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 characterization of non-pathogenic Fusarium oxysporum capable of increasing and decreasing Fusarium wilt severity

Fusarium wilt of banana is a potentially devastating disease throughout the world. Options for control of the causal organism, Fusarium oxysporum f.sp. cubense (Foc) are limited. Suppressive soil sites have previously been identified where, despite the presence of Foc, Fusarium wilt does not develop. In order to understand some aspects of this disease suppression, endophytic Fusarium oxysporum isolates were obtained from banana roots. These isolates were genetically characterized and compared with an isolate of Fusarium oxysporum previously identified as being capable of suppressing Fusarium wilt of banana in glasshouse trials. Three additional isolates were selected for glasshouse trials to assess suppression of Fusarium wilt in two different cultivars of banana, Cavendish and Lady Finger. One isolate (BRIP 29089) was identified as a potential biocontrol organism, reducing the disease severity of Fusarium wilt in Lady Finger and Cavendish cultivars. Interestingly, one isolate (BRIP 45952) increased Fusarium wilt disease severity on Cavendish. The implications of an isolate of Fusarium oxysporum, non-pathogenic on banana, increasing disease severity and the potential role of non-pathogenic isolates of Fusarium oxysporum in disease complexes are discussed.

Firearm Owner's Identification Card Act (FOID) : Deadly Weapons Article : frequently asked questions.

"6/04"--Colophon.

Identification and analysis of venom gland-specific genes from the coastal taipan (Oxyuranus scutellatus) and related species

Australian terrestrial elapid snakes contain amongst the most potently toxic venoms known. However, despite the well-documented clinical effects of snake bite, little research has focussed on individual venom components at the molecular level. To further characterise the components of Australian elapid venoms, a complementary (cDNA) microarray was produced from the venom gland of the coastal taipan (Oxyuranus scutellatus) and subsequently screened for venom gland-specific transcripts. A number of putative toxin genes were identified, including neurotoxins, phospholipases, a pseudechetoxin-like gene, a venom natriuretic peptide and a nerve growth factor together with other genes involved in cellular maintenance. Venom gland-specific components also included a calglandulin-like protein implicated in the secretion of toxins from the gland into the venom. These toxin transcripts were subsequently identified in seven other related snake species, producing a detailed comparative analysis at the cDNA and protein levels. This study represents the most detailed description to date of the cloning and characterisation of different genes associated with envenomation from Australian snakes.

Neural network assisted autotuning of industrial PID controllers

A recent servey (1) has reported that the majority of industrial loops are controlled by PID-type controllers and many of the PID controllers in operation are poorly tuned. poor PID tuning is due to the lack of a simple and practical tuning method for avarage users, and due to the tedious procedurs involved in the tuning and retuning of PID controllers.

Multiloop controller and reference generator for a dynamic voltage restorer implementation

This paper deals with the design and analysis of a Dynamic Voltage Restorer output voltage control. Such control is based on a multiloop strategy, with an inner current PID regulator and an outer P+Resonant voltage controller. The inner regulator is applied on the output inductor current. It will be also demonstrated how the load current behavior may influence in the DVR output voltage, which justifies the need for the resonant controller. Additionally, it will be discussed the application of a modified algorithm for the identification of the DVR voltage references, which is based on a previously presented positive sequence detector. Since the studied three-phase DVR is assumed to be based on three identical H-bridge converters, all the analysis and design procedures were realized by means of single-phase equivalent circuits. The discussions and conclusions are supported by theoretical calculations, nonlinear simulations and some experimental results. ©2008 IEEE.

Gestão de risco: a importância da resiliência em eventos indesejáveis

Atualmente, ferramentas e dados estatísticos são muito utilizados para avaliar as condições perigosas enquanto, por outro lado, as pessoas usam o julgamento para perceber o risco, que tem como base a cultura do risco. A percepção do risco muda conforme o ambiente no qual a pessoa está imersa, e se diferencia conforme a cultura. O objetivo desta pesquisa é conhecer qual o papel dos diversos atores envolvidos na gestão de riscos e como a resiliência ajuda nos eventos indesejáveis. Foram investigados onze eventos indesejáveis, com dez entrevistados em seis organizações, com o objetivo de identificar e analisar como a gestão de risco, a resiliência e a percepção do risco interagem. A análise multifacetada reforçou a importância dos aspectos de resiliência para uma gestão de risco eficaz. A participação dos possíveis envolvidos no evento, desde o contexto da gestão, reforçado pelo controle compartilhado, identificação das habilidades individuais não prescritas, incentivo à cooperação entre esses atores, comunicação eficaz e simplificação dos processos são aspectos integradores a uma gestão de risco. Como oportunidade de investigação futura, a pesquisa reforça a necessidade de analisar aspectos da cultura organizacional abrangendo as ciências sociais: antropologia, sociologia, psicodinâmica do trabalho, sociologia da ética e cultura país como agente consciente e experimentador da realidade.

Test and evaluation of the AMF, Inc. and Fairchild Industries experimental safety vehicles. Volume II. ESV program summary. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Human factors criteria for vehicle controls and displays. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Human factors criteria for vehicle controls and displays. Appendix B. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Human factors criteria for vehicle controls and displays. Appendix A. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

System identification and control of an aerobot drive system

Fast thrust changes are important for authoritive control of VTOL micro air vehicles. Fixed-pitch rotors that alter thrust by varying rotor speed require high-bandwidth control systems to provide adequate performace. We develop a feedback compensator for a brushless hobby motor driving a custom rotor suitable for UAVs. The system plant is identified using step excitation experiments. The aerodynamic operating conditions of these rotors are unusual and so experiments are performed to characterise expected load disturbances. The plant and load models lead to a proportional controller design capable of significantly decreasing rise-time and propagation of disturbances, subject to bus voltage constraints.

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