Construction crew productivity prediction is one of the most important issues that affect realistic construction duration and cost. Use different search algorithms like Feed Forward Neural Network, Ant Colony, Artificial Bee Particle Swarm Optimization, Radial Based Networks Self Organizing Maps for problem have been discussed in previous studies. However, significant effect coherence between n...

Condit ion m onitoring (CM) of gearboxes is a necessary act ivit y due to the crucial im portance of gearboxes in power t ransm ission in m ost indust rial applicat ions. There has long been pressure to im prove m easuring techniques and develop analyt ical tools for early fault detect ion in gearboxes. This thesis develops new gearbox m onitoring m ethods by dem onst rat ing that operat ing pa...

This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) model...

As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general r...

A new empirical technique to construct predictive models of vacuum pyrolysis process is presented in this study. Pyrolysis of biomass for preparing bio-oil was studied on a vacuum pyrolysis system, where rape straw was chosen as the raw material. The experiments ran based on orthogonal experimental design method. The operation factors of the system including pyrolysis temperature, system pressu...

Differential protection, as the key protection element in power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential needs an additional block distinguish between internal faults and The system should, first, be able perform based on raw data, second, learn fully temporal features sudden changes signals, and, t...

Microarrays are being used to express thousands of genes at a time which is helpful to diagnose and cure many diseases with higher accuracy using diagnostic classifiers. However, 90% of the time gene expression datasets contain multiple missing values because of slide scratches, hybridization error, image corruption and etc. These missing values affect classifiers accuracy as most of the classi...

Researchers in robotics and other human-related fields have been studying human motion behaviors to understand and mimic them in humanoid motion prediction, obstacle avoidance, and ergonomic studies. Human motion, however, is not an easy system or kinematic to study when it includes highly complex relationships between factor—such as human anthropometry and speed and the output motion profile f...

To improve overall fault detection and diagnostic (FDD) performance, many of the techniques proposed for automated FDD rely on the use of steady-state models for expected values of operating states under normal operating conditions. Furthermore, on-line measurements and models for overall performance (e.g., EER) are useful in evaluating whether faults are serious enough for service to be perfor...