Robust Stability and Stabilization Of TCP‐Networked Control Systems with Multiple Delay System Modeling

This paper presents a new model for networked control systems (NCSs) under transmission control protocol (TCP) as a multiple‐delay system by considering both sensor to controller and controller to actuator delays. An analytical TCP model has been considered for the network part, and an active queue management (AQM) controller is designed to regulate the desired queue length, which ensures holding the network induced delay and its variation within their lower bounds. The model is assumed to possess structured uncertainties due to the stochastic nature of the network. Robust stability and stabilization conditions are derived in terms of linear matrix inequalities (LMIs) by applying the Lyapunov‐Krasovskii stability criterion. Illustrative examples are presented and it has been shown that the proposed method will obtain less conservative results compared to the existing approaches in the literature.     

A selective encryption for H.264/AVC videos based on scrambling

H.264/Advanced Video Coding (H.264/AVC) is one of the video compression standards that is 50% more efficient than previously introduced standards. Given that the H.264/AVC standard is considered and used in a variety of video applications, it is essential to provide a suitable solution for video encryption with good security, high encryption speed, and to prevent bitrate increases. In this paper, we propose a method selective encryption of H.264/AVC for the digital rights management (DRM) applications. In this method, discrete cosine transform (DCT) coefficients, which affect the texture and content of the H.264/AVC video during compression, are encrypted after the zigzag scanning and based on Context-Adaptive Binary Arithmetic Coding (CABAC). The experimental results and encryption efficiency analysis demonstrate that the proposed method with the format compliance has good security and high encryption speed and it can prevent the bitrate from rising and is thus usable in industrial and the DRM applications.

     

Synergistic effect of nanoparticles and Thalassospira sp. isolated from Bahregan area in biodegradation of oil sludge

Oil sludge is obtained from the contaminated site of Bahregan area in the Persian Gulf in Iran. Chemotaxis was used for the isolation of alkane-degrading bacteria from oil sludge and the alk genes were determined with specific primers. bac1 identified in Thalassospira was selected as a powerful strain for biodegradation of oil sludge. Biodegradation of oil sludge by bac1 in the presence of nanoparticles was investigated by GC-MS analysis. Synergistic effect of Fe₂O₃ and Fe₃O₄ nanoparticles on bacteria increases the biodegradation of oil sludge and produces cyclosiloxane compound which is used in the field of medicine; however, the Thalassospira reduced its toxicity in the environment.     

Prediction of Crack Nucleation in Rough Line-Contact Fretting via Continuum Damage Mechanics Approach

The crack nucleation behavior of rough surfaces in line contact is investigated by means of a thermodynamically based continuum damage mechanics technique. The deterministic approach is employed to investigate the effect of roughness on the surface tractions and contact stresses. In order to treat the effect of high stress gradients, a special averaging technique, proposed previously for the case of smooth surface, is adopted in this study. The predictions of the crack nucleation life are compared with the relevant experimental data in the literature and indicate the validity of the analysis.     

Sweet lemon mechanical damage detection using image processing technique and UV radiation

Non-destructive measurement of qualitative parameters of agricultural produce is quite beneficial in the postharvest operations. The consequential effect of mechanical damage in citrus fruits is rarely visible in their appearance compared to other commodities. The purpose of this study was to propose a fast, non-destructive method for sweet lemon mechanical damage detection using image processing technique and UV radiation. For this purpose, 135 sweet lemons were tested based on a completely randomized factorial design. In order to examine mechanical damage, the independent variables included drop height, fruit diameter and tempering period (holding time at room temperature after treatment). Fruits were dropped from heights of 2, 2.5 and 3 m onto the ground. Then images were captured under UV light having a wavelength of 365 nm, 1, 3 and 6 days after treatment. The images were sent to a PC and analyzed using MATLAB software. “Green Spot Index” or GSI was defined to show the extent of mechanical damage. Results of the analysis of variance showed that the percentage of green spots on fruit skin is significant at the 1% level considering the main and double interaction effects of drop height and fruit diameter. Green spot index significantly increases with the level of mechanical damage. Accuracy of the developed method in differentiating the bruised and undamaged fruits was found to be 100%.     

Effect of soft‐storey mechanism caused by infill elimination on displacement demand in nonlinear static procedure using coefficient method

In recent earthquakes, many buildings have been damaged due to the soft‐storey mechanism failure. The seismic design codes for buildings do not contain enough criteria to predict the real displacement of such buildings. This paper focuses on evaluating the nonlinear displacement of buildings that fail in soft‐storey mechanism form. Results show that the nonlinear static procedure with coefficient method, which is described in Chapter 3 of ASCE/SEI 41‐06, does not have sufficient accuracy for estimation of structure displacement demand in such buildings. In this paper, the coefficient methodology is used for evaluating the target displacement for 5‐storey, 8‐storey and 15‐storey special moment resisting steel frames. For this purpose, dynamic nonlinear time‐history analysis has been applied for the mentioned structures having a soft‐storey mechanism failure form. The numerical results of storey displacement and interstorey drift were compared with those values obtained from the coefficient method described in Chapter 3 of ASCE/SEI 41‐06. Copyright © 2012 John Wiley & Sons, Ltd.     

Immobilization of palladium on benzimidazole functionalized mesoporous silica nanoparticles: catalytic efficacy in Suzuki–Miyaura reaction and nitroarenes reduction

Journal of Porous Materials - Benzimidazole functionalized mesoporous silica nanoparticles immobilized Pd(0)/Pd(II) has been proposed as an efficient catalyst for the one-pot preparation of biaryls...     

电池充电管理：新兴趋势

简介手机、便携式媒体播放器、便携式导航工具及蓝芽（Bluetooth）耳机等新一代手持装置不断缩小体积外型,同时加强效能与功能。就充电管理而言,这些强化的功能使得设计工程人员必须同时顾及用户的便利性与体验感受,以及整体充电时间与成本的均衡。     

Comparative Numerical Investigation on TiO2/Water Nanofluid Turbulent Flow by Implementation of Single Phase and Two Phase Approaches

In this article, turbulent TiO₂/water nanofluid flow and convective heat transfer in a horizontal tube is numerically investigated. Four computer codes were developed for each model (single phase, volume of fluid, mixture, and Eulerian) to simulate this problem. The Finite volume method is applied to solve the two-dimensional steady state governing equations. The results are compared with each other and an experimental work. A model with the most similar results to those of the experimental data and less amount of time for the CPU usage is chosen to develop two correlations for Nusselt number and friction factor based on dimensionless numbers.     

A local linear radial basis function neural network for financial time-series forecasting

In this paper a Local Linear Radial Basis Function Neural Network (LLRBFN) is presented. The difference between the proposed neural network and the conventional Radial Basis Function Neural Network (RBFN) is connection weights between the hidden layer and the output layer which are replaced by a local linear model in the LLRBFN. A modified Particle Swarm Optimization (PSO) with hunter particles is introduced for training the LLRBFN. The proposed methods have been applied for prediction of financial time-series and the result shows the feasibility and effectiveness.