Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers’ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers’ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.     

Fast evolving neuro-fuzzy model and its application in online classification and time series prediction

A strategy for improving speed of the previously proposed evolving neuro-fuzzy model (ENFM) is presented in this paper to make it more appropriate for online applications. By considering a recursive extension of Gath?CGeva clustering, the ENFM takes advantage of elliptical clusters for defining validity region of its neurons which leads to better modeling with less number of neurons. But this necessitates the computing of reverse and determinant of the covariance matrices which are time consuming in online applications with large number of input variables. In this paper a strategy for recursive estimation of singular value decomposition components of covariance matrices is proposed which converts the burdensome computations to calculating reverse and determinant of a diagonal matrix while keeping the advantages of elliptical clusters. The proposed method is applied to online detection of epileptic seizures in addition to prediction of Mackey?CGlass time series and modeling a time varying heat exchanger. Simulation results show that required time for training and test of fast ENFM is far less than its basic model. Moreover its modeling ability is similar to the ENFM which is superior to other online modeling approaches.     

Creating corroborated crisis reports from social media data through formal concept analysis

During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake.     

Online learning of task-driven object-based visual attention control

We propose a biologically-motivated computational model for learning task-driven and object-based visual attention control in interactive environments. In this model, top-down attention is learned interactively and is used to search for a desired object in the scene through biasing the bottom-up attention in order to form a need-based and object-driven state representation of the environment. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Top-down attention is learned by the U-TREE algorithm which successively grows an object-based binary tree. Internal nodes in this tree check the existence of a specific object in the scene by biasing the early vision and the object recognition parts. Its leaves point to states in the action value table. Motor actions are associated with the leaves. After performing a motor action, the agent receives a reinforcement signal from the critic. This signal is alternately used for modifying the tree or updating the action selection policy. The proposed model is evaluated on visual navigation tasks, where obtained results lend support to the applicability and usefulness of the developed method for robotics.     

Disturbance and recovery of trunk mechanical and neuromuscular behaviours following prolonged trunk flexion: influences of duration and external load on creep-induced effects

Trunk flexion results in adverse mechanical effects on the spine and is associated with a higher incidence of low back pain. To examine the effects of creep deformation on trunk behaviours, participants were exposed to full trunk flexion in several combinations of exposure duration and external load. Trunk mechanical and neuromuscular behaviours were obtained pre- and post-exposure and during recovery using sudden perturbations. Intrinsic trunk stiffness decreased with increasing flexion duration and in the presence of the external load. Recovery of intrinsic stiffness required more time than the exposure duration and was influenced by exposure duration. Reflexive trunk responses increased immediately following exposure but recovered quickly (～2.5 min). Alterations in reflexive trunk behaviour following creep deformation exposures may not provide adequate compensation to allow for complete recovery of concurrent reductions in intrinsic stiffness, which may increase the risk of injury due to spinal instability. STATEMENT OF RELEVANCE: An increased risk of low back injury may result from flexion-induced disturbances to trunk behaviours. Such effects, however, appear to depend on the type of flexion exposure, and have implications for the design of work involving trunk flexion.     

Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.     

Wear Resistance of Amorphous-Crystalline Coatings with Lubricants

The results of tests of a Zr–Al–B boundary friction detonation coating in a wide range of changes under friction conditions have been presented. A comparative analysis of the obtained characteristics of friction and wear in order to evaluate the tribotechnical properties of amorphous-crystalline coatings has been carried out. These results have been compared with parallel tests of coatings based on tungsten carbide, samples of hardened steel and bronze, and the bearing of the sliding layer. The formation of solid solutions has been established and the introduction of oxygen in zirconium that corresponds to the formation on the friction surfaces of the secondary structures of the first type, a characteristic feature of which is their surface localization, ultra-dispersed structure, and ability to minimize disruption and shield unacceptable adhesion phenomena. The use of the Auger electron microscope confirmed that oxygen completely replaces the sulfur in the surface structures.     

Steam gasification of bitumen oil in presence of Ni/dolomite catalysts

An important step in developing clean energy and reducing greenhouse gas emissions is the use of clean technologies such as gasification. In this work, we were used a bubbling fluidized bed (BFB) to convert the bitumen oil into a clean syngas in presence of three kinds of Ni/dolomite catalysts. While all three catalysts were acceptable in the process of tar elimination, but DN-22 was more successful in eliminating tar than the other two samples, which might be due to the higher amount of CaO in the composition DN-22. With increase in Ni, the amount of eliminated tars in the tar cracking process was also increased due to the delay in the deactivation of the dolomite catalyst.     

Influence of top‐section design and draft‐tube height on the performance of airlift bioreactors containing water‐in‐oil microemulsion

The mixing and mass transfer characteristics of draft‐tube airlift bioreactors (DTAB) for a water‐in‐kerosene microemulsion, as a cold model of petroleum biodesulfurization, were studied. Incomplete gas disengagement at the top‐section of the DTAB and hence high gas recirculation were obtained with the microemulsion system for all the top‐section configurations employed in the present study especially at the high airflow rates. The ratio (S) of the volumes of the riser and the downcomer to the top‐section together with the gas disengagement abilities of the gas separator were both found to affect the mixing performance of the DTAB employed for the microemulsion system. Increase in the draft‐tube height resulted in significant increase in the mixing time (t_m) and a slight increase in the overall volumetric oxygen transfer coefficient (k_La). Increase in the diameter of the top‐section and the height of the liquid above the draft‐tube led to a decrease in k_La, the latter effect being less prominent. New correlations were developed that predicted the mixing time and oxygen transfer coefficients obtained in the present work with reasonable accuracy. Copyright © 2004 Society of Chemical Industry