• International journal of advanced smart convergence
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• v.10 no.2
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• pp.79-85
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• 2021

Speech recognition technology refers to a technology in which a computer interprets the speech language spoken by a person and converts the contents into text data. This technology has recently been combined with artificial intelligence and has been used in various fields such as smartphones, set-top boxes, and smart TVs. Examples include Google Assistant, Google Home, Samsung's Bixby, Apple's Siri and SK's NUGU. Google and Daum Kakao offer free open APIs for speech recognition technologies. This paper selects three APIs that are free to use by ordinary users, and compares each recognition rate according to the three types. First, the recognition rate of "numbers" and secondly, the recognition rate of "Ga Na Da Hangul" are conducted, and finally, the experiment is conducted with the complete sentence that the author uses the most. All experiments use real voice as input through a computer microphone. Through the three experiments and results, we hope that the general public will be able to identify differences in recognition rates according to the applications currently available, helping to select APIs suitable for specific application purposes.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• The Journal of Industrial Distribution & Business
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• v.14 no.2
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• pp.21-33
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• 2023

Purpose: After using the Internet, the world is changing through several paradigms, and the retail industry, which is essential to living in the world, is also changing rapidly. In this review paper, the requirements that the retail industry should consider and prepare in accordance with the rapidly changing paradigm were reviewed according to the current situation of the times. Research design, data, and methodology: It is a review of technological development using PRISMA flow diagram, retail change, and necessity in April 2022, and a review of the digital environment to be applied to the retail industry in the future. Results As the current situation and changes of retail, and the development of IT technology, reviews on the retail business applying the 4th Industrial Revolution, the Internet of Things and artificial intelligence were collected, and the direction of the retail industry was suggested. Conclusions: The direction for the retail industry in preparation for developing technologies was presented. In addition, this study is a review paper that suggests the need for research on active introduction of new technologies to the beauty market that is very close to human life and economically helpful as IT technology for the 4th industrial revolution develops rapidly.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.86-102
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• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.891-894
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• 2024

The quest for reliability in Artificial Intelligence (AI) is progressively urgent, especially in the field of next generation wireless networks. Future Beyond 5G (B5G)/6G networks will connect a huge number of devices and will offer innovative services invested with AI and Machine Learning tools. Wireless communications, in general, and medium access control (MAC) techniques were among the fields that were heavily affected by this improvement. This study presents the applications and services of future communication networks. This study details the Medium Access Control (MAC) scheduler of Beyond-5G/6G from 3^rd Generation Partnership (3GPP) and highlights the current open research issues which are yet to be optimized. This study provides an overview of how AI plays an important role in improving next generation communication by solving MAC-layer issues such as resource scheduling and queueing. We will select C-V2X as our use case to implement our proposed MAC scheduling model.

• Journal of Korean Philosophical Society
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• v.147
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• pp.117-146
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• 2018

This paper discusses, whether religion is possible even in the age of artificial intelligence, and whether humans alone are the subject of religious faith or ultra intelligent machines with human minds can be also subjects of faith. In order for ultra intelligent machines to be subjects of faith in the same conditions as humans, they must be able to have unique characteristics such as emotion, will, and self-consciousness. With the advent of ultra intelligent machines with the same level of cognitive and emotional abilities as human beings, the religious actions of artificial intelligence will be inevitable. The ultra intelligent machines after 'singularity' will go beyond the subject of religious belief and reign as God who can rule humans, nature and the world. This is also the common view of Morabeck, Kurzweil and Harari. Leonhart also reminds us that technological advances should make us used to the fact that we are now 'gods'. But we fear we may face distopia despite the general affluence of the 'Star Trec' economy. For this reason, even if a man says he has learned the religious truth, one can't help but wonder if it is true. Kant and Bloch are thinkers who critically reflected on our religious ideals and highest concept in different world-view premises. Kant's concept of God as 'idea of pure reason' and 'postulate of practical reason', can seem like a 'god of gap' as Jesse Bering said earlier. Kant recognized the need for religious faith only on a strict basis of moral necessity. The subjects of religious faith should always strive to do the moral good, but such efforts themselves were not enough to reach perfection and so postulated immortality of the soul. But if an ultra intelligent machines that has emerged above a singularity is given a new status in an intellectual explosion, it can reach its morality by blocking evil tendencies and by the infinite evolution of super intelligence. So it will no longer need Kant's 'Postulate for continuous progress towards greater goodness', 'Postulate for divine grace' and 'Postulate for infinite expansion of the kingdom of God on earth.' Artificial intelligence robots would not necessarily consider religious performance in the Kant's meaning, and therefore religion will also have to be abolished. Ernst Bloch transforms Kant's postulate to be Persian dualism. Therefore, in Bloch, even though the ultra intelligent machines is a divine being, one must critically ask whether it is a wicked or a good God. Artificial intelligence experts warn that ultra intellectual machine as Pandora's gift will bring disaster to mankind. In the Kant's Matrix, a ultra intelligent machines, which is the completion of morality and God itself, may fall into a bad god in Bloch's Matrix. Therefore, despite the myth of singularity, we still believe that ultra intelligent machines, whether as God leads us to the completion of one of our only religious beliefs, or as bad god to the collapse of mankind through complete denial of existence.

• Korean Journal of Cognitive Science
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• v.32 no.4
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• pp.169-191
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• 2021

With the development of artificial intelligence(AI), the jobs of many human experts are threatened, and this also applies to the legal profession. This study attempted to investigate whether AI can actually replace humans in the legal profession, especially the role of judges making final judgments. For this purpose, from the perspective of uniqueness neglect, this study was conducted to confirm the effect of involvement and the severity on acceptance of the judgment made by the AI judge (Experiment 1) and the AI jury (Experiment 2). The involvement was manipulated as if the subject who was sentenced for committing a crime was his or her family (mother, father) or stranger, and the severity was manipulated by the extent of the damage, the perception of the crime, and the number of applied crimes. In Experiment 1, the interactive effect of involvement and severity was found. Specifically, when the involvement was low, the acceptance of AI judges was higher in high severity (vs. low severity). Conversely, when the involvement was high, the acceptance of AI judges was higher in low severity (vs. high severity). The same interactions as in Experiment 1 occurred in Experiment 2. Specifically, when the involvement was low, a larger number of AI jury members were allocated in high severity (vs. low severity). On the other hand, when the involvement was high, the number of AI juries increased in low severity (vs. high severity). This study has implications in that it is the first experimental study in Korea on artificial intelligence legal judgment and that it presents the prospects for the jobs of legal experts.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.83-101
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• 2021

This study is to investigate, using AHP, what technologies should be applied first to Korean SMEs in order to respond to the 4th industrial revolution and change to a smart enterprise. To this end, technologies related to the 4th industrial revolution and smart factory are synthesized, and the classification criteria of Dae-Hoon Kim et al. (2019) are applied, but additional opinions of experts are collected and related technologies are converted to artificial intelligence (AI), Big Data, and Cloud Computing. As a base technology, mobile, Internet of Things (IoT), block chain as hyper-connected technology, unmanned transportation (autonomous driving), robot, 3D printing, drone as a convergence technology, smart manufacturing and logistics, smart healthcare, smart transportation and smart finance were classified as smart industrial technologies. As a result of confirming the priorities for technical use by AHP analysis and calculating the total weight, manufacturing companies have a high ranking in mobile, artificial intelligence (AI), big data, and robots, while service companies are in big data and robots, artificial intelligence (AI), and smart healthcare are ranked high, and in all companies, it is in the order of big data, artificial intelligence (AI), robot, and mobile. Through this study, it was clearly identified which technologies should be applied first in order to respond to the 4th industrial revolution and change to a smart company.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.