• Journal of Service Research and Studies
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• v.11 no.1
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• pp.1-20
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• 2021

This study was conducted to derive a model of a sustainable economic system for humanity in the era of service economy that requires a paradigm shift. A new long-term sustainable development model has been built on the basis of thousands of years of economic operation experience. Currently, the world is operating the capitalism as the main economic system because there is no better alternative, and the changing economic and social environment such as the advent of the 4th Industrial Revolution is exacerbating the problems of the capitalism, such as job shortages and inequality. In this study, we analyzed the economic management system experienced by human society, and derived an economic system model that is ideal for the modern and future society and is sustainable in the long term. The conditions for a long-term sustainable economic system were presented first. It must be a model that can solve the problems of the current economic system. It must be a model that is faithful to the characteristics of the modern economic society and the nature of the economy itself. And since the new economic system is for humanity, it must be based on the common principles of human society. It should be a model that continuously guarantees core values such as equality and freedom required by human society. After analyzing the problems of the current economic system and analyzing the conditions required for the new system, the basic axioms that the new economic system should be based on were presented, and a desirable model was derived based on this. The structure of the derived model and the specific operation model were presented. In the future, research is needed to specify the operational model so that this model can be settled well in different environments for each country.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.101-108
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• 2020

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.399-406
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• 2018

Much research has been done using the KDDCup99 data set to study intrusion detection using artificial intelligence. Previous studies have shown that the performance of the SMO (SVM) algorithm is superior. However, intrusion detection studies of new intrusion types not used in training are insufficient. In this paper, a model was created using the instances of weka's SMO and KDDCup99 training data set, kddcup.data.gz. We tested existing instances(292,300) of the corrected.gz file and new intrusions(18,729). In general, intrusion labels not used in training are not tested, so new intrusion labels were changed to normal. Of the 18,729 new intrusions, 1,827 were classified as intrusions. 1,827 instances classified as new intrusions are buffer_overflow. Three, neptune. 392, portsweep. 164, ipsweep. 9, back. 511, imap. 1, satan. Dogs, 645, nmap. 102.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.61-66
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• 2017

As the fourth industrial revolution becomes more popular and advanced services such as artificial intelligence and Internet of Things technology are widely commercialized, awareness of the importance of software is spreading. Recently, software education has been taught not only in elementary school and college but also in college. Also, there is a growing interest in computational thinking needed to solve problems through computing methodology and model. The purpose of this study is to design an app inventor course for non-majors software education based on computational thinking. As a result of the study, six detailed competencies of computational thinking were derived, and six detailed competencies were mapped to the app inventor learning elements. In addition, based on the computational thinking modeling, I designed an app inventor class for students who participated in IT curriculum of university liberal arts curriculum.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.7
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• pp.157-164
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• 2020

As the consumption of fishery products continues to increase, aquaculture industry has emerged instead of fishing industry facing limitations of fish stock resources. Recently, smart fish farming industry has rapidly developed through convergence with 4th Industrial Revolution technology. Accordingly, it is important to derive a future model of smart fish farming platforms in order to secure the superiority of the aquaculture industry and the technology standard hegemony. In this study, the future direction of smart fish farm platform was derived through the analysis of environment related to politics, economy, society, and technology related to smart fish farming by applying PEST methodology of macro-environment analysis. It is expected that it will help the public and industrial circles in planning and implementing related projects by including the entire process of value chain of aquaculture industry of breeding, production, management and distribution, and by presenting advanced models based on artificial intelligence and digital twin.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.391-398
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• 2017

In this paper, we compared the performance of "Network Intrusion Detection System based on attack feature selection using fuzzy control language"[1] and "Intelligent Intrusion Detection System Model for attack classification using RNN"[2]. In this paper, we compare the intrusion detection performance of two techniques using KDD CUP 99 dataset. The KDD 99 dataset contains data sets for training and test data sets that can detect existing intrusions through training. There are also data that can test whether training data and the types of intrusions that are not present in the test data can be detected. We compared two papers showing good intrusion detection performance in training and test data. In the comparative paper, there is a lack of performance to detect intrusions that exist but have no existing intrusion detection capability. Among the attack types, DoS, Probe, and R2L have high detection rate using fuzzy and U2L has a high detection rate using RNN.

• Journal of the Korean Society for Nondestructive Testing
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• v.19 no.5
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• pp.369-377
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• 1999

Recently, it is gradually raised necessity that thickness of thin film is measured accuracy and managed in industrial circles and medical world. Ultrasonic signal processing method is likely to become a very powerful method for NDE method of detection of microdefects and thickness measurement of thin film below the limit of ultrasonic distance resolution in the opaque materials, provides useful information that cannot be obtained by a conventional measuring system. In the present research. considering a thin film below the limit of ultrasonic distance resolution sandwiched between three substances as acoustical analysis model, demonstrated the usefulness of ultrasonic signal processing technique using information of ultrasonic frequency for NDE of measurements of thin film thickness. Accordingly, for the detection of delamination between the junction condition of boundary microdefect of thin film sandwiched between three substances the results from digital image processing.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1357-1363
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• 2018

In addition to advances in information technology, machine learning approach is applied to a variety of applications, and is expanding to a variety of areas. In this paper, we propose a software analysis method that applies linear regression to analyse software similarity from the code distribution of the software. The characteristics of software can be expressed by instructions contained within the program, so the distribution information of instructions is used as learning data. In addition, a learning procedure with the learning data generates a linear regression model for software similarity analysis. The proposed method is evaluated with real world Java applications. The proposed method is expected to be used as a basic technique to determine similarity of software. It is also expected to be applied to various software analysis techniques through machine learning approaches.