An optimized digital architecture exploiting distributed and multilayer hierarchical control and real-time communication for Flying Capacitor Passive Cross-Connected arms Modular Multilevel converter (FC-PCC MMC) base...
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ISBN:
(数字)9781728193878
ISBN:
(纸本)9781728193878
An optimized digital architecture exploiting distributed and multilayer hierarchical control and real-time communication for Flying Capacitor Passive Cross-Connected arms Modular Multilevel converter (FC-PCC MMC) based drive systems is proposed, designed and validated. The proposed solution, relies on two fundamental components: a Multi-Point Low Voltage Differential Signaling (M-LVDS) bus and a bi-directional optical wireless transceiver, recently introduced to the market. As a result, the necessary isolation barrier between ground and each Sub-Module (SM) can be crossed at a very high data rate. The real-time constraints and requirements have been analyzed and discussed in order to design a control strategy and communication infrastructure for proper, safe and reliable converter operation. Based on this analysis, a distributed control algorithm and the related communication infrastructure are proposed in this paper. Moreover, an innovative isolated communication bus is adopted, which aims at reducing the overall system size and cost. The effectiveness of the proposal is demonstrated by accurate simulation of a 22 kW Permanent Magnet Synchronous Machine (PMSM) drive, including the effect of communication and processing latency. The results show that proper operation is obtained even at rated torque from zero to rated speed, while the voltage ripple across the SM capacitors is kept limited thanks to the optimized control strategy proposed in [1]. A prototype of the converter has been designed and is ready to be manufactured.
The long-time behavior of many complex molecular systems can often be described by Markovian dynamics in a slow subspace spanned by a few reaction coordinates referred to as collective variables (CVs). However, determ...
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The long-time behavior of many complex molecular systems can often be described by Markovian dynamics in a slow subspace spanned by a few reaction coordinates referred to as collective variables (CVs). However, determining CVs poses a fundamental challenge in chemical physics. Depending on intuition or trial and error to construct CVs can lead to non-Markovian dynamics with long memory effects, hindering analysis. To address this problem, we continue to develop a recently introduced deep-learning technique called spectral map [J. Rydzewski, J. Phys. Chem. Lett. 14, 5216-5220 (2023)]. Spectral map learns slow CVs by maximizing a spectral gap of a Markov transition matrix describing anisotropic diffusion. Here, to represent heterogeneous and multiscale free-energy landscapes with spectral map, we implement an adaptive algorithm to estimate transition probabilities. Through a Markov state model analysis, we validate that spectral map learns slow CVs related to the dominant relaxation timescales and discerns between long-lived metastable states.
In the production process of high-end PP-R pipes, mixing different colored raw material particles can result in uneven color in the final product, affecting its appearance quality. in addition, color mixing can reduce...
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In the production process of high-end PP-R pipes, mixing different colored raw material particles can result in uneven color in the final product, affecting its appearance quality. in addition, color mixing can reduce the physical properties of the pipes, impacting their durability and safety. To address this issue, we propose a visual, non-destructive inspection solution based on image processing technology. The solution aims to enhance detection efficiency and accuracy by reducing background interference and enabling adaptive adjustments in various environments. Initially, the K-Means image segmentation algorithm is employed to eliminate complex background factors from the original image, significantly improving image segmentation accuracy. Subsequently, the Gaussian mixture model algorithm is utilized to automatically extract the color threshold of the foreground image after background removal, facilitating adaptive algorithm adjustments. Finally, the mean value algorithm is introduced to swiftly and accurately identify plastic particles of different colors using the automatically obtained color thresholds. Experimental results demonstrate that this method can quickly and accurately identify different color particles and effectively support the rejection of impurity particles. Through this approach, the algorithm achieves an average detection accuracy of 99.3%.
We introduce the cache-adaptive model, which generalizes the external-memory model to apply to environments in which the amount of memory available to an algorithm can fluctuate. The cache-adaptive model applies to op...
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Brain Computer Interfaces are used to obtain relevant information from the electroencephalogram (EEG) with a concrete objective. The evoked potentials related to movement are much demanded nowadays, in particular the ...
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ISBN:
(数字)9781728127828
ISBN:
(纸本)9781728127828
Brain Computer Interfaces are used to obtain relevant information from the electroencephalogram (EEG) with a concrete objective. The evoked potentials related to movement are much demanded nowadays, in particular the ones associated to imagery movement. The objective of this work is to develop simple algorithms to imagery motion detection that can be included in a non-invasive wearable that everybody can use in a comfortable way for new services and applications. A wearable implies low resources, which is the most important requirement that the algorithms have. A public database with 105 subjects doing an upper-limb imagery movement is used. We have developed two algorithms (FBA and BLA) based on three characteristics of the signal (correlation, wavelet energy per segment and wavelet energy per electrode). They are tested for different number of electrodes and frequency bands. The best performance is found for 6 electrodes. The beta band is not the only band who achieves good performances. In fact, in this study the range between 25 Hz - 30 Hz has obtained the best performance using 6 electrodes. The conclusions show that these simple algorithms not fit well with the wearable requirements. However, it shows the need of adaptive algorithms to bypass the differences between subjects. Also, it affirms that more electrodes not lead to a better information, as well as, less electrodes not lead to a worse information. The same goes for frequency, where not only the beta band have the information required that fits our needs.
We consider a Cahn–Hilliard-type phase-field model for phase separation and large deformations in battery electrode particles. For the numerical solution we employ an hp-adaptive finite element solution algorithm cou...
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Many modern machine learning problems require solving min-max saddle point games, whose computational complexity is NP-hard in general. To overcome this issue, most available algorithms aim for finding a first-order N...
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ISBN:
(纸本)9781665405409
Many modern machine learning problems require solving min-max saddle point games, whose computational complexity is NP-hard in general. To overcome this issue, most available algorithms aim for finding a first-order Nash equilibrium solution that always exists under mild assumptions. However, the proposed algorithms for obtaining such solutions are non-adaptive and also exhibit slow convergence rates in real settings. Further, most algorithms are centralized in nature and cannot be adapted to a decentralized architecture in a straightforward manner. This study aims to address these issues by introducing general two-step adaptive algorithms for obtaining first-order Nash equilibrium solutions of min-max games in both single-node and decentralized architectures. We also obtain the non-asymptotic convergence rates of the algorithms assuming the objective functions satisfy the weak Minty variational inequality condition which is standard in recent literature. Finally, we illustrate the performance of the proposed algorithms by using them to train neural networks which are more robust against adversarial attacks compared to neural networks trained using existing algorithms.
In this paper, we design and implement a k-multipath routing algorithm that allows a given source node send samples of data to a given sink node in a large scale sensor networks. Construction and dynamic selection of ...
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The constrained least mean square (CLMS) algorithm is one of the most popular online linear-equality-constrained beamforming algorithms. This paper demonstrates for the first time that it solves a deterministic minimu...
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The constrained least mean square (CLMS) algorithm is one of the most popular online linear-equality-constrained beamforming algorithms. This paper demonstrates for the first time that it solves a deterministic minimum-disturbance optimization problem in an exact manner. Such a framework is employed to insert the coefficient reusing technique into the algorithm, engendering a new low-complexity constrained adaptive filter, designated as RC-CLMS, that trades convergence rate for asymptotic performance. A stochastic model that predicts the average evolution of adaptive weights is derived. Through simulations, the advanced reusing coefficient extension of the constrained least mean-square algorithm enhanced the asymptotic signal-to-interference-plus-noise ratio and decreased the steady-state mean output energy. Furthermore, the resulting beam pattern is analyzed with an antenna analysis tool, confirming the efficacy of the advanced algorithm in a realistic setting, when the electromagnetic coupling between the antennas is taken into account.
This paper explores the possibility of applying non model-based controllers to wind energy conversion systems (WECS), and proposes the application of a novel data-driven control algorithm based on Model-Free adaptive ...
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ISBN:
(纸本)9781665467612
This paper explores the possibility of applying non model-based controllers to wind energy conversion systems (WECS), and proposes the application of a novel data-driven control algorithm based on Model-Free adaptive Control. The proposed technique makes use of an equivalent dynamic linearization model obtained adopting a dynamic linearization technique based on pseudo-partial derivatives. A stability proof of convergence of the closed loop system is provided, proving the asymptotical vanishing of the tracking error. The proposed approach has been applied to the problem of efficiency maximization of a 5MW wind turbine operating in the region of medium wind speed using the recognized high-fidelity simulation tool FAST by NREL. The obtained realistic validation data seem to support the theoretical development and confirms the potential interest on data driven controllers for WECS, in view of their flexibility, effectiveness, low cost and possible interoperability with smart production lines.
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