• The Research Journal of the Costume Culture
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• v.31 no.3
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• pp.346-360
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• 2023

The pataphysics implemented by digital technology differs from the form of objects in the real world and is used throughout the cultural industry. This study aims to analyze the expression method of pataphysics as applied to modern fashion and derive its impact on the fashion industry. The research analyzes fashion images, shows, films, displays, and e-commerce, since 2016, when pataphysics began to be used in the fashion domain. Pataphysics, created by Alfred Zaire, appeared as an overlapping phenomenon that reflects physical phenomena in the virtual world. The expression method of pataphysics applied to modern fashion was divided into an augmented reality method based on immersion and interaction, a virtual platform-oriented metaverse, and a virtual model expressing a processed self. The influence of pataphysics applied to modern fashion is as follows. In the field of design, pataphysics affects the development of contemplative designs for innovation and creativity. Second, digital technology can expand the role of fashion at the intersection of art and fashion that takes a novel perspective through pataphysics. Third, e-commerce positively affects efficient production and consumption through virtual and economic models. In conclusion, this study's findings are expected to play a positive role in promoting creativity and innovation by introducing new perspectives and ideas into modern fashion through pataphysics.

• Ocean Systems Engineering
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• v.12 no.3
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• pp.301-333
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• 2022

Herein, we report meaningful and selective review of the progress made on 'Vortex Induced Vibration (VIV)' and 'Vortex Induced Motion (VIM)' of 'Structures of Specific Shapes (SoSS)' subjected to steady uniform flow and of relevance to/in marine structures. Important and critical elements of the numerical methods, experimental methods, and physical ideas are listed and analysed critically and the limitations of the current state of art of VIV/VIM are discussed in-detail. Our focus and aim are to analyse the existing researches with respect to the application in analyses, design and production of marine structures and the reported reviews centre on these only. We identify the critical and important issues that exist in the current literature and utilise these issues to highlight the challenges that need to be tackled to design and develop new age marine structures that can exist and operate safely in the areas of dominance by the VIV/VIM. Finally, we also identify some areas for future scope of research on VIV/VIM.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.647-649
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• 2023

Sentiment analysis as part of natural language processing (NLP) has received much attention following the demand to understand people's opinions. Aspect-based sentiment analysis (ABSA) is a fine-grained subtask from sentiment analysis that aims to classify sentiment at the aspect level. Throughout the years, researchers have formulated ABSA into various tasks for different scenarios. Unlike the early works, the current ABSA utilizes many elements to improve performance and provide more details to produce informative results. These ABSA formulations have provided greater challenges for researchers. However, it is difficult to explore ABSA's works due to the many different formulations, terms, and results. In this paper, we conduct a comparative analysis of recent studies on ABSA. We mention some key elements, problem formulations, and datasets currently utilized by most ABSA communities. Also, we conduct a short review of the latest papers to find the current state-of-the-art model. From our observations, we found that span-level representation is an important feature in solving the ABSA problem, while multi-task learning and generative approach look promising. Finally, we review some open challenges and further directions for ABSA research in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2483-2504
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• 2023

Most existing low-light enhancement algorithms either use a large number of training parameters or lack generalization to real-world scenarios. This paper presents a novel lightweight and robust pixel-wise polynomial approximation-based deep network for low-light image enhancement. For mapping the low-light image to the enhanced image, pixel-wise higher-order polynomials are employed. A deep convolution network is used to estimate the coefficients of these higher-order polynomials. The proposed network uses multiple branches to estimate pixel values based on different receptive fields. With a smaller receptive field, the first branch enhanced local features, the second and third branches focused on medium-level features, and the last branch enhanced global features. The low-light image is downsampled by the factor of 2b-1 (b is the branch number) and fed as input to each branch. After combining the outputs of each branch, the final enhanced image is obtained. A comprehensive evaluation of our proposed network on six publicly available no-reference test datasets shows that it outperforms state-of-the-art methods on both quantitative and qualitative measures.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.767-777
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• 2023

In modern society, user privacy is emerging as an important issue as closed-circuit television (CCTV) systems increase rapidly in various public and private spaces. If CCTV cameras monitor sensitive areas or personal spaces, they can infringe on personal privacy. Someone's behavior patterns, sensitive information, residence, etc. can be exposed, and if the image data collected from CCTV is not properly protected, there can be a risk of data leakage by hackers or illegal accessors. This paper presents an innovative approach to "machine learning based reversible chaotic masking method for user privacy protection in CCTV environment." The proposed method was developed to protect an individual's identity within CCTV images while maintaining the usefulness of the data for surveillance and analysis purposes. This method utilizes a two-step process for user privacy. First, machine learning models are trained to accurately detect and locate human subjects within the CCTV frame. This model is designed to identify individuals accurately and robustly by leveraging state-of-the-art object detection techniques. When an individual is detected, reversible chaos masking technology is applied. This masking technique uses chaos maps to create complex patterns to hide individual facial features and identifiable characteristics. Above all, the generated mask can be reversibly applied and removed, allowing authorized users to access the original unmasking image.

• New & Renewable Energy
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• v.19 no.4
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• pp.53-60
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• 2023

The Computational Fluid Dynamics (CFD) model is a method of studying the flow phenomenon of fluid using a computer and finding partial differential equations that dominate processes such as heat dispersion through numerical analysis. Through CFD, a lot of information about flow disorders such as speed, pressure, density, and concentration can be obtained, and it is used in various fields from energy and aircraft design to weather prediction and environmental modeling. The simulation used for fluid analysis in this study utilized Gexcon's (FLACS) CODE, such as Norway, through overseas journals, for the accuracy of the analysis results through many experiments. It was analyzed that a technology for treating two or more catalysts with physical properties under low-temperature atmospheric pressure conditions could not be found in the prior art. Therefore, it would be desirable to establish a continuous plan by reinforcing data that can prove the effectiveness of producing efficient synthetic oil (renewable oil) through the application that pyrolysis under low-temperature and atmospheric pressure conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.438-455
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• 2024

Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.331-342
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• 2023

Purpose - This study aims to develop a valid and appropriate method for measuring the economic value of intangible cultural resources. Design/methodology/approach - Building upon the concepts explored in many studies on the total value regulation of public goods or environmental goods, which are non-market value commodities, with a focus on the intangible cultural property Arirang, this study aims to formulate a new economic value concept for cultural resources that contributes to the overall economic total value, including non-use value. Based on this foundation, the study aim to identify and apply the most efficient model(CVM) among economic value measurement methods, as suggested by Tietenberg (2003). Findings - This involves estimating economic value through consumer behavior, encompassing the use or experience of cultural resources, as well as utilizing statements to estimate economic methods through consumer surveys. Only by presenting individual resource economic values of cultural resources in objective figures can a foundation be established for creating budgets and organizational structures to promote projects and policies. Research implications or Originality - Appropriate decisions can then be made by comparing these values with the expected costs in the management and planning process.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.40-52
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• 2024

Cyber-Physical Systems (CPS) are introduced as complex, interconnected systems that combine physical components with computational elements and networking capabilities. They bridge the gap between the physical world and the digital world, enabling the monitoring and control of physical processes through embedded computing systems and networked communication. These systems introduce several security challenges. These challenges, if not addressed, can lead to vulnerabilities that may result in substantial losses. Therefore, it is crucial to thoroughly examine and address the security concerns associated with CPS to guarantee the safe and reliable operation of these systems. To handle these security concerns, different existing security requirements methods are considered but they were unable to produce required results because they were originally developed for software systems not for CPS and they are obsolete methods for CPS. In this paper, a Security Requirements Engineering Methodology for CPS (CPS-SREM) is proposed. A comparison of state-of-the-art methods (UMLSec, CLASP, SQUARE, SREP) and the proposed method is done and it has demonstrated that the proposed method performs better than existing SRE methods and enabling experts to uncover a broader spectrum of security requirements specific to CPS. Conclusion: The proposed method is also validated using a case study of the healthcare system and the results are promising. The proposed model will provide substantial advantages to both practitioners and researcher, assisting them in identifying the security requirements for CPS in Industry 4.0.