Type 1 protein phosphatases (PP1s) are crucial in various plant cellular processes. Their function is controlled by regulators known as PP1-interacting proteins (PIPs), often intrinsically disordered, such as Inhibitor 2 (I2), conserved across kingdo...
Chlorophyll biosynthesis plays a vital role in chloroplast development and photosynthesis in plants. In this study, we identified an orthologue of the rice gene TDR (Oryza sativa L., Tapetum Degeneration Retardation) in wheat (Triticum aestivum L.) c...
Permanent magnet synchronous motor (PMSM) drive systems are commonly utilized in mobile electric drive systems due to their high efficiency, high power density, and low maintenance cost. To reduce the tracking error of the permanent magnet synchronou...
This paper presents a novel, robust, and reliable control strategy for renewable energy production systems, leveraging artificial neural networks (ANNs) to optimize performance and efficiency. Unlike conventional ANN approaches that rely on perturbat...
Most assassin bugs are predators that act as important natural enemies of insect pests. Mitochondrial (mt) genomes of these insects are double-strand circular DNAs that encode 37 genes. In the present study, we explore the duplication and rearrangeme...
This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistan...
To explore the variation and relationship between gene rearrangement and phylogenetic effectiveness of mitogenomes among lineages of the diversification of the tribe Tagiadini in the subfamily Pyrginae, we sequenced the complete mitogenome of Odontop...
Pollen fertility plays an important role in the application of heterosis in wheat (Triticum aestivum L.). However, the key genes and mechanisms underlying pollen abortion in K-type male sterility remain unclear. TAA1a is an essential gene for pollen...
Starting from the fact that in sensorless control systems of the Permanent Magnet Synchronous Motor (PMSM), the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Dis...
In this article, starting from a benchmark represented by a Direct Current-to-Direct Current (DC-DC) three-phase power electronic converter used as an interface and interconnection between the grid and a DC microgrid, we compare the performances of a...
The high-performance control of permanent magnet synchronous motor (PMSM) drives is challenged by operating uncertainties and coupled electromechanical dynamics. This paper proposes a temporal-feature-enhanced reinforcement learning control approach...
8 February 2022
The ability of infants to track transitional probabilities (Statistical Learning—SL) and to extract and generalize high-order rules (Rule Learning—RL) from sequences of items have been proposed as being pivotal for the acquisition of lang...
Reinforcement learning (RL) has become an effective paradigm for enabling autonomous robots to acquire navigation policies directly from interaction with complex and uncertain environments. Nevertheless, autonomous path planning for Skid-Steer Mobile...
Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status...
Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potenti...
The aim of the present work was to sensorially characterize different commercial categories of Iberian dry-cured loins (varying genetic and feeding background) using a novel dynamic sensory technique, and to explore consumers preferences applying a r...
Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs ca...
Owing to the complexity involved in training an agent in a real-time environment, e.g., using the Internet of Things (IoT), reinforcement learning (RL) using a deep neural network, i.e., deep reinforcement learning (DRL) has been widely adopted on an...
The improvement of the partially integrated reinforcement learning-model predictive control (RL-MPC) system is developed in the paper by introducing the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD...
In numerous episodic reinforcement learning (RL) environments, SARSA-based methodologies are employed to enhance policies aimed at maximizing returns over long horizons. Traditional SARSA algorithms face challenges in achieving an optimal balance bet...
Traditional control strategies, such as droop-frequency and PI controllers, often show limited adaptability when the system operates under highly variable renewable generation and load conditions. In order to overcome this limitation, this study prop...
Domain randomization in the context of Reinforcement learning (RL) involves training RL agents with randomized environmental properties or parameters to improve the generalization capabilities of the resulting agents. Although domain randomization ha...
Lower-limb rehabilitation exoskeletons have the potential to improve gait recovery after stroke by providing intensive and repetitive training. However, conventional control strategies often rely on fixed control parameters and exhibit limited adapta...
This paper investigates the use of reinforcement learning (RL) algorithms to learn adaptive hedging strategies for derivatives under realistic market conditions, incorporating permanent market impact, execution slippage, and transaction costs. Market...
Reinforcement learning has shown strong potential for sequential control problems, providing an effective framework for adaptive policy derivation in complex and dynamic environments. However, standalone RL controllers may behave unstably or too aggr...
A two-layer control framework for unmanned underwater vehicle (UUV) navigation is proposed, combining a lower-layer active disturbance rejection controller (ADRC) with an upper-layer safe reinforcement learning (RL) policy for obstacle-avoidance navi...
This article presents the control of a grid connected PV (GC-PV) array system, starting from a benchmark. The control structure used in this article was a cascade-type structure, in which PI or synergetic (SYN) controllers were used for the inner con...
This paper proposes an AI-based trading framework that integrates supervised price forecasting with reinforcement learning (RL)-based decision-making. The objective is to enhance both profitability and risk management in cryptocurrency trading by equ...
Unmanned aerial vehicle (UAV) path planning in unknown 3D environments remains challenging due to missing prior maps, irregular terrain, and real-time obstacle avoidance requirements. This paper proposes TD3-DLSAs, a novel framework that integrates a...
In this paper, based on a benchmark on the performance of a Unified Power Quality Conditioner (UPQC), the improvement of this performance is presented comparatively by using Proportional Integrator (PI)-type controllers optimized by a Grey Wolf Optim...
Reward design is one of the most influential factors in determining the performance of reinforcement learning (RL) algorithms in continuous-control tasks. This paper presents a comparative study of three reward formulations for training a TD3 agent t...
Machine learning has been applied by more and more scholars in the field of quantitative investment, but traditional machine learning methods cannot provide high returns and strong stability at the same time. In this paper, a multimodal model based o...
This work provides a systematic literature review on reinforcement learning (RL) for integrated maximum power point tracking (MPPT) and battery management in photovoltaic (PV) systems. As PV installations increasingly incorporate battery energy stora...
An important process in the mining industry is material handling, where trucks are responsible for transporting materials extracted by shovels to different locations within the mine. The decision about the destination of a truck is very important to...
In this work, we propose the use of a reinforcement learning (RL) agent for the control of a cascade hydropower plant system. Generally, this job is handled by power plant dispatchers who manually adjust power plant electricity production to meet the...
This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algor...
Starting from the general topology and the main elements that connect a microgrid represented by a DC power source to the main grid, this article presents the performance of the control system of a DC-AC converter. The main elements of this topology...
Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances rema...
Real-time isolated signal control (RISC) at an intersection is of interest in the field of traffic engineering. Energizing RISC with reinforcement learning (RL) is feasible and necessary. Previous studies paid less attention to traffic engineering co...
Development of distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted an increasing surge of interest lately. Generally speaking, conventional Model-Based (MB) or Model-Free (MF) RL algorithms are not directly applicable to th...
As artificial intelligence continues to evolve, reinforcement learning (RL) has shown remarkable potential for solving complex sequential decision problems and is now applied in diverse areas, including robotics, autonomous vehicles, and financial an...
Planetary soft landing has been studied extensively due to its promising application prospects. In this paper, a soft landing control algorithm based on deep reinforcement learning (DRL) with good convergence property is proposed. First, the soft lan...
In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima...
The spectroscopic and biological properties of two 1,3,4-thiadiazole derivatives, 4-chloro-6-{5-[(3-chlorophenyl)amino]-1,3,4-thiadiazol-2-yl}benzene-1,3-diol (PhTD-ClB) and 4-ethyl-6-[5-(furan-2-yl)-1,3,4-thiadiazol-2-yl]benzene-1,3-diol (FTD-EtB),...
In Hymenoptera, the monophyly of Evaniomorpha has been the focus of debate among different scholars. In this study, we sequenced two mitochondrial genomes of Dendrocerus (Hymenoptera: Megaspilidae) to analyze the mitochondrial genomic features of Den...
We introduce cumulative tic-tac-toe, a novel variant of the classic
Various edge collaboration schemes that rely on reinforcement learning (RL) have been proposed to improve the quality of experience (QoE). Deep RL (DRL) maximizes cumulative rewards through large-scale exploration and exploitation. However, the exist...
Dexterous object manipulation using anthropomorphic robot hands is of great interest for natural object manipulations across the areas of healthcare, smart homes, and smart factories. Deep reinforcement learning (DRL) is a particularly promising appr...
Starting from the nonlinear operating equations of the permanent magnet synchronous motor (PMSM) and from the global strategy of the field-oriented control (FOC), this article compares the linear and nonlinear control of a PMSM. It presents the linea...
This paper proposes a deep reinforcement learning-based predictive control scheme to address cushion pressure prediction and stabilization in hovercraft systems subject to modeling complexity, dynamic instability, and system delay. Notably, this work...
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