Abd Manan Samad

Aerial mapping using high resolution digital camera and unmanned aerial vehicle for Geographical Information System

In aerial photogrammetry, aerial photographs are acquired using aerial camera and light aircraft as a platform. The aerial photographs are usually processed for mapping such as for production of topographic map and other special purpose map known as thematic map. However, this method is expensive and suitable for large area but it is not practical for mapping small area. This study concentrates on the use of high resolution digital camera and a very light platform known as unmanned aerial vehicle (UAV) as data acquisition system in capturing digital aerial photographs. The acquired digital aerial photographs were processed using image processing software to produce digital map and digital orthophoto. The results showed that an accuracy of sub-meter can be obtained using the employed method. In Geographical Information System (GIS), it is quite common that topographic map and orthophoto are used as a base map. Hence, the findings from this study could also be used as an input for GIS. Not to forget, the photogrammetric technique could be used not only for mapping but it could also be used for any environmental protection and conservation.

Model identification and controller design for real-time control of hydraulic cylinder

Hydraulic cylinder has been widely used as an actuator in industrial equipments and processes due to its linear movements, fast response and accurate positioning of heavy load. The nonlinear properties of hydraulic cylinder has challenged researchers to design a suitable controller for position control, motion control, and tracking control. This paper presents model identification and controller design using pole-placement method for real-time control of hydraulic cylinder. The plant mathematical model was approximated using Matlab system identification toolbox from open-loop input-output experimental data. The simulation studies and real-time studies were done using Visual C++ console programming. The simulation and real-time results were compared and they show about similar performances.

Flood water level modeling and prediction using NARX neural network: Case study at Kelang river

Flood disaster has becomes major threat around the world because it causes loss of lives and damages to property. Thus, reliable flood prediction is very much needed in order to reduce the effects of flood disaster. Hence, an accurate flood water level prediction is an important task to achieve. Since flood water level fluctuation is highly nonlinear, it is very difficult to predict the flood water level. Artificial Neural Network is well known technique is solving nonlinear cases and Nonlinear Auto Regressive with Exogenous Input (NARX) model is one class of Artificial Neural Network model. Thus, this paper proposes flood water level modeling and prediction using Nonlinear Auto Regressive with Exogenous Input (NARX) model to overcome the nonlinearity problem and come out with an advanced neural network model for the prediction of flood water level 10 hours in advance. The input and output parameters used in this model are based on real-time data obtained from Department of Irrigation and Drainage Malaysia. Results showed that NARX model successfully predicted the flood water level 10 hours ahead of time.

Flood water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor

Flood water level prediction has long been the earliest forecasting problems that have attracted the interest of many researchers. Accurate prediction of flood water level is extremely importance as an early warning system to the public to inform them about the possible incoming flood disaster. Using the collected data at the upstream and downstream station of a river, this paper proposes a modelling of flood water level at downstream station using back propagation neural network (BPN). In order to improve the prediction values, an extended Kalman filter was introduced at the output of the BPN. The introduction of extended Kalman filter at the output of BPN shows significant improvement to the prediction and tracking performance of the actual flood water level.

Real-time control of non-minimum phase electro-hydraulic system using trajectory-adaptive ZPETC

Hydraulic actuator has been widely used in industrial equipments and processes principally due to its high-power density and system solution that it can provided. The natural nonlinear property of hydraulic cylinder has challenged researchers in designing suitable controller for positioning control, motion control and tracking control. This paper proposes a controller design using trajectory-adaptive ZPETC without factorization of zeros and implementing real-time control to non-minimum phase electro-hydraulic system. Simulation and real-time experimental results were compared and evaluated and they show interesting tracking performances.

Trajectory zero phase error tracking control using comparing coefficients method

This paper presents the studies on trajectory zero phase error tracking control without factorisation of zeros polynomial where the controller parameters are determined using comparing coefficients methods. The controller was applied to two types of third-order non-minimum phase plant. The first plant was having a zero outside and far from the unity circle. Another plant was having a zero outside and near to the unity circle. Simulation and experimental results will be presented to discuss its tracking performance.

Artificial neural network modelling and flood water level prediction using extended Kalman filter

Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.

New Artificial Neural Network and Extended Kalman Filter hybrid model of flood prediction system

Accurate prediction of flood water level is a difficult task to achieve due to the nonlinearity of the water level itself and lacking of input parameters to the neural network model. Although Artificial Neural Network is proven to be the best model of flood water level prediction, suitable model parameters need to be chosen for training purposes in order to arrive to an optimal model with smallest error. A new Back Propagation Neural Network model (BPN) for the prediction of flood water level 3 hours ahead of time is developed in this study. This optimized BPN model offers advantages of parameter analysis method instead of trial and error method for choosing the optimized BPN model parameters. However, the simulated results of BPN model required improvement as the model could not able to track the actual water level precisely. Hence, this paper proposes BPN model with integration of EKF at the output. Performance indices result such as Akaikes Final Prediction Error(FPE), Loss Function(V) and Root Mean Square Error (RMSE) from this hybrid model outperform the BPN model result.

A study on flood risk assessment for Bandar Segamat sustainability using remote sensing and GIS approach

Flood is one of the natural disaster usually take place that in the southern states of Malaysia such as Johor, Pahang and Malacca during rainy season. The number of area affected from this event has increase over the past years. This paper describes a study made to explore the application of GIS and Remote Sensing in flood risk assessment for Bandar Segamat sustainability. The objective is to explore and understand the flood risk assessment characteristics, Remote Sensing (RS) and Geographic Information System (GIS). Open source software such as QuantimGIS and Infoworks RS is used as tools in processing the data. The result show a significant movement of water in Segamat city which causing such loss in terms of property damages and lives. A simulation of flood risk and sustainability at Bandar Segamat was developed which indicate the most, moderate and less affected area in Segamat. The main contribution of the study was to prove that anyone that involved directly or indirectly in GIS or RS field are able to perform the similar study without the need of high cost, great experience and with basic knowledge of the field study.

An Unmanned Aerial Imagery Capturing System (UAiCs): A review of flight mission planning

The main purpose to plan the flight planning for Unmmaned Aerial Imagery Capturing System (UAiCs) is to find out the best flight path and camera exposure station. Besides that, flight planning can help in reducing operational cost and obtain the optimum result. Flight planning is the most important step in Photogrammetry and need to be planned properly before further operation is carried out. By utilizing these new technology of capturing the aerial imagery, the process becomes easier and the data captured is up to date. In terms of knowing the area need to be surveyed until the generation of the product. the process is more convenient and all is computerized now. The objective of this paper is to review the standard flight mission process in capturing aerial imagery using the UAiCs technology. The expected result will shows that the important of the flight planning process flow that will lead to a good result of data captured.