Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks

In wireless multimedia sensor networks (WMSNs) a sensor node may have different types of sensor which gather different kinds of data. To support quality of service (QoS) requirements for multimedia applications having a reliable and fair transport protocol is necessary. One of the main objectives of the transport layer in WMSNs is congestion control. We observe that the information provided may have different levels of importance and argue that sensor networks should be willing to spend more resources in disseminating packets carrying more important information. Some applications of WMSNs may need to send real time traffic toward the sink node. This real time traffic requires low latency and high reliability so that immediate remedial and defensive actions can be taken when needed. Therefore, similar to wired networks, service differentiation in wireless sensor networks is also an important issue. We present a priority-based rate control mechanism for congestion control and service differentiation in WMSNs. We distinguish high priority real time traffic from low priority non-real time traffic, and service the input traffic based on its priority. Simulation results confirm the superior performance of the proposed model with respect to delays, delay variation and loss probability.     

Multi-criteria expertness based cooperative Q-learning

One of the most influential points in cooperative learning is the type of exchanging information. If the content of exchanging information among agents is rich, cooperation gives rise to better results. To extract proper knowledge of agents during the cooperation process, some expertness measures that assign expertness levels to the other agents are used. In this paper, a new method named Multi-Criteria Expertness based cooperative Q-learning (MCE) is proposed that utilizes all of the expertness measures and attempts to enrich the exchanging information more efficiently. In MCE, all expertness measures are considered simultaneously and collective knowledge is equal to the combination of learned knowledge by each of expertness measures. The experimental results confirm outstanding performance of the proposed method on a sample maze world and a hunter-prey problem.     

A review on the application of structured sparse representation at image annotation

The increasing number of images on the Web and other information environments, needs efficient management and suitable retrieval especially by computers. Image annotation is a process which produces words for a digital image based on its content. Users prefer an image search based on text queries and keywords which has increased the use of image annotation. In this paper, we discuss the applicability of structured sparse representations at image annotation. First the components of image annotation and sparse representation are reviewed. Then, we survey the structure of sparse representation based on the image annotation algorithms. Next, the comparison of algorithm has been presented. Finally the paper concludes with some major challenges and open issues in image annotation using structured sparse representations.     

Optimal design of gas turbine CHP plant with preheater and HRSG

Located in the south of Iran, Jiroft Paper Mill Company requires an integrated combined heat and power plant, which can provide 50 MW of electric power and 100 ton h^?1 saturated steam at 13 bar, to produce paper from an adjacent eucalyptus forest. The plant is composed of an air compressor, combustion chamber, air preheater, turbine, as well as a heat recovery steam generator. The design parameters of the plant were chosen as: compressor pressure ratio (r_c), compressor isentropic efficiency (η_AC), gas turbine isentropic efficiency (η_T), combustion chamber inlet temperature (T₃), and turbine inlet temperature (T₄). In order to optimally find the design parameters a thermoeconomic approach has been followed. An objective function representing the total cost of the plant in terms of dollar per second was defined as the sum of the operating cost related to the fuel consumption and the capital investment for equipment purchase and maintenance costs. Subsequently, different parts of the objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using sequential quadratic programming. The influence of changes in the demanded power and steam on the design parameters has also been studied for 40, 50, 60, and 70 MW of net power output, and 100, 120, and 150 ton h^?1 of saturated steam mass flow rate. Finally, the sensitivity analysis of change in design parameters with change in fuel or investment cost was performed. Copyright © 2009 John Wiley & Sons, Ltd.     

The relative performance of alternative oxygen carriers for liquid chemical looping combustion and gasification

The relative performance of different potential liquid oxygen carriers within a novel system that can be configured for either chemical looping gasification or combustion is assessed. The parameters considered here are the melting temperature, the Gibbs free energy, reaction enthalpy, exergy and energy flows, syngas quality and temperature difference between the two reactors. Results show that lead, copper and antimony oxides are meritorious candidates for the proposed systems. Antimony oxide was found to offer strong potential for high quality syngas production because it has a reasonable oxygen mass ratio for gasification. A sufficiently low operating temperature to be compatible with concentrated solar thermal energy and a propensity to generate methane. In contrast, copper and lead oxides offer greater potential for liquid chemical looping combustion because they have higher oxygen mass ratio and a higher operating temperature, which enables better efficiency from a power plant. For all three metal oxides, the production of methane via the undesirable methanation reaction is less than 2% of the product gasses for all operating temperatures and an order of magnitude lower for lead.     

A study on high ratio cup drawing by Maslennikov’s process

Deep drawing of sheet metals using Maslennikov’s technique has been analyzed by analytical and finite element simulation approaches. A new friction model based on local contact conditions has been used in the finite element (FE) simulations of the process. Compared to traditional Coulomb friction model, the results of FE simulations with the new friction model show good correlation with analytical calculations. The effects of key process parameters such as rubber ring thickness, ring inner diameter, die hole diameter, and die profile radius on the results have been investigated. The results showed that very deep cups without thinning in the side wall portion can be achieved with this process. Based on the results of FE analysis, it was found that the maximum drawing ratio can be achieved by adopting a combination of process parameters which correspond to points nearest to the fracture limit.     

Analysis of deep drawing of sheet metal using the Marform process

This paper deals with the deep drawing of metal cups using the Marform process. Using this technique, higher limiting drawing ratios can be obtained compared with the conventional deep drawing process. The analytical model of the process is presented initially, followed by the finite element simulations using ABAQUS software. A new friction model based on local contact conditions is presented and used in the finite element (FE) simulations of the process. Compared with traditional Coulomb friction model, the results of the FE simulations with the new friction model showed good correlation with experimental results. The results showed that the maximum thinning occurs at the punch profile portion, and by increasing the forming pressure, thinning of the sheet metal propagates from the punch profile portion to the side wall. At low forming pressures, wrinkles appear in the flange, whilst at higher pressures, fracture is the main defect of the Marform process.     

An Investigation on Reasons Causing Inferiority in Unlubricated Sliding Wear Performance of 60NiTi as Compared to 440C Steel

60NiTi is gaining recognition as an alternative to 440C steel in ball bearing components due to its intrinsic corrosion resistance and unusually high static load capacity. 440C steel and 60NiTi exhibit comparable Rockwell hardness and would be expected to exhibit similar sliding wear behavior using hardness based models. However, results show that under unlubricated sliding conditions, 60NiTi shows inferior wear properties than 440C steel. In this study, a series of indentation and single pass scratching experiments are conducted to elucidate the reasons behind this unexpected observation. Moreover, sliding wear tests carried out under moderate and extreme tensile stress conditions were used to identify sliding conditions under which these materials exhibit similar and dissimilar behavior. The results show that 440C steel exhibits more microscopic plasticity than 60NiTi, halting the propagation of generated tensile microcracks. In contrast, the intrinsic brittleness of 60NiTi leads to the formation and growth of microcracks between the shear bands causing subsequent wear particle generation. These lead to the occurrence of wear through more aggressive abrasion processes in 60NiTi than 440C steel. These findings help explain why 60NiTi performs well when lubricated. 60NiTi is expected to tolerate ～912?MPa tensile stress before yielding. Under good lubricated conditions where a perfect lubricating film is formed, friction induced tensile stresses fall below the tensile strength of 60NiTi and wear is prevented. However, inadequate lubrication combined with high contact stress leads to damage and wear.     

Concrete bridge surface damage detection using a single‐stage detector

Early and timely detection of surface damages is important for maintaining the functionality, reliability, and safety of concrete bridges. Recent advancement in convolution neural network has enabled the development of deep learning‐based visual inspection techniques for detecting multiple structural damages. However, most deep learning‐based techniques are built on two‐stage, proposal‐driven detectors using less complex image data, which could be restricted for practical applications and possible integration within intelligent autonomous inspection systems. In this study, a faster, simpler single‐stage detector is proposed based on a real‐time object detection technique, You Only Look Once (YOLOv3), for detecting multiple concrete bridge damages. A field inspection images dataset labeled with four types of concrete damages (crack, pop‐out, spalling, and exposed rebar) is used for training and testing of YOLOv3. To enhance the detection accuracy, the original YOLOv3 is further improved by introducing a novel transfer learning method with fully pretrained weights from a geometrically similar dataset. Batch renormalization and focal loss are also incorporated to increase the accuracy. Testing results show that the improved YOLOv3 has a detection accuracy of up to 80% and 47% at the Intersection‐over‐Union (IoU) metrics of 0.5 and 0.75, respectively. It outperforms the original YOLOv3 and the two‐stage detector Faster Region‐based Convolutional Neural Network (Faster R‐CNN) with ResNet‐101, especially for the IoU metric of 0.75.