Dynamic multi-objective optimization problems (DMOPs) are prevalent in daily life, and the aim of dealing with DMOPs is to track the moving Pareto Front(PF) and find a series of Pareto Sets (PS) at different moments. ...
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—Modern communication systems need to fulfill multiple and often conflicting objectives at the same time. In particular, new applications require high reliability while operating at low transmit powers. Moreover, rel...
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This article presents an innovative analysis of the development and implementation of double degree programs in higher education, focusing on the emerging fields of electric vehicles and automotive electronics. The st...
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Automated monitoring of sow welfare and behaviors is a crucial tool in precision swine farming, giving farmers access to continuous streams of sow health information. Monitoring the activity of the sows helps farmers ...
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Traffic in backbone networks is characterized by strong seasonality, with clear patterns visible in various services and applications based on their usage throughout the day. Data-driven networks can learn these patte...
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ISBN:
(数字)9783903176669
ISBN:
(纸本)9798331505158
Traffic in backbone networks is characterized by strong seasonality, with clear patterns visible in various services and applications based on their usage throughout the day. Data-driven networks can learn these patterns to manage resources more efficiently as they become increasingly saturated. In this paper, we explore the benefits of traffic prediction and grooming across different traffic patterns. To achieve this, we simulate network operations using uniform sets of time-varying connection requests, where all demands in a simulation share the same traffic pattern related to a specific network-based service or application. Our goal is to thoroughly evaluate the robustness of the proposed techniques across diverse scenarios. The results will facilitate the design of future application-aware algorithms for the most efficient handling of each traffic pattern.
CLLC resonant converter can achieve zero voltage switching (ZVS) turn-on and low current turn-off for the primary side, both zero current switching (ZCS) and ZVS for the secondary side switches, which makes it a great...
CLLC resonant converter can achieve zero voltage switching (ZVS) turn-on and low current turn-off for the primary side, both zero current switching (ZCS) and ZVS for the secondary side switches, which makes it a great candidate for today's high-frequency, high-efficiency applications. However, for high power applications like electrical vehicle (EV) charger, photovoltaic (PV) power station, energy storage, and railway auxiliary power supply applications, the wide input and/or output voltage range is usually a challenge for resonant converters. Besides, the influence of the secondary-side devices' junction caps to commutation is no longer negligible, but rarely analyzed in literature. In this paper, a control strategy of CLLC converter to boost the output voltage is reviewed. The design of magnetic component, an integrated transformer, is introduced. The process of conventional commutation strategy is analyzed for the cases where the secondary side junction caps are comparable to the primary side ones. Then another commutation strategy is compared, which could reduce the circulating energy by half. Finally, a 100 kHz 30 kW SiC based CLLC converter prototype verifies the proposed design and achieves a peak efficiency higher than 99%.
Large volumes of distributed energy resources (DERs), such as solar photovoltaic (PV) plants are integrated into the power distribution system due to increased awareness of climate change. These DERs introduce variabl...
Large volumes of distributed energy resources (DERs), such as solar photovoltaic (PV) plants are integrated into the power distribution system due to increased awareness of climate change. These DERs introduce variable and uncertain generation sources due to changing weather conditions. This makes operations and controls challenging and complex. To better understand and manage the dynamic nature of solar PV power plants, digital twins (DTs) will be needed. DTs based on artificial intelligence (AI) methods can be applied to replicate the dynamics of PV plants. This study utilizes a popular paradigm of AI - neural networks to create a variety of data-driven DT (DD-DT) prediction models for a 1 MW solar PV plant located at Clemson University in South Carolina, USA. State-of-the-art internet of things (IoT) based real-time measurements are used to develop the DD-DTs. Typical results for short-term PV power prediction for DTs implemented using multilayer perceptron neural networks (MLPNNs) and Elman recurrent neural networks (ERNNs) are presented in this paper.
Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-...
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This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human concepts in an image and generates the Wei...
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This paper presents a powerline energy harvesting circuit to power wireless sensor nodes for powerline safety monitoring. The magnetic harvester is a ring-shaped nano-crystalline magnetic core, which transforms the ac...
This paper presents a powerline energy harvesting circuit to power wireless sensor nodes for powerline safety monitoring. The magnetic harvester is a ring-shaped nano-crystalline magnetic core, which transforms the ac powerline current to ac voltage. The major building blocks of the circuit are a buck-boost converter operating in discontinuous conduction mode (DCM) and a microcontroller unit (MCU) for maximum power point tracking (MPPT). The MPPT algorithm based on the perturb and observe senses the current flowing into the load and adjusts the duty cycle of the buck-boost converter to match the source impedance. The magnetic core delivers 6.98 W to an optimal $200\ \Omega$ resistor directly attached to the core under the powerline current of 30 A. The output power of the proposed circuit is 4.86 W with the optimal load resistance of $R_{L}=250\ \Omega$ , resulting in the conversion efficiency of 70%.
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