• Wind and Structures
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• v.36 no.6
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.72-81
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• 2023

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

• Smart Media Journal
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• v.12 no.5
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• pp.46-57
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• 2023

As the COVID-19 pandemic and the development of IT technology have led to the gradual popularization of remote and telecommuting, cloud computing technology is advancing, and cyber attack techniques are becoming more sophisticated and advanced. In response to these trends, companies are increasingly moving away from traditional perimeter-based security and adopting Zero Trust to strengthen their security. Zero Trust, based on the core principle of doubting and not trusting everything, identifies all traffic and grants access permissions through a strict authentication process to enhance security. In this paper, we analyze the background of Zero Trust adoption and the adoption policies and trends of countries that are proactively promoting its implementation. Additionally, we propose necessary efforts from governments and organizations to strengthen corporate security and considerations for companies when applying Zero Trust.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.285-294
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• 2023

Porous ceramics are used in various industrial applications based on their physical properties, including isolation, storage, and thermal barrier properties. However, traditional manufacturing environments require additional steps to control artificial pores and limit deformities, because they rely on limited molding methods. To overcome this drawback, many studies have recently focused on fabricating porous structures using additive manufacturing techniques. In particular, the binder jet technology enables high porosity and various types of designs, and avoids the limitations of existing manufacturing processes. In this study, we investigated process optimization for manufacturing porous ceramic filters using the binder jet technology. In binder jet technology, the flowability of the powder used as the base material is an important factor, as well as compatibility with the binder in the process and for the final print. Flow agents and secondary binders were used to optimize the flowability and compatibility of the powders. In addition, the effects of the amount of added glass frit, and changes in sintering temperature on the microstructure, porosity and mechanical properties of the final printed product were investigated.

• Journal of English Language & Literature
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• v.57 no.6
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• pp.1089-1109
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• 2011

In an attempt to demonstrate in context of Nietzsche's "overman" (ubermensch) and Heidegger's "Being-in-the-World" (Dasein) the collective human efforts to overcome humanism in crisis, I will provide the ground for the poetics of overcoming, the ground which are based upon the double movements of transhumanism and transnationalism. For this purpose, I will turn to the theories of two distinctive poets who reveal and disreveal their truths about the subjecthood or the subjectivity in terms of overcoming: Christopher Dewdney for posthuman transhumanity and Dionisio D. Martinez for transnational cultural contamination Transhumanism represented by Christopher Dewdney manifests an interfusion of outside and inside, thereby collapsing the boundary between the mind and the world, and provides a breakthrough from the limitedly defined mind to the transhuman perspective of overcoming by using terminalogy and techniques from science and technology. The emerging transhumanism reflects the growing interdependence between humans and bio technologies, and suggests a potential improvement of human beings. The main argument of transhumanism is that we humans can and should continue to develop in all possible directions, by overcoming our human limitations by shedding the body and having the disembodied consciousness which will liberate our mind. Kwame Anthony Appiah's "cultural contamination" is another form of overcoming as well as a way to otherness, a counter-ideal of cultural purity which sustains authentic culture, reversing the traditional binary opposition between enriching authenticity and threatening hybridization. Dionisio Martinez's poetry sublimates the negative side of Appiah's concept of contamination, by redeeming the value of the Appiah's list of the ideal of contamination such as hybridity, impurity, intermingling, the transformation that comes of new and unexpected combinations of human beings, a bit of this and a bit of that is how newness enters the world. When a poetic subject is doubly exiled and doubly homeless away from his/her native homeland and home of native language, one has no more identification with the authentic culture of both home and away, but rather anticipates a new identity as a transnational subject to cross the bridge beyond cultural authenticity and to enter into the field of cultural contamination.

• Annual Conference of KIPS
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.207-215
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• 2023

The ship system is complex and advanced, and the operation relationship between each element is very high. So it is necessary to approach it in terms of an overall and integrated system in addition to the traditional sequential approach of finding and removing the direct cause of the accident when analyzing the accident. In this study, it is analyzed the recent fire accidents on ships occurred the Korean terrestrial water using a STAMP methodology that is different from conventional accident analysis techniques. This analysis reviews a range of factors, including safety requirements to prevent fires in ships, inappropriate decisions and actions, situations, equipment defects, and recommendations derived from accident analysis results. Through a comprehensive approach to accident prevention using STAMP, alternative evaluations are presented at the component level within the entire system of ships, and they are systematically used for accident prevention and risk evaluation as well as simple accident analysis.

• Wind and Structures
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• v.37 no.6
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• pp.445-460
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• 2023

Accurate estimation of modal parameters (i.e., natural frequency, damping ratio) of tall buildings is of great importance to their structural design, structural health monitoring, vibration control, and state assessment. Based on the combination of variational mode decomposition, smoothed discrete energy separation algorithm-1, and Half-cycle energy operator (VMD-SH), this paper presents a method for structural modal parameter estimation. The variational mode decomposition is proved to be effective and reliable for decomposing the mixed-signal with low frequencies and damping ratios, and the validity of both smoothed discrete energy separation algorithm-1 and Half-cycle energy operator in the modal identification of a single modal system is verified. By incorporating these techniques, the VMD-SH method is able to accurately identify and extract the various modes present in a signal, providing improved insights into its underlying structure and behavior. Subsequently, a numerical study of a four-story frame structure is conducted using the Newmark-β method, and it is found that the relative errors of natural frequency and damping ratio estimated by the presented method are much smaller than those by traditional methods, validating the effectiveness and accuracy of the combined method for the modal identification of the multi-modal system. Furthermore, the presented method is employed to estimate modal parameters of a full-scale tall building utilizing acceleration responses. The identified results verify the applicability and accuracy of the presented VMD-SH method in field measurements. The study demonstrates the effectiveness and robustness of the proposed VMD-SH method in accurately estimating modal parameters of tall buildings from acceleration response data.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.911-917
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• 2023

In today's world, we find ourselves facing energy crises due to factors such as war and climate crises. To prepare for these energy crises, many researchers continue to study systems related to energy monitoring and conservation, such as energy management systems, energy monitoring, and energy conservation. In line with these efforts, nations are making it mandatory for energy-consuming facilities to implement these systems. However, these facilities, limited by space and energy constraints, are exploring ways to improve. This research explores the operation of a data collection system using low-performance embedded devices. In this context, it proves that an optimized version of RocksDB, a Key-Value store, outperforms traditional databases when it comes to time-series data. Furthermore, a comprehensive database evaluation tool was employed to assess various databases, including optimized RocksDB and regular RocksDB. In addition, heterogeneous databases and evaluations are conducted using a UD Benchmark tool to evaluate them. As a result, we were able to see that on devices with low performance, the time required was up to 11 times shorter than that of other databases.

• Fashion & Textile Research Journal
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• v.25 no.5
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• pp.560-567
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• 2023

This study aims to rediscover the industrial value of a borderless service in the hyper-connected era by producing fashion content at the forefront of the cultural industry as XR content and contributing to developing fashion content for edutech. The research method employed design aesthetic theory, while the empirical proposal utilized scientific knowledge information to build a framework for 3D convergence content. The characteristics of fashion content exhibitions that apply the neumorphism technique are as follows: The first is a virtual space that produces clothing culture by type. Africa, where dyeing and crafts are developed, selects a product-oriented exhibition type; Asia, where weaving and textiles are excellent, selects a random movement type; and Europe, where the evolution of clothing design over time is evident, selects a guided movement type to create a three-dimensional fashion edutech. The goal was to produce content. The second is creative reproducibility, which combines a new fashion design that embraces the aura of the original with a trendy sense. The realistic folk costume style of the original allowed for its implementation in the AR exhibition space using historical traditional style techniques such as weaving and textiles. The third is building organic, modular content. By designing and then saving/editing/arranging the basic VP zone for each style, learners and instructors can freely edit the content for each fashion class topic and create various presentations to ensure that it functions as non-face-to-face edutech content around the world.