Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Antireflection coating on InP for semiconductor detectors

Aluminum nitride thin film by RF magnetron sputtering is used to produce antireflection coating on InP. The index of refection variation of aluminum nitride for different thickness at different wavelength in the range of 400 to 1500 nm is investigated using reflection spectroscopy. Subsequent Ar+ ion implantation at 30 keV with different doses on these coated layers has been performed. The morphology of aluminum nitride after ion implantation is characterized using atomic force microscopy AFM.     

Parallel adaptive discontinuous Galerkin approximation for thin layer avalanche modeling

This paper describes the development of highly accurate adaptive discontinuous Galerkin schemes for the solution of the equations arising from a thin layer type model of debris flows. Such flows have wide applicability in the analysis of avalanches induced by many natural calamities, e.g. volcanoes, earthquakes, etc. These schemes are coupled with special parallel solution methodologies to produce a simulation tool capable of very high-order numerical accuracy. The methodology successfully replicates cold rock avalanches at Mount Rainier, Washington and hot volcanic particulate flows at Colima Volcano, Mexico.     

A new approach for constructing the concept map

In recent years, e-learning system has become more and more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners’ historical testing records. Phase 1 is used to preprocess the testing records; i.e., transform the numeric grade data, refine the testing records, and mine the association rules from input data. Phase 2 is used to transform the mined association rules into prerequisite relationships among learning concepts for creating the concept map. Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic data, apply Education Theory to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also discussed. Moreover, we also develop a prototype system of TP-CMC and then use the real testing records of students in junior high school to evaluate the results. The experimental results show that our proposed approach is workable.     

Protection switching schemes and mapping strategies for fail-operational hard real-time NoCs

Communication infrastructures designed for mixed-critical MPSoCs must provide isolation of traffic, hard real-time guarantees, and fault-tolerance. In previous work, we proposed the combination of protection-switching with a hybrid Time-Division-Multiplexed (TDM) and packet-switched Network-on-Chip (NoC) to achieve all three goals. In this paper, we present an FPGA implementation of such a NoC with all its features. We give synthesis results for the hybrid NoC, including the network interface, and show that our router uses over 32% fewer LUTs and registers than a competitive state-of-the-art router for mixed-critical MPSoC. We then explore different channel and task mapping strategies for critical applications which use protection switching and evaluate the effect these mappings have on the best-effort (BE) traffic in the system. Results show, that spreading out the critical traffic rather than naively dividing the system in critical and non-critical application domains is advantageous or even necessary in many cases and can allow for up to 13% more BE traffic. We give a comprehensive trade-off analysis of three protection switching schemes—1:n, 1:1, and 1+1—and show that 1+1 protection has less than half the worst case latency for critical traffic that 1:n and 1:1 protection have. At the same time, 1+1 protection, on average, only causes a 1.18% earlier saturation rate for BE traffic, which we consider to be affordable. We conclude that 1+1 protection is ideally suited for use in mixed-critical systems with high safety requirements.     

Distributed and incremental travelling salesman algorithm on time-evolving graphs

The Journal of Supercomputing - Travelling salesman problem (TSP) is a graph problem that has been widely used in many applications, especially for transportation and logistics. Because TSP is a NP...     

A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals

Neural Computing and Applications - This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat...     

An adaptive single-point algorithm for global numerical optimization

This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution.