• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.464-473
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• 2022

In this paper, about a lightweight design that satisfies the performance of UHF-GPS Antenna used in autonomous underwater vehicle is proposed. Structural analysis, watertight external pressure test and non-destructive testing used in the design process are decided in consideration of structural safety for operating external forces in the underwater environment. First, the material of radome is selected for the performance of the UHF-GPS Antenna for communication with the carrier on the underwater operation in consideration of the 20 bar pressure generated. And the material of radome as PA-GF is selected by conducting electromagnetic field analysis and structural analysis and by considering high strength, rigidity and high dielectric constant. Electromagnetic field analysis and structural analysis by the thickness of radome are additionally performed in order to satisfy the required weight of UHF-GPS antenna. After selecting the final model, its structural safety is verified through watertight external pressure test and non-destructive testing.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Advances in aircraft and spacecraft science
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• v.10 no.3
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• pp.223-243
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• 2023

There is a burgeoning demand for minimizing the mass of satellites because of its direct impact on reducing launch-to-orbit cost. This must be done without compromising the structure's efficiency. The present paper introduces a relatively low-cost and easily implementable approach for optimizing structural mass to a maximum natural frequency. The natural frequencies of the satellite are of utmost pertinence to the application requirements, as the sensitive electronic instrumentation and onboard computers should not be affected by the vibrations of the satellite structure. This methodology is applied to a realistic model of Al-Azhar University micro-satellite in partnership with the Egyptian Space Agency. The procedure used in structural design can be summarized in two steps. The first step is to select the most favorable primary structural configuration among several different candidate variants. The nominated variant is selected as the one scoring maximum relative dynamic stiffness. The second step is to use perforation patterns reduce the overall mass of structural elements in the selected variant without changing the weight. The results of the presented procedure demonstrate that the mass reduction percentage was found to be 39% when compared to the unperforated configuration that had the same plate thickness. The findings of this study challenge the commonly accepted notion that isogrid perforations are the most effective means of achieving the goal of reducing mass while maintaining stiffness. Rather, the study highlights the potential benefits of exploring a wider range of perforation unit cells during the design process. The study revealed that rectangular perforation patterns had the lowest efficiency in terms of modal stiffness, while triangular patterns resulted in the highest efficiency. These results suggest that there may be significant gains to be made by considering a broader range of perforation shapes and configurations in the design of lightweight structures.

• Journal of the Korean Society of Safety
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• v.39 no.2
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• pp.38-43
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• 2024

Recently, with the expanding market for electronic devices and electric vehicles, secondary battery usage has been on the rise. Lithium-ion batteries are particularly popular due to their fast charging times and lightweight nature compared to other types of batteries. A secondary battery consists of four components: anode, cathode, electrolyte, and separator. Generally, the positive and negative electrode materials of secondary batteries are composed of an active material, a binder, and a conductive material. Acetylene Black (AB) is utilized to enhance conductivity between active material particles or metal dust collectors, preventing the binder from acting as an insulator. However, when recycling waste batteries that have been subject to high usage, there is a risk of fire and explosion accidents, as accurately identifying the characteristics of Acetylene Black dust proves to be challenging. In this study, the lower explosion limit for Acetylene Black dust with an average particle size of 0.042 ㎛ was determined to be 153.64 mg/L using a Hartmann-type dust explosion device. Notably, the dust did not explode at values below 168 mg, rendering the lower explosion limit calculation unfeasible. Analysis of explosion delay times with varying electrode gaps revealed the shortest delay time at 3 mm, with a noticeable increase in delay times for gaps of 4 mm or greater. The findings offer fundamental data for fire and explosion prevention measures in Acetylene Black waste recycling processes via a predictive model for lower explosion limits and ignition delay time.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.4
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• pp.16-21
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• 2023

Although the global number of landmines is on a declining trend, the damages caused by previously buried landmines persist. In light of this, the present study contemplates solutions to issues and constraints that may arise due to the improvement of mine detection equipment and the reduction in the number of future soldiers. Current mine detectors lack data storage capabilities, posing limitations on data collection for research purposes. Additionally, practical data collection in real-world environments demands substantial time and manpower. Therefore, in this study, gprMax simulation was utilized to generate data. The lightweight CNN-based model, MobileNet, was trained and validated with real data, achieving a high identification rate of 97.35%. Consequently, the potential integration of technologies such as deep learning and simulation into geographical detection equipment is highlighted, offering a pathway to address potential future challenges. The study aims to somewhat alleviate these issues and anticipates contributing to the development of our military capabilities in becoming a future scientific and technological force.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.428-435
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• 2024

In this paper, a human activity regeneration (HAR) system based on multiple input multiple output frequency modulation continuous wave (MIMO FMCW) radar was designed and implemented. Using point cloud data from MIMO radar sensors has advantages in terms of privacy, safety, and accuracy. For the implementation of the HAR system, a customized neural network based on PointPillars and depthwise separate convolutional neural network (DS-CNN) was developed. By processing high-resolution point cloud data through a lightweight network, high accuracy and efficiency were achieved. As a result, the accuracy of 98.27% and the computational complexity of 11.27M multiply-accumulates (Macs) were achieved. In addition, the developed neural network model was implemented on Raspberry-Pi embedded system and it was confirmed that point cloud data can be processed at a speed of up to 8 fps.

• Advances in materials Research
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• v.13 no.5
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• pp.391-416
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• 2024

Hybrid composite materials are widely used in various load-bearing structural components of micro - mini UAVs. However, the design of thin laminates for better impact resistance remains a challenge, despite the strong demand for lightweight structures. This work aims to assess the low-velocity impact (LVI) behaviour of thin quasi-isotropic woven carbon/ aramid epoxy hybrid laminates using experimental and numerical techniques. Drop tower impact test with 10 J and 15 J impact energies is performed on carbon/epoxy laminates having aramid layers at different sequences and locations. The impact behaviour is experimentally evaluated using force-time, force-deformation, and energy-time histories considering delamination threshold load, peak load, and laminate deflection. Ultrasonic C-scan is performed on the post-impact samples to analyse the insidious damage profile at different impact energies. The experimental data is further utilized to numerically simulate LVI behaviour by employing the representative volume element model. The numerical results are in good agreement with the experimental data. Numerical and experimental approach predicts that the hybrid laminates with aramid layers at both impact and non-impact sides of the laminate exhibits significant improvement in the overall impact behaviour by having a subcritical damage morphology compared to carbon/epoxy laminate. A combined numerical-experimental approach is proposed for evaluating the effective impact performance.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of the Korea Institute of Building Construction
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• v.19 no.1
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• pp.9-18
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• 2019

Concrete, which is the most widely used building material in modern times, has been improved not only in strength but also in structural performance such as increase in toughness and ductility, weight reduction, and improvement in quality of human life. Due to the surge in demand for the building, there is a tendency to be used variously from architectural panel and architecture to interior accessories. In Korea, a light-transmitting concrete, LEFC(Light Emotion Friendly Concrete), that insert plastic rods to stimulate emotional sensation through the combination of light and concrete has developed. In previous research, it was confirmed that the use of a synthetic foam agent rather than an animal foam agent did not cause a fogging phenomenon. In this study, lightweight by applying foaming agent to LEFC and two types of fiber (Nylon Fiber, Polyvinyl Alcohol) were compared to achieve to investigate the fiber to be applied in future. An equation that can predict the loss and adhesion reduction of the concrete section according to the diameter of the rod (5mm, 10mm) and the interval (10mm, 15mm, 20mm) was proposed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.89-95
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• 2024

Nowadays, the competitiveness of a company depends on the ability of all organizational members to share and utilize the organizational knowledge accumulated by the organization. As if to prove this, the world is now focusing on ChetGPT service using generative AI technology based on LLM (Large Language Model). However, it is still difficult to apply the ChetGPT service to work because there are many hallucinogenic problems. To solve this problem, sLLM (Lightweight Large Language Model) technology is being proposed as an alternative. In order to construct sLLM, corporate data is essential. Corporate data is the organization's ERP data and the company's office document knowledge data preserved by the organization. ERP Data can be used by directly connecting to sLLM, but office documents are stored in file format and must be converted to data format to be used by connecting to sLLM. In addition, there are too many technical limitations to utilize office documents stored in file format as organizational knowledge information. This study proposes a method of storing office documents in DB format rather than file format, allowing companies to utilize already accumulated office documents as an organizational knowledge system, and providing office documents in data form to the company's SLLM. We aim to contribute to improving corporate competitiveness by combining AI technology.