Incorporate ACO routing algorithm and mobile sink in wireless sensor networks

Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field

An approach to adaptive inference engine for rule-based consultation systems

Today, expert systems are widely used in business, science, engineering, agriculture, manufacturing, medicine, video games, and virtually every other field. In fact, it is difficult to think of a field in which expert systems are not used today (Lozano-Perez T., Kaelbling L., 2003). In the field of consultation, expert system has been applied very early, for example, MYCIN (Buchanan, B. G. and Shortliffe, E. H., 1984) can be seen one of the earliest applications of the expert system. Although there are many commercial products of expert system shells that can be applied to build consultation systems, they have shown some drawbacks: Accepting only one determined language for knowledge representation. Therefore, once one decided to use an expert system shell, it is not easy to make a change of it later because of having to edit the whole knowledge base in the new language again. Being so passive in comparison with human being. They can only do exactly what the knowledge engineer specified in the knowledge base. Therefore, it requires a lot of efforts for preparing knowledge base Using system resource is not optimal, when applying them to consultation systems. To deal with the above problems, this dissertation is aimed at presenting an approach of an adaptive inference engine for rule-based consultation systems, which is a traditional inference engine with some additional abilities: Being able to learn different languages for knowledge representations through training. Being able to find out by itself which question should be asked next without any interference from human being. The “Matching” can recognize which information is required for the reasoning, then it creates new rules for them and adds the rules into its knowledge base. Being able to find out by itself an optimal reasoning strategy (forward chaining, backward chaining or a mixture of both). It does not need any setting from human being for the reasoning strategy. The “Matching” determines it from the beginning and at run-time. Being able to find out syntax errors in rules. This is the evidence that the inference engine understands its rules and understands what it is doing as well. Being able to learn from experience to improve its performance Beside concepts and implementation of the adaptive inference engine, a mathematical evaluation on the effectiveness of the new matching algorithm is also presented

Recurrent Neural Networks: Error Surface Analysis and Improved Training

Recurrent neural networks (RNNs) have powerful computational abilities and could be used in a variety of applications; however, training these networks is still a difficult problem. One of the reasons that makes RNN training, especially using batch, gradient-based methods, difficult is the existence of spurious valleys in the error surface. In this work, a mathematical framework was developed to analyze the spurious valleys that appear in most practical RNN architectures, no matter their size. The insights gained from this analysis suggested a new procedure for improving the training process. The procedure uses a batch training method based on a modified version of the Levenberg-Marquardt algorithm. This new procedure mitigates the effects of spurious valleys in the error surface of RNNs. The results on a variety of test problems show that the new procedure is consistently better than existing training algorithms (both batch and stochastic) for training RNNs. Also, a framework for neural network controllers based on the model reference adaptive control (MRAC) architecture was developed. This architecture has been used before, but the difficulties in training RNNs have limited its use. The new training procedures have made MRAC more attractive. The updated MRAC framework is very flexible, and incorporates disturbance rejection, regulation and tracking. The simulation and testing results on several real systems show that this type of neural control is well-suited for highly nonlinear plants.Electrical Engineerin

Mechanical Attributes of Fractal Dragons

Fractals are ubiquitous natural emergences that have gained increased attention in engineering applications, thanks to recent technological advancements enabling the fabrication of structures spanning across many spatial scales. We show how the geometries of fractals can be exploited to determine their important mechanical properties, such as the first and second moments, which physically correspond to the center of mass and the moment of inertia, using a family of complex fractals known as the dragons

Imposition of physical parameters in dissipative particle dynamics

In the mesoscale simulations by the dissipative particle dynamics (DPD), the motion of a fluid is modelled by a set of particles interacting in a pairwise manner, and it has been shown to be governed by the Navier–Stokes equation, with its physical properties, such as viscosity, Schmidt number, isothermal compressibility, relaxation and inertia time scales, in fact its whole rheology resulted from the choice of the DPD model parameters. In this work, we will explore the response of a DPD fluid with respect to its parameter space, where the model input parameters can be chosen in advance so that (i) the ratio between the relaxation and inertia time scales is fixed; (ii) the isothermal compressibility of water at room temperature is enforced; and (iii) the viscosity and Schmidt number can be specified as inputs. These impositions are possible with some extra degrees of freedom in the weighting functions for the conservative and dissipative forces. Numerical experiments show an improvement in the solution quality over conventional DPD parameters/weighting functions, particularly for the number density distribution and computed stresses

Further Discussion On Educational Issues And Case Teaching Method For Economic Students Via Case Studies

Vietnam has begun to apply case stude method teaching for economic and business students in clleges and universities because it has provided them with real situation and problem solving skills. By using experiences, synthesis and quantitative methods ombined with dialectical materialism methods, this study prove examples of case studies in Bahrain and in Vietnam, in the context og educationin globalization, it is better to propose innovative educational methods for strengthening educational policies and for educating studets also, for instance, in economic field. In developing countries such as Vietnam, in Asia and in the world, case teaching method has been usedf widely for economic major students in clooges and unievrsities. Therefore, This study will menitone case method benefits such as real worl problems, solvin gproblem skill enhancement and representative and ttractiveness for learning

Single phase second order sliding mode controller for complex interconnected systems with extended disturbances and unknown time-varying delays

Novel results on complex interconnected time-delay systems with single phase second order sliding mode control is investigated. First, a reaching phase in traditional sliding mode control (TSMC) is removed by using a novel single phase switching manifold function. Next, a novel reduced order sliding mode observer (ROSMO) with lower dimension is suggested to estimate the unmeasurable variables of the plant. Then, a new single phase second order sliding mode controller (SPSOSMC) is established based on ROSMO tool to drive the state variables into the specified switching manifold from beginning of the motion and reduce the chattering in control input. Then, a stability condition is suggested based on the well-known linear matrix inequality (LMI) method to ensure the asymptotical stability of the whole plant. Finally, an illustrated example is simulated to validate the feasible application of the suggested technique

BEM-RBF approach for viscoelastic flow analysis

A new BE-only method is achieved for the numerical solution of viscoelastic flows. A decoupled algorithm is chosen where the fluid is considered as being composed of an artificial Newtonian component and the remaining component is accordingly defined from the original constitutive equation. As a result the problem is viewed as that of solving for the flow of a Newtonian liquid with the non-linear viscoelastic effects acting as a pseudo body force. Thus the general solution is obtained by adding a particular solution to the homogeneous one. The former is obtained by a BEM for the base Newtonian fluid and the latter is obtained analytically by approximating the pseudo body force in terms of suitable radial basis functions (RBFs). Embedded in the approximation of the pseudo body force is the calculation of the polymer stress. This is achieved by solving the constitutive equation using RBF networks (RBFNs). Both the calculations of the particular solution and the polymer stress are therefore meshless and the resultant BEM-RBF method is a BE-only method. The complete elimination of any structured domain discretisation is demonstrated with a number of flow problems involving the Upper Convected Maxwell (UCM) and the Oldroyd-B fluids