• Composites Research
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• v.36 no.1
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• pp.59-67
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• 2023

Damage caused by impact on a vehicle composed of CFRP(carbon fiber reinforced plastic) composite to reduce weight in the aerospace industries is related to the safety of passengers. Therefore, it is important to understand the damage behavior of materials that is invisible in impact situations, and research through the FEM(finite element model) is needed to simulate this. In this study, FEM suitable for predicting damage behavior was constructed for impact analysis of unidirectional laminated composite. The calibration parameters of the MAT_54 Enhanced Composite Damage material model in LS-DYNA were acquired by inverse estimation through ANN(artificial neural network) model. The reliability was verified by comparing the result of experiment with the results of the ANN model for the obtained parameter. It was confirmed that accuracy of FEM can be improved through optimization of calibration parameters.

• Journal of Powder Materials
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• v.29 no.6
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• pp.459-467
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• 2022

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3080-3087
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• 2023

Thermoelectric (TE) materials working in radioisotope thermoelectric generators are irradiated by neutrons throughout its service; thus, investigating the neutron irradiation stability of TE devices is necessary. Herein, the influence of neutron irradiation with fluences of 4.56 × 10¹⁰ and 1 × 10¹³ n/cm² by pulsed neutron reactor on the electrical and thermal transport properties of n-type Bi₂Te_2.7Se_0.3 and p-type Bi_0.5Sb_1.5Te₃ thermoelectric alloys prepared by cold-pressing and molding is investigated. After neutron irradiation, the properties of thermoelectric materials fluctuate, which is related to the material type and irradiation fluence. Different from p-type thermoelectric materials, neutron irradiation has a positive effect on n-type Bi₂Te_2.7Se_0.3 materials. This result might be due to the increase of carrier mobility and the optimization of electrical conductivity. Afterward, the effects of p-type and n-type TE devices with different treatments on the output performance of TE devices are further discussed. The positive and negative effects caused by irradiation can cancel each other to a certain extent. For TE devices paired with p-type Bi_0.5Sb_1.5Te₃ and n-type Bi₂Te_2.7Se_0.3 thermoelectric legs, the generated power and conversion efficiency are stable after neutron irradiation.

• Clean Technology
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• v.29 no.1
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• pp.53-58
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• 2023

This study shows the summary of the economic performance of excess electricity conversion to hydrogen as well as methane and returned conversion to electricity using a fuel cell. The methane production process has been examined in a previous study. Here, this study focuses on the conversion of methane to electricity. As a part of this study, capital expenditure (CAPEX) is estimated under various sized plants (0.3, 3, 9, and 30 MW). The study shows a method for economic optimization of electricity generation using a fuel cell. The CAPEX and operating expenditure (OPEX) as well as the feed cost are used to calculate the discounted cash flow. Then the levelized cost of returned electricity (LCORE) is estimated from the discounted cash flow. This study found the LCORE value was ¢10.2/kWh electricity when a 9 MW electricity generating fuel cell was used. A methane production plant size of 1,500 Nm³/hr, a methane production cost of $11.47/mcf, a storage cost of $1/mcf, and a fuel cell efficiency of 54% were used as a baseline. A sensitivity analysis was performed by varying the storage cost, fuel cell efficiency, and excess electricity cost by ±20%, and fuel cell efficiency was found as the most dominating parameter in terms of the LCORE sensitivity. Therefore, for the best cost-performance, fuel cell manufacturing and efficiency need to be carefully evaluated. This study provides a general guideline for cost performance comparison with LCORE.

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.106-116
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• 2023

In this paper, we designed a coaxial dual camera incorporating two optical systems-one for the visible rays and the other for far-infrared ones-with the aim of capturing images in both wavelength ranges. The far-infrared system, which uses an uncooled detector, has a sensor array of 640×480 pixels. The visible ray system has 1,945×1,097 pixels. The coaxial dual optical system was designed using a hot mirror beam splitter to minimize heat transfer caused by infrared rays in the visible ray optical system. The optimization process revealed that the final version of the dual camera system reached more than 90% of the fusion performance between two separate images from dual systems. Multiple rigorous testing processes confirmed that the coaxial dual camera we designed demonstrates meaningful design efficiency and improved image conformity degree compared to existing dual cameras.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.343-352
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• 2020

Modular integrated construction (MiC) is an innovative construction method where components of a building are manufactured in an offsite factory, trucked to the job site in sections, set in place with cranes, and assembled together to form a whole building. Where circumstances merit, favorable conditions exist and implemented effectively; MiC improves project performance. However, several key factors need to converge during implementation to realize the full benefits of MiC. Thus, a thorough understanding of the factors which are critical to the success of MiC projects is imperative. Drawing on a systematic review of 47 empirical studies, this research identified 25 key success factors (KSFs) for MiC projects. Of these, the five topmost cited KSFs for MiC projects include effective working collaboration and communication among project participants; standardization, optimization, automation and benchmarking of best practices; effective supply chain management; early design freeze and completion; and efficient procurement method and contracting. The study further proposed a conceptual model of the KSFs, highlighting the interdependences of people, processes, and technology-related KSFs for the effective accomplishment of MiC projects. The set of KSFs is practically relevant as they constitute a checklist of items for management to address and deal with during the planning and execution of MiC projects. They also provide a useful basis for future empirical studies tailored towards measuring the performance and success of MiC projects. MiC project participants and stakeholders will find this research useful in reducing failure risks and achieving more desired performance outcomes. One potential impact of the study is that it may inform, guide, and improve the successful implementation of MiC projects in the construction industry. However, the rigor of the analysis and relative importance ranking of the KSFs were limited due to the absence of data.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.105-114
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• 2023

In this paper, we propose a deployment and operation scheme of UxNB network toward mobile IAB. By operating a UxNB network based on SDN(Software Defined Network), UxNBs are deployed in areas where mobile communication services are desired. After deploying UxNB in the service area, IAB can be set up to perform mobile communication services. For this purpose, this paper first proposes a UxNB Network Controller consisting of a UAV Controller and an SDN Controller, and proposes the necessary functions. Next, we present a scenario in which a UxNB network can be deployed and operated in detail step by step. We also discuss the location of the UxNB network controller, how to deliver control commands from the UAV controller to the UxNB, how to apply IAB for UxNB networks, optimization of UxNB networks, RLF(radio link failure) recovery in UxNB networks, and future research on security in UxNB networks. It is expected that the proposed UxNB Network Controller architecture and UxNB network deployment and operation will enable seamless integration of UxNB networks into Mobile IAB.

• Journal of Aerospace System Engineering
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• v.18 no.1
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• pp.72-78
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• 2024

It is essential to coincide with moving target planet at future arrival changing point during space flight time in an interplanetary orbit design. Transition orbit elements can be obtained from traditional Lambert solutions by adjusting initial and final positions include flight time. Two-point boundary values of orbits can be selected in the design process. From this point of view, interplanetary orbits are infinite if they can be acquired from departure velocity without limit. However, appropriate and optimized procedures are needed to obtain an optimum interplanetary orbit to meet given conditions. The departure velocity is highly dependent on space launch vehicle's ability up to now. In this paper, algorithms of professor Howard Curtis at Embry-Riddle Aeronautical University were applied to obtain Lambert solution and orbit elements.