基于DWT-LSSVM的图像压缩算法

为了进一步提高图像压缩效率和质量,提出一种离散小波变换（DWT）和最小二乘支持向量机（LSSVM）相融合的图像压缩方法（DWT-LSSVM）。采用DWT对图像分解,得到低频系数和高频系数,采用LSSVM归学习逼近高频系数,并采用混沌粒子群算法对LSSVM参数进行优化,对支持向量、权重和低频系数进行编码,得到数据压缩数据流。仿真结果表明,DWT-LSSVM获得了较高的压缩比,可以较好满足图像传输的实时性要求。     

Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform

One of the research problems investigated these days is early fault detection. To this end, advanced signal processing algorithms are employed. The present paper makes an attempt at early fault detection in a gearbox. In order to evaluate its technical condition, artificial neural networks were used. Early fault detection based on support vector machines is a relatively new and rarely employed method for evaluating condition of machines, particularly gearboxes. The available literature offers very promising results of using this method. In order to compare the obtained results, a multilayer perceptron network was created. Such standard neural network ensures high effectiveness. The vibration signal obtained from a sensor is seldom a material for direct analysis. First, it needs to be processed to bring out the informative part of the signal. To this end, a wavelet transform was used. The presented results concern both a “raw” vibration signal and processed one, investigated for two neural networks. The wavelet transform has proved to improve significantly the accuracy of condition evaluation and the results obtained by the two networks are consistent with one another.     

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12-NASA MDP and 12-Java open-source projects), where NASA MDP is considered as a large-scale dataset and Java open-source projects are considered as a small-scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large-scale and small-scale datasets and overcome the limitations in the previous studies.     

ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix

We propose a systematic ECG quality classification method based on a kernel support vector machine（KSVM） and genetic algorithm（GA） to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function（GRBF） is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function（MHWF）. GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search（GS） method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive（TP）, false positive（FP）, and classification accuracy were used as the assessment indices. For training database set A（1000 recordings）, the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were： TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B（500 recordings）, the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.     

A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization

Fault detection and diagnosis have an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance technique that is applicable in the fault diagnosis of rotating machinery faults. A unique flexible algorithm is proposed for classifying the condition of centrifugal pump based on support vector machine hyper-parameters optimization and artificial neural networks (ANNs) which are composed of eight distinct steps. Artificial neural networks (ANNs), support vector classification with genetic algorithm (SVC-GA) and support vector classification with particle swarm optimization (SVC-PSO) algorithm have been considered in a flexible algorithm to perform accurate classification in the manufacturing area. SVC-GA, SVC-PSO and ANN have been used together due to their importance and capabilities in classifying domain. Also, the superiority of the proposed hybrid algorithm (SVC with GA and PSO) is shown by comparing its results with SVC performance. Two types of faults through six features, flow, temperature, suction pressure, discharge pressure, velocity, and vibration, have been classified with proposed integrated algorithm. To test the robustness of the efficiency results of the proposed method, the ability of proposed flexible algorithm in dealing with noisy and corrupted data is analyzed.