Optimization of Helicopter Rotor Using Polynomial and Neural Network Metamodels

This study aims to determine optimal locations of dual trailing-edge flaps and blade stiffness to achieve minimum hub vibration levels in a helicopter, with low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithm to determine the flap time history for vibration minimization. Using the aeroelastic analysis, it is found that the objective functions are highly nonlinear and polynomial response surface approximations cannot describe the objectives adequately. A neural network is then used for approximating the objective functions for optimization. Pareto-optimal points minimizing both helicopter vibration and flap power ale obtained using the response surface and neural network metamodels. The two metamodels give useful improved designs resulting in about 27% reduction in hub vibration and about 45% reduction in flap power. However, the design obtained using response surface is less sensitive to small perturbations in the design variables.

Evidence for follicle-stimulating hormone mediation in the hemiorchidectomy-induced compensatory increase in the function of the remaining testis of the adult male bonnet monkey (Macaca radiata)

Hemiorchidectomy (HO) in the adult male bonnet monkey results in a selective increase in circulating concentrations of FSH and testosterone, and this is accompanied by compensatory increase in sperm production by the remaining testis. We investigated the possible role of increased FSH concentration that occurs after HO in the compensatory increase in the activity of the remaining testis. Of eight adult male bonnet monkeys that underwent HO, four received i.v. injections every other day for 30 days of a well-characterized ovine FSH antiserum (a/s) that cross-reacts with monkey FSH. The remaining four males received normal monkey serum (NMS) as control treatment in a protocol similar to that employed for ais-treated males. Blood samples were collected between 2100 and 2200 h before and 1/2, 1, 3, 5, 7, 14, 22, and 29 days after HO. Testicular weight, number of 3 beta-hydroxy steroid dehydrogenase-positive (3 beta-HSD+) cells, and DNA flow cytometric analysis of germ cell populations were obtained for testes collected before and at the termination of NMS or ais treatment. In NMS-treated males, circulating serum FSH concentrations progressively increased to reach a maximal level by Day 7 after HO (1.95 +/- 0.3 vs. 5.6 +/- 0.7 ng/ml on Days -1 and 7, respectively). Within 30 min of ais injection, FSH antibodies were detected in circulation, and the antibody level was maintained at a constant level between Day 7 and end of treatment (exhibiting 50-60% binding to I-125-hFSH). Although circulating mean nocturnal serum testosterone concentration showed an initial decrease, it rose gradually to pre-HO concentrations by Day 7 in NMS-treated males. In contrast, nocturnal mat serum testosterone concentrations in a/s-treated males remained lower than in NMS-treated controls (p < 0.05) up to Day 22 and thereafter only marginally increased. Testicular weights increased (p < 0.05) over the pre-HO weight in NMS- but not in ais-treated males. After HO, the number of 3 beta-HSD+ cells (Leydig cells) was markedly increased but was significantly (p < 0.05) higher in NMS-treated males compared to a/s-treated males. A significant (p < 0.05) reduction in the primary spermatocyte population of germ cells was observed in ais-treated compared to NMS-treated males. These results suggest that the increased FSH occurring after HO could be intimately involved in increasing the compensatory functional activity of the remaining testis in the male bonnet monkey.

Mathematical modelling of neurons and neural networks

The basic concepts and techniques involved in the development and analysis of mathematical models for individual neurons and networks of neurons are reviewed. Some of the interesting results obtained from recent work in this field are described. The current status of research in this field in India is discussed

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

Male-biased adult sex ratios and their significance for cooperative breeding in dhole, Cuon alpinus, packs

Two free-ranging packs of dholes (Asiatic wild dog, Cuon alpinus) were monitored for a period of 6 yr (Sep. 1990-Sep. 1996) in the Mudumalai sanctuary, southern India. Demographic data on age structure, litter-size, sex ratio and age and sex specific dispersal were collected. Behavioural data on social interactions and reproductive behaviour among pack members were obtained to determine the frequencies of dominant and subordinate behaviours shown by malt: and female pack members and a measure of each male's reproductive access to females. Behaviours displayed by pack members at dens were recorded to determine whether any age- or sex-specific role specialization existed during pup care. Tenures for dominant males and females within the pack were calculated to ascertain the rate of breeding vacancies occurring within packs. Approximate levels of genetic relatedness within packs were determined by studying pedigrees. In most years one study pack had a male-biased adult sex ratio. This was caused by almost twofold higher dispersal of adult females over adult males. A considerable variance existed in the percentage of sub-adults dispersing from the two packs. Differences existed in the frequencies of dominant and subordinate behaviours shown by males. For males, dominance ranks and ranks based on submissive behaviours were not correlated with frequencies of reproductive behaviours. Subordinate males also displayed reproductive behaviours. In packs, dominant males had lower tenures than dominant females indicating that among males breeding vacancies arose more quickly. The litter size was found to be negatively correlated with the age of the breeding female. There were no significant differences across individuals of varying age or sex classes in the display of pup care behaviours. Significant differences did exist among individual adults. Genetic relatedness among packs tended to vary temporally as a consequence of possible mating by subordinate animals and immigration of new males into the pack. In conclusion, it appears that males delay dispersal and cooperate within their natal packs because of the variety of reproductive strategies they could pursue within. A combination of ecological constraints and the difficulties of achieving breeding status within non-natal packs may make early dispersal and independent breeding less beneficial.

On the problem of spurious patterns in neural associative memory models

The problem of spurious patterns in neural associative memory models is discussed, Some suggestions to solve this problem from the literature are reviewed and their inadequacies are pointed out, A solution based on the notion of neural self-interaction with a suitably chosen magnitude is presented for the Hebb learning rule. For an optimal learning rule based on linear programming, asymmetric dilution of synaptic connections is presented as another solution to the problem of spurious patterns, With varying percentages of asymmetric dilution it is demonstrated numerically that this optimal learning rule leads to near total suppression of spurious patterns. For practical usage of neural associative memory networks a combination of the two solutions with the optimal learning rule is recommended to be the best proposition.

Susceptibility of sperm chromatin to acid denaturation in situ: A study in endogenous FSH-deprived adult male bonnet monkeys (Macaca radiata)

Acid denaturation of calf thymus DNA in vitro followed by acridine orange (AO) binding induced a 112% increase in the emission of red, a 58% decrease in green, and a consequential decrease in the ratio of green:red fluorescences from 1.7 to 0.9. This metachromatic property of AO on binding to DNA following acid denaturation was utilized to study the susceptibility of normal and ovine follicle-stimulating hormone (oFSH) actively immunized bonnet monkey spermatozoa voided throughout the year. For analyses, the scattergram generated by the emission of red and green fluorescences by 10,000 AO-bound sperm from each semen sample was divided into 4 quadrant zones representing percentage cells fluorescing high green-low red (Q1), high green-high red (Q2), low green-low red (Q3) and low green-high red. (Q4). Normal monkey sperm obtained during the months of July-December exhibited 76, 13, and 11% cells in Q2, Q3, and Q4 quadrants, respectively. However, during January-June, when the females of the species are markedly subfertile, noncycling, and amenorrhoeic, the spermatozoa ejaculated by the male monkeys exhibited 38, 39, and 23% sperm in Q2, Q3, and Q4, respectively, the differences being highly significant (p < .01-.001). FSH deprivation induced significant shifts in fluorescence emissions, from respective controls, with 39, 33, and 28% cells in Q2, Q3, and Q4, respectively, during July-December, and 15, 48, and 37% sperm in Q2, Q3, and Q4 quadrants, respectively, during January-June. It is postulated that the altered kinetics of germ cell transformations and the deficient spermiogenesis observed earlier following FSH deprivation in these monkeys may have induced the enhanced susceptibility to acid denaturation in sperm.

Transitional intermittency detection by neural network

A neural network has been used to predict the flow intermittency from velocity signals in the transition zone in a boundary layer. Unlike many of the available intermittency detection methods requiring a proper threshold choice in order to distinguish between the turbulent and non-turbulent parts of a signal, a trained neural network does not involve any threshold decision. The intermittency prediction based on the neural network has been found to be very satisfactory.

Glassy behavior in neural network models of associative memory

Neural network models of associative memory exhibit a large number of spurious attractors of the network dynamics which are not correlated with any memory state. These spurious attractors, analogous to "glassy" local minima of the energy or free energy of a system of particles, degrade the performance of the network by trapping trajectories starting from states that are not close to one of the memory states. Different methods for reducing the adverse effects of spurious attractors are examined with emphasis on the role of synaptic asymmetry. (C) 2002 Elsevier Science B.V. All rights reserved.

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. (C) 2002 Elsevier Science Ltd. All rights reserved.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.