In the aftermath of increasingly frequent catastrophic events, a typical scenario is critical infrastructure (CI) units being supported by available backup sources with a weak power grid that can be intermittent or ab...
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With the integration of loads such as pulse power loads, a new control challenge is presented in meeting their high ramp rate requirements. Existing onboard generators are ramp rate limited. The inability to meet the ...
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With the integration of loads such as pulse power loads, a new control challenge is presented in meeting their high ramp rate requirements. Existing onboard generators are ramp rate limited. The inability to meet the load power due to ramp rate limitation may lead to instability. The addition of energy storage elements in addition to the existing generators proves a viable solution in addressing the control challenges presented by high ramp rate loads. A distributed energy management strategy maximizing generator efficiency and minimizing energy storage degradation is developed that facilitates an optimal adaptive power split between generators and energy storage elements. The complex structure of the energy storage degradation model makes it tough for its direct integration into the optimization problem and is not practical for real-time implementation. A degradation heuristic to minimize absolute power extracted from the energy storage elements is proposed as a degradation heuristic measure. The designed strategy is tested through a numerical case study of a consolidated shipboard power system model consisting of a single generator, energy storage element, and load model. The results show the impact of the designed energy management strategy in effectively managing energy storage health.
Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ...
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Low-rank structures have been observed in several recent empirical studies in many machine and deep learning problems, where the loss function demonstrates significant variation only in a lower dimensional subspace. W...
Low-rank structures have been observed in several recent empirical studies in many machine and deep learning problems, where the loss function demonstrates significant variation only in a lower dimensional subspace. While traditional gradient-based optimization algorithms are computationally costly for high-dimensional parameter spaces, such low-rank structures provide an opportunity to mitigate this cost. In this paper, we aim to leverage low-rank structures to alleviate the computational cost of first-order methods and study Adaptive Low-Rank Gradient Descent (AdaLRGD). The main idea of this method is to begin the optimization procedure in a very small subspace and gradually and adaptively augment it by including more directions. We show that for smooth and strongly convex objectives and any target accuracy $\epsilon$ , AdaLRGD's complexity is $\mathcal{O}(r\ln(r/\epsilon))$ for some rank $r$ no more than dimension $d$ . This significantly improves upon gradient descent's complexity of $\mathcal{O}(d\ln(1/\epsilon))$ when $r\ll d$ . We also propose a practical implementation of AdaLRGD and demonstrate its ability to leverage existing low-rank structures in data.
This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their struc...
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ISBN:
(数字)9798350373974
ISBN:
(纸本)9798350373981
This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their structural properties by modeling internal dynamics in stand-alone modules (components, areas) using the fundamental conservation laws between energy stored and generated, and then constraining explicitly their Tellegen’s quantities (power and rate of change of power). In this paper we derive, by following the same principles, a transformed state-space model for a general nonlinear system. Using this model we show the existence of an area-level interaction variable, intVar, whose rate of change depends solely on the area internal power imbalance and is independent of the model complexity used for representing individual module dynamics in the area. Given these structural properties of stand-alone modules, we define in this paper for the first time an inter-area variable as the difference of power wave incident to tie-line from Area I and the power reflected into tie-lie from Area II. Notably, these power waves represent the interaction variables associated with the two respective interconnected areas. We illustrate these notions using a linearized case of two lossless inter-connected areas, and show the existence of a new inter-area mode when the areas get connected. We suggest that lessons learned in this paper open possibilities for computationally-efficient modeling and control of inter-area oscillations, and offer further the basis for modeling and control of dynamics in changing systems comprising faster energy conversion processes.
We propose a coarse symbol timing scheme for wireless orthogonal frequency division multiplexing (OFDM) systems using an optimal correlation-based circular-shifted preamble (CSP). Our proposed scheme is implemented by...
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We propose a coarse symbol timing scheme for wireless orthogonal frequency division multiplexing (OFDM) systems using an optimal correlation-based circular-shifted preamble (CSP). Our proposed scheme is implemented by off-line correlation-ranking and on-line processing. The on-line processing derives an optimal CSP for transmission according to the off-line correlation-ranking, and employs a delayed multiplication operation for symbol timing. The conducted simulation results show that our proposed scheme outperforms the other existing representative schemes over typical multi-path fading channels in terms of mean square error (MSE) while keeping the computational complexity low. IEEE
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples....
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample’s deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.
Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly prevalent in the commercial and defense sectors, operating throughout the continental United States airspace. UAVs have wing sizes ranging from 0.2...
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ISBN:
(数字)9798331508180
ISBN:
(纸本)9798331508197
Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly prevalent in the commercial and defense sectors, operating throughout the continental United States airspace. UAVs have wing sizes ranging from 0.25 centimeters to 61 meters, and they perform a myriad of roles from job site surveillance to 3D mapping of agricultural fields. However, a UAV is not a standalone system, but joins a ground control system, pilot and communications network to form an Unmanned Aircraft System (UAS) platform. With the various UAS platforms of different UAV sizes and missions, the Federal Aviation Administration (FAA) is developing a UAS Traffic Management (UTM) system to safely integrate them into the airspace. UAS ground systems require ingestion, processing, compilation and dissemination of weather data specific to a local area. The NOAA Advanced Quantitative Precipitation Information (AQPI) system has proven to reliably perform these functions. Expanding the mission for a system changes the requirements originally used to design the system. The ability of a system to adjust is the foundation of System Adaptability. Using the concept of System Adaptability and leveraging the existing Digital Twin of the Bay Area AQPI, this paper presents the theoretical application of adapting the San Francisco Bay Area AQPI system to provide meteorological information to UAS platforms operating under the Bay Area UTM.
In this paper, we introduce a novel approach that unifies Automatic Speech Recognition (ASR) and speaker diarization in a cohesive framework. Utilizing the synergies between the two tasks, our method effectively extra...
In this paper, we introduce a novel approach that unifies Automatic Speech Recognition (ASR) and speaker diarization in a cohesive framework. Utilizing the synergies between the two tasks, our method effectively extracts speaker-specific information from the lower layers of a pretrained Conformer-based ASR model while leveraging the higher layers for enhanced diarization performance. In particular, the integration of ASR contextual details into the diarization process has been demonstrated to be effective. Results on the DIHARD III dataset indicate that our approach achieves a Diarization Error Rate (DER) of 10.52%, which can be further reduced to 10.39% when integrating ASR features into the diarization model. These findings highlight the potential of our approach, suggesting competitive performance against other state-of-the-art systems. Additionally, our framework’s ability to simultaneously generate text transcripts for each speaker marks a distinct advantage, which can further enhance ASR capabilities and transition towards an end-to-end multitask framework encompassing both ASR and speaker diarization.
Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods...
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