Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance *** studies have mostly concentrate...
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Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance *** studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power ***-power consumer appliances have comparable power consumption patterns,which can complicate the detection task and can be mistaken as *** research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures.A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power *** dimension,Hurst exponent,multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various‘turn-on’appliance signatures for *** classifiers,i.e.,deep neural network,support vector machine,decision trees,and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted *** simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers,achieving an accuracy of up to 96%in classifying low-power appliances.
In today's networking environments caching algorithms plays a very important role to get the best use of resources and maximizing performance of networks. In this paper, we conduct a comparative analysis of the th...
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Several cutting-edge modern technologies, including hologram technology, have emerged due to the tremendous advancements of our era. The science of holography is used to make holograms, which are 3D images with lifeli...
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Recent advances in deep learning have led to the widespread use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) across various fields. For aircraft attitude estimation, CNNs can effectivel...
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Feature selection (FS) is one of the basic preprocessing steps in data mining and is a challenging binary optimization problem. FS is the process of determining the subset that can best represent the dataset by removi...
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With the development of deep learning in recent years, code representation learning techniques have become the foundation of many softwareengineering tasks such as program classification [1] and defect detection. Ear...
With the development of deep learning in recent years, code representation learning techniques have become the foundation of many softwareengineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.
A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation ...
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The Internet of Things (IoT) offers vast potential to enhance the quality of life, but the excessive visual data generated during environmental monitoring presents significant challenges. Existing visual data minimiza...
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We proposed new prediction models based on multilayer perceptron(MLP)which successfully predict the maximum run-up of landslide-generated tsunami waves and assess the role of parameters affecting *** input is approxim...
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We proposed new prediction models based on multilayer perceptron(MLP)which successfully predict the maximum run-up of landslide-generated tsunami waves and assess the role of parameters affecting *** input is approximately 55,000 rows of data generated through an analytical solution employing slide’s cross section,initial submergence,vertical thickness,horizontal length,beach slope angle and the maximum run-up itself,along with its occurrence *** parameters are first ranked through a feature selection algorithm and six models are constructed for a 9,000-row randomly sampled *** MLP-based models led predictions with a minimum Mean Absolute Percentage Error of 1.1%and revealed that vertical slide thickness has the largest impact on the maximum tsunami run-up,whereas beach slope angle has minimal *** parison with existing literature showed the reliability and applicability of the offered *** methodology introduced here can be suggested as fast and flexible method for prediction of landslide-induced tsunami run-up.
Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate ...
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Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.
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