• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.897-906
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• 2022

In this paper, we propose a novel MAC protocol based on the harmonic transmission of ACK, called HAT-LoRa, for improving the reliability and the utilization in multiple gateway LoRa Networks. LoRa is basically vulnerable to collision due to the primitive pure ALOHA-like MAC. Whereas data frame delivery can be guaranteed by the transparent bridge of multiple receiving gateways, ACK is still transmitted by a single gateway in LoRa Network. HAT-LoRa provides the augmented reception opportunity of ACK via the simultaneous transmissions of identical ACK in multiple spreading factors. The proposed method reduces the expected transmission times of ACK double gateway environment as well as single gateway environment, by 55 and 60% in maximum, by 35% and 40% in average, in a single- and double-gateway environment, respectively. Especially, it outperforms under the environment where the distance between end device and gateways are similar to each other.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.827-833
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• 2022

This study aims to prevent secondary traffic accidents with high severity by overcoming the limitations of existing traffic information collection systems through analysis of traffic information collection detectors and various algorithms used to detect unexpected situations. In other words, this study is meaningful present that analyzing the 'unexpected situation that causes secondary traffic accidents' and 'Existing traffic information collection system' accordingly presenting a solution that can preemptively prevent secondary traffic accidents, intelligent traffic information collection system that enables accurate information collection on all sections of the road. As a result of the experiment, the reliability of data transmission reached 97% based on 95%, the data transmission speed averaged 209ms based on 1000ms, and the network failover time achieved targets of 50sec based on 120sec.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.308-309
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• 2022

The Ministry of Oceans and Fisheries is providing maritime safety services using combine limited artificial intelligence technologies through the operation of the Korean e-Navigation service, and research is needed to improve reliability and quality to secure the competitiveness of the system. However, linking real-time operating systems requires a separate system configuration that can be linked after processing personal information security with minimal performance impact. To solve this problem, this study will make a basic design of a big-data maritime information gateway system of the Korean e-Navigation service that minimizes the impact of performance and reflects the security of personal information.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.124-130
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• 2023

Recently, fan out wafer level packaging, which enables high integration, miniaturization, and low cost, is being rapidly applied in the semiconductor industry. In particular, FOWLP is attracting attention in the mobile and Internet of Things fields, and is recognized as a core technology that will lead to technological advancements such as 5G, self-driving cars, and artificial intelligence in the future. However, as chip density and package size within the package increase, FOWLP warpage is emerging as a major problem. These problems have a direct impact on the reliability and electrical performance of semiconductor products, and in particular, cause defects such as vacuum leakage in the manufacturing process or lack of focus in the photolithography process, so technical demands for solving them are increasing. In this paper, warpage simulation according to the thickness of FOWLP material was performed using finite element analysis. The thickness range was based on the history of similar packages, and as a factor causing warpage, the curing temperature of the materials undergoing the curing process was applied and the difference in deformation due to the difference in thermal expansion coefficient between materials was used. At this time, the stacking order was reflected to reproduce warpage behavior similar to reality. After performing finite element analysis, the influence of each variable on causing warpage was defined, and based on this, it was confirmed that warpage was controlled as intended through design modifications.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.559-571
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• 2021

Along with the increase prevalence of computers, the number of malware distributions by attackers to ordinary users has also increased. Research to detect malware continues to this day, and in recent years, research on malware detection and analysis using AI is focused. However, the AI algorithm has a disadvantage that it cannot explain why it detects and classifies malware. XAI techniques have emerged to overcome these limitations of AI and make it practical. With XAI, it is possible to provide a basis for judgment on the final outcome of the AI. In this paper, we conducted malware group classification using XGBoost and Random Forest, and interpreted the results through SHAP. Both classification models showed a high classification accuracy of about 99%, and when comparing the top 20 API features derived through XAI with the main APIs of malware, it was possible to interpret and understand more than a certain level. In the future, based on this, a direct AI reliability improvement study will be conducted.

• Journal of The Korea Institute of Healthcare Architecture
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• v.29 no.4
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• pp.45-56
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• 2023

Purpose: Recently, Korea has been promoting smart cities that combine artificial intelligence(AI), big data, ICT, and the Internet of Things(IoT), and these technologies are being applied to housing services and are developing into smart housing services. This study try to analyze what is the most necessary and important the AI smart housing services for the housing disadvantaged persons through a survey of experts and the housing disadvantaged persons. And by collecting these necessary and important services, we aim to present elements and directions for the AI smart housing services policy for the housing disadvantaged persons. Methods: Firstly, we asked 11 experts, Secondly, the desire and necessity for the above smart housing service was identified through an online survey targeting the housing disadvantaged persons. Thirdly, the survey was analyzed and reliability was measured through descriptive statistical analysis using SPSS program. Fourthly, based on the results of descriptive statistics analysis, the necessity and importance of AI smart housing services from the perspective of the housing disadvantaged were derived. Results: The results of this study are that firstly, both experts and the housing disadvantaged persons viewed safety and health-related services as the most important and necessary among AI smart housing services, secondly, there is a difference in perspectives on the services that should be priority between experts and people with disabilities, and lastly there are differences in perspectives and needs for services that should be priority between the disabled and the elderly.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.324-327
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• 2022

Crack inspection of aged structures is mostly conducted by inspectors using surveying tools on site and visually inspecting them. This method greatly depends on professional worker, and consumes a lot of time and money. An artificial intelligence image classification algorithm is used to make reliable and objective judgments. Since 2018, image augmentation techniques have been used in the image pre-processing stage as they lead to high performance improvement. In this study, an analysis algorithm for cracks in concrete structures was developed using image augmentation techniques, in which the accuracy and speed according to the augmentation ratio were compared and measured for optimization. As a result, it was found that 8 times of image augmentation was appropriate when the accuracy was improved and economic feasibility was taken into account.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.285-293
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• 2021

With the advent of the 4th Industrial Revolution, AI utilization capabilities are being emphasized in various industries, but AI education design and curriculum research as universal education is currently lacking. This study offers a design for universal AI education to further cultivate its use in universities. For the AI basic education design, a questionnaire was conducted for experts three times, and the reliability of the derived design contents was verified by reflecting the results. As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived were data structure understanding and processing, visualization, word cloud, public data utilization, and machine learning concept understanding and utilization. The educational design content derived through this study is expected to increase the value of competency-centered AI universal education in the future.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.