The paper present design of a control structure that enables integration of a Kinematic neural controller for trajectory tracking of a nonholonomic differential two wheeled mobile robot, then proposes a Kinematic neural controller to direct a National Instrument mobile robot (NI Mobile Robot). The controller is to make the actual velocity of the wheeled mobile robot close the required velocity by guarantees that the trajectory tracking mean squire error converges at minimum tracking error. The proposed tracking control system consists of two layers; The first layer is a multi-layer perceptron neural network system that controls the mobile robot to track the required path , The second layer is an optimization layer ,which is implemented based on hybrid Crossoved Firefly Algorithm with Artificial Bee Colony (CFA-ABC) to tune the controller's parameters to achieve the optimal path. The performance of the hybrid optimization algorithm is verified by various benchmark functions. The simulation results show that the utilizing of CFA and (CFA-ABC ) are better than the original Firefly Algorithm. A simulation example is given to indicate the effectiveness of the proposed algorithm, the results have been done using MATLAB (R2013b), and all trajectory tracking results with two reference trajectories (circular and lemniscates ) are presented.
The unconventional techniques called “the quick look techniques”, have been developed to present well log data calculations, so that they may be scanned easily to identify the zones that warrant a more detailed analysis, these techniques have been generated by service companies at the well site which are among the useful, they provide the elements of information needed for making decisions quickly when time is of essence. The techniques used in this paper are:
- Apparent resistivity Rwa
- Rxo /Rt
The above two methods had been used to evaluate Nasiriyah oil field formations (well-NS-3) to discover the hydrocarbon bearing formations. A compu
... Show MoreClassification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE), Border
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Calculating the Inverse Kinematic (IK) equations is a complex problem due to the nonlinearity of these equations. Choosing the end effector orientation affects the reach of the target location. The Forward Kinematics (FK) of Humanoid Robotic Legs (HRL) is determined by using DenavitHartenberg (DH) method. The HRL has two legs with five Degrees of Freedom (DoF) each. The paper proposes using a Particle Swarm Optimization (PSO) algorithm to optimize the best orientation angle of the end effector of HRL. The selected orientation angle is used to solve the IK equations to reach the target location with minimum error. The performance of the proposed method is measured by six scenarios with different simulated positions of the legs. The proposed
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This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord
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Traditionally, path selection within routing is formulated as a shortest path optimization problem. The objective function for optimization could be any one variety of parameters such as number of hops, delay, cost...etc. The problem of least cost delay constraint routing is studied in this paper since delay constraint is very common requirement of many multimedia applications and cost minimization captures the need to
distribute the network. So an iterative algorithm is proposed in this paper to solve this problem. It is appeared from the results of applying this algorithm that it gave the optimal path (optimal solution) from among multiple feasible paths (feasible solutions).
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Designing and Standardizing two tests for motor coordination timing for youth basketball players Research submitted by Prof. Faris sami & asst. prof. Wasan hanoon ali & asst. prof. Feras muttasher Baghdad University-College Of Physical Education and Sport Sciences Motor coordination in basketball is considered one of the most important factors for success in skill performance accuracy and speed due to the defensive and offensive situations of the game. The problem of the research lies in the lack of tests that can specify the growth of motor coordination through which the relative change for a number of players can be noticed due to practice and training. The subjects of the research were (30) young league players of National Center for gif
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