Emmanuel Karlo Nyarko

Solving the parameter identification problem of mathematical models using genetic algorithms

A method for solving the parameter identification problem for ordinary second order differential equations using genetic algorithms is given. The method is tested on two numerical examples.

A modification of the DIRECT method for Lipschitz global optimization for a symmetric function

In this paper, we consider a global optimization problem for a symmetric Lipschitz continuous function. An efficient modification of the well-known DIRECT (DIviding RECTangles) method called SymDIRECT is proposed for solving this problem. The method is illustrated and tested on several standard test functions. The application of this method to solving complex center-based clustering problems for the data having only one feature is particularly presented.

A neural network based modelling and sensitivity analysis of damage ratio coefficient

The level of structural damage after an earthquake can often be expressed using the damage ratio (DR) coefficient. This coefficient can be calculated using different formulas. A previously valorised new original formula for damage ratio derived for regular structures is implemented. This formula uses the structure response parameters of a single degree of freedom (SDOF) model. The structure response parameters of the SDOF model are obtained by analyzing a large number of non-linear numeric structure responses using earthquakes of different intensities as load input. In this paper, a multilayer perceptron (MLP) neural network is used to model the relationship between the structure parameters (natural period, elastic base shear capacity, post-elastic stiffness and damping) of an SDOF model and the damage ratio (DR) coefficient. The influence of the individual structure parameters on the damage level of a structure is then determined by performing a sensitivity analysis procedure on the trained MLP neural network.

Place recognition based on matching of planar surfaces and line segments

This paper considers the potential of using three-dimensional (3D) planar surfaces and line segments detected in depth images for place recognition. A place recognition method is presented that is based on matching sets of surface and line features extracted from depth images provided by a 3D camera to features of the same type contained in a previously created environment model. The considered environment model consists of a set of local models representing particular locations in the modeled environment. Each local model consists of planar surface segments and line segments representing the edges of objects in the environment. The presented method is designed for indoor and urban environments. A computationally efficient pose hypothesis generation approach is proposed that ranks the features according to their potential contribution to the pose information, thereby reducing the time needed for obtaining accurate pose estimation. Furthermore, a robust probabilistic method for selecting the best pose hypothesis is proposed that allows matching of partially overlapping point clouds with gross outliers. The proposed approach is experimentally tested on a benchmark dataset containing depth images acquired in the indoor environment with changes in lighting conditions and the presence of moving objects. A comparison of the proposed method to FAB-MAP and DLoopDetector is reported.

Data Preprocessing in Data Based Process Modeling

Abstract Abstract Important process variables which give information about the final product quality cannot often be measured by a sensor. The alternative procedure is estimation of these difficult-to-measure process variables for which it is necessary to have an appropriate process model. Process model building is based on plant data, taken from the process database. Since the quality of the built model depends heavily on the modeling data informativity, a preparatory part of modeling, in which analysis and preprocessing of available measured data are performed, is a very important step in such process modeling. The analysis and preprocessing of plant data obtained from an oil distillation process are showed in the paper. The results show that, apart from the regression method applied, selection of easy-to-measure variables which will be used in the model building and filtering of easy-to-measure variables significantly affects process model prediction capabilities.

Fast Pose Tracking Based on Ranked 3D Planar Patch Correspondences

A fast robot pose tracking algorithm based on planar segments extracted from range images is presented. A range image obtained from a 3D sensor is transformed to a 2.5D triangle mesh from which planar segments are extracted. Using information provided by each planar segment based on its size and orientation, a directed search hypothesis generation algorithm using a tree structure is presented. The presented approach is experimentally evaluated using 3D data obtained by a Kinect sensor mounted on a mobile robot. Results indicate that the proposed method is much faster than similar previously proposed methods.

Wound detection and reconstruction using RGB-D camera

The advent of inexpensive RGB-D sensors pioneered by the original Kinect sensor, has paved the way for a lot of innovations in computer and robot vision applications. In this article, we propose a system which uses the new Kinect 2 sensor in a medical application for the purpose of detection and 3D reconstruction of chronic wounds. Wound detection is based on a per block classification of wound tissue using color histograms and the nearest neighbor approach. The 3D reconstruction is similar to KinectFusion where ICP is used for determining the rigid body transformation, color enhanced TSDF is applied for scene fusion, while the marching cubes algorithm is used for creating a surface mesh. The entire system is implemented in CUDA which enables real-time operation. The end result of the developed system is a precise 3D colored model which can be used for determining a correct therapy and treatment of chronic wounds.

Estimation of difficult-to-measure process variables using neural networks - a comparison of simple MLP and RBF neural network properties

In this paper, two different artificial neural networks are tested and compared with regard to their application in the estimation of difficult-to-measure process variables. Two of the most commonly used neural networks, the MLP (multilayer perceptron) and RBF (radial basis function) neural networks, with simple structure and standard training methods are chosen as examples. Neural network training is based on available data from a database of process variables measured over a long time period. The database in this paper is obtained using a simulation model of a real process. Without going deeper into theoretical background, relative properties of these neural networks are given through the results obtained by testing the trained networks and analysis performed on these results.

Evaluation of color and texture descriptors for matching of planar surfaces in global localization scheme

This paper presents a systematic study about the applicability of color/texture descriptors in a global localization system based on planar surface segments. Two comprehensive experiments regarding matching of planar surface segments and robot pose hypothesis evaluation were conducted. The experiments show that using color/texture descriptors to prune potential surface pairs in the initial correspondence phase and to provide additional information in the hypothesis evaluation phase of a feature-based localization scheme can result in considerable speedup of the localization process and help distinguish between geometrically similar places. An experimental benchmark which enables researchers to evaluate the performance of color and texture descriptors in the context of mobile robot localization based on planar surface segments is presented. Indoor global localization system based on planar segments with visual descriptors.Applicability of 6 color and 3 texture descriptors is systematically analyzed.Performance increase in initial correspondence and pose hypothesis evaluation phases.Evaluation benchmark for visual descriptors in global localization is proposed.

Detection, Reconstruction and Segmentation of Chronic Wounds Using Kinect v2 Sensor

The advent of inexpensive RGB-D sensors pioneered by the original Kinect sensor, has paved the way for a lot of innovations in computer and robot vision applications. In this article, we propose a system which uses the new Kinect v2 sensor in a medical application for the purpose of detection, 3D reconstruction and segmentation of chronic wounds. Wound detection is based on a per block classification of wound tissue using colour histograms and nearest neighbour approach. The 3D reconstruction is similar to KinectFusion where ICP is used for determining rigid body transformation. Colour enhanced TSDF is applied for scene fusion, while the Marching cubes algorithm is used for creating the surface mesh. The wound contour is extracted by a segmentation procedure which is driven by geometrical and visual properties of the surface. Apart from the segmentation procedure, the entire system is implemented in CUDA which enables real-time operation. The end result of the developed system is a precise 3D coloured model of the segmented wound, and its measurable properties including perimeter, area and volume, which can be used for determining a correct therapy and treatment of chronic wounds. All experiments were conducted on a medical wound care model.