Journal of Advances in Computer Engineering and Technology
Volume:5 Issue: 1, Winter 2019

Quadrotor control has been noted for its trouble as the consequence of the high maneuverability, system nonlinearity and strongly coupled multivariable. This paper deals with the simulation depend on proposed controller of a quadrotor that can overcome this trouble. The mathematical model of quadrotor is determined using a Newton-Euler formulation. Fractional Order Proportional Integral Derivative (FOPID) controller tuned by genetic algorithm (GA) is investigated to control and stabilization the position and attitude of quadrotor using feedback linearization. This controller is used as a reference to compare its results with Proportional Integral Derivative (PID) controller tuned by GA. The control structure performance is evaluated through the response and minimizing the error of the position and attitude. Simulation results, demonstrates that position and attitude control using FOPID has fast response, better steady state error and RMS error than PID. By simulation the two controllers are tested under the same conditions using SIMULINK under MATLAB2015a.

This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, while other portions were used to check and test the generalization ability of the ANFIS model. Water level predictions were made for 24 hours, 48 hours and 72 hours, in which training, checking and testing of the model were performed for each of the prediction periods. The model results from the training, checking and testing data groups show that 48 hours prediction has the least Root Mean Square Error (RMSE) of 0.05426, 0.06298 and 0.05355 for training, checking and testing data groups respectively, showing that the prediction is most accurate for 48 hours.

In path planning Problems, a complete description of robot geometry, environments and obstacle are presented; the main goal is routing, moving from source to destination, without dealing with obstacles. Also, the existing route should be optimal. The definition of optimality in routing is the same as minimizing the route, in other words, the best possible route to reach the destination. In most of the routing methods, the environment is known, although, in reality, environments are unpredictable;But with the help of simple methods and simple changes in the overall program, one can see a good view of the route and obstacles ahead. In this research, a method for solving robot routing problem using cellular automata and genetic algorithm is presented.In this method, the working space model and the objective function calculation are defined by cellular automata, and the generation of initial responses and acceptable responses is done using the genetic algorithm.During the experiments and the comparison we made, we found that the proposed algorithm yielded a path of 28.48 if the lengths of the paths obtained in an environment similar to the other algorithm of 15 / 32, 29.5 and 29.49, which is more than the proposed method.

In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mining techniques. Sports data mining assists coaches and managers in result prediction, player performance assessment, player injury prediction, sports talent Identification and game strategy evaluation. Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty: the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, coaching strategy, etc. So even for experts, it is very hard to predict the exact results of individual matches. The present study reviews previous research on data mining systems to predict sports results and evaluates the advantages and disadvantages of each system.

In this paper, we study intrinsic vs. extrinsic motivation in players playing an electrical engineering gaming environment. We used UNTANGLED, a highly interactive game to conduct this study. This game is developed to solve complex mapping problem from electrical engineering using human intuitions. Our goal is to find whether there are differences in the ways anonymous players solved electrical engineering puzzles in an electronic gaming environment when motivated to play competitively, as compared to self-regulated play. For our experiments, we used puzzles from four games from UNTANGLED. A one-way analysis of variance (ANOVA) was calculated on participants’ scores, type of plays, number of plays, and time spent playing, as both self-regulated and competitive players. We also examined difference between the type of moves used by the competitive and self-regulated players. Our results support the theory of motivation as being internally embedded in learners. The results also demonstrate that a self-regulated learner does not require motivation to improve one’s performance.

Big data sizes are constantly increasing. Big data analytics is where advanced analytic techniques are applied on big data sets. Analytics based on large data samples reveals and leverages business change. The popularity of big data analytics platforms, which are often available as open-source, has not remained unnoticed by big companies. Google uses MapReduce for PageRank and inverted indexes. Facebook uses Apache Hadoop to analyse their data and created Hive. eBay uses Apache Hadoop for search optimization and Twitter uses Apache Hadoop for log file analysis and other generated data[ 1]. Different Big data analytics platform providers are providing different types of facilities. To select those analytics platform for our business and public sector institutions purpose we follow multiple criteria. Multiple criteria decision making (MCDM) is mostly used in ranking one or more alternatives from finite set of available alternatives with respect to multiple criteria. Among many multi-criteria techniques, MAXMIN, MAXMAX, SAW, AHP, TOPSIS, SMART, ELECTRE are the most frequently used methods. The TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) methods are simplicity, rationality, comprehensibility, good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form.