In this paper, a combination of fractional order control and a classical vector control scheme is proposed to drive the PMSM speed. The new proposed control scheme with fractional order property is based on IMC struct...
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ISBN:
(数字)9798350361025
ISBN:
(纸本)9798350361032
In this paper, a combination of fractional order control and a classical vector control scheme is proposed to drive the PMSM speed. The new proposed control scheme with fractional order property is based on IMC structure which is known for its robustness to modelling error and Bode’s ideal transfer function chosen as the reference model which exhibits iso-damping property. Simulation results show that the proposed control scheme tracks the PMSM speed at specified values and compensates the effect of load torque. Moreover, the time constant and fractional order of the Bode’s ideal transfer function can be used as tuning parameters to improve the PMSM speed and current responses.
Asymmetric heat-transfer systems, often referred to as thermal diodes or thermal rectifiers, have garnered increasing interest due to their wide range of application possibilities. Most of those previous macroscopic a...
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Asymmetric heat-transfer systems, often referred to as thermal diodes or thermal rectifiers, have garnered increasing interest due to their wide range of application possibilities. Most of those previous macroscopic asymmetric thermal devices either resort to nonlinear thermal conductivities with strong temperature dependence that may be quite limited by or fixed in natural materials or rely on active modulation that necessitates auxiliary energy payloads. Here, we establish a straightforward strategy of passively realizing asymmetric heat transfer with linear conductive materials. The strategy also introduces an interrogative perspective into the previous design of passive asymmetric heat transfer utilizing nonlinear thermal conductivity, correcting the misconception that thermal rectification is impossible with separable nonlinear thermal conductivity. The nonlinear-perturbation mode can be versatilely engineered to produce an effective and wide-ranging perturbation in heat conduction, which imitates and bypasses intrinsic thermal nonlinearity constraints set by naturally occurring counterparts. Independent experimental characterizations of surface thermal radiation and thermal convection verify that heat exchange between a graded linear thermal metamaterial and the ambient surroundings can be tailored to achieve macroscopic asymmetric heat transfer. Our work is envisaged to inspire conceptual models for heat-transfer control, serving as a robust and convenient platform for advanced thermal management and thermal computation.
Generating logical qubits, essential for error detection and correction in quantum computation, remains a critical challenge in continuous-variable (CV) optical quantum information processing. The Gottesman-Kitaev-Pre...
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With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain i...
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We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems...
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We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.
ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative e...
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This paper presents a comprehensive study on the reconfiguration of power distribution networks (PDN) within interconnected microgrids (MGs), utilizing the Artificial Hummingbird Algorithm (AHA), Particle Swarm Optimi...
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This paper presents a comprehensive study on the reconfiguration of power distribution networks (PDN) within interconnected microgrids (MGs), utilizing the Artificial Hummingbird Algorithm (AHA), Particle Swarm Optimization (PSO), and the Slime Mould Algorithm (SMA) for optimization. The objective is to minimize power loss and improve voltage profiles across the interconnected systems while maintaining a radial topology within each microgrid and the overall interconnection. Building on previous work, which focused solely on horizontal interconnection in two 20-bus systems, this study expands the scope by incorporating vertical and diagonal interconnection scenarios for the 20-bus systems. Additionally, it explores these configurations within a 69-bus system model. Simulation results demonstrate that while all three algorithms improve power loss reduction and voltage stability, AHA consistently outperforms PSO and SMA. The findings contribute to a robust framework for PDN reconfiguration, addressing the complexities of various interconnection configurations and providing valuable insights into enhancing the performance and sustainability of interconnected MGs.
In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target esti...
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We propose a generalized system of nonequilibrium cavity QED with interacting dipoles coupled to a single-mode of an electromagnetic field in strong-, ultrastrong-, and deep-strong-coupling regimes. To illustrate the ...
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We propose a generalized system of nonequilibrium cavity QED with interacting dipoles coupled to a single-mode of an electromagnetic field in strong-, ultrastrong-, and deep-strong-coupling regimes. To illustrate the applicability of the system, an extended Dicke model is developed for atoms undergoing Raman transitions between the ground states in the presence of laser fields and considering dipole-dipole interactions; the latter has been neglected in many previous works. We have studied the effect of a ferroelectric and an antiferroelectric arrangement on the phase transition for both a finite and an infinite number of dipoles. An additional superradiant phase is observed in the deep-strong-coupling regime due to influence of the dipole-dipole interaction term. A high degree of dipole-dipole entanglement occurs for the antiferroelectric arrangement in the deep-strong regime, whereas it gets disentangled quite rapidly for the ferroelectric arrangement. A sharp transition of system parameters is observed in the ultrastrong-coupling regime and beyond. The dipole-dipole interaction also influences the spectra of the system, inducing a significant shift in the peaks, and modifies the average number of photons emitted.
Due to the importance and sensitivity of medical data, the security protection and privacy preservation of the Healthcare Internet of Things (IoT) are current research hotspots. However, existing research schemes stil...
Due to the importance and sensitivity of medical data, the security protection and privacy preservation of the Healthcare Internet of Things (IoT) are current research hotspots. However, existing research schemes still suffer from incomplete security properties, imperfect authentication mechanisms, and inadequate privacy preservation. Therefore, this paper presents SECP-AKE, a secure and efficient certificateless-password-based authenticated key exchange protocol for IoT-based smart healthcare, which enables batch authentication, resists physical attacks, and provides strong anonymity. Specifically, using certificateless cryptography, the SECP-AKE protocol enables batch authentication of authorized users and devices while also resolving the key escrow problem. In particular, the SECP-AKE protocol incorporates Physical Unclonable Functions (PUFs) to resist physical attacks, thus enhancing device security and ensuring reliable medical service delivery. Additionally, the design of a pseudonym update mechanism can achieve user unlinkability, thereby providing enhanced privacy preservation. The results from both formal verification using SVO logic and informal security analyses demonstrate that the SECP-AKE protocol is secure and offers more comprehensive security properties. Meanwhile, the use of a well-known automated security verification tool Scyther further evaluates the protocol’s security reliability. Ultimately, comparative experiments on communication overhead and computational overhead demonstrate that the SECP-AKE protocol is efficient and feasible compared to state-of-the-art existing works.
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