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Application of computer technology in Piano Teaching in Normal Universities

Application of computer technology in Piano Teaching in Norm...

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Internet Technology and Educational Informization (ITEI), international conference on

作者： Baihui Yan Fei Ren Jiangxi Science & Technology Normal University Nanchang Jiangxi China

ISBN: (纸本)9781665480512

Based on the resource integration and resource coordination of computer technology, piano teaching, as a discipline, will effectively use computer technology to mine valuable data information for teaching from massive data. The application of computer technology has subverted and promoted the traditional piano teaching mode in normal universities. Due to the new system analysis algorithm added by computer technology in the teaching process of piano teaching in normal universities, with the continuous expansion of the amount of data in piano teaching, the requirements for the system storage performance and operation speed of piano teaching in normal universities are also increasing. In order to further improve the comprehensive performance of computer technology in the piano teaching system model of normal universities, this paper proposes a hybrid model learning method, which is a hybrid incremental learning method using computer a model and data. This method can quickly carry out professional teaching for learners, and establish a dynamic learning scheme for learners through data analysis.

关键词： Learning systems Data analysis Computational modeling Education Music Production Data models

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Synchronous and Asynchronous Implementation of Radix-2 Booth Multiplication algorithm

Synchronous and Asynchronous Implementation of Radix-2 Booth...

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international conference on Electrical & Electronic Engineering (ICEEE)

作者： Nahin Ul Sadad Afsana Afrin Md. Nazrul Islam Mondal Rajshahi University of Engineering & Technology Rajshahi-6204 Bangladesh

ISBN: (纸本)9781665482820

Multiplication is one of the most common operations used in any program. Program working on massively large data always requires high computation power. In the age of big data, conventional general-purpose CPU based on Von Neumann architecture is no longer enough to satisfy high computation demand. Field Programmable Gate Array (FPGA) can perform hardware acceleration of any program. Since multiplier is the slowest component in any hardware accelerator, thus faster and re-configurable multiplier which can handle integers of any size must be implemented on FPGA. In this paper, we implemented both synchronous and asynchronous radix-2 booth multiplier using Verilog HDL on a Xilinx FPGA. We found that simulation time of asynchronous radix-2 booth multiplier is faster than synchronous radix-2 booth multiplier but synchronous radix-2 booth multiplier consumes fewer resources than asynchronous radix-2 booth multiplier.

关键词： Computational modeling computer architecture Logic gates Big Data Arrays Hardware design languages Field programmable gate arrays

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Study on Multi-source Mixed Partial Discharge Signals Separation Method Based on AMUSE algorithm

Study on Multi-source Mixed Partial Discharge Signals Separa...

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Weizhi Sun Hu Sheng Liang Shen School of Computer and Communication Engineering Dalian Jiaotong University Dalian China

ISBN: (纸本)9781665402682

Partial discharge is one of the main factors that harm the insulation of high voltage electrical equipment. When multiple insulation defects appear in the equipment at the same time, it will lead to the generation of hybrid partial discharge signal, which brings great challenges to the subsequent fault type identification work. This research studies the mixed-signal separation method of two uncorrelated insulation defects in electrical equipment. A blind source separation algorithm based on multiple unknown signal extraction algorithms is used to separate partial discharge mixed signals, and similar coefficients are used to evaluate the separation performance and anti- interference performance of the algorithm. The simulation results show that the method can effectively separate the mixed partial discharge signal, and the similarity coefficient between the separated signal and the original signal is more than 0.893, and it has good anti-interference performance. It provides an effective method for accurately judging the type of insulation defects and effectively avoiding the decline of electrical equipment insulation.

关键词： Partial discharges Fault diagnosis Insulation Technological innovation simulation Interference High-voltage techniques

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Cascaded Adaptive Nonlinear Functional Link Networks for modeling and Predicting Crude Oil Prices Time Series Data 3rd

Cascaded Adaptive Nonlinear Functional Link Networks for Mod...

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3rd international conference on Communications and Cyber Physical Engineering, ICCCE 2020

作者： Nayak, Sarat Chandra Kulukarni, Santosh V. Jilla, Karthik Department of Computer Science and Engineering CMR College of Engineering and Technology Hyderabad501401 India Department of Mechanical Engineering CMR College of Engineering and Technology Hyderabad501401 India

ISBN: (纸本)9789811579608

Contrast to traditional neural networks functional link neural network (FLNN) is preferred for its single layer structural design, lower computational complexity and higher convergence rate. It achieves high dimensional representation space of input patterns through functional expansion of input signals. However, its nonlinear approximation capability is limited up to certain extent. Further improvement in performance may require enlargement in dimensionality of the input pattern, which increases the computational overhead significantly. Chebyshev FLNN (CFLNN) is a special case of FLNN and has universal approximation capacity along with faster convergence. Legendre neural network (LeNN) uses simple polynomial expansion functions and posses computational gain over FLNN. This paper develops two cascaded neural networks in order to improve the performance of FLNN. The first model combines the input expansion capacity of FLNN and better approximation of CFLNN to develop a model termed as CCFLNN. Similarly, the second model takes the advantages of FLNN and LeNN to develop another model termed as CLeFLNN. The weight and bias vectors are adjusted by gradient descent based back propagation learning method. The proposed models are evaluated on forecasting crude oil prices. Extensive simulation outcome and comparative performance investigation suggests suitability of the proposed model. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Backpropagation

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Research on quantitative multi-factor stock selection model based on machine learning

Research on quantitative multi-factor stock selection model ...

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Ming Yang School of Computer Science and Technology Shandong University of Finance and Economics Jinan China

ISBN: (纸本)9781665402682

With the popularization of computer technology, the quantitative investment business in China has been developing rapidly, but it started late compared to the developed markets, and its related theories and applications have received very high attention in the investment field in China. In this paper, we focus on the effectiveness of multi-factor stock selection model applied to A-share market and use machine learning algorithms to improve the performance of the model. First, this paper selects 10 relatively independent valid factors from 44 candidate factors by validity testing and redundancy removal; second, builds a multifactor model and tests its validity, and introduces a hedging mechanism to further reduce the maximum retracement of the model; then changes the factor retracement period to select the model with the best effect for backtesting; finally, combines the AdaBoost algorithm with the multi-factor model and and the traditional Finally, the stock selection effect of combining AdaBoost algorithm with multi-factor model and and traditional multi-factor model is compared. The study finds that the machine learning AdaBoost [1] algorithm can enhance the stock selection effect of the traditional multi-factor model, and this study has a significant role in the future development of this field.

关键词： Technological innovation Machine learning algorithms Sensitivity Computational modeling Redundancy Diversity reception Machine learning

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Gymnasium simulation Design and Implementation Based on 3D Virtual Building

Gymnasium Simulation Design and Implementation Based on 3D V...

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international conference on Applied Machine Learning (ICAML)

作者： Yiren Zhou Hua Luo Tingting Yang Zhiqiang Guo Nanchang Institute of Technology Nanchang Jiangxi China

ISBN: (纸本)9781665421263

Based on the three-dimensional virtual building-simulation design and implementation method of the gymnasium, when the building space is subjected to virtual reality and simulation design, based on the constructed building space coordinate system and scale, the position and parameters of the building are used to construct a mathematical model of the building space component., The mathematical model of each component is integrated to build a mathematical model of the overall building space. OpenGL virtual reality technology is used to expand the target building based on the mathematical model of the target building space. The target building is given materials and texture characteristics to obtain the ideal 3D virtual view of the building space. The 3D virtual view of the building space is 3D rendered and displayed. The vivid 3D virtual renderings of the building space are animated using animation design technology. The experimental results show that the proposed method has good point-line rendering and overall rendering effect, can obtain more realistic 3D virtual building based gym simulation design results, and has high interactivity and practicability.

关键词： Solid modeling Three-dimensional displays Design methodology Buildings Earthquakes Virtual reality Rendering (computer graphics)

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A heuristic algorithm of social influence based on K-shell value diversity

A heuristic algorithm of social influence based on K-shell v...

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Baojun Fu Jianpei Zhang Rui Xiong College of Computer Science and Technology Harbin Engineering University HEU Harbin China College of Computer Science and Information Engineering Harbin Normal University HNU Harbin China

ISBN: (纸本)9781665402682

The measurement of node importance in complex networks and the influence maximization problem are hot issues in the field of network science in recent years. At present, node importance can usually be measured by degree index, betweenness index, tightness index, etc. In addition, K-shell, PageRank, eigenvector centrality and other indicators can also be used to evaluate node importance. However, these evaluation indicators have their own limitations. Based on K-shell, this paper proposes a heuristic algorithm -- KDP algorithm, which not only combines the diversity of the distribution of adjacent nodes, but also takes into account the importance of its neighbor nodes. In this paper, the independent cascade model is used to carry out the simulation experiment of node influence on multiple real complex networks. The experiment proves that KDP index can measure the propagation ability of nodes more accurately and has good time efficiency compared with other node importance evaluation indexes.

关键词： Technological innovation Heuristic algorithms Complex networks Time measurement Indexes Cultural differences

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Numerical analysis of the stability of nonequilibrium 3D evolutionary transfer processes in the network 3

Numerical analysis of the stability of nonequilibrium 3D evo...

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3rd international Scientific conference on Applied Physics, Information Technologies and Engineering, APITECH-III 2021

作者： Provotorov, V.V. Part, A.A. Shleenko, A.V. Sergeev, S.M. Voronezh State University 1 Universitetskaya sq. Voronezh394006 Russia Military Training and Scientific Center Air Force "Air Force Academy named after Professor N.E. Zhukovsky and Yu.A. Gagarin" 54 "A" Old Bolsheviks str. Voronezh394064 Russia Orel State University 95 Komsomol'skaya str. Orel302026 Russia Peter the Great St. Petersburg Polytechnic University 29 Polytechnitcheskaya str. St. Petersburg195251 Russia

Analytical methods for solving various problems of an applied nature (for example, non-stationary transfer problems over network hydro, gas and heat carriers), whose mathematical models use the formalisms of evolutionary differential systems, are possible with rare exceptions. That is why the construction of numerical and simulation models for the use of quantitative analysis methods becomes a universal research tool, if at the same time the implementation of these models on a computer is carried out – in other words, a complex of software engineering of the process under study is formed. The study uses the method of semi-discretization by a time variable of the mathematical model of the evolutionary non-equilibrium process of continuous medium transfer, which remains one of the most effective methods for analyzing applied problems. In this case, the elliptic operator of the mathematical model has a special basis (a system of eigenfunctions), which is why the analysis is reduced to the study of a boundary value problem for elliptic-type equations with a spatial variable changing on a network-like domain. The paper presents the conditions for unambiguous weak solvability of a differential-difference system, which is a difference analogue in the time variable of the original system, and the way of constructing an algorithm for finding an approximate solution is indicated. The study contains an analysis of the stability and convergence of difference schemes of evolutionary network-like nonequilibrium processes of continuous media transfer over network carriers and includes an analysis of the correctness of the mathematical model of this process. The results of the work are applicable in the framework of oil and gas engineering to the study of issues of stabilization and parametric optimization of the processes of transportation of liquid media through spatial networks. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Numerical simulation and Mathematical Modelling of Machine Learning Based Improved Crypto Currency Forecasting System 3rd

Numerical Simulation and Mathematical Modelling of Machine L...

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3rd international conference on Communication, Networks and Computing, CNC 2022

作者： Sharma, Ravi Bhushan Sharma, Himani Goyal Iqbal, Md. Asif Department of Computer Science Engineering Chandigarh University Chandigarh India Department of Electrical Engineering Chandigarh University Gharuan Chandigarh India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9783031431449

The crypto currency market is a rapidly growing trading and investment industry that has attracted merchants, investors, and entrepreneurs on a global scale never seen before in this century. By providing comparison studies and insights from the pricing data of crypto currency marketplaces, it will aid in documenting the behavior and habits of such a lucratively demanding and rapidly growing sector. The bitcoin market is at one of its all-time highs in 2021. The introduction of new exchanges has made crypto currencies more accessible to the general public, enhancing their attractiveness. This, together with the launch of several genuine crypto initiatives by some of the founders, has resulted in a surge in crypto currency users and interest. Virtual currencies are growing in popularity, with corporations like Microsoft, Dell, and Tesla all accepting them. As the number of individuals using decentralised digital currencies grows, As new currencies gain popularity, it's more important than ever to properly educate the public about them so that they understand what they possess and how their money is being invested. The algorithm predicts that the high, low, and open prices will all be close to the same value on the same day. The coefficients indicate that the high price has the biggest influence on the closing price. Contrary to popular assumption, "volume" does not add to the data on the closing price provided by the other factors. We also observed that the "marketcap" argument offers no more information. This is odd because market cap is often determined by the price of a product. After removing the two previously mentioned factors, we found no discernible difference in adjusted R2. It's worth mentioning that the model was built using data from the same time period. At the same moment as the closing price, the variables we obtained are made public. We are hopeful that the model will be able to forecast the closing price based on the existing high and low values for

关键词： Machine learning

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Extended LDA Based Topic Analysis for Big Sentiment Data 3

Extended LDA Based Topic Analysis for Big Sentiment Data

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2020 3rd international conference on modeling, simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Xie, Shengzhen Sui, Ling Li, Jiaxin Kou, Kaiqi Wang, Zhiqiong School of Computer Science and Engineering Northeastern University Shenyang China College of Medicine and Biological Information Engineering Northeastern University Shenyang China

Owing to the massive emergence of multi-modal data related to big sentiment events, the topic analysis of the text cannot accurately reflect the topic distribution of the whole data set. There are some problems such as semantic deficiency and difficulty in integration. Therefore, the extended LDA(eLDA) based topic analysis for big sentiment data is posed. Firstly, the semantic analysis of text and image data is carried out by probability generation method respectively. The semantic correlation between multi-modal data is used for visual topic learning, a visual topic model is established, and the mapping between visual data and text topics is realized. Furthermore, the fusion of text, audio, video and image data is realized, and multimodal theme analysis is carried out accordingly. The experimental results show that the multimodal topic analysis method can effectively obtain the subject words related to the semantics of big sentiment data. The effect of the subject words tracked by the multi-modal data analysis method is better than that of the single text subject words extraction, which can provide basis for emotional analysis of big sentiment events. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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