This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why ...
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This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control form...
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This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.
With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducte...
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This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning ...
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The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus. The lack of...
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The significant impact of stress on health necessitates accurate assessment methods,where traditional questionnaires lack reliability and *** advancements like wearables with electrocardiogram(ECG)and galvanic skin re...
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The significant impact of stress on health necessitates accurate assessment methods,where traditional questionnaires lack reliability and *** advancements like wearables with electrocardiogram(ECG)and galvanic skin response(GSR)sensors face accuracy and artifact *** biosensors detecting cortisol,a critical stress hormone,present a promising ***,existing cortisol assays,requiring saliva,urine,or blood,are complex,expensive,and unsuitable for continuous *** study introduces a passive,molecularly imprinted polymer-radio-frequency(MIP-RF)wearable sensing system for real-time,non-invasive sweat cortisol *** system is wireless,flexible,battery-free,reusable,environmentally stable,and designed for long-term monitoring,using an inductance-capacitance *** transducer translates cortisol concentrations into resonant frequency shifts with high sensitivity(~160 kHz/(log(μM)))across a physiological range of 0.025–1μ*** with near-field communication(NFC)for wireless and battery-free operation,and threedimensional(3D)-printed microfluidic channel for in-situ sweat collection,it enables daily activity cortisol level *** of cortisol circadian rhythm through morning and evening measurements demonstrates its effectiveness in tracking and monitoring sweat cortisol levels.A 28-day stability test and the use of cost-effective 3D nanomaterials printing enhance its economic viability and *** innovation paves the way for a new era in realistic,on-demand health monitoring outside the laboratory,leveraging wearable technology for molecular stress biomarker detection.
With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of *** the number of Vehic...
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With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of
The existing method of using large pre-trained models with prompts for zero-shot text classification possesses powerful representation ability and scalability. However, its commercial availability is relatively limite...
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Time variant coverage, called sweep coverage in wireless sensor networks has got attention from various re-searchers in recent time. In this problem, a set of mobile sensors are collectively monitoring certain area of...
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In this work, a prototype system has been designed with a 0.18-μm CMOS technology to capture perspiration rate in daily life. To calculate an amount of perspiration, a temperature sensor is necessary concurrently wit...
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