Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking

A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multiclassifier jointly using iterative reweighed least squares algorithm. Combined with robust sparse coding, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between robust sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness

Robust and accurate online pose estimation algorithm via efficient three‐dimensional collinearity model

In this study, the authors propose a robust and high accurate pose estimation algorithm to solve the perspective‐N‐point problem in real time. This algorithm does away with the distinction between coplanar and non‐coplanar point configurations, and provides a unified formulation for the configurations. Based on the inverse projection ray, an efficient collinearity model in object–space is proposed as the cost function. The principle depth and the relative depth of reference points are introduced to remove the residual error of the cost function and to improve the robustness and the accuracy of the authors pose estimation method. The authors solve the pose information and the depth of the points iteratively by minimising the cost function, and then reconstruct their coordinates in camera coordinate system. In the following, the optimal absolute orientation solution gives the relative pose information between the estimated three‐dimensional (3D) point set and the 3D mode point set. This procedure with the above two steps is repeated until the result converges. The experimental results on simulated and real data show that the superior performance of the proposed algorithm: its accuracy is higher than the state‐of‐the‐art algorithms, and has best anti‐noise property and least deviation by the influence of outlier among the tested algorithms

A SOM-Based Membrane Optimization Algorithm for Community Detection

The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. Aimed at community detection in complex networks, this paper proposed a membrane algorithm based on a self-organizing map (SOM) network. Firstly, community detection was transformed as discrete optimization problems by selecting the optimization function. Secondly, three elements of the membrane algorithm, objects, reaction rules, and membrane structure were designed to analyze the properties and characteristics of the community structure. Thirdly, a SOM was employed to determine the number of membranes by learning and mining the structure of the current objects in the decision space, which is beneficial to guiding the local and global search of the proposed algorithm by constructing the neighborhood relationship. Finally, the simulation experiment was carried out on both synthetic benchmark networks and four real-world networks. The experiment proved that the proposed algorithm had higher accuracy, stability, and execution efficiency, compared with the results of other experimental algorithms

A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data

Student achievement prediction is one of the most important research directions in educational data mining. Student achievement directly reflects students’ course mastery and lecturers’ teaching level. Especially for the achievement prediction of college students, it not only plays an early warning and timely correction role for students and teachers, but also provides a method for university decision-makers to evaluate the quality of courses. Based on the existing research and experimental results, this paper proposes a student achievement prediction model based on evolutionary spiking neural network. On the basis of fully analyzing the relationship between course attributes and student attributes, a student achievement prediction model based on spiking neural network is established. The evolutionary membrane algorithm is introduced to learn hyperparameters of the model, so as to improve the accuracy of the model in predicting student achievement. Finally, the proposed model is used to predict student achievement on two benchmark student datasets, and the performance of the prediction model proposed in this paper is analyzed by comparing with other experimental algorithms. The experimental results show that the model based on spiking neural network can effectively improve the prediction accuracy of student achievement

Characteristic Analysis of Ambient Air Pollutants during Summer Season in Shenyang City

Shenyang City was the political, economic and cultural centre of the northeast China and was also a heavily polluted industrial city. The understanding of the distribution characteristics of air pollutants concentration and changes was still lacked. To reduce the impact of disturbing factors such as firecrackers in the traditional Chinese festivals, the observation period of the monitoring data was selected from 17th May to 21st July in 2016. The data sources were picked from eight national monitoring stations and the daily average concentration of the main air pollutants that included PM2.5, PM10, SO2, NO2 and O3. The overall analysis of distribution characteristics of the air pollutant was shown that the principal pollutants with highest frequency were O3 and PM10, the average proportion was 74.1% and 20.8% respectively

Characteristic Analysis of Ambient Air Pollutants during Summer Season in Shenyang City

Shenyang City was the political, economic and cultural centre of the northeast China and was also a heavily polluted industrial city. The understanding of the distribution characteristics of air pollutants concentration and changes was still lacked. To reduce the impact of disturbing factors such as firecrackers in the traditional Chinese festivals, the observation period of the monitoring data was selected from 17th May to 21st July in 2016. The data sources were picked from eight national monitoring stations and the daily average concentration of the main air pollutants that included PM2.5, PM10, SO2, NO2 and O3. The overall analysis of distribution characteristics of the air pollutant was shown that the principal pollutants with highest frequency were O3 and PM10, the average proportion was 74.1% and 20.8% respectively