Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris

Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai 325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap. Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam membuat kajian yang akan datang

Navigation and Control of Automated Guided Vehicle using Fuzzy Inference System and Neural Network Technique

Automatic motion planning and navigation is the primary task of an Automated Guided Vehicle (AGV) or mobile robot. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. Artificial Intelligence based decision making systems have become increasingly more successful as they are capable of handling large complex calculations and have a good performance under unpredictable and imprecise environments. This research focuses on developing Fuzzy Logic and Neural Network based implementations for the navigation of an AGV by using heading angle and obstacle distances as inputs to generate the velocity and steering angle as output. The Gaussian, Triangular and Trapezoidal membership functions for the Fuzzy Inference System and the Feed forward back propagation were developed, modelled and simulated on MATLAB. The reserach presents an evaluation of the four different decision making systems and a study has been conducted to compare their performances. The hardware control for an AGV should be robust and precise. For practical implementation a prototype, that functions via DC servo motors and a gear systems, was constructed and installed on a commercial vehicle

A fuzzy-logic controller for an autonomous vehicle operation in an unknown environment

A controller is developed to guide a four-wheeled vehicle through an unknown environment. The vehicle is equipped with an ultrasonic sensor that can rotate to survey the neighboring environment. A new path planning algorithm is developed that reduces the computational time while avoiding obstacles. The vehicle uses a fuzzy-logic controller to determine the corresponding change in steering. While fuzzy-logic controllers exhibit robustness under varying operating conditions, it is difficult to design a good controller when observations about the system are scarce or when the system has large number of inputs and outputs. Due to this fact, the performance of the fuzzy-logic controller is improved using nonlinear programming techniques. The algorithm automatically generates the fuzzy rules and redefines the shape of the membership sets of input and output variables for an optimal performance of the controller. The effects of changing: the velocity of the vehicle, the range of the ultrasonic sensor, and the time step of the controller of the autonomous vehicle are discussed

CONTROLAB MUFA: a multi-level fusion architecture for intelligent navigation of a telerobot

This paper proposes a MUlti-level Fusion Architecture (MUFA) for controlling the navigation of a tele-commanded Autonomous Guided Vehicle (AGV). The architecture combines ideas derived from the fundamental concepts of sensor fusion and distributed intelligence. The focus of the work is the development of an intelligent navigation system for a tricycle drive AGV with the ability to move autonomously within any office enviromnent, following instructions issued by client stations connected to the office network and reacting accordingly to different situations found in the real world. The modules which integrate the MUFA architecture are discussed and results of some simulation experiments are presented

Automated steering design using Neural Network

If you don't move forward-you begin to move backward. Technological advancement today has brought us to a frontier where the human has become the basic constraint in our ascent towards safer and faster transportation. Human error is mostly responsible for many road traffic accidents which every year take the lives of lots of people and injure many more. Driving protection is thus a major concern leading to research in autonomous driving systems. Automatic motion planning and navigation is the primary task of an automated guided vehicle or mobile robots. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. In this research our artificial intelligence system is based on neural network model for navigation of an AGV in unpredictable and imprecise environment. A five layered with gradient descent momentum back-propagation system which uses heading angle and obstacle distances as input. The networks are trained by real data obtained from vehicle tracking live test runs. Considering the high amount of risk of testing the vehicle in real space-time conditions, it would initially be tested in simulated environment with the use of MATLAB®. The hardware control for an AGV should be robust and precise. An Aerial and a Grounded prototype were developed to test our neural network model in real time situation

Usage of Automatic Guided Vehicle Systems and Multi-agent Technology in higher education

Today, smart manufacturing is differentiated from many other initiatives by its emphasis on human ingenuity. Human capabilities must be enhanced by intelligently designing a customized solution for a specific domain. For example, Industry 4.0 is based on collaborative robots that digitize and simplify manufacturing processes. In fact, Automatic Guided Vehicles (AGVs) are widely used in intelligent industries due to their productivity, flexibility, and versatility. They are widely considered as one of the most important tools for flexible logistics in workshops. They can move materials and products without a predefined route. Many commercially available AGVs provide a self-guided navigation system to find their way to target workstations. However, many developers and producers of industrial robots face several challenges in designing AGV systems, such as the difficulty of defining a decentralized system decision as well as the discontinuity and complexity of the design process. One of the relevant research areas related to our AGV solution is the establishment of the human-machine industrial relationship and the creation of safe operation side by side

Outdoor navigation of mobile robots

AGVs in the manufacturing industry currently constitute the largest application area for mobile robots. Other applications have been gradually emerging, including various transporting tasks in demanding environments, such as mines or harbours. Most of the new potential applications require a free-ranging navigation system, which means that the path of a robot is no longer bound to follow a buried inductive cable. Moreover, changing the route of a robot or taking a new working area into use must be as effective as possible. These requirements set new challenges for the navigation systems of mobile robots. One of the basic methods of building a free ranging navigation system is to combine dead reckoning navigation with the detection of beacons at known locations. This approach is the backbone of the navigation systems in this study. The study describes research and development work in the area of mobile robotics including the applications in forestry, agriculture, mining, and transportation in a factory yard. The focus is on describing navigation sensors and methods for position and heading estimation by fusing dead reckoning and beacon detection information. A Kalman filter is typically used here for sensor fusion. Both cases of using either artificial or natural beacons have been covered. Artificial beacons used in the research and development projects include specially designed flat objects to be detected using a camera as the detection sensor, GPS satellite positioning system, and passive transponders buried in the ground along the route of a robot. The walls in a mine tunnel have been used as natural beacons. In this case, special attention has been paid to map building and using the map for positioning. The main contribution of the study is in describing the structure of a working navigation system, including positioning and position control. The navigation system for mining application, in particular, contains some unique features that provide an easy-to-use procedure for taking new production areas into use and making it possible to drive a heavy mining machine autonomously at speed comparable to an experienced human driver.reviewe