A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Factorized Smith Method for A Class of High-Ranked Large-Scale T-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked T-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Numerical and theoretical study of large-scale failure of strata overlying sublevel caving mines with steeply dipping discontinuities

The deformation and fracture evolution mechanisms of the strata overlying mines mined using sublevel caving were studied via numerical simulations.Moreover,an expression for the normal force acting on the side face of a steeply dipping superimposed cantilever beam in the surrounding rock was deduced based on limit equilibrium theory.The results show the following:(1)surface displacement above metal mines with steeply dipping discontinuities shows significant step characteristics,and(2)the behavior of the strata as they fail exhibits superimposition characteristics.Generally,failure first occurs in certain superimposed strata slightly far from the goaf.Subsequently,with the constant downward excavation of the orebody,the superimposed strata become damaged both upwards away from and downwards toward the goaf.This process continues until the deep part of the steeply dipping superimposed strata forms a large-scale deep fracture plane that connects with the goaf.The deep fracture plane generally makes an angle of 12°-20°with the normal to the steeply dipping discontinuities.The effect of the constant outward transfer of strata movement due to the constant outward failure of the superimposed strata in the metal mines with steeply dipping discontinuities causes the scope of the strata movement in these mines to be larger than expected.The strata in the metal mines with steeply dipping discontinuities mainly show flexural toppling failure.However,the steeply dipping structural strata near the goaf mainly exhibit shear slipping failure,in which case the mechanical model used to describe them can be simplified by treating them as steeply dipping superimposed cantilever beams.By taking the steeply dipping superimposed cantilever beam that first experiences failure as the key stratum,the failure scope of the strata(and criteria for the stability of metal mines with steeply dipping discontinuities mined using sublevel caving)can be obtained via iterative computations from the key stratum,moving downward toward and upwards away from the goaf.

Review of the Configuration and Transient Stability of Large-scale Renewable Energy Generation through Hybrid DC Transmission

Based on the complementary advantages of Line Commutated Converter(LCC)and Modular Multilevel Converter(MMC)in power grid applications,there are two types of hybrid DC system topologies:one is the parallel connection of LCC converter stations and MMC converter stations,and the other is the series connection of LCC and MMC converter stations within a single station.The hybrid DC transmission system faces broad application prospects and development potential in large-scale clean energy integration across regions and the construction of a new power system dominated by new energy sources in China.This paper first analyzes the system forms and topological characteristics of hybrid DC transmission,introducing the forms and topological characteristics of converter-level hybrid DC transmission systems and system-level hybrid DC transmission systems.Next,it analyzes the operating characteristics of LCC and MMC inverter-level hybrid DC transmission systems,provides insights into the transient stability of hybrid DC transmission systems,and typical fault ride-through control strategies.Finally,it summarizes the networking characteristics of the LCC-MMC series within the converter station hybrid DC transmission system,studies the transient characteristics and fault ridethrough control strategies under different fault types for the LCC-MMC series in the receiving-end converter station,and investigates the transient characteristics and fault ride-through control strategies under different fault types for the LCC-MMC series in the sending-end converter station.

Assessing cutter-rock interaction during TBM tunnelling in granite:Large-scale standing rotary cutting tests and 3D DEM simulations

The widespread utilisation of tunnel boring machines(TBMs)in underground construction engineering requires a detailed investigation of the cutter-rock interaction.In this paper,we conduct a series of largescale standing rotary cutting tests on granite in conjunction with high-fidelity numerical simulations based on a particle-type discrete element method(DEM)to explore the effects of key cutting parameters on the TBM cutter performance and the distribution of cutter-rock contact stresses.The assessment results of cutter performance obtained from the cutting tests and numerical simulations reveal similar dependencies on the key cutting parameters.More specifically,the normal and rolling forces exhibit a positive correlation with penetration but are slightly influenced by the cutting radius.In contrast,the side force decreases as the cutting radius increases.Additionally,the side force shows a positive relationship with the penetration for smaller cutting radii but tends to become negative as the cutting radius increases.The cutter's relative effectiveness in rock breaking is significantly impacted by the penetration but shows little dependency on the cutting radius.Consequently,an optimal penetration is identified,leading to a low boreability index and specific energy.A combined Hertz-Weibull function is developed to fit the cutter-rock contact stress distribution obtained in DEM simulations,whereby an improved CSM(Colorado School of Mines)model is proposed by replacing the original monotonic cutting force distribution with this combined Hertz-Weibull model.The proposed model outperforms the original CSM model as demonstrated by a comparison of the estimated cutting forces with those from the tests/simulations.The findings from this work that advance our understanding of TBM cutter performance have important implications for improving the efficiency and reliability of TBM tunnelling in granite.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Digital Application Objectives and Benefit Analysis of BIM Technology in Large-Scale Comprehensive Development Projects

This paper discusses the digital application and benefit analysis of building information model(BIM)technology in the large-scale comprehensive development project of the Guangxi headquarters base.The project covers a total area of 92,100 square meters,with a total construction area of 379,700 square meters,including a variety of architectural forms.Through three-dimensional modeling and simulation analysis,BIM technology significantly enhances the design quality and efficiency,shortens the design cycle by about 20%,and promotes the collaboration and integration of project management,improving the management efficiency by about 25%.During the construction phase,the collision detection and four-dimensional visual management functions of BIM technology have improved construction efficiency by about 15%and saved the cost by about 10%.In addition,BIM technology has promoted green building and sustainable development,achieved the dual improvement of technical and economic indicators and social and economic benefits,set an example for enterprises in digital transformation,and opened up new market businesses.

Large-scale controllable fabrication of aluminum nanobowls for surface plasmon-enhanced fluorescence

Al nanoparticles(NPs)exhibit excellent localized surface plasmon resonance(LSPR)properties and have been considered a promising alternative to plasmonic Au or Ag NPs.However,it remains difficult to fabricate Al NPs with uniform size and controllable morphology over a large area on substrates,which seriously hinders the in-depth exploration of their properties and applications.Herein,we have developed a self-assembly nanoparticle template method to realize the controllable preparation of bowl-shaped Al NPs(Al nanobowls(Al NBs))with tunable sizes from 36 to 131 nm on the substrate surface,accompanied by tunable LSPR spectral responses from 272 to 480 nm.Among them,131 nm Al NBs exhibit superior fluorescence enhancement ability(1932.2-fold)and a low detection limit(78.6 pM)towards 5-carboxyfluorescein,exceeding comparable Ag NBs and Au nanospheres(NSs).This can be attributed to the strong electromagnetic enhancement induced by the LSPR effect and the effective inhibition of fluorescence quenching caused by the self-passivated oxide layer.Therefore,the successful fabrication of Al NBs on substrates is of vital significance for their promising applications,including surface-enhanced spectroscopy,sensitive fluorescence detection,light-harvesting devices,biosensing,and ultraviolet(UV)plasmonics.

Research on Comprehensive Control of Power Quality of Port Distribution Network Considering Large-Scale Access of Shore Power Load

In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is proposed.Based on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is constructed.Based on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered comprehensively.The proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution networks.The simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimization algorithm has good convergence and global optimization finding capability.

Application of Large-Scale Systems Theory in Fire Control Systems

According the to the model of single tank fire control system, this paper makes use of decomposition-coordination theory of large-scale systems to discuss the shooting problem of multiple tank the control systems, and alalyzes specifically the three-tank the control systems and models.the process was imitated in computer, and the optimal shooting element of 3TS system was got. In addition, probability of hitting the target was calculated.