• Computers and Concrete
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• v.21 no.5
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• pp.495-504
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• 2018

The possibility of earthquakes in vulnerable regions indicates that efficient technique is required for seismic protection of buildings. During the recent decades, the concept is moving towards the insertion of base isolation on seismic prone buildings. So, investigation of structural behavior is a burning topic for buildings to be isolated in base level by bearing device. This study deals with the incorporation of base isolation system and focuses the changes of structural responses for different types of Lead Rubber Bearing (LRB) isolators. A number of sixteen model buildings have been simulated selecting twelve types of bearing systems as well as conventional fixed-base (FB) scheme. The superstructures of the high-rise buildings are represented by finite element assemblage adopting multi-degree of freedoms. Static and dynamic analyses are carried out for FB and base isolated (BI) buildings. The dynamic analysis in finite element package has been performed by the nonlinear time history analysis (THA) based on the site-specific seismic excitation and compared employing eminent earthquakes. The influence of the model type and the alteration in superstructure behavior of the isolated buildings have been duly assessed. The results of the 3D multistory structures show that the lateral forces, displacement, inertia and story accelerations of the superstructure of the seismic prone buildings are significantly reduced due to bearing insertion. The nonlinear dynamic analysis shows 12 to 40% lessening in base shear when LRB is incorporated leading to substantial allowance of horizontal displacement. It is revealed that the LRB isolators might be potential options to diminish the respective floor accelerations, inertia, displacements and base shear whatever the condition coincides. The isolators with lower force intercept but higher isolation period is found to be better for decreasing base shear, floor acceleration and inertia force leading to reduction of structural and non-structural damage. However, LRB with lower isolator period seems to be more effective in dropping displacement at bearing interface aimed at reducing horizontal shift of building structure.

• CELLMED
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• v.10 no.3
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• pp.25.1-25.7
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• 2020

Allergic Rhinitis (AR) is an IgE (immunoglobin-E) mediated inflammatory condition of upper respiratory tract; main clinical features involve runny nose, sneezing, nasal obstruction, itching and watery eyes. AR is a global problem and has large variations in incidences, currently affects up to 20% - 40% of the population worldwide. It may not be a life-threatening disease per se but indisposition from the condition can be severe and has the potential to adversely affect the daily functioning of life. Classical yoga literature indicates that, components of yoga have been used to treat numerous inflammatory conditions including upper respiratory tract. A few yoga intervention studies reported improvement in lung capacity, Nasal air flow and symptoms of allergic rhinitis. This review examined various anti-inflammatory pathways mediated through Yoga that include downregulation of pro-inflammatory cytokines and upregulation of anti-inflammatory cytokines. The hypothalaminic-pitutary-adrenal (HPA) axis and vagal efferent stimulation has been reported to mediate anti-inflammatory effect. A significant reduction is also reported in other inflammatory biomarkers like- TNF-alpha, nuclear factor kappa B (NF-κB), plasma CRP and Cortisol level. Neti, a yogic nasal cleansing technique, reported beneficial effect on AR by direct physical cleansing of thick mucus, allergens, and inflammatory mediator from nasal mucosa resulting in improved ciliary beat frequency. We do not find any study showing effect of yoga on neurogenic inflammation. In summary, Integrated Yoga Therapy may have beneficial effect in reducing symptoms and improving quality of life for patients with allergic rhinitis. Yoga may reduce inflammation through mediating neuro-endocrino-immunological network. Future studies are needed to explore the mechanism how yoga might modulate immune inflammation cascade and neurogenic inflammation at the cellular level in relevance to allergic rhinitis; the effects of kriyas (yogic cleansing techniques) also need to be evaluated in early and late phase of AR. So the proposed model could guide future research.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.125-132
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• 2005

Domestic construction firms make every effort to save cost and, contrarily, enhance quality for competitive advantage in the market. Structural work of building construction takes chaise of the total cost and schedule, thus elaborate planning and management of the work help to lead the project into a successful way. Therefore, the idea to save time and cost and enhance constructability securing quality and safety of the work should be developed after analyzing the designed documents and site conditions comprehensively in the initial construction planning phase. Value Engineering (VE) technique is introduced in the substructural work in this paper to save cost creatively and systematically in the design and construction phase. A practical VE model that is applied to the underground building work systematically is proposed to save cost and it applies to the actual project to confirm the effectiveness of the model.

• Journal of Navigation and Port Research
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• v.34 no.2
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• pp.131-136
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• 2010

The aim of the paper is to solve the problem of customer reduction due to the difficulty of parts sourcing which impacts production delay and delivery delay in SC networks. Furthermore, this paper is to suggest the new inventory policy of MTS in order to solve the problem of current inventory policy. In order to compare two policies, a LCD maker is selected as a case study and the real data for 2007 years is used for simulation input. The maker uses MTO policy for parts sourcing which has the problem of lead time even if it has some advantage of inventory cost. Based on current process. The simulation program of AS-IS model and TO-BE model using ARENA 10 version is developed for evaluation. In a result, the order number of two policies shows that MTO is 52 and MTS is 53. However the quantity of order shows big difference such that MTO is 168,460 and MTS is 225,106. Particularly, the lead time of new inventory policy shows much shorter that that of MTO such that MTO 100 is days and MTS is 16 days. In spite of short lead time by MTS policy, new policy has to take burden of inventory cost per year. Total inventory cost per year by MTS policy is US$ 11,254 and each part inventory cost is that POL is US$ 1,807, LDI is US$ 2,166 and Panel is US$ 7,281. The implication of the research is that the company has to consider the cost and the service simultaneously in deciding the inventory policy. In the paper, even if the optimal point of deciding is put into tactical area, the ground of decision is suggested in order to improve the problem in SC networks.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.64.1-64.1
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• 2011

MIRIS (Multipurpose Infra-Red Imaging System), is a small infrared space telescope which is being developed by KASI, as the main payload of Science and Technology Satellite 3 (STSAT-3). Two wideband filters (I and H) of the MIRIS enables us to study the cosmic infrared background by detecting the absolute background brightness. The narrow band filter for Paschen ${\alpha}$ emission line observation will be employed to survey the Galactic plane for the study of warm ionized medium and interstellar turbulence. The opto-mechanical design of the MIRIS is optimized to operate around 200K for the telescope, and the cryogenic temperature around 90K for the sensor in the orbit, by using passive and active cooling technique, respectively. The engineering and qualification model of the MIRIS has been fabricated and successfully passed various environmental tests, including thermal, vacuum, vibration and shock tests. The flight model was also assembled and is in the process of system optimization to be launched in 2012 by a Russian rocket. The mission operation scenario and the data reduction software is now being developed. After the successful mission of FIMS (the main payload of STSAT-1), MIRIS is the second Korean space telescope, and will be an important step towards the future of Korean space astronomy.

• Computers and Concrete
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• v.24 no.4
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• pp.379-397
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• 2019

Steel-concrete composite beams are widely employed in constructions and their performance at the serviceability stage is of concern among practitioners and design regulations. In this context, an accurate evaluation of long-term deflections via various rheological concrete models is needed. In this work, the performance and predict capability of some concrete creep and shrinkage models ACI, CEB, B3, FIB and GL2000 are ascertained, and compared by using statistical bias indicators. Ten steel-concrete composite beams with existing experimental and numerical results are then modeled for this purpose. The proposed modeling technique uses the finite element method, where the concrete slab and steel beam are modeled with shell finite elements. Concrete is considered as an aging viscoelastic material and cracking is treated with the common smeared approach. The results show that when the experimental ultimate shrinkage strain is used for calibration, all studied rheological models predict nearly similar deflections, which agree with the experimental data. In contrast, significance differences are encountered for some models, when none calibration is made prior to. A value between twenty and thirty times the cracking strain is recommended for the ultimate tensile strain in the tension stiffening model. Also, increasing the relative humidity and decreasing the ambient temperature can lead to a substantial reduction of slab cracking for beams under negative flexure. Finally, there is not a unique rheological model that clearly excels in all scenarios.

• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.2
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• pp.97-104
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• 2005

A simple but efficient grid generation technique by using the modified compressible form of stream function has been formulated. Transformation of a physical plane to a streamline plane, the Von Mises Transformation, has been widely used to solve the differential equations governing flow phenomena, however, limitation arises in low velocity region of boundary layer, mixing layer and wake region where the relatively large grid spacing is inevitable. Modified Von Mises Transformation with simple mathematical adjustment for the stream function is suggested and applied to solve the confined coaxial turbulent jet mixing with simple $\kappa-\epsilon$ turbulence model. Comparison with several experimental data of axial mean velocity, turbulent kinetic energy, and Reynolds shear stress distribution shows quite good agreement in the mixing layer except in the centerline where the turbulent kinetic energy distributions were somewhat under estimated. This formulation is strongly suggested to be utilized specially for free turbulent mixing layers in axisymmetric flow conditions such as the investigation of mixing behavior, jet noise production and reduction for Turbofan engines.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of Advanced Navigation Technology
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• v.28 no.5
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• pp.650-656
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• 2024

This study proposes an AI-based image restoration technique to reduce image distortion caused by lighting and noise in nighttime environments and improve the performance of infrared detection systems. A synthetic image dataset was constructed using visible light images under various lighting conditions and ISO settings, and deep learning models (AutoEncoder and U-Net) were trained to assess image restoration performance. Experimental results show that the Multi-ISO model (9-channel) outperforms the Single-ISO model (3-channel), especially when utilizing input data with multiple ISO values. This study demonstrates that AI models can be effectively trained using synthetic data, even when real data collection is challenging, and can be applied to image restoration tasks. These findings are expected to contribute to enhancing the performance of optical detection systems through AI-based technology.