<span lang="EN-GB">The operational performance of a three phase induction motor is impaired by unbalanced voltage supply due to the generation of negative sequence currents, and negative sequence torque which increase motor losses and also trigger torque pulsations. In this study, data mining approach was applied in developing a predictive model using the historical, simulated operational data of a motor for classifying sample motor data under the appropriate type of voltage supply i.e. balanced (BV) and unbalance voltage supply (UB = 1% to 5%). A dataset containing the values of a three phase induction motor’s performance parameter values was analysed using KNIME (Konstanz Information Miner) analytics platform. Three predictive models; the Naïve Bayes, Decision Tree and the Probabilistic Neural Network (PNN) Predictors were deployed for comparative analysis. The dataset was divided into two; 70% for model training and learning, and 30% for performance evaluation. The three predictors had accuracies of 98.649%, 100% and 98.649% respectively, and this confirms the suitability of data mining methods for predictive evaluation of a three phase induction motor’s performance using machine learning</span>

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The aim of this paper is the presentation and verification of computer vision algorithms in order to measure the geometric parameters of soft robots. The materials from which soft robots are made from possess large deformations. Embedded sensors or visual processing algorithms are often used to obtain measurement performance data from these robots. Integration of embedded sensors with soft robots can be cumbersome and expensive, also limiting the performance of a soft actuator. In this paper, implemented visual processing algorithms (thresholding, SAD, SSD and ZNCC) to measure performance data such as angle of motion, degree of bending, radius of curvature in real-time implemented with OpenCV libraries and Webcam is described. Soft RGB colour markers were also produced and firmly glued into the body of the soft robot with no hindrance to movement. Some concepts of visual processing applied include colour tracking, template matching and camera calibration. The execution of vision based motion control to a variety of soft actuators such as bending and wedge-shaped soft actuators was described.

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