Abnormal Data Detection of Guidance Angle Based On SMP-SVDD For Seeker

The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, aiming to detect abnormal data in seeker pitch angle deviation data, a method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed to detect abnormal data in guidance angle data. On the one hand, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, and the particle swarm optimization is used to find the optimal kernel, which improves the detection accuracy. On the other hand, the constrained quadratic programming problem is smooth and differentiable, and the conjugate gradient method can be applied to reduce the complexity of problem solving. Through simulation experiments, it is verified that the SMP-SVDD method has higher detection accuracy and faster calculation speed compared with different detection methods in different guidance stages.

Energy Characteristics of Seismic Waves on Cardox Blasting Tube

In order to study the energy characteristics of seismic waves on the liquid CO2 blasting system, the blasting seismic wave signal of liquid CO2 blasting was obtained by on-site microseismic monitoring tests. The adaptive optimal kernel time-frequency analysis method was used to study the basic time-frequency properties of the seismic wave signal. Combining wavelet packet transform decomposition and reconstruction and adaptive optimal kernel time-frequency analysis method, the liquid CO2 energy distribution of the seismic wave signal was further analyzed. And the energy regression model of seismic wave source of liquid CO2 blasting system was discussed. The results show that the vibration velocity is at a low level, and the main frequency range is between 30 and 70 Hz, and the duration is about 20-30 ms. The energy is mainly distributed in 0-125 Hz, which is composed of two main regions. The power function model can be used to describe the attenuation law of the seismic wave energy. The energy conversion coefficient and characteristic coefficient of the source of liquid CO2 blasting system were defined and analyzed. Combined with the empirical formula of the Sadovsky vibration velocity, the energy regression model of the seismic wave source of liquid CO2 blasting system was obtained.

Principal Component Analysis and Support Vector Machine on Ice Entrainment Through a Sluice Gate

The entrainment and accumulation of ice floes in front of the sluice gates are closely related to the water transport efficiency and safe operation of the channel during an ice period. A flume study is carried out for a sluice gate with free outflow. A framework of stacking ensemble models is used to analyze the data, which consists of a two-level structure including the principal component analysis (PCA) and the support vector machine (SVM) algorithms. Based on the mechanism of ice floe accumulation, ten input characteristics of the machine learning (ML) model are selected. The PCA method is used to eliminate redundant information. The first principal component, with a contribution rate of 71.76%, and the second principal component, with a contribution of rate 15.64%, are extracted as the inputs of the SVM model, and the state of the floating ice in front of the gate is used to determine the classification labels. The 5-fold cross-validation method is used to train the model. The training results showed that the Gaussian radial basis functions (RBF) were the optimal kernel function. The performance of the developed model is measured using area under curve (AUC), accuracy (Acc) and F1-score (F1) values as statistical indicators. The results showed that the established PCA-SVM model improves the Bernoulli naive Bayes (Bernoulli NB) classifier and K-nearest neighbors’ algorithm (KNN) models. It increasing the AUC value by 11% and 5%, the Acc value by 16% and 17%, and the F1 value by 17% and 2%, respectively.

Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering

Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

Optimal Kernel Selection Based on GPR for Adaptive Learning of Mean Throughput Rates in LTE Networks

Machine learning models and algorithms have been employed in various applications, from prognostic scrutinizing, learning and revealing patterns in data, knowledge extracting, and knowledge deducing. One promising computationally efficient and adaptive machine learning method is the Gaussian Process Regression (GPR). An essential ingredient for tuning the GPR performance is the kernel (covariance) function. The GPR models have been widely employed in diverse regression and functional approximation purposes. However, knowing the right GPR training to examine the impacts of the kernel functions on performance during implementation remains. In order to address this problem, a stepwise approach for optimal kernel selection is presented for adaptive optimal prognostic regression learning of throughput data acquired over 4G LTE networks. The resultant learning accuracy was statistically quantified using four evaluation indexes. Results indicate that the GPR training with the mertern52 kernel function achieved the best user throughput data learning among the ten contending Kernel functions.