Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

Retrieval of flower videos based on a query with multiple species of flowers

Searching,recognizing and retrieving a video of interest froma large collection of a video data is an instantaneous requirement.This requirement has been recognized as an active area of research in computer vision,machine learning and pattern recognition.Flower video recognition and retrieval is vital in the field of floriculture and horticulture.In this paper we propose a model for the retrieval of videos of flowers.Initially,videos are represented with keyframes and flowers in keyframes are segmented from their background.Then,the model is analysed by features extracted from flower regions of the keyframe.A Linear Discriminant Analysis(LDA)is adapted for the extraction of discriminating features.Multiclass Support VectorMachine(MSVM)classifier is applied to identify the class of the query video.Experiments have been conducted on relatively large dataset of our own,consisting of 7788 videos of 30 different species of flowers captured from three different devices.Generally,retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species.In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.