An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm

The implementation of content-based image retrieval(CBIR)mainly depends on two key technologies:image feature extraction and image feature matching.In this paper,we extract the color features based on Global Color Histogram(GCH)and texture features based on Gray Level Co-occurrence Matrix(GLCM).In order to obtain the effective and representative features of the image,we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively.And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain way.Image feature matching mainly depends on the similarity between two image feature vectors.In this paper,we propose a novel similarity measure method based on k-Nearest Neighbors(kNN)and fuzzy mathematical algorithm(SBkNNF).Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure method.Using the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query image.And using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved image.Calculating the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved image.Extensive experiments are carried out on three data sets:WANG data set,Corel-5k data set and Corel-10k data set.The experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.

Mathematical Algorithm for Calculating the Velocity Vectors of Fluid by CTA in Spherical Coordinates

This paper presents a mathematical algorithm that determines the fluid flow velocity vector （direction, intensity and orientation）, based on measured voltages on multi-channel hot-wire anemometer. As the voltage on Constant Temperature hot-wire Anemometer （CTA） is non-linear function of velocity and angle of the fluid, inverse function is also non-linear and has several mathematically correct solutions. In the Laboratory of Non-linear Mechanics at the Faculty of Mechanical Engineering in Ljubljana, the authors have decided to try developing multi-charmel hot-wire anemometer with constant temperature at which it is possible to select physically correct solutions from several mathematically correct solutions. The mathematical algorithm works correctly if the range of instrument operation is limited for the value of spherical angles ｜φ｜≤ 60°and ｜ψ｜＇1 ≤ 58°.

Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems(DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.

Algorithm for repairing the damaged images of grain structures obtained from the cellular automata and measurement of grain size

Computational models are developed to create grain structures using mathematical algorithms based on the chaos theory such as cellular automaton, geometrical models, fractals, and stochastic methods. Because of the chaotic nature of grain structures, some of the most popular routines are based on the Monte Carlo method, statistical distributions, and random walk methods, which can be easily programmed and included in nested loops. Nevertheless, grain structures are not well defined as the results of computational errors and numerical incon- sistencies on mathematical methods. Due to the finite definition of numbers or the numerical restrictions during the simulation of solidifica- tion, damaged images appear on the screen. These images must be repaired to obtain a good measurement of grain geometrical properties. Some mathematical algorithms were developed to repair, measure, and characterize grain structures obtained from cellular automata in the present work. An appropriate measurement of grain size and the corrected identification of interfaces and length are very important topics in materials science because they are the representation and validation of mathematical models with real samples. As a result, the developed al- gorithms are tested and proved to be appropriate and efficient to eliminate the errors and characterize the grain structures.

The Energy Optimization Mathematic Algorithm on Multi-energy Resource Powertrain of Fuel Cell Vehicle

In order to solve the core issue of the energy regulation （ER） on multi-energy resource powertrain of fuel cell vehicle, the work functions of each component were defined; the mathematical algorithm model of energy regulation was established and the relevant solution was found. This algorithm was evaluated successfully on the hardware in loop （FILL） platform under three typical urban running cycles. The results showed ER control target had been realized and the mathematical algorithm was effective and reasonable. Based on the HIL simulation, some conclusions and ER strategies were made. According to the different power component parameters and real time control request, this algorithm should be modified and calibrated for application in the actual control system.

The Distribution Search:An O(n) Expected Time Search

Provided an algorithm for the distribution search and proves the time complexity of the algorithm. This algorithm uses a mathematical formula to search n elements in the sequence of n elements in O(n)expected time,and experimental reesult proves that distribution search is superior to binary search.