• Journal of the Korean GEO-environmental Society
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• v.20 no.9
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• pp.13-19
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• 2019

Dynamic earth pressure is considered an important parameter in the design of embedded structures. In current engineering design simplified methods developed either for yielding or non-yielding structures are utilized to predict resultant dynamic pressure. The applicability of these equations to embedded structures have not yet been reported. In this study we perform a suite of equivalent linear time history analysis for a range of embedded structure configurations. Numerically calculated dynamic pressure is shown to depend on the flexibility ratio (F), aspect ratio (L/H) of the embedded structure, and ground motion. Increase in L/H and intensity increases the magnitude of dynamic pressure. An increase in F decreases the dynamic pressure. Overall, the trends highlight the need for development of new method that accounts for F and L/H to calculate the dynamic pressure for the performance-based design of embedded structures.

• IEMEK Journal of Embedded Systems and Applications
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• v.1 no.1
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• pp.1-7
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• 2006

In this paper, a novel power management structure for an energy harvesting pervasive system is proposed. The system considers the power state of each subsystem to assign proper power sources. The switch matrix structure utilizes each power source to reduce the peak current of the battery. The power management structure can be interfaced to an embedded system power supply without significant design change.

• Earthquakes and Structures
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• v.3 no.3_4
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• pp.549-561
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• 2012

Many building foundations are embedded, however it is not easy to compact the backfill around the foundation especially for the deeply embedded ones. The soil condition around the embedded foundation may affect the seismic response of a building due to the weak contact between the soil and the foundation. In this paper, the response accelerations in the short-period range and at the period of 1 second (in the long-period range) for a seismic design spectrum specified in the IBC design code were compared considering perfect and poor backfills to investigate the effect of backfill compaction around the embedded foundation. An in-house finite-element software (P3DASS) which has the capability of horizontal pseudo-3D seismic analysis with linear soil layers was used to perform the seismic analyses of the structure-soil system with an embedded foundation. Seismic analyses were carried out with 7 bedrock earthquake records provided by the Pacific Earthquake Engineering Research Center (PEER), scaling the peak ground accelerations to 0.1 g. The results indicate that the poor backfill is not detrimental to the seismic response of a building, if the foundation is not embedded deeply in the soft soil. However, it is necessary to perform the seismic analysis for the structure-soil system embedded deeply in the soft soil to check the seismic resonance due to the soft soil layer beneath the foundation, and to compact the backfill as well as possible.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.4
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• pp.169-177
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• 2021

In this paper, horizontal seismic responses of a structure built on a sunken mat foundation were compared with those built on a solid embedded mat foundation to investigate the effect of a sunken mat foundation on the horizontal response of a structure. Seismic analyses of a structure laid on the embedded mat foundation were performed by utilizing a pseudo-3D finite element software of P3DASS. Three bedrock earthquake records downloaded from the Pacific Earthquake Engineering Research Center database were scaled to reproduce weak-moderate earthquakes. Weak, medium, and stiff soil layers were considered for the seismic analyses of the structure-foundation-soil system. Parametric studies were performed for foundation radius, foundation embedment depth, and shear wave velocity of a soil layer to investigate their effect on the seismic response spectrum. The study result showed that the design spectrum of a structure built on a sunken mat foundation was similar to that with a solid embedded mat foundation showing a slight difference due to almost the same seismic base motion beneath both embedded foundations.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.4
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• pp.1-6
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• 2005

Recently embedded passive technology which fabricate passive elements such as resistors and capacitors at the inner layer of PCB(Printed Circuit Board) is used to make high performance IT products. However, embedded capacitor has limit in full range circuit applications because of the low capacitance density. In this paper, a new embedded capacitor which has wrinkled electrodes and dielectric layer was proposed to overcome the limits. FEM(Finite Elements Method) technique was used to evaluate capacitance density of the wrinkled type embedded capacitor. Capacitance density of the wrinkled type embedded capacitor is larger than that of conventional planar type embedded capacitor by about 25.6%$\sim$39.6%. In case of thin film type embedded capacitor, proposed wrinkled structure has more enhanced effect on the capacitance density.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.475-478
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• 2007

A solution for carbon nanotube based field emission displays has been designed and built. The solution makes use of structure layout to control electron beam trajectories, uniformity by use of ballasting, emission anomalies eliminated by selective carbon nanotube growth and invisible spacers to maintain the vacuum gap.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.273-279
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• 2015

The vehicle has lots of embedded systems. Each of systems has its own role. In case of the vehicle, simple failure of system can be critical to driver. Therefore all of embedded system should be managed based on importance factors to be effective. In this paper, we consider the resilience as the importance factor for the driving system with ACC(Adaptive Cruise Control). We propose metrics to calculate the resilience of the embedded system. To get the resilience of system, we calculate the reliability and the resilience of nodes in the system using its failure rate. The resilience of whole system can be presented by the resilience of nodes and its weight. We calculate the resilience and compare the centralized structure and the distributed structure.

• Geomechanics and Engineering
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• v.15 no.2
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• pp.783-791
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• 2018

In consideration of the mesoscopic structure of soil-rock mixtures in which the rock aggregates are wrapped by soil at normal temperatures, a two-layer embedded model of single-inclusion composite material was built to calculate the shear modulus of soil-rock mixtures. At a freezing temperature, an interface ice interlayer was placed between the soil and rock interface in the mesoscopic structure of the soil-rock mixtures. Considering that, a three-layer embedded model of double-inclusion composite materials and a multi-step multiphase micromechanics model were then built to calculate the shear modulus of the frozen soil-rock mixtures. Given the effect of pore structure of soil-rock mixtures at normal temperatures, its shear modulus was also calculated by using of the three-layer embedded model. Experimental comparison showed that compared with the two-layer embedded model, the effect predicted by the three-layer embedded model of the soil-rock mixtures was better. The shear modulus of the soil-rock mixtures gradually increased with the increase in rock regardless of temperature, and the increment rate of the shear modulus increased rapidly particularly when the rock content ranged from 50% to 70%. The shear modulus of the frozen soil-rock mixtures was nearly 3.7 times higher than that of the soil-rock mixtures at a normal temperature.

• Journal of Technology Innovation
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• v.14 no.2
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• pp.143-165
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• 2006

By analyzing five in-depth case studies of suppliers in newly emerging Vietnam's motorcycle industry, this paper explores the differences in patterns and processes of capability improvement of suppliers who participate in different kinds of supplier-assembler network with different structures. The paper finds the correlation between the kinds of suppliers' capabilities improved and the structure of networks they participate in. While suppliers in arm-length networks can improve more upstream capabilities (structure design, process desist), suppliers in embedded networks can improve more downstream capabilities (process design, process setup, process maintenance and delivery control). Two capability improvement patterns of firms in newly emerging economy are indicated. The first pattern is asymmetrical improvement, either upstream or downstream capabilities, by participating in either arm-length or embedded networks. This pattern obstructs the suppliers to meet the requirements of new buyers rho come from different kinds of network. The second pattern is symmetrical improvement by joining both arm-length and embedded networks.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.