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Effect of Neutrinos on Angular Momentum of Dark Matter Halo

Research in Astronomy and Astrophysics 2023年第8期23卷 355-363页

作者： Yu Chen Chang-Zhi Lu Yu Lu Tingting Zhang Tong-Jie Zhang Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Department of Astronomy Beijing Normal UniversityBeijing 100875China Beijing Planetarium Beijing Academy of Science and TechnologyBeijing 100044China College of Command and Control Engineering PLA Army Engineering UniversityNanjing 210017China Institute for Astronomical Science Dezhou UniversityDezhou 253023China

Massive neutrinos are expected to affect the large-scale structure formation,including the major component of solid substances,dark matter *** halos are influenced by neutrinos is vital and interesting,and angular momentum(AM)as a significant feature provides a statistical perspective for this *** halos from TianNu N-body cosmological simulation with the co-evolving neutrino particles,we obtain some concrete ***,by comparing the same halos with and without neutrinos,in contrast to the neutrino-free case,over 89.71%of halos have smaller halo moduli,over 71.06%have smaller particle-mass-reduced(PMR)AM moduli,and over 95.44%change their orientations of less than 0°.***,the relative variation of PMR modulus is more visible for low-mass ***,to explore the PMR moduli of halos in dense or sparse areas,we divide the whole box into big cubes,and search for halos within a small spherical cell in a single *** the two-level divisions,we discover that in denser cubes,the variation of PMR moduli with massive neutrinos decreases more *** distinction suggests that neutrinos exert heavier influence on halos'moduli in compact *** massive neutrinos,most halos(86.60%)have lower masses than without neutrinos.

关键词： (cosmology:)dark matter-neutrinos-(cosmology:)large-scale structure of universe-methods data analysis

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An Energy Stable Bound-Preserving Finite Volume Scheme for the Allen-Cahn Equation Based on Operator Splitting Method

SSRN

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SSRN 2024年

作者： Peng, Gang Li, Yuan Command and Control Engineering College Army Engineering University of PLA Nanjing China Department of Basic Education Army Engineering University of PLA Nanjing China

In this paper, an energy stable bound-preserving finite volume scheme is constructed for the Allen-Cahn equation. The first-order operator splitting method is used to split the original equation into a nonlinear equation and a heat equation in each time interval. The nonlinear equation is solved by the backward Euler scheme, and the heat equation is discretized by the extremum-preserving scheme. The harmonic averaging points on cell facets are employed to define auxiliary unknowns, which enable our discrete scheme to be applicable to unstructured meshes. The energy stable and bound-preserving analysis of the finite volume scheme is also presented. Numerical experiments illustrate that this linear numerical scheme is practical and accurate in solving the Allen-Cahn equation. © 2024, The Authors. All rights reserved.

关键词： Nonlinear equations

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Recognizing knee pathologies by using gait dynamics via kernel principal component analysis and deterministic learning theory

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 15535-15543页

作者： Deng, Muqing Feng, Xiaoreng Zeng, Wei Cao, Jiuwen Zhang, Yu Zheng, Tongjia Institute of Information and Control Hangzhou Dianzi University Hangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Orthopaedics and Traumatology Queen Mary Hospital the University of Hong Kong Pok Fu Lam Hong Kong College of Mechanical and Electrical Engineering Longyan University Longyan China Department of Orthopaedic Surgery Guangzhou General Hospital of Guangzhou Military Command Guangzhou China College of Engineering University of Notre Dame Notre DameIN United States

This study concentrates on dynamical motion characteristics of knees during treadmill walking, and describes the application of kernel principal component analysis and deterministic learning theory to the detection of abnormal gait dynamics induced by knee pathologies. First, twelve-dimensional knee joint rotation and translation parameters are measured by a marker-based motion analysis system during treadmill walking. Second, the dimension of the gait parameter signal is reduced based on kernel principal component analysis. Third, gait dynamics underlying time-varying gait parameter signal is captured by using localized radial basis function neural networks through deterministic learning mechanism. This kind of dynamics information represents the frame-to-frame temporal change of kinematics and kinetics modifications during treadmill walking, which is shown to be sensitive to the variance during knee pathologies. Gait patterns are represented as the gait dynamics underlying time-varying gait parameters. The training patters under different knee pathologies further constitute a uniform training dataset, containing all kinds of gait dynamics under different knee pathologies. By comparing the training patterns with a test knee gait pattern to be recognized, a set of recognition errors are generated. The average L1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test pattern. A rapid recognition scheme can be achieved according to the smallest error principle. Experimental results show that encouraging recognition accuracy can be achieved. The proposed method is sensitive for the detection of knee pathologies and is capable of discharge of patients without knee pathologies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Errors

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Study on optimal conditions of flocculation in deinking wastewater treatment

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Journal of engineering and Applied Science 2021年第1期68卷 1-14页

作者： Li, Ming Hu, Kaitang Wang, Jin Department of Mathematics & Applied Sciences University of Michigan-Flint Flint United States Department of Printing Engineering Yuncheng Professional Technology College Shanxi China Department of Chemical Control Command Engineering Beijing Institute of Chemical Defense Beijing China

Flocculation is an important method to treat paper manufacturing wastewater. Coagulants and flocculants added to wastewater facilitate the aggregation and sedimentation of various particles in the wastewater and lead to the formation of floc networks which can be easily removed using physical methods. The goal of this paper is to determine the optimal hydraulic conditions using machine learning in order to enable efficient flocculation and improve performance during the treatment of deinking wastewater. Experiments using polymerized aluminum chloride as flocculant to treat deinking wastewater were carried out. Based on the orthogonal array test, 16 different combinations of hydraulic conditions were chosen to investigate the performance of flocculation, which was indicated by the turbidity of the solution after treatment. To develop a model representing the relationship between the hydraulic conditions and the performance of wastewater treatment, the machine learning methods, support vector regression and Gaussian process regression, were compared, whereby the support vector regression method was chosen. According to the fitness function derived from the support vector regression model, a genetic algorithm was applied to evaluate the optimal hydraulic conditions. Based on the optimal conditions determined by the genetic algorithm and real-life experience, a set of hydraulic conditions were implemented experimentally. After treatment under higher stirring speed at 120 rpm for 1 min and lower stirring speed at 20 rpm for 5 min at a temperature of 20 °C, the turbidity of deinking wastewater was measured as 1 NTU. The turbidity reduction was as high as 99.6%, which indicated good performance of the deinking wastewater treatment. © 2021, The Author(s).

关键词： Genetic algorithms

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Artificial Intelligence Based Employee Attrition Analysis and Prediction

Artificial Intelligence Based Employee Attrition Analysis an...

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Computing and Informatics (ICCI), International Conference on

作者： Nazli Sila Ozakca Ali Bulus Aydin Cetin Havelsan Inc. Command Control and Defence technologies Ankara Turkiye Computer Engineering Department Faculty of Technology Gazi University Ankara Turkiye

The use of classification models to predict the probability of an employee leaving the job can greatly improve the ability of human resources to intervene in a timely manner and rectify the situation to prevent attrition. After the so-called “The Great Resignation”, which took place after the pandemic period, studies on attrition have increased in order to regain the lost workforce and increase the loyalty of existing employees. In Turkey, however, studies in this field remain insufficient. The aim of this study is to predict the possible loss of employees, to take relevant measures and to reduce the financial losses of companies. The study was made available as SaaS (Software as a Service) based in order to facilitate accessibility to the target audience. For the analysis phase, the columns in the data set were analyzed with Pandas and Scikit-Learn library. Exploratory Data Analysis was performed by visualizing the analyzed data with Plotly and Seaborn libraries. Various insights were obtained with these inferences. The data was subjected to pre-processing stages such as cleaning, missing data completion, scaling, feature selection, data set balancing, dimension reduction, etc. to make it useful. Logistic Regression, KNN, SVM, Desicion Tree, Random Forest, ADABoost and Naive Bayes were used to train the model. In order to enrich the data set and increase the efficiency of the model, artificial intelligence-based synthetic data generation (Data Augmentation) was performed. In case of missing columns in the trained model, KNN-Data Imputation, one of the missing data completion methods, was used. In the optimization process of the model, hyperparameter optimizations were performed to achieve maximum efficiency with improvements. 5-fold crossvalidation prevented the model from over-learning. Performance was analyzed on the basis of Accuracy, Recall, Precision and F1-score metrics and the success criterion was determined as F1-Score. The model was presented as a web ser

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Optical fiber splicing defect segmentation using hybrid active contour

Scientific Visualization

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Scientific Visualization 2021年第5期12卷 61-78页

作者： Azizi, A. Azizi, Z. Department of Electrical Engineering University of Mohamed Khider Biskra Algeria Laboratory of Identification Command Control and Communication 'LI3C' University of Mohamed Khider Biskra Algeria

In this paper, we are interested to segment splicing hot (infra-red) images using active contours in order to extract defects for the fiber splicing process on fiber splicer machine. The segmentation is assured by using a hybrid edge and region active contour;where region information uses local statistics to solve the problem of highlight or intensity inhomogeneity, whereas edge information based on Local Binary Patterns LBP, smoothes homogeneous regions and enhance contour information. Experimental results on hot images captured from the fiber splicer machine illustrate the effective performance of the proposed method. © 2020 National Research Nuclear University. All rights reserved.

关键词： Defects

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A Method of Random Forest Classification based on Fuzzy Comprehensive Evaluation 8

A Method of Random Forest Classification based on Fuzzy Comp...

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8th International Conference on Dependable Systems and Their Applications, DSA 2021

作者： Zhu, Jing Huang, Song Shi, Yaqing Wu, Kaishun Wang, Yanqiu Command and Control Engineering College Army Engineering University of PLA Training Management Department Navy Command College Nanjing China Baopo Technology Co. LTD Nanjing China

ISBN: (纸本)9781665443913

At present, the method of random forest classification is one of the most common classifier. However, when there are many attributes in the acquired data set and the weights of attributes are not the same or close to each other, the direct use of this method for data training will not only ignore the correlation between attributes, but also make it difficult to reflect the advantages of different attribute weights, resulting in a negative impact on the accuracy of the final results. In addition, if there are many attributes in the data set, in order to meet the normalization of attribute weights, the values of the weights will inevitably be small, which will also lead to the loss of information, resulting in a negative impact on the final training result of the random forest. In order to solve these problems, this paper proposes a random forest method based on fuzzy comprehensive evaluation, which can not only considers the correlation between attributes, but also preserves the information in the original data set to the greatest extent, and at the same time significantly improves the accuracy of the results of random forest training. © 2021 IEEE.

关键词： Decision trees

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Design and Tuning of Fuzzy controller using Human Inspired Algorithm for Speed control of PMSM

Design and Tuning of Fuzzy Controller using Human Inspired A...

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Electronics, Energy and Measurement (IC2EM), International Conference on

作者： Tahar Nouaoui Abdelhakim Dendouga Abdelmalik Bendaikha Laboratory of identification command control and comunication University of Biskra Biskra Algeria Electrical engineering department University of Biskra Biskra Algeria Electrical engineering department University of M'sila Msila Algeria

designing a decent controller for a speed control of an alternative current (AC) machine, such as the PMSM can be very challenging, the conventional PID and PI controllers are the most used in the industry, but they lack the robustness and the rapid response of A fuzzy controller(FC), the fuzzy controller however needs a very precise scaling of its inputs, outputs, and its membership functions (MFS), this could be done with some serious user experience, or via trial and error technique, which is very time consuming and inconsistent, in this paper a human inspired optimization algorithm called Sewing Training-Based Optimization (STBO) was used to tune the parameters of a specially designed FC, within an hour of tuning the obtained FC managed to achieve significantly better speed tracking, and produce way less current harmonics than the conventional PID and PI controllers.

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Fractional Order PID Tuned using Symbiotic Organism Search Algorithm for Field Oriented control of The Permanent Magnet Synchronous Motor

Fractional Order PID Tuned using Symbiotic Organism Search A...

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Advances in Electronics, control and Communication Systems (ICAECCS), International Conference on

作者： Tahar Nouaoui Abdelhakim Dendouga Abdelmalik Bendaikha Laboratory of identification commandcontrol and comunication University of Biskra Biskra Algeria Electrical engineering department University of Biskra Biskra Algeria Electrical engineering department University of M’sila M’sila Algeria

This paper presents the model of the permanent magnet synchronous motor (PMSM) controlled by the field oriented control (FOC) technique using the fractional order proportional, integral, and derivative (FOPID) regulators. To bring out the maximum potential of the FOPID controller the algorithm of symbiotic organism search (SOS) has been used to obtain the gains of the latter. SOS, FOC, and fractional calculus were explained. The tuning efficiency of SOS has been compared to that of the algorithm of particle swarm optimization (PSO) when tuning the FOPID controllers for the PMSM under identical conditions and constraints. SOS algorithm managed to achieve better fitness than PSO. The obtained controllers were tested under various speeds and load charges. The inspection of the simulated results of speed, torque and induced currents proved the supremacy of SOS over PSO when tuning the parameters of the FOPID.

关键词： Symbiosis Torque Regulators Permanent magnet motors Linear programming Search problems Organisms

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Random Scaling-Based Bat Algorithm for Greenhouse Thermal Model Identification and Experimental Validation 4th

Random Scaling-Based Bat Algorithm for Greenhouse Thermal Mo...

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4th International Conference on Electrical engineering and control Applications, ICEECA 2019

作者： Guesbaya, Mounir Megherbi, Hassina Megherbi, Ahmed Chaouki Laboratory of Identification Command Control and Communication University of Mohamed Kheider Biskra Algeria Department of Electrical Engineering University of Mohamed Kheider Biskra Algeria

ISBN: (纸本)9789811564024

In this paper, we propose a Random Scaling-based Bat Algorithm (RSBA) for parametric identification of a greenhouse thermal model. The proposition includes modifying the exploitation of the standard BA by making the scaling parameter changes randomly over the iterations. The proposed thermal model identification method has been assessed first on a simulated greenhouse thermal model with known parameters. The simulation results have shown the superiority of the proposed RSBA compared to the standard BA in term of convergence and performance accuracy. To experimentally investigate the proposed identification method, we used a greenhouse prototype under arid climate conditions located in Mziraa, Biskra, Algeria. The obtained prediction results are found to be in a good agreement with the measured ones which show the effectiveness of the proposed RSBA in identifying the real greenhouse thermal model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Greenhouses

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