The method of structural identification dynamic systems with a hysteresis in the conditions of uncertainty is developed. The method is based on selection of the special set containing the information on properties of a nonlinear part system. The virtual structure (VS) which allows the make the decision about hysteresis structure is offered. The concept of structural identifiability of nonlinear dynamic systems is introduced. Structural identifiability is a necessary condition of obtaining the original form of hysteresis. The criterion of structural identifiability is proposed. The solution of a problem selection the class of the functions belonging to hysteresis to nonlinearities is given.
The procedure of structural identification of hysteresis functions is developed. Procedure realization is based on the phenomenological analysis of structure VS. Defini-tion of features and properties of the VS is the goal of phenomenological analysis. Each non-linearity introduces the features in the behavior of the system. Therefore, their detection gives only the concrete analysis of VS.
Algorithms of estimation structural parameters the hysteresis in the conditions of uncertainty are offered. They analyze the data in special structural space and are based on the application of secant method VS. Such approach gives adequate estimations of parameters hysteresis. The method of the structurally-frequency analysis is offered for check of the obtained results and estimations. It is based on the analysis of fragments VS in two planes. Such analysis allows the make a decision about hysteresis structure. We show that the offered methodology is applicable to unstable dynamic systems. Results of the computer simulation are given.

With the advent of new thumbprint identification techniques, accurate personal identification is now easy and cheaper with approximately zero false acceptance rates. This paper focuses on developing an advance feature for thumbprint based identification systems with the help of soft computing and 2D transformation which makes the technique more flexible and Fidel. The thumbprint images of individuals were scanned with the help of H3 T&A terminal for collecting self generated datasets. The thumbprints of self generated and standard datasets were trained to form a refined set which includes linear and angular displacements of thumbprint images. The new obtained features of refined datasets were stored in the database for further identification.
In the proposed technique, the minutiae coordinates and orientation angles of the thumbprint of a person to be identified are computed and merged together for comparison. The minutia coordinates and orientation angles of a person are compared with the minutiae trained set values stored in the database at different linear and angular rotations for identity verification. The proposed technique was tested on fifty persons self generated and standard datasets of FVC2002, FVC2004 and CASIA databases. In the experimentation and result analysis we observed that the proposed technique accurately identifies a person on the basis of minutiae features of a thumbprint with low FNMR (False Non-Match Rate) values.

In this paper we are analyzing the steganographic data hiding using the least significant bit technique. This paper describes the particle swarm optimisation. The particle swarm optimisation algorithm is applied to the spatial domain technique. The improved algorithm called the accelerated particle swarm optimisation converges faster than the usual particle swarm optimisation and improves the performance. This paper also analyses the modified particle swarm optimisation on the spatial domain technique which improved the PSNR and also reduced the computation time.

Cascading failures play an important role in creation of blackout. These events consist of lines and generators outages. Online values of voltage, current, angle, and frequency are changing during the cascading events. The percent of blackout can be estimated during the disturbance by neural network. Proper indices must be defined for this purpose. These indices can be computed by online measurement from WAMs. In this paper, voltage, load, lines, and generators indices are defined for estimating the percent of blackout during the disturbance. These indices are used as the inputs of neural networks. A new combinational structure of neural network is used for this purpose. Proposed method is implemented on 39-bus New-England test system.

In this paper, a novel idea is being presented to perform Opinion Mining in a very simple and efficient manner with the help of the One-Level-Tree (OLT) based approach. To recognize opinions specific for features in customer reviews having a variety of features commingled with diverse emotions. Unlike some previous ventures entirely using one-time structured or filtered data but this is solely based on unstructured data obtained in real-time from Twitter. The hybrid approach utilizes the associations defined in Dependency Parsing Grammar and fully employs Double Propagation to extract new features and related new opinions within the review. The Dictionary based approach is used to expand the Opinion Lexicon. Within the dependency parsing relations a new relation is being proposed to more effectively catch the associations between opinions and features. The three new methods are being proposed, termed as Double Positive Double Negative (DPDN), Catch-Phrase Method (CPM) & Negation Check (NC), for performing criteria specific evaluations. The OLT approach conveniently displays the relationship between the features and their opinions in an elementary fashion in the form of a graph. The proposed system achieves splendid accuracy across all domains and also performs better than the state-of-the-art systems.

In this paper, we consider a method for solving a linear programming problem with fuzzy objective and coefficient matrix, where the fuzzy numbers are supposed to be triangular. By the proposed method, the Decision Maker will have the flexibility of choosing. The solving method is based on the Pareto algorithm, which converts the problem to a weighted-objective linear programming. For more illustration, after discussing the problem and the algorithm, we present an example, which its solutions are independent from the objective weights.

This paper will establish the importance and significance of studying the fractional-order control of nonlinear dynamical systems. The foundation and the sources related to this research scope is going to be set. Then, the paper incorporates a brief overview on how this study is performed and present the organization of this study. The present work investigates the effectiveness of the physical-fractional and biological-genetic operators to develop an Optimal Form of Fractional-order PID Controller (O2Fo-PIDC). The newly developed Fo-PIDC with optimal structure and parameters can, also, improve the performances required in the modeling and control of modern manufacturing-industrial process (MIP). The synthesis methodology of the proposed O2Fo-PIDC can be viewed as a multi-level design approach. The hierarchical Multiobjective genetic algorithm (MGA), adopted in this work, can be visualized as a combination of structural and parametric genes of a controller orchestrated in a hierarchical fashion. Then, it is applied to select an optimal structure and knowledge base of the developed fractional controller to satisfy the various design specification contradictories (simplicity, accuracy, stability and robustness).