scholarly journals Where, What, When, and Why Is Bottom Mapping Needed? An On-Line Application to Set Priorities Using Expert Opinion

Geosciences ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 379 ◽  
Author(s):  
Matthew Kendall ◽  
Ken Buja ◽  
Charles Menza ◽  
Tim Battista
Keyword(s):  
Cluster Analysis ◽  
Expert Opinion ◽  
Lake Michigan ◽  
Region Of Interest ◽  
Mapping Data ◽  
Seafloor Mapping ◽  
Spatial Framework ◽  
On Line ◽  
Common Goals ◽  
Multivariate Cluster

Globally, there is a lack of resources to survey the vast seafloor areas in need of basic mapping data. Consequently, smaller areas must be prioritized to address the most urgent needs. We developed a systematic, quantitative approach and on-line application to gather mapping suggestions from diverse stakeholders. Participants are each provided with 100 virtual coins to place throughout a region of interest to convey their mapping priorities. Inputs are standardized into a spatial framework using a grid and pull-down menus. These enabled participants to convey the types of mapping products that they need, the rationale used to justify their needs, and the locations that they prioritize for mapping. This system was implemented in a proposed National Marine Sanctuary encompassing 2784 km2 of Lake Michigan, Wisconsin. We demonstrate key analyses of the outputs, including coin counts, cell ranking, and multivariate cluster analysis for isolating high priority topics and locations. These techniques partition the priorities among the disciplines of the respondents, their selected justifications, and types of desired map products. The results enable respondents to identify potential collaborations to achieve common goals and more effectively invest limited mapping funds. The approach can be scaled to accommodate larger geographic areas and numbers of participants and is not limited to seafloor mapping.

scholarly journals Assessing the Pollution Level in the Kuban River Basin by Multivariate Cluster Analysis

2020 ◽  
Vol 17 (4) ◽  
pp. 73-80 ◽  
Author(s):  
Vera Snezhko ◽  
Dmitrii Benin ◽  
Artem Lukyanets ◽  
Larisa Kondratenko
Keyword(s):  
Cluster Analysis ◽  
River Basin ◽  
Ecological Status ◽  
Chemical System ◽  
Pollution Level ◽  
Multivariate Cluster Analysis ◽  
Water Chemical Composition ◽  
Aluminium Copper ◽  
Kuban River ◽  
Multivariate Cluster

Considering features of hydrological conditions for hydro-chemical system, this paper analyses the performance of the hydro-ecological status of the Kuban river basin.. The results of the study on water chemical composition depending on the distance from the source are presented. By comparing the results with the reference values of water quality, increased aluminium, zinc, and copper content was established. Respective dendrograms of hydro-ecological studies obtained according to performed analysis for the Kuban River and its tributaries are presented. The relevance of the findings received is p<0.0005 and the correlation coefficient corresponds to 0.935...1. The results of multivariate cluster analysis showed that the Kuban basin has an increased content of particular heavy metals such as aluminium, copper, and zinc.

The Application of Mathematical Statistics in Urban Geography Research

2014 ◽  
Vol 919-921 ◽  
pp. 1630-1633
Author(s):  
Xiu Feng Ma
Keyword(s):  
Cluster Analysis ◽  
Regression Analysis ◽  
20Th Century ◽  
Statistical Methods ◽  
Urban Geography ◽  
Mathematical Statistics ◽  
Multivariate Cluster Analysis ◽  
Important Position ◽  
Multivariate Cluster

In the middle of the 20th century, geography has experienced a number "revolution". statistical occupies more and more important position in the urban geography research. This paper analyzes the application of statistics in the development of the urban geography. And with the method of case study, illustrates the multivariate cluster analysis, regression analysis and other statistical methods in the study of urban geography specific applications

Cluster and canonical variate analyses in multilocation trials of linseed

2003 ◽  
Vol 140 (3) ◽  
pp. 297-304 ◽  
Author(s):  
W. ADUGNA ◽  
M. T. LABUSCHAGNE
Keyword(s):  
Cluster Analysis ◽  
Block Design ◽  
Canonical Variate Analysis ◽  
Canonical Variate ◽  
Good Seed ◽  
Randomized Block Design ◽  
Variate Analysis ◽  
Testing Environments ◽  
Multivariate Cluster ◽  
Multilocation Trials

Multivariate cluster and canonical variate analyses were undertaken for 10 genotypes of linseed (Linum usitatissimum L.) that were tested in a four-times replicated randomized block design across 18 environments (six localities by 3 years) of Ethiopia. The main aims of this study were to determine the similarities and differences of the genotypes and their testing environments, and to compare applicability of the two statistical methods. Cluster analysis grouped the genotypes into five classes in accordance with their original sources. The six locations and 18 environments were stratified into four and seven clusters, respectively. Three sites (Bekoji, Kulumsa and Sinana) were separately stratified, while three other ones (Holetta, Asasa and Adet) showed closer similarity. Canonical variate analysis indicated that ‘D33C’ and ‘D24C’ were distinguished from the other genotypes by their high oil contents. ‘N10D’ and ‘Norlin’ had closer values and were thus preferred for their good seed yield and earliness. Days to flowering and maturity, oil contents and lodging per cent played major roles in discriminating the genotypes. Comparison of the two methods showed clearer differentiation by cluster analysis than canonical variate analysis. Canonical variate analysis also contributed information on how each variable discriminated the genotypes and their test environments. Thus, both methods complement each other in providing useful information for more efficient variety development programmes.

scholarly journals Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 713 ◽  
Author(s):  
Jiaming Han ◽  
Zhong Yang ◽  
Hao Xu ◽  
Guoxiong Hu ◽  
Chi Zhang ◽  
...  
Keyword(s):  
High Voltage ◽  
Transmission Lines ◽  
Energy Supply ◽  
Region Of Interest ◽  
Aerial Images ◽  
Line Detection ◽  
Complex Background ◽  
Average Running Time ◽  
Cascaded Model ◽  
On Line

Insulator missing fault is a serious accident of high-voltage transmission lines, which can cause abnormal energy supply. Recently, a lot of vision-based methods are proposed for detecting an insulator missing fault in aerial images. However, these methods usually lack efficiency and robustness due to the effect of the complex background interferences in the aerial images. More importantly, most of these methods cannot address the insulator multi-fault detection. This paper proposes an unprecedented cascaded model to detect insulator multi-fault in the aerial images to solve the existing challenges. Firstly, a total of 764 images are adopted to create a novel insulator missing faults dataset ‘IMF-detection’. Secondly, a new network is proposed to locate the insulator string from the complex background. Then, the located region that contains the insulator string is set to be an RoI (region of interest) region. Finally, the YOLO-v3 tiny network is trained and then used to detect the insulator missing faults in the RoI region. Experimental results and analysis validate that the proposed method is more efficient and robust than some previous works. Most importantly, the average running time of the proposed method is about 30ms, which demonstrates that it has the potential to be adopted for the on-line detection of insulator missing faults.

On-Line Task Decomposition for Collaborative Surveillance of Marine Environment by a Team of Unmanned Surface Vehicles

2017 ◽  
Author(s):  
Shaurya Shriyam ◽  
Brual C. Shah ◽  
Satyandra K. Gupta
Keyword(s):  
Maximum Velocity ◽  
Region Of Interest ◽  
Surface Currents ◽  
Time Step ◽  
Physical Constraints ◽  
Unmanned Surface Vehicles ◽  
Optimal Region ◽  
On Line ◽  
The Given ◽  
Effective Distribution

In this paper, we introduce an approach for decomposing exploration tasks among multiple Unmanned Surface Vehicles (USVs) in port regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environment on the physical constraints of the USVs. The performance of the USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths as a result of tides, and weather. In our approach, we want the team of USVs to explore certain region of the harbor. The algorithm has to decompose the region of interest into multiple sub-regions by considering the maximum operating velocity of each USV in the given environmental conditions. The algorithm overlays a 2D grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, each USV covers the maximum area that is possible by operating at maximum velocity at each time-step. The objective is to minimize the time taken for the last USV to finish claiming its area exploration. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments with unknown velocity profiles.

On-line determination of ore type using cluster analysis and neural networks

1995 ◽  
Vol 8 (6) ◽  
pp. 637-648 ◽  
Author(s):  
S Laine ◽  
H Lappalainen ◽  
S.-L Jämsä-Jounela
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
On Line
Sign in / Sign up

Export Citation Format

Share Document