• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.503-510
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• 2011

This paper presents a method of optimizing the blade pitch and generator torque controllers which have been already designed for an existing wind turbine generator system. Since the highly nonlinear and uncertain characteristics of the wind turbine generator can not be fully considered in the controller design phase, some parameters such as control gains must be tuned during the field implementation phase. In this paper, nonlinear simulation software, which is based high fidelity wind turbine model, and optimization technique are effectively combined and used to tune a set of gains for the blade pitch and the generator torque controllers. Simulation results show that the baseline controllers can be effectively optimized to reduce the errors in wind turbine rotor speed and generator power output controls as well as twisting of the high and low speed shafts.

• BioChip Journal
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• v.12 no.4
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• pp.332-339
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• 2018

The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to $80{\mu}V$ while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of $2{\mu}W$ under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves $60G{\Omega}$ of input impedance and input referred noise of $8.5nv/{\sqrt{Hz}}$ over the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists.

• Journal of Astronomy and Space Sciences
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• v.39 no.4
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• pp.159-167
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• 2022

Because of the small number of spacecraft available in the Earth's magnetosphere at any given time, it is not possible to obtain direct measurements of the fundamental quantities, such as the magnetic field and plasma density, with a spatial coverage necessary for studying, global magnetospheric phenomena. In such cases, empirical as well as physics-based models are proven to be extremely valuable. This requires not only having high fidelity and high accuracy models, but also knowing the weakness and strength of such models. In this study, we assess the accuracy of the widely used Tsyganenko magnetic field models, T96, T01, and T04, by comparing the calculated magnetic field with the ones measured in-situ by the GOES satellites during geomagnetically disturbed times. We first set the baseline accuracy of the models from a data-model comparison during the intervals of geomagnetically quiet times. During quiet times, we find that all three models exhibit a systematic error of about 10% in the magnetic field magnitude, while the error in the field vector direction is on average less than 1%. We then assess the model accuracy by a data-model comparison during twelve geomagnetic storm events. We find that the errors in both the magnitude and the direction are well maintained at the quiet-time level throughout the storm phase, except during the main phase of the storms in which the largest error can reach 15% on average, and exceed well over 70% in the worst case. Interestingly, the largest error occurs not at the Dst minimum but 2-3 hours before the minimum. Finally, the T96 model has consistently underperformed compared to the other models, likely due to the lack of computation for the effects of ring current. However, the T96 and T01 models are accurate enough for most of the time except for highly disturbed periods.

• Steel and Composite Structures
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• v.43 no.4
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• pp.447-456
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• 2022

Recent experimental studies showed that deep steel I-shaped profiles classified as high ductility class sections in seismic design international codes exhibit low deformation capacity when subjected to cyclic loading. This paper presents an innovative retrofit solution to increase the rotation capacity of beams using bonded carbon fiber reinforced polymers (CFRP) patches validated with advanced finite element analysis. This investigation focuses on the flexural cyclic behaviour of I-shaped hot rolled steel deep section used as beams in moment-resisting frames (MRF) retrofitted with CFRP patches on the web. The main goal of this CFRP reinforcement is to increase the rotation capacity of the member without increasing the overstrength in order to avoid compromising the strong column-weak beam condition in MRF. A finite element model that simulates the cyclic plasticity behavior of the steel and the damage in the adhesive layer is developed. The damage is modelled using the cohesive zone modelling (CZM) technique that is able to capture the crack initiation and propagation. Details on the modelling techniques including the mesh sensitivity near the fracture zone are presented. The effectiveness of the retrofit solution depends strongly on the selection of the appropriate adhesive. Different adhesive types are investigated where the CZM parameters are calibrated from high fidelity fracture mechanics tests that are thoroughly validated in the literature. This includes a rigid adhesive commonly found in the construction industry and two tough adhesives used in the automotive industry. The results revealed that the CFRP patch can increase the rotation capacity of a steel member considerably when using tough adhesives.

• Steel and Composite Structures
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• v.45 no.3
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• pp.389-408
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• 2022

Residual capacity is defined as the load carrying capacity of an RC column after undergoing severe damage. Evaluation of residual capacity of RC columns is necessary to avoid damage initiation in RC structures. The central aspect of the current research is to propose an empirical formula to estimate the residual capacity of RC columns after undergoing severe damage. This formula facilitates decision making of whether a replacement or a repair of the damaged column is adequate for further use. Available literature mainly focused on the simulation of explosion loads by using simplified pressure time histories to develop residual capacity of RC columns and rarely simulated the actual explosive. Therefore, there is a gap in the literature concerning general relation between blast damage of columns with different explosive loading conditions for a reliable and quick evaluation of column behavior subjected to blast loading. In this paper, the Arbitrary Lagrangian Eulerian (ALE) technique is implemented to simulate high fidelity blast pressure propagations. LS-DYNA software is utilized to solve the finite element (FE) model. The FE model is validated against the practical blast tests, and outcomes are in good agreement with test results. Multivariate linear regression (MLR) method is utilized to derive an analytical formula. The analytical formula predicts the residual capacity of RC columns as functions of structural element parameters. Based on intensive numerical simulation data, it is found that column depth, longitudinal reinforcement ratio, concrete strength and column width have significant effects on the residual axial load carrying capacity of reinforced concrete column under blast loads. Increasing column depth and longitudinal reinforcement ratio that provides better confinement to concrete are very effective in the residual capacity of RC column subjected to blast loads. Data obtained with this study can broaden the knowledge of structural response to blast and improve FE models to simulate the blast performance of concrete structures.

• Journal of Animal Reproduction and Biotechnology
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• v.38 no.3
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• pp.99-108
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• 2023

Background: Efficient gene editing technology is needed for successful knock-in. Homologous recombination (HR) is a major double-strand break repair pathway that can be utilized for accurately inserting foreign genes into the genome. HR occurs during the S/G2 phase, and the DNA mismatch repair (MMR) pathway is inextricably linked to HR to maintain HR fidelity. This study was conducted to investigate the effect of inhibiting MMR-related genes using CdCl₂, an MMR-related gene inhibitor, on HR efficiency in HC11 cells. Methods: The mRNA and protein expression levels of MMR-related genes (Msh2, Msh3, Msh6, Mlh1, Pms2), the HR-related gene Rad51, and the NHEJ-related gene DNA Ligase IV were assessed in HC11 cells treated with 10 μM of CdCl₂ for 48 hours. In addition, HC11 cells were transfected with a CRISPR/sgRNA expression vector and a knock-in vector targeting Exon3 of the mouse-beta casein locus, and treated with 10 μM cadmium for 48 hours. The knock-in efficiency was monitored through PCR. Results: The treatment of HC11 cells with a high-dose of CdCl₂ decreased the mRNA expression of the HR-related gene Rad51 in HC11 cells. In addition, the inhibition of MMR-related genes through CdCl₂ treatment did not lead to an increase in knock-in efficiency. Conclusions: The inhibition of MMR-related gene expression through high-dose CdCl₂ treatment reduces the expression of the HR-related gene Rad51, which is active during recombination. Therefore, it was determined that CdCl₂ is an inappropriate compound for improving HR efficiency.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3700-3716
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• 2024

Owing to spatial effects and vortex flow, flow in research reactors that use plate-type fuels can be maldistributed to the parallel channels of the core, which significantly impacts the reactor safety. In this study, the core flow of an advanced research reactor was measured in a real-scale facility under various hydraulic conditions. For flow measurement, integrated pressure lines were embedded in the mockups of 22 fuel assemblies and six fission molybdenum assemblies. Each assembly mockup was individually calibrated to obtain the relationship between the pressure drop and flow rate. Real-scale facility, which implements the characteristics of the hydraulic conditions in research reactors, was then used to evaluate the assembly-to-assembly flow distribution under normal operating condition, a partially withdrawn condition for the follower fuel assemblies, no flow for the pool water management system, and 1:1.5 asymmetric inlet flow condition. As a parallel channel system, core flow distribution was analyzed with conventional header design approach. Taking into account the measuring uncertainty, the core flow was uniformly distributed within 5 % under all conditions. This was mainly because the core flow resistance was sufficiently high and the vortex flow was minimized by the perforated plate.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3210-3223
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• 2024

International Commission on Radiological Protection (ICRP) recently developed the adult and pediatric meshtype reference computational phantoms (MRCPs) in high-quality/fidelity mesh format, featuring high deformability into various body sizes and poses. Utilizing this feature, the adult MRCPs-based body-size-dependent phantom library was developed for individualized dosimetry. To complete the full phantom library set, the present study produced the pediatric-MRCPs-based body-size-dependent pediatric phantom library. The library comprises a total of 637 phantoms (356 males and 281 females) with varying standing heights and body weights, covering a wide range of body sizes (i.e., including from 1st to 99th percentile height and weight values) for infants, children, and adolescents, offering a realistic representation of body shapes by reflecting ten secondary anthropometric parameters. The phantoms were automatically constructed utilizing automatic deformation program. The dosimetric impact of the library was investigated by calculating organ doses for external exposures to broad parallel photon beams in anterior-posterior direction. Compared with the values of the pediatric MRCPs, significant differences were observed at energies <0.05 MeV, showing larger values for underweight phantom and smaller values for obese phantom. The results highlight the importance of using the pediatric phantom library for accurate dose estimates of individual children with various body sizes.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1286-1288
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• 2024

The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.