• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.6
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• pp.393-400
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• 2021

As the recent climate problems are getting worse year after year, the demands for clean energy materials have highly increased in modern society. However, the candidate material classes for clean energy expand rapidly and the outcomes are too complex to be interpreted at laboratory scale (e.g., multicomponent materials). In order to overcome these issues, the first-principles calculations are becoming attractive in the field of material science. The calculations can be performed rapidly using virtual environments without physical limitations in a vast candidate pool, and theory can address the origin of activity through the calculations of electronic structure of materials, even if the structure of material is too complex. Therefore, in terms of the latest trends, we report academic progress related to the first-principles calculations for design of efficient electrocatalysts. The basic background for theory and specific research examples are reported together with the perspective on the design of novel materials using first-principles calculations.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.192-196
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• 2018

The mobile agent paradigm can be exploited in a variety of ways, ranging from low-level system administration tasks to middle ware to user-level applications. Mobile agents can be useful in building middle-ware services such as active mail systems, distributed collaboration systems, etc. An active mail message is a program that interacts with its recipient using a multimedia interface, and adapts the interaction session based on the recipient's responses. The mobile agent paradigm is well suitable to this type of application, since it can carry a sender-defined session protocol along with the multimedia message. Mobile agent communication is possible via method invocation on virtual references. Agents can make synchronous, one-way, or future-reply type invocations. Multicasting is possible, since agents can be aggregated hierarchically into groups. A simple check-pointing facility has also been implemented. Another proposed solution is to use multi agent computer systems to access, filter, evaluate, and integrate this information. We will present the overall architectural framework, our agent design commitments, and agent architecture to enable the above characteristics. Besides, the each information needed a mobile agent system such as text, graphic, image, audio and video etc, constructed a great capacity multimedia database system. However, they have problems in establishing connections over multiple subnetworks, such as no end-to-end connections, transmission delay due to ATM address resolution, no QoS protocols. We propose a new connection management scheme in the thesis to improve the connection management involved of mobile agent systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5588-5613
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• 2018

Virtualization technology has been widely applied in the area of computer security research that provides a new method for system protection. It has been a hotspot in system security research at present. Virtualization technology brings new risk as well as progress to computer operating system (OS). A multi-level perception security model using virtualization is proposed to deal with the problems of over-simplification of risk models, unreliable assumption of secure virtual machine monitor (VMM) and insufficient integration with virtualization technology in security design. Adopting the enhanced isolation mechanism of address space, the security perception units can be protected from risk environment. Based on parallel perceiving by the secure domain possessing with the same privilege level as VMM, a mechanism is established to ensure the security of VMM. In addition, a special pathway is set up to strengthen the ability of information interaction in the light of making reverse use of the method of covert channel. The evaluation results show that the proposed model is able to obtain the valuable risk information of system while ensuring the integrity of security perception units, and it can effectively identify the abnormal state of target system without significantly increasing the extra overhead.

• Convergence Security Journal
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• v.23 no.4
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• pp.3-15
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• 2023

As remote services and remote work become commonplace, the use of the Metaverse has grown. This allows transactions like real estate and finance in virtual Second Life. However, conducting economic activities in the Metaverse presents unique security challenges compared to the physical world and conventional cyberspace. To address these, the paper proposes solutions centered on authentication and privacy. It suggests improving data preprocessing based on Metaverse data's uniqueness and introduces a new authentication service using NFTs while adhering to W3C's DID framework. The system is implemented using Hyperledger Indy blockchain, and its success is confirmed through implementation analysis.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.123-127
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• 2024

Cloud computing services has gained increasing popularity in recent years for supporting various on demand and scalable services for IT consumers where there is a need of less investment towards infrastructure. While storage architecture of cloud enjoys a more robust and fault-tolerant cloud computing network, such architecture also poses a number of security challenges especially when applied in applications related to social networks, Financial transactions, etc. First, as data are stored and maintained by individual virtual machines so Cloud resources are prone to hijacked. Such attacks allow attackers to create, modify and delete machine images, and change administrative passwords and settings successfully. hence, it is significantly harder to ensure data security. Second, Due to dynamic and shared nature of the Cloud, data may be compromised in many ways. Last but not least, Service hijacking may lead to redirect client to an illegitimate website. User accounts and service instances could in turn make a new base for attackers. To address the above challenges, we propose in this paper a distributed data access control scheme that is able to fulfil fine-grained access control over cloud data and is resilient against strong attacks such as compromise and user colluding. The proposed framework exploits a novel cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it for cloud computing with respect to security requirements

• International conference on construction engineering and project management
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• 2024.07a
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• pp.791-798
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• 2024

Construction industry records poor safety records annually due to a large number of injuries and accidents on construction jobsite. In order to improve existing safety performance, object detection approaches have been extensively studied using vision-sensing techniques and deep learning algorithms. Unfortunately, an insufficient number of datasets (e.g., images) and challenges that reside in manually collecting quality datasets constitute a significant hurdle in fully deploying object recognition approaches in real construction sites. Although advanced technologies (e.g., virtual reality) have attempted to address such challenges, they have achieved limited success because they still rely on labor-intensive work. A promising alternative is to adopt generative AI-based data augmentation methods attributed to their efficiency in creating realistic visual datasets and proven performance. However, there remain critical knowledge gaps on how such alternatives can be effectively employed by safety managers on real construction sites in terms of practicability and applications. In this context, this study establishes a framework that can identify effective strategies for improving object detection performance (e.g., accuracy) using generative AI technologies. The outcome of this study will contribute to providing guidelines and best practices for practitioners as well as researchers by exploring different generative AI-driven augmentation approaches and comparing the corresponding results in a quantitative manner.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1065-1072
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• 2024

Previous approaches to 3D excavator pose estimation via synthetic data training utilized a single virtual excavator model, low polygon objects, relatively poor textures, and few background objects, which led to reduced accuracy when the resulting models were tested on differing excavator types and more complex backgrounds. To address these limitations, the authors present a realism-centric synthetization and labeling approach that synthesizes results with improved image quality, more detailed excavator models, additional excavator types, and complex background conditions. Additionally, the data generated includes dense pose labels and depth maps for the excavator models. Utilizing the realism-centric generation method, the authors achieved significantly greater image detail, excavator variety, and background complexity for potentially improved labeling accuracy. The dense pose labels, featuring fifty points instead of the conventional four to six, could allow inferences to be made from unclear excavator pose estimates. The synthesized depth maps could be utilized in a variety of DNN applications, including multi-modal data integration and object detection. Our next step involves training and testing DNN models that would quantify the degree of accuracy enhancement achieved by increased image quality, excavator diversity, and background complexity, helping lay the groundwork for broader application of synthetic models in construction robotics and automated project management.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.3-11
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• 2023

To address the high accident rate in the construction industry, there is a growing interest in implementing virtual reality (VR)-based construction safety training. However, existing training approaches often failed to consider learners' individual characteristics, resulting in inadequate training for some individuals. This study aimed to investigate the impact of personal characteristics on learning performance in VR-based construction safety training using machine learning and SHAP (SHAPley Additional exPlanations). This study revealed that age exerted the greatest influence on learning performance, while work experience had the least impact. Furthermore, age exhibited a negative relationship with learning performance, indicating that the introduction of VR-based construction safety training can be effective for younger individuals. On the other hand, academic degree, qualifications, and work experience exhibited a positive relationship. To enhance learning performance for individuals with lower academic degree, it is necessary to provide content that is easier to understand. The lower qualifications and work experience have minimal impact on learning performance, so it is important to consider other learners' characteristics so as to provide appropriate educational content. This study confirmed that personal characteristics can significantly affect learning performance in VR-based construction safety training, highlighting the potential for leveraging these findings to provide effective safety training for construction workers.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.419-425
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• 2024

As digital communication gains prominence, there is an increasing trend in uncivil behaviors like rude or hateful comments and the empathetic actions towards them, highlighting the need for social efforts to address these issues. As part of these endeavors, we investigated how avatar feedback in a virtual news portal affects users' empathy towards uncivil comments. We defined both posting and empathizing with uncivil comments as antisocial actions. To this end, we posted socially controversial news in a virtual space and provided feedback in two forms when participants selected uncivil comments: text-only feedback and feedback accompanied by an avatar. We then assessed the impact of this feedback on behavioral conformity, guilt, and self-image concern through surveys. Our results showed that avatar-provided feedback significantly influenced participants' social responses more than text-based feedback. Interaction with avatars notably increased participants' behavioral conformity, guilt, and self-image concern. We concluded that avatar-based interactions can positively influence users' social behaviors and attitudes, suggesting their potential in fostering a more civil and responsible digital communication culture.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.11-21
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• 2024

This study explores the use of virtual reality (VR) technology to identify and label key segments of the golf swing. To address the limitations of existing VR devices, we developed a platform to collect kinematic data from various VR devices using the OpenVR SDK (Software Development Kit) and SteamVR, and developed a semi-automated labeling technique to identify and label temporal changes in kinematic behavior through LSTM (Long Short-Term Memory)-based time series data analysis. The experiment consisted of 80 participants, 20 from each of the following age groups: teenage, young-adult, middle-aged, and elderly, collecting data from five swings each to build a total of 400 kinematic datasets. The proposed technique achieved consistently high accuracy (≥0.94) and F1 Score (≥0.95) across all age groups for the seven main phases of the golf swing. This work aims to lay the groundwork for segmenting exercise data and precisely assessing athletic performance on a segment-by-segment basis, thereby providing personalized feedback to individual users during future education and training.