Neuromorphic learning with Mott insulator NiO

2021 ◽  
Vol 118 (39) ◽  
pp. e2017239118
Author(s):  
Zhen Zhang ◽  
Sandip Mondal ◽  
Subhasish Mandal ◽  
Jason M. Allred ◽  
Neda Alsadat Aghamiri ◽  
...  
Keyword(s):  
Building Blocks ◽  
Mott Insulator ◽  
Biological Species ◽  
Time Interval ◽  
Learning Behavior ◽  
Experimental Approaches ◽  
Artificial Neural ◽  
The Stability ◽  
External Stimuli ◽  
Catastrophic Interference

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

Statistical Management of Food Production Processes

2020 ◽  
pp. 30-35
Author(s):  
N.A. Jurk ◽  
Keyword(s):  
Production Process ◽  
Control Chart ◽  
Food Production ◽  
Time Interval ◽  
Bakery Products ◽  
Shewhart Control ◽  
Manufactured Products ◽  
Mnemonic Technique ◽  
The Stability

The article presents scientific research in the field of statistical controllability of the food production process using the example of bakery products for a certain time interval using statistical methods of quality management. During quality control of finished products, defects in bakery products were identified, while the initial data were recorded in the developed form of a checklist for registering defects. It has been established that the most common defect is packaging leakage. For the subsequent statistical assessment of the stability of the production process and further analysis of the causes of the identified defect, a Shewhart control chart (p-card by an alternative feature) was used, which allows you to control the quality of manufactured products by the number of defects detected. Analyzing the control chart, it was concluded that studied process is conditionally stable, and the emerging defects are random. At the last stage of the research, the Ishikawa causal diagram was used, developed using the 6M mnemonic technique, in order to identify the most significant causes that affect the occurrence of the considered defect in bakery products. A more detailed study will allow the enterprise to produce food products that meet the established requirements.

scholarly journals On the Numerical Simulation of HPDEs Using θ-Weighted Scheme and the Galerkin Method

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 78
Author(s):  
Haifa Bin Jebreen ◽  
Fairouz Tchier
Keyword(s):  
Differential Equation ◽  
Galerkin Method ◽  
Linear Ordinary Differential Equation ◽  
Time Interval ◽  
Hyperbolic Partial Differential Equation ◽  
Time Step ◽  
Numerical Examples ◽  
One Dimensional ◽  
The Stability ◽  
The Galerkin Method

Herein, an efficient algorithm is proposed to solve a one-dimensional hyperbolic partial differential equation. To reach an approximate solution, we employ the θ-weighted scheme to discretize the time interval into a finite number of time steps. In each step, we have a linear ordinary differential equation. Applying the Galerkin method based on interpolating scaling functions, we can solve this ODE. Therefore, in each time step, the solution can be found as a continuous function. Stability, consistency, and convergence of the proposed method are investigated. Several numerical examples are devoted to show the accuracy and efficiency of the method and guarantee the validity of the stability, consistency, and convergence analysis.

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

Effect of the ionizing radiation on the catalytic activity of the BASF K-3-10 catalyst in the low-temperature conversion of carbon monoxide by water vapour. Stability of the radiation catalytic effects

1986 ◽  
Vol 51 (8) ◽  
pp. 1571-1578 ◽  
Author(s):  
Alois Motl
Keyword(s):  
Carbon Monoxide ◽  
Catalytic Activity ◽  
Low Temperature ◽  
Catalytic Effect ◽  
Catalytic Properties ◽  
Beta Radiation ◽  
Fast Neutrons ◽  
Time Interval ◽  
The Stability ◽  
Catalytic Effects

The radiation catalytic properties of the BASF K-3-10 catalyst were studied, namely the dependence of these effects on the time interval between the catalyst irradiation and the reaction itself and also on the length of the catalyst use. The catalytic effects decrease exponentially with the interval between the irradiation and the reaction if the catalyst is kept in the presence of air. The stability of effects induced by various types of radiations increases in the sequence beta radiation - gamma radiation - fast neutrons. The radiation catalytic effect stability in the reaction increases in the same sequence.

scholarly journals Recent Progress in the Synthesis and Characterization of Diarylethene-based MOFs

2014 ◽  
Vol 70 (a1) ◽  
pp. C1223-C1223
Author(s):  
Jason Benedict ◽  
Ian Walton ◽  
Dan Patel ◽  
Jordan Cox
Keyword(s):  
Chemical Properties ◽  
Building Blocks ◽  
Synthesis And Characterization ◽  
Next Generation ◽  
Design Synthesis ◽  
Thermally Induced ◽  
Potential Applications ◽  
External Stimuli ◽  
Physical And Chemical

Metal-organic Frameworks (MOFs) remain an extremely active area of research given the wide variety of potential applications and the enormous diversity of structures that can be created from their constituent building blocks. While MOFs are typically employed as passive materials, next-generation materials will exhibit structural and/or electronic changes in response to applied external stimuli including light, charge, and pH. Herein we present recent results in which advanced photochromic diarylethenes are combined with MOFs through covalent and non-covalent methods to create photo-responsive permanently porous crystalline materials. This presentation will describe the design, synthesis, and characterization of next-generation photo-switchable diarylethene based ligands which are subsequently used to photo-responsive MOFs. These UBMOF crystals are, by design, isostructural with previously reported non-photoresponsive frameworks which enables a systematic comparison of their physical and chemical properties. While the photoswitching of the isolated ligand in solution is fully reversible, the cycloreversion reaction is suppressed in the UBMOF single crystalline phase. Spectroscopic evidence for thermally induced cycloreversion will be presented, as well as a detailed analysis addressing the limits of X-ray diffraction techniques applied to these systems.

Stability of a growing cylindrical blob

2017 ◽  
Vol 827 ◽  
Author(s):  
R. Krechetnikov
Keyword(s):  
Potential Flow ◽  
Lateral Acceleration ◽  
Time Interval ◽  
Time Varying ◽  
Standard Analysis ◽  
Physical Mechanisms ◽  
Stage Of Development ◽  
Instability Development ◽  
The Stability ◽  
State Assumption

The stability of an accelerating cylindrical blob of a time-varying radius is considered with the goals of understanding the effects of time dependence of the underlying base state on a Rayleigh–Plateau instability as well as of evaluating a contribution due to a lateral acceleration of the blob, treated as a perturbation here. All of the key processes contributing to instability development are dissected, with analytical analyses of the exact incompressible inviscid potential flow formulation. Herein, without invoking the ‘frozen’ base state assumption, the entire time interval of the evolution of a perturbation is explored, discerning physical mechanisms at each stage of development. It transpires that the stability picture proves to be cardinally different from Rayleigh’s standard analysis.

Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks

2020 ◽  
Vol 26 (1) ◽  
pp. 130-151 ◽  
Author(s):  
Atsushi Masumori ◽  
Lana Sinapayen ◽  
Norihiro Maruyama ◽  
Takeshi Mita ◽  
Douglas Bakkum ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Changes ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Cultured Neurons ◽  
Autonomous Regulation ◽  
Artificial Neural ◽  
Living Organisms ◽  
External Stimuli

Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.

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