Non-conventional control of the flexible pole-cart balancing problem

Emerging techniques of intelligent or learning control seem attractive for applications in manufacturing and robotics. It is however important to understand the capabilities of such control systems. In the past the inverted pendulum has been used as a test case. The thesis begins with an examination of whether the inverted pendulum or polecart balancing problem is a representative problem for experimentation for learning controllers for complex nonlinear systems. Results of previous research concerning the inverted pendulum problem are presented to show that this problem is not sufficiently testing. This thesis therefore concentrates on the control of the inverted pendulum with an additional degree of freedom as a testing demonstrator problem for learning control system experimentation. A flexible pole is used in place of a rigid one. The transverse displacement of the flexible pole adds a degree of freedom to the system. The dynamics of this new system are more complex as the system needs additional parameters to be defIned due to the pole's elastic deflection. This problem also has many of the signifIcant features associated with flexible robots with lightweight links as applied in manufacturing. Novel neural network and fuzzy control systems are presented that control such a system both in simulation and real time. A fuzzy-genetic approach is also demonstrated that allows the creation of fuzzy control systems without the use of extensive knowledge

Fuzzy Logic Implementation for Power Efficiency and Reliable Irrigation System (PERIS) of Tomatoes Smart Farm

This paper presents an intelligent motor speed controller for Three Phase Motor with Variable Frequency Driver (VFD) for irrigation system of Smart Farming using fuzzy logic algorithm developed inside a Micro-Control Unit (MCU) environment or MCU on Power Efficiency and Reliable Irrigation System (PERIS). The desired motor speed controller is obtained using fuzzy inputs that consider three phenomenon such as: availability of energy within the system, reservoir water level and environment temperature. These fuzzy inputs are feedback data from the water reservoir level sensor (plant water requirements), environment/temperature sensor and current sensor. Different frequencies were used to test the controller’s performance in real time undergoing different water level and power load variations. The whole system is powered by photovoltaic cells, it can quickly and accurately calculate water demand amounts of crops, which can provide a scientific basis for power-savings and water-savings for irrigation. Experiment results showed that the developed controller is efficient, reliable and robust

A Caching Algorithm for Information Centric Network Using Fuzzy Logic

The internet today has evolved from information superhighway to a household necessity that offers more than just information. Nowadays, the internet serves a lot of purpose. It is a tool for not just information but entertainment that offers music, graphics and videos that is available for downloading or streaming. It has also evolved to be a medium of communication that offers a global link from people around the globe. From emails, short message services and even voice communication, the internet has all of these to offer. The former information superhighway is today a social media platform that is open to all ages to all variety of users. With this development, it is logical to think that the current internet network scheme should also be subjected to evolution. The emerging Information Centric Network is quite a good fit to the future of internet. The idea to be concerned to the content that is to be accessed more than the identity of the one accessing the content is tailor-fit to the current application of internet. In a nutshell, ICN requires node with caching functionality. An effective caching algorithm is a great help to attain the very purpose of ICN which is to come up with an efficient network. Meanwhile, fuzzy logic, which has proven to be effective in control or optimization applications, can also be applied in improving caching functionality of ICN. This paper explores the application of fuzzy logic to the caching algorithm that can be used to further improve current information centric networks. The results were obtained from hypothetical data because this is just to prove that fuzzy logic can be applied in the caching dynamics of Information Centric Network

Object Detection in X-ray Images Using Transfer Learning with Data Augmentation

Object detection in X-ray images is an interesting problem in the field of machine vision. The reason is that images from an X-ray machine are usually obstructed with other objects and to itself, therefore object classification and localization is a challenging task. Furthermore, obtaining X-ray data is difficult due to an insufficient dataset available compared with photographic images from a digital camera. It is vital to easily detect objects in an X-ray image because it can be used as decision support in the detection of threat items such as improvised explosive devices (IED’s) in airports, train stations, and public places. Detection of IED components accurately requires an expert and can be achieved through extensive training. Also, manual inspection is tedious, and the probability of missed detection increases due to several pieces of baggage are scanned in a short period of time. As a solution, this paper used different object detection techniques (Faster R-CNN, SSD, R-FCN) and feature extractors (ResNet, MobileNet, Inception, Inception-ResNet) based on convolutional neural networks (CNN) in a novel IEDXray dataset in the detection of IED components. The IEDXray dataset is an X-ray image of IED replicas without the explosive material. Transfer learning with data augmentation was performed due to limited X-ray data available to train the whole network from scratch. Evaluation results showed that individual detection achieved 99.08% average precision (AP) in mortar detection and 77.29% mAP in three IED components

Female Voice Recognition Using Artificial Neural Networks and MATLAB Voicebox Toolbox

Voice and speaker recognition performances are measured based on the accuracy, speed and robustness. These three key performance indicators are primarily dependent on voice feature extraction method and voice recognition algorithm used. This paper aims to discuss various researches in speech recognition that has yielded high accuracy rates of 95% and above. The extracted MFCCs from MATLAB Voicebox toolbox were used as inputs to the multilayer Artificial Neural Networks (ANN) for female voice recognition algorithm. This study explored the recognition performance of the neural networks using variable number of hidden neurons and layers, and determine the architecture that would provide the optimum performance in terms of high recognition rate. MATLAB simulation resulted to a training and testing recognition rate of 100.00% when using 3-hidden-layer neural network from speech samples of a single-speaker, and highest training recognition rate of 98.11% and testing recognition rate of 87.20% when using 4-hidden-layer neural network from speech samples of several speakers. When tested with homonyms, the best recognition rate was 75.00% from a 3-hidden-layer neural network trained from a single-speaker, and 81.91% from a 4- hidden-layer neural network trained from multiple speakers. The deviation in recognition rates were primarily attributed to the variations made in the number of input neurons, hidden layers, and neurons of the speech recognition neural network

Optimization of CO2 Laser Cutting Parameters Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Laser cutting is a manufacturing technology that uses laser light to cut almost any materials. This type of cutting technology has been applied in many industrial applications. Problems seen with a laser is the cutting efficiency and the quality wherein these two parameters are both affected by the laser power and its process speed. This study presents the modelling and simulation of an intelligent system for predicting and optimising the process parameters of CO2 laser cutting. The developed model was trained and tested using actual data gathered from actual laser cut runs. For the system parameters, two inputs were used: the type of material used and the material thickness (mm). For the desired response, the output is the process speed or cutting rate (mm/min). Adaptive neuro-fuzzy inference system (ANFIS) was the tool used to model the optimisation cutting process. Moreover, grid partition (GP) and subtractive clustering were both used in designing the fuzzy inference system (FIS). Among the training models used, GP Gaussian bell membership function (Gbellmf) provided the highest performance with an accuracy of 99.66%

Automation and Control for Adaptive Management System of Urban Agriculture Using Computational Intelligence

It has been predicted by the United Nations that the world population will increase to 9.8 billion in 2050. This causes agricultural development areas to be transformed into urban areas. This urbanization and increase in population density cause food insecurity. Urban agriculture using precision farming becomes a feasible solution to meet the growing demand for food and space. An adaptive management system (AMS) is necessary for such farm to provide an artificial environment suitable to produce cultivars effectively. This research proposes the development of a computational intelligence-based urban farm automation and control system utilizing machine learning and fuzzy logic system models. A quality assessment is employed for adjusting the environmental parameters with respect to the cultivars’ requirements. The system is composed of sensors for data acquisition and actuators for model-dictated responses to stimuli. Data logging was done wirelessly through a router that would collect and monitor data through a cloud-based dashboard. The model intended for training from the acquired data undergo statistical comparative analysis and least computational cost analysis to optimize the performance. The system performance was evaluated by monitoring the conditions of the sensors and actuators. Experiment results showed that the proposed system is accurate, robust, and reliable

Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

Fuzzy Logic - Controls, Concepts, Theories and Applications

This book introduces new concepts and theories of Fuzzy Logic Control for the application and development of robotics and intelligent machines. The book consists of nineteen chapters categorized into 1) Robotics and Electrical Machines 2) Intelligent Control Systems with various applications, and 3) New Fuzzy Logic Concepts and Theories. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic control systems