Building heating, ventilating, and air conditioning(HVAC) systems have one of the largest energy footprint worldwide, which necessitates the design of intelligent control algorithms that improve the energy utilization...
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Building heating, ventilating, and air conditioning(HVAC) systems have one of the largest energy footprint worldwide, which necessitates the design of intelligent control algorithms that improve the energy utilization while still providing thermal comfort. In this work, the authors formulate the HVAC equipment dynamics in the setting of a two-player non-zero-sum cooperative game, which enables two decision variables(mass flow rate and supply air temperature) to perform joint optimization of the control utilization and thermal setpoint tracking by simultaneously exchanging their policies. The HVAC zone serves as a game environment for these two decision variables that act as two players in a game. It is assumed that dynamic models of HVAC equipment are not available. Furthermore, neither the state nor any estimates of HVAC disturbance(heat gains, outside variations, etc.) are accessible,but only the measurement of the zone temperature is available for feedback. Under these constraints,the authors develop a new data-driven Q-learning scheme employing policy iteration and value iteration with a bias compensation mechanism that accounts for unmeasurable disturbances and circumvents the need of full-state measurement. The proposed algorithms are shown to converge to the optimal solution corresponding to the generalized algebraic Riccati equations(GAREs) in dynamic games.
In this paper, we study stealthy cyber-attacks on actuators of cyber-physical systems(CPS), namely zero dynamics and controllable attacks. In particular, under certain assumptions, we investigate and propose condition...
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In this paper, we study stealthy cyber-attacks on actuators of cyber-physical systems(CPS), namely zero dynamics and controllable attacks. In particular, under certain assumptions, we investigate and propose conditions under which one can execute zero dynamics and controllable attacks in the CPS. The above conditions are derived based on the Markov parameters of the CPS and elements of the system observability matrix. Consequently, in addition to outlining the number of required actuators to be attacked, these conditions provide one with the minimum system knowledge needed to perform zero dynamics and controllable cyber-attacks. As a countermeasure against the above stealthy cyber-attacks, we develop a dynamic coding scheme that increases the minimum number of the CPS required actuators to carry out zero dynamics and controllable cyber-attacks to its maximum possible value. It is shown that if at least one secure input channel exists, the proposed dynamic coding scheme can prevent adversaries from executing the zero dynamics and controllable attacks even if they have complete knowledge of the coding system. Finally, two illustrative numerical case studies are provided to demonstrate the effectiveness and capabilities of our derived conditions and proposed methodologies.
Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile *** this pap...
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Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile *** this paper,we introduce a cost-effective and energy-efficient mmWave-NOMA system that exploits sparse antenna arrays in the *** analysis shows that utilizing low-weight and small-sized sparse antennas in the Base Station(BS)leads to better outage probability *** also introduce an optimum low complexity Equilibrium Optimization(EO)-based algorithm to further improve the outage *** simulation and analysis results show that the systems equipped with sparse antenna arrays making use of optimum beamforming vectors outperform the conventional systems with uniform linear arrays in terms of outage probability and sum rates.
Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memris...
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Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.
In the electric power equipment industry,various insulating materials and accessories are manufactured using petroleum-based epoxy ***,petrochemical resources are gradually becoming *** addition,the global surge in pl...
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In the electric power equipment industry,various insulating materials and accessories are manufactured using petroleum-based epoxy ***,petrochemical resources are gradually becoming *** addition,the global surge in plastic usage has consistently raised concerns regarding greenhouse gas emissions,leading to worsening global ***,to facilitate eco-friendly policies,industrialising epoxy systems applicable to high-pressure components using bio-based epoxy composites is *** results of the characterisation conducted in this research regarding bio-content were confirmed through thermogravimetric analysis and differential scanning calorimetry,which showed that as the bio-content increased,the thermal stability *** the operating temperature of 105℃ for the insulation spacer,structurally,no issues would be encountered if the spacer was manufactured with a bio-content of 20%(bio 20%).Subsequent tensile and flexural strength measurements revealed mechanical properties equivalent to or better than those of their petroleum-based *** impact strength tended to decrease with increasing *** the dielectric properties confirmed that the epoxy composite containing 20%biomaterial is suitable for manufacturing insulation ***,a series of tests conducted after spacer fabrication confirmed the absence of internal metals and bubbles with no external discolouration or cracks observed.
This paper presents a novel integrated sensing and communication (ISAC) framework that leverages recent advancements in reconfigurable distributed antennas and reflecting surfaces (RDARS). RDARS is a programmable stru...
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In this paper the authors consider the operational problem of optimal signalling and control,called control-coding capacity(with feedback),C_(FB) in bits/second,of discrete-time nonlinear partially observable stochast...
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In this paper the authors consider the operational problem of optimal signalling and control,called control-coding capacity(with feedback),C_(FB) in bits/second,of discrete-time nonlinear partially observable stochastic systems in state space form,subject to an average cost constraint.C_(FB) is the maximum rate of encoding signals or messages into randomized controller-encoder strategies with feedback,which control the state of the system,and reproducing the messages at the output of the system using a decoder or estimator with arbitrary small asymptotic error *** the first part of the paper,the authors characterize C_(FB) by an information theoretic optimization problem of maximizing directed information from the inputs to the outputs of the system,over randomized strategies(controller-encoders).The authors derive equivalent characterizations of C_(FB),using randomized strategies generated by either uniform or arbitrary distributed random variables(RVs),sufficient statistics,and a posteriori distributions of nonlinear filtering *** the second part of the paper,the authors analyze C_(FB) for linear-quadratic Gaussian partially observable stochastic systems(LQG-POSSs).The authors show that randomized strategies consist of control,estimation and signalling parts,and the sufficient statistics are,two Kalman-filters and an orthogonal innovations *** authors prove a semi-separation principle which states,the optimal control strategy is determined explicitly from the solution of a control matrix difference Riccati equation(DRE),independently of the estimation and signalling ***,the authors express the optimization problem of C_(FB) in terms of two filtering matrix DREs,a control matrix DRE,and the covariance of the innovations *** the paper,the authors illustrate that the expression of C_(FB) includes as degenerate cases,problems of stochastic optimal control and channel capacity of information transmission.
The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of *** authors model the system using an automaton and study the general notion of detectab...
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The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of *** authors model the system using an automaton and study the general notion of detectability where a given set of state pairs needs to be(eventually or periodically)distinguished in any estimate of the state of the *** authors adopt the ALTER sensor attack model from previous work and formulate four notions of CA-detectability in the context of this attack model based on the following attributes:strong or weak;eventual or *** authors present verification methods for strong CA-detectability and weak *** authors present definitions of strong and weak periodic CA-detectability that are based on the construction of a verifier automaton called the augmented *** development also resulted in relaxing assumptions in prior results on D-detectability,which is a special case of CA-detectability.
Schizophrenia (SC) is a complex mental disorder with diverse symptoms that make diagnosis challenging. This study aims to enhance diagnostic accuracy for SC using deep learning techniques applied to resting-state func...
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Schizophrenia (SC) is a complex mental disorder with diverse symptoms that make diagnosis challenging. This study aims to enhance diagnostic accuracy for SC using deep learning techniques applied to resting-state functional MRI (rsfMRI) data, capturing both spatial and temporal features of brain activity. We introduced a novel slice-wise classification approach using convolutional neural networks (CNNs) to analyze brain activity from rsfMRI images. Preprocessing included normalization, noise reduction, and contrast enhancement. The study utilized data from 158 subjects, including 83 schizophrenia patients and 75 healthy controls. We combined CNN-extracted features with traditional machine learning models such as support vector machines (SVM), random forest (RF), and gradient boosting (GB) to boost classification performance. Transfer learning using pre-trained models like VGG16, ResNet50, and Xception was applied to leverage advanced feature extraction. Model performance was evaluated based on precision, accuracy, recall, and F1 score. The CNN-based approach achieved significant improvements in classification accuracy, with a peak accuracy of 98.67%. The hybrid models combining CNN features with SVM, RF, and GB achieved accuracies of 97.01%, 98.44%, and 98.84%, respectively. The CNN model alone achieved a precision of 0.9826, recall of 1.0, and F1 score of 0.9912. Pre-trained models also demonstrated high performance, with ResNet50 achieving an accuracy of 98.71%. Brain regions such as the frontal lobe (slices 5 and 9) and temporal lobe (slices 15 and 25) were identified as key areas with significant differences between schizophrenia patients and healthy controls. This study demonstrates the effectiveness of deep learning models, particularly CNNs and hybrid approaches with traditional machine learning models, in enhancing schizophrenia diagnosis using rsfMRI data. Identifying key brain regions provides insights into schizophrenia’s neurobiological underpinnings. Fu
Non-linear optics is a branch of optics that studies the intriguing and sometimes unexpected ways in which light and matter interact at high intensities, when the polarization density does not respond linearly to the ...
Non-linear optics is a branch of optics that studies the intriguing and sometimes unexpected ways in which light and matter interact at high intensities, when the polarization density does not respond linearly to the electric field of the light. The pursuit of the perfect non-linear optical material has been ongoing ever since the pioneering experiment on second harmonic generation carried out by Franken in 1961 [1]. Indeed,
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