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

Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring

In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments

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

Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools

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

Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

Classification of lettuce life or growth stages is an effective tool for measuring the performance of an aquaponics system. It determines the balance in water nutrients, adequate temperature and lighting, other environmental factors, and the system’s productivity to sustain cultivars. This paper proposes a classification of lettuce life stages planted in an aquaponics system. The classification was done using the texture features of the leaves derived from machine vision algorithms. The attributes underwent three different feature selection processes, namely: Univariate Selection (US), Recursive Feature Elimination (RFE), and Feature Importance (FI) to determine the four most significant features from the original eight attributes. The features selected were used for training four estimators from Decision Trees Classifier (DTC), Gaussian Naïve Bayes (GNB), Stochastic Gradient Descent (SGD), and Linear Discriminant Analysis (LDA). The models trained using DTC and SGD were then optimized as they have hyperparameters for tuning. A comparative analysis among Machine Learning (ML) algorithms was conducted to identify the best-performing model with the given application. The best features were derived from US and FI as they have the same top four features using the DTC estimator optimized with the hyperparameters tuned to max depth having 5, criterion equated to ‘Gini', and splitter was set to 'Best'. The accuracy obtained from cross-validation evaluation resulted in 87.92%. Considering consistency with hold-out validation, LDA outperforms optimized DTC even with lower accuracy of 86.67%. This accuracy of LDA outperformed DTC due to its sufficient fit for generalizing the testing data on classifying lettuce growth stage

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%