scholarly journals KOREAN LUNAR LANDER – CONCEPT STUDY FOR LANDING-SITE SELECTION FOR LUNAR RESOURCE EXPLORATION

2016 ◽  
Vol XLI-B4 ◽  
pp. 417-423 ◽  
Author(s):  
Kyeong Ja Kim ◽  
Christian Wöhler ◽  
Gwang Hyeok Ju ◽  
Seung–Ryeol Lee ◽  
Alexis P. Rodriguez ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Mineral Resources ◽  
Landing Site ◽  
National Space ◽  
Landing Sites ◽  
Fuzzy C Means Clustering ◽  
Resource Exploration ◽  
Local Geology ◽  
Lunar Lander ◽  
Remote Sensing Methods

As part of the national space promotion plan and presidential national agendas South Korea’s institutes and agencies under the auspices of the Ministry of Science, Information and Communication Technology and Future Planning (MSIP) are currently developing a lunar mission package expected to reach Moon in 2020. While the officially approved Korean Pathfinder Lunar Orbiter (KPLO) is aimed at demonstrating technologies and monitoring the lunar environment from orbit, a lander – currently in pre-phase A – is being designed to explore the local geology with a particular focus on the detection and characterization of mineral resources. In addition to scientific and potential resource potentials, the selection of the landing-site will be partly constrained by engineering constraints imposed by payload and spacecraft layout. Given today’s accumulated volume and quality of available data returned from the Moon’s surface and from orbital observations, an identification of landing sites of potential interest and assessment of potential hazards can be more readily accomplished by generating synoptic snapshots through data integration. In order to achieve such a view on potential landing sites, higher level processing and derivation of data are required, which integrates their spatial context, with detailed topographic and geologic characterizations. We are currently assessing the possibility of using fuzzy c-means clustering algorithms as a way to perform (semi-) automated terrain characterizations of interest. This paper provides information and background on the national lunar lander program, reviews existing approaches – including methods and tools – for landing site analysis and hazard assessment, and discusses concepts to detect and investigate elemental abundances from orbit and the surface. This is achieved by making use of manual, semi-automated as well as fully-automated remote-sensing methods to demonstrate the applicability of analyses. By considering given boundary conditions, concrete procedures for determining potential landing sites of the Korean lunar lander could be proposed.

scholarly journals KOREAN LUNAR LANDER – CONCEPT STUDY FOR LANDING-SITE SELECTION FOR LUNAR RESOURCE EXPLORATION

2016 ◽  
Vol XLI-B4 ◽  
pp. 417-423
Author(s):  
Kyeong Ja Kim ◽  
Christian Wöhler ◽  
Gwang Hyeok Ju ◽  
Seung–Ryeol Lee ◽  
Alexis P. Rodriguez ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Mineral Resources ◽  
Landing Site ◽  
National Space ◽  
Landing Sites ◽  
Fuzzy C Means Clustering ◽  
Resource Exploration ◽  
Local Geology ◽  
Lunar Lander ◽  
Remote Sensing Methods

As part of the national space promotion plan and presidential national agendas South Korea’s institutes and agencies under the auspices of the Ministry of Science, Information and Communication Technology and Future Planning (MSIP) are currently developing a lunar mission package expected to reach Moon in 2020. While the officially approved Korean Pathfinder Lunar Orbiter (KPLO) is aimed at demonstrating technologies and monitoring the lunar environment from orbit, a lander – currently in pre-phase A – is being designed to explore the local geology with a particular focus on the detection and characterization of mineral resources. In addition to scientific and potential resource potentials, the selection of the landing-site will be partly constrained by engineering constraints imposed by payload and spacecraft layout. Given today’s accumulated volume and quality of available data returned from the Moon’s surface and from orbital observations, an identification of landing sites of potential interest and assessment of potential hazards can be more readily accomplished by generating synoptic snapshots through data integration. In order to achieve such a view on potential landing sites, higher level processing and derivation of data are required, which integrates their spatial context, with detailed topographic and geologic characterizations. We are currently assessing the possibility of using fuzzy c-means clustering algorithms as a way to perform (semi-) automated terrain characterizations of interest. This paper provides information and background on the national lunar lander program, reviews existing approaches – including methods and tools – for landing site analysis and hazard assessment, and discusses concepts to detect and investigate elemental abundances from orbit and the surface. This is achieved by making use of manual, semi-automated as well as fully-automated remote-sensing methods to demonstrate the applicability of analyses. By considering given boundary conditions, concrete procedures for determining potential landing sites of the Korean lunar lander could be proposed.

Complex Resistivity Method and its Application in Mineral Resource Exploration

2011 ◽  
Vol 396-398 ◽  
pp. 115-118
Author(s):  
Xin Gong Tang ◽  
Xing Bing Xie ◽  
Liang Jun Yan
Keyword(s):  
Oil And Gas ◽  
Supply And Demand ◽  
Mineral Resources ◽  
Underground Water ◽  
Electromagnetic Method ◽  
Complex Resistivity ◽  
West China ◽  
Controlled Source ◽  
Resistivity Method ◽  
Resource Exploration

Complex resistivity (CR) is one of an electromagnetic method which plays an important role in the exploration of oil and gas, underground water as well as solid mineral resources in recent years. Nowadays China is under fast developing and there is still a big gap between the supply and demand of mineral resources. As an effective controlled source electromagnetic method, CR method can be easily used to judge the content of resources, determine the target reservoir and select a favorable drilling area. In this paper, an introduction to CR method and its application in copper mine exploration in west China is present. The result shows that CR is an effective electromagnetic method in the exploration of deep mineral resources.

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

scholarly journals Lunar Lander Landing Site Decision in Low-Fuel Case

2016 ◽  
Vol 54 ◽  
pp. 09003
Author(s):  
Min-Guk Seo ◽  
Seong-Min Hong ◽  
Min-Jea Tahk
Keyword(s):  
Landing Site ◽  
Lunar Lander

Development of a sea-ice workstation for the automated monitoring of sea ice

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 155-160
Author(s):  
Diane Boardman ◽  
David Darwin ◽  
Jolyon Martin ◽  
Neil Mclntyre ◽  
Ken Sullivan
Keyword(s):  
Sea Ice ◽  
Weather Forecasting ◽  
Earth Observation ◽  
Polar Regions ◽  
Regional Survey ◽  
Ship Routing ◽  
Automated Mapping ◽  
National Space ◽  
High Resolution Imagery ◽  
Resource Exploration

AbstractUK-based operations that range from ship routing and resource exploration to weather forecasting and glaciology have direct and growing interests in the oceans of the polar regions. Typically, information describing sea-ice conditions in localised regions is required on short time scales. To explore this market, the UK's Defence Research Agency, as part of a programme of the British National Space Centre, has commissioned the development of a prototype sea-ice workstation by a consortium led by Earth Observation Sciences Ltd.The sea-ice workstation (SIWS) uses data from several current earth observation sensors, thereby combining the advantages of regional survey, all-weather capability, and high-resolution imagery. The workstation has been designed to run with a minimum of operator intervention in order to optimise speed of operation and ensure consistency of results. The geophysical processing chains generate charts of the ice edge, ice type, ice concentration, ice-motion vectors, and sea-surface temperatures.Although taking full advantage of developments made elsewhere, the project has also made significant progress in research into the automated mapping of ice types. Existing ice-motion algorithms have been significantly enhanced as well. Considerable emphasis is being placed on the validation of the results from the system in order to assess their quality, this being one of the major concerns of potential users. The sea-ice workstation was completed in July 1994 and will form the basis for a series of evaluations that are intended to assess the value of the system for mapping and monitoring sea ice.

scholarly journals Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images

2021 ◽  
Vol 11 (7) ◽  
pp. 656
Author(s):  
Si-Wa Chan ◽  
Wei-Hsuan Hu ◽  
Yen-Chieh Ouyang ◽  
Hsien-Chi Su ◽  
Chin-Yao Lin ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Hyperspectral Image ◽  
Clustering Algorithms ◽  
Ground Truth ◽  
Breast Tumors ◽  
Tumor Detection ◽  
Intravoxel Incoherent Motion ◽  
Detection Methods ◽  
Clustering Methods ◽  
Fuzzy C Means Clustering

Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel CEM (K-CEM) and iterative CEM (I-CEM)—were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types.

scholarly journals Leaf Segmentation of Rosette Plants using Rough K-Means in CIELab Color Space

2021 ◽  
Author(s):  
Arunita Das ◽  
Daipayan Ghosal ◽  
Krishna Gopal Dhal
Keyword(s):  
Clustering Algorithms ◽  
Color Space ◽  
Ground Truth ◽  
Accurate Analysis ◽  
Partitional Clustering ◽  
Modern Agriculture ◽  
Cielab Color Space ◽  
Fuzzy C Means Clustering ◽  
Leaf Segmentation ◽  
Cielab Color

Segmentation of Plant Images plays an important role in modern agriculture where it can provide accurate analysis of a plant’s growth and possi-ble anomalies. In this paper, rough set based partitional clustering technique called Rough K-Means has been utilized in CIELab color space for the proper leaf segmentation of rosette plants. The eÿcacy of the proposed technique have been analysed by comparing it with the results of tra-ditional K-Means and Fuzzy C-Means clustering algorithms. The visual and numerical results re-veal that the RKM in CIELab provides the near-est result to the ideal ground truth, hence the most eÿcient one.

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