This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring...
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ISBN:
(数字)9798350374605
ISBN:
(纸本)9798350386165
This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed
The requirement of Device-to-device (D2D) communication is increasing day-by-day for rapid data transmission by bypassing the base station. Communication can be established either through cooperative devices or non-co...
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This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes sub...
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ISBN:
(数字)9798350384147
ISBN:
(纸本)9798350384154
This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.
Having access to a reliable and accurate prediction of the short-term power demand is a fundamental step for the widespread adoption of Electric Vehicles (EVs), as their charges may have a significant impact on the po...
Having access to a reliable and accurate prediction of the short-term power demand is a fundamental step for the widespread adoption of Electric Vehicles (EVs), as their charges may have a significant impact on the power system balancing. In this direction, we propose a short-term load demand predictor, based on distributed Long Short-Term Memory Networks, that employs consensus and fully-decentralized Federated Learning (FL) algorithms to seek cooperation among multiple points of charge without the requirement of sharing any user-related data.
This paper proposes a linear quadratic regulator (LQR)-based controller for a grid-connected photovoltaic (PV) with a supercapacitor (SC) system to ensure a smooth transition of control mode when the SC is unavailable...
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ISBN:
(数字)9798350376067
ISBN:
(纸本)9798350376074
This paper proposes a linear quadratic regulator (LQR)-based controller for a grid-connected photovoltaic (PV) with a supercapacitor (SC) system to ensure a smooth transition of control mode when the SC is unavailable (completely charged) during fault operations. The SC improves the low-voltage ride-through (LVRT) capability of the PV system by quickly balancing power between the PV system and the grid. When the SC reaches its full charge capacity, the DC-link voltage will be controlled by the inverter’s controller, and the PV system may need to curtail its power generation. During the transition, fluctuations in the DClink voltage can occur which can compromise the stability of the PV system, especially when the control mode switching happens during severe fault operations. The proposed LQR-based controller is validated on a hardware testbed platform to demonstrate its effectiveness in reducing DC-link overvoltage during the control mode transitions. As a result, the PV system can remain compliant with LVRT requirements while maintaining stability during fault operations.
With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, thi...
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In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse. This complexity ari...
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Modeling trajectories in cigarette smoking prevalence, initiation and quitting for populations and subgroups of populations is important for policy planning and evaluation. This paper proposes an agent-based model (AB...
ISBN:
(纸本)9798331534202
Modeling trajectories in cigarette smoking prevalence, initiation and quitting for populations and subgroups of populations is important for policy planning and evaluation. This paper proposes an agent-based model (ABM) design for simulating the smoking behaviors of a population using the Capability, Opportunity, Motivation - Behavior (COM-B) model. Capability, Opportunity and Motivation are modeled as latent composite attributes which are composed of observable factors associated with smoking behaviors. Three forms of the COM-B model are proposed to explain the transitions between smoking behaviors: initiating regular smoking uptake, making a quit attempt and quitting successfully. The ABM design follows object-oriented principles and extends an existing generic software architecture for mechanism-based modeling. The potential of the model to assess the impact of smoking policies is illustrated and discussed.
This paper addresses the problem of adaptive output regulation of a minimum-phase MIMO LTI system in the presence of un-modeled (fast) input dynamics. The adoption of a post-processing tunable internal model makes it ...
This paper addresses the problem of adaptive output regulation of a minimum-phase MIMO LTI system in the presence of un-modeled (fast) input dynamics. The adoption of a post-processing tunable internal model makes it possible to implement standard methods for the analysis of two-time-scale systems. The proposed adaptation law guarantees, under suitable hypotheses, convergence to zero of the regulation error as well as of the parameter estimation error.
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