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Internet of Things Enabled Smart Energy Management of distributed Energy Resources using Self-Adaptive Physics-Informed neural network Optimized with Green Anaconda Optimization 4

Internet of Things Enabled Smart Energy Management of Distri...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ghongade, Rahul D. Arunaa, J. Sri Latha Mageshwari, P.S. Vinoth, K. Shanmugathai, M. Mercy Rani, J. Femila P. R. Pote Patil College of Engineering and Management Department of Electronics and Telecommunication Engineering Maharashtra Amravati444602 India Dr. M. G. R Educational and Research Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai600095 India Tamil Nadu Kavaraipettai601206 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Sri Sairam Engineering College Department of English Tamil Nadu Chennai600044 India Department of Mathematics Tamil Nadu Dindigul624622 India

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 neural network (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 neural network (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

关键词： Recurrent neural networks

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Graph Attention neural network distributed Model Training

Graph Attention Neural Network Distributed Model Training

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IEEE World AI IoT Congress (AIIoT)

作者： Esmaeilzadeh, Armin Kambar, Mina Esmail Zadeh Nojoo Heidari, Maryam Univ Nevada Las Vegas NV 89154 USA George Mason Univ Fairfax VA 22030 USA

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 neural network 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.

关键词： natural language processing NLP machine learning distributed machine learning distributed systems big data pytorch

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distributed neural network-Based DDoS Detection in Vehicular Communication Systems 16

Distributed Neural Network-Based DDoS Detection in Vehicular...

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16th International Conference on Signal processing and Communication System, ICSPCS 2023

作者： Jaton, Nicholas Gyawali, Sohan Qian, Yi University of Nebraska-Lincoln Department of Electrical and Computer Engineering OmahaNE United States East Carolina University Department of Technology Systems GreenvilleNC United States

ISBN: (纸本)9798350333510

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 being attacked so that the system can take steps to mitigate harm from the attacker. Vehicular communication systems face significant risks from distributed denial of service (DDoS) attacks. During a DDoS attack, multiple nodes are used to flood the target with an overwhelming amount of communication packets. In this paper, we first survey the current MDS literature and how it is used to detect and mitigate DDoS attacks. We then propose a new distributed multilayer perceptron classifier (MLPC) for DDoS detection and evaluate the performance of the proposed detection scheme in vehicular communication systems. For the evaluations using simulations, two specific implementations of the attacks are conducted. Apache Spark is then used to create the distributed MLPC. The median F1-score for this MLPC method was 95%. The proposed method outperformed linear regression and support vector machines, which achieved 89% and 88% respectively, but is unable to perform better than random forests and gradient boosted trees which both achieved a 97% F1-score. Using Amazon Web Services (AWS), it is determined that model training and detection time are not significantly increased with the inclusion of additional nodes after three nodes including the master. © 2023 IEEE.

关键词： neural networks

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Single-channel distributed Raman temperature sensing based on a 1-dimensional convolutional neural network

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OPTICAL FIBER TECHNOLOGY 2024年 88卷

作者： Renner, Esther Mampilli, John S. Amer, Nadia Schmauss, Bernhard Friedrich Alexander Univ Erlangen Nurnberg Inst Microwaves & Photon Cauerstr 9 D-91058 Erlangen Germany Max Planck Inst Sci Light Staudtstr 2 D-91058 Erlangen Germany

We present a simple and cost-efficient single-channel Raman distributed temperature sensing (DTS) system based on temperature prediction by a 1-dimensional convolutional neural network (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.

关键词： Optical fiber sensing Raman distributed temperature sensing Incoherent optical frequency domain reflectometry Convolutional neural network

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distributed Radar Interval Distance Estimation Based on Deep neural network 14

Distributed Radar Interval Distance Estimation Based on Deep...

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14th IEEE International Conference on Wireless Communications and Signal processing (WCSP)

作者： Li, Xiayu Chen, Peng Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

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 neural network-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.

关键词： distributed radar radar calibration radar interval distance

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Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

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INTERNATIONAL JOURNAL OF network MANAGEMENT 2024年第3期34卷 e2256-e2256页

作者： Karthik, V. Lakshmi, R. Abraham, Salini Ramkumar, M. Sri Krishna Coll Engn & Technol Elect & Commun Engn Coimbatore 641008 Tamil Nadu India Siddharth Inst Engn & Technol Elect & Elect Engn Puttur 517583 Andhra Pradesh India Jaibharath Coll Management & Engn Technol Elect & Commun Engn Cochin 683556 Kerala India

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 neural network (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 neural network (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 neural network (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

关键词： attack detection distributed denial of service software defined network vehicular ad hoc network

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SMPS System using neural network Controller with distributed Pass Gates Optimized for Deep neural network Loads 31

SMPS System using Neural Network Controller with Distributed...

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IEEE 31st International Symposium on Industrial Electronics (ISIE)

作者： Keskin, Mustafa Miao, Guoqing Wadhwa, Sameer Qualcomm Mixed Signal Circuit Design San Diego CA 92121 USA

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 neural network (DNN) and Convolutional neural network (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.

关键词： voltage regulators machine learning deep neural networks

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Removal of Gaussian distributed Noise in Images with Deep neural network Models 30

Removal of Gaussian Distributed Noise in Images with Deep Ne...

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30th IEEE Signal processing and Communications Applications Conference (SIU)

作者： Kus, Zeki Aydin, Musa Fatih Sultan Mehmet Vaktif Univ Bilgisayar Muhendisligi Istanbul Turkey

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 neural network-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

关键词： Deep neural networks Convolutional neural networks Image Denoising

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A Real-Time Load Forecasting Approach Based on Graph Convolution neural network in a distributed IoT Energy System

A Real-Time Load Forecasting Approach Based on Graph Convolu...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Pan, Li Gan, Jixiang Liu, Qi Zhang, Yonghong Liu, Xiaodong School of Software Nanjing University of Information Science and Technology Nanjing China School of Automation Nanjing University of Information Science and Technology Nanjing China School of Computing Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350304602

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 system that can centrally process load data is proposed, and a KGLSTM network based on graph neural networks and long short-term memory networks is introduced. In our proposed system, smart sockets are used for the collecting and processing of load data. The proposed KGLSTM network is based on a dual-branch structure. One branch extracts medium and short-term load data features by using LSTM, and the other branch extracts medium and long-term load data features by using graph neural networks. In terms of the training of model, a dynamic weighted loss function is proposed to guide the training, which improves the load prediction performance for peak load intervals. Finally, by comparing with advanced models, the effectiveness of KGLSTM and weighted KGLSTM for load prediction is validated, and the proposed two models also have a great performance in peak forecasting. © 2023 IEEE.

关键词： Long short-term memory

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DCNNet: A distributed Convolutional neural network for Remote Sensing Image Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Zhang, Ting Wang, Zhirui Cheng, Peirui Xu, Guangluan Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Aerosp Informat Res Inst Chinese Acad Sci Key Lab Network Informat Syst Technol NIST Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100190 Peoples R China

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 neural network (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.

关键词： Feature extraction Image classification Remote sensing Convolutional neural networks Convolution Visualization neural networks Attention mechanism distributed network progressive inference remote sensing (RS) image classification self-distillation

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