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Papers by destaw addis
Neurocomputing, 2012
Classifying power quality (PQ) disturbances is one of the most important issues for power quality... more Classifying power quality (PQ) disturbances is one of the most important issues for power quality control. A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed. The original power quality signals are analysed by the S-transform and processed into a complex matrix named the S-matrix. Eighteen types of time-frequency features are extracted from the S-matrix. Then, after comparing the classification abilities of different feature combinations, a selected subset with 2 features is used as the input vector of the PNN. Finally, the PNN is trained and tested with simulated samples. By reducing the number of features in the PNN's input vector, the new classification system reduces the time required for learning and the computational costs associated with the features and the PNN's memory space. The simulation results show that 8 types of PQ disturbance signals with 2 types of complex disturbances were classified precisely and that the new PNN-based approach more accurately classified PQ disturbances compared to back propagation neural network (BPNN) and radial basis function neural network (RBFNN) approaches.
In this work a new Integral transform, namely Elzaki Transform was applied to solve 1-dimensional... more In this work a new Integral transform, namely Elzaki Transform was applied to solve 1-dimensional heat equation to obtained the exact solutions.It is moreeasier, efficient,simple and powerful tool for solving 1-dimensional heat equation. Some examples are solved to show the ability of this method.
Neurocomputing, 2012
Classifying power quality (PQ) disturbances is one of the most important issues for power quality... more Classifying power quality (PQ) disturbances is one of the most important issues for power quality control. A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed. The original power quality signals are analysed by the S-transform and processed into a complex matrix named the S-matrix. Eighteen types of time-frequency features are extracted from the S-matrix. Then, after comparing the classification abilities of different feature combinations, a selected subset with 2 features is used as the input vector of the PNN. Finally, the PNN is trained and tested with simulated samples. By reducing the number of features in the PNN's input vector, the new classification system reduces the time required for learning and the computational costs associated with the features and the PNN's memory space. The simulation results show that 8 types of PQ disturbance signals with 2 types of complex disturbances were classified precisely and that the new PNN-based approach more accurately classified PQ disturbances compared to back propagation neural network (BPNN) and radial basis function neural network (RBFNN) approaches.
International Journal of Multidisciplinary Research and Development, Apr 1, 2018
A three-dimensional object can be measured and represented as a gray scale set of points through ... more A three-dimensional object can be measured and represented as a gray scale set of points through the use of LiDAR sensor with high spatial accuracy. The ability to quickly measure and represent tree canopy profile in 3D is extremely useful in many applicat ions in precision agriculture. In this paper, we developed a software program to characterize a tree canopy profile and to reconstruct 3D image in real time. The developed program can process 2D laser sensor raw data to reconstruct a pseudo-color 3D image in real time. The program was written in C++ and MATLAB programing languages. The software program was tested on specially designed indoor LiDAR-based canopy profile measurement platform. During the test two artificial trees (tree-1 and tree-2) were used as target objects. The reconstructed 3D image and data analysis results showed that the performance of the developed program to reconstruct 3D image in real time is relatively well at low travel speed ranged from 1.0m/s to 2m/s a...
Journal of Sensors, 2017
In real outdoor canopy profile detection, the accuracy of a LIDAR scanner to measure canopy struc... more In real outdoor canopy profile detection, the accuracy of a LIDAR scanner to measure canopy structure is affected by a potentially uneven road condition. The level of error associated with attitude angles from undulations in the ground surface can be reduced by developing appropriate correction algorithm. This paper proposes an offline attitude angle offset correction algorithm based on a 3D affine coordinate transformation. The validity of the correction algorithm is verified by conducting an indoor experiment. The experiment was conducted on an especially designed canopy profile measurement platform. During the experiment, an artificial tree and a tree-shaped carved board were continuously scanned at constant laser scanner travel speed and detection distances under simulated bumpy road conditions. Acquired LIDAR laser scanner raw data was processed offline by exceptionally developed MATLAB program. The obtained results before and after correction method show that the single attitude angle offset correction method is able to correct the distorted data points in tree-shaped carved board profile measurement, with a relative error of 5%, while the compound attitude angle offset correction method is effective to reduce the error associated with compound attitude angle deviation from the ideal scanner pose, with relative error of 7%.
Information Processing in Agriculture
Neurocomputing, 2012
Classifying power quality (PQ) disturbances is one of the most important issues for power quality... more Classifying power quality (PQ) disturbances is one of the most important issues for power quality control. A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed. The original power quality signals are analysed by the S-transform and processed into a complex matrix named the S-matrix. Eighteen types of time-frequency features are extracted from the S-matrix. Then, after comparing the classification abilities of different feature combinations, a selected subset with 2 features is used as the input vector of the PNN. Finally, the PNN is trained and tested with simulated samples. By reducing the number of features in the PNN's input vector, the new classification system reduces the time required for learning and the computational costs associated with the features and the PNN's memory space. The simulation results show that 8 types of PQ disturbance signals with 2 types of complex disturbances were classified precisely and that the new PNN-based approach more accurately classified PQ disturbances compared to back propagation neural network (BPNN) and radial basis function neural network (RBFNN) approaches.
In this work a new Integral transform, namely Elzaki Transform was applied to solve 1-dimensional... more In this work a new Integral transform, namely Elzaki Transform was applied to solve 1-dimensional heat equation to obtained the exact solutions.It is moreeasier, efficient,simple and powerful tool for solving 1-dimensional heat equation. Some examples are solved to show the ability of this method.
Neurocomputing, 2012
Classifying power quality (PQ) disturbances is one of the most important issues for power quality... more Classifying power quality (PQ) disturbances is one of the most important issues for power quality control. A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed. The original power quality signals are analysed by the S-transform and processed into a complex matrix named the S-matrix. Eighteen types of time-frequency features are extracted from the S-matrix. Then, after comparing the classification abilities of different feature combinations, a selected subset with 2 features is used as the input vector of the PNN. Finally, the PNN is trained and tested with simulated samples. By reducing the number of features in the PNN's input vector, the new classification system reduces the time required for learning and the computational costs associated with the features and the PNN's memory space. The simulation results show that 8 types of PQ disturbance signals with 2 types of complex disturbances were classified precisely and that the new PNN-based approach more accurately classified PQ disturbances compared to back propagation neural network (BPNN) and radial basis function neural network (RBFNN) approaches.
International Journal of Multidisciplinary Research and Development, Apr 1, 2018
A three-dimensional object can be measured and represented as a gray scale set of points through ... more A three-dimensional object can be measured and represented as a gray scale set of points through the use of LiDAR sensor with high spatial accuracy. The ability to quickly measure and represent tree canopy profile in 3D is extremely useful in many applicat ions in precision agriculture. In this paper, we developed a software program to characterize a tree canopy profile and to reconstruct 3D image in real time. The developed program can process 2D laser sensor raw data to reconstruct a pseudo-color 3D image in real time. The program was written in C++ and MATLAB programing languages. The software program was tested on specially designed indoor LiDAR-based canopy profile measurement platform. During the test two artificial trees (tree-1 and tree-2) were used as target objects. The reconstructed 3D image and data analysis results showed that the performance of the developed program to reconstruct 3D image in real time is relatively well at low travel speed ranged from 1.0m/s to 2m/s a...
Journal of Sensors, 2017
In real outdoor canopy profile detection, the accuracy of a LIDAR scanner to measure canopy struc... more In real outdoor canopy profile detection, the accuracy of a LIDAR scanner to measure canopy structure is affected by a potentially uneven road condition. The level of error associated with attitude angles from undulations in the ground surface can be reduced by developing appropriate correction algorithm. This paper proposes an offline attitude angle offset correction algorithm based on a 3D affine coordinate transformation. The validity of the correction algorithm is verified by conducting an indoor experiment. The experiment was conducted on an especially designed canopy profile measurement platform. During the experiment, an artificial tree and a tree-shaped carved board were continuously scanned at constant laser scanner travel speed and detection distances under simulated bumpy road conditions. Acquired LIDAR laser scanner raw data was processed offline by exceptionally developed MATLAB program. The obtained results before and after correction method show that the single attitude angle offset correction method is able to correct the distorted data points in tree-shaped carved board profile measurement, with a relative error of 5%, while the compound attitude angle offset correction method is effective to reduce the error associated with compound attitude angle deviation from the ideal scanner pose, with relative error of 7%.
Information Processing in Agriculture