• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.428-442
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• 2023

In this study, we have a goal to develop usability evaluation guidelines for heuristic-based artificial intelligence-based Software as a Medical Device (SaMD) in the medical field. We conducted a gap analysis between medical hardware (H/W) and non-medical software (S/W) based on ten heuristic principles. Through severity assessments, we identified 69 evaluation domains and 112 evaluation criteria aligned with the ten heuristic principles. Subsequently, we categorized each evaluation domain into five types, including user safety, data integrity, regulatory compliance, patient therapeutic effectiveness, and user convenience. We proposed usability evaluation guidelines that apply the newly derived heuristic-based Software as a Medical Device (SaMD) evaluation factors to the risk management process. In the discussion, we also have proposed the potential applications of the research findings and directions for future research. We have emphasized the importance of the judicious application of AI technology in the medical field and the evaluation of usability evaluation and offered valuable guidelines for various stakeholders, including medical device manufacturers, healthcare professionals, and regulatory authorities.

• Journal of Music and Human Behavior
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• v.21 no.3
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• pp.91-112
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• 2024

The purpose of this study is to examine the guidelines related to generative AI provided by universities in North America and Europe, which show a proactive interest in and attitude towards AI ethics, and to explore how these guidelines can be applied to teacher training institutions that prepare music educators. The main findings of this study are as follows. First, from an educational perspective, most universities investigated in this study allow instructors to decide on the tools, methods, and extent of AI utilization in teaching and learning, while preemptively preventing students from using generative AI inappropriately. Additionally, they encourage the active use of generative AI in research and learning. Second, the governance guidelines provided by universities include aspects such as privacy protection, transparency, fairness, accountability, and academic integrity. Third, in terms of operational aspects, universities emphasize the importance of periodically monitoring the use of generative AI, ensuring that the guidelines are being actively followed, and exploring processes to adapt to rapidly changing AI tools and environments. Fourth, as recommendations for music teacher education derived from the analysis of generative AI guidelines, the study highlights the importance of critical thinking regarding the use and outputs of generative AI, the development of agency in music creation using AI, the copyright issues related to AI-generated music outputs, and the need for discussions on the scope, process, and value of music, musical activities, and music education in light of the rapid advancements in generative AI.

• Convergence Security Journal
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• v.24 no.2
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• pp.63-70
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• 2024

With the development of artificial intelligence technology, our lives are changing in innovative ways, but at the same time, new ethical issues are emerging. In particular, issues of discrimination due to algorithm and data bias, deep fakes, and personal information leakage issues are judged to be social priorities that must be resolved as artificial intelligence services expand. To this end, this paper examines the concept of artificial intelligence and ethical issues from the perspective of artificial intelligence ethics, and includes each country's ethical guidelines, laws, artificial intelligence impact assessment system, artificial intelligence certification system, and the current status of technologies related to artificial intelligence algorithm transparency to prevent this. We would like to examine and suggest institutional improvement measures to strengthen artificial intelligence ethics.

• Convergence Security Journal
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• v.23 no.3
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• pp.65-71
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• 2023

The importance of combat drones has been highlighted through the recent outbreak of the Russia-Ukraine war. The combat drones play a significant role as a a game changer that alters the conventional wisdom of traditional warfare. Many pundits expect the role of combat swarm drones would be more crucial in the future warfare. In this regard, this paper aims to analyze the development of artificial intelligence-enabled combat swarm drones. To transform the human-operated swarm drones into fully autonomous weaponry system our suggestions are as follows. Developments of (1) AI algorithms for optimized swarm drone operations, (2) decentralized command and control system, (3) inter-drones' mission analysis and allocation technology, (4) enhanced drone communication security and (5) set up of ethical guideline for the autonomous system. Specifically, we suggest the development of AI algorithms for drone collision avoidance and moving target attacks. Also, in order to adjust rapidly changing military environment, decentralized command and control system and mission analysis allocation technology are necessary. Lastly, cutting-edging secure communication technology and concrete ethical guidelines are essential for future AI-enabled combat swarm drones.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.51-58
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• 2022

Microcontroller (MCU) is designed to properly utilize each module through programming by connecting various modules to Advanced Microcontroller Bus Architecture (AMBA). General-purpose MCUs are designed for consumers to use them appropriately in their research or industry area. However, in a specific area such as networking and AI autonomous vehicles, it is necessary to design MCU suitable for the field directly. However, there is a significant barrier for most consumers to directly design an MCU. In this paper, we provide a development guideline that can easily design an MCU for education or research purpose. First, we introduce AMBA system with open IPs, and we verify that the module operates properly through AMBA and interrupt operation. Finally, the MCU system is designed as an on-chip.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.1-8
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• 2024

The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.

• Journal of Veterinary Science
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• v.21 no.3
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• pp.34.1-34.10
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• 2020

Background: A nationwide outbreak of foot-and-mouth disease (FMD) in South Korea caused massive economic losses in 2010. Since then, the Animal and Plant Quarantine Agency (QIA) has enhanced disinfection systems regarding livestock to prevent horizontal transmission of FMD and Avian influenza (AI). Although the amount of disinfectant used continues to increase, cases of FMD and AI have been occurring annually in Korea, except 2012 and 2013. Objectives: This study measured the concentration of the disinfectant to determine why it failed to remove the horizontal transmission despite increased disinfectant use. Methods: Surveys were conducted from February to May 2017, collecting 348 samples from disinfection systems. The samples were analyzed using the Standards of Animal Health Products analysis methods from QIA. Results: Twenty-three facilities used inappropriate or non-approved disinfectants. Nearly all sampled livestock farms and facilities-93.9%-did not properly adjust the disinfectant concentration. The percentage using low concentrations, or where no effective substance was detected, was 46.9%. Furthermore, 13 samples from the official disinfection station did not use effective disinfectant, and-among 72 samples from the disinfection station-88.89% were considered inappropriate concentration, according to the foot-and-mouth disease virus guidelines; considering the AIV guideline, 73.61% were inappropriate concentrations. Inappropriate concentration samples on automatic (90.00%) and semi-automatic (90.90%) disinfection systems showed no significant difference from manual methods (88.24%). Despite this study being conducted during the crisis level, most disinfectants were used inappropriately. Conclusions: This may partially explain why horizontal transmission of FMD and AI cannot be effectively prevented despite extensive disinfectant use.

• Reproductive and Developmental Biology
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• v.41 no.2
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• pp.25-31
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• 2017

Effective estrus detection and artificial insemination (AI) are necessary for profitable management of dairy herd. In current study, 45 crossbred lactating cows have been selected with the complaint of unobserved oestrus for more than sixty days postpartum. All cows had functional corpus luteum as examined by transrectal ultrasonography. Cows were treated with $PGF_2{\alpha}$ analogue and AI was performed with observed oestrus and then single dose of GnRH was administered. Similar synchronization protocol has been repeated after 14 days in cows that did not repose to first treatment. Remaining cows received additional $PGF_2{\alpha}$ after 14 days of second treatment and timed AI was performed following GnRH administration. Among 45 cows, 28.89% showed estrus after first treatment and 78.79% responded to second hormonal intervention. A higher conception rate (88.89% vs 26.66 and 72.72%) was observed in cows after triple administration of $PGF_2{\alpha}$ and timed AI. We noticed a significant differences in body condition score (BCS, 1~5 scale), postpartum period, and daily milk production between cows that either responded of non-responded following first and second hormonal treatment. In addition, there was a significant positive correlation between daily milk production and BCS, age and postpartum days, milk production and estrus/BCS, and milk production/BCS/estrus and conception rate. Depending upon the findings we conclude that hormonal intervention with $PGF_2{\alpha}$ and GnRH enhances postpartum ovarian cyclicity and help decreasing the days open of dairy herd. Therefore, this finding might provide an excellent guideline for target breeding system for profitable dairy herd management.

• Korean Journal of Community Nutrition
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• v.16 no.5
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• pp.548-558
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• 2011

The purpose of this study was to compare eating habits and food attitudes between depressed and normal adults. The subjects were selected (n = 6217) from those who participated in the 2008 Korean National Health and Nutrition Examination Survey (KNHANES IV). The subjects were divided into the depressed (DG, n = 841) and normal groups (NG, n = 3969). DG was those who have depression now or who have experienced depressing feelings more than two weeks or per year. The general characteristics, anthropometric measurement, eating habits, the dietary guideline recognition and practices were compared by using chi-square test and t-test. Also the partial correlations were analyzed by SAS (Statistical analysis system, version 9.1) program. There was a significantly higher rate of DG among the female (74.32%), with low education (44.6%) and low-income (32.0%) subjects (p < 0.001). DG showed significantly lower snacking and dining out. There was significantly higher rate of DG who responded "none" in frequency of snack (27.10%) and dining out (29.50%) (p < 0.001). Also DG showed significantly lower rate of the subjects who ate with the family than NG. Also, DG showed significantly lower dietary guideline recognition level and practice than NG. Correlation between depression symptom and various factors showed that positive correlation with low snack intake and dining out frequencies. However, correlation was relatively weak. In conclusion, eating habits and recognition levels and practice of dietary guidelines of DG were significantly different from NG. DG showed significantly lower frequencies of snack, dining out, and eating with family.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.1-7
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• 2021

In the latest trend of machine translation research, the model is pretrained through a large mono lingual corpus and then finetuned with a parallel corpus. Although many studies tend to increase the amount of data used in the pretraining stage, it is hard to say that the amount of data must be increased to improve machine translation performance. In this study, through an experiment based on the mBART model using parallel corpus filtering, we propose that high quality data can yield better machine translation performance, even utilizing smaller amount of data. We propose that it is important to consider the quality of data rather than the amount of data, and it can be used as a guideline for building a training corpus.