• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.225-238
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play that reflects the social activities of gamers: In a Massively Multi-user Online Role Playing Game (MMORPG), party play log includes a distinguished information that can classify game users under normal-user and abnormal-user. That is because the bot users' main activities target on the acquisition of cyber assets. Through a statistical analysis of user behaviors in game activity logs, we establish the threshold levels of the activities that allow us to identify game bots. Also, we build a knowledge base of detection rules based on this statistical analysis. We apply these rule reasoner to the sixth most popular online game in the world. As a result, we can detect game bot users with a high accuracy rate of 95.92%.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.341-348
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• 2022

Formulaic sequences are continuous or discontinuous series of words that are seemingly treated like single units. Formulaic sequences play a key role in language development, and formulaic sequences acquisition determines the success or failure of language development. This study proposes a reading aloud activity as a way for international students to learn formulaic sequences. A class focused on reading aloud was conducted with 41 international students taking a general English course at a university in Seoul. For 15 weeks, video lectures and real-time Zoom classes were conducted in parallel. The animated film Frozen was used as course material. In the video lectures, the teacher interpreted the movie script in easy Korean and read aloud formulaic sequences. Students were tasked with reading the sentences with formulaic sequences aloud, recording themselves reading aloud, and submitting their recordings. During real-time class meetings, students performed the activity of reading aloud the formulaic sequences they had studied in the video lectures. There was a significant increase in the interpretation and sentence writing of formulaic sequences in participants' post-evaluation compared to the pre-evaluation. Through the study's survey, students exhibited positive views in the affective domains.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.136-143
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• 2022

The pilot's role is absolutely critical to the safety of an airline. It can be predicted that there is a difference in the acquisition of pilot competency required in actual flight at the initial flight training stage. In this study, the progress and effects of education and training for each background are analyzed, and differentiated competency-based training is to be studied. By sampling a sample of civil airline pilots, we are trying to determine whether there is a difference in the results of the regular proficiency check among the eight competencies defined by ICAO according to military and civil backgrounds. Accordingly, an independent t-test was conducted to test the average difference between the two groups, from the military and civilian origin, to confirm the average difference in the piloting competency between the groups. Based on these results, it will be helpful to design future training courses.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.119-125
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• 2022

In the study, we endeavored to investigate the effect of phenylephrine, isoprenaline and prazosin on the tissue-specific vascular contractility and to determine the mechanism involved. There were few reports addressing the question whether thin or thick filament modulation is included in phenylephrine, isoprenaline and prazosin-induced regulation. We hypothesized that isoprenaline and prazosin play a role in tissue-dependent regulation of vascular contractility. Denuded arterial muscles of Sprague-Dawley male rats were suspended in organ baths and isometric tensions were transduced and recorded using isometric transducers and an automatic data acquisition system. Interestingly, sustained continuous contraction of thoracic and abdominal aorta. Furthermore, isoprenaline and prazosin together with phenylephrine inhibited transiently and persistently vasoconstriction of thoracic and abdominal aorta suggesting that additional mechanisms (e.g. decreased receptor density, chemical interaction, postreceptor signaling or distribution of agonists) might be included in the modulation of vascular contractility.

• Journal of Industrial Convergence
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• v.20 no.7
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• pp.131-137
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• 2022

In the study, we endeavored to assess the convergence effect of Silybum marianum-derived silymarin and epidemiologically-correlated alcohol intake on vascular contractility and to determine the mechanism involved. There were few reports addressing the question whether thin or thick filament modulation is included in ethanol and silymarin-induced regulation. We hypothesized that ethanol at a low concentration and silymarin play a role in agonist-dependent regulation of vascular contractility. Denuded arterial muscles of Sprague-Dawley male rats were suspended in organ baths and isometric tensions were transduced and recorded using isometric transducers and an automatic data acquisition system. Interestingly, both silymarin and ethanol didn't encourage silymarin alone-induced inhibition in agonists-induced contraction suggesting that endothelial nitric oxide synthesis might be involved in ethanol or silymarin-induced modulation of vascular contractility and additional pathways besides endothelial nitric oxide synthesis such as ROCK inactivation might be involved in the silymarin-induced modulation of vascular contractility.

• Korean Journal of Culture and Social Issue
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• v.26 no.3
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• pp.329-349
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• 2020

Confirmation bias is well known to be the cause of widespread misjudgment in the field of forensic decision-making. In this study, we examined the psychological mechanisms by which confirmation bias affects intentionality judgment in serious injury and death cases that combine the moral characteristics of the perpetrator and victim differently. As a result, participants perceived the case as a more typical criminal case when both the perpetrator and victim were bad people, and gave higher intention to perpetrators' actions in these typical crimes. In particular, it was found that people with a high degree of confirmation bias highly judge the intention of the offenders in a consistent way with the stereotype of criminal cases. However, in serious criminal cases, the moderate effect of confirmation bias has disappeared and only the effect of crime typicality has existed. Finally, we discussed implications of this study and ways to reduce bias in intentionality judgment.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.103-123
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• 2023

This study empirically investigated the relationship between workers' safety behavior and safety leadership, trust in leader, workers' involvement, and safety knowledge in the enterprise, and analyzed the role of the factors to identify and analyze factors that enhance workers' safety behavior that contribute to the prevention of major accidents in the enterprise. When industrial accidents occur, companies have to bear huge loss costs due to direct costs of compensating the victims and indirect costs such as human loss, material loss, production loss, and time loss. Based on the results of previous studies, this study investigated the effects of managerial safety leadership and workers' trust in leader on safety behavior through the mediation of workers' involvement and safety knowledge among production, technical, and labor workers in the manufacturing industry. Statistical analysis was conducted on 271 manufacturing workers using SPSS and PLS. The results showed that safety leadership and trust in leader can lead to workers' involvement and have a positive effect on workers' safety knowledge acquisition, which can lead to workers' safety behavior.

• Food Science of Animal Resources
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• v.43 no.5
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• pp.792-804
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• 2023

Non-aureus staphylococci (NAS), particularly antimicrobial-resistant NAS, have a substantial impact on human and animal health. In the current study, we investigated (1) the species profiles of NAS isolates collected from healthy broilers, farm environments, and farm workers in Korea, (2) the occurrence of antimicrobial-resistant NAS isolates, especially methicillin resistance, and (3) the genetic factors involved in the methicillin and fluoroquinolone resistance. In total, 216 NAS isolates of 16 different species were collected from healthy broilers (n=178), broiler farm environments (n=18), and farm workers (n=20) of 20 different broiler farms. The two most dominant broiler-associated NAS species were Staphylococcus agnetis (23.6%) and Staphylococcus xylosus (22.9%). Six NAS isolates were mecA-positive carrying staphylococcal cassette chromosome mec (SCCmec) II (n=1), SCCmec IV (n=1), SCCmec V (n=2), or nontypeable SCCmec element (n=2). While two mecA-positive Staphylococcus epidermidis isolates from farm workers had SCCmec II and IV, a mecA-positive S. epidermidis isolate from broiler and a Staphylococcus haemolyticus isolate farm environment carried SCCmec V. The occurrence of multidrug resistance was observed in 48.1% (104/216 isolates) of NAS isolates with high resistance rates to β-lactams (>40%) and fusidic acid (59.7%). Fluoroquinolone resistance was confirmed in 59 NAS isolates (27.3%), and diverse mutations in the quinolone resistance determining regions of gyrA, gyrB, parC, and parE were identified. These findings suggest that NAS in broiler farms may have a potential role in the acquisition, amplification, and transmission of antimicrobial resistance.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.77-87
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• 2023

Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.