• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Information Systems Review
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• v.23 no.3
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• pp.201-223
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• 2021

Smart home is an advanced Internet of Things (IoT) service that enhances the convenience of human daily life and improves the quality of life at home. Recently, with the emergence of smart home products and services to which artificial intelligence (AI) technology is applied, interest in smart home is increasing. To gain a competitive edge in the smart home market, companies are providing "personalized service" to users, which is a key service that can promote smart home use. This study investigates the factors affecting the value awareness of personalized service and intention to use smart home. This research focuses on four-dimensional motivated innovativeness (cognitive, functional, hedonic, and social innovativeness) and privacy risk awareness as key factors that influence the value awareness of personalized service of smart home. In particular, this study conducts a comparative analysis between the generation MZ (young people in late teens to 30s), who are showing socially differentiated characteristics, and the generation X and baby boomers in 40s to 50s or older. Based on the analysis results, this study derives the distinctive characteristics of generation MZ that are different from the older generation, and provides academic and practical implications for expanding the use of smart home services.

• Journal of The Korean Association For Science Education
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• v.28 no.8
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• pp.955-963
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• 2008

This study was conducted to assess any change in students' views on the nature of science (NOS) after lessons through the activities of model construction and pattern identification. The instrument used to examine NOS views was the Views of Nature of Science questionnaire (VNOS). Four students' responses on VNOS before and after instruction were analyzed. The two levels of their views, novice and expert, were judged by the authors based on criteria set by several science educators. The instruction consisted of six hours of the so-called black box and cube activities developed for model construction and pattern identification, respectively. Students' views were at the novice level in definition of scientific theory, tentativeness of scientific knowledge, difference of hypotheses, theories and laws, model construction, and creativity and imagination in experiments and investigations. Students' views on NOS knowledge such as model and theory have improved for two students after instruction. The improvement seemed to be due to an explicit approach using the activities of model construction and pattern identification. The factors of changes and no-changes of views on NOS were identified and discussed in terms of improvement of the views.

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.77-90
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• 2024

This research aims to investigate methods of leveraging generative artificial intelligence in service sectors where consumer sentiment and experience are paramount, focusing on minimizing hallucination phenomena during usage and developing strategic services tailored to consumer sentiment and experiences. To this end, the study examined both mechanical approaches and user-generated prompts, experimenting with factors such as business item definition, provision of persona characteristics, examples and context-specific imperative verbs, and the specification of output formats and tone concepts. The research explores how generative AI can contribute to enhancing the accuracy of personalized content and user satisfaction. Moreover, these approaches play a crucial role in addressing issues related to hallucination phenomena that may arise when applying generative AI in real services, contributing to consumer service innovation through generative AI. The findings demonstrate the significant role generative AI can play in richly interpreting consumer sentiment and experiences, broadening the potential for application across various industry sectors and suggesting new directions for consumer sentiment and experience strategies beyond technological advancements. However, as this research is based on the relatively novel field of generative AI technology, there are many areas where it falls short. Future studies need to explore the generalizability of research factors and the conditional effects in more diverse industrial settings. Additionally, with the rapid advancement of AI technology, continuous research into new forms of hallucination symptoms and the development of new strategies to address them will be necessary.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.539-549
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• 2024

This study employed text mining techniques like frequency analysis, word clouds, and LDA topic modeling to assess consumer satisfaction and dissatisfaction with Hyundai Motor Company in the Chinese market, compared to brands such as Toyota, Volkswagen, Buick, and Geely. Focusing on compact vehicles from these brands between 2021 and 2023, this study analyzed customer reviews. The results indicated Hyundai Avante's positive factors, including a long wheelbase. However, it also highlighted dissatisfaction aspects like Manipulate, engine performance, trunk space, chassis and suspension, safety features, quantity and brand of audio speakers, music membership service, separation band, screen reflection, CarLife, and map services. Addressing these issues could significantly enhance Hyundai's competitiveness in the Chinese market. Previous studies mainly focused on literature research and surveys, which only revealed consumer perceptions limited to the variables set by the researchers. This study, through text mining and comparing various car brands, aims to gain a deeper understanding of market trends and consumer preferences, providing useful information for marketing strategies of Hyundai and other brands in the Chinese market.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.100-112
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• 2024

This study investigates the ethical and legal implications of utilizing artificial intelligence (AI) in human resource management, with a particular focus on AI interviews in the recruitment process. AI, defined as the capability of computer programs to perform tasks associated with human intelligence such as reasoning, learning, and adapting, is increasingly being integrated into HR practices. The deployment of AI in recruitment, specifically through AI-driven interviews, promises efficiency and objectivity but also raises significant ethical and legal concerns. These concerns include potential biases in AI algorithms, transparency in AI decision-making processes, data privacy issues, and compliance with existing labor laws and regulations. By analyzing case studies and reviewing relevant literature, this paper aims to provide a comprehensive understanding of these challenges and propose recommendations for ensuring ethical and legal compliance in AI-based HR practices. The findings suggest that while AI can enhance recruitment efficiency, it is imperative to establish robust ethical guidelines and legal frameworks to mitigate risks and ensure fair and transparent hiring practices.

• Science of Emotion and Sensibility
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• v.27 no.2
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• pp.3-14
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• 2024

An understanding of the influence of emotional context on memory retrieval is crucial to our comprehensive understanding of human cognition. While previous research focused primarily on visual stimuli to address this relationship, this study ventures into the realm of speech-based emotional contexts. Building on previous findings, we examine the effects of arousal and the valence of verbal contexts on memory, with particular focus on mitigating the serial position effect. In Study 1, we investigated how the arousal level of verbal context in the middle of a word list affects memory retention. Our results demonstrated detriment to the memory of later parts of the word list when exposed to low-arousal contexts. In Study 2, we controlled for arousal levels and examined the impact of valence on memory. We found that negative verbal contexts impair the memory of the word when presented together. Our findings suggest that speech-based emotional contexts do not facilitate verbal memory processing. In particular, negative emotional contexts were found to reinforce the serial position effect. Negative emotional contexts tend to disrupt task performance and fail to elicit memory-enhancing effects, especially when both the context and memory stimulus are verbal. These insights offer a valuable contribution to our understanding of the nuances of auditorily delivered emotional context in verbal memory processes.

• Smart Media Journal
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• v.13 no.8
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• pp.79-86
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• 2024

In design studies, there has been extensive prior research on accident and disaster prevention, but it mainly focuses on visualization methods to improve visibility. Most accidents and disasters are caused by human factors. However, there is little design research that considers human characteristics that manifest in dangerous situations. This study aims to identify the concepts and characteristics of safety information design that reflects human traits. It examines the concepts of risk and safety from a social science perspective and looks into human visual information processing from a cognitive neuroscience perspective. Additionally, it explores the relationship between human information processing and accident rates in dangerous situations from a psychological perspective. Based on these analyses, the study distinguishes between conventional information design and safety information design, ultimately identifying the features of safety information design that incorporates human traits. The key findings are as follows: First, information should be presented considering the human tendency to suspend rational judgment in dangerous situations. Second, appropriately dispersing or concentrating attention according to the level of risk can serve as an opportunity to minimize harm. Third, it was found that sensory and perceptual characteristics should be given top priority in the field of risk and safety information design. Through these findings, the study concludes that a design approach that reflects human traits in safety information design can ultimately be a key indicator for improving safety levels.