The problem of non-collocated vibration absorption is targeted utilizing a multiparameter delay-based controller. The combined objective is to achieve closed-loop stability and vibration suppression at a target locati...
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The problem of non-collocated vibration absorption is targeted utilizing a multiparameter delay-based controller. The combined objective is to achieve closed-loop stability and vibration suppression at a target location, which is different from the position of the absorber. This problem can be translated into an optimization problem of shaping the poles of a closed-loop system with delays subject to zero-location constraints. The solution approach involves remodelling the closed-loop system as a system of delay-differential algebraic equations (DDAEs) which allows to systematically account for delays in the control law, thereby enabling a straightforward technique for computation of controller parameters. Finally, the proposed design method is validated by simulation on a lumped parameter model of a mass-spring-damper system.
With the growing integration of solar power gener-ation into smart grids, accurate solar irradiance forecasting is of paramount importance for efficient grid operation and renew-able energy management. In this paper, ...
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ISBN:
(数字)9798331521035
ISBN:
(纸本)9798331521042
With the growing integration of solar power gener-ation into smart grids, accurate solar irradiance forecasting is of paramount importance for efficient grid operation and renew-able energy management. In this paper, a data decomposition approach with two different methods of deep learning and grid search optimization was combined to develop a better hybrid model to forecast solar irradiance. The proposed model combines spatial and temporal information to improve forecasting accuracy by considering the impact of meteorological constraints such as global horizontal irradiance, temperature, relative humid-ity, wind speed, cloud type, direct normal irradiance, diffuse horizontal irradiance, and solar zenith angle. In addition to the model architecture, this work incorporates hyper-parameter optimization to fine-tune the parameters of the model for optimal performance. The design of the model ensures that the system adapts to the specific characteristics of the solar irradiance data and meteorological conditions under consideration. The proposed hybrid model was evaluated using real-world data to outperform traditional forecasting methods in terms of accuracy. The results of the proposed hybrid model indicate better prediction ability as measured by four parameters (lower RMSE and MAE, fewer epochs, and a higher R2 co-efficient).
Fixed channel assignment in wireless communication networks is an important combinatorial optimization problem that must be solved for application problems. Since it is NP-hard, many different heuristics are proposed ...
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Fixed channel assignment in wireless communication networks is an important combinatorial optimization problem that must be solved for application problems. Since it is NP-hard, many different heuristics are proposed for its solution. We consider two types of interference conditions for channel assignment: a co-channel interference within distance of two cells and a adjacent channel interference within the same and the adjacent cells. Our goal is to minimize or disallow these two types of interference in order to achieve optimal channel assignment. First we present our recursive search algorithm together with the neighborhood improvement structure. Then we minimize or disallow these two types of interference in order to achieve optimal channel assignment. We suggest a general approach combining several important heuristics. Our experimental results show that our algorithm improves over known approaches.
Our research has been focused on developing human-robot interactions. We attempted to give a robotic arm has a consciousness resembling that of a human or an animal, so we have improved a hierarchical structure model,...
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Our research has been focused on developing human-robot interactions. We attempted to give a robotic arm has a consciousness resembling that of a human or an animal, so we have improved a hierarchical structure model, which defines the relationship between the consciousness field and the behavior module in the robotic arm. This model is called Consciousness-Based Architecture (CBA) and can be used to select the action of a robot. Indeed, the robot is programmed to select its own action by evaluating the changing situation based on its motivation value and image recognition. We have investigated the application of brain-inspired technology such as a Self Organizing Map (SOM) into the robotic arm. In this paper, we introduce a prototype function for autonomous action selection and show a classification of the robotic arm's behavior using SOM combined with a CBA module.
This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and with actual mea...
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Different in-plane mismatch was introduced by varying the Ga 0.92In0.08As(p+) epilayer thickness (h-0.1, 0.25, 0.5 and lum) on GaAs(n)/GaAs(n+) structure. For sample with h-lum, quasi-Fermi level pinning effect was ob...
Different in-plane mismatch was introduced by varying the Ga 0.92In0.08As(p+) epilayer thickness (h-0.1, 0.25, 0.5 and lum) on GaAs(n)/GaAs(n+) structure. For sample with h-lum, quasi-Fermi level pinning effect was observed in low temperature forward Current-Voltage characteristic due to high density misfit dislocation. From Vint measurement at low frequency limit in C-2 vs Voltage plot, interface state density Nss was obtained. From Capacitance-Voltage measurements at different frequencies, single-level interface state density Ns was estimated using Schokley-Read-Hall statistics. Both Nss and Ns show their linear relation with epilayer in-plane mismatch. Admittance Spectroscopy shows an interface trap level at about Ev + 0.36 eV with the hole capture cross section cp = 2.7×10-15 cm-2 for the h-lum sample, and at Ev + 0.21 eV with cP = 2.4×10-16 cm-2 for the h-0.5um sample.
In a recent paper [1], we developed a target capturing strategy for swarms composed of double integrator agents. In this paper, we build on the results in [1] and focus on the implementation of the methodology for a s...
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ISBN:
(数字)9781665405935
ISBN:
(纸本)9781665405942
In a recent paper [1], we developed a target capturing strategy for swarms composed of double integrator agents. In this paper, we build on the results in [1] and focus on the implementation of the methodology for a swarm composed of quadrotor UAVs. We utilize both MATLAB and ROS environments under realistic conditions for that purpose. In ROS, we utilize Ultra-Violet LED blinkers based Ultra-Violet Direction And Ranging (UVDAR) system for relative localization. The sensing and localization errors serve as realistic errors for the assumptions in [1] for the uncertainties of the velocity and acceleration of the target. We verify in a realistic scenario that the agents converge to a user-defined ellipsoidal ring around the target despite the uncertainties and the more realistic agent dynamics.
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed se...
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ISBN:
(数字)9781728113982
ISBN:
(纸本)9781728113999
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings, the algorithms had better come with the same type of reliability, robustness, and safety bounds that are hallmarks of control theory, or failures could be catastrophic. Thus, as learning algorithms are increasingly and more aggressively deployed in safety critical settings, it is imperative that control theorists join the conversation. The goal of this tutorial paper is to provide a starting point for control theorists wishing to work on learning related problems, by covering recent advances bridging learning and control theory, and by placing these results within an appropriate historical context of system identification and adaptive control.
In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation...
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In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time.
Polycrystalline PbSe for mid-wave IR (MWIR) photodetector is an attractive material option due to high operating/ambient temperature operation and relatively easy and cheap fabrication process, making it candidate for...
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