A Comparison Study of Underwater and Land Flexible Manipulators

New results for experimental comparison of flexible underwater and land manipulator have studied in this research. A new experimental rig and test basin have designed to present the manipulators behavior. Several electronic devices used to capture the data that relates with land and underwater manipulators. Experimental parts have consisted on studying of hub-angle and vibration of end-point as in-line forces affection under static and moving waters conditions as a distributed flow speeds. The experimental outcomes appeared that the in-line forces influence on land manipulator case is more than the underwater manipulator. The outcomes revealed that angular displacement influence and vibration at the end-point of the land manipulator is unlike with underwater manipulator at disturbance cases about 80% and 59% for the first amplitude respectively while, very few vibration has been recorded for underwater manipulator behavior at static and moved waters after first amplitude compared with land manipulator

Finite element method for dynamic modelling of an underwater flexible single-link manipulator

In order to control the angular displacement of the hub and to suppress the vibration at the end point of an underwater flexible single-link manipulator system efficiently, it is required to obtain an adequate model of the structure. In this study, a mathematical model of an underwater flexible single-link manipulator system has been developed and modelled as a pinned-free, an Euler-Bernoulli flexible beam using finite element method based on Lagrangian approach analysis. Damping, hub inertia and payload are incorporated in the dynamic model, which is then represented in a state-space form. The simulation algorithm was developed using matlab and its performance, on the basis of accuracy in characterizing the behavior of the manipulator, is assessed

Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting

Flood, which is the most common natural disaster that occurs worldwide, causes massive casualties and damages to people and environment respectively. Hence, flood prediction is integral to minimise the damage and loss of life, while simultaneously aiding the government authorities and even the private sector in making accurate decisions when faced with incoming flood. Therefore, this present study had imputed the missing hydrological data using five imputation methods, namely Neural Network (NN), Moving Median (MM), Iterative Algorithm (IA), Nonlinear Iterative Partial Least Square (NIPALS), and Combined Correlation with Inversed Distance (CCID) imputation methods. Next, a newly developed hybrid deep learning (DL) algorithm is proposed to predict the daily water level in selected rivers that flow through Kelantan. The proposed model was then compared with two benchmark models, namely single Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN). The outcomes revealed that the MM imputation method resulted in higher accuracy with the lowest Root Mean Square Error (RMSE) for all rainfall and streamflow stations, in comparison to the other imputation methods. The experimental results portrayed that the proposed model achieved the best prediction accuracy in all performance measurements. The Mean Arctangent Absolute Percentage Error (MAAPE) results for all rivers ranged at 1-12%, which signified higher accuracy. Essentially, the proposed model may facilitate the government authorities and private sector to predict and plan better when dealing with the occurrence of flood