• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.279-290
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• 2022

A superconducting fault current limiter(SFCL) is a power device that exploits superconducting transition to control currents and enhances the flexibility, stability and reliability of the power system within a few milliseconds. With a high phase transition speed, high critical current densities and little AC loss, high-temperature superconducting (HTS) wires are suitable for a resistive-type SFCL. However, HTS wires due to the lack of optimization research are rather inefficient to directly apply to a fault current limiter in terms of the design and capacity, for the existing method relied the characteristics. Therefore, in order to develop a suitable wire for an SFCL, it is necessary to enhance critical current uniformity, select optimal stabilizer materials and conducted research on the development of uniform stabilizer layering technology. The high temperature superconducting wires manufactured by this study get an average critical current of 804 A/12mm-width at the length of 710m; therefore, conducted research was able to secure economic performance by improving efficiency, reducing costs, and reducing size.

• Steel and Composite Structures
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• v.48 no.2
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.571-578
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• 2010

Manning's roughness coefficients were reevaluated for the computation of river flow of the Han River, the Nakdong River and the Geum River. The roughness coefficients were estimated by two methods. One is based on the assumption that roughness is primarily a function of grain diameter and the other is based on the findings that roughness may vary significantly with the flow discharge. The roughness coefficients adopted in each river improvement master plan have been compared with those obtained using the FLDWAV in this study, and their applicabilities have been reviewed, using the FLDWAV and HEC-RAS models. The design flood water levels computed by the abovementioned models with the roughness coefficients proposed in this study have shown good agreement with the measurements of time variation. The roughness coefficients computed using the FLDWAV model showed nearly no close correlation with the various hydraulic characteristic factors, such as grain size and river depth, etc.. Finally the design flood water levels and levee safety about the downstream part from the Paldang Dam of the Han River has been reviewed using HEC-2 model with roughness coefficients of this study and the results indicated that some parts of the existing levees were short of safety.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.1001-1007
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• 2023

Dose limits are not applied to medical radiation exposure therefore justification and optimization should be essential for protecting radiation. This study explores methods to reduce exposure dose undergoing general radiation exam by bolus(tissue equivalent material) with keeping image quality. Hand PA projection with 50 kVp, 5 mAs, SID 100 cm, and 8×10 inch is referred by covered bolus of thickness 0, 3, 5, 8, and 10 mm for evaluation entrance dose and SNR. The entrance dose (μGy) to the hand by bolus thickness was 125.41±0.288, 106.85±0.255, 104.97±0.221, 91.68±0.299, and 90.94±0.106 showing a significant reduction in radiation exposure depending on if the bolus was used and bolus thickness. The SNR of the image was 13.997, 13.906, 12.240, 12.538, and 12.548 at each bolus thickness, showing no significant difference. It was confirmed that if appropriate thickness and size of bolus is used depending on the type of radiological imaging exam and the body site, a significant radiation dose reduction effect can be achieved without deteriorating image quality.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.61-71
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• 2006

An MP3 encoder chip has been designed and fabricated with the hardware and software co-design concepts. In the aspect of the software. the calculation cycles of the distortion control loop. which requires most of the calculation cycles in MP3 encoding procedure. have been reduced to $67\%$ of the original algorithm through the 'scale factor Pre-calculation'. By using a floating Point 32 bit DSP core and designing the FFT block with the hardware. we can get the additional reduction of the calculation cycles in addition to the software optimization. The designed chip has been verified using HW emulation and fabricated via 0.25um CMOS technology The fabricated chip has the size of $6.2{\time}6.2mm^2$ and operates normally on the test board in the qualitative and quantitative aspect.

• Resources Recycling
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• v.33 no.2
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• pp.37-45
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• 2024

Molybdenum is used in various industries because of its high heat and corrosion resistance. It was selected as a critical mineral in Korea. However, there have been recent challenges in production because of the increased depth and decreased grade of molybdenum veins. Consequently, it is necessary to enhance the effectiveness of the molybdenum beneficiation process. In this study, a basic evaluation of beneficiation characteristics was conducted to enhance the effectiveness of the domestic molybdenum ore beneficiation process. The properties of the beneficiation process were assessed using mineralogical analysis, work index, and flotation kinetics. The results revealed that the allowable particle size of the molybdenum ore for liberation was ~100 ㎛. In addition, the work index was calculated to be 14.57 kWh/t. The operating conditions in the flotation units were achieved by determining the optimal flotation time for each process based on flotation kinetics. Finally, the characteristics of molybdenum ore beneficiation provided in this study can be utilized to diagnose the grinding and flotation processes of large-scale molybdenum beneficiation plants.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1749-1773
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• 2024

Recent advances in deep neural networks (DNNs) have greatly improved the accuracy and universality of various intelligent applications, at the expense of increasing model size and computational demand. Since the resources of end devices are often too limited to deploy a complete DNN model, offloading DNN inference tasks to cloud servers is a common approach to meet this gap. However, due to the limited bandwidth of WAN and the long distance between end devices and cloud servers, this approach may lead to significant data transmission latency. Therefore, device-edge collaborative inference has emerged as a promising paradigm to accelerate the execution of DNN inference tasks where DNN models are partitioned to be sequentially executed in both end devices and edge servers. Nevertheless, collaborative inference in heterogeneous edge environments with multiple edge servers, end devices and DNN tasks has been overlooked in previous research. To fill this gap, we investigate the optimization problem of collaborative inference in a heterogeneous system and propose a scheme CIS, i.e., collaborative inference scheme, which jointly combines DNN partition, task offloading and scheduling to reduce the average weighted inference latency. CIS decomposes the problem into three parts to achieve the optimal average weighted inference latency. In addition, we build a prototype that implements CIS and conducts extensive experiments to demonstrate the scheme's effectiveness and efficiency. Experiments show that CIS reduces 29% to 71% on the average weighted inference latency compared to the other four existing schemes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.4
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• pp.62-67
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• 2024

In this paper, we developed and optimized a chitosan-based polymer microbial bead carrier that is cell-friendly, has a high moisture absorption rate, and effectively provides the conditions for microbial biomineral formation as an optimal microbial carrier that protects microorganisms in concrete, and evaluated the self-healing performance of mortar using it. In order to incorporate circular-shaped microbial endospores, a circular-shaped microbial bead carrier was developed by combining chitosan and alginate polymers, and the amount of calcium carbonate produced could be actively controlled by adjusting the composition of the carrier. The amount of biominerals formed and the size of crystals were maximized in the hydrogel bead carrier containing chitosan, and in the case of mortar cracks using this, it was confirmed that self-healing of cracks with a maximum crack width of 0.3mm was achieved within 96 hours after crack generation.