To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Deep Learning Approach to Multi-phenomenological Nuclear Fuel Cycle Signals for Nonproliferation Applications

In order to reduce the time required for data analysis and decision-making relevant to nuclear proliferation detection, Artificial Intelligence (AI) techniques are applied to multi-phenomenological signals emitted from nuclear fuel cycle facilities to identify non-human readable characteristic signatures of operations for use in detecting proliferation activities. Seismic and magnetic emanations were collected in the vicinity of the High Flux Isotope Reactor (HFIR) and the McClellan Nuclear Research Center (MNRC). A novel bi-phenomenology DL network is designed to test the viability of transfer learning between nuclear reactor facilities. It is found that the network produces an 84.1% accuracy (99.4% without transient states) for predicting the operational state of the MNRC reactor when trained on the operational state of the HFIR reactor. In comparison, the best performing traditional ML single-phenomenology algorithm, K-Means, produces a 67.8% prediction accuracy (80.5% without transient states)

Feature Driven Learning Techniques for 3D Shape Segmentation

Segmentation is a fundamental problem in 3D shape analysis and machine learning. The abil-ity to partition a 3D shape into meaningful or functional parts is a vital ingredient of many down stream applications like shape matching, classification and retrieval. Early segmentation methods were based on approaches like fitting primitive shapes to parts or extracting segmen-tations from feature points. However, such methods had limited success on shapes with more complex geometry. Observing this, research began using geometric features to aid the segmen-tation, as certain features (e.g. Shape Diameter Function (SDF)) are less sensitive to complex geometry. This trend was also incorporated in the shift to set-wide segmentations, called co-segmentation, which provides a consistent segmentation throughout a shape dataset, meaning similar parts have the same segment identifier. The idea of co-segmentation is that a set of same class shapes (i.e. chairs) contain more information about the class than a single shape would, which could lead to an overall improvement to the segmentation of the individual shapes. Over the past decade many different approaches of co-segmentation have been explored covering supervised, unsupervised and even user-driven active learning. In each of the areas, there has been widely adopted use of geometric features to aid proposed segmentation algorithms, with each method typically using different combinations of features. The aim of this thesis is to ex-plore these different areas of 3D shape segmentation, perform an analysis of the effectiveness of geometric features in these areas and tackle core issues that currently exist in the literature.Initially, we explore the area of unsupervised segmentation, specifically looking at co-segmentation, and perform an analysis of several different geometric features. Our analysis is intended to compare the different features in a single unsupervised pipeline to evaluate their usefulness and determine their strengths and weaknesses. Our analysis also includes several features that have not yet been explored in unsupervised segmentation but have been shown effective in other areas.Later, with the ever increasing popularity of deep learning, we explore the area of super-vised segmentation and investigate the current state of Neural Network (NN) driven techniques. We specifically observe limitations in the current state-of-the-art and propose a novel Convolu-tional Neural Network (CNN) based method which operates on multi-scale geometric features to gain more information about the shapes being segmented. We also perform an evaluation of several different supervised segmentation methods using the same input features, but with vary-ing complexity of model design. This is intended to see if the more complex models provide a significant performance increase.Lastly, we explore the user-driven area of active learning, to tackle the large amounts of inconsistencies in current ground truth segmentation, which are vital for most segmentation methods. Active learning has been used to great effect for ground truth generation in the past, so we present a novel active learning framework using deep learning and geometric features to assist the user in co-segmentation of a dataset. Our method emphasises segmentation accu-racy while minimising user effort, providing an interactive visualisation for co-segmentation analysis and the application of automated optimisation tools.In this thesis we explore the effectiveness of different geometric features across varying segmentation tasks, providing an in-depth analysis and comparison of state-of-the-art methods

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

Extension and hardware implementation of the comprehensive integrated security system concept

Merged with duplicate record (10026.1/700) on 03.01.2017 by CS (TIS)This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.The current strategy to computer networking is to increase the accessibility that legitimate users have to their respective systems and to distribute functionality. This creates a more efficient working environment, users may work from home, organisations can make better use of their computing power. Unfortunately, a side effect of opening up computer systems and placing them on potentially global networks is that they face increased threats from uncontrolled access points, and from eavesdroppers listening to the data communicated between systems. Along with these increased threats the traditional ones such as disgruntled employees, malicious software, and accidental damage must still be countered. A comprehensive integrated security system ( CISS ) has been developed to provide security within the Open Systems Interconnection (OSI) and Open Distributed Processing (ODP) environments. The research described in this thesis investigates alternative methods for its implementation and its optimisation through partial implementation within hardware and software and the investigation of mechanismsto improve its security. A new deployment strategy for CISS is described where functionality is divided amongst computing platforms of increasing capability within a security domain. Definitions are given of a: local security unit, that provides terminal security; local security servers that serve the local security units and domain management centres that provide security service coordination within a domain. New hardware that provides RSA and DES functionality capable of being connected to Sun microsystems is detailed. The board can be used as a basic building block of CISS, providing fast cryptographic facilities, or in isolation for discrete cryptographic services. Software written for UNIX in C/C++ is described, which provides optimised security mechanisms on computer systems that do not have SBus connectivity. A new identification/authentication mechanism is investigated that can be added to existing systems with the potential for extension into a real time supervision scenario. The mechanism uses keystroke analysis through the application of neural networks and genetic algorithms and has produced very encouraging results. Finally, a new conceptual model for intrusion detection capable of dealing with real time and historical evaluation is discussed, which further enhances the CISS concept

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern

Computational and Numerical Simulations

Computational and Numerical Simulations is an edited book including 20 chapters. Book handles the recent research devoted to numerical simulations of physical and engineering systems. It presents both new theories and their applications, showing bridge between theoretical investigations and possibility to apply them by engineers of different branches of science. Numerical simulations play a key role in both theoretical and application oriented research

Evidences of Equal Error Rate Reduction in Biometric Authentication Fusion

Multimodal biometric authentication (BA) has shown perennial successes both in research and applications. This paper casts a light on why BA systems can be improved by fusing opinions of different experts, principally due to diversity of biometric modalities, features, classifiers and samples. These techniques are collectively called variance reduction (VR) techniques. A thorough survey was carried out and showed that these techniques have been employed in one way or another in the literature, but there was no systematic comparison of these techniques, as done here. Despite the architectural diversity, we show that the improved classification result is due to reduced (class-dependent) variance. The analysis does not assume that scores to be fused are uncorrelated. It does however assume that the class-dependent scores have Gaussian distributions. As many as 180 independent experiments from different sources show that such assumption is acceptable in practice. The theoretical explanation has its root in regression problems. Our contribution is to relate the reduced variance to a reduced classification error commonly used in BA, called Equal Error Rate. In addition to the theoretical evidence, we carried out as many as 104 fusion experiments using commonly used classifiers on the XM2VTS multimodal database to measure the gain due to fusion. This investigation leads to the conclusion that different ways of exploiting diversity incur different hardware and computation cost. In particular, higher diversity incurs higher computation and sometimes hardware cost and vice-versa. Therefore, this study can serve as an engineering guide to choosing a VR technique that will provide a good trade-off between the level of accuracy required and its associated cost

Development of security mechanisms for a multi-agent cyber-physical conveyor system using machine learning

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáOne main foundation of the Industry 4.0 is the connectivity of devices and systems using Internet of Things technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. CPS requires the use of Artificial Intelligence (AI) techniques, such as Multi-Agent Systems (MAS), allowing the incorporation of intelligence into the CPS through autonomous, proactive and cooperative entities. The adoption of this new generation of systems in the industrial environment opens new doors for various attacks that can cause serious damage to industrial production systems. This work presents the development of security mechanisms for systems based on MAS, where these mechanisms are used in an experimental case study that consists of a multiagent cyber-physical conveyor system. For this purpose, simple security mechanisms were employed in the system, such as user authentication, signature and message encryption, as well as other more complex mechanisms, such as machine learning techniques that allows the agents to be more intelligent in relation to the exchange of messages protecting the system against attacks, through the classification of the messages as reliable or not, and also an intrusion detection system was carried out. Based on the obtained results, the efficient protection of the system was reached, mitigating the main attack vectors present in the system architecture.Uma das principais bases da Indústria 4.0 é a conectividade de dispositivos e sistemas utilizando as tecnologias da Internet das Coisas, onde os sistemas ciber-físicos atuam como a infraestrutura principal com base em estruturas distribuídas e descentralizadas. Os sistemas ciber-físicos requerem o uso de técnicas de Inteligência Artificial, como por exemplo, Sistemas Multi-Agentes, permitindo a incorporação de inteligência nos sistemas ciber-físicos através de entidades autônomas, proativas e cooperativas. A adoção dessa nova geração de sistemas no ambiente industrial abre novas portas para vários ataques que podem causar sérios danos aos sistemas de produção industrial. Este trabalho apresenta o desenvolvimento de mecanismos de segurança para sistemas baseados em sistemas multi-agentes, em que esses mecanismos são utilizados em um caso de estudo experimental que consiste em um sistema de transporte ciber-físico baseado em sistemas multi-agentes. Para isso, mecanismos simples de segurança foram empregados no sistema, como autenticação do usuário, assinatura e criptografia de mensagens, além de outros mecanismos mais complexos, como técnicas de aprendizagem de máquina, que permite que os agentes sejam mais inteligentes em relação à troca de mensagens, protegendo o sistema contra ataques, através da classificação das mensagens como confiáveis ou não, e também foi realizado um sistema de detecção de intrusões. Com base nos resultados obtidos, obteve-se uma proteção eficiente do sistema, mitigando os principais vetores de ataque presentes na arquitetura do sistema