Rajprasad Rajkumar

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.

A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine

This work implements a new text document classifier by integrating the K-nearest neighbor (KNN) classification approach with the support vector machine (SVM) training algorithm. The proposed Nearest Neighbor-Support Vector Machine hybrid classification approach is coined as SVM-NN. The KNN has been reported as one of the widely used text classification approaches due to its simplicity and efficiency in handling various types of text classification tasks. However, there exists a major problem of the KNN in determining the appropriate value for parameter K in order to guarantee high classification effectiveness. This is due to the fact that the selection of the value of parameter K has high impact on the accuracy of the KNN classifier. Other than determining the optimal value of parameter K, the KNN is also a lazy learning method which keeps the entire training samples until classification time. Hence, the computational process of the KNN has become intensive when the value of parameter K increases. In this paper, we propose the SVM-NN hybrid classification approach with the objective that to minimize the impact of parameter on classification accuracy. In the training stage, the SVM is utilized to reduce the training samples for each of the available categories to their support vectors (SVs). The SVs from different categories are used as the training data of nearest neighbor classification algorithm in which the Euclidean distance function is used to calculate the average distance between the testing data point to each set of SVs of different categories. The classification decision is made based on the category which has the shortest average distance between its SVs and the testing data point. The experiments on several benchmark text datasets show that the classification accuracy of the SVM-NN approach has low impact on the value of parameter, as compared to the conventional KNN classification model.

PIPELINE DEFECT PREDICTION USING SUPPORT VECTOR MACHINES

Oil and gas pipeline condition monitoring is a potentially challenging process due to varying temperature conditions, harshness of the flowing commodity and unpredictable terrains. Pipeline breakdown can potentially cost millions of dollars worth of loss, not to mention the serious environmental damage caused by the leaking commodity. The proposed techniques, although implemented on a lab scale experimental rig, ultimately aim at providing a continuous monitoring system using an array of different sensors strategically positioned on the surface of the pipeline. The sensors used are piezoelectric ultrasonic sensors. The raw sensor signal will be first processed using the discrete wavelet transform (DWT) as a feature extractor and then classified using the powerful learning machine called the support vector machine (SVM). Preliminary tests show that the sensors can detect the presence of wall thinning in a steel pipe by classifying the attenuation and frequency changes of the propagating lamb waves. The SVM algorithm was able to classify the signals as abnormal in the presence of wall thinning.

Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach

This paper proposes an automatic folder allocation system for text documents through the implementation of a hybrid classification method which combines the Bayesian (Bayes) approach and the Support Vector Machines (SVMs). Folder allocation for text documents in computer is typically executed manually by the user. Every time the user creates text documents by using text editors or downloads the documents from the internet, and wishes to store these documents on the computer, the user needs to determine and allocate the appropriate folder in which to store these new documents. This situation is inconvenient as repeating the folder allocation each time a text document is stored becomes tedious especially when the numbers and layers of folders are huge and the structure is complex and continuously growing. This problem can be overcome by implementing Artificial Intelligence machine learning methods to classify the new text documents and allocate the most appropriate folder as the storage for them. In this paper we propose the Bayes-SVMs hybrid classification framework to perform the tedious task of automatically allocating the right folder for text documents in computers.

Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach

Highlights? This paper presents an oil and gas pipeline failure prediction system using LRUT in conjunction with Euclidean-SVM approach. ? The conventional NDT pipeline failure prediction systems are deployed at pre-determined intervals. ? LRUT pipeline failure prediction system with Euclidean-SVM provides continuous monitoring for pipelines and makes decision without human errors and misinterpretations. ? In contrast to the conventional SVM, Euclidean-SVM is less dependent on the choice of the kernel function and parameters. ? Experimental results show that Euclidean-SVM has more consistent and better classification performance as compared to the conventional SVM. This paper presents an intelligent failure prediction system for oil and gas pipeline using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Since the past decade, the incidents of oil and gas pipeline leaks and failures which happened around the world are becoming more frequent and have caused loss of life, properties and irreversible environmental damages. This situation is due to the lack of a full-proof method of inspection on the condition of oil and gas pipelines. Onset of corrosion and other defects are undetected which cause unplanned shutdowns and disruption of energy supplies to consumers. Existing failure prediction systems for pipeline which use non-destructive testing (NDTs) methods are accurate, but they are deployed at pre-determined intervals which can be several months apart. Hence, a full-proof and reliable inspection method is required to continuously monitor the condition of oil and gas pipeline in order to provide sufficient information and time to oil and gas operators to plan and organize shutdowns before failures occur. Permanently installed long range ultrasonic transducers (LRUTs) offer a solution to this problem by providing an inspection platform that continuously monitor critical pipeline sections. Data are acquired in real-time and processed to make decision based on the condition of the pipe. The continuous nature of the data requires an automatic decision making software rather than manual inspection by operators. Support Vector Machines (SVMs) classification approach has been increasingly used in a multitude of domains including LRUT and has shown better performance than other classification algorithms. SVM is heavily dependent on the choice of kernel functions as well as fine tuning of the kernel and soft margin parameters. Hence it is unsuitable to be used in continuous monitoring of pipeline data where constant modifications of kernels and parameters are not unrealistic. This paper proposes a novel classification technique, namely Euclidean-Support Vector Machines (Euclidean-SVM), to make a decision on the integrity of the pipeline in a continuous monitoring environment. The results show that the classification accuracy of the Euclidean-SVM approach is not dependent on the choice of the kernel function and parameters when classifying data from pipes with simulated defects. Irrespective of the kernel function and parameters chosen, classification accuracy of the Euclidean-SVM is comparable and also higher in some cases than using conventional SVM. Hence, the Euclidean-SVM approach is ideally suited for classifying data from the oil and gas pipelines which are continuously monitored using LRUT.

Using unsupervised clustering approach to train the Support Vector Machine for text classification

The use of learning algorithms for text classification assumes the availability of a large amount of documents which have been organized and labeled correctly by human experts for use in the training phase. Unless the text documents in question have been in existence for some time, using an expert system is inevitable because manual organizing and labeling of thousands of groups of text documents can be a very labor intensive and intellectually challenging activity. Also, in some new domains, the knowledge to organize and label different classes might not be unavailable. Therefore unsupervised learning schemes for automatically clustering data in the training phase are needed. Furthermore, even when knowledge exists, variation is high when the subject under classification depends on personal opinions and is open to different interpretations. This paper describes a methodology which uses Self Organizing Maps (SOM) and alternatively does the automatic clustering by using the Correlation Coefficient (CorrCoef). Consequently the clusters are used as the labels to train the Support Vector Machine (SVM). Experiments and results are presented based on applying the methodology to some standard text datasets in order to verify the accuracy of the proposed scheme. We will also present results which are used to evaluate the effect that dimensionality reduction and changes in the clustering schemes have on the accuracy of the SVM. Results show that the proposed combination has better accuracy compared to training the learning machine using the expert knowledge.

Optimization of Multi-Pole Permanent Magnet Synchronous Generator Based 8 Blade Magnetically Levitated Variable Pitch Low Speed Vertical Axis Wind Turbine

The paper presents a new Vertical Axis Wind Turbine (VAWT) design by using magnetic levitation (Maglev) and Permanent Magnet Synchronous Generator (PMSG). A lab prototype of VAWT was built which was run at low wind speed of around 3 to 5 meter per second. The bearing was replaced by Neodymium Magnet to avoid the friction which in turns reduces the losses and increase the efficiency. A Prototype version of PMSG was built which could generate voltage from the turbine even in low rotational speed. Suitable turbine blade angle was also determined using trial and error method.

A novel tree trunk detection method for oil-palm plantation navigation

A novel tree trunk detection method for oil palm plantations is proposed.A combination of colour images and depth information is used for detection.The proposed method produced a 97.8% tree trunk detection rate in field tests. This paper presents a novel tree trunk detection algorithm that uses the Viola and Jones detector along with a proposed pre-processing method, combined with tree trunk detection via depth information. The proposed method tackles the issue of the high false positive rate when the Viola and Jones detector is used on its own, due to the low contrast between tree trunks and the surrounding environment. The pre-processing method uses colour space combination and segmentation to eliminate the ground not covered by trees from the images and feeding the resulting image into a cascade detector for identifying the location of the trunks in the image. Depth information is obtained via the use of the Microsoft KINECT sensor to further increase the accuracy of the detector. Our proposed method had better performance when compared to both Neural Network based and Support Vector Machine based detectors with a detection rate of 91.7% and had the lowest false acceptance rate out of other detectors, including the original Viola and Jones detector. The performance of the proposed method was also tested on live video feeds with the use of a robot prototype in an oil-palm plantation, which proved the high accuracy of the method, with a 97.8% detection rate. The inclusion of depth information resulted in more accurate detections during low levels of light and at night, where reliance on pure depth information resulted in a 100% detection rate of tree trunks within the range of the sensor.

The performance of a solar PV system using supercapacitor and varying loads

This paper analyses the application of supercapacitors in a standalone off-grid solar PV system. The solar PV system at University of Nottingham Malaysia Campus (UNMC) was tested using a programmable load. The programmable load was used to apply various load values to the system. The results on the effect of using different loads will be analysed and tested with and without a supercapacitor bank. Results show that the supercapacitor can supply peak current demand and preserve battery state of charge during the day. This system can be implemented in rural areas or small industries.

A Comparative Analysis of Three-Phase, Multi-Phase and Dual Stator Axial Flux Permanent Magnet Synchronous Generator for Vertical Axis Wind Turbine

In this paper, the performances of all the three kinds of Axial type Multi-Pole Permanent Magnet Synchronous Generators (PMSG) namely Three-phase, Multi-phase or Five Phase and Double Stator fixed in Vertical Axis Wind Turbine (VAWT) were investigated and compared in order to get an optimal system. MATLAB/Simulink had been used to model and simulate the wind turbine system together with all the three types Permanent Magnet Generators. It was observed from the result that with the increasing number of pole in both low and high wind speed, the five phase generator produced more power than the other two generators. In general, it was observed that the responses of the Multi-phase generator at both high and low speed wind showed promising aspect towards the system followed by Dual Stator. But with the change of the variables such as wind velocity, turbine height, radius, area together with the generator pole pairs and stator resistance, the optimum system should be chosen by considering the trade-off between different configurations which were firmly analyzed and described in this paper.