In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent...In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.展开更多
The paper evaluates the suitability of examples used in developing averaging techniques of multi-objective optimization (MOO). Most of the examples used for proposing these techniques were not suitable. The results of...The paper evaluates the suitability of examples used in developing averaging techniques of multi-objective optimization (MOO). Most of the examples used for proposing these techniques were not suitable. The results of these examples have also not been interpreted correctly. An appropriate example has also been solved with existing and improved averaging techniques of multi-objective optimization.展开更多
This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distributio...This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.展开更多
This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from g...This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.展开更多
Efficient resource provisioning,allocation,and computation offloading are critical to realizing lowlatency,scalable,and energy-efficient applications in cloud,fog,and edge computing.Despite its importance,integrating ...Efficient resource provisioning,allocation,and computation offloading are critical to realizing lowlatency,scalable,and energy-efficient applications in cloud,fog,and edge computing.Despite its importance,integrating Software Defined Networks(SDN)for enhancing resource orchestration,task scheduling,and traffic management remains a relatively underexplored area with significant innovation potential.This paper provides a comprehensive review of existing mechanisms,categorizing resource provisioning approaches into static,dynamic,and user-centric models,while examining applications across domains such as IoT,healthcare,and autonomous systems.The survey highlights challenges such as scalability,interoperability,and security in managing dynamic and heterogeneous infrastructures.This exclusive research evaluates how SDN enables adaptive policy-based handling of distributed resources through advanced orchestration processes.Furthermore,proposes future directions,including AI-driven optimization techniques and hybrid orchestrationmodels.By addressing these emerging opportunities,thiswork serves as a foundational reference for advancing resource management strategies in next-generation cloud,fog,and edge computing ecosystems.This survey concludes that SDN-enabled computing environments find essential guidance in addressing upcoming management opportunities.展开更多
Crowd evacuation simulation is an essential element when it comes to planning and preparation in evacuation management.This paper presents the survey based on systematic literature review(SLR)technique that aims to id...Crowd evacuation simulation is an essential element when it comes to planning and preparation in evacuation management.This paper presents the survey based on systematic literature review(SLR)technique that aims to identify the crowd evacuation under microscopic model integrated with soft computing technique from previous works.In the review process,renowned databases were searched to retrieve the primary articles and total 38 studies were thoroughly studied.The researcher has identified the potential optimization factors in simulating crowd evacuation and research gaps based on acquired issues,limitation and challenges in this domain.The results of this SLR will serve as a guideline for the researchers that have same interest to develop better and effective crowd evacuation simulation model.The future direction from this SLR also suggests that there is a potential to hybrid the model with softcomputing optimization focusing on latest nature-inspired algorithms in improving the crowd evacuation model.展开更多
Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effec...Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.展开更多
In the evaluation of cementing quality,quantitatively assessing cement density is crucial along with identifying the cementation degree at the interface using acoustic logging.While the 137Cs-based formation density l...In the evaluation of cementing quality,quantitatively assessing cement density is crucial along with identifying the cementation degree at the interface using acoustic logging.While the 137Cs-based formation density logging method is well-suited for density calculation,its reliance on open-hole environmental measurements poses challenges when inspecting cement density.This work focuses on the quantitative calculation of cement density while considering the radioactive hazards to the environment caused by 137Cs source.The proposed approach utilizes a measurement system consisting of an X-Ray source and four gamma detectors.The gamma spectrum characteristics of each detector are analyzed,and the energy spectrum recorded by each detector is distinguished by different energy windows.A forward model is established to relate the gamma counts of each energy window to the formation and cement parameters.By employing a regularized Newton's method based on optimization technique,cement density can be calculated with a controllable error margin of within 0.015 g/cm^(3).Furthermore,even though X-Ray detection has lower sensitivity to formation parameters compared to 137Cs,this method is capable of estimating formation density.Overall,the proposed approach enables the quantitative calculation of cement density and semi-quantitative calculation of formation density,therefore is of significance to the comprehensive evaluation of cementing quality.展开更多
The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blo...The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.展开更多
Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection.The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the...Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection.The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation process.Because time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difficulties.One of the most significant requirements is to design optimal solutions to optimize their use.There is no particular technique that can answer all optimization issues.The proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized diagnosis.The accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization algorithm.The total sensitivity and specificity attained in the proposed system are 99.56%and 99.73%respectively.The proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional methods.The proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefficient.The model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.展开更多
The problem of evaluating the sensitivity of non-trivial boundary conditions to the onset of azimuthal combustion instability is a longstanding challenge in the development process of modern gas turbines.The difficult...The problem of evaluating the sensitivity of non-trivial boundary conditions to the onset of azimuthal combustion instability is a longstanding challenge in the development process of modern gas turbines.The difficulty lies in how to describe three-dimensional in-and outlet boundary conditions in an artificial computational domain.To date,the existing analytical models have still failed to quantitatively explain why the features of the azimuthal combustion instability of a combustor in laboratory environment are quite different from that in a real gas turbine,making the stability control devices developed in laboratory generally lose the effectiveness in practical applications.To overcome this limitation,we provide a novel theoretical framework to directly include the effect of non-trivial boundary conditions on the azimuthal combustion instability.A key step is to take the non-trivial boundary conditions as equivalent distributed sources so as to uniformly describe the physical characteristics of the inner surface in an annular enclosure along with different in-and outlet configurations.Meanwhile,a dispersion relation equation is established by the application of three-dimensional Green's function approach and generalized impedance concept.Results show that the effects of the generalized modal reflection coefficients on azimuthal unstable modes are extremely prominent,and even prompt the transition from stable to unstable mode,thus reasonably explaining why the thermoacoustic instability phenomena in a real gas turbine are difficult to observe in an isolated combustion chamber.Overall,this work provides an effective tool for analysis of the azimuthal combustion instability including various complicated boundary conditions.展开更多
Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and e...Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and exchanges model parameters to obtain an accurate model without raw data transmission.However,the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models.Moreover,the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources.In view of this,we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks(FLVN).We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles.Further,to fortify the FL reliability,we adopt the consortium blockchain to oversee the reputation informa-tion,which boasts tamper-proof and interference-resistant qualities.Finally,we formulate the resource scheduling problem by jointly optimizing the computation capability,the transmission power,and the number of local training rounds,aiming to minimize the cost of clients while guaranteeing accuracy.To this end,we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy.Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.展开更多
While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),...While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.展开更多
The Internet of Things(IoT)has become an integral part of various industries,from smart cities to healthcare,driving the need for energy-efficient and stable devices,especially in complex and unpredictable environment...The Internet of Things(IoT)has become an integral part of various industries,from smart cities to healthcare,driving the need for energy-efficient and stable devices,especially in complex and unpredictable environments.This research investigates the optimization of energy consumption and the enhancement of stability in IoT devices operating in such environments.The study addresses key challenges,including resource constraints,fluctuating environmental conditions,and the increasing complexity of IoT networks.It explores various energy optimization techniques,such as low-power communication protocols,edge and cloud computing,and machine learning models,that help reduce energy usage while maintaining performance.Furthermore,it examines stability enhancement strategies,including fault-tolerant mechanisms,resilient network architectures,and real-time monitoring and adaptive control,that ensure the continuous and reliable operation of IoT devices despite external disruptions.The findings of this research contribute to the development of next-generation IoT systems that are both energy-efficient and resilient,thereby promoting sustainable deployment in real-world applications.展开更多
What is the scientific question being addressed?To provide expertise on the optimal timing and surgical techniques for managing patients with aortic annular involvement due to infective endocarditis.
The time-scaling transformation is a widely used approach within the computational framework of control parameterization for optimizing the switching times of control variables.However,the conventional time-scaling tr...The time-scaling transformation is a widely used approach within the computational framework of control parameterization for optimizing the switching times of control variables.However,the conventional time-scaling transformation has the limitation that the switching times and the number of switches for each control component must be the same.In this paper,we present a novel technique to solve constrained optimal control problems that allows for adaptively optimizing the switching times for each control component.Numerical results demonstrate that this proposed method provides better flexibility in control strategy and yields improved performance.展开更多
This special issue focuses on simulation optimization(SO),which combines stochastic simulation with optimization techniques to address complex decisionmaking problems under uncertainty.SO has gained increasing importa...This special issue focuses on simulation optimization(SO),which combines stochastic simulation with optimization techniques to address complex decisionmaking problems under uncertainty.SO has gained increasing importance in areas such as transportation,supply chain and manufacturing,providing solutions where traditional analytical methods may fail.Recent advances in computing,data analytics,and artificial intelligence have significantly enhanced the efficiency and capability of SO methods,enabling the development of innovative techniques.This special issue aims to highlight recent theoretical and practical advancements in SO,showcasing its potential to solve real-world problems and inspiring further research and application in this dynamic field.展开更多
One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which make...One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.展开更多
The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpasse...The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%.展开更多
A two-step methodology was used to address and improve the power quality concerns for the PV-integrated microgrid system. First, partial shading was included to deal with the real-time issues. The Improved Jelly Fish ...A two-step methodology was used to address and improve the power quality concerns for the PV-integrated microgrid system. First, partial shading was included to deal with the real-time issues. The Improved Jelly Fish Algorithm integrated Perturb and Obserb (IJFA-PO) has been proposed to track the Global Maximum Power Point (GMPP). Second, the main unit-powered via DC–AC converter is synchronised with the grid. To cope with the wide voltage variation and harmonic mitigation, an auxiliary unit undergoes a novel series compensation technique. Out of various switching approaches, IJFA-based Selective Harmonic Elimination (SHE) in 120° conduction gives the optimal solution. Three switching angles were obtained using IJFA, whose performance was equivalent to that of nine switching angles. Thus, the system is efficient with minimised higher-order harmonics and lower switching losses. The proposed system outperformed in terms of efficiency, metaheuristics, and convergence. The Total Harmonic Distortion (THD) obtained was 1.32%, which is within the IEEE 1547 and IEC tolerable limits. The model was developed in MATLAB/Simulink 2016b and verified with an experimental prototype of grid-synchronised PV capacity of 260 W tested under various loading conditions. The present model is reliable and features a simple controller that provides more convenient and adequate performance.展开更多
文摘In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.
文摘The paper evaluates the suitability of examples used in developing averaging techniques of multi-objective optimization (MOO). Most of the examples used for proposing these techniques were not suitable. The results of these examples have also not been interpreted correctly. An appropriate example has also been solved with existing and improved averaging techniques of multi-objective optimization.
文摘This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.
文摘This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.
文摘Efficient resource provisioning,allocation,and computation offloading are critical to realizing lowlatency,scalable,and energy-efficient applications in cloud,fog,and edge computing.Despite its importance,integrating Software Defined Networks(SDN)for enhancing resource orchestration,task scheduling,and traffic management remains a relatively underexplored area with significant innovation potential.This paper provides a comprehensive review of existing mechanisms,categorizing resource provisioning approaches into static,dynamic,and user-centric models,while examining applications across domains such as IoT,healthcare,and autonomous systems.The survey highlights challenges such as scalability,interoperability,and security in managing dynamic and heterogeneous infrastructures.This exclusive research evaluates how SDN enables adaptive policy-based handling of distributed resources through advanced orchestration processes.Furthermore,proposes future directions,including AI-driven optimization techniques and hybrid orchestrationmodels.By addressing these emerging opportunities,thiswork serves as a foundational reference for advancing resource management strategies in next-generation cloud,fog,and edge computing ecosystems.This survey concludes that SDN-enabled computing environments find essential guidance in addressing upcoming management opportunities.
基金This work was supported by Fundamental Research Grant Scheme(Ministry of Higher Edu-cation Malaysia):[Grant Number FRGS/1/2019/ICT02/UTM/02/13].
文摘Crowd evacuation simulation is an essential element when it comes to planning and preparation in evacuation management.This paper presents the survey based on systematic literature review(SLR)technique that aims to identify the crowd evacuation under microscopic model integrated with soft computing technique from previous works.In the review process,renowned databases were searched to retrieve the primary articles and total 38 studies were thoroughly studied.The researcher has identified the potential optimization factors in simulating crowd evacuation and research gaps based on acquired issues,limitation and challenges in this domain.The results of this SLR will serve as a guideline for the researchers that have same interest to develop better and effective crowd evacuation simulation model.The future direction from this SLR also suggests that there is a potential to hybrid the model with softcomputing optimization focusing on latest nature-inspired algorithms in improving the crowd evacuation model.
基金This work was supported in part by the MOE ARF Tier 2 under Grant MOE2015-T2-2-104the Singapore University of Technology and Design-Zhejiang University(SUTD-ZJU)Research Collaboration under Grant SUTD-ZJU/RES/01/2016and the SUTD-ZJU Research Collaboration under Grant SUTD-ZJU/RES/05/2016.
文摘Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.
基金support of the National Natural Science Foundation of China(U23B20151 and 52171253)National Science Foundation for Young Scientists of Sichuan(2025ZNSFSC1168).
文摘In the evaluation of cementing quality,quantitatively assessing cement density is crucial along with identifying the cementation degree at the interface using acoustic logging.While the 137Cs-based formation density logging method is well-suited for density calculation,its reliance on open-hole environmental measurements poses challenges when inspecting cement density.This work focuses on the quantitative calculation of cement density while considering the radioactive hazards to the environment caused by 137Cs source.The proposed approach utilizes a measurement system consisting of an X-Ray source and four gamma detectors.The gamma spectrum characteristics of each detector are analyzed,and the energy spectrum recorded by each detector is distinguished by different energy windows.A forward model is established to relate the gamma counts of each energy window to the formation and cement parameters.By employing a regularized Newton's method based on optimization technique,cement density can be calculated with a controllable error margin of within 0.015 g/cm^(3).Furthermore,even though X-Ray detection has lower sensitivity to formation parameters compared to 137Cs,this method is capable of estimating formation density.Overall,the proposed approach enables the quantitative calculation of cement density and semi-quantitative calculation of formation density,therefore is of significance to the comprehensive evaluation of cementing quality.
文摘The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.
文摘Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection.The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation process.Because time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difficulties.One of the most significant requirements is to design optimal solutions to optimize their use.There is no particular technique that can answer all optimization issues.The proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized diagnosis.The accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization algorithm.The total sensitivity and specificity attained in the proposed system are 99.56%and 99.73%respectively.The proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional methods.The proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefficient.The model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.
基金supported by the Science Center for Gas Turbine Project of China (No.P2022-B-II-013-001)the National Natural Science Foundation of China (No.52106038).
文摘The problem of evaluating the sensitivity of non-trivial boundary conditions to the onset of azimuthal combustion instability is a longstanding challenge in the development process of modern gas turbines.The difficulty lies in how to describe three-dimensional in-and outlet boundary conditions in an artificial computational domain.To date,the existing analytical models have still failed to quantitatively explain why the features of the azimuthal combustion instability of a combustor in laboratory environment are quite different from that in a real gas turbine,making the stability control devices developed in laboratory generally lose the effectiveness in practical applications.To overcome this limitation,we provide a novel theoretical framework to directly include the effect of non-trivial boundary conditions on the azimuthal combustion instability.A key step is to take the non-trivial boundary conditions as equivalent distributed sources so as to uniformly describe the physical characteristics of the inner surface in an annular enclosure along with different in-and outlet configurations.Meanwhile,a dispersion relation equation is established by the application of three-dimensional Green's function approach and generalized impedance concept.Results show that the effects of the generalized modal reflection coefficients on azimuthal unstable modes are extremely prominent,and even prompt the transition from stable to unstable mode,thus reasonably explaining why the thermoacoustic instability phenomena in a real gas turbine are difficult to observe in an isolated combustion chamber.Overall,this work provides an effective tool for analysis of the azimuthal combustion instability including various complicated boundary conditions.
基金supported by the National Natural Science Foundation of China (Nos.61901015,62301017).
文摘Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and exchanges model parameters to obtain an accurate model without raw data transmission.However,the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models.Moreover,the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources.In view of this,we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks(FLVN).We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles.Further,to fortify the FL reliability,we adopt the consortium blockchain to oversee the reputation informa-tion,which boasts tamper-proof and interference-resistant qualities.Finally,we formulate the resource scheduling problem by jointly optimizing the computation capability,the transmission power,and the number of local training rounds,aiming to minimize the cost of clients while guaranteeing accuracy.To this end,we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy.Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.
文摘While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.
文摘The Internet of Things(IoT)has become an integral part of various industries,from smart cities to healthcare,driving the need for energy-efficient and stable devices,especially in complex and unpredictable environments.This research investigates the optimization of energy consumption and the enhancement of stability in IoT devices operating in such environments.The study addresses key challenges,including resource constraints,fluctuating environmental conditions,and the increasing complexity of IoT networks.It explores various energy optimization techniques,such as low-power communication protocols,edge and cloud computing,and machine learning models,that help reduce energy usage while maintaining performance.Furthermore,it examines stability enhancement strategies,including fault-tolerant mechanisms,resilient network architectures,and real-time monitoring and adaptive control,that ensure the continuous and reliable operation of IoT devices despite external disruptions.The findings of this research contribute to the development of next-generation IoT systems that are both energy-efficient and resilient,thereby promoting sustainable deployment in real-world applications.
基金funded by the Natural Sciences Foundation of Gansu(No.23JRRA0972)the Natural Sciences Foundation of Fujian(No.2022J05105)+3 种基金the Science and Technology Planning Project of Chengguan District(2022RCCX0023)the Cuiying Graduate Supervisor Applicant Training Program of Lanzhou University Second Hospital(No.201709)the Cuiying Scientific and Technological Innovation Program of Lanzhou University Second Hospital(No.CY2022-MS-A03)the Talent Introduction Plan of the Lanzhou University Second Hospital(No.YJRCKYQDJ-2021-02).
文摘What is the scientific question being addressed?To provide expertise on the optimal timing and surgical techniques for managing patients with aortic annular involvement due to infective endocarditis.
基金Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice(No.22DZ2229014)Science and Technology Commission of Shanghai Municipality(No.20JC1413900).
文摘The time-scaling transformation is a widely used approach within the computational framework of control parameterization for optimizing the switching times of control variables.However,the conventional time-scaling transformation has the limitation that the switching times and the number of switches for each control component must be the same.In this paper,we present a novel technique to solve constrained optimal control problems that allows for adaptively optimizing the switching times for each control component.Numerical results demonstrate that this proposed method provides better flexibility in control strategy and yields improved performance.
文摘This special issue focuses on simulation optimization(SO),which combines stochastic simulation with optimization techniques to address complex decisionmaking problems under uncertainty.SO has gained increasing importance in areas such as transportation,supply chain and manufacturing,providing solutions where traditional analytical methods may fail.Recent advances in computing,data analytics,and artificial intelligence have significantly enhanced the efficiency and capability of SO methods,enabling the development of innovative techniques.This special issue aims to highlight recent theoretical and practical advancements in SO,showcasing its potential to solve real-world problems and inspiring further research and application in this dynamic field.
文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.
文摘The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%.
文摘A two-step methodology was used to address and improve the power quality concerns for the PV-integrated microgrid system. First, partial shading was included to deal with the real-time issues. The Improved Jelly Fish Algorithm integrated Perturb and Obserb (IJFA-PO) has been proposed to track the Global Maximum Power Point (GMPP). Second, the main unit-powered via DC–AC converter is synchronised with the grid. To cope with the wide voltage variation and harmonic mitigation, an auxiliary unit undergoes a novel series compensation technique. Out of various switching approaches, IJFA-based Selective Harmonic Elimination (SHE) in 120° conduction gives the optimal solution. Three switching angles were obtained using IJFA, whose performance was equivalent to that of nine switching angles. Thus, the system is efficient with minimised higher-order harmonics and lower switching losses. The proposed system outperformed in terms of efficiency, metaheuristics, and convergence. The Total Harmonic Distortion (THD) obtained was 1.32%, which is within the IEEE 1547 and IEC tolerable limits. The model was developed in MATLAB/Simulink 2016b and verified with an experimental prototype of grid-synchronised PV capacity of 260 W tested under various loading conditions. The present model is reliable and features a simple controller that provides more convenient and adequate performance.
