Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of l...
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Deep neural networks (DNNs) emerge as a key component in various applications. However, the ever-growing DNN size hinders efficient processing on hardware. To tackle this problem, on the algorithmic side, compressed D...
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Manufacturing plays a significant role in economic development, production, exports, and job creation, which ultimately contribute to improving the quality of life. The presence of manufacturing defects is, however, i...
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Manufacturing plays a significant role in economic development, production, exports, and job creation, which ultimately contribute to improving the quality of life. The presence of manufacturing defects is, however, inevitable leading to products being discarded, i.e. scrapped. In some cases, defective products can be repaired through rework. Scrap and rework cause a longer completion time, which can contribute to orders being shipped late. Moreover, the presence of uncertainties and combinatorial complexity significantly increases the difficulty of complex manufacturing scheduling. This paper tackles this challenge, exemplified by a case study on stochastic job-shop scheduling in low-volume, high-variety manufacturing contexts. To ensure on-time delivery, high-quality solutions are required, and near-optimal solutions must be obtained within strict time constraints to ensure smooth operations on the job-shop floor. To efficiently solve the stochastic job-shop scheduling (JSS) problem, a recently-developed Surrogate "Level-Based" Lagrangian Relaxation is used to reduce computational effort while efficiently exploiting the geometric convergence potential inherent to Polyak's step-sizing formula thereby leading to fast convergence. Numerical testing demonstrates that the new method is two orders of magnitude faster as compared to commercial solvers. Note to Practitioners-Manufacturing defects leading to scrap or rework create significant challenges, from both computational as well as on-time delivery standpoints. To assist practitioners in overcoming these challenges, this paper presents a case study of stochastic job-shop scheduling as well as Surrogate "Level-Based" Lagrangian Relaxation as an efficient solution methodology, which not only reduces the time required to obtain high-quality, near-optimal solutions for complex scheduling problems but also minimizes the dependence on hyperparameters. As a result, the method becomes more user-friendly, reducing the need f
Soft robotics has gained considerable attention in recent years for its structural flexibility and inherent safety in environmental interactions. To address the pitfalls of pneumatic actuation systems, namely sluggish...
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Soft robotics has gained considerable attention in recent years for its structural flexibility and inherent safety in environmental interactions. To address the pitfalls of pneumatic actuation systems, namely sluggish response and inefficiencies, this paper introduces the design and fabrication of a Hydraulic-Driven Soft Robotic Arm (HDSRA). The study establishes a comprehensive platform that integrates actuation, sensing, and control software to provide an experimental prototype for validating control algorithms. A novel fabrication technique utilizing water-soluble PVA for single-step mold creation enhances the structural reliability of soft actuators. Through closed-loop control experiments, the HDSRA demonstrates rapid and precise tracking of 1Hz signals, encompassing sine, square, and ramp waves, thus confirming the platform's reliability. This foundational work lays a robust foundation for future research and the verification of control algorithms in HDSRAs.
Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying st...
Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying stability analyses and mathematical proofs rely on unrealistic assumptions which limit not only the performance, but also the implementation of such controllers in real-world scenarios. In this work the limbic system inspired control (LISIC) framework is revisited, introducing three contributions that facilitate the implementation of this type of controller in real-time. First, an extension enabling the implementation of LISIC to the domain of SISO affine systems is proposed. Second, a strategy for resetting the controller’s Neural Network (NN) weights is developed, in such a way that now it is possible to deal with piece-wise smooth references and impulsive perturbations. And third, for the case when a nominal model of the system is available, a technique is proposed to compute a set of optimal NN reset weight values by solving a convex constrained optimization problem. Numerical simulations addressing the stabilization of an unmanned aircraft system via the robust LISIC demonstrate the advantages obtained when adopting the extension to SISO systems and the two NN weight reset strategies.
This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or fu...
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We study optimal procedures for estimating a linear functional based on observational data. In many problems of this kind, a widely used assumption is strict overlap, i.e., uniform boundedness of the importance ratio,...
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Partial discharge (PD) is a common and detrimental phenomenon that can cause damage and potential breakdown in insulation systems. According to the air breakdown voltage behaviors shown by Paschen's curve, from se...
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Partial discharge (PD) is a common and detrimental phenomenon that can cause damage and potential breakdown in insulation systems. According to the air breakdown voltage behaviors shown by Paschen's curve, from sea level to high altitude, the partial discharge inception voltage (PDIV) of the air is expected to decrease with decreasing pressure. So far, there has not been a widely accepted PD testing method for aviation wires in low pressure environments with pulse width modulated (PWM) voltage excitations. There have been studies of the PD behaviors of aviation wires with conventional triangular impulses, ac voltages, and PWM pulses with relatively low $\text{dv}/\text{dt}$ . But existing test methods and study results cannot be directly utilized to test and predict PDIV of aviation wires when high $\text{dv}/\text{dt}$ PWM excitation is applied with wide-bandgap (WBG) power devices, such as Silicon Carbide (SiC) Metal Oxide Semiconductor Field Effect Transistors (MOSFETs). Thus, this paper aims to provide more insights on PD behaviors of aviation wires under high $\text{dv}/\text{dt}$ PWM excitations and possible ways to improve the existing PD test methods. A test setup including an ultra-high $\text{dv}/\text{dt}$ PWM generator, a test sample fixture, and associated PD sensors including a photomultiplier tube (PMT), a high frequency current transducer (HFCT), and an ultra-high-frequency (UHF) antenna will be introduced. Experimental results and associated analysis for aviation wire test samples at various pressures under high $\text{dv}/\text{dt}$ PWM excitations will be presented. The effect of the rise time of the PWM will be discussed in detail. Suggestions on how to improve test methods and test standards will be provided.
Denoising-based diffusion models have attained impressive image synthesis;however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of eff...
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