Acquiring labelled data for machine learning tasks, for example, for software performance prediction, remains a resource-intensive task. This study extends our previous work by introducing a batch-mode deep active lea...
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Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,o...
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Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,oil spill identification,among many *** machine learning algorithms are given less and less attention in the era of big ***,a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer *** number of relevant articles has nearly doubled every year since *** in remote sensing technology,as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale,have opened up the possibilities for a wide range of modern ***,there are some challenges related to the availability of annotated data,the complex nature of data,and model parameterization,which strongly impact *** this article,a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures(vintage Convolutional Neural Network,CNN;Fully Convolutional Networks,FCN;encoder-decoder,recurrent networks;attention models,and generative adversarial models).The characteristics,capabilities,and limitations of current DL models were examined,and potential research directions were discussed.
The landscape of deep learning has vastly expanded the frontiers of source code analysis, particularly through the utilization of structural representations such as Abstract Syntax Trees (ASTs). While these methodolog...
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Acquiring labelled data for machine learning tasks, for example, for software performance prediction, remains a resource-intensive task. This study extends our previous work by introducing a batch-mode deep active lea...
Acquiring labelled data for machine learning tasks, for example, for software performance prediction, remains a resource-intensive task. This study extends our previous work by introducing a batch-mode deep active learning approach tailored for regression in graph-structured data. Our framework leverages the source code conversion into Flow Augmented-AST graphs (FA-AST), subsequently utilizing both supervised and unsupervised graph embeddings. In contrast to single-instance querying, the batch-mode paradigm adaptively selects clusters of unlabeled data for labelling. We deploy an array of base kernels, kernel transformations, and selection methods, informed by both Bayesian and non-Bayesian strategies, to enhance the sample efficiency of neural network regression. Our experimental evaluation, conducted on multiple real-world software performance datasets, demonstrates the efficacy of the batch mode deep active learning approach in achieving robust performance with a reduced labelling budget. The methodology scales effectively to larger datasets and requires minimal alterations to existing neural network architectures.
This study adopts a game-theoretical approach to examine the impact of introducing retired electrical battery-based (REVB-based) energy storage systems on the energy storage market and the electrical grid. A bi-level ...
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ISBN:
(数字)9798331520090
ISBN:
(纸本)9798331520106
This study adopts a game-theoretical approach to examine the impact of introducing retired electrical battery-based (REVB-based) energy storage systems on the energy storage market and the electrical grid. A bi-level Nash-Stackelberg game is formulated with three players: power stations (PSs), energy storage aggregators (ESAs), and energy storage service operators (ESSOs). In this framework, power stations participate in the day-ahead market and develop bidding strategies for electricity supply. Energy storage service operators, equipped with either standard storage systems or REVB-based systems, determine optimal bidding strategies for charging and discharging power. These strategies aim to provide load-shaving during peak-load periods and enhance the integration of solar power during base-load periods. The standard and REVB-based energy storage systems differ in their maximum charging-discharging cycles, investment and maintenance costs. The ESA acts as an intermediary between the day-ahead market and the energy storage market, aggregating the charging and discharging power from ESSOs. Specifically, during peak-load periods, the ESA arbitrages between the two markets by selling stored discharging power purchased from ESSOs to the day-ahead market. Conversely, during base-load periods, the ESA purchases charging power from the day-ahead market to charge energy storage systems and purchases charging capacity from ESSOs to facilitate the accommodation of renewable energy. The ESA is regarded as the leader in the Stackelberg-Nash game, with PSs and ESSOs acting as the followers. Additionally, incentive compensation is considered to address potential profit losses incurred by the ESA during baseload periods. Numerical simulations are conducted to explore how the introduction of REVB-based energy storage systems influences profits and system performance within the energy storage and day-ahead markets.
This study adopts a game-theoretical approach to examine the impact of introducing retired electrical battery-based (REVB-based) energy storage systems on the energy storage market and the electrical grid. A bi-level ...
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There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artifici...
There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the SpaceCube GR740 Host for Onboard Science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard Space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.
Next-generation spacecraft developers are earnestly investigating the application of artificial intelligence (AI) algorithms onboard to enable new mission concepts for space exploration and science. However, the curre...
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Next-generation spacecraft developers are earnestly investigating the application of artificial intelligence (AI) algorithms onboard to enable new mission concepts for space exploration and science. However, the current generation of radiation-hardened processors are inferior compared to commercial-off-the-shelf alternatives in terms of the computational performance required by modern AI applications. To address this disparity, space-system designers have started employing novel radiation-tolerant architectures combining both commercial and radiation-hardened components to mitigate radiation effects at a system level. Unfortunately, developing single-board computers with radiation-tolerant, high-performance processors is challenging because designers must balance the sparce selection of radiation-hardened power converters and high-reliability decoupling capacitors with SmallSat/CubeSat area constraints and limited thermal conduction. Consequently, the next-generation of space processors, including the AMD-Xilinx Versal Adaptive Compute Acceleration Platform require demanding power solutions capable of supplying core rails with 0.8V ± 18mV and currents up to 150 A. In this paper, we present a multifaceted analysis of the power system and decoupling network for a future Versal-based design. We developed preliminary power estimates based on expected processor and FPGA resource utilization for common AI processing applications. These estimates drive the power system requirements and a comparative analysis of single-phase integrated converters and multi-phase discrete converters for high-current FPGA supplies. We develop a tradespace between the number of phases, input voltage, load current, switching frequency, power efficiency, and printed circuit board area. Finally, we design and simulate four power delivery networks based on commercial, high-reliability, and flight-qualified capacitors to compare the efficacy of 0201 decoupling capacitors in flight missions.
Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that ex...
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Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.
Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive ...
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Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.
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