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Analysis of Rice Field Drought Area Using Unmanned Aerial Vehicle (UAV) and Geographic Information System (GIS) Methods (무인항공기와 GIS를 이용한 논 가뭄 발생지역 분석)

  • Park, Jin Ki;Park, Jong Hwa
    • Journal of The Korean Society of Agricultural Engineers
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    • v.59 no.3
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    • pp.21-28
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    • 2017
  • The main goal of this paper is to assess application of UAV (Unmanned Aerial Vehicle) remote sensing and GIS based images in detection and measuring of rice field drought area in South Korea. Drought is recurring feature of the climatic events, which often hit South Korea, bringing significant water shortages, local economic losses and adverse social consequences. This paper describes the assesment of the near-realtime drought damage monitoring and reporting system for the agricultural drought region. The system is being developed using drought-related vegetation characteristics, which are derived from UAV remote sensing data. The study area is $3.07km^2$ of Wonbuk-myeon, Taean-gun, Chungnam in South Korea. UAV images were acquired three times from July 4 to October 29, 2015. Three images of the same test site have been analysed by object-based image classification technique. Drought damaged paddy rices reached $754,362m^2$, which is 47.1 %. The NongHyeop Agricultural Damage Insurance accepted agricultural land of 4.6 % ($34,932m^2$). For paddy rices by UAV investigation, the drought monitoring and crop productivity was effective in improving drought assessment method.

Thruster Modeling for Underwater Vehicle with Ambient Flow Velocity and its Incoming Angle (외부 유체의 영향을 고려한 무인잠수정의 추진기 모델)

  • Kim, Jin-Hyun;Chung, Wan-Kyun
    • The Journal of Korea Robotics Society
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    • v.2 no.2
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    • pp.109-118
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    • 2007
  • The thruster is the crucial factor of an underwater vehicle system, because it is the lowest layer in the control loop of the system. In this paper, we propose an accurate and practical thrust modeling for underwater vehicles which considers the effects of ambient flow velocity and angle. In this model, the axial flow velocity of the thruster, which is non-measurable, is represented by ambient flow velocity and propeller shaft velocity. Hence, contrary to previous models, the proposed model is practical since it uses only measurable states. Next, the whole thrust map is divided into three states according to the state of ambient flow and propeller shaft velocity, and one of the borders of the states is defined as Critical Advance Ratio (CAR). This classification explains the physical phenomenon of conventional experimental thrust maps. In addition, the effect of the incoming angle of ambient flow is analyzed, and Critical Incoming Angle (CIA) is also defined to describe the thrust force states. The proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within 2N force error. The comparison results show that the new model's matching performance is significantly better than conventional models'.

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Challenges and Issues of Resource Allocation Techniques in Cloud Computing

  • Abid, Adnan;Manzoor, Muhammad Faraz;Farooq, Muhammad Shoaib;Farooq, Uzma;Hussain, Muzammil
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.7
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    • pp.2815-2839
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    • 2020
  • In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.). This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power. Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing. More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives. Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.

Comparison of Four Different Ordination Methods for Patterning Water Quality of Agricultural Reservoirs

  • Bae, Mi-Jung;Kwon, Yong-Su;Hwang, Soon-Jin;Park, Young-Seuk
    • Korean Journal of Ecology and Environment
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    • v.41 no.spc
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    • pp.1-10
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    • 2008
  • We patterned water quality of agricultural reservoirs according to the differences of six physico-chemical environmental factors (TN, TP, DO, BOD, COD, and SS) using four different ordination methods: Principal Components Analysis (PCA), Detrended Correspondence Analysis (DCA), Nonmetric Multidimensional Scaling (NMS), and Isometric Feature Mapping (Isomap). The data set was obtained from the water quality monitoring networks operated by the Ministry of Agriculture and Forestry and the Ministry of Environments. Chlorophyll-${\alpha}$ displayed the highest correlation with COD, followed by TP, BOD, SS, and TN (p<0.01), while negatively correlated with altitude and bank height of the reservoirs (p<0.01). Although four different ordination methods similarly patterned the reservoirs according to the gradient of nutrient concentration, PCA and NMS appeared to be the most efficient methods to pattern water quality of reservoirs based on the explanation power. Considering variable scores in the ordination map, the concentration of nutrients was positively correlated with Chl-${\alpha}$, while negatively correlated with altitude and bank height. These ordination methods may help to pattern agricultural reservoirs according to their water quality characteristics.

Development of Empirical Space Weather Models based on Solar Information

  • Moon, Yong-Jae;Kim, Rok-Soon;Park, Jin-Hye;Jin, Kang
    • The Bulletin of The Korean Astronomical Society
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    • v.36 no.2
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    • pp.90.1-90.1
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    • 2011
  • We are developing empirical space weather (geomagnetic storms, solar proton events, and solar flares) forecast models based on solar information. These models have been set up with the concept of probabilistic forecast using historical events. Major findings can be summarized as follows. First, we present a concept of storm probability map depending on CME parameters (speed and location). Second, we suggested a new geoeffective CME parameter, earthward direction parameter, directly observable from coronagraph observations, and demonstrated its importance in terms of the forecast of geomagnetic storms. Third, the importance of solar magnetic field orientation for storm occurrence was examined. Fourth, the relationship among coronal hole-CIR-storm relationship has been investigated, Fifth, the CIR forecast based on coronal hole information is possible but the storm forecast is challenging. Sixth, a new solar proton event (flux, strength, and rise time) forecast method depending on flare parameters (flare strength, duration, and longitude) as well as CME parameter (speed, angular width, and longitude) has been suggested. Seventh, we are examining the rates and probability of solar flares depending on sunspot McIntosh classification and its area change (as a proxy of flux change). Our results show that flux emergence greatly enhances the flare probability, about two times for flare productive sunspot regions.

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Data Mining-Aided Automatic Landslide Detection Using Airborne Laser Scanning Data in Densely Forested Tropical Areas

  • Mezaal, Mustafa Ridha;Pradhan, Biswajeet
    • Korean Journal of Remote Sensing
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    • v.34 no.1
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    • pp.45-74
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    • 2018
  • Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.

Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression

  • Zhang, Wengang;Goh, Anthony T.C.
    • Geomechanics and Engineering
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    • v.10 no.3
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    • pp.269-284
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    • 2016
  • Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

Assessment and Classification of Meteorological Drought Severity in North Korea (북한의 지역별 기상학적 가뭄의 평가와 유형분류)

  • Yoo, Seung-Hwan;Nam, Won-Ho;Jang, Min-Won;Choi, Jin-Yong
    • Journal of The Korean Society of Agricultural Engineers
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    • v.50 no.4
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    • pp.3-15
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    • 2008
  • North Korea is one of the most vulnerable countries of the world for drought but still it is difficult to find scientific researches for understanding of the drought characteristics. This study analyzed the temporal and spatial distribution of meterological drought severity and classified the drought development types in North Korea. All eleven drought indices were tested such as seasonal rainfall, PDS, SPI and so on, and then drew the drought risk map by each indicator using frequency analysis and GIS(Geographic Information Systems) for twenty one meteorological stations. In addition meteorological drought characteristics in North Korea was classified to six patterns on Si/Gun administrative units using cluster analysis on the drought indicators. The cluster III has the strongly drought-resistant area due to sufficient rainfall and the cluster V was considered as the most drought-vulnerable area, Pungsan and Sinpo, because of the severest drought condition for eight drought indicators. The results of this study are expected to be provided for the basic understanding of regionalized drought severity and characteristics confronting the risk of drought from climate variations in North Korea.

An Analysis of Human Factor and Error for Human Error of the Semiconductor Industry (반도체 산업에서의 인적오류에 대한 인적요인과 과오에 대한 분석)

  • Yun, Yong-Gu;Park, Beom
    • Proceedings of the Safety Management and Science Conference
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    • 2007.04a
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    • pp.113-123
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    • 2007
  • Through so that accident of semiconductor industry deduces unsafe factor of the person center on unsafe behaviour that incident history and questionnaire and I made starting point that extract very important factor. It served as a momentum that make up base that analyzes factors that happen based on factor that extract factor cause classification for the first factor, the second factor and the third factor and presents model of human error. Factor for whole defines factor component for human factor and to cause analysis 1 stage in human factor and step that wish to do access of problem and it do analysis cause of data of 1 step. Also, see significant difference that analyzes interrelation between leading persons about human mistake in semiconductor industry and connect interrelation of mistake by this. Continuously, dictionary road map to human error theoretical background to basis traditional accidental cause model and modern accident cause model and leading persons. I wish to present model and new model in semiconductor industry by backbone that leading persons of existing scholars who present model of existent human error deduce relation. Finally, I wish to deduce backbone of model of pre-suppression about accident leading person of the person center.

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A Study on the Pedestrian Detection on the Road Using Machine Vision (머신비전을 이용한 도로상의 보행자 검출에 관한 연구)

  • Lee, Byung-Ryong;Truong, Quoc Bao;Kim, Hyoung-Seok;Bae, Yong-Hwan
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.5
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    • pp.490-498
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    • 2011
  • In this paper, we present a two-stage vision-based approach to detect multi views of pedestrian in road scene images. The first stage is HG (Hypothesis Generation), in which potential pedestrian are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map, and different colors between road background and pedestrian's clothes to determine the leg position of pedestrian, then a novel symmetry peaks processing is performed to define how many pedestrians is covered in one potential candidate region. Finally, the real candidate region where pedestrian exists will be constructed. The second stage is HV (Hypothesis Verification). In this stage, all hypotheses are verified by Support Vector Machine for classification, which is robust for multi views of pedestrian detection and recognition problems.