Developing the Processing Maps Using the Hyperbolic Sine Constitutive Equation

Hot compression tests were performed on a duplex stainless steel at temperatures ranging from 1223 K to 1473 K (950 °C to 1200 °C) and strain rates from 0.001 to 100 s^?1. The constitutive analysis of flow stress was carried out using the hyperbolic sine function, and the material constants were determined at two typical strains of 0.3 and 0.7. The power dissipation map, instability map, and processing map for the material were developed for strains of 0.3 and 0.7. The developed processing maps were based on the hyperbolic sine as well as the conventional power-law constitutive equations. The efficiency of power dissipation (η) varied from 12 to 60 pct over the studied temperature and strain rate. The highest value of η was obtained at strain rates below 0.01 s^?1, whereas the lowest value of η was observed at the intermediate strain rates. The instability region in sin h-based processing map was only observed in the range of 1423 K to 1473 K (1150 °C to 1200 °C) and at a strain rate of 100 s^?1, while the conventional processing map did not predict any instability region. Optical microscopy observations were more consistent with the results of the sin h-based processing map and indicated that the instability regime at high temperatures and high strain rates was due to the development of adiabatic shear bands.     

Applying reinforcement learning for web pages ranking algorithms

Ranking web pages for presenting the most relevant web pages to user's queries is one of the main issues in any search engine. In this paper, two new ranking algorithms are offered, using Reinforcement Learning (RL) concepts. RL is a powerful technique of modern artificial intelligence that tunes agent's parameters, interactively. In the first step, with formulation of ranking as an RL problem, a new connectivity-based ranking algorithm, called RL_Rank, is proposed. In RL_Rank, agent is considered as a surfer who travels between web pages by clicking randomly on a link in the current page. Each web page is considered as a state and value function of state is used to determine the score of that state (page). Reward is corresponded to number of out links from the current page. Rank scores in RL_Rank are computed in a recursive way. Convergence of these scores is proved. In the next step, we introduce a new hybrid approach using combination of BM25 as a content-based algorithm and RL_Rank. Both proposed algorithms are evaluated by well known benchmark datasets and analyzed according to concerning criteria. Experimental results show using RL concepts leads significant improvements in raking algorithms.     

Task space control of a welding robot using a fuzzy coordinator

This paper introduces a fuzzy coordinator as a novel application of fuzzy controller. A control transformation from the task space to the joint space is required to control a robot manipulator in the task space. Because the actuators operate in the joint space while the manipulator is controlled in the task space. A conflict between two spaces is produced due to using an imprecise transformation. Fuzzy coordinator coordinates two spaces by modifying the control transformation affected by uncertainties. The fuzzy coordinator is designed simply and operates as a robust controller. The role of fuzzy coordinator is analyzed and illustrated in the robust control of a welding robot in the task space. A circular trajectory is planned for a welding task performed by a SCARA robot. The fuzzy coordinator is then used to improve the performance of control system affected by imprecise transformations including the imprecise path transformation and the approximated feedback linearization.     

Dynamic Resource Allocation for Multimedia Document Retrieval over High Speed LANs

The emergence of gigabit local area networks (G-LANs) has spurred a tremendous interest in supporting networked multimedia applications over a LAN. In this paper, we propose a mechanism for dynamically allocating network resources in asynchronous LANs. Presentation of multimedia objects with required play-out quality requires Quality of Service (QoS) guarantees by the underlying networking infrastructure. Existing asynchronous LANs, such as Ethernet, do not support the notion of QoS due to their asynchronous media access protocol. For such networks, we propose a dynamic bandwidth management scheme that uses the concept of Time Division Multiple Access (TDMA). Significant performance improvement is observed through experimental results. In particular, the transmission rates for multimedia hosts improve significantly with low jitter variations in media streams. We also propose a framework for graceful degradation of play-out quality of multimedia objects in case the LAN's total capacity is not sufficient to meet the overall demand.     

An OWA‐Based Ranking Approach for University Books Recommendation

Generally the book recommendation approaches are personalized in nature, that is, they utilize the users’ purchasing behavior to recommend them the book similar to their preferences. The main problem with the personalized recommendation is its knowledge requirement about users’ past preferences. As a result, these techniques fail in producing appropriate recommendation for a new user whose preferences are not known. The personalized recommendation also needs extra space to store the users’ preferences. In this paper, a framework to recommend books to university students for their studies is presented. In order to answer which books are to be included in the syllabus, a specialized way of recommendation, where recommendations from experts of the subjects at different universities are considered, is presented. We have suggested a ranked recommendation approach for books, which employ Ordered Weighted Aggregation (OWA), a fuzzy‐based aggregation, to aggregate the several ranking of the top universities. On the one hand, it does not need user prior preferences, and on the other hand, it eases the complexities of personalized recommendation to huge number of users and replaces it with a single ranked recommendation. The experimental results are compared with the existing positional aggregation algorithm that demonstrates significant improvement in the results with respect to various performance metrics.     

Information schema constructs for defining warehouse databases of genotypes and phenotypes of system manifestation features

Our long-term objective is to develop a software toolbox for pre-embodiment design of complex and heterogeneous systems, such as cyber-physical systems. The novelty of this toolbox is that it uses system manifestation features (SMFs) for transdisciplinary modeling of these systems. The main challenges of implementation of the toolbox are functional design- and language-independent computational realization of the warehouses, and systematic development and management of the various evolving implements of SMFs (genotypes, phenotypes, and instances). Therefore, an information schema construct (ISC) based approach is proposed to create the schemata of the associated warehouse databases and the above-mentioned SMF implements. ISCs logically arrange the data contents of SMFs in a set of relational tables of varying semantics. In this article we present the ISCs necessary for creation of genotypes and phenotypes. They increase the efficiency of the database development process and make the data relationships transparent. Our follow-up research focuses on the elaboration of the SMF instances based system modeling methodology.     

A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

Multiresolution segmentation (MRS) is one of the most commonly used image segmentation algorithms in the remote-sensing community. This algorithm has three user-defined parameters: scale, shape, and compactness. The scale parameter (SP) is the most crucial one in determining the average size of the image segments generated. Since setting this parameter typically requires a trial-and-error process, automatically estimating it can expedite the segmentation process. However, most of automatic approaches are still iterative and can lead to a time-consuming process. In this article, we propose a new, non-iterative framework for estimating the SP with an emphasis on extracting individual urban buildings. The basis of the proposed method is to investigate the feasibility of associating the size of urban buildings with a corresponding ‘optimal’ (or at least reasonable) SP using an explicit mathematical equation. Using the proposed method, these two variables are related to each other by constructing a mathematical (regression) model. In this framework, the independent variables were chosen to be the typical size of buildings in a given urban area and the spatial resolution of the image under consideration; and the dependent variable was chosen to be the corresponding optimal SP. To assess the potential of the proposed approach, two regression models that yielded explicit equations (i.e. degree-2 polynomial (DP), and regression tree (RT)) were employed. In addition, as a sophisticated and versatile nonlinear model, support vector regression (SVR) was utilized to further measure the performances of DP and RT models compared with it. According to the comparisons, the DP model was selected as a representative of the proposed approach. In the end, to evaluate the proposed methodology, we also compared the results derived from the DP model with those derived from the Estimation of Scale Parameter (ESP) tool. Based on our experiments, not only did the DP model produce acceptable results, but also it outperformed ESP tool in this study for extracting individual urban buildings.     

Relativistic modelling of a superdense star containing a charged perfect fluid

The paper presents a variety of classes of interior solutions of the Einstein-Maxwell field equations for a static, spherically symmetric distribution of a charged fluid of well-behaved nature. They describe perfect fluid balls with positive finite central pressure and density; their ratio is less than one (c = 1), and the causality condition is obeyed at the center. The outmarch of pressure, density, pressuredensity ratio and the adiabatic speed of sound is a monotonic decrease, in a physically appealing manner. A certain class of these well-behaved solutions is studied extensively. For this class, the mass of the configuration is maximized. In particular, for a surface density ?? _b = 2×10¹⁴ g/cm³ we obtain a star with a maximummass of 3.47M _??, a radius of 15.21 km and the central redshift 1.014385.     

An RL-based scheduling algorithm for video traffic in high-rate wireless personal area networks

The emerging high-rate wireless personal area network (WPAN) technology is capable of supporting high-speed and high-quality real-time multimedia applications. In particular, video streams are deemed to be a dominant traffic type, and require quality of service (QoS) support. However, in the current IEEE 802.15.3 standard for MAC (media access control) of high-rate WPANs, the implementation details of some key issues such as scheduling and QoS provisioning have not been addressed. In this paper, we first propose a Markov decision process (MDP) model for optimal scheduling for video flows in high-rate WPANs. Using this model, we also propose a scheduler that incorporates compact state space representation, function approximation, and reinforcement learning (RL). Simulation results show that our proposed RL scheduler achieves nearly optimal performance and performs better than F-SRPT, EDD + SRPT, and PAP scheduling algorithms in terms of a lower decoding failure rate.