• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2241-2242
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• 2006

As developing the information society, Lighting Emitted diode(LED) which is light source for illumination of next generation is attracted public attention. LED have many problem as narrow light view angle, high price, drift phenomenon of color coordinate, high heating problem for lower power, lower weight and small size. So, many researches have continued in a illumination as LED module type. in this problem, heating problem is very important and difficult and that is caused in decreasing phenomenon of brightness and drift phenomenon of color coordinate. so the problem of heating is urgent question for illumination of LED. In this paper, structural design of PCB changed as two type for solving the heating problem. also the properties of heating is analysed and optical properties is measured with heating image camera and spectrometer according to change in this design.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1275-1276
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• 2006

As developing the information society, Lighting Emitted diode(LED) which is light source for illumination of next generation is attracted public attention. LED have many problem as narrow light view angle, high price, drift phenomenon of color coordinate, high heating problem for lower power, lower weight and small size. So, many researches have continued in a illumination as LED module type. in this problem, heating problem is very important and difficult and that is caused in decreasing phenomenon of brightness and drift phenomenon of color coordinate. so the problem of heating is urgent question for illumination of LED. In this paper, structural design of PCB changed as two type for solving the heating problem. also the properties of heating is analysed and optical properties is measured with heating image camera and spectrometer according to change in this design.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.504-507
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• 2011

3D graphic softwares have brought design spaces beyond the limitations of Euclidean space. Moreover, as computational geometry has been considered together with algorithms, generative algorithms are being evolved. Recently 3D graphic softwares with the embedded generative algorithms allow designers to design free form curves and surfaces in a systematic way. While architectural design has been greatly affected by the advancement of 3D graphic technology, such attention has not given in the realm of structural design. Grasshopper is a platform in Rhino to deal with these Generative Algorithms and Associative modelling techniques. This study has tried to develop a module for preliminary structural configuration using Rhino with Grasshopper. To verify the proposed concept in this study, a module for designing a basic type of suspension structure is introduced.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.138-141
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• 2006

The application of photovoltaics into building as integrated building components has been paid more attention worldwide. Photovoltaics or solar electric modules are solid state devices, directly converting solar radiation into electricity; the process does not require fuel and any moving parts, and produce no pollutants. And the prefab building method is very effective because the pre-manufactured building components is simply assembled to making up buildings in the construction fields especially the sandwich panel. So, the purpose of this research is to integrated prefab building materials depending on the cooling type of PV modules. It is concluded that the prediction of BIPV system's performance should be based on the more accurate PV module temperature. From the basis of these results on the correlation of temperature and irradiation were obtained.

• Journal of the Korean Solar Energy Society
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• v.35 no.3
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• pp.81-91
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• 2015

LED has received intensive research attention due to its long life, high efficacy, fast response and wide colour availability, and has secured extensive application areas. However, LED chips within the modules convert only fraction of electric energy into light, and majority of supplied energy needs to be dissipated as heat, which challenges in the performance and life of the LED modules. IEC 62717 specifies the performance requirements for LED modules together with the test methods and conditions. The present study examined the influence of different design parameters on performance temperature through series of experiments and numerical simulations. The economic means to change the module performance temperature during the measurement of mandatory markings were suggested based on predicted cooling performances.

• Journal of Integrative Natural Science
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• v.2 no.4
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• pp.237-242
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• 2009

As semitransparent dye-sensitized solar cells (DSSCs) have advanced to large-scale applications from lab-level research, the large-scale performance has attracted much attention. Modules of DSSCs have been investigated to optimize the efficiency as a $TiO_2$ systhesis temperature and a surface treatment of $TiCl_4$ aqueous solution. Essentially, these semitransparent modules have an extended structure with lab-scale works with the exception of the dimensions and methods for the series connection. The $5cm{\times}6.5cm$ modules have shown an efficiency of about 6% without a scattering layer. While the fill factors of modules depend on the width of each $TiO_2$ unit cell, they are much less dependent on the lengths of the unit cells.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.60-79
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• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• Genomics & Informatics
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• v.13 no.4
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• pp.132-136
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• 2015

Studies of cancer heterogeneity have received considerable attention recently, because the presence or absence of resistant sub-clones may determine whether or not certain therapeutic treatments are effective. Previously, we have reported G64, a co-regulated gene module composed of 64 different genes, can differentiate tumor intra- or inter-subpopulations in lung adenocarcinomas (LADCs). Here, we investigated whether the G64 module genes were also expressed distinctively in different subpopulations of other cancers. RNA sequencing-based transcriptome data derived from 22 cancers, except LADC, were downloaded from The Cancer Genome Atlas (TCGA). Interestingly, the 22 cancers also expressed the G64 genes in a correlated manner, as observed previously in an LADC study. Considering that gene expression levels were continuous among different tumor samples, tumor subpopulations were investigated using extreme expressional ranges of G64-i.e., tumor subpopulation with the lowest 15% of G64 expression, tumor subpopulation with the highest 15% of G64 expression, and tumor subpopulation with intermediate expression. In each of the 22 cancers, we examined whether patient survival was different among the three different subgroups and found that G64 could differentiate tumor subpopulations in six other cancers, including sarcoma, kidney, brain, liver, and esophageal cancers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.139-144
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• 2023

Recently, many manufacturing companies are paying more attention to energy efficiency due to increased energy costs. Energy-efficient scheduling of production systems is a good method for energy efficiency improvement and cost reduction. In this research, we assumed the tire production problem and aim to construct a production scheduling considering specific shape module types, ordered amount for each tire, number of production modules, and the production time for each type. To facilitate effective production scheduling, we considered the types and number of shape modules currently available, and tire types that can be selected to be produced in the next stage were used as additional inputs, In addition to that, additional production was permitted to reduce the halt of production processing. Thus, an average production module utilization rate of about 62 percent was obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1545-1559
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• 2023

In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.