• Journal of the Korean Society of Industry Convergence
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• v.22 no.6
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• pp.785-795
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• 2019

This paper is a study on the modeling of the quantity estimation model for offshore plant Material handling equipment in FEED(Front End Engineering Design) verification stage using system engineering approach which is an engineering design methods. The relevant engineering execution procedure is not systemized although the operation method and Material handling equipment selection with weight and space constraints is a key part of the FEED. Using the system engineering process, the stakeholder requirements analysis process, the system requirements analysis, and the final system architecture design were sequentially performed, and the process developed through the functional development diagram and Requirement traceability matrix (RTM) was verified. In addition, based on the established process, we propose a Material handling quantity estimation model and Quantity calculation verification Table that can be applied at the FEED verification stage and we verify the applicability through case studies.

• Convergence Security Journal
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• v.19 no.2
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• pp.147-156
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• 2019

The purpose of this study is provide appropriate arms control measures between South and North Koreas by comparing and analyzing the former proposals and agreements in the past and the current Moon Government's approach representing the 9.19 military agreement. For this, I established the most appropriate framework for analysis through comparing and analyzing the arms control theories. The policies of the past governments and of the current Moon Jae-in government are analyzed. The most highlighted characteristic was that the arms control policies were projected by not from the military confidence, but political confidence building measures or both concurrently. In conclusion, I suggested the strategies of projecting confidence building measures and arms control or disarmament in the process of projecting the peace settlement. Nonetheless, the most important point is that the policies of arms control and unification should be pushed ahead complementally.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.309-315
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• 2021

Pycnogenol extracted from pine bark is a component with great antibacterial activity and antioxidant effect. It is applied as a natural anti-inflammatory agent with various medical effects including anti-inflammatory effects, regulation of blood pressure, regulation of the immune system, and inhibition of cancer cell growth. However, research related to cosmetics is limited. Therefore, in this study, the effect of Pycnogenol on the skin was studied through a clinical approach. Changes in skin condition were observed after using cosmetics with Pycnogenol and without Pycnogenol for 6 weeks for 10 clinicians in each group. We observed the effect of pore reduction, wrinkle reduction around eyes, a decrease of the number and angle of loose pores, and reduction of pigmentation. Therefore, cosmetics containing Pycnogenol have the effect of improving skin problems of aging skin.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.255-264
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• 2021

In this paper, we propose operations and maintenance (O&M) planning approach for floating offshore wind farm using the mathematical optimization. To be specific, we present a MILP (Mixed Integer Linear Programming that suggests the composition of vessels, technicians, and maintenance works on a weekly basis. We reflect accessibility to wind turbines based on weather data and loss of power generation using the Jensen wake model to identify downtime cost that vary from time to time. This paper also includes a description of two-stage approach for maintenance planning & detailed scheduling and numeric analysis of the number of vessels and technicians on the O&M cost. Finally, the MILP model could be utilized in order to establish the suitable and effective maintenance planning reflecting domestic situation.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.51-62
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• 2022

AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.21-26
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• 2023

The development and distribution approach of applications is transitioning from a monolithic architecture to microservices and containerization, a lightweight virtualization technology, is becoming a core IT technology. However, unlike traditional virtual machines based on hypervisors, container technology does not provide concrete security boundaries as it shares the same kernel. According to various preceding studies, there are many security vulnerabilities in most container images that are currently shared. Accordingly, attackers may attempt exploitation by using security vulnerabilities, which may seriously affect the system environment. Therefore, in this study, we propose an efficient automated deployment pipeline design to prevent the distribution of container images with security vulnerabilities, aiming to provide a secure container environment. Through this approach, we can ensure a safe container environment.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.119-124
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• 2023

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

• Convergence Security Journal
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• v.23 no.3
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• pp.3-11
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• 2023

Zero Trust, characterized by the principle of "Never Trust, Always Verify," represents a novel security paradigm. The proliferation of remote work and the widespread use of cloud services have led to the establishment of Work From Anywhere (WFA) environments, where access to corporate systems is possible from any location. In such environments, the boundaries between internal and external networks have become increasingly ambiguous, rendering traditional perimeter security models inadequate to address the complex and diverse nature of cyber threats and attacks. This research paper introduces the implementation principles of Zero Trust and focuses on the Micro Segmentation approach, highlighting its relevance in mitigating the limitations of perimeter security. By leveraging the risk management framework provided by the National Institute of Standards and Technology (NIST), this paper proposes a comprehensive procedure for the adoption of Zero Trust. The aim is to empower organizations to enhance their security strategies.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.101-111
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• 2024

In this paper, we propose a concpetual architecture and its implementation approach for contextualizing unstructured CCTV-video frame data into structured XML-video textual data by using the deep-learning neural network models and frameworks. Conclusively, through the conceptual architecture and the implementation approach proposed in this paper, we can eventually realize and implement the so-called sharable working and experiencing knowledge management platforms to be adopted to smart factories in various industries.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.19-25
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• 2024

The worldwide aging population trend is causing an increase in the incidence of major retinal diseases that can lead to blindness, including glaucoma, cataract, and macular degeneration. In the field of ophthalmology, there is a focused interest in diagnosing diseases that are difficult to prevent in order to reduce the rate of blindness. This study proposes a deep learning approach to accurately diagnose ocular diseases in fundus photographs using less data than traditional methods. For this, Convolutional Neural Network (CNN) models capable of effective learning with limited data were selected to classify Conventional Fundus Images (CFI) from various ocular disease patients. The chosen CNN models demonstrated exceptional performance, achieving high Accuracy, Precision, Recall, and F1-score values. This approach reduces manual analysis by ophthalmologists, shortens consultation times, and provides consistent diagnostic results, making it an efficient and accurate diagnostic tool in the medical field.