• Title/Summary/Keyword: map models

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Cloud Computing Platforms for Big Data Adoption and Analytics

  • Hussain, Mohammad Jabed;Alsadie, Deafallah
    • International Journal of Computer Science & Network Security
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    • v.22 no.2
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    • pp.290-296
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    • 2022
  • Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

Study of the Derive of Core Habitats for Kirengeshoma koreana Nakai Using HSI and MaxEnt (HSI와 MaxEnt를 통한 나도승마 핵심서식지 발굴 연구)

  • Sun-Ryoung Kim;Rae-Ha Jang;Jae-Hwa Tho;Min-Han Kim;Seung-Woon Choi;Young-Jun Yoon
    • Korean Journal of Environment and Ecology
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    • v.37 no.6
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    • pp.450-463
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    • 2023
  • The objective of this study is to derive the core habitat of the Kirengeshoma koreana Nakai utilizing Habitat Suitability Index (HSI) and Maximum Entropy (MaxEnt) models. Expert-based models have been criticized for their subjective criteria, while statistical models face difficulties in on-site validation and integration of expert opinions. To address these limitations, both models were employed, and their outcomes were overlaid to derive the core habitat. Five variables were identified through a comprehensive literature review and spatial analysis based on appearance coordinates. The environmental variables encompass vegetation zone, forest type, crown density, annual precipitation, and effective soil depth. Through surveys involving six experts, importance rankings and SI (Suitability Index) scores were established for each variable, subsequently facilitating the creation of an HSI map. Using the same variables, the MaxEnt model was also executed, resulting in a corresponding map, which was merged to construct the definitive core habitat map. Out of 16 observed locations of K. koreana, 15 were situated within the identified core habitat. Furthermore, an area historically known to host K. koreana but not verified in the present, Mt. Yeongchwi, was found to lack a core habitat. These findings suggest that the developed models exhibit a high degree of accuracy and effectively reflect the current ecological landscape.

Modeling Aided Lead Design of FAK Inhibitors

  • Madhavan, Thirumurthy
    • Journal of Integrative Natural Science
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    • v.4 no.4
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    • pp.266-272
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    • 2011
  • Focal adhesion kinase (FAK) is a potential target for the treatment of primary cancers as well as prevention of tumor metastasis. To understand the structural and chemical features of FAK inhibitors, we report comparative molecular field analysis (CoMFA) for the series of 7H-pyrrolo(2,3-d)pyrimidines. The CoMFA models showed good correlation between the actual and predicted values for training set molecules. Our results indicated the ligand-based alignment has produced better statistical results for CoMFA ($q^2$ = 0.505, $r^2$ = 0.950). Both models were validated using test set compounds, and gave good predictive values of 0.537. The statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The contour map from 3D-QSAR models explains nicely the structure-activity relationships of FAK inhibitors and our results would give proper guidelines to further enhance the activity of novel inhibitors.

FDDI Throughput and Application Analysis of MAP Network Construction in Manufactruing Environment (제조 환경에서 MAP 네트워크 체제의 FDDI 효율과 적용 해석)

  • Kim, Jeong-Ho;Lee, Min-Nam;Lee, Sang-Beom
    • The Transactions of the Korea Information Processing Society
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    • v.2 no.1
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    • pp.95-105
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    • 1995
  • An appendix to the MAP 3.0 specification notes that there are primary advantages to use of fiber optics : noise immunity, ability to run in difficult electrical environments, safety and high data rates. All of these may be quite useful in various manufacturing environments. In this paper, we study on construction schmes for a fiber-based 802.4 MAP system including the use of both bus and star topologies. We suggest passive star network and FDDI network for manufacturing environment. And then, we propose the FDDI protocol including the use a dual ring topology running at 100 Mbps to physical and datalink layer of MAT specification and analysis it's protocol and topology for abilities in manufacturing environments, We evaluate about applications service, time-critical processing and topology of two models in manufacturing environment.

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Real-time Roadmap Generation and Updating Method between Heterogeneous Navigation Systems for Unknown Roads in Cloud Computing Environment (클라우드 환경에서 이기종 네비게이션간 새로운 지도 정보 추출 및 업데이트 방법)

  • Lee, Seung-Gwan;Choi, Jin-Hyuk
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.4
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    • pp.179-187
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    • 2011
  • Multiple roadmap DB providers are already available in these days, and try to reduce unknown roads in their own roadmaps. However, cooperation models or Win-Win approaches between roadmap providers are not considered yet. Thus, In this paper, We proposed a cloud-oriented real-time roadmap generation and update method between heterogeneous navigation systems for unknown roads. With the proposed method, the roadmap DB providers update the own roadmap DB for navigation systems in real time. Also, they can provide the complete roadmap without unknown roads to users instantly. Therefore, the proposed method can reduce the costs of an actual traveling test and the maintenance for the roadmap DB provides. Thus, the cloud-oriented roadmap generation method can more efficiently update the unknown road information.

Research of Communication Coverage and Terrain Masking for Path Planning (경로생성 및 지형차폐를 고려한 통신영역 생성 방법)

  • Woo, Sang Hyo;Kim, Jae Min;Beak, InHye;Kim, Ki Bum
    • Journal of the Korea Institute of Military Science and Technology
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    • v.23 no.4
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    • pp.407-416
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    • 2020
  • Recent complex battle field demands Network Centric Warfare(NCW) ability to control various parts into a cohesive unit. In path planning filed, the NCW ability increases complexity of path planning algorithm, and it has to consider a communication coverage map as well as traditional parameters such as minimum radar exposure and survivability. In this paper, pros and cons of various propagation models are summarized, and we suggest a coverage map generation method using a Longley-Rice propagation model. Previous coverage map based on line of sight has significant discontinuities that limits selection of path planning algorithms such as Dijkstra and fast marching only. If there is method to remove discontinuities in the coverage map, optimization based path planning algorithms such as trajectory optimization and Particle Swarm Optimization(PSO) can also be used. In this paper, the Longley-Rice propagation model is used to calculate continuous RF strengths, and convert the strength data using smoothed leaky BER for the coverage map. In addition, we also suggest other types of rough coverage map generation using a lookup table method with simple inputs such as terrain type and antenna heights only. The implemented communication coverage map can be used various path planning algorithms, especially in the optimization based algorithms.

Comparison of Orthophotos and 3D Models Generated by UAV-Based Oblique Images Taken in Various Angles

  • Lee, Ki Rim;Han, You Kyung;Lee, Won Hee
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.3
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    • pp.117-126
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    • 2018
  • Due to intelligent transport systems, location-based applications, and augmented reality, demand for image maps and 3D (Three-Dimensional) maps is increasing. As a result, data acquisition using UAV (Unmanned Aerial Vehicles) has flourished in recent years. However, even though orthophoto map production and research using UAVs are flourishing, few studies on 3D modeling have been conducted. In this study, orthophoto and 3D modeling research was performed using various angle images acquired by a UAV. For orthophotos, accuracy was evaluated using a GPS (Global Positioning System) survey that employed VRS (Virtual Reference Station) acquired checkpoints. 3D modeling was evaluated by calculating the RMSE (Root Mean Square Error) of the difference between the outline height values of buildings obtained from the GPS survey to the corresponding 3D modeling height values. The orthophotos satisfied the acceptable accuracy of NGII (National Geographic Information Institute) for a 1/500 scale map from all angles. In the case of 3D modeling, models based on images taken at 45 degrees revealed better accuracy of building outlines than models based on images taken at 30, 60, or 75 degrees. To summarize, it was shown that for orthophotos, the accuracy for 1/500 maps was satisfied at all angles; for 3D modeling, images taken at 45 degrees produced the most accurate models.

Comparison of Deep Learning-based CNN Models for Crack Detection (콘크리트 균열 탐지를 위한 딥 러닝 기반 CNN 모델 비교)

  • Seol, Dong-Hyeon;Oh, Ji-Hoon;Kim, Hong-Jin
    • Journal of the Architectural Institute of Korea Structure & Construction
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    • v.36 no.3
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    • pp.113-120
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    • 2020
  • The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

Effects of Ginsenosides $Rg_3$ and $Rh_2$ OH the Proliferation of Prostate Cancer Cells

  • Kim Hyun-Sook;Lee Eun-Hee;Ko Sung-Ryong;Choi Kang-Ju;Park Jong-Hee;Im Dong-Soon
    • Archives of Pharmacal Research
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    • v.27 no.4
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    • pp.429-435
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    • 2004
  • Ginseng has an anti-cancer effect in several cancer models. This study was to characterize active constituents of ginseng and their effects on proliferation of prostate cancer cell lines, LNCaP and PC3. Cell proliferation was measured by $[^3H]$thymidine incorporation, the intracellular calcium concentration by a dual-wavelength spectrophotometer system, effects on mite-gen-activated protein (MAP) kinases by Western blotting, and cell attachment and morphologic changes were observed under a microscope. Among 11 ginsenosides tested, ginsenosides $Rg_3\;and\;Rh_2$ inhibited the proliferation of prostate cancer cells. $EC_{50}s\;of\;Rg_3\;and\;Rh_2$ on PC3 cells were $8.4{\mu}M\;and\;5.5{\mu}M$, respectively, and $14.1{\mu}M\;and\;4.4{\mu}M$ on LNCaP cells, respectively. Both ginsenosides induced cell detachment and modulated three modules of MAP kinases activities differently in LNCaP and PC3 cells. These results suggest that ginsenosides $Rg_3\;and\;Rh_2$-induced cell detachment and inhibition of the proliferation of prostate cancer cells may be associated with modulation of three modules of MAP kinases.

Groundwater pollution risk mapping using modified DRASTIC model in parts of Hail region of Saudi Arabia

  • Ahmed, Izrar;Nazzal, Yousef;Zaidi, Faisal
    • Environmental Engineering Research
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    • v.23 no.1
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    • pp.84-91
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    • 2018
  • The present study deals with the management of groundwater resources of an important agriculture track of north-western part of Saudi Arabia. Due to strategic importance of the area efforts have been made to estimate aquifer proneness to attenuate contamination. This includes determining hydrodynamic behavior of the groundwater system. The important parameters of any vulnerability model are geological formations in the region, depth to water levels, soil, rainfall, topography, vadose zone, the drainage network and hydraulic conductivity, land use, hydrochemical data, water discharge, etc. All these parameters have greater control and helps determining response of groundwater system to a possible contaminant threat. A widely used DRASTIC model helps integrate these data layers to estimate vulnerability indices using GIS environment. DRASTIC parameters were assigned appropriate ratings depending upon existing data range and a constant weight factor. Further, land-use pattern map of study area was integrated with vulnerability map to produce pollution risk map. A comparison of DRASTIC model was done with GOD and AVI vulnerability models. Model validation was done with $NO_3$, $SO_4$ and Cl concentrations. These maps help to assess the zones of potential risk of contamination to the groundwater resources.