A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.

     

Automatic navigation of mobile robots in unknown environments

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work.     

Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha^?1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (R²) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production.     

Effect of Shot Peening Operation on the Microstructure and Wear Behavior of AZ31 Magnesium Alloy

Protection of Metals and Physical Chemistry of Surfaces - Shot peening is a treatment used to increase surface hardness and wear resistance. In this study, the effect of shot peening on the...     

Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window

Neural Computing and Applications - This study addresses a many-to-many hub location-routing problem where the best-found locations of hubs and the best-found tours for each hub are determined with...     

An Anti-Gray Hole Attack Scheme in Mobile Ad Hoc Network

International Journal of Wireless Information Networks - Dynamic variation of network topology in mobile ad hoc networks (MANET) forces network nodes to work together and rely on each other for...     

Quantitative characterization of carrier injection across metal–organic interfaces using Bardeen theory

We have investigated the contact between a metal and an organic/polymeric (o/p) material and we have introduced a relation for carrier injection using Bardeen theory. A series of narrow barriers is considered in the semiconductor side to account for the localized nature of the carriers in the o/p material. As an application of the model, we have calculated the hopping rate of carriers in terms of the contact parameters. Also, we have discussed the hopping of carriers deep into the organic dielectric. Finally, we have explored the hopping rate in practical contacts between polyfluorene-based polymers and different electrodes.     

Target tracking of autonomous robotic vehicles using range‐only measurements: a switched logic‐based control strategy

This paper considers the pursuing or target tracking problem where an autonomous robotic vehicle is required to move towards a maneuvering target using range‐only measurements. We propose a switched logic‐based control strategy to solve the pursuing problem that can be described as comprising a continuous cycle of two distinct phases: (1) the determination of the bearing, and (2) the steering control of the vehicle to follow the direction computed in the previous step while the range is decreasing. We provide guaranteed conditions under which the switched closed‐loop system achieves convergence of the relative distance error to a small neighborhood around zero. Simulation results are presented and discussed.Copyright © 2011 John Wiley & Sons, Ltd.     

A neural approach for estimation of per capita electricity consumption due to age and income

Electricity consumption is influenced by number of adults and children and their relationship at household level. Household income also plays a critical role on expenditure on electricity. Accordingly, this article presents a joint probability model of electricity demand based on occupants’ age grades and household income levels. A bottom-up strategy is developed using a micro level database of 70 Australian households. A neural regression-generalization technique is devised to estimate electricity demand using back-propagation and cognitive mapping. The aggregated result is then validated against 2012 Australian national census. Accordingly, the model is improved based on a top-down review. The results show per capita electricity demand by adult and child at 0.408 kW (69 kWh/week) and 0.226 kW (38 kWh/week), respectively. The equivalent dollar values are $13.6/week and $7.6/week in 2012. At macro level, the model reveals per capita demand by all individuals at 0.324 kW (54.35 kWh/week) equivalent to dollar value of $10.87/week, across Australia. The results also show higher percentage of per capita demand for adults in high and medium income classes, and the otherwise for low income class. Ratio of child’s demand over adult’s demand is highest among the low income households, and lowset among the middle income households, while best balance between adult and child per capita demand belongs to the high income.