A hybrid evolutionary graph-based multi-objective algorithm for layout optimization of truss structures

In this paper a new graph-based evolutionary algorithm, gM-PAES, is proposed in order to solve the complex problem of truss layout multi-objective optimization. In this algorithm a graph-based genotype is employed as a modified version of Memetic Pareto Archive Evolution Strategy (M-PAES), a well-known hybrid multi-objective optimization algorithm, and consequently, new graph-based crossover and mutation operators perform as the solution generation tools in this algorithm. The genetic operators are designed in a way that helps the multi-objective optimizer to cover all parts of the true Pareto front in this specific problem. In the optimization process of the proposed algorithm, the local search part of gM-PAES is controlled adaptively in order to reduce the required computational effort and enhance its performance. In the last part of the paper, four numeric examples are presented to demonstrate the performance of the proposed algorithm. Results show that the proposed algorithm has great ability in producing a set of solutions which cover all parts of the true Pareto front.     

High-pressure torsion of TiFe intermetallics for activation of hydrogen storage at room temperature with heterogeneous nanostructure

TiFe is a potential candidate for the stationary hydrogen storage systems, but it requires initial activation to absorb hydrogen. This study shows that TiFe processed by high-pressure torsion (HPT) absorbs and desorbs 1.7 wt.% hydrogen at room temperature without activation. The absorption pressure decreases from 2 MPa in the first hydrogenation cycle to 0.7 MPa in the latter cycles. The HPT-processed TiFe exhibits heterogeneous microstructures composed of nanograins, coarse-grains, amorphous-like phases and disordered phases with a high hardness of ∼1050 Hv.     

Magnetic charged system search: a new meta-heuristic algorithm for optimization

An improved version of the charged system search (CSS) algorithm is introduced which is called magnetic charged system search (MCSS). In the new algorithm, magnetic forces are considered in addition to electrical forces, using the Biot–Savart law. Each charged particle (CP), as a search agent, exerts magnetic forces on other CPs based on the variation of its objective function value during its last movement and its distance between other CPs. This additional force provides useful information for the optimization process and enhances the performance of the CSS algorithm. The efficiency of the MCSS is examined by application of this algorithm to well-known mathematical benchmarks and three well-studied engineering design problems. The results are compared to those of the CSS, and the improvements are highlighted.     

Principles and Applications of Light Backscattering Imaging in Quality Evaluation of Agro-food Products: a Review

In recent years, due to the increasing consciousness of quality in the food and health sector, much progress has been made in developing non-invasive techniques for the evaluation or inspection of internal qualitative parameters of fruits, vegetables, and processed foodstuffs considering, e.g., moisture content, soluble solid content, acidity, and mechanical properties. This paper reviews the theoretical and technical principles as well as the recent achievements and applications of light backscattering imaging for nondestructive evaluation of food and agricultural produce. The results suggest the potential use of this emerging technique in the food industry. Further attempts are pointed out to improve its performance through utilizing advanced image processing coupled with artificial intelligence techniques.     

On‐line system identification of structures using wavelet‐Hilbert transform and sparse component analysis

A new wavelet‐Hilbert transform based sparse component analysis (WHT‐SCA) method is presented for online system identification in indeterminate conditions. The instantaneous phase ratios of output signals are obtained by using a wavelet‐Hilbert transform based filter; and the out‐of‐phase data, that causes errors in identification accuracy, is detected and eliminated. Then, modal parameters of the structure are identified through existing relationships between the dispersion of filtered data in the frequency domain. Subsequently, to demonstrate the capability of the online identification, a new controller is introduced by combining the WHT‐SCA and a semi‐active tuned mass damper (STMD), resulting in creation of smart structures. The performance of the proposed method and controller is investigated through examples. The results demonstrate that, modal parameters of structures are identified accurately even with noise contamination and limited number of sensors. Also, the STMD is effectively robust against any variations in modal parameters of the structure.     

Heterogeneous Cooperative Localization for Social Networks

Location-aware techniques has become a hot research topic with great value in commercial and military applications. Cooperative localization, which utilizes multiple sensors in portable devices to estimate locations of the mobile users in the social networks, is one of the most promising solution for the indoor geo-location. Traditional cooperative localization methods are based on ranging techniques, they are highly dependent on the distance interpreted from the received signal strength (RSS) or time of arrival from anchors. However, a precise ranging procedure demands high performance hardware which would increase the cost to the current mobile platform. In this paper, we describes four ranging-free probabilistic cooperative localization algorithms: centroid scheme, nearest neighbor scheme, kernel scheme and AP density scheme to improve the accuracy for the indoor geo-location using current mobile devices. Since the GPS sensor embedded in the smart phone is able to provide accurate location information in the outdoor area, those mobile nodes can be used as calibrated anchors. The position of the indoor mobile node can be estimated by exchanging locations and RSSs from shared wireless access points information between the target node and anchor nodes. An empirical evaluation of the system is given to demonstrate the feasibility of these cooperative localization algorithms by reporting the results in a real-world environments, e.g. suburban area and city downtown. Moreover, we compared our results with the WiFi positioning system made by Skyhook Wireless to validate the accuracy of the proposed algorithms. Meanwhile, a Monte Carlo simulation is carried out to evaluate the performance of the cooperative algorithms under different scenarios. Results show that given the same scenario setting, the AP density scheme and kernel scheme outperform than other schemes.     

Modeling the Effect of Human Body on TOA Based Indoor Human Tracking

In time-of-arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line of sight (NLOS) scenario and result in significant ranging error. However, the previous studies on the behavior of indoor TOA ranging did not take the effects of human body into account. In this paper, measurement of TOA ranging error has been conducted in a typical indoor environment and sources of inaccuracy in TOA-based indoor localization have been analyzed. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of transmitter–receiver, signal to noise ratio and bandwidth of the system, which clearly shows the effects of human body on TOA ranging.     

Activation of TiFe for hydrogen storage by plastic deformation using groove rolling and high-pressure torsion: Similarities and differences

Intermetallics of TiFe were processed using three different routes: annealing, plastic deformation using groove rolling and severe plastic deformation using high-pressure torsion (HPT). Hydrogen absorption was less than 0.2 wt.% in the coarse-grained annealed sample because of difficult activation. The groove-rolled sample, with subgrain structure and high density of dislocations and cracks, absorbed 0.3, 1.0, 1.4 and 1.7 wt.% of hydrogen in the first, second, third and fourth hydrogenation cycles, respectively. The HPT-processed sample, containing nanograins, absorbed 1.7–2 wt.% of hydrogen in any hydrogenation cycles. Both samples activated by groove rolling and HPT were not deactivated by long time exposure to the air. No surface segregation was detected after groove rolling, while the HPT-processed sample exhibited surface segregation. The current study confirmed the significance of plastic deformation and formation of grain boundaries and cracks on activation for hydrogen storage.     

A Novel Modeling, Simulation and Optimization Approach of Crude Oil Cold Stripping Process

Cold stripping is the most common process for crude oil sweetening in oilfields particularly at offshore installations because of its low price and relatively easy operating conditions in comparison with other sweetening processes. In this paper the cold stripping process in tray column has been modeled mathematically in static and dynamic modes, and solved with the MATLAB software. This process has been used in the existing treatment facilities of an offshore oil production complex for verifying the model results. With the help of HYSYS software the effective parameters on the process have been discussed and the optimized conditions finalized after some plant modifications for improving the performance of stripper columns have been proposed.     

On the estimation of the K parameter for the Rice fadingdistribution

We study the statistical performance of two moment-based estimators for the K parameter of Rice fading distribution, as less complex alternatives to the maximum-likelihood estimator. Our asymptotic analysis reveals that both estimators are nearly asymptotically efficient, and that there is a compromise between the computational simplicity and the statistical efficiency of these two estimators. We also show, by Monte Carlo simulation, that the fading correlation among the envelope samples deteriorates the performance of both estimators. However, the simpler estimator, which employs the second and the fourth moments of the signal envelope, appears to be more suitable for real-world applications