Inverse induction motor modelling of a submarine propulsion system

An inherent trade-off exists in simulation model development and employment: a trade-off between the level of detail simulated and the simulation models computational cost. It is often desirable to simulate a high level of detail to a high degree of accuracy. However, due to the nature of design optimisation, which requires a large number of design evaluations, the application of such simulation models can be prohibitively expensive. A induction motor modelling approache to reduce the computational cost while maintaining a high level of detail and accuracy in the final design is presented. © 2012 IEEE.

Updated correlation between aircraft smoke number and black carbon concentration

Aircraft emissions of black carbon (BC) contribute to anthropogenic climate forcing and degrade air quality. The smoke number (SN) is the current regulatory measure of aircraft particulate matter emissions and quantifies exhaust plume visibility. Several correlations between SN and the exhaust mass concentration of BC (CBC) have been developed, based on measurements relevant to older aircraft engines. These form the basis of the current standard method used to estimate aircraft BC emissions (First Order Approximation version 3 [FOA3]) for the purposes of environmental impact analyses. In this study, BC with a geometric mean diameter (GMD) of 20, 30, and 60 nm and filter diameters of 19 and 35 mm are used to investigate the effect of particle size and sampling variability on SN measurements. For BC with 20 and 30 nm GMD, corresponding to BC emitted by modern aircraft engines, a smaller SN results from a given CBC than is the case for BC with 60 nm GMD, which is more typical of older engines. An updated correlation between CBC and SNthat accounts for typical size of BC emitted by modern aircraft is proposed. An uncertainty of ±25% accounts for variation in GMD in the range 20-30 nm and for the range of filter diameters. The SN-CBC correlation currently used in FOA3 underestimates by a factor of 2.5-3 for SN <15, implying that current estimates of aircraft BC emissions derived from SN are underestimated by the same factor. Copyright © American Association for Aerosol Research.

Monitoring bioaccumulation and toxic effects of hexachlorobenzene using the polyurethane foam unit method in the microbial communities of the Fuhe River, Wuhan

Hexachlorobenzene (HCB) is a chlorinated aromatic hydrocarbon that was widely used for seed dressing in prevention of fungal growth on crops, and also as a component of fireworks, ammunition, and synthetic rubbers. Because of its resistance to degradation and mobility, HCB is widely distributed throughout the environment and is accumulated through food chains in different ecosystems. In this study, a preliminary investigation was carried out on the bioaccumulation and the toxic effects of HCB in the microbial (protozoan in particular) communities in the Fuhe River, Wuhan, a water body receiving industrial wastewaters containing HCB and other pollutants, using the standardized polyurethane foam units (PFU) method. Field samples were taken from eight stations established along the Fuhe River in January and August 2006. The concentration ratios of HCB in microbial communities and in water were 9.66-18.64, and the microbial communities accumulated 13.29-56.88 mu g/L of HCB in January and 0.82-10.25 mu g/L HCB in August. Correlation analysis showed a negative correlation between the HCB contents in the microbial assemblage, and the number of species and the diversity index of the protozoan communities. This study demonstrated the applicability of the PFU method in monitoring the effects of HCB on the level of microbial communities.

An ultra-low power CMOS random number generator

This paper proposes an ultra-low power CMOS random number generator (RING), which is based on an oscillator-sampling architecture. The noisy oscillator consists of a dual-drain MOS transistor, a noise generator and a voltage control oscillator. The dual-drain MOS transistor can bring extra-noise to the drain current or the output voltage so that the jitter of the oscillator is much larger than the normal oscillator. The frequency division ratio of the high-frequency sampling oscillator and the noisy oscillator is small. The RNG has been fabricated in a 0.35 mu m CMOS process. It can produce good quality bit streams without any post-processing. The bit rate of this RNG could be as high as 100 kbps. It has a typical ultra-low power dissipation of 0.91 mu W. This novel circuit is a promising unit for low power system and communication applications. (c) 2007 Elsevier Ltd. All rights reserved.

Effect of copolymerized ethylene unit on the crystallization behavior of poly(propylene-co-ethylene)s

The crystallization behavior of two kinds of commercial poly(propylene-co-ethylene)s (PPE1, PPE2) with similar average molecular weight and molecular weight distribution, isotacticity and copolymerized ethylene unit content and their fractions was investigated by differential scanning calorimetry (DSC), transmission electron microscopy (TEM) and polarized optical microscopy (POM) techniques. The results indicate that the PPE1 isothermally crystallized films possess thicker and less cross-hatched lamellar structure than those of the PPE2. As for the fractionated samples, the thin films of low temperature (less than or equal to 90 degreesC) fractions (PPE1-80, PPE2-80) of both PPE1 and PPE2 exhibit similar crystallization behavior, while for the high temperature ( greater than or equal to 95 degreesC) fractions (PPE1-108, PPE2-108), the crystalline morphology has marked differences. Compared with PPE2-108, the PPE1-108 isothermally crystallized thin films possess thicker lamellae and less crosshatched lamellar structure, while for the fibrous crystal number, the former is less than that of the latter. The main reason to create the crystallization behavior differences between the two PPEs and their fractions is due to the effect of molecular chain structure, i.e. the different distribution of copolymerized ethylene unit in polypropylene chains.

Cardo polyarylethersulfones and polyaryletherketones bearing alkyl substituents on the phenylene unit

A series of cardo polyaryletherketones and polyaryletersulfones containing alkyl substituents of a different kind, number and volume were synthesized from bis(4-nitrophenyl)ketone or bis(4-fluorophenyl)sulfone with various alkyl substituted phenolphthaleins by polycondensation using K2CO3 as catalyst. Their chemical and aggregation structures were confirmed by FT-IR, H-1-NMR and WAXD. The resulting polymers were soluble in a variety of common polar solvents and, transparent, colorless, and tough films could be easily cast from 1,1,2-trichluoroethane solution. Their tensile strength, elongation at break and tensile modulis were in the range of 70.5 similar to 97.1MPa, 4.49%similar to7.81%, and 1.69 similar to2.27GPa, respectively. The prepared polymers had reasonably high glass transition temperatures at 207 to 269 degreesC, and showed fairly good thermal stability with 5% thermal decomposition loss above 410 degreesC.

Using triazine as coupling unit for intramolecular ferromagnetic coupling of multiradicals

Novel high spin tri-, tetra-, pentaradicals, composed of triazine coupling units and cationic amino radical spin centers (+ . NH) under various configurations and linkages, are predicted from AM1-CI calculations. It is found that for charged planar multiradicals the stability of high spin ground states depends on both the molecular configuration and the number of end groups. Generally, cyclic 1,3-bridged charged multiradicals (S less than or equal to 5/2) possess more stable high spin ground states than their isomers under the branched 1,3,5,-bridged configuration. Therefore, it is suggested that in the design of planar high spin molecules with stable high spin ground states, less end groups and all the supposed spin centers and/or the coupling units should be under the same structural situation. (C) 1999 Elsevier Science B.V. All rights reserved.

Neural Modeling and Imaging of the Cortical Interactions Underlying Syllable Production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequence to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

Cerebellar Learning in an Opponent Motor Controller for Adaptive Load Compensation and Synergy Formation

This paper shows how a minimal neural network model of the cerebellum may be embedded within a sensory-neuro-muscular control system that mimics known anatomy and physiology. With this embedding, cerebellar learning promotes load compensation while also allowing both coactivation and reciprocal inhibition of sets of antagonist muscles. In particular, we show how synaptic long term depression guided by feedback from muscle stretch receptors can lead to trans-cerebellar gain changes that are load-compensating. It is argued that the same processes help to adaptively discover multi-joint synergies. Simulations of rapid single joint rotations under load illustrates design feasibility and stability.

A Self-Organizing Neural Model for Motor Equivalent Phoneme Production

This paper describes a model of speech production called DIVA that highlights issues of self-organization and motor equivalent production of phonological units. The model uses a circular reaction strategy to learn two mappings between three levels of representation. Data on the plasticity of phonemic perceptual boundaries motivates a learned mapping between phoneme representations and vocal tract variables. A second mapping between vocal tract variables and articulator movements is also learned. To achieve the flexible control made possible by the redundancy of this mapping, desired directions in vocal tract configuration space are mapped into articulator velocity commands. Because each vocal tract direction cell learns to activate several articulator velocities during babbling, the model provides a natural account of the formation of coordinative structures. Model simulations show automatic compensation for unexpected constraints despite no previous experience or learning under these constraints.

A Neural Network Model of Speech Acquisition and Motor Equivalent Speech Production

This article describes a neural network model that addresses the acquisition of speaking skills by infants and subsequent motor equivalent production of speech sounds. The model learns two mappings during a babbling phase. A phonetic-to-orosensory mapping specifies a vocal tract target for each speech sound; these targets take the form of convex regions in orosensory coordinates defining the shape of the vocal tract. The babbling process wherein these convex region targets are formed explains how an infant can learn phoneme-specific and language-specific limits on acceptable variability of articulator movements. The model also learns an orosensory-to-articulatory mapping wherein cells coding desired movement directions in orosensory space learn articulator movements that achieve these orosensory movement directions. The resulting mapping provides a natural explanation for the formation of coordinative structures. This mapping also makes efficient use of redundancy in the articulator system, thereby providing the model with motor equivalent capabilities. Simulations verify the model's ability to compensate for constraints or perturbations applied to the articulators automatically and without new learning and to explain contextual variability seen in human speech production.

Laminar Cortical Dynamics of Cognitive and Motor Working Memory, Sequence Learning and Performance: Toward a Unified Theory of How the Cerebral Cortex Works

How do the layered circuits of prefrontal and motor cortex carry out working memory storage, sequence learning, and voluntary sequential item selection and performance? A neural model called LIST PARSE is presented to explain and quantitatively simulate cognitive data about both immediate serial recall and free recall, including bowing of the serial position performance curves, error-type distributions, temporal limitations upon recall, and list length effects. The model also qualitatively explains cognitive effects related to attentional modulation, temporal grouping, variable presentation rates, phonemic similarity, presentation of non-words, word frequency/item familiarity and list strength, distracters and modality effects. In addition, the model quantitatively simulates neurophysiological data from the macaque prefrontal cortex obtained during sequential sensory-motor imitation and planned performance. The article further develops a theory concerning how the cerebral cortex works by showing how variations of the laminar circuits that have previously clarified how the visual cortex sees can also support cognitive processing of sequentially organized behaviors.

How Spinal Neural Networks Reduce Discrepancies between Motor Intention and Motor Realization

This paper attempts a rational, step-by-step reconstruction of many aspects of the mammalian neural circuitry known to be involved in the spinal cord's regulation of opposing muscles acting on skeletal segments. Mathematical analyses and local circuit simulations based on neural membrane equations are used to clarify the behavioral function of five fundamental cell types, their complex connectivities, and their physiological actions. These cell types are: α-MNs, γ-MNs, IaINs, IbINs, and Renshaw cells. It is shown that many of the complexities of spinal circuitry are necessary to ensure near invariant realization of motor intentions when descending signals of two basic types independently vary over large ranges of magnitude and rate of change. Because these two types of signal afford independent control, or Factorization, of muscle LEngth and muscle TEnsion, our construction was named the FLETE model (Bullock and Grossberg, 1988b, 1989). The present paper significantly extends the range of experimental data encompassed by this evolving model.