The Internet of Things (IoT) seeks to establish a vast network comprising billions of interconnected devices to enable seamless data exchange and intelligent interactions between people and objects. This network is ch...
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The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean *** the past,generation systems depended on non-renewable sources such as oil,coal,and ***,this pap...
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The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean *** the past,generation systems depended on non-renewable sources such as oil,coal,and ***,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,*** performance analysis of the considered grid-connected PV system is carried out using power system simulator for engineering(PSS/E)*** the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered *** results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power *** total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 *** connection between solar stations and the national grid makes the system more efficient.
This work introduces novel architecture components and training procedures to create augmented neural networks with the ability to process data bidirectionally via an end-to-end approximate inverse. We develop pseudoi...
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We explore the use of caching both at the network edge and within User Equipment(UE)to alleviate traffic load of wireless *** develop a joint cache placement and delivery policy that maximizes the Quality of Service(Q...
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We explore the use of caching both at the network edge and within User Equipment(UE)to alleviate traffic load of wireless *** develop a joint cache placement and delivery policy that maximizes the Quality of Service(QoS)while simultaneously minimizing backhaul load and UE power consumption,in the presence of an unknown time-variant file *** file requests in a time slot being affected by download success in the previous slot,the caching system becomes a non-stationary Partial Observable Markov Decision Process(POMDP).We solve the problem in a deep reinforcement learning framework based on the Advantageous Actor-Critic(A2C)algorithm,comparing Feed Forward Neural Networks(FFNN)with a Long Short-Term Memory(LSTM)approach specifically designed to exploit the correlation of file popularity distribution across time *** results show that using LSTM-based A2C outperforms FFNN-based A2C in terms of sample efficiency and optimality,demonstrating superior performance for the non-stationary POMDP *** caching at the UEs,we provide a distributed algorithm that reaches the objectives dictated by the agent controlling the network,with minimum energy consumption at the UEs,and minimum communication overhead.
Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from...
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Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from the patient information which creates an imbalance in class distribution as the number of normal persons is more than the number of patients and contains a large number of features to represent a sample. It tends to the machine learning algorithms biased toward the majority class which degrades their classification performance for minority class samples and increases the computation overhead. Therefore, oversampling, feature selection and feature weighting-based four strategies are proposed to deal with the problems of class imbalance and high dimensionality. The key idea behind the proposed strategies is to generate a balanced sample space along with the optimal weighted feature space of the most relevant and discriminative features. The Synthetic Minority Oversampling Technique is utilized to generate the synthetic minority class samples and reduce the bias toward the majority class. An Improved Elephant Herding Optimization algorithm is applied to select the optimal features and weights for reducing the computation overhead and improving the interpretation ability of the learning algorithms by providing weights to relevant features. In addition, thirteen methods are developed from the proposed strategies to deal with the problems of high-dimensionality and imbalanced data. The optimized k-Nearest Neighbor (k-NN) learning algorithm is utilized to perform classification. The performance of the proposed methods is evaluated and compared for sixteen high-dimensional imbalanced medical datasets. Further, Freidman’s mean rank test is applied to show the statistical difference between the proposed methods. Experimental and statistical results show that the proposed Feature Weighting followed by the Feature Selection (FW–FS) method performed significantly b
Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Indu...
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Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.
作者：
Tsai, Shang-YenChang, Yun-HanChow, Chi-WaiDepartment of Photonics
Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Photonics
Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Chiao Tung University Hsinchu30010 Taiwan
We propose and experimentally demonstrate for the first time up to the authors' knowledge a wide field-of-view (FOV) water-to-air optical transmission using rolling-shutter (RS) based optical camera communication ...
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This article presents a method for designing transmit beampattern in 4-D imaging automotive multiple-input-multiple-output (MIMO) radars, employing the distance between the designed and desired beampatterns as the des...
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Photo sensing capability of an artificial synaptic device make it more valuable for brain-inspired computing systems, which can conquer the von Neumann bottleneck. In this letter, Ba0.7 Sr0.3 TiO3 (BST) based single l...
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Nanoelectromechanical systems(NEMS)incorporating atomic or molecular layer van der Waals materials can support multimode resonances and exotic nonlinear *** we investigate nonlinear coupling of closely spaced modes in...
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Nanoelectromechanical systems(NEMS)incorporating atomic or molecular layer van der Waals materials can support multimode resonances and exotic nonlinear *** we investigate nonlinear coupling of closely spaced modes in a bilayer(2L)molybdenum disulfide(MoS_(2))nanoelectromechanical *** model the response from a drumhead resonator using equations of two resonant modes with a dispersive coupling term to describe the vibration induced frequency shifts that result from the induced change in *** employ method of averaging to solve the equations of coupled modes and extract an expression for the nonlinear coupling coefficient(λ)in closed *** thermomechanical noise spectral measurements are used to calibrate the vibration amplitude of mode 2(a_(2))in the displacement *** drive mode 2 near its natural frequency and measure the shifted resonance frequency of mode 1(f_(1s))resulting from the dispersive *** model yieldsλ=0.027±0.005 pm^(-2)·μs^(-2) from thermomechanical noise measurement of mode *** model also captures an anomalous frequency shift of the undriven mode 1 due to nonlinear coupling to the driven mode 2 mediated by large dynamic *** study provides a direct means to quantifyingλby measuring the thermomechanical noise in NEMS and will be valuable for understanding nonlinear mode coupling in emerging resonant systems.
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