检索结果-内蒙古大学图书馆

Application of Innovative Teaching Methods in Music Education Based on Network Control and Optimized Centralized algorithms

引用

International Journal of High Speed Electronics and Systems 2025年第0期

作者： He, Zhaozhe Changde College College of Humanlities and Arts Changde415000 China

The traditional music education model faces challenges such as uneven distribution of teaching resources and lack of personalized teaching. With the rapid advancement of artificial intelligence (AI) and computer technology, especially the application of network control and centralized optimization algorithms, new paths have been opened up for innovation in music education. This paper focuses on building an innovative music education teaching platform that integrates network control and optimized centralized algorithms, aiming to explore its potential and practical value in improving the effectiveness of music education. The study designed a rigorous experimental plan, randomly assigning students to the experimental group and the control group and using the innovative teaching platform and traditional teaching mode for two months of music learning, respectively. The experimental results showed that the experimental group students were significantly better than the control group in terms of learning efficiency, skill improvement and creativity stimulation. Meanwhile, the satisfaction survey of students and teachers further confirms that the innovative teaching platform has received widespread praise. These findings strongly demonstrate that innovative music education teaching methods based on network control and optimized centralized algorithms have significant advantages in enhancing the effectiveness of music education. © World Scientific Publishing Company.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Current strategy of non-model-based bioprocess optimizations with genetic algorithms in bioscience - A systematic review

引用

Computers in Biology and Medicine 2025年 192卷 110247-110247页

作者： Surma, Robert Wojcieszyńska, Danuta Mulla, Sikandar I. Guzik, Urszula Institute of Biology Biotechnology and Environmental Protection Faculty of Natural Sciences University of Silesia in Katowice Jagiellonska 28 Katowice40-032 Poland Department of Biochemistry School of Allied Health Sciences REVA University Bangalore India

This article describes genetic algorithms (GAs), a widely used group of nature-inspired metaheuristics, and presents examples of their application in model-free optimization of bioprocesses. This approach is mainly used to solve optimization problems expressed through mathematical models. However, there are many situations in which laboratory optimization with GAs can be performed. In many cases, GAs have been reported to be superior to other popular optimization methods. Hence, their use is particularly recommended when multiple variables need to be studied simultaneously, the search space is large, and/or little is known about the interactions between individual factors. Despite their usefulness and simplicity, the number of reported experimental examples of non-model-based optimization using GAs remains limited. Real-world experimental evaluations, as opposed to mathematical fitness functions, are neither classified nor explicitly defined in the literature. The authors propose the term "Reality-Based Genetic algorithms" and express hope for its widespread adoption. There is a significant need for both theoretical and empirical research on the parameter configurations of genetic algorithms for experimental optimization, and the authors anticipate that this gap will be addressed in the future. In the meantime, it is recommended to either use configurations that have been proven successful in similar studies or to experiment with different configurations to generate comparative data for future research. © 2025 Elsevier Ltd

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Riemannian Online Optimistic algorithms with Dynamic Regret

引用

IEEE Transactions on Automatic Control 2025年

作者： Wang, Xi Yuan, Deming Hong, Yiguang Hu, Zihao Wang, Lei Shi, Guodong University of New South Wales School of Electrical Engineering & Telecommunications SydneyNSW2052 Australia Nanjing University of Science and Technology School of Automation Nanjing210094 China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Hong Kong University of Science and Technology Department of Mathematics Hong Kong Zhejiang University College of Control Science and Engineering Zhejiang 310058 China The University of Sydney Australian Center for Robotics School of Aerospace Mechanical and Mechatronic Engineering NSW2006 Australia

In this paper, we consider Riemannian online convex optimization with dynamic regret, which involves minimizing the cumulative loss difference between a learner's decisions and a sequence of adaptive decisions across dynamic environments over time on Riemannian manifolds. First, we propose Riemannian Online Optimistic Gradient Descent (R-OOGD), which extends the classical optimistic algorithms to Riemannian manifolds, requiring one gradient inquiry per round. We then propose Riemannian Adaptive Online Optimistic Gradient Descent (R-AOOGD) by incorporating the meta-expert framework into R-OOGD. We analyze the dynamic regret of R-OOGD and R-AOOGD in terms of the regularity of the sequence of cost functions and comparators. The dynamic regret bound matches the results in Euclidean space and with a scale adjustment of O(1+κ D2/√1-KD2) related to the sectional curvature lower bound κ, sectional curvature upper bound and feasible set diameter. Finally, we provide numerical experiments to illustrate the performance of the proposed algorithms. © 1963-2012 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

A unified framework to experiment with algorithm optimality and efficiency

引用

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2021年第6期29卷 1793-1810页

作者： Angel Velazquez-Iturbide, J. Rey Juan Carlos Univ Higher Tech Sch Comp Engn C Tulipan S-N Madrid 28933 Spain

It is common in algorithm courses to assess the correctness of algorithms via testing as well as conducting experiments to measure their run-time performance. However, two severe problems must be addressed: both activities are conducted using different procedures and tools, and algorithm assessment systems do not support well optimization problems due to their unique characteristics. Given the relevant role of optimization algorithms in computing, we present a unified framework to support experimentation with both criteria, that is, optimality and efficiency. The contributions of the article are twofold. First, we present a unified framework to experiment with both criteria, by using explicit principles and by presenting its instantiation in the AlgorEx system. The homogeneous treatment of optimality and time efficiency contributes to smoother integration of experimentation into a course syllabus and to easier adoption of the system. Second, we present our experience in several academic years in an algorithm course. Initially, it was noticed that students had severe difficulties dealing with experimentation, but their academic performance sharply increased by fully integrating experimentation along the complete course. The article also identifies some opportunities for extension in AlgorEx.

关键词： educational assessment experimental algorithmics optimality optimization algorithms software tool time efficiency

来源：评论

学校读者我要写书评

暂无评论

Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

引用

ENGINEERING WITH COMPUTERS 2021年第4期37卷 2717-2734页

作者： Jing, Hongjun Rad, Hima Nikafshan Hasanipanah, Mandi Armaghani, Danial Jahed Qasem, Sultan Noman Xian Univ Sci & Technol Sch Architecture & Civil Engn Xian 710054 Peoples R China Tabari Univ Babol Coll Comp Sci Babol Sar Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Modeling Evolutionary Algorithms Simulat & Artifi Ho Chi Minh City Vietnam Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Comp Sci Dept Riyadh Saudi Arabia Taiz Univ Fac Appl Sci Comp Sci Dept Taizi Yemen

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang-Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R-2) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R-2 of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.

关键词： UCS ANFIS Stochastic fractal search optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

The optimization methods for the thickness of the layer estimation using reflected waves

引用

Procedia Computer Science 2022年 207卷 1105-1112页

作者： Jerzy Podhajecki Elżbieta Kawecka The Jacob of Paradies University Faculty of Technology Teatralna 25 66-400 Gorzów Wielkopolski Poland

The paper presents a method that allows the thickness of a compact bone layer and longitudinal wave velocity in the bone to be determined simultaneously with the use of reflected waves, with particular emphasis on the case of layers when the layer thickness is of the same order as the ultrasonic wavelength. This paper presents the use of optimization methods for estimation the thickness of a layer using inverse method. The studied structure is a multilayer structure composed of two layers solid and porous material. To solve the inverse problem, a multi-layer model and ultrasonic signals obtained from cortical and cancellous bone phantoms by the reflection method was used. The genetic algorithm implemented in the Matlab® package optimization Toolbox applied to find solution the optimization problem. The results of simulation and experiments demonstrate that the thickness of layer was obtained with good accuracy.

关键词： inverse problems multi-layer model optimization algorithms genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Bilevel Evolutionary Multi-objective Algorithm with Multiple Lower-level Search Modes

引用

IEEE Transactions on Evolutionary Computation 2025年

作者： Yang, Ning Liu, Hai-Lin Guangdong University of Technology School of Automation Ganzhou China Jiangxi University of Science and Technology Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control School of Information Engineering Ganzhou China Guangdong University of Technology Center for Mathematics and Interdisciplinary Sciences School of Mathematics and Statistics Guangzhou China

In bilevel optimization, the upper-level optimization problem (ULOP) requires to be solved under the constraint of the inner lower-level optimization problem (LLOP). However, it is computationally expensive to always consider the constraint caused by the LLOP in a higher priority because heavy evaluation budgets are required for validating the constraint satisfaction. From this aspect, this paper investigates bilevel evolutionary multi-objective optimization with multiple lower-level search modes (BLEMO-MLS). Assisted by self-learning and reinforcement, BLEMO-MLS could adaptively adjust the priority of considering more on the upper-level objective optimization or the constraint caused by the LLOP. In BLEMO-MLS, three lower-level search modes are designed to handle the constraint caused by the LLOP. The former two search modes have higher priorities on the satisfaction of the constraint caused by the LLOP, where lower-level decisions of solutions are optimized by a hybrid lower-level search method, while the last search mode has a higher priority on the upper-level objective optimization with the constraint caused by the LLOP temporarily ignored. Through self-learning and reinforcement, BLEMO-MLS dynamically selects proper lower-level search modes in the bilevel optimization process, aiming to obtain approximate bilevel Pareto-optimal solutions fulfilling the constraint caused by the LLOP as much as possible. Compared with five existing bilevel evolutionary algorithms, BLEMO-MLS could effectively solve bilevel multi-objective optimization problems with function evaluations saved in both levels. © 1997-2012 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on the Digital Transformation of the Enterprise Marketing Ecosystem Based on Genetic algorithms

引用

International Journal of High Speed Electronics and Systems 2024年第0期

作者： Chen, Minghui Institute of Economics and Management Nanchang Institute of Science and Technology Jiangxi Nanchang330108 China

This paper examines the current state of the Enterprise Marketing Ecosystem (EME), especially in the context of Digital Transformation (DT), highlighting both the challenges and opportunities it presents. A significant focus is placed on the potential of genetic algorithms to address the complex optimization problems inherent in this transformation. The core issues faced by enterprises in their digital marketing evolution are elaborated, setting the stage for the exploration of genetic algorithms in this domain. The research delves into the specific steps of the proposed method, utilizing the multi-objective genetic algorithm NSGA-II to optimize various aspects of market strategies. The methodology includes the generation of an initial population, evaluation of fitness according to the tailored needs of the marketing ecosystem, selection, crossover, and mutation operations, followed by the formation of a new generation of solutions. The termination criteria of the algorithm are also discussed. Experimental results are presented, demonstrating the effectiveness of the genetic algorithm in enhancing the DT of the EME. Comparisons with traditional approaches reveal significant improvements in various key performance metrics, underscoring the algorithm's capability in navigating the complexities of digital marketing optimization. © 2025 World Scientific Publishing Company.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints

引用

IISE TRANSACTIONS 2021年第1期53卷 21-38页

作者： Ivanov, Dmitry Sokolov, Boris Chen, Weiwei Dolgui, Alexandre Werner, Frank Potryasaev, Semyon Berlin Sch Econ & Law Supply Chain & Operat Management Berlin Germany St Petersburg Inst Informat & Automat RAS SPIIRAS St Petersburg Russia Rutgers State Univ Dept Supply Chain Management Piscataway NJ USA CNRS LS2N IMT Atlantique Nantes France Otto von Guericke Univ Magdeburg Germany

We study the problem of scheduling in manufacturing environments which are dynamically reconfigurable for supporting highly flexible individual operation compositions of the jobs. We show that such production environments yield the simultaneous process design and operation sequencing with dynamically changing hybrid structural-logical constraints. We conceptualize a model to schedule jobs in such environments when the structural-logical constraints are changing dynamically and offer a design framework of algorithmic development to obtain a tractable solution analytically within the proven axiomatic of the optimal control and mathematical optimization. We further develop an algorithm to simultaneously determine the process design and operation sequencing. The algorithm is decomposition-based and leads to an approximate solution of the underlying optimization problem that is modeled by optimal control. We theoretically analyze the algorithmic complexity and apply this approach on an illustrative example. The findings suggest that our approach can be of value for modeling problems with a simultaneous process design and operation sequencing when the structural and logical constraints are dynamic and interconnected. Utilizing the outcomes of this research could also support the analysis of processing dynamics during the operations execution.

关键词： Reconfigurable manufacturing systems dynamics flexible machines flexible jobs optimal control optimization algorithms Industry 4 0 digital manufacturing cloud manufacturing

来源：评论

学校读者我要写书评

暂无评论

Fast Sidelobe Calculation and Suppression in Arbitrary Two-Dimensional Phased Arrays

引用

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2021年第6期20卷 928-932页

作者： Miao, Ke Zhang, Yi Sun, Houjun Beijing Inst Technol Beijing Key Lab Millimeter Wave & Terahertz Tech Sch Informat & Elect Beijing Peoples R China Beijing Inst Technol Sch Phys Beijing 100081 Peoples R China

Sidelobe calculation is a common procedure in phased array synthesis. However, the traditional sidelobe calculation method through pattern simulation and peak searching costs a large amount of time. In this letter, we derive a formula for fast sidelobe level calculation in arbitrary 2-D phased arrays. The formula describes the relationship between sidelobe (change throughout the article) level and the element feeding excitations. The result of numerical experiments shows that the proposed method is as accurate as the search method. When applying our formula in a genetic algorithm for sidelobe suppression, the proposed method consumes 0.1% of the computing time compared with the search method in a nonuniformly distributed array of 50 elements.

关键词： Phased arrays Genetic algorithms Search methods optimization Sun Radar antennas Planar arrays Array synthesis genetic algorithm (GA) optimization algorithms sidelobe calculation sidelobe suppression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：