Optimal Number Of Zones Research Articles

DELIMITATION OF HOMOGENEOUS ZONES IN VINEYARDS USING GEOSTATISTICS AND MULTIVARIATE ANALYSIS OF DIFFERENT VEGETATION INDICES

Vegetation indices (VIs) are quantitative measures used to describe the distribution and spatial variability of the vegetation cover of natural or cultivated areas. The aim of this study was to delimit homogeneous zones (HZs) of different VIs using geostatistics and multivariate analysis in order to identify vegetation patterns in Cabernet Franc and Cabernet Sauvignon vineyards. The evaluation was performed in two vineyards in the municipality of Espírito Santo do Pinhal in the state of São Paulo, Brazil. Reflectance (ρ) was measured at three wavelengths of the electromagnetic spectrum (670, 730, and 780 nm) at canopy height in georeferenced points along planting rows using the Crop Circle ACS-430 active sensor. Nine VIs were calculated based on the ratios between the ρ values. Geostatistical data analysis allowed the spatial prediction of VIs by ordinary kriging interpolation. Principal component analysis and fuzzy k-means clustering were applied for HZs delimitation and the optimal number of zones was defined according to cluster validity functions. Despite the variations of the VIs spatial distribution patterns, the multivariate analysis resulted in a representative categorization of the grapevine vegetative vigor and delimitation of HZs for this characteristic. This was validated according to the observed significant differences between VIs.

Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: A case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran

Determination of TOC is critical to the evaluation of every source rock unit. Methods which are dependent upon extensive laboratory testing are limited by the availability and integrity of the rock samples. Prediction of TOC (Total Organic Carbon) from well Log data being available for the majority of wells being drilled provides rapid evaluation of organic content, producing a continuous record while eliminating sampling issues. Therefore, the ideal method for determining the TOC fraction within source rock units would utilize common well log data. So a model was developed to formulate TOC values in the absence of laboratory TOC measurements from conventional well log data. Consequently, with the assistance of FL (Fuzzy Logic), TOC estimated from well log data with an overall prediction accuracy of 0.9425 for the test set. Following that TOC content of the Kazhdumi formation optimally has been divided into 4 zones using K-means cluster analysis, since searching for patterns is one of the main goals in data mining. There is a general increase in TOC from zone 1 to zone 4. The optimal number of zones has been detected by means of the knee method that finds the “knee” in a number of clusters vs. Compactness, Davies-Bouldin and Silhouette values. In the last step, using SVM (Support Vector Machine) and ANN (Artificial Neural Network) algorithms, two commonly used techniques, classification rules developed to predict the source rock class-membership (zones) from well log data. The proposed method is found effective in directly extracting patterns from well log data after defining classification rules. Quantitative comparisons of the results from ANN and SVM depicts that for classification problem of source rock zonation SVM with RBF (Radial Basis Function) kernel readily outperforms ANN in term of classification accuracy (0.9077 and 0.9369 for ANN and SVM, respectively), reduced computational time and highly repeatable results. This method would enable a more elaborate assessment of Kazhdumi formation to be undertaken by providing a comprehensive quick look results derived directly from well log data while using conventional methods one can’t define patterns within the data without grouping data manually.

A Novel Zone Division Approach for Power System Fault Detection Using ANN-Based Pattern Recognition Technique

This paper presents a waveform analysis-based approach for detection and classification of short-circuit faults in large power networks. To reduce the computational burden in dealing with a large volume of waveform data, a novel zone detection method has been used where a large power network is divided into optimal number of zones with manageable number of buses and lines. A first module of the artificial neural network-based classifier has been developed to perform an “exploratory global search” to find the faulty zone, which is then refined to a “local search” within a zone, by a second module of classifier for determination of exact fault location and fault type. The elementary waveform data are being captured by disturbance recorders placed at strategic buses, termed as “monitoring locations.” Feature extraction, which is typically the underlying principle of any waveform analysis-based fault detection approach, is implemented by the extended Kalman filter. The proposed method has been successfully tested on the IEEE 57 bus network with encouraging results.

ADAPTIVE ZONING DESIGN BY SUPERVISED LEARNING USING MULTI-OBJECTIVE OPTIMIZATION

Zoning is a widespread feature extraction technique for handwritten digit recognition, since it is able to handle handwritten pattern variability. Static techniques for zoning design have recently been superseded by adaptive techniques, in which zoning design is considered as the result of an optimization procedure. This paper presents a new learning strategy to optimal zoning design using multi-objective genetic algorithm. More precisely, the nondominant sorting genetic algorithm II (NSGA II) has been applied to define, in a single process, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests, carried out in the field of handwritten digit recognition, show the effectiveness of this new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.

A Dynamic Multi-Zone Partitioning Scheme for Solving Detailed Chemical Kinetics in Reactive Flow Computations

A dynamic multi-zone (DMZ) partitioning scheme is proposed for enhancing the computational efficiency associated with solving detailed chemistry in reactive flow calculations. The DMZ scheme employs a novel and highly flexible evolutionary data clustering algorithm to partition a group of computational cells into zones for solving the chemistry source terms at each hydrodynamic time step. The algorithm dynamically determines the optimal number of zones for each time step of the calculation based on the thermochemical conditions in the cells and user-specified dispersion thresholds. After solving the chemistry equations on zonal level, the DMZ scheme maps the updated solution back to the cells while preserving the initial parameter stratification. The DMZ scheme is implemented into a KIVA3V-CHEMKIN code and benchmarked against fully resolved (non-partitioned) calculations using direct-injection compression-ignition engine cases. The benchmarking results show that the DMZ scheme accurately reproduces the pressure and key species profiles obtained from the fully resolved calculations in simulations that range from homogeneous to highly stratified. Using a computational mesh containing 15,000 cells, the DMZ scheme achieved a 20- and 8-fold speedup for the moderately and severely stratified operating conditions, respectively. Based on the benchmark calculations, recommended values of the user-specified thresholds are proposed.

Mobility model and performance analysis for zone-based registration in CDMA mobile communication system

In this paper, we describe a mobility model and analysis for zone-based registration in the CDMA mobile communication system. We first propose a simple and proper mobility function for single zone-based registration. We establish the circumstances of multiple zone-based registration, which is based on the mobility function of single zone-based registration, and perform computer simulations for the circumstances. Using the simulation results, we obtain the optimal number of zones, N, that minimizes signaling traffic on radio channels. Results show that, in most cases, N is 2. We also show that multiple zone-based registration has lower signaling traffic on radio channels than single zone-based registration, and zone?based registration must be implemented as multiple zone?based registration.