• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.19 no.4
• /
• pp.479-489
• /
• 2021

The Republic of Korea is expected to participate in the denuclearization verification activities by the International Atomic Energy Agency (IAEA) in case any neighboring countries declared denuclearization. In this study, samples for the verification of nuclear activities in undeclared areas were selected for the denuclearization of neighboring countries, and the appropriateness of the procedures was considered. If a country with nuclear weapons declares denuclearization, it must be accompanied by the IAEA's verification regarding nuclear materials and weapons in the declared and undeclared areas. The analysis of the process samples or on-site environmental samples and the verification of undeclared nuclear facilities and materials aid in uncovering any evidence of concealment of nuclear activity in undeclared areas. Therefore, a methodology was established for effective sampling and analysis in accordance with proper procedures. Preparations for sampling in undeclared areas were undertaken for various potential scenarios, such as, the establishment of zones according to radiation dose, methods of supplying electricity, wireless communication networks, targets of sampling according to characteristics of nuclides, manned sampling method, and unmanned sampling method. Through this, procedures were established for pre- and post-site settings in preparation for hazards and limiting factors at nuclear inspection sites.

• International Journal of Contents
• /
• v.18 no.3
• /
• pp.21-33
• /
• 2022

Previous research has provided inconsistent findings as to whether socially (in)competent individuals benefit from social networking sites (SNSs) use. Based on the rich-get-richer model, some studies have shown that socially competent individuals expand their existing networks even further via SNSs use. Based on the poor-get-richer model, other studies have shown that those with poor social skills can achieve beneficiary outcomes from SNSs use by overcoming their deficient social resources of offline environments. The present study is devised to add evidence regarding how and why social skills are related to SNSs use. To this end, we tested the relationships between social competence and three types of Facebook communication activities: interaction, self-presentation, and passive observation. Further, drawing on the social cognitive theory, the mediating role of outcome expectancy in the relationship between social competence and Facebook communication activities was examined. Using an online survey in South Korea (N = 708), it was found that individuals with higher social competence were more likely than those with lower social competence to engage in interaction, self-presentation, passive observation on Facebook. Moreover, these relationships were mediated by outcome expectancy that the desired social outcomes could be achieved as a result of Facebook use.

• International journal of advanced smart convergence
• /
• v.11 no.3
• /
• pp.102-118
• /
• 2022

With the development of digital content software production technology and the technological progress of related hardware, the social status quo in the post-epidemic era, the popularization and application of 5G networks, the market and consumers' increasing demand for digital content products, artificial intelligence, virtual digital human, virtual Idols, virtual live, self-media content and metaverse-related content industries are all developing rapidly. Virtual idols, virtual digital human, etc. are not only accelerating innovation in production technology. The economic cost, technical difficulty and time requirements of production are also greatly reduced. With the arrival and development of the Metaverse, the author believes that the content industry with virtual digital humans as the core will continue to develop in the direction of refinement, specialization, facilitation and customization. In this article, we will analyze and study the production of virtual digital human based on Photoscan technology and Unreal Engine 5 Metahuman software, and discuss the application status and future development of related content.

• Journal of Daesoon Thought and the Religions of East Asia
• /
• v.2 no.1
• /
• pp.85-109
• /
• 2022

Yiguandao missions arrived in Korea no later than 1947. Despite many obstacles, including war and internal dissension, the movement has flourished in South Korea. Today there are three active major lineages and another seven smaller networks. This article relates the movement's overall development in Korea. We begin by discussing key missions dispatched to Korea by Yiguandao's founder Zhang Tianran. The northern port city of Tianjin was key to this effort, in particular a single temple, the Hall of Morality. In Korea the leaders found an unfamiliar cultural landscape that was soon engulfed in war. The Yiguandao missions tended to develop independently, without coordination. In an effort to unify the movement, the Morality Foundation was established in Busan in 1952. The article shows how Yiguandao's subsequent success in Korea is connected to the development of indigenous leadership. Local Korean leadership ousted Chinese members from the Morality Foundation in 1954, and this branch has continued under Korean leadership to this day. The ousted Chinese leaders continued to develop their own lineages. Two major leaders, Zhang Ruiquan and Kim Bokdang, were able to establish enduring legacies. A final section looks at organizational traits that will determine the movement's future prospects in modern Korean society.

• International journal of advanced smart convergence
• /
• v.11 no.2
• /
• pp.7-12
• /
• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• Structural Monitoring and Maintenance
• /
• v.9 no.4
• /
• pp.337-357
• /
• 2022

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

• International journal of advanced smart convergence
• /
• v.12 no.1
• /
• pp.53-58
• /
• 2023

With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a "black box" model, Shapley values help us to alleviate the "black box" problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses K-nearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.

• International journal of advanced smart convergence
• /
• v.12 no.2
• /
• pp.84-89
• /
• 2023

In recent years, with the breakthrough of Artificial Intelligence (AI) technology in deep learning algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), AI generation technology has rapidly expanded in various sub-sectors in the art field. 2022 as the explosive year of AI-generated art, especially in the creation of AI-generated art creative design, many excellent works have been born, which has improved the work efficiency of art design. This study analyzed the application design characteristics of AI generation technology in two sub fields of artistic creative design of AI painting and AI animation production , and compares the differences between traditional painting and AI painting in the field of painting. Through the research of this paper, the advantages and problems in the process of AI creative design are summarized. Although AI art designs are affected by technical limitations, there are still flaws in artworks and practical problems such as copyright and income, but it provides a strong technical guarantee in the expansion of subdivisions of artistic innovation and technology integration, and has extremely high research value.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.736-743
• /
• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.592-595
• /
• 2013

Advent of Wireless Sensor Networks (WSN) has provided potentials to a variety of construction applications. It is well appreciated that WSNs have advantages over traditional wired system, such as ease of installation and maintenance with increased cost savings and efficiencies. However, the obstruction of wireless signal from physical objects in the heterogeneous construction environment often brings challenges to WSN measurement system. This paper analyzed the obstruction characteristic of construction environment where construction materials, equipment, and built structures obstruct the wireless signal yielding negative effect of measurement system. By adopting evaluation criteria, such as packet reception rate, field experiments have been implemented to quantitatively identify the interference of wireless signal from penetration, reflection, and network traffic under the construction environment. The results show that reliable performance of wireless sensor in construction environment depends on the optimal separation distance between a receiver and a transmitter, obstruction types, obstruction thickness, and transmission interval. In addition, the methodology and experimental results of this paper could be used in the practical design of network topology when hundreds of sensor nodes form a mesh network in the large scale construction applications.