To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries pre...
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To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles'degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM)network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.
Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the mo...
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Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the modern energy system with rapid *** renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental *** develop energy resources,electric power generation involved huge *** power and output voltages are plays important role in our work but it not considered in the existing *** considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy *** input information is collected from two input *** that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC *** layer 1 is transferred to hidden layer *** activation is employed for determining the output voltage with help of the *** last,the output layer offers the last value in GPIC-MDCNNC *** designed method was confirmed using one and multiple sources by stable and unpredictable input ***-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art *** control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.
This paper presents a high gain, compact size and dual band rectangular patch antenna for 5G applications. To enhance the gain of antenna, an equilateral triangle slots on the upper rectangular patch are constructed. ...
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Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a d...
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Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a delay-independent dynamical event-triggering controller is considered, operating the same way as the intermittent coupling and excluding the Zeno behavior naturally.
Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources includi...
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With the recent increase in the number of Internet of things(IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices ...
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With the recent increase in the number of Internet of things(IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning(RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffic transmission path and provide qualified IoT services.
We study the problem of learning feature repre-sentations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide sufficient and necessary conditions for...
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The stability of an HVDC transmission network is very important for the reliable transfer of power from renewable energy resources. Therefore, this paper proposes a region of attraction stability analysis method for g...
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The brain tumor (BT) is a severe condition caused by abnormal cell growth. If left untreated, the BT may result in a variety of harsh conditions, including death. As a consequence of the significance of automatic BT d...
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