HUMAN ROBOT INTERACTION THROUGH SEMANTIC INTEGRATION OF MULTIPLE MODALITIES, DIALOG MANAGEMENT, AND CONTEXTS

The hypothesis for this research is that applying the Human Computer Interaction (HCI) concepts of using multiple modalities, dialog management, context, and semantics to Human Robot Interaction (HRI) will improve the performance of Instruction Based Learning (IBL) compared to only using speech. We tested the hypothesis by simulating a domestic robot that can be taught to clean a house using a multi-modal interface. We used a method of semantically integrating the inputs from multiple modalities and contexts that multiplies a confidence score for each input by a Fusion Weight, sums the products, and then uses the input with the highest product sum. We developed an algorithm for determining the Fusion Weights. We concluded that different modalities, contexts, and modes of dialog management impact human robot interaction; however, which combination is better depends on the importance of the accuracy of learning what is taught versus the succinctness of the dialog between the user and the robot

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

Usage of fuzzy classification algorithms in brain-computer interfaces

Selles lõputöös uuritakse hägusate klassifikatsioonialgoritmide kasutamist elektroentsefalograafial (electroencephalography, EEG) põhinevates aju-arvuti liidestes (brain-computer interfaces, BCI). Uuritakse olemasolevat kirjandust BCI-des kasutatavate klassifikatsioonialgoritmide, hägusate algoritmide olemuse ja nende kasutamise kohta BCI-des. Hägusate algoritmide potentsiaalsete eeliste demonstreerimiseks realiseeritakse lihtne aju-arvuti liides, mis võimaldab kasutajal liigutada kursorit arvuti ekraanil. Testid selle rakendusega näitavad, et hägusad algoritmid sellist tüüpi rakendustes ei oma eelist traditsiooniliste algoritmide üle.In this thesis, the usage of fuzzy classification algorithms in brain-computer interfaces (BCI) based on electroencephalography (EEG) is researched. We review the existing literature on BCI, the traditional crisp algorithms often used in BCI for classification, fuzzy classification algorithms and their application in BCI. A simple BCI system is implemented that allows the user to move a cursor on the computer screen. Tests conducted with this application show that fuzzy classification algorithms do not have advantage over crisp classification algorithms in this kind of BCI systems

Algorithms for enhancing pattern separability, feature selection and incremental learning with applications to gas sensing electronic nose systems

Three major issues in pattern recognition and data analysis have been addressed in this study and applied to the problem of identification of volatile organic compounds (VOC) for gas sensing applications. Various approaches have been proposed and discussed. These approaches are not only applicable to the VOC identification, but also to a variety of pattern recognition and data analysis problems. In particular, (1) enhancing pattern separability for challenging classification problems, (2) optimum feature selection problem, and (3) incremental learning for neural networks have been investigated;Three different approaches are proposed for enhancing pattern separability for classification of closely spaced, or possibly overlapping clusters. In the neurofuzzy approach, a fuzzy inference system that considers the dynamic ranges of individual features is developed. Feature range stretching (FRS) is introduced as an alternative approach for increasing intercluster distances by mapping the tight dynamic range of each feature to a wider range through a nonlinear function. Finally, a third approach, nonlinear cluster transformation (NCT), is proposed, which increases intercluster distances while preserving intracluster distances. It is shown that NCT achieves comparable, or better, performance than the other two methods at a fraction of the computational burden. The implementation issues and relative advantages and disadvantages of these approaches are systematically investigated;Selection of optimum features is addressed using both a decision tree based approach, and a wrapper approach. The hill-climb search based wrapper approach is applied for selection of the optimum features for gas sensing problems;Finally, a new method, Learn++, is proposed that gives classification algorithms, the capability of incrementally learning from new data. Learn++ is introduced for incremental learning of new data, when the original database is no longer available. Learn++ algorithm is based on strategically combining an ensemble of classifiers, each of which is trained to learn only a small portion of the pattern space. Furthermore, Learn++ is capable of learning new data even when new classes are introduced, and it also features a built-in mechanism for estimating the reliability of its classification decision;All proposed methods are explained in detail and simulation results are discussed along with directions for future work

A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work

Online Novelty Detection System: One-Class Classification of Systemic Operation

Presented is an Online Novelty Detection System (ONDS) that uses Gaussian Mixture Models (GMMs) and one-class classification techniques to identify novel information from multivariate times-series data. Multiple data preprocessing methods are explored and features vectors formed from frequency components obtained by the Fast Fourier Transform (FFT) and Welch\u27s method of estimating Power Spectral Density (PSD). The number of features are reduced by using bandpower schemes and Principal Component Analysis (PCA). The Expectation Maximization (EM) algorithm is used to learn parameters for GMMs on feature vectors collected from only normal operational conditions. One-class classification is achieved by thresholding likelihood values relative to statistical limits. The ONDS is applied to two different applications from different application domains. The first application uses the ONDS to evaluate systemic health of Radio Frequency (RF) power generators. Four different models of RF power generators and over 400 unique units are tested, and the average robust true positive rate of 94.76% is achieved and the best specificity reported as 86.56%. The second application uses the ONDS to identify novel events from equine motion data and assess equine distress. The ONDS correctly identifies target behaviors as novel events with 97.5% accuracy. Algorithm implementation for both methods is evaluated within embedded systems and demonstrates execution times appropriate for online use