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This paper addresses the distributed formation containment control problem of Euler–Lagrangian systems(ELSs) with unknown control directions by two layers, namely, time-varying formation for the leaders and containment formation control for the followers. We first estimate the desired states and velocities for the leaders and the followers in finite time. Second, novel adaptive neutral networks are used to compensate the model uncertainties. Third, using the Nussbaum gain technique, the proposed control laws can guarantee the ultimate convergence of error signals to zero and that all of the signals in the closed-loop system are bounded. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract With the development of Web service technology, more and more enterprises choose to publish their own services on the Internet. However, with the increasing demands of users, it is difficult for a single service to meet the complex user requirements. To address this challenge, multiple services can be integrated by leveraging the service-oriented architecture (SOA) to generate a value-added service, referred to a service composition, where the component services are loosely coupled. However, due to the dynamic running environment, the performance of each component service (including reliability) may fluctuate. This will introduce cascading effects, which could cause the entire service system to fail. Since component services run in a dynamic environment, the parameters used to conduct reliability prediction are difficult to obtain. Therefore, online reliability prediction that ensures the runtime quality poses a grand challenge. This paper analyzes the historical response time series and throughput time series of component services, and predicts the reliability in the near future. To guarantee the stable and continuous operation of a service system, we proposed an online reliability time series prediction method by combining a Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The proposed approach, referred to as CL-ROP, is able to predict the reliability of a service system in the near future. We conducted a series of experiments over real service data and compared with other competitive approaches to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A neutral network is a subgraph of a Hamming graph, and its principal eigenvalue determines its robustness: the ability of a population evolving on it to withstand errors. Here we consider the most robust small neutral networks: the graphs that interpolate pointwise between hypercube graphs of consecutive dimension (the point, line, line and point in the square, square, square and point in the cube, and so on). We prove that the principal eigenvalue of the adjacency matrix of these graphs is bounded by the logarithm of the number of vertices, and we conjecture an analogous result for Hamming graphs of alphabet size greater than two. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the problem on an unique equilibrium solution of the bidirectional associative memory neural networks with distributed time-varying delays is investigated via Lyapunov stability theory. By using topological degree theory and M-matrix theory, several sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of equilibrium solution are derived. An illustrative example is given to demonstrate the effectiveness of the obtained results. [Copyright &y& Elsevier]

Abstract: We are developing a system to predict the arrival of interplanetary (IP) shocks at the Earth. These events are routinely detected by the Electron, Proton, and Alpha Monitor (EPAM) instrument aboard NASA’s ACE spacecraft, which is positioned at Lagrange Point 1 (L1). In this work, we use historical EPAM data to train an IP shock forecasting algorithm. Our approach centers on the observation that these shocks are often preceded by identifiable signatures in the energetic particle intensity data. Using EPAM data, we trained an artificial neural network to predict the time remaining until the shock arrival. After training this algorithm on 37 events, it was able to forecast the arrival time for 19 previously unseen events. The average uncertainty in the prediction 24h in advance was 8.9h, while the uncertainty improved to 4.6h when the event was 12h away. This system is accessible online, where it provides predictions of shock arrival times using real-time EPAM data. [Copyright &y& Elsevier]

Abstract: For LHC, the hadronic calorimetry of the ATLAS detector is performed by Tilecal, a scintillating tile calorimeter. For calibration purposes, a fraction of the Tilecal modules is placed in particle beam lines. Despite beam high quality, experimental beam contamination is observed and this masks the actual performance of the calorimeter. For optimizing the calibration task, an online neural particle classifier was developed for Tilecal. Envisaging a neural trigger for incoming particles, a neural process runs integrated to the data acquisition task and performs online training for particle identification. The neural classification performance is evaluated by correlating the neural response to classical methodology, confirming an ability for outsider identification at levels as high as 99.3%. [Copyright &y& Elsevier]

Abstract: The paper is devoted to the problem of time series enhancement, which is based on the analysis of local regularity. The model construction using this analysis does not require any a priori assumption on the structure of the noise and the functional relationship between original signal and noise. The signal itself may be nowhere differentiable with rapidly varying local regularity, what is overcome with the help of the new technique of increasing the local Hölder regularity of the signal under research. A new signal with prescribed regularity is constructed using the genetic algorithm. This approach is applied to enhancement of time series in the paleoclimatology, solar physics, dendrochronology, meteorology and hydrology. [Copyright &y& Elsevier]

Abstract: We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application. [Copyright &y& Elsevier]

The work aimed to determine the effectiveness of the treatment of wastewater generated by the meat industry in a hybrid system combining biological methods of activated sludge (in an SBR reactor) and reverse osmosis. The tests carried out on the wastewater from the meat processing plant Uni-Lang in Wrzosowa showed that the biological treatment resulted in sufficient removal of contaminants from the wastewater, which consequently could be discharged into receiving water. In order to make it possible for the wastewater to be reused in the production cycle, it was additionally treated with reverse osmosis. The research was described mathematically by the program MATLAB using artificial neural networks. The program enables a prediction of the results for the treatment of wastewater over a range of tested values. [Copyright &y& Elsevier]