Unpredictable Oscillations Research Articles

Generation of Synchronous Unpredictable Oscillations by Coupled Hopfield Neural Networks

Generation of Synchronous Unpredictable Oscillations by Coupled Hopfield Neural Networks

Application of single wrist-wearable accelerometry for objective motor diary assessment in fluctuating Parkinson’s disease

Advanced Parkinson’s disease (PD) is characterized by motor fluctuations including unpredictable oscillations remarkably impairing quality of life. Effective management and development of novel therapies for these response fluctuations largely depend on clinical rating instruments such as the widely-used PD home diary, which are associated with biases and errors. Recent advancements in digital health technologies provide user-friendly wearables that can be tailored for continuous monitoring of motor fluctuations. Their criterion validity under real-world conditions using clinical examination as the gold standard remains to be determined. We prospectively examined this validity of a wearable accelerometer-based digital Parkinson’s Motor Diary (adPMD) using the Parkinson’s Kinetigraph (PKG®) in an alternative application by converting its continuous data into one of the three motor categories of the PD home diary (Off, On and Dyskinetic state). Sixty-three out of 91 eligible participants with fluctuating PD (46% men, average age 66) had predefined sufficient adPMD datasets (>70% of half-hour periods) from 2 consecutive days. 92% of per-protocol assessments were completed. adPMD monitoring of daily times in motor states showed moderate validity for Off and Dyskinetic state (ICC = 0.43–0.51), while inter-rating methods agreements on half-hour-level can be characterized as poor (median Cohen’s κ = 0.13–0.21). Individualization of adPMD thresholds for transferring accelerometer data into diary categories improved temporal agreements up to moderate level for Dyskinetic state detection (median Cohen’s κ = 0.25–0.41). Here we report that adPMD real-world-monitoring captures daily times in Off and Dyskinetic state in advanced PD with moderate validities, while temporal agreement of adPMD and clinical observer diary data is limited.

Dynamics of Hopfield-Type Neural Networks with Modulo Periodic Unpredictable Synaptic Connections, Rates and Inputs.

In this paper, we rigorously prove that unpredictable oscillations take place in the dynamics of Hopfield-type neural networks (HNNs) when synaptic connections, rates and external inputs are modulo periodic unpredictable. The synaptic connections, rates and inputs are synchronized to obtain the convergence of outputs on the compact subsets of the real axis. The existence, uniqueness, and exponential stability of such motions are discussed. The method of included intervals and the contraction mapping principle are applied to attain the theoretical results. In addition to the analysis, we have provided strong simulation arguments, considering that all the assumed conditions are satisfied. It is shown how a new parameter, degree of periodicity, affects the dynamics of the neural network.

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.

Unpredictable oscillations of SICNNs with delay

We rigorously prove that unpredictable oscillations take place in the dynamics of shunting inhibitory cellular neural networks (SICNNs) with delay when rectangular input currents generated by an unpredictable sequence are utilized. The existence, uniqueness, and exponential stability of such oscillations are discussed. The contraction mapping principle is applied to achieve the theoretical results. Numerical simulations supporting the presence of unpredictable oscillations are provided, and the transfer of unpredictable behavior between SICNNs under unidirectional coupling is demonstrated. It is also shown by means of the delayed feedback control method that the obtained unpredictable behavior is controllable. Moreover, an application to secure communication is discussed.

Unpredictable Oscillations for Hopfield-Type Neural Networks with Delayed and Advanced Arguments

This is the first time that the method for the investigation of unpredictable solutions of differential equations has been extended to unpredictable oscillations of neural networks with a generalized piecewise constant argument, which is delayed and advanced. The existence and exponential stability of the unique unpredictable oscillation are proven. According to the theory, the presence of unpredictable oscillations is strong evidence for Poincaré chaos. Consequently, the paper is a contribution to chaos applications in neuroscience. The model is inspired by chaotic time-varying stimuli, which allow studying the distribution of chaotic signals in neural networks. Unpredictable inputs create an excitation wave of neurons that transmit chaotic signals. The technique of analysis includes the ideas used for differential equations with a piecewise constant argument. The results are illustrated by examples and simulations. They are carried out in MATLAB Simulink to demonstrate the simplicity of the diagrammatic approaches.

Simulations of a flexible 100 kWel PEM Fuel Cell power plant for the provision of grid balancing services

The continuous growth of non-programmable renewable energy resources penetration leads to unpredictable oscillations of the net load faced by dispatchable power plants, hindering the reliability and stability of the electric grid and requiring additional flexible resources. The EU project GRASSHOPPER focuses on MW-scale Fuel Cell Power Plant (FCPP) based on low temperature PEM technology. The project aims to setup and demonstrate a 100 kWel PEM FCPP, flexible in power output and designed to provide grid support. This work presents a dynamic simulation model of the FCPP, developed to simulate plant flexible operation and identify the best management strategy, aiming at optimizing the efficiency while reducing the degradation rate. Cold start up simulations, according to a warm-up procedure limiting stack degradation, result in a time to operation equal to 26 minutes. A sensitivity analysis is performed to determine which parameters mostly influence the warm-up duration, showing that it is possible to reduce start-up time substantially (e.g. down to 3 minutes with component preheating). On the other hand, simulations at variable load along the entire range of operation (20-100 kWel), according to grid balancing requirements, show that the plant is able to ramp up and down between the minimum to the maximum load in about 40 seconds.

ВЛИЯНИЕ ГАЗА В ЗАЗОРАХ ЛАБИРИНТНЫХ УПЛОТНЕНИЙ НА ДИНАМИЧЕСКОЕ СОСТОЯНИЕ РОТОРА

When transporting natural gas over long distances, it is necessary to build gas compressor stations that compensate for the gas energy losses due to friction. The uninterrupted supply of natural gas to consumers depends on their reliability. Current trends in increasing power with a simultaneous decrease structure stiffness leads to the occurrence of previously unpredictable oscillations of the compressor rotor. The article describes the main causes of compressor rotor vibrations and some methods of dealing with them. Due to the limitations of the considered phenomena with this methods, the article proposes the use multiphysics modeling of gas and structure interaction using the 2FSI approach. Simulation performed for a simplified labyrinthseal compressor rotor model. To solve the problem, the ANSYS software product was chosen, which implements the 2FSI method. The calculations were carried out on a high-performance computing complex PNRPU. For the model structure, an analysis of natural frequencies is performed and a Campbell diagram is constructed. The trajectories analysis of a point on shaft rotation axis at different rotation frequencies of the rotor is carried out. The influence of the imbalance on the dynamics of the system is noted. Further, the results of numerical modeling of the structure taking into account gas dynamics are presented and the results comparison by dynamic modeling of the structure is carried out. To identify the factors influencing the rotor dynamics, taking into account gas dynamics, numerical calculations were carried out with various options for taking into account the effect of gravity and imbalance.

Inertial Neural Networks with Unpredictable Oscillations

In this paper, inertial neural networks are under investigation, that is, the second order differential equations. The recently introduced new type of motions, unpredictable oscillations, are considered for the models. The motions continue a line of periodic and almost periodic oscillations. The research is of very strong importance for neuroscience, since the existence of unpredictable solutions proves Poincaré chaos. Sufficient conditions have been determined for the existence, uniqueness, and exponential stability of unpredictable solutions. The results can significantly extend the role of oscillations for artificial neural networks exploitation, since they provide strong new theoretical and practical opportunities for implementation of methods of chaos extension, synchronization, stabilization, and control of periodic motions in various types of neural networks. Numerical simulations are presented to demonstrate the validity of the theoretical results.

Strongly Unpredictable Oscillations of Hopfield-Type Neural Networks

In this paper, unpredictable oscillations in Hopfield-type neural networks is under investigation. The motion strongly relates to Poincaré chaos. Thus, the importance of the dynamics is indisputable for those problems of artificial intelligence, brain activity and robotics, which rely on chaos. Sufficient conditions for the existence and uniqueness of exponentially stable unpredictable solutions are determined. The oscillations continue the line of periodic and almost periodic motions, which already are verified as effective instruments of analysis and applications for image recognition, information processing and other areas of neuroscience. The concept of strongly unpredictable oscillations is a significant novelty of the present research, since the presence of chaos in each coordinate of the space state provides new opportunities in applications. Additionally to the theoretical analysis, we have provided strong simulation arguments, considering that all of the assumed conditions are fulfilled.

Shunting inhibitory cellular neural networks with strongly unpredictable oscillations

The paper considers a new type of solutions for shunting inhibitory cellular neural networks (SICNNs), strongly unpredictable oscillations. The conditions for the existence, uniqueness and stability of the solutions are determined. Numerical examples are given to show the feasibility of the obtained results.

Economic analysis of the threats posed to the harvesting of ornamental fish by the operation of the Belo Monte hydroelectric dam in northern Brazil

The capture of ornamental fish is an important economic activity in Brazil. Most (79 %) of the live aquarium fish exported from this country are captured in the Amazon region, in particular in the basins of the Negro, Xingu, and Tapajós rivers. The capture of ornamental fish in the Xingu basin is now under threat from the impacts caused by the construction of the Belo Monte hydroelectric dam, which has resulted in habitat loss and changes in the river’s hydrological cycle. An economic assessment of this fishery is needed to determine the magnitude of the possible impacts for the fishers over the long term. The present study estimated the costs and profitability of the fisheries that target ornamental species prior to the impoundment of the river, based on the records of the catches and economic income of 36 vessels. The results were positive, indicating that this activity was economically viable at that time. The harvesting of ornamental fish requires a mean initial investment of US$ 2,640.14, with a net profitability of 28.63 % for wooden canoes and 18.47 % for aluminum boats, providing the fishers with a net income of US$16.80 per fishing day. However, this income is extremely unstable due to the fluctuations in the capturability of the target species over the course of the year, and unpredictable oscillations in the demand for the most commercially valuable species. The predicted changes in the hydrological systems following the construction of the dam will likely result in a decrease in the economic yield of the fishery. Studies of this type are fundamentally important for the establishment of effective public policies for the fishery sector, and the mediation of socio-environmental impacts in the Amazon region.

Deep Learning From Spatio-Temporal Data Using Orthogonal Regularizaion Residual CNN for Air Prediction

Air pollution is harmful to human health and restricts economic development, so predicting when and where air pollution will occur is a challenging and important issue, especially in fields of urban planning, factory production and human activities. In this paper, we propose a deep Spatio-Temporal Orthogonal Regularization Residual CNN (ST-OR-ResNet) for air prediction. Deep Convolutional Neural Network (CNN) is presented to capture the complex spatio-temporal relation of the dynamic biased meteorological data. Residual learning is designed to avoid unpredictable oscillations when training the network and verifying errors. For the issue of characteristic statistical migration and saddle point proliferation in deep network, the orthogonality regularizations are designed to stabilize the back-propagation errors, utilizing various advanced analytical tools such as restricted isometry property without extra hassle. We then benchmark their effects on public real-world datasets to demonstrate that ST-OR-ResNet has better predictive performance than the state-of-the-art methods.

Neighboring optimal guidance and constrained attitude control applied to three-dimensional lunar ascent and orbit injection

Future human or robotic missions to the Moon will require efficient ascent path and accurate orbit injection maneuvers, because the dynamical conditions at injection affect the subsequent phases of spaceflight. This research is focused on the original combination of two techniques applied to lunar ascent modules, i.e. (i) the recently-introduced variable-time-domain neighboring optimal guidance (VTD-NOG), and (ii) a constrained proportional-derivative (CPD) attitude control algorithm. VTD-NOG belongs to the class of implicit guidance approaches, aimed at finding the corrective control actions capable of maintaining the spacecraft sufficiently close to the reference trajectory. CPD pursues the desired attitude using thrust vector control and side jet system, while constraining the rates of both the thrust deflection angle and the roll control torque. After determining the optimal two-dimensional ascent path, which represents the reference trajectory, VTD-NOG & CPD is applied in the presence of nonnominal flight conditions, namely those due to navigation and actuation errors, incorrect initial position, unpredictable oscillations of the propulsive thrust, and imperfect modeling of the spacecraft mass distribution and variation. These stochastic deviations are simulated in the context of extensive Monte Carlo campaigns, and yield three-dimensional perturbed trajectories. The numerical results obtained in this work unequivocally demonstrate that VTD-NOG & CPD represents an accurate and effective methodology for guidance and control of lunar ascent path and orbit injection.

Pressure dependency of localization degree in heavy fermion CeIn3: A density functional theory analysis.

Two dramatic discrepancies between previous reliable experimental and ab initio DFT results are identified to occur at two different pressures in CeIn3, as discussed through the paper. We physically discuss sources of the phenomena and indicate how to select an appropriate functional for a given pressure. We show that these discrepancies are due to the inaccuracy of the DFT + U scheme with arbitrary Ueff and that hybrid functionals can provide better agreement with experimental data at zero pressure. The hybrid B3PW91 approach provides much better agreement with experimental data than the GGA + U. The DFT + U scheme proves to be rather unreliable since it yields completely unpredictable oscillations for the bulk modulus with increasing values of Ueff. Our B3PW91 results show that the best lattice parameter (bulk modulus) is obtained using a larger value of α parameter, 0.4 (0.3 or 0.2), than that of usually considered for the AFM phase. We find that for hybrid functionals, the amount of non-local exchange must first be calibrated before conclusions are drawn. Therefore, we first systematically optimize the α parameter and using it investigate the magnetic and electronic properties of the system. We present a theoretical interpretation of the experimental results and reproduce them satisfactorily.

Replicator dynamics of reward & reputation in public goods games

Public goods games have become the mathematical metaphor for game theoretical investigations of cooperative behavior in groups of interacting individuals. Cooperation is a conundrum because cooperators make a sacrifice to benefit others at some cost to themselves. Exploiters or defectors reap the benefits and forgo costs. Despite the fact that groups of cooperators outperform groups of defectors, Darwinian selection or utilitarian principles based on rational choice should favor defectors. In order to overcome this social dilemma, much effort has been expended for investigations pertaining to punishment and sanctioning measures against defectors. Interestingly, the complementary approach to create positive incentives and to reward cooperation has received considerably less attention—despite being heavily advocated in education and social sciences for increasing productivity or preventing conflicts. Here we show that rewards can indeed stimulate cooperation in interaction groups of arbitrary size but, in contrast to punishment, fail to stabilize it. In both cases, however, reputation is essential. The combination of reward and reputation result in complex dynamics dominated by unpredictable oscillations.

Phase synchronization and collective instability in oscillating bubble clouds.

The effect of bubble interactions within a bubble cloud under ultrasonic forcing is investigated. Under certain conditions, phase synchronization is found, revealing parameter regimes where the dynamics of the bubble system are primarily determined by the interactions of the bubbles, rather than the applied ultrasonic acoustic field. Due to the Bjerknes interaction, the stability thresholds for critical pressure and size leading to unpredictable oscillations for bubble clouds are lower than the stability thresholds for single bubbles. Interestingly, numerical simulations confirm that in the regimes where bubbles exhibit chaotic oscillations, the dynamics are determined by a complicated geometric object called a low-dimensional strange attractor which is qualitatively the same as that for the chaotic oscillations of a single bubble. Thus an averaged equation may also be low-dimensional. The effects of synchronization on Landau damping are discussed. The effect of delays between the interactions is also investigated. The implications for applications in high-intensity focused ultrasound, harmonic imaging, and targeted drug delivery will be discussed.

The principles of the treatment of pancreatic diabetes

In pancreatic diabetes the whole of the Langerhans islets are damaged, which means that both the beta-cells and the alpha-cells are damaged. Unpredictable oscillations of blood glucose level are common due to malabsorption and maldigestion and one may count with explicit proneness to hypoglycaemia. The defence against hypoglycaemia also diminishes due to the damage of glucagon secretion, which is often aggravated by alcohol consumption. If normoglycaemia in pancreas diabetes cannot be achieved with appropriate diet, insulin treatment is necessary. Oral antidiabetic agents are not expedient. The best metabolic state can be achieved at the expense of the least amount of hypoglycaemia by giving the necessary minimum portions of insulin in an intensive, conservative insulin therapy. There is not enough experience with insulin analogs in pancreatic diabetes, but it seems expedient to use long-acting insulin analogs. The treatment of pancreas diabetes is a teamwork in which everybody has a decisive role, the gastroenterologist, the diabetologist, the dietitian and the diabetes nurse as well.

Riddled basins of coupled elastic arches

Riddled basins are shown to occur in a simple mechanical device consisting of masses and springs. Convergence probabilities versus deviations in the initial conditions are quantified by three independent methods, all leading to scale-invariance over many decades. Conditions exist for the occurrence of two-state on-off intermittency involving switching between predictable and unpredictable oscillations.

Nonlinear oscillations due to spurious energisation of transformers

Spurious energisation occurs when at least one phase of an otherwise de-energised three-phase transformer remains energised. A transformer spuriously energised will certainly overheat although the probability of its occurrence is low. However, unpredictable nonlinear oscillations, which may be either periodic or quasiperiodic, will be setup as a consequence. The author uses the method of multiple-scales to analytically predict the type of oscillations when a transformer is spuriously energised. This will aid in identifying the conditions that cause qualitative change in the type of oscillation. The author shows that spurious energisation can result in either nonresonant or resonant modes depending on the parameters of the power network. The nature of oscillations in nonresonant mode are of vivid variety. However, the oscillations in the resonant mode are generally periodic.