The problem of structural identification linear dynamic systems on the basis of the analysis Lyapunov exponent in the conditions of uncertainty is considered. The method of estimation the general solution system on the basis of application static model is developed. Defini-tion of Lyapunov exponent (LE) on the analysis of a coefficient structural properties system is grounded. On the basis of a coefficient of structural properties the special structures reflecting change LE are introduced. The criterion of estimation an order system on the basis of the analysis behaviour these structures are offered. The decision-making method about type of roots dynamic system on the basis of the analysis of time series and the structures reflecting change LE is developed. Two approaches to an estimation of the largest LE and the Perron bottom indexes are offered. The first approach to identification of a change in a coefficient of structural properties with the help secant method for various classes of roots is grounded. The second approach is the structurally-frequency method grounded on definition of estimations LE by means of the analysis of local minima of structures offered in work. The frequency method which is a modification of a method a bar graph in the statistical theory is applied to a validation of the obtained estimations. Results of simulation confirm effectiveness of the offered methods, structures and procedures.

Local feature detection and description are widely used for object recognition such as augmented reality applications. There have been a number of evaluations and comparisons between feature detectors and descriptors and between their different implementations. Those evaluations are carried out on random sets of image structures. However, feature detectors and descriptors respond differently depending on the image structure. In this paper, we evaluate the overall performance of the most efficient detectors and descriptors in terms of speed and efficiency. The evaluation is carried out on a set of images of different object classes and structures with different geometric and photometric deformations. This evaluation would be useful for detecting the most suitable detector and descriptor for a particular object recognition application. Moreover, multi-object applications such as digilog books could change the detector and descriptor used based on the current object. From the results, it has been observed that some detectors perform better with certain object classes. Differences in performance of the descriptors vary with different image structures.

Liquid level control is important for ensuring energy and material balance in many installations but it also difficult as the plant is nonlinear, inertial and with model uncertainties. Fuzzy logic controllers (FLCs) are successfully applied to ensure system stability and robustness by simple means and a model-free design. This paper suggests a procedure for off-line tuning of the many FLC parameters based on optimization of a suggested multi-objective function defined on several system performance indices using genetic algorithms (GAs). First, a model-free FLC is empirically tuned, then applied for real time control of the plant and the necessary data recorded and used to GA parameter optimize a TSK plant model of an accepted structure. The validated on different set of experimental data model is employed in FLC closed loop system simulation experiments to evaluate the fitness function in the GA optimization of the FLC pre-processing and post-processing parameters. The procedure is applied for the real time PI/PID FLC level control in a laboratory-scale tank system. The improvement of the system performance indices due to the GA optimization is estimated in level real time control.

Feature selection is one of the most important preprocessing steps for a data mining, pattern recognition or machine learning problem. Finding an optimal subset of features, among all the combinations is a NP-Complete problem. Lot of research has been done in feature selection. However, as the sizes of the datasets are increasing and optimality is a subjective notion, further research is needed to find better techniques. In this paper, a genetic algorithm based feature subset selection method has been proposed with a novel feature evaluation measure as the fitness function. The evaluation measure is different in three primary ways a) It considers the information content of the features apart from relevance with respect to the target b) The redundancy is considered only when it is over a threshold value c) There is lesser penalization towards cardinality of the subset. As the measure accepts value of few parameters, this is available for tuning as per the need of the particular problem domain. Experiments conducted over 21 well known publicly available datasets reveal superior performance. Hypothesis testing for the accuracy improvement is found to be statistically significant.

The need for autonomous Unmanned Aerial Vehicles (UAVs) is very interesting nowadays. Autonomous UAVs provide the possibility of performing tasks and missions that are currently hazardous or can cost humans or money, enable autonomous search, persistent combat intelligence, surveillance and reconnaissance (ISR), and many other applications. This paper presents an overview of autopilot design with a detailed design of longitudinal autopilot of a Small Unmanned Aerial Vehicle (SUAV). The designed autopilot is applied to an Ultrastick-25e fixed wing UAV depending on longitudinal linear model and analytic linear model with trimmed values of straight and leveling scenario. The longitudinal motion controller design is started with the design of most inner loop (pitch rate feedback) of the longitudinal system, then pitch tracker design with a Proportional Integral (PI)- controller. The guidance and control system is related with the design of altitude hold controller with P-controller as an example of outer loop controller design. The performance of two classic controller approaches for the design of autopilot are compared and evaluated for both linear and non-linear models. The proposed controller is chosen for design due to its higher performance than the classic one. At last the climbing turn scenario is applied to the whole autopilot (longitudinal and lateral) for the evaluation process. The results show a good performance in both disturbance rejection and robustness against sensors noise.

Today’s Food Industry has responsibility to provide most consuming food for people. These foods are consumed by large area of society. So they are important source of causes of diseases and food poisoning. Monitoring system have been created to control these diseases and they are used in duration of production step of food supply chain. Hazard Assurance Critical Control Point (HACCP) is regarded as best method in safety system. Necessity to create integrated HACCP system forced factories to use intelligent methods to build HACCP for every production. This paper proposes Case-Based Reasoning (CBR) technique and use of paired comparisons tables and similarity equations to create HACCP for food system of Sabz Nam Company. Our system is an intelligent system has based on RFID and it works as consulter by generating five proper safety suggestion to food expert. Finally we assess accuracy and efficiency of proposed system on real data of Sabz Nam Company.

Software reuse is the process of implementing or updating software systems using existing software components. A good software reuse process facilitates the increase of productivity, quality and reliability. It decreases the cost and implementation time as compared to develop new system. Despite its many benefits we cannot achieve its full benefits. The reason behind this is that software reuse is often done in an informal and haphazard way. If done systematically, then we can achieve its full benefits. This research proposes a method through which we will classify the reusable components in proper way to get the full benefits of reusability. We classify the reusable components according to their clusters. Clusters are made on the basis of parameters provided with components. We develop an algorithm for assigning clusters to the reusable components.

A problem that humans must face very often is that of having to add, melt or synthesize information, that is, combine together a series of data from various sources to reach a certain conclusion or make a certain decision. This involves the use of one or more aggregation operators capable to provide a collective preference relation. These operators must be chosen according to specific criteria taking into account the characteristic properties of each operator. Some conditions to be taken into account to identify them are the following: axiomatic strength, empirical setting, adaptability, numerical efficiency, compensation and compensation range, added behavior and scale level required of the membership functions. It is possible to establish a general list of possible mathematical properties whose verification might be desirable in certain cases: boundary conditions, continuity, not decreasing monotony, symmetry, idempotence, associativity, bisymmetry, self-distributivity, compensation, homogeneity, translativity, stability, ?-comparability, sensitivity and locally internal functions. For analyze the attitudinal character of the aggregation operator the following measures are studied: disjunction degree (orness), dispersion, balance and divergence. In this paper, a review of these issues is presented.