Optimal parameter estimation of polymer electrolyte membrane fuel cells model with chaos embedded particle swarm optimization

Polymer electrolyte membrane fuel cells (PEMFC) are generally preferred in engineering applications due to their energy conversion efficiency, high power density, and low operating temperatures. In recent years, it has come to the fore in electric vehicles and unmanned aerial vehicle applications, which have critical and strategic importance. However, researchers use fuel cells in many different applied-theoretical studies. The models they use to increase the accuracy of these studies should be very similar to the real PEMFC. Therefore, in this paper, chaos embedded particle swarm optimization algorithm (CEPSO) and a new objective function are proposed for the first time in the literature to find the unknown parameters of PEMFC heaps more realistically. Three commercial types of PEMFCs stack namely 250 W Stack, BCS-500 W, and Nedstack PS6, which are commonly investigated in the literature, were numerically simulated to show the effectiveness of the proposes for parameter determining. The success of the suggestions is shown by the results obtained.     

Phosphate adsorption characteristics of alunite to be used as a cement additive

The adsorption of phosphate from aqueous solution by alunite has been investigated as a function of calcination temperature, particle size, pH, agitation time and initial phosphate concentration. Phosphate adsorption was seen to increase with increasing calcination temperature, decreasing adsorbent particle size and pH. The adsorption isotherm data were fitted to the Langmuir isotherm. The alunite exhibited the highest phosphate uptake capacity at 1073 K calcination temperature, at a particle size of 90-150 μm, at the initial pH of 5.0, at an equilibrium time of 60 min and at the initial phosphate concentration of 20×10⁻⁴ mol/l. The adsorption capacity, Q, was 4.697×10⁻³ mol/g at initial pH 5.0.     

Enhancement of pacemaker induced stochastic resonance by an autapse in a scale-free neuronal network

An autapse is an unusual synapse that occurs between the axon and the soma of the same neuron. Mathematically, it can be described as a self-delayed feedback loop that is defined by a specific time-delay and the so-called autaptic coupling strength. Recently, the role and function of autapses within the nervous system has been studied extensively. Here, we extend the scope of theoretical research by investigating the effects of an autapse on the transmission of a weak localized pacemaker activity in a scale-free neuronal network. Our results reveal that by mediating the spiking activity of the pacemaker neuron, an autapse increases the propagation of its rhythm across the whole network, if only the autaptic time delay and the autaptic coupling strength are properly adjusted. We show that the autapse-induced enhancement of the transmission of pacemaker activity occurs only when the autaptic time delay is close to an integer multiple of the intrinsic oscillation time of the neurons that form the network. In particular, we demonstrate the emergence of multiple resonances involving the weak signal, the intrinsic oscillations, and the time scale that is dictated by the autapse. Interestingly, we also show that the enhancement of the pacemaker rhythm across the network is the strongest if the degree of the pacemaker neuron is lowest. This is because the dissipation of the localized rhythm is contained to the few directly linked neurons, and only afterwards, through the secondary neurons, it propagates further. If the pacemaker neuron has a high degree, then its rhythm is simply too weak to excite all the neighboring neurons, and propagation therefore fails.     

Dispositional resistance to change: Measurement equivalence and the link to personal values across 17 nations.

The concept of dispositional resistance to change has been introduced in a series of exploratory and confirmatory analyses through which the validity of the Resistance to Change (RTC) Scale has been established (S. Oreg, 2003). However, the vast majority of participants with whom the scale was validated were from the United States. The purpose of the present work was to examine the meaningfulness of the construct and the validity of the scale across nations. Measurement equivalence analyses of data from 17 countries, representing 13 languages and 4 continents, confirmed the cross-national validity of the scale. Equivalent patterns of relationships between personal values and RTC across samples extend the nomological net of the construct and provide further evidence that dispositional resistance to change holds equivalent meanings across nations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quaternion-based Robust Trajectory Tracking Control of a Quadrotor Hover System

This paper presents a robust nonlinear output feedback control method that achieves three degree of freedom (3-DOF) attitude trajectory tracking of a hover system test bed. The proposed control method formally incorporates dynamic model uncertainty in addition to test bed voltage constraints. To reduce the computational requirement in the closed-loop system, constant feedforward estimates of the input-multiplicative parametric uncertainty are utilized in lieu of adaptive parameter estimates. To eliminate the need for angular rate measurements, the control design employs a bank of dynamic filters, which operates as a velocity estimator in the closed-loop system. A rigorous error system development and Lyapunov-based stability analysis are presented to prove asymptotic 3-DOF attitude trajectory tracking control. Computer simulation and experimental results are also included to illustrate the performance of the attitude control method using the Quanser 3-DOF hover system test bed.     

Predicting peak pinch strength: Artificial neural networks vs. regression

Cumulative trauma disorders (CTDs) of the upper extremities are one of the major ergonomics areas of research. Pinching is a common risk factor associated with the development of hand/wrist CTDs. The capacity standards of peak pinch strength for various postures are needed to design the tasks in harmony with the workers. This paper describes the formulation, building and comparison of pinch strength prediction models that were obtained using two approaches: Statistical and artificial neural networks (ANN). Statistical and ANN models were developed to predict the peak chuck pinch strength as a function of different combinations of five elbow and seven shoulder flexion angles, and several anthropometric and physiological variables. The two modeling approaches were compared. The results indicate ANN models to provide more accurate predictions over the standard statistical models.     

Clustering-based diversity improvement in top-N recommendation

The major aim of recommender algorithms has been to predict accurately the rating value of items. However, it has been recognized that accurate prediction of rating values is not the only requirement for achieving user satisfaction. One other requirement, which has gained importance recently, is the diversity of recommendation lists. Being able to recommend a diverse set of items is important for user satisfaction since it gives the user a richer set of items to choose from and increases the chance of discovering new items. In this study, we propose a novel method which can be used to give each user an option to adjust the diversity levels of their own recommendation lists. Experiments show that the method effectively increases the diversity levels of recommendation lists with little decrease in accuracy. Compared to the existing methods, the proposed method, while achieving similar diversification performance, has a very low computational time complexity, which makes it highly scalable and allows it to be used in the online phase of the recommendation process.     

Energy-performance trade-offs for spatial access methods on memory-resident data

The proliferation of mobile and pervasive computing devices has brought energy constraints into the limelight. Energy-conscious design is important at all levels of system architecture, and the software has a key role to play in conserving battery energy on these devices. With the increasing popularity of spatial database applications, and their anticipated deployment on mobile devices (such as road atlases and GPS-based applications), it is critical to examine the energy implications of spatial data storage and access methods for memory resident datasets. While there has been extensive prior research on spatial access methods on resource-rich environments, this is, perhaps, the first study to examine their suitability for resource-constrained environments. Using a detailed cycle-accurate energy estimation framework and four different datasets, this paper examines the pros and cons of three previously proposed spatial indexing alternatives from both the energy and performance angles. Specifically, the Quadtree, Packed R-tree, and Buddy-Tree structures are evaluated and compared with a brute-force approach that does not use an index. The results show that there are both performance and energy trade-offs between the indexing schemes for the different queries. The nature of the query also plays an important role in determining the energy-performance trade-offs. Further, technological trends and architectural enhancements are influencing factors on the relative behavior of the index structures. The work in the query has a bearing on how and where (on a mobile client or/and on a server) it should be performed for performance and energy savings. The results from this study will be beneficial for the design and implementation of embedded spatial databases, accelerating their deployment on numerous mobile devices. Received: November 11, 2001 / Accepted: March 12, 2002 Published online: November 14, 2002 This paper is a significantly extended version of preliminary work that appeared in the Proceedings of the Very Large Databases (VLDB) 2001 Conference. The extensions include (i) a comparison of indexing alternatives carrying out the operations in a brute-force manner; (ii) observations showing that datasets do play a role in power consumption; (iii) architectural solutions to address the cache and memory hotspots for energy; and (iv) benefits when off-loading the work to a server over a wireless medium compared to doing everything on the handheld device.     

Optimal work-rest cycles for an isometric intermittent gripping task as a function of force, posture and grip span

The aim of this study was to investigate the maximum acceptable contraction frequencies (i.e. work-rest cycles) for an isometric-intermittent handgrip task as a function of grip span, applied force and shoulder posture using psychophysical and physiological approaches. Twelve healthy males served as subjects. The three grip spans investigated were the optimal, 2 cm narrower than the optimal, and 2 cm wider than the optimal. The grip force levels studied were 15% and 30% of maximum voluntary grip force and the two shoulder postures were 25 degrees flexion and 30 degrees abduction. The psychophysical results indicate that subjects work faster with the narrower grip span at 15% of maximum voluntary grip force level in comparison to working with the optimal and the wider spans. However, when the task required 30% of maximum grip force level, the subjects worked faster with the optimal grip span. These findings were supported by the results of electromyography, heart rate, blood pressure and perceived discomfort. The study suggests that grip span of a tool is an important factor to be considered in predicting optimal work-rest cycles for hand grip tasks, and the optimum setting of grip span of the hand-tool depends on the required task force level. That is, the optimality is relative rather than absolute. In addition, it appears that weaker subjects can work at a higher rate than stronger ones at the same relative force level.     

An Accurate Indoor RSSI Localization Algorithm Based on Active RFID System with Reference Tags

Radio frequency identification technologies are popular since their cost is very low and its data transmission based upon radio-wave communications. Where, the objects that are attached to tags are located using the reference tags. However, RSSI information suffers from the multipath propagation and anisotropic interference. So, the localization accuracy will be affected severely. Also, the multipath-propagation increases whenever the reference-tags increase, and so does for the cost, and signal interference. This paper presents a boundary virtual reference label algorithm for improving the indoor-efficiency by inserting some virtual reference tags on the boundary with establishing a linear regression model that eliminates unwanted tag information from the estimation procedure. The Results show that the localisation precision of the proposed approach has significantly increased, at least 78% without adding extra reference tags or radio frequency interference which represents a significant improvement over other algorithms .