Velocity Distributions in Spatially Varied Flow with Increasing Discharge

The experimental study presented in this paper explores the distribution of the mean velocities for a channel receiving spatially varied (SV) inflow from directly above the centerline of the channel via sets of nozzles. The velocity components u, v, and w have been measured using a laser Doppler velocimeter. Initially the validity of the law of the wall in the channel receiving SV inflow was investigated. It was found that the measured velocity profile within the inertial sublayer region near the channel bed falls below the line representing the log law. Further, if the SV inflow enters at the center of the channel, the degree that the data depart from the log law increases from the sidewall toward the center of the channel. The structures of the velocity distributions are also presented in detail. The results include both near field, that is the area close to where the flow enters the channel, and far field measurements. It was found that the influence of the SV inflow on velocity profiles is largely contained within the SV inflow zone and that the influence within this zone reduces in the downstream direction. At any one section within this zone the influence reduces from the center of the channel toward the sidewall. In addition, some features of the secondary flow established by the SV inflow are described.     

Failure Analysis of AISI 321 Austenitic Stainless Steel Water Piping in a Power Plant

The objective of this work was to analyze a reported pitting damage mechanism in water piping of a power plant. The investigated material was a longitudinal-seam welded pipe which was made of AISI 321 austenitic stainless steel. Pitting occurred on the internal surfaces of the pipe and adjacent to the girth welds. Optical emission spectrometry, metallography, and electrochemical testing were the techniques employed to analyze the failure. The investigation showed that the flowing water contained chloride ions in which AISI 321 austenitic stainless steel is not immune against pitting corrosion. In addition, the polarization data showed the susceptibility of the material to pitting corrosion in the service conditions. Furthermore, the result of double loop electrochemical potentiokinetic reactivation tests showed that the material was sensitized during welding. It was found that the damage was due to pitting corrosion in chloride-containing water. Thus, it was recommended to replace the pipe with material having higher pitting resistance equivalent number to be more resistant against pitting corrosion.     

An uncertainty compensator for robust control of wheeled mobile robots

This paper proposes an uncertainty compensator to design a novel robust control for mobile robots with dynamic and kinematic uncertainties. A novel gradient-based adaptive fuzzy estimator is developed to compensate uncertainties with minimum required feedback signals. As a novelty, the proposed approach uses the tracking error and its first time derivative to form the estimation error of uncertainty, and guarantees that both the estimation error and tracking error converge asymmetrically to ignorable value. Advantages of the proposed robust control are simplicity in design, robustness against uncertainties, guaranteed stability, and good control performance. The control approach is verified by stability analysis. Simulation results and experimental results illustrate the effectiveness of the proposed control. Experimental evaluation of the proposed controller is expressed for two different low-cost nonholonomic wheeled mobile robots. The proposed control design is compared with an adaptive control approach to confirm the superiority of the proposed approach in terms of precision, simplicity of design, and computations.     

LAMR: learning automata based multicast routing protocol for multi-channel multi-radio wireless mesh networks

Multicast routing is a crucial issue in wireless networks in which the same content should be delivered to a group of recipients simultaneously. Multicast is also considered as a key service for audio and video applications as well as data dissemination protocols over the last-mile backhaul Internet connectivity provided by multi-channel multi-radio wireless mesh networks (MCMR WMNs). The multicast problem is essentially related to a channel assignment strategy which determines the most suitable channel-radio associations. However, channel assignment brings about its own complications and hence, solving the multicast problem in MCMR WMNs will be more complicated than that of traditional networks. This problem has been proved to be NP-hard. In the major prior art multicast protocols developed for these networks, channel assignment and multicast routing are considered as two separate sub-problems to be solved sequentially. The work in this article is targeted at promoting the adoption of learning automata for joint channel assignment and multicast routing problem in MCMR WMNs. In the proposed scheme named LAMR, contrary to the existing methods, these two sub-problems will be solved conjointly. Experimental results demonstrate that LAMR outperforms the LCA and MCM proposed by Zeng et al. (IEEE Trans. Parallel. Distrib. Syst. 21(1):86–99, 2010) as well as the genetic algorithm-, tabu search-, and simulated annealing-based methods by Cheng and Yang (Int. J. Appl. Soft Comput. 11(2):1953–1964, 2011) in terms of achieved throughput, end-to-end delay, average packet delivery ratio, and multicast tree total cost.     

Swallow swarm optimization algorithm: a new method to optimization

This paper presents an exposition of a new method of swarm intelligence–based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO.     

Increasing image compression rate using steganography

The goal of image compression is to remove the redundancies for minimizing the number of bits required to represent an image while steganography works by embedding the secret data in redundancies of the image in invisibility manner. Our focus in this paper is the improvement of image compression through steganography. Even if the purposes of digital steganography and data compression are by definition contradictory, we use these techniques jointly to compress an image. Hence, two schemes exploring this idea are suggested. The first scheme combines a steganographic algorithm with the baseline DCT-based JPEG, while the second one uses this steganographic algorithm with the DWT-based JPEG. In this study data compression is performed twice. First, we take advantage of energy compaction using JPEG to reduce redundant data. Second, we embed some bit blocks within its subsequent blocks of the same image with steganography. The embedded bits not only increase file size of the compressed image, but also decrease the file size further more. Experimental results show for this promising technique to have wide potential in image coding.     

Verification of two machine learning approaches for cloud masking based on reflectance of channel IR3.9 using Meteosat Second Generation over Middle East maritime

ABSTRACT

Most cold channels of Meteosat Second Generation (MSG) satellites can distinguish between the sea and ice cloud tops, except for the IR3.9 channel because of the close reflectance and radiance values of the IR3.9 channel for maritime, low-level cloud and ice cloud tops. In this article, we introduce and evaluate two machine learning methods for cloud masking of Spinning Enhanced Visible and Infrared Imager (SEVIRI) images in the day and night that use the reflectance value of the IR3.9 channel. We reached a good correlation by comparing the results of the modelled cloud masking of Meteosat satellite images with MODIS (Moderate Resolution Imaging Spectroradiometer) and CLM (Cloud Mask product of EUMETSAT) images in a way that the coefficient of determination (R²) value was 92.34%, 89.91% and 83.69%, 78.23% in the cold season and 90.17%, 87.09% and 80.37%, 76.48% in the warm season, respectively, using the CHAID (chi-squared automatic interaction detection) decision tree and RBF (radial basis function) neural network approaches.     

Task-space control of robots using an adaptive Taylor series uncertainty estimator

An uncertainty estimation and compensation can improve the performance of control systems due to structured and unstructured uncertainty. This paper presents a robust task-space control approach using an adaptive Taylor series uncertainty estimator for electrically driven robot manipulators. It is worth noting that not only the lumped uncertainty is estimated and employed in the indirect form of robust controller, but also the upper bound of approximation error is estimated to form a robustifying term and the asymptotic convergence of tracking error and its time derivative are proven based on stability analysis. Finally, the effectiveness of the proposed controller is shown through simulation and comparison with two valuable control schemes applied on the Selective Compliance Assembly Robot Arm (SCARA) robot manipulator.