Internet of Things (IoT)-enabled Smart Energy Management (SEM) in distributed Energy Resources (DERs), while crucial for optimizing energy distribution and resource management faces challenges such as data inconsisten...
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ISBN:
(纸本)9798331523923
Internet of Things (IoT)-enabled Smart Energy Management (SEM) in distributed Energy Resources (DERs), while crucial for optimizing energy distribution and resource management faces challenges such as data inconsistencies and the variability of renewable energy generation. These issues result in inaccurate demand forecasts, leading to suboptimal allocation of resources and inefficient Energy Management (EM). Additionally, as energy demand and generation patterns are influenced by factors like weather conditions, time of day, and energy consumption behaviors, prediction models may struggle to capture these dynamic changes, affecting the reliability of forecasts. To address these limitations, this manuscript proposes a novel approach for energy demand prediction. Data is collected from IoT-enabled sensors monitoring DERs. The data undergoes pre-processing, where the Fast Resampled Iterative Filtering (FRIF) method is used to eliminate missing values and normalize the inputs. The Self-Adaptive Physics-Informed neuralnetwork (SAPINN) model then utilizes the processed data to forecast energy demand, renewable energy generation, and storage levels. Green Anaconda Optimization (GAO) is applied to optimize the weight parameters of the SAPINN model. The proposed SAPINN-GAO method is implemented using the MATLAB platform and compared with existing models, such Stacked Convoluted Bi-Directional Gated Attention network-Hybrid Darts Seagull Optimizer (SConBGAN-HDSO), Recurrent neuralnetwork (RNN), and Support Vector Machine-Particle Swarm Optimization (SVM-PSO). The SAPINN-GAO method achieves an accuracy of 99.2%, precision of 99.2%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in energy demand prediction. The SAPINN-GAO method's higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for energy demand, renewable energy generation, and storage levels forecasting in IoT-enabled SEM syst
The scale of neural language models has been increasing significantly over recent years. As a result, the time complexity of training larger language models and resource utilization has been increasing at a higher rat...
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ISBN:
(纸本)9781665484534
The scale of neural language models has been increasing significantly over recent years. As a result, the time complexity of training larger language models and resource utilization has been increasing at a higher rate as well. In this research, we propose a distributed implementation of a Graph Attention neuralnetwork model with 120 million parameters and train it on a cluster of eight GPUs. We demonstrate three times speedup in model training while keeping the stability of accuracy and loss rates during training and testing compared to single GPU instance training.
As modern vehicular communication systems advance, the demand for robust security measures becomes increasingly critical. A misbehavior detection systems (MDS) is a tool developed to detect if a vehicular network is b...
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We present a simple and cost-efficient single-channel Raman distributed temperature sensing (DTS) system based on temperature prediction by a 1-dimensional convolutional neuralnetwork (1D-CNN) from the Raman anti- St...
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We present a simple and cost-efficient single-channel Raman distributed temperature sensing (DTS) system based on temperature prediction by a 1-dimensional convolutional neuralnetwork (1D-CNN) from the Raman anti- Stokes backscatter trace. The proposed Raman DTS system is based on incoherent optical frequency domain reflectometry with homodyne down-conversion with excitation of spontaneous Raman backscattering by an Lband laser diode and detection of the Raman anti-Stokes in the optical C-band. A 1D-CNN is employed to predict the spatially resolved temperature profile along the fiber from the obtained anti-Stokes backscatter trace only and thus, solves the problem of temperature referencing for single-channel Raman DTS systems. The network was trained on three different scenarios, consisting of uniform and non-uniform temperature profiles along the fiber in a temperature range from 0 degrees C to 60 degrees C. The obtained results show that the measurement and signal processing pipeline presented here is capable of predicting the temperature distribution to an accuracy of approximately 1 K in the tested scenarios.
In recent years, millimeter wave (mmWave) radar has played an indispensable role in several applications. mmWave radars can measure the distance, speed, and angle of objects, but the transmit power of a single mmWave ...
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ISBN:
(纸本)9781665450850
In recent years, millimeter wave (mmWave) radar has played an indispensable role in several applications. mmWave radars can measure the distance, speed, and angle of objects, but the transmit power of a single mmWave radar is limited. By deploying multiple mmWave radars in a distributed manner and fusing signals from them, detection results will be improved. Before data fusion, it is necessary to accurately measure the external parameters between different radars to complete the coordinates calibration of the radar network. Current methods focus on the calibration method by jointly observing moving objects in overlapping view fields of the radar network. The calibration process requires one target to move within a defined area. Because the radar cross section (RCS) characteristics of the target in all directions are usually inconsistent, if the reflected signal of this target is weak during the calibration process, the error of this method will be relatively large. This paper proposes a new neuralnetwork-based method to estimate the interval distance between different radars without passing through a moving target. The distance estimation error of the proposed network can reach within 0.1 m, which is smaller than the calibration method based on moving objects. Through the verification of actual measured data, the proposed network can more accurately estimate the interval distance between radars.
Software defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN-integrated...
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Software defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN-integrated VANET (SDN-int-VANET) has numerous benefits, but it is susceptible to threats like distributed denial of service (DDoS). Several methods were suggested for DDoS attack detection (AD), but the existing approaches to optimization have given a base for enhancing the parameters. An incorrect selection of parameters results in a poor performance and poor fit to the data. To overcome these issues, residual-based temporal attention red fox-convolutional neuralnetwork (RTARF-CNN) for detecting DDoS attacks in SDN-int-VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre-processed data with the help of stacked contractive autoencoders (St-CAE), which reduces the processing time of the introduced method. The selected features are classified by residual-based temporal attention-convolutional neuralnetwork (RTA-CNN). The weight parameter of RTA-CNN is optimized with the help of red fox optimization (RFO) for better classification. The introduced method is implemented in the PYTHON platform. The RTARF-CNN attains 99.8% accuracy, 99.5% sensitivity, 99.80% precision, and 99.8% specificity. The effectiveness of the introduced technique is compared with the existing approaches. Residual-based temporal attention red fox-convolutional neuralnetwork (RTARF-CNN) for detecting DDoS attacks in SDN-int-VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre-p
Switched Mode Power Supplies (SMPS) and Low Drop-Out Regulators (LDOs) are extensively used in many Power-Management (PM) systems providing large currents to dynamic loads with excellent power efficiencies. Especially...
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ISBN:
(纸本)9781665482400
Switched Mode Power Supplies (SMPS) and Low Drop-Out Regulators (LDOs) are extensively used in many Power-Management (PM) systems providing large currents to dynamic loads with excellent power efficiencies. Especially in recent years, these blocks are used to provide a dedicated power supply to different consumer electronics and applications such as Central processing Units (CPU), Application processors (AP), and Communication Processors (CP) of mobile phones, tablets, or computers. These PM systems have digital and/or mixed-signal controllers on CMOS Integrated-Circuits (IC). Separately, there is also enormous interest to Artificial Intelligence and Machine Learning (AIML) devices and applications. Hence, very complex Deep neuralnetwork (DNN) and Convolutional neuralnetwork (CNN) systems requiring great computational power are increasingly implemented. In this paper, a novel idea is proposed to develop intelligent SMPS and LDO blocks being capable of managing drastic load changes and activity due to AIML algorithms and highly complex DNN structures.
The removal of noise caused by environmental factors in microscopic imaging studies has become an important research topic in the field of medical imaging. In the medical imaging stage made with any digital microscopy...
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ISBN:
(纸本)9781665450928
The removal of noise caused by environmental factors in microscopic imaging studies has become an important research topic in the field of medical imaging. In the medical imaging stage made with any digital microscopy method (Confocal, Fluorescence, etc.), undesirable noises are added to the image obtained due to factors stemming from excessive or low illumination, high or low temperature, or electronic circuit equipment. The most basic noise model formed due to these environmental factors mentioned is the Gaussian normal distribution or a characteristic function close to this distribution. It is widely known that spatial filters (mean, median, Gaussian smoothing) are applied to eliminate Gaussian noise in digital image processing. However, undesirable results may occur in the images obtained when spatial filters are used to remove the noise in the images. In particular, because high frequencies are suppressed in images where spatial filters are applied, details are lost in the final image, and a blurred image is obtained. For this reason, four different convolutional neuralnetwork-based models are used for noise removal and to improve the PSNR values in this study. As a result, the modified U-Net improved the PSNR values for different noise levels as follows: +6.23, +7.88 and +10.52 dB
With the development of edge devices, smart sockets are now capable of handling various power load data, which provide a new solution for edge computing and real-time load forecasting. In this paper, a distributed sys...
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With the development of information technology, multiplatform collaborative collection and processing of remote sensing (RS) images has become a significant trend. However, the existing models are challenging to achie...
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With the development of information technology, multiplatform collaborative collection and processing of remote sensing (RS) images has become a significant trend. However, the existing models are challenging to achieve accurate and efficient image interpretation on RS multiplatform systems. To solve this problem, we propose a novel distributed convolutional neuralnetwork (DCNNet) and demonstrate the superiority of our method in RS image classification. First, a progressive inference mechanism is introduced to support most images to be classified in advance with satisfactory accuracy, which minimizes redundant cloud transmission and achieves higher inference acceleration. Meanwhile, a distributed self-distillation paradigm is designed to integrate and refine in-depth features, performing efficient knowledge transfer between the terminals and the cloud network. Second, a multiscale feature fusion (MSFF) module is presented to extract valid receptive fields and assign weights to crucial channel dimension features. Finally, a sampling augmentation (SA) attention is proposed to enhance the effective feature representation of RS images through a bottom-up and top-down feedforward structure. We conducted extensive experiments and visual analyses on three benchmark scene classification datasets and one fine-grained dataset. Compared with the existing methods, DCNNet consolidates several advantages in terms of accuracy, computation, transmission, and processing efficiency into a single framework for multiplatform RS image classification.
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