• Korean Journal of Culture and Social Issue
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• v.22 no.2
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• pp.165-182
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• 2016

Happiness research has primarily been conducted based on the American model of happiness. The agentic concept of happiness in the West emphasizes the positive feeling state stemming from individual achievement and positive interpersonal relationships. However, previous studies on lay theories of happiness in other East Asian countries, such as China and Japan, have suggested that these meanings of happiness differ from those of the Western cultural context. The present study examined the lay theory of happiness among Koreans using qualitative and quantitative approaches. Furthermore, the authors compared the Korean model of happiness with that of the Japanese and Americans from Uchida and Kitayama (2009). The findings from the present research indicate that the Korean model of happiness involves both positive and negative states and consequences of happiness, unlike the uniformly positively connoted happiness in Western cultural contexts. The paper concludes with a discussion of the implications of the current findings on happiness research in the Korean culture.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.495-501
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• 2023

P-3C/K aircraft for maritime patrols that Republic of Korea Navy is using, is equipped with a variety of sensors and communication devices. Collected data from the aircraft is managed as tactical information by flight operators and stored. When the flight mission is completed, this information is transferred to tactical support center on the ground and played back or used for follow-up work through a analysis tool. During a flight mission, there are tens of thousands of detection objects within an hour in KADIZ (Korea air defense identification zone). In contrast, in TSD (tactical situation display), which displays a map when using the analysis tool, all detected objects are expressed as symbols. The increase in display symbols has a significant impact on the TSD image updating and consequently interferes with the smooth operation of operators. In this paper, we propose applying multiple threads and multiple layers to improve the performance of existing TSD. And the performance improvement is proven through the execution results.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.87-99
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• 2024

This study aims to identify the competencies required for webtoon artists to survive in the future era of AI commercialization. It explores the current and future use of AI in webtoons, and predicts the role of artists in the future webtoon industry. The study finds that AI will replace human workers in some areas, but human empathy-related fields can be sustained. Artist roles like story projectors, Visual directors, and AI editors were identified as potential models for the changing role of artists. To address terminology ambiguity, a three-step AI categorization mechanical type AI, humanoid type AI, and transcendent type AI was proposed for a more realistic separation of AI capabilities. The researcher suggested these findings as guidelines for developing skills in emerging artists or re-skilling existing ones, emphasizing collaboration with AI for mutual growth rather than a negative acceptance of new technology.

• Smart Media Journal
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• v.13 no.6
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• pp.44-53
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• 2024

Companies in modern society are increasingly recognizing sentiment classification as a crucial task, emphasizing the importance of accurately understanding consumer opinions opinions across various platforms such as social media, product reviews, and customer feedback for competitive success. Extensive research is being conducted on sentiment classification as it helps improve products or services by identifying the diverse opinions and emotions of consumers. In sentiment classification, fine-tuning with large-scale datasets and pre-trained language models is essential for enhancing performance. Recent advancements in artificial intelligence have led to high-performing sentiment classification models, with the ELECTRA model standing out due to its efficient learning methods and minimal computing resource requirements. Therefore, this paper proposes a method to enhance sentiment classification performance through efficient fine-tuning of various datasets using the KoELECTRA model, specifically trained for Korean.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• Information Systems Review
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• v.25 no.2
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• pp.31-56
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• 2023

As the COVID-19 pandemic is prolonged, non-contact work has increased, as well as the demand for automation of simple and repetitive questions and tasks with success of using them. Therefore, companies are attempting to expand the area of automated business and apply various technologies such as AI to complex and various business processes of E2E to provide automation of all business. However, the extension to Intelligent Process Automation (IPA) is still in its beginning stage so that it is difficult to find practical use cases and related solutions. In this aspect, it is safe to say that there is insufficient evidence for companies which have various and complex enterprise processes to make a decision about the adoption. In this study, to solve this problem, a Hyper Automation Platform (HAP) that consists of RPA, Chatbot, and AI technology was proposed. Moreover, an implementation method that can bring intelligent process automation using HAP, and practical use-cases were provided so that it makes it possible to review the implementation of the HAP objectively and comprehensively. This study is meaningful and valuable to check the feasibility of the Hyper Automation concept and to actively utilize HAP.

• The Journal of Bigdata
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• v.8 no.2
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• pp.27-36
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• 2023

The logistics industry is becoming increasingly important as it is closely linked to our daily lives, storing and transporting the goods and resources that businesses and consumers need. With its growing importance, the logistics industry strives to provide efficient and sustainable services through innovations and artificial intelligence are being used to optimize logistics networks, make transport more environmentally friendly, and more. Research and improvements are being undertaken in various domains, such as logistics network optimization and environmentally friendly transportation through technological innovation and artificial intelligence; however, there still needs to be more research in certain aspects of the logistics industry. In particular, devising an optimized stowage plan for vehicle carriers, considering various factors, involves a significant degree of complexity and remains an under-researched area. This paper studies the loading and unloading algorithms that enable to quickly and efficiently establish stowage plans for vehicle carriers, reflecting a variety of considerations and rules for stowage planning.

• Sleep Medicine and Psychophysiology
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• v.7 no.2
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• pp.96-101
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• 2000

Objectives: Through comparing sleep variables and autonomic activities among three shifts in shift workers, the authors intended to clarify which shift is most tolerable and to identify the characteristics of their psychological and physical problems. This study is also expected to help shift workers to adapt themselves to their work more effectively. Methods: Fifty one shift workers took part in this study. They were working in a rapidly rotating system in which they worked for 3 days in one shift with one day off between each shift. Based on a sleep diary, sleep latency (SL), sleep period time (SPT), and number of wake after sleep onset (NWASO) were estimated and compared among the three shifts. In assessing sleepiness, Epworth sleepiness scale (ESS) and visual analogue scale (VAS) were used. To evaluate mood states among the three shifts, profile of mood states (POMS) was administered. Heart rate variability (HRV), and the level of adrenaline and noradrenaline were measured to assess autonomic activities. HRV included low frequency power (LF), high frequency power (HF), and LF/HF. Results: SPT was significantly lengthened during the evening shift and SL was shortened during the night shift. The workers showed a drop in alertness at wake-up during morning shift and a drop in alertness at work during night shift. During night shift the subjects complained of physical fatigue and cognitive decline. Comparison of HRV showed that parasympathetic activity was most prominent during the evening shift. Secretion of adrenaline and noradrenaline decreased during the evening shift, though statistically not significant. Conclusion: We found that the evening shift was most tolerable among the three shifts. It is recommended that morning light exposure be done during the morning shift and nocturnal light exposure during the night shift.

• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.61-99
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• 2016

This research investigated that participants' response time and recognition in the decision-making situation would vary according to either rational or emotional stimuli and analyzed how brain functions are related to each type of stimuli by means of fMRI. We tried to address the difference of cognitive processing between rational stimuli and emotional stimuli in the perspective of information processing theory. In order to achieve the research purpose above, we conducted two kinds of experiment studies. In study 1, subjects conducted decision-making task which selected which kind of information type the stimuli was after stimuli - rational stimuli or emotional stimuli - was randomly seen during experiment. During this experiment, we investigated the effect of each stimuli by measuring the duration from the onset time at which stimuli was shown to the response time at which subjects conducted decision-making. Furthermore, we compared the brain functions by finding out what kinds of brain areas were activated during the decision-making task. In study 2, subjects conducted recognition task at which subjects made a decision whether the stimuli was sees in the previous experiment or not. During the second experiment, we investigated the recognition effect by measuring the memory for each stimuli type. Moreover, we compared the cognitive processes during recognition by analyzing the differences of brain area functions. The results of two experiments above were as following. Firstly, regarding the response time as the effect of stimuli, we found that the effect of emotional stimuli was higher than that of rational stimuli. And regarding the recognition as the effect of stimuli, it was found that the effect of rational stimuli was higher than that of emotional stimuli. Secondly, the explanation about the characteristics of cognitive processes with the result of behavioral response by analyzing brain functions was as following. First of all, regarding the decision-making task which conducted for analyzing the effect of response time, the relatively high activated brain areas of rational stimuli were related with the functions of movement control or working memory, and the relatively high activated brain areas of emotional stimuli were connected with the functions of lingual processing.

• Korean Journal of Cognitive Science
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• v.20 no.2
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• pp.125-154
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• 2009

This paper describes the possibility of human physiological data, especially brain-wave activity, to detect cognitive overload, a phenomenon that may occur while learner uses an e-learning system. If it is found that cognitive overload to be detectable, providing appropriate feedback to learners may be possible. To illustrate the possibility, while engaging in cognitive activities, cognitive load levels were measured by EEG (electroencephalogram) to seek detection of cognitive overload. The task given to learner was a computerized listening and recall test designed to measure working memory capacity, and the test had four progressively increasing degrees of difficulty. Eight male, right-handed, university students were asked to answer 4 sets of tests and each test took from 61 seconds to 198 seconds. A correction ratio was then calculated and EEG results analyzed. The correction ratio of listening and recall tests were 84.5%, 90.6%, 62.5% and 56.3% respectively, and the degree of difficulty had statistical significance. The data highlighted learner cognitive overload on test level of 3 and 4, the higher level tests. Second, the SEF-95% value was greater on test3 and 4 than on tests 1 and 2 indicating that tests 3 and 4 imposed greater cognitive load on participants. Third, the relative power of EEG gamma wave rapidly increased on the 3rd and $4^{th}$ test, and signals from channel F3, F4, C4, F7, and F8 showed statistically significance. These five channels are surrounding the brain's Broca area, and from a brain mapping analysis it was found that F8, right-half of the brain area, was activated relative to the degree of difficulty. Lastly, cross relation analysis showed greater increasing in synchronization at test3 and $4^{th}$ at test1 and 2. From these findings, it is possible to measure brain cognitive load level and cognitive over load via brain activity, which may provide atimely feedback scheme for e-learning systems.