Automated neural network-based instrument validation system

In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex hon-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant-wide. Due to the large number of sensors to be monitored and the limited computational capability of an artificial neural network model, a variable grouping process was developed for dividing the sensor variables into small correlated groups which the neural networks can handle. A modification of a statistical method, called Beta method, as well as a principal component analysis (PCA)-based method of estimating the number of neural network hidden nodes was developed. Another development in this dissertation is the sensor fault detection method. The commonly used Sequential Probability Ratio Test (SPRT) continuously measures the likelihood ratio to statistically determine if there is any significant calibration change. This method requires normally distributed signals for correct operation. In practice, the signals deviate from the normal distribution causing problems for the SPRT. A modified SPRT (MSPRT) was developed to suppress the possible intermittent alarms initiated by spurious spikes in network prediction errors. These methods were applied to data from the Tennessee Valley Authority (TVA) fossil power plant Unit 9 for testing. The results show that the average detectable drift level is about 2.5% for instruments in the boiler system and about 1% in the turbine system of the Unit 9 system. Approximately 74% of the process instruments can be monitored using the methodologies developed in this dissertation

Design and analysis of adaptive noise subspace estimation algorithms

Ph.DDOCTOR OF PHILOSOPH

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition

Learning from small and imbalanced dataset of images using generative adversarial neural networks.

The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and semi-supervised learning have not lived up to expectations. Some of these techniques do not account for other classification challenges, such as a class-imbalance problem. Thus, these techniques mostly underperform when compared with fully supervised approaches. In this thesis, we propose new methods to train a deep model on image classification with a limited number of labeled examples. This was achieved by extending state-of-the-art generative adversarial networks with multiple fake classes and network switchers. These new features enabled us to train a classifier using large unlabeled data, while generating class specific samples. The proposed model is label agnostic and is suitable for different classification scenarios, ranging from weakly supervised to fully supervised settings. This was used to address classification challenges with limited labeled data and a class-imbalance problem. Extensive experiments were carried out on different benchmark datasets. Firstly, the proposed approach was used to train a classification model and our findings indicated that the proposed approach achieved better classification accuracies, especially when the number of labeled samples is small. Secondly, the proposed approach was able to generate high-quality samples from class-imbalance datasets. The samples' quality is evident in improved classification performances when generated samples were used in neutralising class-imbalance. The results are thoroughly analyzed and, overall, our method showed superior performances over popular resampling technique and the AC-GAN model. Finally, we successfully applied the proposed approach as a new augmentation technique to two challenging real-world problems: face with attributes and legacy engineering drawings. The results obtained demonstrate that the proposed approach is effective even in extreme cases

Complexity in Developmental Systems: Toward an Integrated Understanding of Organ Formation

During animal development, embryonic cells assemble into intricately structured organs by working together in organized groups capable of implementing tightly coordinated collective behaviors, including patterning, morphogenesis and migration. Although many of the molecular components and basic mechanisms underlying such collective phenomena are known, the complexity emerging from their interplay still represents a major challenge for developmental biology. Here, we first clarify the nature of this challenge and outline three key strategies for addressing it: precision perturbation, synthetic developmental biology, and data-driven inference. We then present the results of our effort to develop a set of tools rooted in two of these strategies and to apply them to uncover new mechanisms and principles underlying the coordination of collective cell behaviors during organogenesis, using the zebrafish posterior lateral line primordium as a model system. To enable precision perturbation of migration and morphogenesis, we sought to adapt optogenetic tools to control chemokine and actin signaling. This endeavor proved far from trivial and we were ultimately unable to derive functional optogenetic constructs. However, our work toward this goal led to a useful new way of perturbing cortical contractility, which in turn revealed a potential role for cell surface tension in lateral line organogenesis. Independently, we hypothesized that the lateral line primordium might employ plithotaxis to coordinate organ formation with collective migration. We tested this hypothesis using a novel optical tool that allows targeted arrest of cell migration, finding that contrary to previous assumptions plithotaxis does not substantially contribute to primordium guidance. Finally, we developed a computational framework for automated single-cell segmentation, latent feature extraction and quantitative analysis of cellular architecture. We identified the key factors defining shape heterogeneity across primordium cells and went on to use this shape space as a reference for mapping the results of multiple experiments into a quantitative atlas of primordium cell architecture. We also propose a number of data-driven approaches to help bridge the gap from big data to mechanistic models. Overall, this study presents several conceptual and methodological advances toward an integrated understanding of complex multi-cellular systems

A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

In this thesis, we tackle the problem of extending neural network navigation algorithms for various types of mobile robots and 2-dimensional range sensors. We propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and mobile robot platforms. Moreover, this method allows the neural networks to be trained using only one type of 2-dimensional range sensor, which contributes positively to reducing the time required for training the networks. Experimental results carried out in simulation environments demonstrate the effectiveness of our approach in mobile robot navigation for different kinds of robots and sensors. Therefore, the successful implementation of our method provides a solution to apply mobile robot navigation algorithms to various robot platforms