Response Times Reconstructor Based on Mathematical Expectation Quotient for a High Priority Task over RT-Linux

Every computer task generates response times depending on the computer hardware and software. The response times of tasks executed in real-time operating systems such as RT-Linux can vary as their instances evolve even though they always execute the same algorithm. This variation decreases as the priority of the tasks increases; however, the minimum and maximum response times are still present in the same task, and this complicates its monitoring, decreasing its level of predictability in case of contingency or overload, as well as making resource sizing difficult. Therefore, the need arises to propose a model capable of reconstructing the dynamics of response times for the instances of a task with high priority in order to analyze their offline behavior under specific working conditions. For this purpose, we develop the necessary theory to build the response time reconstruction model. Then, to test the proposed model, we set up a workbench consisting of a single board computer, PREEMPT_RT, and a high priority task generated by the execution of a matrix inversion algorithm. This work demonstrates the application of the theory in an experimental process, presenting a way to model and reconstruct the dynamics of response times by a high-priority task on RT-Linux.

A Methodological Approach to Analyzing Digital Game-Based FL Use and Learning

The potential of digital gaming to facilitate foreign language (FL) learning has been established in many empirical investigations; however, the pedagogical implications of these investigations remain rather limited. A potential reason for this limitation is that the FL learning potential of digital games is embedded in the gaming ecology and shaped by different forces in that ecology. However, to date most empirical studies in the field have focused primarily on the linguistic behavior of gamers rather than the gaming ecology. A potential reason for this is the lack of a robust methodological approach to examining game-based language use as an ecological, multidimensional activity. To address this research gap, this chapter proposes the diamond reconstruction model, a dynamic, multidimensional, and ecology-sensitive approach to de- and re-constructing game-based FL use. Grounded in theories of gameplay, and informed by a conceptual model of game-based FL use, the model reconstructs gameplay episodes by gathering detail-rich data on social, cognitive, and virtual dimensions.

Compressive Video Sensing Based on Intra-Inter-Frame Constraints and Genetic Algorithm

Establishing structured reconstruction models and efficient reconstruction algorithms according to practical engineering needs is of great concern in the applied research of Compressed Sensing (CS) theory. Targeting problems during high-speed video capture, the paper proposes a set of video CS scheme based on intra-frame and inter-frame constraints and Genetic Algorithm (GA). Firstly, it employs the intra-frame and inter-frame correlation of the video signals as the priori information, creating a video CS reconstruction model on the basis of temporal and spatial similarity constraints. Then it utilizes overcomplete dictionary of Ridgelet to divide the video frames into three structures, smooth, single-oriented, or multijointed. Video frames cluster according to the structure using Affinity Propagation (AP) algorithm, and finally clusters are reconstructed using evolutionary algorithm. It is proved efficient in terms of reconstruction result in the experiment.

An accelerated majorization-minimization algorithm with convergence guarantee for non-Lipschitz wavelet synthesis model *

We consider a wavelet based image reconstruction model with the ℓ p (0 < p < 1) quasi-norm regularization, which is a non-convex and non-Lipschitz minimization problem. For solving this model, Figueiredo et al (2007 IEEE Trans. Image Process. 16 2980–2991) utilized the classical majorization-minimization framework and proposed the so-called Isoft algorithm. This algorithm is computationally efficient, but whether it converges or not has not been concluded yet. In this paper, we propose a new algorithm to accelerate the Isoft algorithm, which is based on Nesterov’s extrapolation technique. Furthermore, a complete convergence analysis for the new algorithm is established. We prove that the whole sequence generated by this algorithm converges to a stationary point of the objective function. This convergence result contains the convergence of Isoft algorithm as a special case. Numerical experiments demonstrate good performance of our new algorithm.

Fast Reconstruction Method of the Stress Field for the Steam Turbine Rotor Based On Deep Fully Convolutional Network

Since it is difficult to directly measure the transient stress of a steam turbine rotor in operation, a rotor stress field reconstruction model based on deep fully convolutional network for the start-up process is proposed. The stress distribution in the rotor can be directly predicted based on the temperature of a few measurement points. First, the finite element model is used to accurately simulate the temperature and stress field of the rotor start-up process, generating training data for the deep learning method. Next, data of only 15 temperature measurement points are arranged to predict the stress distribution in critical area of the rotor surface, with the accuracy (R2-score) reaching 0.997. The time cost of the trained neural network model at a single case is 1.42s in CPUs and 0.11s in GPUs, shortened by 97.3% and 99.8% with comparison to finite element analysis, respectively. In addition, the influence of the number of temperature measurement points and the training size are discussed, verifying the stability of the model. With the advantages of fast calculation, high accuracy and strong stability, the fast reconstruction model can effectively realize the stress prediction during start-up processes, resulting in the possibility of real-time diagnosis of rotor strength in operation.

Study on 3D Image Reconstruction Model of Sparring Action Based on Graph Neural Network (GNN)

With the advent of the information age, human demand for information is increasing day by day. The emergence of the concept of big data has triggered a new round of technological revolution, and visual information plays an important role in information. In order to obtain a better 3D model, this paper studies the reconstruction model of training motion 3D images based on a graphical neural network algorithm. This paper studies the problem of Sanda from the following two aspects. First, we try to apply two deep learning algorithms, graphical neural network and recurrent neural network, to the boxing movement recognition task and compare the effects with quadratic discriminant analysis and support vector machine. By comparing and analyzing the influence of different network structures on the deep learning algorithm, it is concluded that recurrent neural network has more practical application advantages than graph neural network in network structure parameter tuning.