• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Steel and Composite Structures
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• v.48 no.1
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• pp.19-25
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• 2023

Due to high application of concrete structures in construction industry, however, the quality improvement is essential. One of the new ways for this purpose is adding the nanoparticles to the concrete. In this work, vibration analysis of concrete beams reinforced by graphene oxide (GO) nanoparticles based on mathematical model has been investigated. For the accuracy of the presented model, the experimental study is done for comparing the compressive strength. Since the nanoparticles can not be solved in water without any specific process, at the first, GO nanoparticles should be dispersed in water by using shaker, magnetic striker, ultrasonic devices and finally mechanical mixer. For modelling of the strucuture, sinusoidal shear deformation beam theory (SSDBT) is utilized. Mori-Tanak model model is utilized for obtaining the effective properties of the beam including agglomeration influences. Utilizing the energy method and Hamilton's principal, the motion equations are calculated. The frequency of the concrete beam is obtanied by analytical method. Three samples with 0.02% GO nanoparticles are built and its compressive strength is compared which shows a good accuracy with maximum 1.29% difference with mathematical model and other papers. The aim of this work from the theoretical study is investigating the effects of nanoparticles volume percentage and agglomeration, length and thickness of the beam on the frequency of the structure. The results show that the with enhancing the GO nanoparticles, the frequency is increased. For example, with enhancing the volume percent of GO nanoparticles from zero to 0.08%, the compressive strength is increased 48.91%. and 46.83%, respectively for two cases of with and without agglomeration.

• Industry Promotion Research
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• v.8 no.4
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• pp.257-266
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• 2023

The research focuses on the provincial universities in the western region of China and investigates the research level of 12 provincial universities from 2017 to 2021, considering both static efficiency and dynamic efficiency. The static efficiency is examined using Data Envelopment Analysis (DEA), while the dynamic efficiency is analyzed using the Malmquist model. The analysis results are as follows: the scientific research efficiency of universities in the 12 western provinces is generally not high. Against the background of the "Double First-Class" construction, the overall efficiency of scientific research in universities is showing an increasing trend. The main reason for the increase in scientific research efficiency is the increase in scale efficiency in recent years. The total factor productivity (TFP) of research activities is influenced by the technology progress index and exhibits a pattern of initial increase, followed by a decline, and then an increase again. Research conclusion: Western colleges and universities should reasonably allocate resources for scientific research activities, perfect scientific research mechanisms, improve management standards, promote scientific innovation and corresponding achievements, and ultimately raise the scientific and technological level in western China.

• Advances in concrete construction
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• v.16 no.1
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• pp.69-78
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• 2023

This investigation delves into the adverse repercussions stemming from the impact of arsenic on steel pipes concealed within soil designated for rice cultivation. Simultaneously, the study aims to ascertain effective techniques for detecting arsenic in the soil and to provide strategies for mitigating the corrosion of steel pipes. The realm of nanotechnology presents promising avenues for addressing the intricate intersection of renewable energy, oil, and environmental pollution from a novel perspective. Nanostructured materials, characterized by distinct chemical and physical attributes, unveil novel pathways for pioneering materials that exert a substantial impact across diverse realms of food production, storage, packaging, and quality control. Within the scope of the food industry, the scope of nanotechnology encompasses processes, storage methodologies, packaging paradigms, and safeguards to ensure the safety of consumables. Of particular note, silver nanoparticles, in addition to their commendable antibacterial efficacy, boast anti-fungal and anti-inflammatory prowess, environmental compatibility, minimal irritability and allergenicity, resilience to microbial antagonism, thermal stability, and robustness. Confronting the pressing issue of arsenic contamination within both environmental settings and the food supply is of paramount importance to preserve public health and ecological equilibrium. In response, this study introduces detection kits predicated upon silver nanoparticles, providing an expeditious and economically feasible avenue for identifying arsenic concentrations ranging from 0.5 to 3 ppm within rice. Subsequent quantification employs Hydride Atomic Absorption Spectroscopy (HG-AAS), which features a detection threshold of 0.05 ㎍/l. A salient advantage inherent in the HG-AAS methodology lies in its capacity to segregate analytes from the sample matrix, thereby significantly reducing instances of spectral interference. Importantly, the presence of arsenic in the soil beneath rice cultivation establishes a causative link to steel pipe corrosion, with potential consequences extending to food contamination-an intricate facet embedded within the broader tapestry of renewable energy, oil, and environmental pollution.

• Journal of KIBIM
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• v.13 no.3
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• pp.1-11
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• 2023

In order to use BIM as a tool for improving the productivity and quality of products in the construction industry, a BIM model must be created from the design stage first. Infrastructure structures such as bridges and tunnels are mainly created based on three-dimensional alignment in the generation of BIM models. Especially, generation of BIM models based on three-dimensional linearity has high task difficulty and algorithms for automating BIM modeling for railway infra structures have been suggested in previous studies. This study improved the BIM modeling automation algorithm of railway infrastructures and developed a system based on the algorithm so that it can be easily used by ordinary users. The system was built as an add-in system of Autodesk's Revit. As an improvement first, it is possible to arrange different libraries for each pattern, enabling various uses. In addition, it can be created models of several members with a single process and the system can automatically places structures that are added periodically, such as Rock Bolt and Fore Polling. Finally, 3D length information and volume for each pattern are automatically calculated for more accurate 3D-based volume calculation. This study contributes to increasing user accessibility by building a BIM modeling automation algorithm into a system. The system is expected to improve the efficiency of BIM modeling creation of linear-based infra structures, including railway infrastructure.

• Journal of KIBIM
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• v.13 no.3
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• pp.21-29
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• 2023

ChatGPT, a chatbot based on GPT large language models, has gained immense popularity among the general public as well as domain professionals. To assess its proficiency in specialized fields, ChatGPT was tested on mainstream exams like the bar exam and medical licensing tests. This study evaluated ChatGPT's ability to answer questions related to Building Information Modeling (BIM) by testing it on Korea's BIM expertise exam, focusing primarily on multiple-choice problems. Both GPT-3.5 and GPT-4 were tested by prompting them to provide the correct answers to three years' worth of exams, totaling 150 questions. The results showed that both versions passed the test with average scores of 68 and 85, respectively. GPT-4 performed particularly well in categories related to 'BIM software' and 'Smart Construction technology'. However, it did not fare well in 'BIM applications'. Both versions were more proficient with short-answer choices than with sentence-length answers. Additionally, GPT-4 struggled with questions related to BIM policies and regulations specific to the Korean industry. Such limitations might be addressed by using tools like LangChain, which allow for feeding domain-specific documents to customize ChatGPT's responses. These advancements are anticipated to enhance ChatGPT's utility as a virtual assistant for BIM education and modeling automation.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.208-216
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• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.929-937
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• 2023

This study aims to develop rules for the Determination of Drought Stages at the Local Level based on the drought cases in Gwangju and Jeollanam-do in 2022-2023. Among the eight drought indicators provided, six indicators (Agricultural drought stage (for paddy), Residential & industrial drought stage, SPI-12, Relative agricultural water storage, Residential water consumption change (for domestic use), Residential water consumption change (for non-domestic use) were confirmed to have statistical correlations with the perceptions of local government officials and experts. Additionally, this drought indicator was applied to a decision tree algorithm to develop rules for determining the severity of drought. Although it presented results similar to those of the existing method presented in previous studies, it showed a significant comparative advantage in explaining the temporal and spatial patterns of drought in the Gwangju and Jeollanam-do.

• Economic and Environmental Geology
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• v.57 no.2
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• pp.119-128
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• 2024

In Australia & Canada, architectural rock and aggregate are one of the mineral resources, and related data and information provided integrated with them. In these countries, the provided data and information, through the information system of local government and national geological survey organizations, are interactive maps, geological and thematic maps, exploration data set, 3 dimension geological models, minning rights status, survey reports and related papers etc. However, in case of Korea, aggregate and architectural rock are not assigned as the kind of mineral resources in accordance to domestic mining law, and related geological data and information are not provided from comprehensive mineral information system established in public geoscience organizations. And the administrative and informative management are conducted separately through the different governmental organizations such as Ministry of construction, Korea forest service, geoscience institute & Korea Mine & Reclamation Corporation. For securing the supply of architectural rock and aggregate resources, and for the convenience of their development & utilization, the unified information system and governance reform for the related industry is needed.

• Computers and Concrete
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• v.33 no.2
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.