Generation of linear and nonlinear waves in numerical wave tank using clustering technique-volume of fluid method

A two-dimensional （2D） numerical model is developed for the wave sim- ulation and propagation in a wave flume. The fluid flow is assumed to be viscous and incompressible, and the Navier-Stokes and continuity equations are used as the governing equations. The standard k-e model is used to model the turbulent flow. The Navier- Stokes equations are discretized using the staggered grid finite difference method and solved by the simplified marker and cell （SMAC） method. Waves are generated and propagated using a piston type wave maker. An open boundary condition is used at the end of the numerical flume. Some standard tests, such as the lid-driven cavity, the constant unidirectional velocity field, the shearing flow, and the dam-break on the dry bed, are performed to valid the model. To demonstrate the capability and accuracy of the present method, the results of generated waves are compared with available wave theories. Finally, the clustering technique （CT） is used for the mesh generation, and the best condition is suggested.

Clustering Approach for Analyzing the Student’s Efficiency and Performance Based on Data

The academic community is currently confronting some challenges in terms of analyzing and evaluating the progress of a student’s academic performance. In the real world, classifying the performance of the students is a scientifically challenging task. Recently, some studies apply cluster analysis for evaluating the students’ results and utilize statistical techniques to part their score in regard to student’s performance. This approach, however, is not efficient. In this study, we combine two techniques, namely, k-mean and elbow clustering algorithm to evaluate the student’s performance. Based on this combination, the results of performance will be more accurate in analyzing and evaluating the progress of the student’s performance. In this study, the methodology has been implemented to define the diverse fascinating model taking the student test scores.

Multiobjective Economic/Environmental Dispatch Using Harris Hawks Optimization Algorithm

The eminence of Economic Dispatch(ED)in power systems is signifi-cantly high as it involves in scheduling the available power from various power plants with less cost by compensating equality and inequality constrictions.The emission of toxic gases from power plants leads to environmental imbalance and so it is highly mandatory to rectify this issues for obtaining optimal perfor-mance in the power systems.In this present study,the Economic and Emission Dispatch(EED)problems are resolved as multi objective Economic Dispatch pro-blems by using Harris Hawk’s Optimization(HHO),which is capable enough to resolve the concerned issue in a wider range.In addition,the clustering approach is employed to maintain the size of the Pareto Optimal(PO)set during each itera-tion and fuzzy based approach is employed to extricate compromise solution from the Pareto front.To meet the equality constraint effectively,a new demand-based constraint handling mechanism is adopted.This paper also includes Wind energy conversion system(WECS)in EED problem.The conventional thermal generator cost is taken into account while considering the overall cost functions of wind energy like overestimated,underestimated and proportional costs.The quality of the non-dominated solution set is measured using quality metrics such as Set Spacing(SP)and Hyper-Volume(HV)and the solutions are compared with other conventional algorithms to prove its efficiency.The present study is validated with the outcomes of various literature papers.

Human Errors Monitoring in Electrical Transmission Networks

The electrical transmission networks are complex systems that are commonly run near their operational limits. Such systems can undergo major disturbances that have serious consequences. Individually, these disturbances can be attributed to specific causes, such as lightning strikes, ice storms, equipment failure, shorts resulting from untrimmed trees, excessive customer demand, or human errors. In the paper, the human errors from electrical transmission networks of Romanian Power Grid Company over period of 10 years are analyzed. It is hoped that obtained results will provide engineers a better understanding so they can cater to the needs of the human operators when to implement new interfaces for network monitoring tasks, not for the other technical objectives.

Soft Computing Based Discriminator Model for Glaucoma Diagnosis

In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.

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.

New Results on PWARX Model Identification Based on Clustering Approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.

Joint Design of Clustering and In-cluster Data Route for Heterogeneous Wireless Sensor Networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.