scholarly journals NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY

2017 ◽  
Vol 23 (1) ◽  
pp. 213-233 ◽  
Author(s):  
Yao Yevenyo Ziggah ◽  
Hu Youjian ◽  
Prosper Basommi Laari ◽  
Zhenyang Hui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coordinate Transformation ◽  
Position Error ◽  
Transformation Model ◽  
Network Technology ◽  
Horizontal Position ◽  
Translation Model ◽  
Artificial Neural ◽  
Neural Network Technology

Abstract: Geocentric translation model (GTM) in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP) and new parameters determined using the arithmetic mean (AM) were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM) attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.

scholarly journals Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

2019 ◽  
Vol 23 (1) ◽  
pp. 67-77 ◽  
Author(s):  
Yao Yevenyo Ziggah ◽  
Hu Youjian ◽  
Alfonso Rodrigo Tierra ◽  
Prosper Basommi Laari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coordinate Transformation ◽  
Cross Validation ◽  
Data Partitioning ◽  
Transformation Model ◽  
Data Partition ◽  
Data Set ◽  
Artificial Neural ◽  
Fold Cross Validation

The popularity of Artificial Neural Network (ANN) methodology has been growing in a wide variety of areas in geodesy and geospatial sciences. Its ability to perform coordinate transformation between different datums has been well documented in literature. In the application of the ANN methods for the coordinate transformation, only the train-test (hold-out cross-validation) approach has usually been used to evaluate their performance. Here, the data set is divided into two disjoint subsets thus, training (model building) and testing (model validation) respectively. However, one major drawback in the hold-out cross-validation procedure is inappropriate data partitioning. Improper split of the data could lead to a high variance and bias in the results generated. Besides, in a sparse dataset situation, the hold-out cross-validation is not suitable. For these reasons, the K-fold cross-validation approach has been recommended. Consequently, this study, for the first time, explored the potential of using K-fold cross-validation method in the performance assessment of radial basis function neural network and Bursa-Wolf model under data-insufficient situation in Ghana geodetic reference network. The statistical analysis of the results revealed that incorrect data partition could lead to a false reportage on the predictive performance of the transformation model. The findings revealed that the RBFNN and Bursa-Wolf model produced a transformation accuracy of 0.229 m and 0.469 m, respectively. It was also realised that a maximum horizontal error of 0.881 m and 2.131 m was given by the RBFNN and Bursa-Wolf. The obtained results per the cadastral surveying and plan production requirement set by the Ghana Survey and Mapping Division are applicable. This study will contribute to the usage of K-fold cross-validation approach in developing countries having the same sparse dataset situation like Ghana as well as in the geodetic sciences where ANN users seldom apply the statistical resampling technique.

scholarly journals Problem-Solving Skills Among Precollege Students in Clinical Immunology and Microbiology: Classifying Strategies with a Rubric and Artificial Neural Network Technology

2001 ◽  
Vol 2 (1) ◽  
pp. 25-33
Author(s):  
SUSAN KANOWITH-KLEIN ◽  
MEL STAVE ◽  
RON STEVENS ◽  
ADRIAN M. CASILLAS
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High School Students ◽  
Problem Solving ◽  
Network Technology ◽  
Problem Solving Skills ◽  
Problem Solving Strategies ◽  
Artificial Neural ◽  
Neural Network Technology

Educators emphasize the importance of problem solving that enables students to apply current knowledge and understanding in new ways to previously unencountered situations. Yet few methods are available to visualize and then assess such skills in a rapid and efficient way. Using a software system that can generate a picture (i.e., map) of students’ strategies in solving problems, we investigated methods to classify problem-solving strategies of high school students who were studying infectious and noninfectious diseases. Using maps that indicated items students accessed to solve a software simulation as well as the sequence in which items were accessed, we developed a rubric to score the quality of the student performances and also applied artificial neural network technology to cluster student performances into groups of related strategies. Furthermore, we established that a relationship existed between the rubric and neural network results, suggesting that the quality of a problem-solving strategy could be predicted from the cluster of performances in which it was assigned by the network. Using artificial neural networks to assess students’ problem-solving strategies has the potential to permit the investigation of the problem-solving performances of hundreds of students at a time and provide teachers with a valuable intervention tool capable of identifying content areas in which students have specific misunderstandings, gaps in learning, or misconceptions.

scholarly journals Prediction of dust storms in construction projects using intelligent artificial neural network technology

2019 ◽  
Vol 7 (4) ◽  
pp. 1659
Author(s):  
Salah Kh. Zamim ◽  
Noora Saad Faraj ◽  
Ibrahim A. Aidan ◽  
Faiq M. S. Al-Zwainy ◽  
Mohammed A. AbdulQader ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Construction Projects ◽  
Dust Storms ◽  
Network Technology ◽  
Artificial Neural ◽  
Neural Network Technology
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