electrical system planning of the large-scale offshore wind farm is usually based on N-1 security for equipment lectotype. However, in this method, owing to the aggregation effect in large-scale offshore wind farms, o...
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electrical system planning of the large-scale offshore wind farm is usually based on N-1 security for equipment lectotype. However, in this method, owing to the aggregation effect in large-scale offshore wind farms, offshore electrical equipment operates under low load for long periods, thus wasting resources. In this paper, we propose a method for electrical system planning of the large-scale offshore wind farm based on the N+ design. A planning model based on the power-limited operation of wind turbines under the N+ design is constructed, and a solution is derived with the optimization of the upper power limits of wind turbines. A comprehensive evaluation and game analysis of the economy, risk of wind abandonment, and environmental sustainability of the planned offshore electrical systems have been conducted. Moreover, the planning of an infield collector system, substation, and transmission system of an offshore electrical system based on the N+ design is integrated. For a domestic offshore wind farm, evaluation results show that the proposed planning method can improve the efficiency of wind energy utilization while greatly reducing the investment cost of the electrical system.
The ephemeris and timing errors of low Earth orbit (LEO) satellites are modeled, leading to an approach to disambiguate these errors from pseudorange-type measurements. First, a model is derived describing the ephemer...
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The ephemeris and timing errors of low Earth orbit (LEO) satellites are modeled, leading to an approach to disambiguate these errors from pseudorange-type measurements. First, a model is derived describing the ephemeris error's impact on ranging measurements from LEO space vehicles (SVs) with imprecise ephemerides. A simulation study is presented comparing the impact of ephemeris error on ranging error for 5 LEO constellations (Statlink, OneWeb, Orbcomm, Iridium, and Globalstar) and 5 medium Earth orbit (MEO) constellations (GPS, GLONASS, Galileo, BeiDou-3, and O3B). Second, it is shown that for a particular SV position, the ephemeris error has no effect on range measurements. Next, the ephemeris and timing errors are parametrized by the three-dimensional (3-D) ephemeris error magnitude and its direction angle from the in-track axis. This parametrization is exploited in a proposed algorithm to disambiguate the ephemeris and timing error from the LEO SVs' pseudorange measurements at a reference receiver. The two parameters can be communicated to any unknown receiver listening to the same LEO SVs to correct for ephemerides ranging error, leading to improved positioning, navigation, and timing (PNT) precision. Monte Carlo simulation results are presented demonstrating the efficacy of the proposed algorithm. The simulations considered a reference receiver tracking via pseudorange measurements 22 Starlink and 4 OneWeb LEO SVs with poorly known ephemerides (obtained from two-line element (TLE) files, propagated with SGP4). The proposed algorithm reduced the 3-D position error of all SVs from a few kilometers to less than 120 m. The parameters were communicated to an unknown receiver to correct the LEO ephemerides, after which the receiver estimated its position by fusing its LEO pseudoranges via an extended Kalman filter (EKF), resulting in a horizontal position error of 0.91 m, as compared to 213 m utilizing TLE+SGP4 ephemerides. Two sets of experimental results are pres
Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcia...
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Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcial intelligence models predicting short-term and long-term health outcomes forpatients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solutionfor generating realistic and useful synthetic data that can be used to train such models. Methods: Despitetheir promise, GANs have not been widely adopted in the congenital heart disease research communitydue, in some part, to a lack of knowledge on how to employ them. In this research study, a GAN was usedto generate synthetic data from the Pediatric Heart Network Fontan I dataset. A subset of data consistingof the echocardiographic and BNP measures collected from Fontan patients was used to train the *** sets of synthetic data were created to understand the effect of data missingness on synthetic datageneration. Synthetic data was created from real data in which the missing values were imputed usingMultiple Imputation by Chained Equations (MICE) (referred to as synthetic from imputed real samples). Inaddition, synthetic data was created from real data in which the missing values were dropped (referred to assynthetic from dropped real samples). Both synthetic datasets were evaluated for ffdelity by using visualmethods which involved comparing histograms and principal component analysis (PCA) plots. Fidelitywas measured quantitatively by (1) comparing synthetic and real data using the Kolmogorov-Smirnovtest to evaluate the similarity between two distributions and (2) training a neural network to distinguishbetween real and synthetic samples. Both synthetic datasets were evaluated for utility by training aneural network with synthetic data and testing the neural network on its ability to classify patients thathave ventricular dysfunction using echocardiograph measures an
This article addresses the velocity-free predefined-time consensus tracking for multiagent systems (MASs) with input and output quantization via adaptive sliding mode control (SMC). First, a distributed predefined-tim...
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Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimi...
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Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimization problem for a mmWave cell-free massive MIMO network considering indoor environments. The objective is to minimize the number of deployed access points (APs) for a given environment, bandwidth, AP cooperation, and precoding scheme while guaranteeing the rate requirements of the user equipments (UEs). Considering coherent joint transmission (C-JT) and non-coherent joint transmission (NC-JT), we solve the problem of AP placement, UE-AP association, and power allocation among the UEs and resource blocks jointly. For numerical analysis, we model a mid-sized airplane cabin in ray-tracing as an exemplary case for IDS. Results demonstrate that a minimum data rate of 1 Gbps can be guaranteed with less than 10 APs with C-JT. From a holistic network design perspective, we analyze the trade-off between the required fronthaul capacity and the processing capacity per AP, under different network functional split options. We observe an above 600 Gbps fronthaul rate requirement, once all network operations are centralized, which can be reduced to 200 Gbps under physical layer functional splits. 2002-2012 IEEE.
In modern wireless systems, the feedback of DownLink (DL) Channel State Information (CSI) from User Equipment (UE) to Base Stations (BS) may require substantial computational and feedback bandwidth overheads. A promis...
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The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus ***,as a training area,it lacks appeal and learning motivation due to its conventional presentation of informat...
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The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus ***,as a training area,it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus *** current study introduced the concept of smart learning in this setting to increase interest and motivation for *** neural networks(CNNs)were used for the classification of lotus plant species,for use in the development of a mobile application to display details about each *** scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques(augmentation,dropout,and L2)and hyper parameters(dropout and epoch number).The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models(Inception V3,VGG16,and VGG19)as *** performance of the model was presented in terms of accuracy,F1-score,precision,and recall *** results showed that the CNN model with the augmentation,dropout,and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of *** best proposed model was more accurate than the pre-trained CNN models,especially compared to Inception *** addition,the number of total parameters was reduced by approximately 1.80–2.19 *** findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.
The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud ***,suitable and effectiv...
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The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud ***,suitable and effective applications could be performed to satisfy the applications’latency *** allocation techniques are essential aspects of fog networks which prevent unbalanced load *** resource management techniques can improve the quality of service *** to the limited and heterogeneous resources available within the fog infrastructure,the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT *** has been limited research on resource management strategies in fog networks in recent years,and a limited systematic review has been done to compile these *** article focuses on current developments in resource allocation strategies for fog-IoT networks.A systematic review of resource allocation techniques with the key objective of enhancing QoS is *** involved in conducting this systematic literature review include developing research goals,accessing studies,categorizing and critically analysing the *** resource management approaches engaged in this article are load balancing and task offloading *** the load balancing approach,a brief survey of recent work done according to their sub-categories,including stochastic,probabilistic/statistic,graph theory and hybrid techniques is provided whereas for task offloading,the survey is performed according to the destination of task *** load balancing and task-offloading approaches contribute significantly to resource management,and tremendous effort has been put into this critical ***,this survey presents an overview of these extents and a comparative ***,the study discusses ongoing research issues and potential future directio
In this paper, an advanced algorithm is presented that utilizes artificial neural networks (ANN) for estimating the inertia of synchronous generators (SGs). The algorithm is enhanced by integrating a modified equal ar...
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