Intellectualization of decision making in security systems of protected objects

The paper discusses the prospects for building decision blocks (DB) for modern security systems built on the basis of fiber optic detectors. An algorithm has been developed that makes it possible to train an artificial neural network as part of a DB. Approaches to the formation of a training sample are outlined. To reduce the dimension of the problem of recognizing informative signal features, an extreme filtering algorithm is presented, which makes it possible to increase the speed and accuracy of training.

Wavelet Processing of Acoustic Emission of VT 1-0 Titanium Alloy under Tension

The paper proposes a method for processing acoustic emission signals for calculating informative signal parameters characterizing the stages of plastic deformation and fractures in a loaded titanium alloy. The proposed method has a complex structure that includes digital signal processing algorithms and multivariate data analysis methods. The acoustic emission signals are processed using the mathematical apparatus of the multilevel discrete wavelet transform to obtain the approximation coefficients of the 10-level decomposition. These coefficients characterize the low-frequency features of acoustic emission at various stages of samples loading. The approximation coefficients are further used as informative parameters of acoustic emission signals. Principal components analysis is used to investigate the informative parameters and establish their quantitative relationship with the stages of plastic deformation of titanium by clustering the processed results. Differences in the informative parameters at different stages of plastic deformation of the material are revealed by the following analysis of the clustered results. The obtained results can be used to develop a new generation of diagnostic devices for acoustic emission measurements.

SpeciesRax: A tool for maximum likelihood species tree inference from gene family trees under duplication, transfer, and loss.

Species tree inference from gene family trees is becoming increasingly popular because it can account for discordance between the species tree and the corresponding gene family trees. In particular, methods that can account for multiple-copy gene families exhibit potential to leverage paralogy as informative signal. At present, there does not exist any widely adopted inference method for this purpose. Here, we present SpeciesRax, the first maximum likelihood method that can infer a rooted species tree from a set of gene family trees and can account for gene duplication, loss, and transfer events. By explicitly modelling events by which gene trees can depart from the species tree, SpeciesRax leverages the phylogenetic rooting signal in gene trees. SpeciesRax infers species tree branch lengths in units of expected substitutions per site and branch support values via paralogy-aware quartets extracted from the gene family trees. Using both empirical and simulated datasets we show that SpeciesRax is at least as accurate as the best competing methods while being one order of magnitude faster on large datasets at the same time. We used SpeciesRax to infer a biologically plausible rooted phylogeny of the vertebrates comprising $188$ species from $31612$ gene families in one hour using $40$ cores. SpeciesRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax and on BioConda.

Study of Master Controller Structure for Multiple Air Compressor System

Compressed air is an important medium for energy transfer in industrial processes. It drives various actuators, which create a large force for significant movements and a high operation speed. Thereat, these mechanisms are quite small, and the design is simple and reliable. They are applicable in food and pharmaceutical technologies. Compressed air systems belong to the largest energy consumers at such enterprises. They consist of several compressors, and the drive of one of them is powered by a frequency converter in order to save electrical energy. This is called a multiple air compressor system, and its operation is controlled by a separate controller – master controller. The paper discusses the results of developing the master controller design and its operation algorithm. As an additional informative signal in the developed design of the master controller, air flow rate measurements are used; additionally, the speed of the air flow rate variation is analysed.

STUDYING OF ELECTRICAL PARAMETERS OF PLANT AND SOIL OBJECTS AS ACTIVE-CAPACITIVE BIPOLAR

The key elements of the system for predicting electromagnetic damage are the electromagnetic source and the biological object. There is no complex of analytical models and programs for predicting damaging and critical conditions of electromagnetic impact on biological objects, as well as software components that implement developed mathematical models of real electromagnetic processes occurring in biological structures at different levels of the organization. (Research purpose) The research purpose is in developing a system for determining the electrical parameters of soil and biological objects (to develop ideas about the processes in the structures of different levels of organization of biological objects): in agricultural technologies for diagnostics of plant objects and soil; in laboratory conditions as a medium for creating and studying new electrical technologies, methods of analysis and processing of information signals. (Materials and methods) The method for monitoring electrical properties consists in applying a voltage with a constant and low-frequency component to a plant object, and simultaneously measuring the DC current, capacitive and resistive components of the low-frequency impedance. (Results and discussion) The proposed system for determining the electrical parameters of plant objects and soil allows to visualize the original signal; to perform calculation of informative parameters and statistical processing of the informative signal of a plant object and soil (construction of distribution laws, calculation of variance, mathematical expectation); to calculate the spectrum of the informative signal; to record the values of any of the specified informative parameters, both in real time and at the selected moment; to make time dependencies of informative parameters of plant objects and soil. (Conclusions) Authors implemented modes for measuring local DC resistance and monitoring the capacitive and resistive components in the area of electrical contact of two needles and other measuring electrodes with plant objects in the low-frequency range.

Does Variability in an Implicit Learning Task Help Infants Learn Faster? A Registered Report

Infants are constantly inundated with sensory input, which they must somehow interpret in order to understand the world. They must do this without explicit cues about which parts of the input are going to be useful and which parts can be ignored. We know that infants do implicitly learn complex skills, which shows that they manage to separate informative signal from uninformative noise.In laboratory-based learning tasks, when the signal to be learnt is accompanied by other less reliable signals, this environmental variability seems to cue learners towards the invariant, learnable features and allows them to perform better when tested (e.g. Gómez, 2002; Tummeltshammer & Kirkham, 2013). Here, we build on these previous studies by asking whether variability in the exact realisation of events – noise within the signal to be learnt – is also influencing learning during the stimulus presentation. We will test whether this type ofvariability is, over and above a helpful cue, a crucial and fundamental ingredient for learningthat has so far been absent from most experimental paradigms.Using multi-level modelling to analyse infants’ implicit learning during a novel saccadic serial reaction time task, we aim to show how infants learn a sequence from noisy instantiations of events. If infants learn sequences with added noise faster than those without, we will claim that noise acts on expectations online to shape perception as it happens.

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

Complex signals alter recognition accuracy and conspecific acceptance thresholds

Many aspects of behaviour depend on recognition, but accurate recognition is difficult because the traits used for recognition often overlap. For example, brood parasitic birds mimic host eggs, so it is challenging for hosts to discriminate between their own eggs and parasitic eggs. Complex signals that occur in multiple sensory modalities or involve multiple signal components are thought to facilitate accurate recognition. However, we lack models that explore the effect of complex signals on the evolution of recognition systems. Here, we use individual-based models with a genetic algorithm to test how complex signals influence recognition thresholds, signaller phenotypes and receiver responses. The model has three main results. First, complex signals lead to more accurate recognition than simple signals. Second, when two signals provide different amounts of information, receivers will rely on the more informative signal to make recognition decisions and may ignore the less informative signal. As a result, the particular traits used for recognition change over evolutionary time as sender and receiver phenotypes evolve. Third, complex signals are more likely to evolve when recognition errors are high cost than when errors are low cost. Overall, redundant, complex signals are an evolutionarily stable mechanism to reduce recognition errors. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests’.

Rapid Motor Fluctuations Reveal Short-Timescale Neurophysiological Biomarkers of Parkinson’s Disease

ABSTRACTIdentifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson’s Disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation for deep brain stimulation (DBS). While pathologically-excessive oscillatory activity has been observed and linked to PD motor dysfunction broadly, the extent to which these signals provide quantitative information about disease expression and fluctuations, particularly at short timescales, is unknown. Furthermore, the degree to which informative signal features are similar or different across patients has not been rigorously investigated. Here, we recorded neural activity from the subthalamic nucleus (STN) of patients with PD undergoing awake DBS surgery while they performed an objective, continuous behavioral assessment. This approach leveraged natural motor performance variations as a basis to identify corresponding neurophysiological biomarkers. Using machine learning techniques, we show it was possible to use neural signals from the STN to decode the level of motor impairment at short timescales (as short as one second). Spectral power across a wide range of frequencies, beyond the classic “β” oscillations, contributed to this decoding. While signals providing significant information about the quality of motor performance were found throughout the STN, the most informative signals tended to arise from locations in or near the dorsolateral, sensorimotor portion. Importantly, the informative patterns of neural oscillations were not fully generalizable across subjects, suggesting a patient-specific approach will be critical for optimal disease tracking and closed-loop neuromodulation.