Finite-difference model of land subsidence caused by cluster loads in Zhengzhou,China

Groundwater exploitation has been regarded as the main reason for land subsidence in China and thus receives considerable attention from the government and the academic community.Recently,building loads have been identified as another important factor of land subsidence,but researches in this sector have lagged.The effect of a single building load on land subsidence was neglected in many cases owing to the narrow scope and the limited depth of the additional stress in stratum.However,due to the superposition of stresses between buildings,the additional stress of cluster loads is greater than that of a single building load under the same condition,so that the land subsidence caused by cluster loads cannot be neglected.Taking Shamen village in the north of Zhengzhou,China,as an example,a finite-difference model based on the Biot consolidation theory to calculate the land subsidence caused by cluster loads was established in this paper.Cluster loads present the characteristics of large-area loads,and the land subsidence caused by cluster loads can have multiple primary consolidation processes due to the stress superposition of different buildings was shown by the simulation results.Pore water migration distances are longer when the cluster loads with high plot ratio are imposed,so that consolidation takes longer time.The higher the plot ratio is,the deeper the effective deformation is,and thus the greater the land subsidence is.A higher plot ratio also increases the contribution that the deeper stratigraphic layers make to land subsidence.Contrary to the calculated results of land subsidence caused by cluster loads and groundwater recession,the percentage of settlement caused by cluster loads in the total settlement was 49.43%and 55.06%at two simulated monitoring points,respectively.These data suggest that the cluster loads can be one of the main causes of land subsidence.

Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.

Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

Sensors are considered as important elements of electronic devices.In many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop communications.But,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much needed.However,reducing energy con-sumption can increase the network lifetime in effective manner.For that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes.In that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes.For providing equal rate of energy consumption among nodes,the dimensions of framed clusters are measured.Moreover,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station.The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node.Additionally,the model also provides higher throughput and minimal delay in delivering data packets.

Transformer-based correction scheme for short-term bus load prediction in holidays

To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a Transformer-based scheme with tailored architectural enhancements is proposed.First,the input data are clustered to reduce complexity and capture inherent characteristics more effectively.Gated residual connections are then employed to selectively propagate salient features across layers,while an attention mechanism focuses on identifying prominent patterns in multivariate time-series data.Ultimately,a pre-trained structure is incorporated to reduce computational complexity.Experimental results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00%consistently across all buses evaluated,and the fitting effect of holiday load curves is outstanding.Meanwhile,the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%.The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions,making it better adapted to real-world prediction scenarios.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

Space decomposition based parallelization solutions for the combined finiteediscrete element method in 2D

The combined finiteediscrete element method （FDEM） belongs to a family of methods of computationalmechanics of discontinua. The method is suitable for problems of discontinua, where particles aredeformable and can fracture or fragment. The applications of FDEM have spread over a number of disciplinesincluding rock mechanics, where problems like mining, mineral processing or rock blasting canbe solved by employing FDEM. In this work, a novel approach for the parallelization of two-dimensional（2D） FDEM aiming at clusters and desktop computers is developed. Dynamic domain decompositionbased parallelization solvers covering all aspects of FDEM have been developed. These have beenimplemented into the open source Y2D software package and have been tested on a PC cluster. Theoverall performance and scalability of the parallel code have been studied using numerical examples. Theresults obtained confirm the suitability of the parallel implementation for solving large scale problems. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

High-resolution Load Profile Clustering Approach Based on Dynamic Largest Triangle Three Buckets and Multiscale Dynamic Warping Path Under Limited Warping Path Length

With the popularity of smart meters and the growing availability of high-resolution load data, the research on the dynamics of electricity consumption at finely resolved timescales has become increasingly popular. Many existing algorithms underperform when clustering load profiles contain a large number of feature points. In addition, it is difficult to accurately describe the similarity of profile shapes when load sequences have large fluctuations, leading to inaccurate clustering results. To this end, this paper proposes a high-resolution load profile clustering approach based on dynamic largest triangle three buckets(LTTBs) and multiscale dynamic time warping under limited warping path length(LDTW). Dynamic LTTB is a novel dimensionality reduction algorithm based on LTTB. New sequences are constructed by dynamically dividing the intervals of significant feature points. The extraction of fluctuation characteristics is optimized. New curves with more concentrated features will be applied to the subsequent clustering. The proposed multiscale LDTW is used to generate a similarity matrix for spectral clustering, providing a more comprehensive and flexible matching method to characterize the similarity of load profiles. Thus, the clustering effect of a high-resolution load profile is improved. The proposed approach has been applied to multiple datasets. Experiment results demonstrate that the proposed approach significantly improves the Davies-Bouldin indicator(DBI) and validity index(VI). Therefore, better similarity and accuracy can be achieved using high-resolution load profile clustering.

Electric Load Clustering in Smart Grid:Methodologies,Applications,and Future Trends

With the increasingly widespread of advanced metering infrastructure,electric load clustering is becoming more essential for its great potential in analytics of consumers’energy consumption patterns and preference through data mining.Moreover,a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data,observe the characteristics of load clusters,and classify the components of the total load.This can give rise to the development of related techniques and research in the smart grid,such as demand-side response.This paper summarizes the basic concepts and the general process in electric load clustering.Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages.Afterwards,eight indices widely used to evaluate the validity of electric load clustering are described.Finally,vital applications are discussed thoroughly along with future trends including the tariff design,anomaly detection,load forecasting,data security and big data,etc.

Islands of misfit buildings: Detecting uncharacteristic electricity use behavior using load shape clustering

Many energy performance analysis methodologies assign buildings a descriptive label that represents their main activity,often known as the primary space usage(PSU).This attribute comes from the intent of the design team based on assumptions of how the majority of the spaces in the building will be used.In reality,the way a building’s occupants use the spaces can be different than what was intended.With the recent growth of hourly electricity meter data from the built environment,there is the opportunity to create unsupervised methods to analyze electricity consumption behavior to understand whether the PSU assigned is accurate.Misclassification or oversimplification of the use of the building is possible using these labels when applied to simulation inputs or benchmarking processes.To work towards accurate characterization of a building’s utilization,we propose a modular methodology for identifying potentially mislabeled buildings using distance-based clustering analysis based on hourly electricity consumption data.This method seeks to segment buildings according to their daily behavior and predict which ones are misfits according to their assigned PSU label.This process finds potentially uncharacteristic behavior that could be an indication of mixed-use or a misclassified PSU.Our results on two public data sets,from the Building Data Genome(BDG)Project and Washington DC(DGS),with 507 and 322 buildings respectively,show that 26%and 33%of these buildings are potentially mislabelled based on their load shape behavior.Such information provides a more realistic insight into their true consumption characteristics,enabling more accurate simulation scenarios.Applications of this process and a discussion of limitations and reproducibility are included.