Flow-graph techniques for epicyclic gear train analysis

Flow-graph techniques are applied in this article for the analysis of an epicyclic gear train. A gear system based on this is designed and constructed for use in Numerical Control Systems.

step-up epicyclic gear-train and flowgraph analysis

An epicyclic gear-train system with a speed step-up of 1:10, useful for numerical control work, is presented. Also, the analysis of such a system is carried out using flowgraph techniques.

Improved assessment of aerosol absorption using OMI-MODIS joint retrieval

The Ozone Monitoring Instrument (OMI) aboard EOS-Aura and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS-Aqua fly in formation as part of the A-train. Though OMI retrieves aerosol optical depth (AOD) and aerosol absorption, it must assume aerosol layer height. The MODIS cannot retrieve aerosol absorption, but MODIS aerosol retrieval is not sensitive to aerosol layer height and with its smaller pixel size is less affected by subpixel clouds. Here we demonstrate an approach that uses MODIS-retrieved AOD to constrain the OMI retrieval, freeing OMI from making an a priori estimate of aerosol height and allowing a more direct retrieval of aerosol absorption. To predict near-UV optical depths using MODIS data we rely on the spectral curvature of the MODIS-retrieved visible and near-IR spectral AODs. Application of an OMI-MODIS joint retrieval over the north tropical Atlantic shows good agreement between OMI and MODIS-predicted AODs in the UV, which implies that the aerosol height assumed in the OMI-standard algorithm is probably correct. In contrast, over the Arabian Sea, MODIS-predicted AOD deviated from the OMI-standard retrieval, but combined OMI-MODIS retrievals substantially improved information on aerosol layer height (on the basis of validation against airborne lidar measurements). This implies an improvement in the aerosol absorption retrieval, but lack of UV absorption measurements prevents a true validation. Our study demonstrates the potential of multisatellite analysis of A-train data to improve the accuracy of retrieved aerosol products and suggests that a combined OMI-MODIS-CALIPSO retrieval has large potential to further improve assessments of aerosol absorption.

A new intelligent algorithm for online voltage stability assessment and monitoring

This paper presents a new approach for assessing power system voltage stability based on artificial feed forward neural network (FFNN). The approach uses real and reactive power, as well as voltage vectors for generators and load buses to train the neural net (NN). The input properties of the NN are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The performance of the trained NN is investigated on two systems under various voltage stability assessment conditions. Main advantage is that the proposed approach is fast, robust, accurate and can be used online for predicting the L-indices of all the power system buses simultaneously. The method can also be effectively used to determining local and global stability margin for further improvement measures.

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.

Uniformity in Bias Tilt for Nematics Oriented by Polyvinyl Alcohol Surface Layers

Uniformity in bias tilt, for the polyvinyl alcohol(PVA)surface layer induced orientation of nematic liquid crystals, could be achieved for large area display panels, if one of the transparent electrodes is first directionally rubbed with fine abrasive; then both the electrodes coated with PVA, followed by directionally buffing the chemisorbed layers in the same direction. Uniformity may be due to increased 'train' configuration of the adsorbed macromolecule by falling on to microgrooves and maintaining the same sense of asymmetry for the looped segments.

Focused Prefetching: Performance Oriented Prefetching Based On Commit Stalls

Loads that miss in L1 or L2 caches and waiting for their data at the head of the ROB cause significant slow down in the form of commit stalls. We identify that most of these commit stalls are caused by a small set of loads, referred to as LIMCOS (Loads Incurring Majority of COmmit Stalls). We propose simple history-based classifiers that track commit stalls suffered by loads to help us identify this small set of loads. We study an application of these classifiers to prefetching. The classifiers are used to train the prefetcher to focus on the misses suffered by LIMCOS. This, referred to as focused prefetching, results in a 9.8% gain in IPC over naive GHB based delta correlation prefetcher along with a 20.3% reduction in memory traffic for a set of 17 memory-intensive SPEC2000 benchmarks. Another important impact of focused prefetching is a 61% improvement in the accuracy of prefetches. We demonstrate that the proposed classification criterion performs better than other existing criteria like criticality and delinquent loads. Also we show that the criterion of focusing on commit stalls is robust enough across cache levels and can be applied to any prefetcher without any modifications to the prefetcher.

Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons limultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

Vertical distribution of aerosols over the east coast of India inferred from airborne LIDAR measurements

The information on altitude distribution of aerosols in the atmosphere is essential in assessing the impact of aerosol warming on thermal structure and stability of the atmosphere.In addition, aerosol altitude distribution is needed to address complex problems such as the radiative interaction of aerosols in the presence of clouds. With this objective,an extensive, multi-institutional and multi-platform field experiment (ICARB-Integrated Campaign for Aerosols, gases and Radiation Budget) was carried out under the Geosphere Biosphere Programme of the Indian Space Research Organization (ISRO-GBP) over continental India and adjoining oceans during March to May 2006. Here, we present airborne LIDAR measurements carried out over the east Coast of the India during the ICARB field campaign. An increase in aerosol extinction (scattering + absorption) was observed from the surface upwards with a maximum around 2 to 4 km. Aerosol extinction at higher atmospheric layers (>2 km) was two to three times larger compared to that of the surface. A large fraction (75-85%) of aerosol column optical depth was contributed by aerosols located above 1 km. The aerosol layer heights (defined in this paper as the height at which the gradient in extinction coefficient changes sign) showed a gradual decrease with an increase in the offshore distance. A large fraction (60-75%) of aerosol was found located above clouds indicating enhanced aerosol absorption above clouds. Our study implies that a detailed statistical evaluation of the temporal frequency and spatial extent of elevated aerosol layers is necessary to assess their significance to the climate. This is feasible using data from space-borne lidars such as CALIPSO,which fly in formation with other satellites like MODIS AQUA and MISR, as part of the A-Train constellation.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Sensitivity of Eigen Value to Damage and Its Identification

The reduction in natural frequencies,however small, of a civil engineering structure, is the first and the easiest method of estimating its impending damage. As a first level screening for health-monitoring, information on the frequency reduction of a few fundamentalmodes can be used to estimate the positions and the magnitude of damage in a smeared fashion. The paper presents the Eigen value sensitivity equations, derived from first-order perturbation technique, for typical infra-structural systems like a simply supported bridge girder, modelled as a beam, an endbearing pile, modelled as an axial rod and a simply supported plate as a continuum dynamic system. A discrete structure, like a building frame is solved for damage using Eigen-sensitivity derived by a computationalmodel. Lastly, neural network based damage identification is also demonstrated for a simply supported bridge beam, where the known-pairs of damage-frequency vector is used to train a neural network. The performance of these methods under the influence of measurement error is outlined. It is hoped that the developed method could be integrated in a typical infra-structural management program, such that magnitudes of damage and their positions can be obtained using acquired natural frequencies, synthesized from the excited/ambient vibration signatures.

Developement of DSP based educational kit for power electronics and drives

The educational kit was developed for power electronics and drives. The need and purpose of this kit is to train engineers with current technology of digital control in power electronics. The DSP is the natural choice as it is able to perform high speed calculations required in power electronics. The educational kit consists of a DSP platform using TI DSP TMS320C50 starter kit, an inverter and an induction machine-dc machine set. A set of experiments have been prepared so that DSP programming can be learned easily in a smooth fashion. Here the application presented is open loop V/F control of three phase induction using sine pulse width modulation technique.

A Thermodynamic Proposal for the Exposition of Critically and other Subtle Features in Self-Deflagrating Ammonium Perchlorate Systems

A first comprehensive investigation on the deflagration of ammonium perchlorate (AP) in the subcritical regime, below the low pressure deflagration limit (LPL, 2.03 MPa) christened as regime I$^{\prime}$, is discussed by using an elegant thermodynamic approach. In this regime, deflagration was effected by augmenting the initial temperature (T$_{0}$) of the AP strand and by adding fuels like aliphatic dicarboxylic acids or polymers like carboxy terminated polybutadiene (CTPB). From this thermodynamic model, considering the dependence of burning rate ($\dot{r}$) on pressure (P) and T$_{0}$, the true condensed (E$_{\text{s,c}}$) and gas phase (E$_{\text{s,g}}$) activation energies, just below and above the surface respectively, have been obtained and the data clearly distinguishes the deflagration mechanisms in regime I$^{\prime}$ and I (2.03-6.08 MPa). Substantial reduction in the E$_{\text{s,c}}$ of regime I$^{\prime}$, compared to that of regime I, is attributed to HClO$_{4}$ catalysed decomposition of AP. HClO$_{4}$ formation, which occurs only in regime I$^{\prime}$, promotes dent formation on the surface as revealed by the reflectance photomicrographs, in contrast to the smooth surface in regime I. The HClO$_{4}$ vapours, in regime I$^{\prime}$, also catalyse the gas phase reactions and thus bring down the E$_{\text{s,g}}$ too. The excess heat transferred on to the surface from the gas phase is used to melt AP and hence E$_{\text{s,c}}$, in regime I, corresponds to the melt AP decomposition. It is consistent with the similar variation observed for both the melt layer thickness and $\dot{r}$ as a function of P. Thermochemical calculations of the surface heat release support the thermodynamic model and reveal that the AP sublimation reduces the required critical exothermicity of 1108.8 kJ kg$^{-1}$ at the surface. It accounts for the AP not sustaining combustion in the subcritical regime I$^{\prime}$. Further support for the model comes from the temperature-time profiles of the combustion train of AP. The gas and condensed phase enthalpies, derived from the profile, give excellent agreement with those computed thermochemically. The $\sigma _{\text{p}}$ expressions derived from this model establish the mechanistic distinction of regime I$^{\prime}$ and I and thus lend support to the thermodynamic model. On comparing the deflagration of strand against powder AP, the proposed thermodynamic model correctly predicts that the total enthalpy of the condensed and gas phases remains unaltered. However, 16% of AP particles undergo buoyant lifting into the gas phase in the `free board region' (FBR) and this renders the demarcation of the true surface difficult. It is found that T$_{\text{s}}$ lies in the FBR and due to this, in regime I$^{\prime}$, the E$_{\text{s,c}}$ of powder AP matches with the E$_{\text{s,g}}$ of the pellet. The model was extended to AP/dicarboxylic acids and AP/CTPB mixture. The condensed ($\Delta $H$_{1}$) and gas phase ($\Delta $H$_{2}$) enthalpies were obtained from the temperature profile analyses which fit well with those computed thermochemically. The $\Delta $H$_{1}$ of the AP/succinic acid mixture was found just at the threshold of sustaining combustion. Indeed the lower homologue malonic acid, as predicted, does not sustain combustion. In vaporizable fuels like sebacic acid the E$_{\text{s,c}}$ in regime I$^{\prime}$, understandably, conforms to the AP decomposition. However, the E$_{\text{s,c}}$ in AP/CTPB system corresponds to the softening of the polymer which covers AP particles to promote extensive condensed phase reactions. The proposed thermodynamic model also satisfactorily explains certain unique features like intermittent, plateau and flameless combustion in AP/ polymeric fuel systems.