• Journal of Science Education
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• v.46 no.3
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• pp.255-265
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• 2022

The purpose of this study is to analyze and compare brain activation that appears in the self-regulation process of biology major and non-major college students in life science learning. The self-regulation task implemented a life science learning situation with the concept of biological classification. The brain activation of college students was measured and analyzed by fNIRS. In the assimilation process, bilateral FP and left DLPFC show significant activation, and the two groups show a difference in the left OFC activation related to motivation and reward. In the conflict process, the left DLPFC shows significantly lower activation in common, and the two groups show a difference in activation between BA 46, which is related to recent memory, and BA 47, which is related to long-term memory. In the accommodation process, a significantly high activation was found in right DLPFC in common, and the two groups show a difference in activation between right DLPFC and right FP. These areas are in the right frontal lobe area and are related to the understanding of life science knowledge. As a result of this study, it can be seen that the brain activation patterns of biology major and non-major college students are different in the self-regulation process. In addition, we will propose additional neurological studies on self-regulation and present systems and learning strategies that can be constructed in school settings.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.327-351
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• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.397-412
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• 2023

In order for social robots to take root in human life, it is important to consider the technical implementation of social robots and human psychology toward social robots. This study aimed to derive potential social robot clusters based on the emotions consumers feel about social robot appearance design, and to identify and compare important design characteristics and emotional differences of each cluster. In our study, we established a social robot emotion framework to measure and evaluate the emotions consumers feel about social robots, and evaluated the emotions of social robot designs based on the semantic differential method, an kansei engineering approach. We classified 30 social robots into 4 clusters by conducting a multidimensional scaling method and K-means cluster analysis based on the emotion evaluation results, confirmed the characteristics of design elements for each cluster, and conducted a comparative analysis on consumer emotions. We proposed a strategic direction for successful social robot design and development from a human-centered perspective based on the design characteristics and emotional differences derived for each cluster.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.285-290
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• 2023

Compressors are widely used in high-speed electronic systems and are used to reduce the number of operands in multiplier. The proposed compressor is constructed based on the m-GDI(: modified gate diffusion input) to reduce the propagation delay time. This paper is compared the performance of compressors by applying 4-2, 5-2 and 6-2 m-GDI compressors to the multiplier, respectively. As a simulation results, compared to the 8-bit Dadda multiplier using the 4-2 and 6-2 compressor, the multiplier using the 5-2 compressor is reduced propagation delay time 13.99% and 16.26%, respectively. Also, the multiplier using the 5-2 compressor is reduced PDP(: Power Delay Product) 4.99%, 28.95% compared to 4-2 and 6-2 compressor, respectively. However, the multiplier using the 5-2 compression circuit is increased power consumption by 10.46% compared to the multiplier using the 4-2 compression circuit. In conclusion, the 8-bit Dadda multiplier using the 5-2 compressor is superior to the multipliers using the 4-2 and 6-2 compressors. The proposed circuit is implemented using TSMC 65nm CMOS process and its feasibility is verified through SPECTRE simulation.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.97-107
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• 2009

Television News has significant impact on the information analysis of viewers by delivering world news to anonymous individuals everyday. We need to pay more attention to resolution considering the fact that even slight facial expression and the dress of TV anchor can be noticed by viewers in the high definition age, called HD TV, by radical changes in broadcasting situation. As a result, the beauty of expression that lighting technology has is extremely important in the high definition age. In news broadcast, as a phenomenon according to this change in trend, people have been looking for change in order to break with traditional TV news production by adopting DLP(Digital Lighting Processing) or LED(Light Emitting Diode). This effort has contributed to creating proper picture quality appropriate for HD TV. Nowadays Digital imaging is creating new trend in TV news production method from traditional analog-based lighting environment thanks to the development of IT(Information Technology) and digitalized lighting equipment. This change has led to building of HD studio and appropriate sets and lighting system. There are film set and projector which projects image on the screen and PDP, LCD, and DLP which has been used widely in recent years and LED which is often used as background in news program as examples, which has appeared since 1990s with HD TV. In this article, I analyzed the KBS News 9 lnce 1990s with in order to research the influence of television image component on the alyzed the KBS of TV article, I. I wille uggest the category of TV anchor image formulation in delivering information by means of lnce 1990s with based on the analysis result.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Convergence Security Journal
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• v.22 no.4
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• pp.11-16
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• 2022

Development of science technology make integrated CPS(Cyber-Physical System) appear. In CPS era, cyberspace and physical-space are hard to separate anymore, that is developing toward integrated CPS. The reality is not stopping, that is consistently changing and the concept of space is developing too. But several articles are considering for cyberspace and physical-space separately, and they are developing tailed alternative each case. The theorical approaching that is not considering reality is dwelled on past, and is dangerous from dropping down to floating cloud that is not considering progressed reality. This article is suggested to consider rimland theory to control measures in cyberspace. That is dedicated to integrated approaching from physical-space to cyberspace. And that is developing concreted controling measures in cyberspace. Especially, this article is suggested to policy alternative by analyzing north korea cyber warfare from rimland theory including human sources. Simplicity is the ultimate sophistication. This article make integrated approaching effects about cyberspace and physical-space to preparing in the CPS era.