A Review on Energy-Efficient Smart Home Load Forecasting Techniques

Jaaz, Zahraa A.; Rusli, Mohd Ezanee; Rahmat, Nur Azzamuddin; Khudhair, Inteasar Yaseen; Al_Barazanchi, Israa; Mehdy, Hala Shaker

doi:10.23919/eecsi53397.2021.9624274

Cited by 15 publications

(10 citation statements)

References 43 publications

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“…The toolkit instead has inbuilt knowledge of several simulation protocols. This ensures different investigations use the same protocols [23][24][25][26][27][28][29][30], allowing results to be compared more directly. In addition, by performing specific simulation protocols with a dedicated program, performance optimizations specific to a given protocol are possible with 99.27% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Jabbar¹,

Alwan²,

Ali³

2022

PEN

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Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field. The Convolutional Neural Network (CNN) algorithm offer a valuable way of gaining insight into the dynamic behaviors of the heart, in normal and pathological conditions. Great efforts have been put into modelling the ventricles, whilst the atria have received less focus. This research therefore concentrates on developing models of the heart ECG atria using deep learning. The research developed an experimental result on MIT-BIH dataset for modelling myocyte electrophysiology and excitation waves in 1D & 2D tissues. It includes optimizations such as adaptive stimulus protocols. As examples of application, it is used to investigate effects of a novel anion bearing current on heart atrial excitation and the effect of remodeling on atrial myocyte electrical heterogeneity. A computationally efficient CNN anatomically based model of the heart atria is constructed. The 3D-CNN model includes heterogeneous, biophysically detailed electrophysiology and conduction anisotropy. The full model activates in 121 ms in heart rhythm, in close agreement with clinical ECG data. The model is used, with the toolkit, to investigate the function effects of S140G mutation in MIT-BIH dataset which is associated with familial. The 3D-CNN model forms the core of a boundary element model of the P-wave Body Surface Potential (BSP). The CNN model incorporates representations of the heart blood masses. Generated ECGs show qualitative agreement with clinical data. Their morphology is as expected for a healthy person, with a lead duration of 103 ms. The CNN model is used to verify an existing algorithm for focal atrial tachycardia location and in providing explanation for a novel clinical phenomenon, using CNN with 99.27% accuracy. Models of the human atria and body surface potential are constructed. The models are validated against both experimental and clinical data. These models are suitable to use as the platform for further research.

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Section: Discussionmentioning

confidence: 99%

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Jabbar¹,

Alwan²,

Ali³

2022

PEN

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“…Therefore, the research of wind solar hybrid generator control strategy mainly focuses on the control of AC excitation converter, as shown in Figure 1. The wind-solar complementary generator and the wire-wound induction motor are identical in structure, and their mathematical models are the same [13]. The mathematical model based on the T-shaped equivalent circuit is the most widely used.…”

Section: Methods 21 Fault Area Characteristics Of Distributed Wind So...mentioning

confidence: 99%

Research on fault adaptive fault tolerant control of distributed wind solar hybrid generator

et al. 2023

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Due to the poor accessibility, poor operating conditions, high failure rate, long maintenance time, and difficult maintenance of wind hybrid generators, the economic loss is huge once the failure stops. To this end, the fault adaptive fault-tolerant control of distributed wind and wind hybrid generators is studied, the historical operation data of offshore wind and wind hybrid generators and onshore wind and wind hybrid generators are counted and compared, and the fault characteristics of key components of offshore wind and wind hybrid generators are analyzed. The generator sets are summarized, and the common electrical faults of wind turbines and their impacts on the system are analyzed. This paper summarizes the current research status of fault-tolerant operation of existing offshore wind and wind complementary generators in terms of software fault tolerance and hardware fault tolerance, summarizes the current fault tolerance schemes for offshore wind and wind complementary generators, and analyzes the application feasibility of existing fault tolerance schemes. In addition, the main problems of fault-tolerant offshore wind and solar complementary generator sets are pointed out, and future research hotspots are foreseen.

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“…The framework is quite flexible and may be customized for a variety of healthcare management and monitoring applications to address a wide range of contemporary challenges, including cardiovascular, diabetes, stress, etc [11][12][13]. Comprehensive analysis is done of the most current advancements in D2D services and applications for the healthcare sector; [14,15] examined current approaches with their benefits and drawbacks while concentrating on the three major D2D communication issues. Different uses of the system model have been explored.…”

Section: Literature Surveymentioning

confidence: 99%

Reliable Fuzzy-Based Multi-Path Routing Protocol Based on Whale Optimization Algorithm to Improve QOS in 5G Networks for IOMT Applications

Al-Mukhtar

Jaaz

Hamad

2023

Int. J. Interact. Mob. Technol.

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The Internet of Medical Things (IoMT) faces stiff competition from the 5th Generation (5G) communication standard, which includes attributes like short and long transmission ranges, Device to Device (D2D) connectivity, low latency, and high node density. To function in the linked ecosystem, IoMT based on 5G is anticipated to have a diversity of energy and mobility. It is currently difficult to create an IoMT routing system based on 5G that maximizes energy efficiency, lowers transmission latency, and increases network lifespan. The "Quality of Services (QoS)" in 5G-based IoMT is improved by the Reliable Fuzzy-based Multi-path routing system shown in this study. The Whale Optimization Algorithm (WOA) enhances the routing protocol performance. The residual energy-based Cluster Head (CH) selection strategy rotates the CH location among nodes with greater energy levels than the others. The method chooses the following set of CHs for the network that is suitable for IoMT applications by considering initial energy, residual energy, and an ideal value of CHs. According to the simulation results, our suggested routing technique enhances QoS in comparison to current approaches.

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A Review on Energy-Efficient Smart Home Load Forecasting Techniques

Cited by 15 publications

References 43 publications

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Research on fault adaptive fault tolerant control of distributed wind solar hybrid generator

Reliable Fuzzy-Based Multi-Path Routing Protocol Based on Whale Optimization Algorithm to Improve QOS in 5G Networks for IOMT Applications

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