This paper investigates the stabilization of underactuated vehicles moving in a three-dimensional vector *** vehicle’s model is established on the matrix Lie group SE(3),which describes the configuration of rigid bod...
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This paper investigates the stabilization of underactuated vehicles moving in a three-dimensional vector *** vehicle’s model is established on the matrix Lie group SE(3),which describes the configuration of rigid bodies globally and *** focus on the kinematic model of the underactuated vehicle,which features an underactuation form that has no sway and heave *** compensate for the lack of these two velocities,we construct additional rotation matrices to generate a motion of rotation coupled with ***,the state feedback is designed with the help of the logarithmic map,and we prove that the proposed control law can exponentially stabilize the underactuated vehicle to the identity group element with an almost global domain of ***,the presented control strategy is extended to set-point stabilization in the sense that the underactuated vehicle can be stabilized to an arbitrary desired configuration specified in ***,simulation examples are provided to verify the effectiveness of the stabilization controller.
For consistent identification of a target module in a dynamic network with the local direct method, basically two prime conditions need to be satisfied: (a) a set of structural conditions on the choice of the predicto...
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For consistent identification of a target module in a dynamic network with the local direct method, basically two prime conditions need to be satisfied: (a) a set of structural conditions on the choice of the predictor model, i.e. a set of input and output node variables, and (b) conditions on data-informativity. While for conditions (a) constructive algorithms for node selection have been presented that appropriately guarantee that the identified object can indeed reveal the target module, the requirements for satisfying (b) have not yet been integrated fully. In this paper, we will present simplified path-based results for generic data-informativity, and show how they can be integrated in constructive algorithms for predictor model selection that provide consistent target module estimates. It is shown that data-informativity not only requires a sufficient number of external excitation signals to be present in the network, but also puts restrictions on the structure of the predictor model, i.e. the selection of input and output node variables. Some examples are presented to illustrate the new results.
As a power system operation, a generation schedule indicating optimal on/off and power generation of generators is created so that the power needed by consumers can be generated and transmitted on transmission lines u...
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ISBN:
(数字)9784907764838
ISBN:
(纸本)9798331544461
As a power system operation, a generation schedule indicating optimal on/off and power generation of generators is created so that the power needed by consumers can be generated and transmitted on transmission lines under some uncertainties. To create the economic schedule, we are required to solve a large-scale combinatorial optimization, which takes a long computation time to solve. Therefore, to reduce the computation time, the number of scenarios modeling the uncertainties is reduced based on dual variables which show the importance of the operational constraint violations in the linear relaxed optimization problem. To evaluate the performance of the proposed method, it is applied to the IEEE-118 bus power system model. The results show that the proposed method can reduce the computation time by up to 11% while maintaining the accuracy of the optimization solution.
By using Typhoon HIL simulator, a simulation research method of data injection attack against secondary control DC microgrid is established, which reveals the important influence of the attack function on the effectiv...
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In Ivanovo State Power engineering University (ISPEU, Ivanovo, Russia) digital instrument current and voltage transformers (DCVTs) has developed. Numerous studies and tests of DCVTs have been carried out;these devices...
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In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environm...
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In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan
While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estim...
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With the development of smart sensors,information storage,processing technology and computer performance,large amounts of operating data collected from production process provide opportunities as well as challenges in...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
With the development of smart sensors,information storage,processing technology and computer performance,large amounts of operating data collected from production process provide opportunities as well as challenges in remaining useful life(RUL) *** one hand,data-driven analysis approaches are experiencing a fast *** the other hand,the collected variables may be redundant,noisy and high-dimensional for ***,data dimension reduction is applied for eliminating useless *** from correlation-based methods,causal inference methods can obtain reliable models reflecting causal relationships among interesting ***,the latter is more suitable in data dimension *** this study,we use PCMCI+,a causal discovery method based on graph model,that handles both lagged and contemporaneous relationships in multi-variable time *** validate this method on time series data extracted directly from a medium frequency quenching *** obtained results confirm that PCMCI+is able to recognize causal associations among various sensor *** instance,variables in the same process have relatively larger causal relationships than those in different processes.
Fully harnessing the ocean wave’s renewable energy resources could benefit coastal ***,ocean wave energy harvesting systems encounter several challenges,i.e.,marine uncertainties,long-distance mainte-nance,power fluc...
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Fully harnessing the ocean wave’s renewable energy resources could benefit coastal ***,ocean wave energy harvesting systems encounter several challenges,i.e.,marine uncertainties,long-distance mainte-nance,power fluctuations,irregular wave currents,non-linear generator dynamics,turbine limitations,cost optimization,and power smoothing *** overcome these challenges,this paper proposes a new multi-stage con-trol design approach for performance evaluation of the os-cillating water column(OWC)-based ocean wave energy conversion(OWEC)*** first stage optimizes the Wells turbine by implementing an efficient airflow control *** achieves maximum power-harvesting ability by eliminating stalling *** the second stage,we investigate the robustness of the permanent magnet syn-chronous generator-based OWEC system by designing adaptive back-stepping controllers,taking into account the Lyapunov stability *** accomplishes precise speed regulation for optimal power extraction while delivering reduced delay response and percentage *** ensure the OWEC system’s availability,the third stage incorporates fault-ride-through *** executes a fault reconfig-urable control for a parallel converter configuration,elimi-nating only the faulty leg instead of the entire power *** the fourth stage,a supercapacitors-based energy management system achieves power smoothing,even when the OWC plant output power *** accomplish this by implementing a model predictive control ***,the Matlab/Simulink results verify that the presented mul-ti-stage control for the OWC OWEC system is an effective design approach,offering an optimal,robust,reliable,and power-smoothing solution.
Dear Editor,Two-dimensional(2-D) systems have wide applications in image data processing,gas absorption and fluid dynamics analysis [1]-[3].When there exist abrupt changes in 2-D systems,they are usually modeled by 2-...
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Dear Editor,Two-dimensional(2-D) systems have wide applications in image data processing,gas absorption and fluid dynamics analysis [1]-[3].When there exist abrupt changes in 2-D systems,they are usually modeled by 2-D Markov jump systems(MJSs) or 2-D semi-Markov jump systems(SMJSs).This letter investigates the control of 2-D SMJSs based on a novel mode generation mechanism,which could avoid mode ambiguousness phenomenon caused by the evolution of system mode in two different *** criterion that guarantees the almost surely exponential stability of the system is obtained.A thermal process is studied to demonstrate the availability of the proposed method.
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