• Advances in nano research
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• v.15 no.1
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• pp.49-57
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• 2023

As composite materials are used in many applications, the modern world looks forward to significant progress. An overview of the application of composite fiber materials in sports equipment is provided in this article, focusing primarily on the advantages of these materials when applied to sports equipment, as well as an Analysis of the influence of sports equipment of fiber-reinforced composite material on social sports development. The present study investigated surface morphology and physical and mechanical properties of S-glass fiber epoxy composites containing Al₂O₃ nanofillers (for example, 1 wt%, 2 wt%, 3 wt%, 4 wt%). A mechanical stirrer and ultrasonication combined the Al₂O₃ nanofiller with the matrix in varying amounts. A compression molding method was used to produce sheet composites. A first physical observation is well done, which confirms that nanoparticles are deposited on the fiber, and adhesive bonds are formed. Al₂O₃ nanofiller crystalline structure was investigated by X-ray diffraction, and its surface morphology was examined by scanning electron microscope (SEM). In the experimental test, nanofiller content was added at a rate of 1, 2, and 3% by weight, which caused a gradual decrease in void fraction by 2.851, 2.533, and 1.724%, respectively, an increase from 2.7%. The atomic bonding mechanism shows molecular bonding between nanoparticles and fibers. At temperatures between 60 ℃ and 380 ℃, Thermogravimetric Analysis (TGA) analysis shows that NPs deposition improves the thermal properties of the fibers and causes negligible weight reduction (percentage). Thermal stability of the composites was therefore presented up to 380 ℃. The Fourier Transform Infrared Spectrometer (FTIR) spectrum confirms that nanoparticles have been deposited successfully on the fiber.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.437-443
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• 2023

We investigates MICE participants's evaluation on the application of smart tourism technology by comparing their importance and performance. Date were collected from MICE participants who had attended in hybrid MICE in recent five years, The three dimensions of smart tourism technology were event management, extension of contents, and community. We found that event management and community affected on overall satisfaction of MICE participants. The result of paired t-test revealed that there were significant differences in audience interaction, webinar, virtual showcase, communication by social hub, virtual marketplace, and matchmaking The importance-performance matrix also indicated that matchmaking, virtual marketplace, audience interaction, communication by social hub, and webinar were major weakness and should be improved in the future.

• Journal of IKEEE
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• v.27 no.4
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• pp.624-629
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• 2023

Estimation of signal parameters via rotational invariance techniques (ESPRIT) is an algorithm that estimates the angle of a signal arriving at an array antenna using the shift invariance property of an array antenna. ESPRIT offers the good trade-off between performance and complexity. However, the ESPRIT algorithm still requires high-complexity operations such as covariance matrix and eigenvalue decomposition, so implementation with a hardware processor is essential to estimate the angle of arrival in real time. In addition, ESPRIT processors should have high performance. The performance is related to the number of antennas, and the number of antennas required for each application are different. Therefore, we proposed an ESPRIT processor that provides 2 to 8 variable antenna configurations to meet the performance and complexity requirements according to the applied field. The proposed ESPRIT processor was designed using the Verilog-HDL and implemented on a field programmable gate array (FPGA).

• 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 Advanced Navigation Technology
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• v.25 no.6
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• pp.551-556
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• 2021

In this paper, we deal with the stability condition of linear interval discrete systems with time-varying delays and unstructured uncertainty. For the interval discrete system which has interval matrix as its system matrices, time-varying delay time within some interval value and unstructured uncertainty which can include non-linearity and be expressed by only its magnitude, the stability condition is proposed. Compared with the previous result derived by using a upper bound solution of the Lyapunov equation, the new results are derived by the form of simple inequality based on Lyapunov stability condition and have the advantage of being more effective in stability application. Furthermore, the proposed stable conditions are very comprehensive and powerful, including the previously published stable conditions of various linear discrete systems. The superiority of the new condition is proven in the derivation process, and the utility and superiority of the proposed condition are examined through numerical example.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.49-56
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• 2023

This study proposed a damage location estimation method for automated image-based port infrastructure inspection. Memory efficiency was improved by calculating the homography matrix using feature detection technology and outlier removal technology, without going through the 3D modeling process and storing only damage information. To develop an algorithm specialized for port infrastructure, the algorithm was optimized through ground-truth coordinate pairs created using images of port infrastructure. The location errors obtained by applying this to the sample and concrete wall were (X: 6.5cm, Y: 1.3cm) and (X: 12.7cm, Y: 6.4cm), respectively. In addition, by applying the algorithm to the concrete wall and displaying it in the form of an exterior damage map, the possibility of field application was demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1042-1058
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• 2024

Terahertz (THz) communication is becoming a key technology for future 6G wireless networks because of its ultra-wide band. However, the implementation of THz communication systems confronts formidable challenges, notably beam splitting effects and high computational complexity associated with them. Our primary objective is to design a hybrid precoder that minimizes the Euclidean distance from the fully digital precoder. The analog precoding part adopts the delay-phase alternating minimization (DP-AltMin) algorithm, which divides the analog precoder into phase shifters and time delayers. This effectively addresses the beam splitting effects within THz communication by incorporating time delays. The traditional digital precoding solution, however, needs matrix inversion in THz massive multiple-input multiple-output (MIMO) communication systems, resulting in significant computational complexity and complicating the design of the analog precoder. To address this issue, we exploit the characteristics of THz massive MIMO communication systems and construct the digital precoder as a product of scale factors and semi-unitary matrices. We utilize Schatten norm and Hölder's inequality to create semi-unitary matrices after initializing the scale factors depending on the power allocation. Finally, the analog precoder and digital precoder are alternately optimized to obtain the ultimate hybrid precoding scheme. Extensive numerical simulations have demonstrated that our proposed algorithm outperforms existing methods in mitigating the beam splitting issue, improving system performance, and exhibiting lower complexity. Furthermore, our approach exhibits a more favorable alignment with practical application requirements, underlying its practicality and efficiency.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• Journal of Korean Dental Science
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• v.17 no.2
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• pp.53-63
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• 2024

Purpose: To investigate the effects of simultaneous soft and hard tissue augmentation and the addition of polydeoxyribonucleotide (PDRN) on regenerative outcomes. Materials and Methods: In five mongrel dogs, chronic ridge defects were established in both mandibles. Six implants were placed in the mandible, producing buccal dehiscence defects. The implants were randomly allocated to one of the following groups: 1) control: no treatment; 2) GBR: guided bone regeneration (GBR) only; 3) GBR/PDRN: GBR+PDRN application to bone substitute particles; 4) GBR/CTG: GBR+connective tissue grafting (CTG); 5) GBR/VCMX: GBR+soft tissue augmentation using volume stable collagen matrix (VCMX); and 6) group GBR/VCMX/PDRN: GBR+VCMX soaked with PDRN. The healing abutments were connected to the implants to provide additional room for tissue regeneration. Submerged healing was achieved. The animals were euthanized after four months. Histological and histomorphometric analyses were then performed. Results: Healing abutments were gradually exposed during the healing period. Histologically, minimal new bone formation was observed in the dehiscence defects. No specific differences were found between the groups regarding collagen fiber orientation and density in the augmented area. No traces of CTG or VCMX were detected. Histomorphometrically, the mean tissue thickness was greater in the control group than in the other groups above the implant shoulder (IS). Below the IS level, the CTG and PDRN groups exhibited more favorable tissue thickness than the other groups. Conclusion: Failure of submerged healing after tissue augmentation deteriorated the tissue contour. PDRN appears to have a positive effect on soft tissues.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.157-176
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• 2024

Integrative analysis for better understanding of complex biological systems gains more attention. Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject. In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA). Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD). To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data. It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data. Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method. Additionally, CIA exhibits variations dependent on the weight matrices employed.