Related papers: Energy Constraints Improve Liquid State Machine Pe…
This paper investigates the effects of network constraints in energy system models at transmission level on renewable energy generation and curtailment as the network is being spatially aggregated. We seek to reproduce historically measured…
Energy is now a first-class design constraint along with performance in all computing settings. Energy predictive modelling based on performance monitoring counts (PMCs) is the leading method used for prediction of energy consumption during…
We review recent studies of a colloidal information engine that consists of a bead in water and held by an optical trap. The bead is ratcheted upward without any apparent external work, by taking advantage of favorable thermal fluctuations.…
Pebble bed reactor (PBR) operation presents unique advantages and challenges due to the ability to continuously change the fuel mixture and excess reactivity. Each operation parameter affects reactivity on a different timescale. For…
Liquid State Machine (LSM) is a brain-inspired architecture used for solving problems like speech recognition and time series prediction. LSM comprises of a randomly connected recurrent network of spiking neurons. This network propagates…
Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become…
The integration of synchronous generators and energy storage systems operated through communication networks introduces new challenges and vulnerabilities to the electric grid, where cyber attacks can corrupt sensor measurements or control…
Censoring has been proposed to be utilized in wireless distributed detection networks with a fusion center to enhance network performance in terms of error probability in addition to the well-established energy saving gains. In this paper,…
An accurate energy efficiency analytical model based on a two-mode circuitry was recently proposed; and the model showed that the use of this circuitry can significantly improve a system's energy efficiency. In this paper, we use this…
The rising computational and energy demands of artificial intelligence systems urge the exploration of alternative software and hardware solutions that exploit physical effects for computation. According to machine learning theory, a neural…
Fluctuations in the energy gap and coupling constants in and between chromophores can play important role in the absorption and energy transfer across a collection of two level systems. In a noisy environment, fluctuations can control…
We use ideas from kinetic proofreading, an error-correcting mechanism in biology, to identify new kinetic regimes in non-equilibrium systems. These regimes are defined by the sensitivity of the occupancy of a state of the system to a change…
Equations governing the nonlinear dynamics of complex systems are usually unknown and indirect methods are used to reconstruct their manifolds. In turn, they depend on embedding parameters requiring other methods and long temporal sequences…
Electricity systems are experiencing increased effects of randomness and variability due to emerging stochastic assets. The increased effects introduce new uncertainties into power systems that can impact system operability and reliability.…
The rise of IoT has increased the need for on-edge machine learning, with TinyML emerging as a promising solution for resource-constrained devices such as MCU. However, evaluating their performance remains challenging due to diverse…
Reservoir computing is a very promising approach for the prediction of complex nonlinear dynamical systems. Besides capturing the exact short-term trajectories of nonlinear systems, it has also proved to reproduce its characteristic…
The use of wearable and mobile devices for health monitoring and activity recognition applications is increasing rapidly. These devices need to maximize their accuracy and active time under a tight energy budget imposed by battery and small…
This paper investigates energy-minimization finite-element approaches for the computation of nematic liquid crystal equilibrium configurations. We compare the performance of these methods when the necessary unit-length constraint is…
Liquid scintillator detectors are playing an increasingly important role in low-energy neutrino experiments. In this article, we describe a generic energy response model of liquid scintillator detectors that provides energy estimations of…
The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples. Here, we investigated the role of sleep in improving the…