Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Macroeconomics modelling on UK GDP growth by neural computing

This paper presents multilayer neural networks used in UK gross domestic product estimation. These networks are trained by backpropagation and genetic algorithm based methods. Different from backpropagation guided by gradients of the performance, the genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then uses the analysed results to breed new networks that tend to be better suited to the problems in hand. It is shown that this guided evolution leads to globally optimal networks and more accurate results, with less adjustment of the algorithm needed

Modeling the Drying Kinetics of Green Bell Pepper in a Heat Pump Assisted Fluidized Bed Dryer

In this research, green bell pepper was dried in a pilot plant fluidized bed dryer equipped with a heat pump humidifier using three temperatures of 40, 50 and 60C and two airflow velocities of 2 and 3m/s in constant air moisture. Three modeling methods including nonlinear regression technique, Fuzzy Logic and Artificial Neural Networks were applied to investigate drying kinetics for the sample. Among the mathematical models, Midilli model with R=0.9998 and root mean square error (RMSE)=0.00451 showed the best fit with experimental data. Feed-Forward-Back-Propagation network with Levenberg-Marquardt training algorithm, hyperbolic tangent sigmoid transfer function, training cycle of 1,000 epoch and 2-5-1 topology, deserving R=0.99828 and mean square error (MSE)=5.5E-05, was determined as the best neural model. Overall, Neural Networks method was much more precise than two other methods in prediction of drying kinetics and control of drying parameters for green bell pepper. Practical Applications: This article deals with different modeling approaches and their effectiveness and accuracy for predicting changes in the moisture ratio of green bell pepper enduring fluidized bed drying, which is one of the most concerning issues in food factories involved in drying fruits and vegetables. This research indicates that although efficiency of mathematical modeling, Fuzzy Logic controls and Artificial Neural Networks (ANNs) were all acceptable, the modern prediction methods of Fuzzy Logic and especially ANNs were more productive and precise. Besides, this report compares our findings with previous ones carried out with the view of predicting moisture quotients of other food crops during miscellaneous drying procedures. © 2016 Wiley Periodicals, Inc

Genetic programming for the automatic design of controllers for a surface ship

In this paper, the implementation of genetic programming (GP) to design a contoller structure is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the command forces required to maneuver the ship. The controllers created using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the automatic design of propulsion and navigation controllers for surface ships

Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan

Applications of fuzzy theories to multi-objective system optimization

Most of the computer aided design techniques developed so far deal with the optimization of a single objective function over the feasible design space. However, there often exist several engineering design problems which require a simultaneous consideration of several objective functions. This work presents several techniques of multiobjective optimization. In addition, a new formulation, based on fuzzy theories, is also introduced for the solution of multiobjective system optimization problems. The fuzzy formulation is useful in dealing with systems which are described imprecisely using fuzzy terms such as, 'sufficiently large', 'very strong', or 'satisfactory'. The proposed theory translates the imprecise linguistic statements and multiple objectives into equivalent crisp mathematical statements using fuzzy logic. The effectiveness of all the methodologies and theories presented is illustrated by formulating and solving two different engineering design problems. The first one involves the flight trajectory optimization and the main rotor design of helicopters. The second one is concerned with the integrated kinematic-dynamic synthesis of planar mechanisms. The use and effectiveness of nonlinear membership functions in fuzzy formulation is also demonstrated. The numerical results indicate that the fuzzy formulation could yield results which are qualitatively different from those provided by the crisp formulation. It is felt that the fuzzy formulation will handle real life design problems on a more rational basis