 6 sample primary schools in Nepal. As Nepal is considered to be a developing country with electricity problem to the school, the U-Learning Box, consisting of a small and easy-to-use tablet PC for teacher and a small smart beam with its own battery was evaluated as the optimum solution to support continuous basic English and hygiene education for these schools. And UBT technology using tablet PC was used to evaluate and analyze basic English learning ability of the students, which helped us realized that it is necessary to improve the educational environment and develop suitable educational contents. We hope that the global educational innovation using U-Learning Box and UBT technology will become a successful model for global equality of educational opportunity project for developing countries including Nepal.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1377-1393
• /
• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.12
• /
• pp.1908-1918
• /
• 2022

Recently, ASEAN is not only a new production base but also a consumer market for Korea, and interest in the utilization of production networks in each region is increasing. In particular, urbanization in ASEAN countries is progressing at a relatively fast pace. Each country is promoting smart city projects combined with ICT to improve outdated basic infrastructure facilities such as housing, transportation, logistics, crime prevention, and disaster prevention. The purpose of this study is to develop a web-based smart city export HUB platform so that companies with excellent domestic smart city solutions can participate in smart city construction through networks with ASEAN countries. These platforms can secure the demand for smart city construction in ASEAN countries, and through the establishment of the Korea-ASEAN public-private network, smart cities planned in ASEAN countries can be promoted more innovative. In addition, it is expected to be positioned as a Global smart city platform model by applying to real cities through collaboration with excellent domestic companies.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.12
• /
• pp.1872-1879
• /
• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• The Korean Journal of Franchise Management
• /
• v.7 no.1
• /
• pp.13-20
• /
• 2016

Purpose - In the past, the chain hotel chefs only serve food to their customers. However recently, the hotel chefs play a pivotal role in hotel including considering various customer preferences, safety and nutrition of food, and increasing profits through effective human resource management and inventory control. With the change of the chain hotel chef's' roles, pygmalion leadership, one of new leadership styles, focuses on the effect that leader's positive expectation let subordinates have motivation and more engage in work. This study investigates the effect of chain hotel chef's pygmalion leadership on leader trust and organizational trust. Research design, data, and methodology - This study was to investigate the structural relationships among chain hotel restaurant chefs' pygmalion leadership, hotel restaurant cooks' leader trust, organizational trust, and teamwork, and how leader trust and organizational trust play mediating roles in the relationship between pygmalion leadership and teamwork. In this model, pygmalion leadership includes 4 dimensions: Climate, Feedback, Input, and Output. Data were collected using self-administered questionnaire survey on cooks of Deluxe hotel restaurants located in Seoul and Gyonggi-Do. The samples for data analyses were 243 excepting unusable responses. Result - The findings can be summarized as follows: First, climate and feedback had a positive effect on leader trust, respectively. Second, feedback and output had a statistically positive effect on organizational trust, respectively. Third, leader trust had positive effects on organizational trust and teamwork. Fourth, organizational trust had a significant effect on teamwork. Conclusions - As a chain hotel chef treats his/her staffs sincerely, they will be more engaged in work by establishing trust in their leader. Ultimately, it leads to higher sales profit and customer satisfaction. In addition, a hotel can encourage chefs and other staffs to treat each other as if the student-instructor relations, not just commanding staffs. Then, cooks build up their trust to their leader and organization for its sustained growth and development, and the internal bond in organization including teamwork is strengthened. Therefore, to strengthen teamwork and organizational trust, there should be active communication, knowledge sharing, goal sharing, and cooperation between chefs and cooks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1773-1793
• /
• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.