• Current Photovoltaic Research
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• v.11 no.3
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• pp.79-86
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• 2023

As we strive to mitigate the environmental impact caused by the use of fossil fuels, the exploration of alternative energy sources has gained significant attention. Solar energy, in particular, has emerged as a promising solution due to its eco-friendly nature and virtually limitless availability. Among the various types of solar cells that harness this abundant energy source, organic solar cells have garnered considerable interest. Organic solar cells feature a photo-active layer composed of organic semiconductors, offering a range of appealing advantages such as cost-effectiveness, flexibility, translucency, and the ability to produce customizable colors. However, the commercialization of organic solar cells has been impeded by certain challenges, notably their relatively low efficiency and stability. To overcome these obstacles and pave the way for wider adoption, researchers have been exploring innovative approaches, including the implementation of ternary blend organic solar cells. This strategy involves introducing a third component into the photo-active layer alongside the organic semiconductors, with the aim of enhancing the overall performance of the solar cell. In this paper, we delve into the issues associated with organic solar cells and focus on one potential solution: ternary blend organic solar cells. Specifically, we examine the application of this approach to PM6:Y6, which stands as one of the most popular combinations of organic semiconductors. By investigating the potential of ternary blends, particularly utilizing PM6:Y6, we aim to accelerate the commercialization of organic solar cells.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.928-934
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• 2009

Although the evolution and deployment of information technologies will undoubtedly play an important role in the current construction industry, many engineers are still unsure of the economic value of using these technologies. Especially for the planning of a construction project, a collaboration system to utilize the whole resources is a essential tool for the successful outcome. A detailed, authoritative, and readily accessible information model is needed to enable engineers to make cost-effective decisions among established and innovative plan alternatives. Most engineers rely on limited private experiences when they create solutions or design alternatives. Initial planning is crucial for the success of the construction project. Most construction projects are done through collaboration of engineers who have different specialized knowledge. Information technologies can dramatically enhance the performance of the collaboration. For the information delivery, we need a mediator between engineers. Object-based 3-D models are useful for the communication and decision assistance for the intelligent project design. In this paper, basic guidelines for the 3-D design according to different construction processes are suggested. Adequate interoperability of 3-D objects from any CAD system is essential for the collaboration. Basic architectures of geometry models and their information layer were established to enable interoperability for design checks, estimation and simulation. A typical international project for roadway was chosen for the pilot project. 3-D GIS model was created and bridge information models were created considering several requirements for planning and decision making of the project. From the pilot test, the integrated construction project planning using 3-D information models was discussed and several guidelines were suggested.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• Steel and Composite Structures
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• v.45 no.3
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• pp.437-454
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• 2022

Elliptic-braced simple resisting frame as a new lateral bracing system installed in the middle bay of frame in building facades has been recently introduced. This system not only creates a problem for opening space from the architectural viewpoint but also improves the structural behavior. Despite the researches on the seismic performance of lateral bracing systems, there are few studies performed on the effect of the stiffness parameters on the elastic story drift and calculation of period in simple braced steel frames. To overcome this shortcoming, in this paper, for the first time, an analytical solution is presented for calculating elastic lateral stiffness in a simple steel frame equipped with elliptic brace subjected to lateral load. In addition, for the first time, in this study, a precise formulation has been developed to evaluate the elastic stiffness variation in a steel frame equipped with a two-dimensional single-story single-span elliptic brace using strain energy and Castigliano's theorem. Thus, all the effective factors, including axial and shear loads as well as bending moments of elliptic brace could be considered. At the end of the analysis, the lateral stiffness can be calculated by an improved and innovative relation through the energy method based on the geometrical properties of the employed sections and specification of the used material. Also, an equivalent element of an elliptic brace was presented for the ease of modeling and use in linear designs. Application of the proposed relation have been verified through a variety of examples in OpenSees software. Based on the results, the error percentage between the elastic stiffness derived from the developed equations and the numerical analyses of finite element models was very low and negligible.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.2
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• pp.131-136
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• 2023

Nanocomposites have been considered innovative composite materials that have multi-functionality and high performance. Because the incorporation of nanoscale fillers may significantly improve the electrical, mechanical, and thermal properties of composites, numerous extensive studies on the characterization of nanocomposites with nanoscale fillers have been performed. In particular, the development of nanocomposites using carbon-based nanoscale fillers (e.g., carbon nanotubes, carbon black, graphene nanoplates) have attracted much interest in the composite field. This paper provides a review of recent advances in the electrical/mechanical characteristics of nanocomposites, which are essential for their practical applications. Furthermore, this paper revisits the recent research on multi-scale modeling, which is a promising approach for predicting the characteristics of nanocomposites. The current challenges and future development potentials for multi-scale modeling are also discussed.

• The Journal of Korean Medicine
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• v.43 no.3
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• pp.16-26
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• 2022

Objectives: Traditional Korean medicine (TKM) has been supplied as part of a national health care system in South Korea under a dual medical system, however it has been difficult to mix western medicine and TKM medical practices in existing hospitals. For the objective of innovative integration between them, Comprehensive and Integrative Medicine Hospital were founded in Daegu, South Korea. Here, we discussed the clinical outcomes of cancer patients who received integrative cancer treatment (ICT). Methods: A total of 678 patients previously diagnosed with cancer were retrospectively checked in our institution for 2 years. After applying inclusion/exclusion criteria, 573 patients were eligible for the final analysis. The overall survival (OS) of these patients in the aftercare period were determined. We looked at how clinical factors and treatments including chemotherapy, complementary and alternative medicine (CAM), and TKM affected the OS. Results: At the first visit, 212 subjects had no evidence of disease after tumor resection and 355 ones with advanced cancer, who receiving ICT including chemotherapy, CAM, and TKM showed better OS compared to the CAM including TKM or the best supportive care (median OS = 216, 78, and 46 days, respectively). The median OS was not reached in TKM only, even though the sample size was small (N=12). Even after adjusting for clinical factors associated to liver, renal, and hematologic manifestation, ICT still remained significant. Conclusions: We demonstrated that ICT might be beneficial for advanced cancer regardless of the performance status, liver and renal function, since it positively affected the OS.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.139-148
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• 2022

The research focuses on the interactional justice of organizational members and supervisors within Chinese organizations. In terms of organizational performance, the focus is on creativity by members, based on the need for innovation today. Furthermore, it verifies the influence of moral leadership on members' innovative behavior and the various effects of trust on members' superiors. This study empirically examined 330 Chinese small practitioners and identified the role of interactional justice in increasing the creative influence of organizational members to date in Chinese small. This study will propose ways to increase the level of creative and reduce the level of procedural justice, and discuss future research directions related to this.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.273-283
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• 2022

Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA's default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.45-56
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• 2024

Faced with aging populations, declining resources, and limited agricultural productivity, rural areas in South Korea require innovative solutions. This study investigated the potential of drone-based vegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with different irrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remote sensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered a comprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysis revealed distinct growth patterns for each lot. LotA(subsurface drainage) displayed early vigor and efficient resource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B (conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 and NDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapid initial growth but faced later resource limitations(peaking at NDVI 0.970 and NDRE 0.695). By monitoring NDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducing costs and environmental impact), improve crop yield and quality (maximizing yield potential), and address rural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizing open-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to a more sustainable and prosperous future for rural communities. Further research integrating additional data and investigating physiological mechanisms can lead to even more effective management strategies and a deeper understanding of VI variations for optimized crop performance.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.