Smart performance optimization of energy-aware scheduling model for resource sharing in 5G green communication systems

This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy-aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.© 2024 The Authors. The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Realization of 485 Level Inverter Using Tri-State Architecture for Renewable Energy Systems

In this paper, a ‘k’-state inverter producing a higher number of voltage levels was designed, and we studied the inverter’s working. Further, a tri-state inverter was derived from the ‘k’-state inverter, which could build a maximum number of output voltage levels with the requirement of fewer components, thereby reducing the cost and size. A single Tri-state architecture generates three direct current (D.C.) voltage levels; therefore, cascading five tri-state architectures can generate 242 levels of DC voltages. Further, the inversion is done via the H bridge, which leads to 485 levels of the output voltage. Algorithms to design the amplitude of voltage sources and the generation of pulses are discussed in this paper. The proposed tri-state inverter takes a significant role in advancing renewable energy systems in utilizing inverter technology. A simulation study validated the operation of the proposed inverter. Moreover, an experimental setup was built for a single-phase 485-level inverter, and the structure’s performance was verified through the experimental results

Smart Load-Based Resource Optimization Model to Enhance the Performance of Device-to-Device Communication in 5G-WPAN

In wireless personal area networks (WPANs), devices can communicate with each other without relying on a central router or access point. They can improve performance and efficiency by allowing devices to share resources directly; however, managing resource allocation and optimizing communication between devices can be challenging. This paper proposes an intelligent load-based resource optimization model to enhance the performance of device-to-device communication in 5G-WPAN. Intelligent load-based resource optimization in device-to-device communication is a strategy used to maximize the efficiency and effectiveness of resource usage in device-to-device (D2D) communications. This optimization strategy is based on optimizing the network’s resource load by managing resource utilization and ensuring that the network is not overloaded. It is achieved by monitoring the current load on the network and then adjusting the usage of resources, such as bandwidth and power, to optimize the overall performance. This type of optimization is essential in D2D communication since it can help reduce costs and improve the system’s performance. The proposed model has achieved 86.00% network efficiency, 93.74% throughput, 91.94% reduced latency, and 92.85% scalability

Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique

Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques

A Case Study on Renewable Energy Sources, Power Demand, and Policies in the States of South India—Development of a Thermoelectric Model

This work aims to perform a holistic review regarding renewable energy mix, power production approaches, demand scenarios, power policies, and investments with respect to clean energy production in the southern states of India. Further, a thermoelectric-generator model is proposed to meet rural demands using a proposed solar dish collector technology. The proposed model is based on the idea of employing a parabolic concentrator and a thermoelectric (TE) module to generate electricity directly from the sun’s energy. A parabolic dish collector with an aperture of 1.11 m is used to collect sunlight and concentrate it onto a receiver plate with an area of 1.56 m in the proposed TE solar concentrator. The concentrated solar thermal energy is converted directly into electrical energy by using a bismuth telluride (BiTe)-based TE module mounted on the receiver plate. A rectangular fin heatsink, coupled with a fan, is employed to remove heat from the TE module’s cool side, and a tracking device is used to track the sun continuously. The experimental results show considerable agreement with the mathematical model as well as its potential applications. Solar thermal power generation plays a crucial part in bridging the demand–supply gap for electricity, and it can be achieved through rural electrification using the proposed solar dish collector technology, which typically has a 10 to 25 kW capacity per dish and uses a Stirling engine to generate power. Here the experimentation work generates a voltage of 11.6 V, a current of 0.7 A, and a power of 10.5 W that can be used for rural electrification, especially for domestic loads

Machine Vision-Based Human Action Recognition Using Spatio-Temporal Motion Features (STMF) with Difference Intensity Distance Group Pattern (DIDGP)

In recent years, human action recognition is modeled as a spatial-temporal video volume. Such aspects have recently expanded greatly due to their explosively evolving real-world uses, such as visual surveillance, autonomous driving, and entertainment. Specifically, the spatio-temporal interest points (STIPs) approach has been widely and efficiently used in action representation for recognition. In this work, a novel approach based on the STIPs is proposed for action descriptors i.e., Two Dimensional-Difference Intensity Distance Group Pattern (2D-DIDGP) and Three Dimensional-Difference Intensity Distance Group Pattern (3D-DIDGP) for representing and recognizing the human actions in video sequences. Initially, this approach captures the local motion in a video that is invariant to size and shape changes. This approach extends further to build unique and discriminative feature description methods to enhance the action recognition rate. The transformation methods, such as DCT (Discrete cosine transform), DWT (Discrete wavelet transforms), and hybrid DWT+DCT, are utilized. The proposed approach is validated on the UT-Interaction dataset that has been extensively studied by past researchers. Then, the classification methods, such as Support Vector Machines (SVM) and Random Forest (RF) classifiers, are exploited. From the observed results, it is perceived that the proposed descriptors especially the DIDGP based descriptor yield promising results on action recognition. Notably, the 3D-DIDGP outperforms the state-of-the-art algorithm predominantly

Machine Vision-Based Human Action Recognition Using Spatio-Temporal Motion Features (STMF) with Difference Intensity Distance Group Pattern (DIDGP)

In recent years, human action recognition is modeled as a spatial-temporal video volume. Such aspects have recently expanded greatly due to their explosively evolving real-world uses, such as visual surveillance, autonomous driving, and entertainment. Specifically, the spatio-temporal interest points (STIPs) approach has been widely and efficiently used in action representation for recognition. In this work, a novel approach based on the STIPs is proposed for action descriptors i.e., Two Dimensional-Difference Intensity Distance Group Pattern (2D-DIDGP) and Three Dimensional-Difference Intensity Distance Group Pattern (3D-DIDGP) for representing and recognizing the human actions in video sequences. Initially, this approach captures the local motion in a video that is invariant to size and shape changes. This approach extends further to build unique and discriminative feature description methods to enhance the action recognition rate. The transformation methods, such as DCT (Discrete cosine transform), DWT (Discrete wavelet transforms), and hybrid DWT+DCT, are utilized. The proposed approach is validated on the UT-Interaction dataset that has been extensively studied by past researchers. Then, the classification methods, such as Support Vector Machines (SVM) and Random Forest (RF) classifiers, are exploited. From the observed results, it is perceived that the proposed descriptors especially the DIDGP based descriptor yield promising results on action recognition. Notably, the 3D-DIDGP outperforms the state-of-the-art algorithm predominantly

A Case Study on Renewable Energy Sources, Power Demand, and Policies in the States of South India—Development of a Thermoelectric Model

This work aims to perform a holistic review regarding renewable energy mix, power production approaches, demand scenarios, power policies, and investments with respect to clean energy production in the southern states of India. Further, a thermoelectric-generator model is proposed to meet rural demands using a proposed solar dish collector technology. The proposed model is based on the idea of employing a parabolic concentrator and a thermoelectric (TE) module to generate electricity directly from the sun’s energy. A parabolic dish collector with an aperture of 1.11 m is used to collect sunlight and concentrate it onto a receiver plate with an area of 1.56 m in the proposed TE solar concentrator. The concentrated solar thermal energy is converted directly into electrical energy by using a bismuth telluride (BiTe)-based TE module mounted on the receiver plate. A rectangular fin heatsink, coupled with a fan, is employed to remove heat from the TE module’s cool side, and a tracking device is used to track the sun continuously. The experimental results show considerable agreement with the mathematical model as well as its potential applications. Solar thermal power generation plays a crucial part in bridging the demand–supply gap for electricity, and it can be achieved through rural electrification using the proposed solar dish collector technology, which typically has a 10 to 25 kW capacity per dish and uses a Stirling engine to generate power. Here the experimentation work generates a voltage of 11.6 V, a current of 0.7 A, and a power of 10.5 W that can be used for rural electrification, especially for domestic loads

Target Object Detection from Unmanned Aerial Vehicle (UAV) Images Based on Improved YOLO Algorithm

Aerial image-based target object detection has several glitches such as low accuracy in multi-scale target detection locations, slow detection, missed targets, and misprediction of targets. To solve this problem, this paper proposes an improved You Only Look Once (YOLO) algorithm from the viewpoint of model efficiency using target box dimension clustering, classification of the pre-trained network, multi-scale detection training, and changing the screening rules of the candidate box. This modified approach has the potential to be better adapted to the positioning task. The aerial image of the unmanned aerial vehicle (UAV) can be positioned to the target area in real-time, and the projection relation can convert the latitude and longitude of the UAV. The results proved to be more effective; notably, the average accuracy of the detection network in the aerial image of the target area detection tasks increased to 79.5%. The aerial images containing the target area are considered to experiment with the flight simulation to verify its network positioning accuracy rate and were found to be greater than 84%. This proposed model can be effectively used for real-time target detection for multi-scale targets with reduced misprediction rate due to its superior accuracy