The doctoral research abstracts. Vol:11 2017 / Institute of Graduate Studies, UiTM

Foreword: Congratulation to IGS on the continuous effort to publish the 11th issue of the Doctoral Research Abstracts which highlights the research in various disciplines from science and technology, business and administration to social science and humanities. This research abstract issue features the abstracts from 91 PhD doctorates who will receive their scrolls in this 86th UiTM momentous convocation ceremony. This is a special year for the Institute of Graduate Studies where we are celebrating our 20th anniversary. The 20th anniversary is celebrated with pride with an increase in the number of PhD graduates. In this 86th convocation, the number of PhD graduates has increased by 30% compared to the previous convocation. Each research produces an innovation and this year, 91 research innovations have been successfully recognized to have made contributions to the body of knowledge. This is in line with this year UiTM theme that is “Inovasi Melonjak Persaingan Global (Innovation Soars Global Competition)”. Embarking on PhD research may not have been an easy decision for many of you. It often comes at a point in life when the decision to further one’s studies is challenged by the comfort of status quo. I would like it to be known that you have most certainly done UiTM proud by journeying through the scholarly world with its endless challenges and obstacles, and by persevering right till the very end. Again, congratulations to all PhD graduates. As you leave the university as alumni we hope a new relationship will be fostered between you and UiTM to ensure UiTM soars to greater heights. I wish you all the best in your future endeavor. Keep UiTM close to your heart and be our ambassadors wherever you go. / Prof Emeritus Dato’ Dr Hassan Said Vice Chancellor Universiti Teknologi MAR

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Evolutionary-based global localization and mapping of three dimensional environments

A fully autonomous robot must obtain and interpret information about the environment to execute several tasks. The mobile robot mapping or SLAM problem is closely related to these abilities. It consists of interpreting the information perceived by its sensors in order to build map and localize itself in it. There are many other robot skills that depend on this task; thus, it is one of the most important problems to be solved by a truly autonomous robot. The objective of this work is to design various specific tools related to the mapping problem in order to improve the autonomy of MANFRED-2, which is a mobile robot fully developed by the Robotics Lab research group of the Systems Engineering and Automation Department of the Carlos III University of Madrid. The localization problem in mobile robotics can be defined as the search of the robot's coordinates in a known environment. If there is no information about the initial location, we are talking about global localization. In this work, we have developed an algorithm that solves this problem in a three-dimensional environment using Differential Evolution, which is a particle-based evolutionary algorithm that evolves in time to the solution that yields the cost function lowest value. The proposed method has many features that make it very robust and reliable: thresholding and discarding mechanisms, different cost functions, effective convergence criteria, and so on. The resulting global localization module has been tested in numerous experiments. The high accuracy of the method allows its application in manipulation tasks. If the environment information is given by laser readings, it is essential to correct the local errors between pairs of scans to improve the map quality, which is called registration or scan matching. We have implemented a scan matching algorithm for three-dimensional environments. It is also based on the Differential Evolution method. The high accuracy and computational effi ciency of the proposed method have been demonstrated with experimental results. The last problem addressed here consists of detecting when the robot is navigating through a known place (loop detection). After that, the accumulated error can be minimized to give consistency to the global map (loop closure). We have developed a loop detection method that compares features extracted from two different scans to obtain a loop indicator. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features describe other several properties (volume, average range, curvature, etc.). The algorithm has been tested with real data to demonstrate its effi ciency. All true loops are correctly detected and no false detections are appreciated when the mobile robot is covering a long trajectory. The results are similar or even better than those obtained by other research groups. In addition, it is a more versatile method because it admits a wide variety of scan properties and different weights in the comparison formula. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Un robot completamente autónomo debe ser capaz de obtener e interpretar la información del entorno para ejecutar diversas tareas. El problema de mapeado o SLAM para robots móviles está estrechamente relacionado con estas habilidades. Consiste en interpretar la infomació percibida por sus sensores para construir un mapa y localizarse. Hay muchas otras tareas que dependen del mapeado, luego este es uno de los problemas más importantes para un robot móvil. El objetivo de este trabajo es el desarrollo de varias herramientas específicas relacionadas con el mapeado de entornos tridimensionales. Con ellas se mejorar a la autonomía del robot manipulador MANFRED-2, que es un robot móvil desarrollado íntegramente en el Robotics Lab del Departamento de Ingeniería de Sistemas y Automática de la Universidad Carlos III de Madrid. El problema de localización para un robot móvil puede ser de nido como la búsqueda de las coordenadas del robot dentro de un entorno conocido. Si no hay información sobre la localización inicial, el problema se denomina localización global. En este trabajo se ha desarrollado un módulo que soluciona este problema para entornos tridimensionales utilizando el algoritmo Differential Evolution, el cual es un filtro evolutivo basado en part culas que evolucionan con el tiempo hacia la solución que tiene asociado un mejor valor para una función de coste dada. El algoritmo desarrollado tiene diversas características que lo hacen muy robusto y fiable: mecanismos de umbralización y descarte, diferentes funciones de coste, criterios de convergencia efectivos, etc. El módulo de localización global se ha probado en m últiples experimentos. La elevada precisión de este método permite que el robot sea utilizado en tareas de manipulación. Si la información del entorno viene dada por barridos de un láser, es muy importante que se pueda corregir el error local entre pares de barridos para mejorar la calidad del mapa. Este proceso se conoce como registro o scan matching. Hemos implementado un algoritmo que resuelve este problema en entornos tridimensionales. Est a tambi en basado en el Differential Evolution. Si se elige la función de forma adecuada es posible resolver el problema de scan matching utilizando este método. La elevada precisión y la eficiencia computacional se han demostrado en los resultados experimentales. El último problema abordado aquí consiste en detectar cuando el robot está navegando por un entorno conocido. Después de esto se podrá minimizar el error acumulado para aumentar la consistencia del mapa. La tarea de detecci on se llama usualmente detección de bucles, mientras que la minimización del error es el cierre del bucle. Se ha desarrollado un algoritmo de detección que extrae las características más importantes de dos barridos del láser para obtener un indicador que es usado como umbral para detectar si el robot está en un lugar que ha visitado previamente. Nuestro método permite tener en cuenta características muy diferentes. Primero, las caractrísticas de superficie permiten incluir las formas geométricas presentes en el barrido (líneas, planos y esferas). Segundo, las características numéricas permiten describir diversas propiedades (volumen, rango medio, curvatura, etc.). El algoritmo ha sido probado con datos reales para demostrar su eficiencia. Todos los bucles son detectados correctamente y no se aprecian falsos positivos cuando el robot está navegando por una trayectoria larga con varios bucles. Los resultados son parecidos o mejores que los que obtienen otros grupos de investigación. Además, este es un m etodo muy versátil pues admite multitud de variables y diferentes pesos en la fórmula de comparación