• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.158-165
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• 2004

The DC motor in a vehicle may cause noise and vibration because of high speed revolution, which can make a driver feel uncomfortable. There have been various studies attempting to solve these problems, focusing mostly on the causes of and ways to reduce noise and vibration. It is suggested that the noise in a DC motor may be primarily due to interaction between a brush and a commutator. Brush noise, the most common noise in a DC motor, results from a brush bounced from the surface of the commutator, fluctuation of the friction between the brush and the commutator, and the impact on the brush when passing over slots of the commutator. Based on the noise test, one of the most important design parameters was shown to be the roundness of the commutator. As the DC motor is used, the roundness of the commutator gets bigger with subsequent increase of the level of brush noise and vibration. There must be a threshold in order to prevent the brush noise from getting worse. Using the method of CAE is more efficient than the real test for purposes of looking for various design parameters to maintain the roundness of the commutator. In this study, the design process to reduce the brush noise is presented with the use of a computer model. The design parameters to reduce the brush noise and vibration are proposed by using FEM. The design parameters are used to reduce the noise and vibration of a DC motor and it is verified with the test results on a fan DC motor in a vehicle. This method may be applicable to various DC motor.

• Structural Engineering and Mechanics
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• v.52 no.3
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• pp.485-505
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• 2014

This study aimed to develop an approach to accurately predict the wind models and wind effects of large wind turbines. The wind-induced vibration characteristics of a 5 MW tower-blade coupled wind turbine system have been investigated in this paper. First, the blade-tower integration model was established, which included blades, nacelle, tower and the base of the wind turbine system. The harmonic superposition method and modified blade element momentum theory were then applied to simulate the fluctuating wind field for the rotor blades and tower. Finally, wind-induced responses and equivalent static wind loads (ESWL) of the system were studied based on the modified consistent coupling method, which took into account coupling effects of resonant modes, cross terms of resonant and background responses. Furthermore, useful suggestions were proposed to instruct the wind resistance design of large wind turbines. Based on obtained results, it is shown from the obtained results that wind-induced responses and ESWL were characterized with complicated modal responses, multi-mode coupling effects, and multiple equivalent objectives. Compared with the background component, the resonant component made more contribution to wind-induced responses and equivalent static wind loads at the middle-upper part of the tower and blades, and cross terms between background and resonant components affected the total fluctuation responses, while the background responses were similar with the resonant responses at the bottom of tower.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4171-4177
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• 2013

Outer tie rod is lighter than other, but there is the trend item weight and the number is increasing due to vehicle performance improvement. Thus, to improve vehicle fuel efficiency, weight lightening is essential. Therefore, this research performed the finite element analysis to investigate the structural performance of the outer tie rod for an electrical vehicle. This study was performed as the preliminary study for a lightweight design of the outer tie rod. The weight of outer tie rod was optimized by adopting the steel material and applying the trial and error method. The strengths due to durability and buckling should be considered in the structural design of an outer tie rod. Furthermore, the meta model-based optimization was applied to obtain its lightweight design, leading to 9 % weigh reduction.

• Nuclear Engineering and Technology
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• v.39 no.3
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• pp.193-206
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• 2007

The Korea Atomic Energy Research Institute has developed an advanced fast reactor concept, KALIMER-600, which satisfies the Generation IV reactor design goals of sustainability, economics, safety, and proliferation resistance. The concept enables an efficient utilization of uranium resources and a reduction of the radioactive waste. The core design has been developed with a strong emphasis on proliferation resistance by adopting a single enrichment fuel without blanket assemblies. In addition, a passive residual heat removal system, shortened intermediate heat-transport system piping and seismic isolation have been realized in the reactor system design as enhancements to its safety and economics. The inherent safety characteristics of the KALIMER-600 design have been confirmed by a safety analysis of its bounding events. Research on important thermal-hydraulic phenomena and sensing technologies were performed to support the design study. The integrity of the reactor head against creep fatigue was confirmed using a CFD method, and a model for density-wave instability in a helical-coiled steam generator was developed. Gas entrainment on an agitating pool surface was investigated and an experimental correlation on a critical entrainment condition was obtained. An experimental study on sodium-water reactions was also performed to validate the developed SELPSTA code, which predicts the data accurately. An acoustic leak detection method utilizing a neural network and signal processing units were developed and applied successfully for the detection of a signal up to a noise level of -20 dB. Waveguide sensor visualization technology is being developed to inspect the reactor internals and fuel subassemblies. These research and developmental efforts contribute significantly to enhance the safety, economics, and efficiency of the KALIMER-600 design concept.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.49-61
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• 2006

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. In this paper, it is extended to the UDAR data compression. A newly developed data compression approach to approximate the UDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. But over the years the TIN data representation has become an important research topic for many researchers due its large data size. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original UDAR data. The results show that this method can be used for significant reduction of data set.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.87-87
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• 2019

The consumption of herbal medicine and related products (herbal products) have increased in South Korea. At the same time the quality, safety, and efficacy of herbal products is being raised. Currently, the herbal products are standardized and controlled according to the requirements of the Korean Pharmacopoeia, the National Institute of Health and the Ministry of Public Health and Social Affairs. The validation of herbal products and their medicinal component is important, since many of these herbal products are composed of two or more medicinal plants. However, there are no tools to support the validation process. Interest in deep learning has exploded over the past decade, for herbal medicine using algorithms to achieve herb recognition, symptom related target prediction, and drug repositioning have been reported. In this study, individual images of four herbs (Panax ginseng C.A. Meyer, Atractylodes macrocephala Koidz, Poria cocos Wolf, Glycyrrhiza uralensis Fischer), actually sold in the market, were achieved. Certain image preprocessing steps such as noise reduction and resize were formatted. After the features are optimized, we applied GoogLeNet_Inception v4 model for herb image recognition. Experimental results show that our method achieved test accuracy of 95%. However, there are two limitations in the current study. Firstly, due to the relatively small data collection (100 images), the training loss is much lower than validation loss which possess overfitting problem. Secondly, herbal products are mostly in a mixture, the applied method cannot be reliable to detect a single herb from a mixture. Thus, further large data collection and improved object detection is needed for better classification.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.1-28
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• 2018

With the growing number of emerging carmakers, automotive parts manufacturers have to penetrate into emerging markets. They can provide large existing carmakers with fully customized parts because of economies scale, but cannot do this for small emerging carmakers due to their small and highly volatile volume order. Once the order by an emerging carmaker is placed, a part manufacturer is exposed to high risks both of decrease in profit margin and high opportunity cost. The platform-based mass customization can be a solution for cost reduction, but the risks of volatility in volume hard to manage. Tackling this issue, we presents a method of optimizing the product portfolio to maximize profits while managing volatility of volume order by emerging carmakers at an affordable level. It is the first robust product portfolio method to keep the scaled deviation of profits at a fixed level under volume order uncertainty. Also, the effect of on the platform-based mass customization on cost is considered. This model can be a building block of conservative market penetration as well as product development strategy while minimizing the financial risks. We conducted an empirical study of a part manufacturer targeting on eighteen automobile manufacturers in North America, Europe and Asia with it powered lift gate.

• Biomedical Science Letters
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• v.27 no.2
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• pp.99-104
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• 2021

Treatment techniques that affect homeostasis by non-invasive regulation in peripheral organs will advance disease research. Here, we demonstrate a non-invasive method of conditioning within an organ using a low-frequency stimulator superposition of alternating microcurrent wave in stages. It is first applied to the inflammatory response in H3N2-infected sinusitis mice. To check the progress of the treatment, mice were sacrificed every week for 3 weeks, nasal tissue was removed, and the inflammatory response was investigated through H & E staining. The low-frequency stimulation treatment group was found to alleviate the proliferation of epithelial cells and invasion of inflammatory cells compared to the control group as the passage of treatment time. The reduction of inflammatory cytokines in the nasal lavage fluid was observed in H3N2-infected sinusitis mice treated with of low-frequency stimulation using superposition of alternating microcurrent wave compared to H3N2-infected sinusitis mice after 3 weeks. These data demonstrate that low-frequency stimulation device in the form of using alternating current wave superposition on within organs provides a new method to regulate specific physiological functions. Therefore, it is necessary to prove the inhibitory effect of low-frequency stimulation using alternating current wave superposition on inflammatory diseases by various methods through further studies and clinical studies.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.193-202
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• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Wind and Structures
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• v.35 no.6
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• pp.419-430
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• 2022

In wind-resistant designs, wind velocity is assumed to be a Gaussian process; however, local complex topography may result in strong non-Gaussian wind features. This study investigates the non-Gaussian wind features over complex terrain under atmospheric turbulent boundary layers by the large eddy simulation (LES) model, and the turbulent inlet of LES is generated by the consistent discretizing random flow generation (CDRFG) method. The performance of LES is validated by two different complex terrains in Changsha and Mianyang, China, and the results are compared with wind tunnel tests and onsite measurements, respectively. Furthermore, the non-Gaussian parameters, such as skewness, kurtosis, probability curves, and gust factors, are analyzed in-depth. The results show that the LES method is in good agreement with both mean and turbulent wind fields from wind tunnel tests and onsite measurements. Wind fields in complex terrain mostly exhibit a left-skewed Gaussian process, and it changes from a softening Gaussian process to a hardening Gaussian process as the height increases. A reduction in the gust factors of about 2.0%-15.0% can be found by taking into account the non-Gaussian features, except for a 4.4% increase near the ground in steep terrain. This study can provide a reference for the assessment of extreme wind loads on structures in complex terrain.