Identification and control of induction machines using artificial neural networks

The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

Identification and control of induction machines using artificial neural networks

This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

Vector control of a high power induction machine

The design and implementation of a high-power (2 MW peak) vector control drive is described. The inverter switching frequency is low, resulting in high-harmonic-content current waveforms. A block diagram of the physical system is given, and each component is described in some detail. The problem of commanded slip noise sensitivity, inherent in high-power vector control drives, is discussed, and a solution is proposed. Results are given which demonstrate the successful functioning of the system

Osteogenic induction of human bone marrow-derived mesenchymal progenitor cells in novel synthetic polymer-hydrogel matrices

The aim of this project was to investigate the in vitro osteogenic potential of human mesenchymal progenitor cells in novel matrix architectures built by means of a three-dimensional bioresorbable synthetic framework in combination with a hydrogel. Human mesenchymal progenitor cells (hMPCs) were isolated from a human bone marrow aspirate by gradient centrifugation. Before in vitro engineering of scaffold-hMPC constructs, the adipogenic and osteogenic differentiation potential was demonstrated by staining of neutral lipids and induction of bone-specific proteins, respectively. After expansion in monolayer cultures, the cells were enzymatically detached and then seeded in combination with a hydrogel into polycaprolactone (PCL) and polycaprolactone-hydroxyapatite (PCL-HA) frameworks. This scaffold design concept is characterized by novel matrix architecture, good mechanical properties, and slow degradation kinetics of the framework and a biomimetic milieu for cell delivery and proliferation. To induce osteogenic differentiation, the specimens were cultured in an osteogenic cell culture medium and were maintained in vitro for 6 weeks. Cellular distribution and viability within three-dimensional hMPC bone grafts were documented by scanning electron microscopy, cell metabolism assays, and confocal laser microscopy. Secretion of the osteogenic marker molecules type I procollagen and osteocalcin was analyzed by semiquantitative immunocytochemistry assays. Alkaline phosphatase activity was visualized by p-nitrophenyl phosphate substrate reaction. During osteogenic stimulation, hMPCs proliferated toward and onto the PCL and PCL-HA scaffold surfaces and metabolic activity increased, reaching a plateau by day 15. The temporal pattern of bone-related marker molecules produced by in vitro tissue-engineered scaffold-cell constructs revealed that hMPCs differentiated better within the biomimetic matrix architecture along the osteogenic lineage.

Fuzzy logic traffic control at a road junction with time-varying flow rates

Fuzzy logic has been applied to control traffic at road junctions. A simple controller with one fixed rule-set is inadequate to minimise delays when traffic flow rate is time-varying and likely to span a wide range. To achieve better control, fuzzy rules adapted to the current traffic conditions are used.

Hierarchical fuzzy logic traffic control at a road junction

Traffic control at road junctions is one of the major concerns in most metropolitan cities. Controllers of various approaches are available and the required control action is the effective green-time assigned to each traffic stream within a traffic-light cycle. The application of fuzzy logic provides the controller with the capability to handle uncertain natures of the system, such as drivers’ behaviour and random arrivals of vehicles. When turning traffic is allowed at the junction, the number of phases in the traffic-light cycle increases. The additional input variables inevitably complicate the controller and hence slow down the decision-making process, which is critical in this real-time control problem. In this paper, a hierarchical fuzzy logic controller is proposed to tackle this traffic control problem at a 2-way road junction with turning traffic. The two levels of fuzzy logic controllers devise the minimum effective green-time and fine-tune it respectively at each phase of a traffic-light cycle. The complexity of the controller at each level is reduced with smaller rule-set. The performance of this hierarchical controller is examined by comparison with a fixed-time controller under various traffic conditions. Substantial delay reduction has been achieved as a result and the performance and limitation of the controller will be discussed.

Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Traffic control at a road junction by a complex fuzzy logic controller is investigated. The increase in the complexity of junction means more number of input variables must be taken into account, which will increase the number of fuzzy rules in the system. A hierarchical fuzzy logic controller is introduced to reduce the number of rules. Besides, the increase in the complexity of the controller makes formulation of the fuzzy rules difficult. A genetic algorithm based off-line leaning algorithm is employed to generate the fuzzy rules. The learning algorithm uses constant flow-rates as training sets. The system is tested by both constant and time-varying flow-rates. Simulation results show that the proposed controller produces lower average delay than a fixed-time controller does under various traffic conditions.

Interaction of NPR1 with basic leucine zipper protein transcription factors that bind sequences required for salicylic acid induction of the PR–1 gene

The Arabidopsis thaliana NPR1 has been shown to be a key regulator of gene expression during the onset of a plant disease-resistance response known as systemic acquired resistance. The npr1 mutant plants fail to respond to systemic acquired resistance-inducing signals such as salicylic acid (SA), or express SA-induced pathogenesis-related (PR) genes. Using NPR1 as bait in a yeast two-hybrid screen, we identified a subclass of transcription factors in the basic leucine zipper protein family (AHBP-1b and TGA6) and showed that they interact specifically in yeast and in vitro with NPR1. Point mutations that abolish the NPR1 function in A. thaliana also impair the interactions between NPR1 and the transcription factors in the yeast two-hybrid assay. Furthermore, a gel mobility shift assay showed that the purified transcription factor protein, AHBP-1b, binds specifically to an SA-responsive promoter element of the A. thaliana PR-1 gene. These data suggest that NPR1 may regulate PR-1 gene expression by interacting with a subclass of basic leucine zipper protein transcription factors.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Induction of anti-chlamydial mucosal immunity by transcutaneous immunization is enhanced by topical application of GM-CSF

Transcutaneous immunization (TCI) involves the direct application of antigen plus adjuvant to skin, taking advantage of the large numbers of Langerhans cells and other resident skin dendritic cells, that process antigen then migrate to draining lymph nodes where immune responses are initiated. We have used this form of immunization to protect mice against genital tract and respiratory tract chlamydial infection. Protection was associated with local antibody responses in the vagina, uterus and lung as well as strong Th1 responses in the lymph nodes draining the reproductive tract and lungs respectively. In this study we show that topical application of GM-CSF to skin enhances the numbers and activation status of epidermal dendritic cells. Topical application of GM-CSF also increased the immune responses elicited by TCI. GM-CSF supplementation greatly increased cytokine (IFNgamma and IL-4) gene expression in lymph node and splenic cells compared to cells from animals immunized without GM-CSF. IgG responses in serum, uterine lavage and bronchoalveolar lavage and IgA responses in vaginal lavage were also increased by topical application of GM-CSF. The studies show that TCI induces protection against genital and respiratory tract chlamydial infections and that topical application of cytokines such as GM-CSF can enhance TCI-induced antibody and cell-mediated immunity.

Comparison of intranasal and transcutaneous immunization for induction of protective immunity against chlamydia muridarum respiratory tract infection

Chlamydia pneumoniae causes a range of respiratory infections including bronchitis, pharyngitis and pneumonia. Infection has also been implicated in exacerbation/initiation of asthma and chronic obstructive pulmonary disease (COPD) and may play a role in atherosclerosis and Alzheimer's disease. We have used a mouse model of Chlamydia respiratory infection to determine the effectiveness of intranasal (IN) and transcutaneous immunization (TCI) to prevent Chlamydia lung infection. Female BALB/c mice were immunized with chlamydial major outer membrane protein (MOMP) mixed with cholera toxin and CpG oligodeoxynucleotide adjuvants by either the IN or TCI routes. Serum and bronchoalveolar lavage (BAL) were collected for antibody analysis. Mononuclear cells from lung-draining lymph nodes were stimulated in vitro with MOMP and cytokine mRNA production determined by real time PCR. Animals were challenged with live Chlamydia and weighed daily following challenge. At day 10 (the peak of infection) animals were sacrificed and the numbers of recoverable Chlamydia in lungs determined by real time PCR. MOMP-specific antibody-secreting cells in lung tissues were also determined at day 10 post-infection. Both IN and TCI protected animals against weight loss compared to non-immunized controls with both immunized groups gaining weight by day 10-post challenge while controls had lost 6% of body weight. Both immunization protocols induced MOMP-specific IgG in serum and BAL while only IN immunization induced MOMP-specific IgA in BAL. Both immunization routes resulted in high numbers of MOMP-specific antibody-secreting cells in lung tissues (IN > TCI). Following in vitro re-stimulation of lung-draining lymph node cells with MOMP; IFNγ mRNA increased 20-fold in cells from IN immunized animals (compared to non-immunized controls) while IFNγ levels increased 6- to 7-fold in TCI animals. Ten days post challenge non-immunized animals had >7000 IFU in their lungs, IN immunized animals <50 IFU and TCI immunized animals <1500 IFU. Thus, both intranasal and transcutaneous immunization protected mice against respiratory challenge with Chlamydia. The best protection was obtained following IN immunization and correlated with IFNγ production by mononuclear cells in lung-draining LN and MOMP-specific IgA in BAL.