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2D Cadastral Coordinate Transformation using extreme learning machine technique

Ziggah Y. Y., Issaka Y., Laari P. B., Hui Z.

Geodesy and Cartography

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2018

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Vol. 67, no. 2

321--343

EN

Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.

2

Application of multidimensional data analysis to chromatography

Satława T., Grabska-Chrząstowska J., Korohoda P.

Image Processing & Communications

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2013

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Vol. 18, no. 2-3

101--108

EN

This work presents analysis of chromatographic signal used to identify substances in samples. First part consists of chromatography overview and description of three classification methods (neural network with backpropagation, probabilistic neural network with Parzen window and support vector machines). Designed algorithm consists of several stages: signal filtering, peak detection and its approximation with sum of two Gaussian functions. The parameters of that two curves are the features vectors describing the peak of the substance. The last step is classification, for which two types of supervised machine learning were compared, based on the whole signal and on features vectors. Both types were tested for different classificators and their parameters. Verification was based on 55 chromatography signals. The best results for both methods of learning were achieved for probabilistic neural networks. The correct classification rate was 82% for the whole signal and 93% for feature vectors.

3

Hierarchical intelligent control of a learning robot using a camera and system of artificial neural networks

Miljković Z.

Journal of Applied Computer Science

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2000

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Vol. 8, nr 2

79-97

EN

This paper presents interaction of mechatronic subsystems in order to achieve an adaptive behavior of learning robots. A learning robot is able to deal flexibly with changes in its environment and to execute intelligent tasks. The control strategy for the learning robot is established by using a recognition system and machine learning. The recognition system that utilizes artificial intelligence techniques is used in order to test and verify the hypothesis that learning robots can achieve sensor-actuator co-ordination and team successfully. The hypothesis is tested and verified on the basis of visual information obtained from the camera and an artificial neural network system. For this purpose the experimental set of software packages Make it, ART-1 Simulator and BPNET, as well as the physical model of anthropomorphic mobile robot Don Kihot with four degrees of freedom, are realized.