Means Clustering Method Research Articles

Classification of eye diseases using optic cup segmentation and optic disc ratio and its implementation using VHDL

The proposed work consists reprocessing using morphological operations and then segmentation of optic disc and optic cup by using the morphological operations. These operations are minimizing errors detection limit of the optic disc due to blood vessels cross. Then, the cup to disc ratio (CDR) is calculated using these operations. The cup to disc ratio is more than 0.3 then patients are glaucoma otherwise normal. Spatially Weighted Fuzzy C Mean (SWFCM) clustering method is used to segment the optic disc and Superpixel algorithm is used to segment the optic cup. The segmented optic disc and cup are then used to compute the CDR for glaucoma screening. Optic disc and cup segmentation of fundus image are considered for Diabetic Retinopathy Detection and Glaucoma detection. In this paper, we are considering one simple image and its part will be implemented by using VHDL family Altra Quartus II, which is a programmable logic device design software produced by Altera. Quartus II enables analysis and synthesis of HDL designs, which enables the developer to compile their designs, perform timing analysis, examine RTL diagrams, simulate a design's reaction to different stimuli, and configure the target device with the programmer. Quartus includes an implementation of VHDL and Verilog for hardware description, visual editing of logic circuits, and vector waveform simulation. The implemented results using MATLAB shows accurate results obtained both for diabetic retinopathy as well as glaucoma.

A bi-level robust optimization model to determine retail electricity price in presence of a significant number of invisible solar sites

Abstract This paper presents a bi-level model for day-ahead electricity pricing and dispatch problems faced by a distributed generation (DG)-owning retailer who plays an intermediary role between the wholesale electricity market and end-use consumers. In this approach, the stochastic programming is addressed in the upper level to study behavior of the retailer in the wholesale electricity market in presence of self-generation facilities, including thermal DGs, wind farms and roof-top photovoltaic (RPV) sites. Regarding increased penetration of RPV sites, a data dimension reduction technique through k -means clustering and principal component analysis (PCA) methods is used to hedge against large-scale output power data of RPV sites. In addition, to forecast day-ahead power output of RPV sites, a similar-day detection (SDD) technique is addressed to investigate the impacts of climate variables, e.g. irradiation, sunshine hours and temperature, on 24-hour-ahead power of RPV sites. In the lower level problem, information gap decision theory (IGDT) is proposed to determine robustness of retail electricity price against uncertain clients’ consumption. In this way, robustness and opportuneness functions are discussed to evaluate immunity against failure and windfall reward, respectively. Finally, numerical results based on actual data from PJM market and North Carolina solar sites are presented to demonstrate the usefulness and proficiency of the proposed framework.

Driving Style Classification Using a Semisupervised Support Vector Machine

Supervised learning approaches are widely used for driving style classification; however, they often require a large amount of labeled training data, which is usually scarce in a real-world setting. Moreover, it is time-consuming to manually label huge amounts of driving data due to uncertainties of driver behavior and variances among the data analysts. To address this problem, a semisupervised approach, a semisupervised support vector machine (S3VM), is employed to classify drivers into aggressive and normal styles based on a few labeled data points. First, a few data clusters are selected and manually labeled using a $ k$ -means clustering method. Then, a specific differentiable surrogate of a loss function is developed, which makes it feasible to use standard optimization tools to solve the nonconvex optimization problem. One of the most popular quasi-Newton algorithms is then used to assign the optimal label to all of the training data. Finally, we compare the S3VM method with a support vector machine method for classifying driving styles from different amounts of labeled data. Experiments show that the S3VM method can improve the classification accuracy by about 10% and reduce the labeling effort by using only a few labeled data clusters among huge amounts of unlabeled data.

Fuzzy Wavelet Polynomial Neural Networks: Analysis and Design

In this study, we propose a concept of fuzzy wavelet polynomial neural networks (FWPNNs) based on concepts and constructs of polynomial neural networks and fuzzy wavelet neurons (FWNs). These networks exhibit a rule-based architecture while each rule in the FWN consists of the premise part and consequence part. The premise part is realized by using C -means clustering method, while the consequence part is realized by means of wavelet functions whose parameters are estimated with the aid of the least square method. In some sense, the FWPNN can be regarded as a generalized fuzzy wavelet neural network (FWNN). Unlike Gaussian membership functions that are commonly utilized to implement the premise part of the rules in typical FWNNs, C -means method is employed here to overcome a possible curse of dimensionality. Polynomial neural networks (PNNs) are used to express the nonlinearity of a complex system. Furthermore, the particle swarm optimization is used to optimize the design parameters of the proposed network. Based on the PNNs and FWNNs, the proposed FWPNNs take advantages of these two neural networks: it exhibits the abilities to describe high-order nonlinear relations between input and output variables and it is beneficial to describe models impacted by uncertainty. The proposed FWPNNs are applied for time-series prediction and regression problems (e.g., control of dynamic plants). Several well-known modeling benchmarks including regression and time series are considered to evaluate the performance of the proposed FWPNNs. A comparative analysis shows that the proposed FWPNNs result in better performance when comparing with some previous models reported in the literature.

An improved method for integrated ecosystem health assessments based on the structure and function of coastal ecosystems: A case study of the Jiangsu coastal area, China

Ecosystem health status assessment is very complex and requires an integrated approach based on combined and synoptic estimates of the environmental background and the study of the structure and function of different ecosystems at a systems level. Here, we present the main results gained over six years (2006–2007, 2009–2012) with the aim of obtaining concise and comprehensive assessment results at 30 routine investigation sites in the Jiangsu coastal area. Our improved approach follows consecutive and interconnected steps. First, we conducted a quantitative assessment of community structure, including species abundance, biomass, and diversity utilizing a multidimensional scaling method. Then, a systemic functional assessment was carried out at the genetic level, including eco-exergy and structural eco-exergy, using a means clustering method, passing through a dimensionless polygonal area to reflect the integrated assessment results. Our results indicate that among the four seasons, the health status was best in autumn, followed by spring and winter, and worst in summer. Among the six assessed years, 2006 had the best health status, whereas 2012 had the worst. The general trend of annual variation in ecosystem health status was in decline. As for the regions, the health status was good or moderate in the southern and northern regions, and worst in the central region. Various human interventions and environmental stresses contributed to the stability and resilience of the ecosystem at different time scales and in different areas; however, these effects were not absolutely negative. This new method has strong applicability for assessing the status of ecosystems composed of several communities, as long as the eco-exergy quality and community structure of the system can be estimated.

Optic Disc and Optic Cup Segmentationbased on Fuzzy C Means Technique

Glaucoma is the major cause of ocular damage and vision loss in which increased Intraocular Pressure (IOP) of the eye progressively damages the optic nerve. In this proposed study, an automatic system is developed for glaucoma detection by extracting vertical Cup to Disc Ratio (CDR) using Fuzzy C Means (FCM) clustering method. CDR is the ratio of optic cup area to the optic disc area, which provides basis for the diagnosis of glaucoma. Region of interest (ROI) extraction through intensity weighted centroid method which is followed by pre-processing and recursively applied FCM for the detection of Optic cup (OC) and Optic disc (OD).

Integrating Contextual Information With ${H}/{\bar {\alpha }}$ Decomposition for PolSAR Data Classification

The use of contextual information is beneficial to improve both the accuracy and reliability of image classification. Based on the robust fuzzy ${c}$ -means (RFCM) clustering method and an adaptive Markov random field model, this letter proposes a contextual ${H}/{\bar {\alpha }}$ classifier for polarimetric synthetic aperture radar images. At each iterative step of RFCM clustering, the prior probability extracted from the local neighborhood is combined with the fuzzy membership derived from inherent polarimetric characteristics, thus the enhanced fuzzy membership is more reliable. In addition, an adaptive smoothing factor is proposed for use during contextual information retrieval, which can prevent oversmoothing and preserve the local spatial details. The experimental results implemented using AIRSAR and ESAR L-band data validate the efficacy of the proposed method. Compared with the iterated Wishart classifier and fuzzy ${H}/{\bar {\alpha }}$ classifier, the proposed method significantly improves the classification accuracy, with less noise and increased preservation of details.

A reconstructed variable regression method for thermal error modeling of machine tools

For the thermal error model of computer numerical control (CNC) machine tools based on empirical modeling method, selecting the appropriate temperature-sensitive points is very important. Owing to the existence of multi-collinearity among temperature variables, the accuracy and robustness of the error model are degraded. Therefore, a reconstructed variable regression (RVR) algorithm is presented firstly in this paper. The reconstructed variable is derived from representative temperature points which are selected from the raw temperature data through fuzzy C means (FCM) clustering method. Then, the reconstructed variable is utilized to build the thermal error model based on regression analysis. Meanwhile, a criterion is proposed to find out the optimal cluster result from the whole results of clustering. RVR algorithm was validated through the thermal error experiments of positioning error on a 3-axis machine tool. The number of temperature measuring points could be reduced from 9 to 2. The results of experiments showed that the fitting accuracy of RVR model could reach 92 % and the prediction accuracy could reach about 90 %. It is verified that the RVR model is of high prediction accuracy and strong robustness which is a great choice for thermal error modeling.

Sequential Radial Basis Function Using Support Vector Machine for Expensive Design Optimization

To improve the efficiency and quality of solving expensive engineering design optimization problems, recently metamodel-based design and optimization technologies have been widely employed. In this paper, a novel adaptive metamodel-based optimization method called sequential radial basis function using support vector machine is proposed to solve the practical engineering optimization problems involving computationally expensive objective and constraints. The proposed metamodel-based optimization method uses the radial basis function metamodel to approximate the expensive simulations in the interesting sampling region, which is a subregion of the design space. To identify the interesting sampling region, a novel feature space fuzzy -means clustering method is proposed to obtain the cluster center of superior cheap points classified by support vector machine. The procedure of sequential radial basis function using support vector machine is presented first, and the interesting sampling region and feature space fuzzy -means clustering methods are discussed in detail. Several numerical benchmark problems are used to compare the proposed method with other well-known metamodel-based design and optimization methods including efficient global optimization, multiple surrogate efficient global optimization methodology, mode-pursuing sampling, constraint importance mode pursuing sampling, and constrained optimization by radial basis function interpolation. The comparison results show that the proposed method generally outperforms the competitive methods in terms of efficiency, convergence, and robustness. Finally, sequential radial basis function using support vector machine is applied in an Earth observation satellite multidisciplinary design optimization problem. The results illustrate that compared with genetic algorithm and sequential quadratic programming, the proposed method can find a better solution with fewer function evaluations, which demonstrates the practicality and effectiveness of the proposed method in solving real-world engineering design optimization problems.

Automated Semantic Knowledge Acquisition From Sensor Data

The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended $k$ - means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.

Large-Scale Design Optimization of PM Machines Over a Target Operating Cycle

A large-scale finite element model-based design optimization algorithm is developed for improving the drive-cycle efficiency of permanent magnet (PM) synchronous machines with wide operating ranges such as those used in traction propulsion motors. The load operating cycle is efficiently modeled by using a systematic k -means clustering method to identify the operating points representing the high-energy-throughput zones in the torque–speed plane. The machine performance is evaluated over these cyclic representative points using a recently introduced computationally efficient finite element analysis, which is upgraded to include both constant torque and field-weakening operations in the evaluation of the machine performance metrics. In contrast with the common practice, which aims at enhancing the rated performance, the entire range of operation is considered in the present design optimization method. Practical operational constraints imposed by the voltage and current limits of the motor-drive system, excessive PM demagnetization, and torque ripple are accounted for during the optimization process. The convergence to the optimal design solutions is expedited by utilizing a new stochastic optimizer. The developed design algorithm is applicable to any configuration of sinewave-drive PM and synchronous reluctance motors over any conceivable load profile. Its effectiveness is demonstrated by optimizing the well-established benchmark design represented by the Toyota Prius Gen. 2 interior PM motor configuration over a compound operating cycle consisting of common U.S. driving schedules. Multiphysics electromagnetic, thermal, and mechanical performance of the optimized design solutions is discussed in a postdesign optimization stage.

Classification of Eye Diseases Using Optic Cup Segmentation and Optic Disc Ratio

The retinal images are widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy, cataract and glaucoma. Normally retinal images are classified manually by clinicians trained in time and process resources. Segmentation imaging of the eye retina provides an important role in the calculation the shape and size of the optic disc and the anterior segment and the abnormal growth of any geometry in the eye region. It automatically and precisely calculates the values of location, position and contour area and a structural part of the image required by ophthalmologists. When there is an elevated intra ocular pressure from the normal condition, the subject is affected by glaucoma and in this condition the retinal nerve fiber layer and the optic disc are affected and this leads to progressive loss of vision. This paper proposes a novel method for glaucoma screening by using optic disc & cup segmentation. But , the segmentation methods suffer from many problems such as the optimization, and initialization inadequate in noisy images results. In this paper, we are tried to locate the most important region of the eye optic disc and optic cup using a morphological operations. The optic disc is the area on the retina optic nerve axons that enter and leave the eye. The proposed work consists pre processing using morphological operations and then segmentation of optic disc and optic cup by using the morphological operations. These operations are minimizing errors detection limit of the optic disc due to blood vessels cross. Then, the cup to disc ratio (CDR) is calculated using these operations. The cup to disc ratio is more than 0.3 then patients are glaucoma otherwise normal. Spatially Weighted Fuzzy C Mean (SWFCM) clustering method is used to segment the optic disc and Superpixel algorithm is used to segment the optic cup. The segmented optic disc and cup are then used to compute the CDR for glaucoma screening. Optic disc and cup segmentation of fundus image is considered for Diabetic Retinopathy Detection and Glucoma detection. The implemented results using MATLAB shows accurate results obtained both for diabetic retinopathy as well as glaucoma.

Maritime Unmanned Vehicle Cruise Path Planning for Maritime Information Collection

Abstract: Maritime Unmanned Vehicle (MUV) as Unmanned Surface Vehicle is introduced as a novel traffic device for continuous maritime traffic information collection and complementing existing methods for Electronic Patrol System (EPS). MUV cruise path planning is studied regarding maximum sailing time, which is aiming at completing MUVs cruise task on a certain number of the areas with relatively high risk needed surveillance. With detailed description of MUV cruise path planning problem, it is Abstracted as travel salesman problem solved by ant colony algorithm in this paper. The approach used to select cruise objects is proposed, especially the cloud model applied to choose cruise navigation segments. The factors including total cruise distance and standard deviation of cruise time, and K -means clustering method are used to evaluate and handle the scenario with MUV navigation constraint. On the basis of above operations, the special scenario is finally converted into several ones without constraint. At last, the cases of Fuzhou Maritime Safety Administration areas are demonstrated. The results indicate that the method proposed for MUV cruise path planning is feasible and effective.

Supervised and Unsupervised Classification Using Mixture Models

This chapter is dedicated to model-based supervised and unsupervised classification. Probability distributions are defined over possible labels as well as over the observations given the labels. To this end, the basic tools are the mixture models. This methodology yields a posterior distribution over the labels given the observations which allows to quantify the uncertainty of the classification. The role of Gaussian mixture models is emphasized leading to Linear Discriminant Analysis and Quadratic Discriminant Analysis methods. Some links with Fisher Discriminant Analysis and logistic regression are also established. The Expectation-Maximization algorithm is introduced and compared to the $K$-means clustering method. The methods are illustrated both on simulated datasets as well as on real datasets using the R software.

K-Means Clustering For Segment Web Search Results

Clustering is the power full technique for segment relevant data into different levels. This study has proposed K- means clustering method for cluster web search results for search engines. For represent documents we used vector space model and use cosine similarity method for measure similarity between user query and the search results. As an improvement of K-means clustering we used distortion curve method for identify optimal initial number of clusters. should exhibit a large degree of similarity while the similarity among different clusters should be minimized. Some of the more familiar clustering methods are: partitioning algorithms based on dividing entire data into dissimilar groups, hierarchical methods, density and grid based clustering, some graph based methods, etc (3). Most popular and widely use partitioning method is K-means clustering method. A. K-means Clustering K-Means Clustering is a method used to classify semi structured or unstructured data sets. This is one of the most commonly and effective methods to classify data because of its simplicity and ability to handle voluminous data sets. It accepts the number of clusters and the initial set of centroids as parameters. The distance of each item in the data set is calculated with each of the centroids of the respective cluster. The item is then assigned to the cluster with which the distance of the item is the least. The centroid of the cluster to which the item was assigned is recalculated. One of the most important and commonly used methods for grouping the items of a data set using K-Means Clustering is calculating the distance of the point from the chosen mean. This distance is usually the Euclidean Distance. ∑ √( )

An Image Processing Technique to Calculate Percentage of Disease Affected Pixels of Paddy Leaf

gladesh is an agricultural country. Paddy is a principle crop of it. Millions of people depend for their living on paddy by way of its farming and processing. Blasts in paddy leaves are the most predominant disease which appears as brown spots on the leaves. If not treated on time, it may cause the great loss. Excessive use of pesticide for treatment of plant diseases increases the cost, environmental pollution and decreases the production. So their use must be optimized. This can be achieved by targeting the disease places, with the appropriate quantity and concentration of pesticide by estimating disease severity using image processing technique. In this paper K- means clustering method has been used to segment the image into three images based on color. Among these images unaffected leaf regions and disease affected regions are used to calculated percentage of affected pixels. By calculating percentage of affected pixels disease severity can be observed which leads to take appropriate measure for treatment.

Granular Model of Long-Term Prediction for Energy System in Steel Industry.

Sound energy scheduling and allocation is of paramount significance for the current steel industry, and the quantitative prediction of energy media is being regarded as the prerequisite for such challenging tasks. In this paper, a long-term prediction for the energy flows is proposed by using a granular computing-based method that considers industrial-driven semantics and granulates the initial data based on the specificity of manufacturing processes. When forming information granules on a basis of experimental data, we propose to deal with the unequal-length temporal granules by exploiting dynamic time warping, which becomes instrumental to the realization of the prediction model. The model engages the fuzzy C -means clustering method. To quantify the performance of the proposed method, real-world industrial energy data coming from a steel plant in China are employed. The experimental results demonstrate that the proposed method is superior to some other data-driven methods and becomes capable of satisfying the requirements of the practically viable prediction.

Weld Detect Identification Using Texture Features and Dynamic Time Warping

In this paper, a simple yet robust algorithm for texture identification using 1 Dimensional Discrete Fourier Transform (1-D DFT) and Dynamic Time Warping (DTW) is presented with illumination variations. In the first stage, several image processing techniques namely Fuzzy C means (FCM) clustering, edge detection, Otsu thresholding and inverse surface thresholding method are utilized to locate the region of interest (ROI) where defects might exist. Next, the image undergoes the feature extraction process using 1-D DFT and finally, the features are classified using DTW. Several defect images consist of 2 types of defect namely the porosity and crack are experimented and classified using the DTW.

Fuzzy-GA modeling in air quality assessment.

In this paper, the authors have suggested and implemented the defined soft computing methods in air quality classification with case studies. The first study relates to the application of Fuzzy C mean (FCM) clustering method in estimating pollution status in cities of Maharashtra State, India. In this study, the computation of weighting factor using a new concept of reference group is successfully demonstrated. The authors have also investigated the efficacy of fuzzy set theoretic approach in combination with genetic algorithm in straightway describing air quality in linguistic terms with linguistic degree of certainty attached to each description using Zadeh-Deshpande (ZD) approach. Two metropolitan cities viz., Mumbai in India and New York in the USA are identified for the assessment of the pollution status due to their somewhat similar geographical features. The case studies infer that the fuzzy sets drawn on the basis of expert knowledge base for the criteria pollutants are not much different from those obtained using genetic algorithm. Pollution forecast using various methods including fuzzy time series forms an integral part of the paper.

Synthetic Rating on Talent Evaluation-Similarity of Subsets

Abstract There are many topics about rating individuals, animals, places, things, or abstract ideas that are actively researched. When rating about an object is needed to form an opinion it is often given by an expert in the field. These ratings vary from one individual expert's rating to another due to the subjective nature of the evaluation process. How can we evaluate the ratings? How can we find the correlations and similarities among these sets? How can we provide a mathematical modelling for a rating problem? This paper provides a procedure for the extension of fuzzy synthetic rating modelling on a sample to the entire data set and introduces a k -means clustering method to check the level to which there exist similarities among the subsets and classify the dataset automatically for a rating problem. The related synthetic rating and an example to illustrate the modelling is given in this work.