This study examined wetland trends in the *** Seaway(~500,000 km^(2))in Canada over the past four *** this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were *** samples were scr...
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This study examined wetland trends in the *** Seaway(~500,000 km^(2))in Canada over the past four *** this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were *** samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged *** spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to ***,a change analysis was conducted to calculate the loss and gain of different wetland ***,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,*** was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 ***,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an ***,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),*** study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.
Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids....
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Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed
The suspended permanent magnet maglev rail transit (SPMMRT), as a new type of rail transit has the advantages of green, intelligent and safety. However, when the vehicle is walking in the rail carriage beam, it will p...
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As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheles...
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computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tack...
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Grape black rot is a devastating disease that affects grape crops globally. Detecting and preventing the disease as early as possible is crucial for minimizing crop loss and increasing yield and quality. In this study...
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Stress concentration factors(SCFs) for welded tubular joints can be decreased by filling the chord with concrete leading to a longer fatigue life. However, there are currently no design formula available in guidelines...
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Stress concentration factors(SCFs) for welded tubular joints can be decreased by filling the chord with concrete leading to a longer fatigue life. However, there are currently no design formula available in guidelines to predict the SCF of concrete-filled circular hollow section(CFCHS) K-joints, thus limiting their applicability in bridge design. To address this gap,finite element models for CFCHS K-joints were developed and compared against test results to ensure their accuracy. Then, a comprehensive parametric study was conducted to establish relationships between maximum SCFs and four variables: brace-to-chord diameter ratio(β), chord diameter-to-thickness ratio(2γ), brace-to-chord thickness ratio(τ), and the angle between braces and chord(θ). A total of 480 FE models were examined under three loading conditions including brace and chord loading: balanced axial force, chord axial force, and chord bending. design equations to predict the maximum SCF for CFCHS Kjoints were established by multiple regression analyses of the numerical results. A comparison of maximum SCFs between circular hollow section(CHS) and CFCHS K-joints was made, and it was concluded that average reductions of 42% and 33% in maximum SCFs in CFCHS K-joints at the locations of the chord and brace were found compared to CHS joints for balanced axial force, respectively. Finally, a case study illustrating how to use the proposed equations for fatigue safety verification was presented.
As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due ...
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Considering the potential benefits to lifespan and performance, zoned flash storage is expected to be incorporated into the next generation of consumer devices. However, due to the limited volatile cache and heterogen...
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Big Data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive...
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