Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm

There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the image is introduced to decompose the aerobics image. According to the denoising and decomposition results, the enhancement of aerobics image is realized by the logarithmic image processing (LIP) model and gradient sharpening method. Finally, the aerobics image after the enhancement is classified by a modal symmetry algorithm. Experimental results show that the proposed method has a good denoising effect and high classification efficiency, which shows that the algorithm has significant effectiveness and high application performance.

Effect on Neural Pattern Classifier for Intelligent Gas Sensor by Increasing Number of Hidden Layer

Neural networks are used to solve complex problem viz., speech and image recognition, pattern recognition (Pattern classification), computer vision etc. Pattern classification by using Back Propagation algorithm for an intelligent gas sensor application is presented. The classifier is trained using published data of thick film tin oxide sensor array. Its superior classification and learning performance is demonstrated for discrimination of alcohols and alcoholic beverages by increasing number of hidden layer. The new model proposed in this article give steep and monotone learning curve and better classification efficiency. Keywords: Neural Network classifier, Back Propagation Algorithm, system error, classification efficiency, learning curve

Development of a New Solver to Model the Fish-Hook Effect in a Centrifugal Classifier

Centrifugal air classifiers are often used for classification of particle gas flows in the mineral industry and various other sectors. In this paper, a new solver based on the multiphase particle-in-cell (MP-PIC) method, which takes into account an interaction between particles, is presented. This makes it possible to investigate the flow process in the classifier in more detail, especially the influence of solid load on the flow profile and the fish-hook effect that sometimes occurs. Depending on the operating conditions, the fish-hook sometimes occurs in such apparatus and lead to a reduction in classification efficiency. Therefore, a better understanding and a representation of the fish-hook in numerical simulations is of great interest. The results of the simulation method are compared with results of previous simulation method, where particle–particle interactions are neglected. Moreover, a validation of the numerical simulations is carried out by comparing experimental data from a laboratory plant based on characteristic values such as pressure loss and classification efficiency. The comparison with experimental data shows that both methods provide similar good values for the classification efficiency d50; however, the fish-hook effect is only reproduced when particle-particle interaction is taken into account. The particle movement prove that the fish-hook effect is due to a strong concentration accumulation in the outer area of the classifier. These particle accumulations block the radial transport of fine particles into the classifier, which are then entrained by coarser particles into the coarse material.

A New Rotor-Type Dynamic Classifier: Structural Optimization and Industrial Applications

Due to the inadequate pre-dispersion and high dust concentration in the grading zone of the turbo air classifier, a new rotor-type dynamic classifier with air and material entering from the bottom was designed. The effect of the rotor cage structure and diversion cone size on the flow field and classification performance of the laboratory-scale classifier was comparatively analyzed by numerical simulation using ANSYS-Fluent. The grinding process performance with an industrial classifier was also tested on-site. The results revealed that an inverted cone-type rotor cage is more suitable for the under-feed classifier. When the rotor cage’s top-surface diameter to bottom-surface diameter ratio was too large or too small, the radial velocity and tangential velocity at the outer surface of the rotor cage greatly fluctuated. Furthermore, the diameter of the diversion cone also affected the axial velocity and radial velocity of the flow field. Models T-C(1-0.8) and T-D(1-0.7) were determined as the best rotor cage structures. Under stable operating conditions, the classification efficiency of the industrial classifier was 87% and the sharpness of separation was 0.58, which meet the industrial requirements for classification efficiency and energy consumption. This present study provides theoretical guidance and engineering application value for air classifiers.

Novel KNN Algorithms for Spherical Regions Based on Clustering and Region Division

The KNN classification algorithm is one of the most commonly used algorithm in the AI field. But classical KNN classification algorithm does not preprocess data before classification calculation, which results in a long time required for classification and a decrease in classification accuracy. To solve the above problems, this paper proposes two improved algorithms, namely KNNTS, and KNNTS-PK+. The two improved algorithms are based on KNNPK+ algorithm, which uses PK-Means + + algorithm to select the center of the spherical region, and sets the radius of the region to form a sphere to divide the data set in the space. The KNNPK+ algorithm improves the classification accuracy on the premise of stabilizing the classification efficiency of KNN classification algorithm. In order to improve the classification efficiency of KNN algorithm on the premise that the accuracy of KNN classification algorithm remains unchanged, KNNTS algorithm is proposed. It uses tabu search algorithm to select the radius of spherical region, and uses spherical region division method with equal radius to divide the data set in space. On the basis of the first two improved algorithms, KNNTS-PK+ algorithm combines them to divide the data sets in space. After preprocessing the data by two methods, experiments are carried out on the new data set and the classification results were obtained. Results revealed show that the two improved algorithms can effectively improve the classification accuracy and efficiency after the data samples are cut reasonably.

Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

Die Systematisierung von Rechtfertigungsgründen im preisbezogenen Behinderungsmissbrauch nach Art. 102 AEUV

Low-price strategies of undertakings with a dominant market position are often subject to an inadequate abuse-analysis evaluation for the individual case. Thus, a systematic integration of grounds of justification in the examination according to Art. 102 TFEU seems overdue. In an interdisciplinary discourse, after a brief dogmatic classification, efficiency- and competition-specific anchor points are explored, which basically allow individual grounds of justification to be categorized according to application. This is followed by a differentiation in structure and content. The integration of reliable economic knowledge is of particular importance.

Effects of Blade Parameters on the Flow Field and Classification Performance of the Vertical Roller Mill via Numerical Investigations

The vertical roller mill is an important crushing and grading screening device widely used in many industries. Its classification efficiency and the pressure difference determine the entire producing capacity and power consumption, respectively, which makes them the two key indicators describing the mill performance. Based on the DPM (Discrete Phase Model) and continuous phase coupling model, the flow field characteristics in the vertical roller mill including the velocity and pressure fields and the discrete phase distributions had been analyzed. The influence of blade parameters like the shape, number, and rotating speed on the flow field and classification performance had also been comprehensively explored. The numerical simulations showed that there are vortices in many zones in the mill and the blades are of great significance to the mill performance. The blade IV not only results in high classification efficiency but also reduces effectively the pressure difference in the separator and also the whole machine. The conclusions of the flow field analysis and the blade effects on the classification efficiency and the pressure difference could guide designing and optimizing the equipment structure and the milling process, which is of great importance to obtain better overall performance of the vertical roller mill.