• Geomechanics and Engineering
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• v.19 no.4
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• pp.307-314
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• 2019

Light construction waste (LCW) particles are pieces of light concrete or insulation wall with light quality and certain strength, containing rich isolated and disconnected pores. Mixing LCW particles with soil can be one of the alternative lightweight soils. It can lighten and stabilize the deep-thick soft soil in-situ. In this study, the unconfined compressive strength (UCS) and its mechanism of Construction Waste Stabilized Lightweight Soil (CWSLS) are investigated. According to the prescription design, totally 35 sets of specimens are tested for the index of dry density (DD) and unconfined compressive strength (UCS). The results show that the DD of CWSLS is mainly affected by LCW content, and it decreases obviously with the increase of LCW content, while increases slightly with the increase of cement content. The UCS of CWSLS first increases and then decreases with the increase of LCW content, existing a peak value. The UCS increases linearly with the increase of cement content, while the strength growth rate is dramatically affected by the different LCW contents. The UCS of CWSLS mainly comes from the skeleton impaction of LCW particles and the gelation of soil-cement composite slurry. According to the distribution of LCW particles and soil-cement composite slurry, CWSLS specimens are divided into three structures: "suspend-dense" structure, "framework-dense" structure and "framework-pore" structure.

• Tunnel and Underground Space
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• v.28 no.4
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• pp.343-357
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• 2018

Algorithm which can analyze the slope failure behavior utilizing the comprehensive information of the dense point of joint poles and the joint set orientations, both of which are obtained statistically, and the defect pattern of pole distribution has been developed. This method overcomes the potential incorrectness of the hemispheric projection method utilizing the joint set orientations only and also enhances the reliability of slope failure analysis. To this end a method capable of calculating the joint dispersion index directly from the joint pole distribution, instead of contour map, has been devised. The representative orientations for the slope failure analysis has been determined by considering the number and orientations of cone angle-dependent joint sets as well as the joint dispersion index. By engaging these representative orientations to the hemispheric projection analysis more reliable slope failure examination has been carried out. Sensitivity analysis for the potentially unstable slope of plane failure mode has been performed. Significance of joint strength index and the external seismic loading on the slope stability has been fully analyzed.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.7
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• pp.568-574
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• 2000

We have been optimized tungsten(W) plug CMP(chemical mechanical polishing) characteristics using two different kinds of component of slurry and two different kinds of pad which have different hardness. The comparison of oxide film roughness on around W plug after polishing has been carried out. And W plug recess for consumable sets and dishing effect at dense area according to the rate of over-polishing has been investigated. Also the analysis of residue on surface after cleaning have been performed. As a experimental result we have concluded that the consumable set of slurry A and hard pad was good for W plug CMP process. After decreasing the rate of chemical reaction of silica slurry and adding two step buffering we could reduce the expanding of W plug void however we are still recognizing to need a more development for those kinds of CMP consumables.

• Korean Institute of Interior Design Journal
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• v.25 no.5
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• pp.42-54
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• 2016

The purpose of this study is to provide a reference for stage planning by analyzing current state of major theaters' stage space and understanding feedback from directors belonged to theaters. Each room in total 18 theaters' stage facilities was measured and their usage and requirements were analyzed on the spot. In addition, each director provided their experiential knowledge about appropriate stage composition for musical performance. The findings are as follow: under stage machinery is not needed for musical. Tour teams prefer to use their own show-deck for set conversion. On the other hand, over stage flying system needs to be able to deal with dense, fast, and accurate scene change. The size and location of the motor room needs to be carefully considered. The number of set battens is directly linked up with the size of the motor room, which should be located lower than the gridiron. As stage sets get bigger and complicated, the number of works at the gridiron is also increasing. The grating floor has to have enough strength coping with machines lifting heavy sets. Most sound control for musical performance is being done at F.O.H. these days rather that in the sound control room. It should equip enough work area and related infra especially for tour teams. 1st gallery needs to have enough effective width, power infra for lighting fixture, and strong guardrail. Lastly, the whole process of parking-unloading-transporting equipments and sets from loading dock to stage and vice versa needs to be efficient, and this has to be carefully considered from early stage of planning.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.51-60
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• 2004

K-means clustering is an iterative algorithm in which items are moved among sets of clusters until the desired set is reached. K-means clustering has been widely used in many applications, such as market research, pattern analysis or recognition, image processing, etc. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using a center of gravity for grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.

• Journal of Powder Materials
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• v.20 no.6
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• pp.445-452
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• 2013

Yttria-stabilized zirconia (YSZ) coatings are fabricated via suspension plasma spray (SPS) for thermal barrier applications. Three different suspension sets are prepared by using a planetary mill as well as ball mill in order to examine the effect of starting suspension on the phase evolution and the microstructure of SPS prepared coatings. In the case of planetary-milled commercial YSZ powder, a deposited thick coating turns out to have a dense, vertically-cracked microstructure. In addition, a dense YSZ coating with fully developed phase can be obtained via suspension plasma spray with suspension from planetary-milled mixture of $Y_2O_3$ and $ZrO_2$.

• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.115-131
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• 2008

Small mobile devices have become commonplace in our everyday life, from cellular phones to PDAs. Discovering dense regions for the mobile devices is one of the problems of grate practical importance. It can be used in monitoring movement of vehicles, concentration of troops, etc. In this paper, we propose a novel algorithm on continuously clustering a large set of mobile objects. We assume that a mobile object reports its position only if it is too far away from the expected position and thus the location data received may be imprecise. To compute the location of each individual object could be costly especially when the number of objects is large. To reduce the complexity of the computation, we want to first cluster objects that are in proximity into a group and treat the members in a group indistinguishable. Each individual object will be examined only when the inaccuracy causes ambiguity in the final results. We conduct extensive experiments on various data sets and analyze the sensitivity and scalability of our algorithms.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Journal of Korea Multimedia Society
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• v.10 no.3
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• pp.316-324
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• 2007

Maximum Intensity Projection (MIP) identifies patients' anatomical structures from MR or CT data sets. Recently, it becomes possible to generate MIP images with interactive speed by exploiting Graphics Processing Unit (GPU) even in large volume data sets. Generally, volume boundary plane is obliquely crossed with view-aligned texture plane in hardware-texture based volume rendering. Since the ray sampling distance is not increased at volume boundary in volume rendering, the aliasing problem occurs due to data loss. In this paper, we propose an efficient method to overcome this problem by Re-rendering volume boundary planes. Our method improves image quality to make dense distances between samples near volume boundary which is a high frequency area. Since it is only 6 clipping planes are additionally needed for Re-rendering, high quality rendering can be performed without sacrificing computational efficiency. Furthermore, our method couldbe applied to Minimum Intensity Projection (MinIP) volume rendering.