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Research on Computer-aided Innovation and Entrepreneurship Education Model and Algorithm

Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Wang, Jingru Xinyu University Jiangxi Xinyu338000 China

With the rapid development of science and technology, computer-aided technology has been widely used in the field of education, but in the field of innovation and entrepreneurship education (Hereinafter referred to as IEE), there are still problems such as uneven distribution of resources and the disconnection between teaching content and actual *** paper puts forward a new model framework of computer-aided IEE, which is student-centered and *** clear teaching objectives, rich course content, diverse teaching methods, and a scientific evaluation system, students' innovative consciousness and entrepreneurial ability are comprehensively *** course covers innovative thinking, business models, market research, and other theoretical knowledge, and introduces virtual reality (VR) and augmented reality (AR) technologies to simulate the real entrepreneurial process and provide a practical *** the aspect of algorithm application, the research combines the content recommendation algorithm and collaborative filtering recommendation algorithm, constructs accurate user portraits by analyzing students' learning behavior data, and matches them according to the characteristics of learning resources to recommend personalized learning resources for *** the same time, the learning path optimization algorithm is developed, and the personalized and efficient learning path is planned by using the shortest path algorithm in graph theory to ensure that students can master the required knowledge in the shortest *** experimental part designed a one-semester teaching experiment, and compared the differences between the experimental group and the control group in academic performance, learning participation, and *** results show that the computer-aided IEE model significantly improves students' academic performance, learning participation, and satisfaction, which verifies the effectiveness of the *** research on the

关键词： optimization algorithms

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Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions

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INFORMATION 2024年第11期15卷 692页

作者： Fergani, Okba Himeur, Yassine Mechgoug, Raihane Atalla, Shadi Mansoor, Wathiq Tkouti, Nacira Univ Biskra Lab Identificat Commande Control & Commun LI3CUB Biskra 07000 Algeria Univ Dubai Coll Engn & Informat Technol POB 14143 Dubai U Arab Emirates Univ Biskra Dept Elect Engn Biskra 07000 Algeria

The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Omega. These findings are derived from extensive experimentation using NASA's worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology.

关键词： Quantum Marine Predator Algorithm (QMPA) Maximum Power Point Tracking (MPPT) photovoltaic systems quantum mechanics optimization algorithms renewable energy efficiency

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Advanced cuttlefish optimizer-random decision forest (ACORDF) based design of fractional order PID controller for higher-order time-delay system

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2021年

作者： George, Thomas Ganesan, V. Mt Zion Coll Engn Kadammanitta India Sathyabama Univ Dept Elect & Telecommun Engn Chennai Tamil Nadu India

Purpose The purpose of this manuscript, a state feedback gain depends on the optimal design of fractional order PID controller to time-delay system is established. In established optimal design known as advanced cuttlefish optimizer and random decision forest that is combined performance of random decision forest algorithm (RDFA) and advanced cuttlefish optimizer (ACFO). Design/methodology/approach The proposed ACFO uses the concept of crossover and mutation operator depend on position upgrading to enhance its search behavior, calculational speed as well as convergence profile at basic cuttlefish optimizer. Findings Fractional order proportional-integrator-derivative (FOPID) controller, apart from as tuning parameters (kp, ki and kd) it consists of two extra tuning parameters lambda and mu. In established technology, the increase of FOPID controller is adjusted to reach needed responses that demonstrated using RDFA theory as well as RDF weight matrices is probable to the help of the ACFO method. The uniqueness of the established method is to decrease the failure of the FOPID controller at greater order time delay method with the help of controller maximize restrictions. The objective of the established method is selected to consider parameters set point as well as achieved parameters of time-delay system. Originality/value In the established technique used to evade large order delays as well as reliability restrictions such as small excesses, time resolution, as well as fixed condition defect. These methods is implemented at MATLAB/Simulink platform as well as outcomes compared to various existing methods such as Ziegler-Nichols fit, curve fit, Wang method, regression and invasive weed optimization and linear-quadratic regression method.

关键词： Control systems optimization algorithms FOPID State feedback gain Three controller parameters Time-delay system Reliability constraints and small overruns

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Instance-Dependent Confidence and Early Stopping for Reinforcement Learning

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Journal of Machine Learning Research 2023年 24卷

作者： Xia, Eric Khamaru, Koulik Wainwright, Martin J. Jordan, Michael I. Department of EECS Massachusetts Institute of Technology CambridgeMA02139 United States Department of Statistics Rutgers University PiscatawayNJ08854 United States Department of EECS and Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States Department of EECS and Statistics University of California Berkeley BerkeleyCA94720 United States

Reinforcement learning algorithms are known to exhibit a variety of convergence rates depending on the problem structure. Recent years have witnessed considerable progress in developing theory that is instance-dependent, along with algorithms that achieve such instance-optimal guarantees. However, important questions remain in how to utilize such notions for inferential purposes, or for early stopping, so that data and computational resources can be saved for "easy" problems. This paper develops data-dependent procedures that output instance-dependent confidence regions for evaluating and optimizing policies in a Markov decision process. Notably, our procedures require only black-box access to an instance-optimal algorithm, and re-use the samples used in the estimation algorithm itself. The resulting data-dependent stopping rule adapts instance-specific difficulty of the problem and allows for early termination for problems with favorable structure. We highlight benefit of such early stopping rules via some numerical studies. ©2023 Eric Xia, Koulik Khamaru, Martin J. Wainwright, and Michael I. Jordan.

关键词： optimization algorithms

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Performance Enhancement of Doubly-Fed Induction Generator-Based-Wind Energy System

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2023年第1期13卷 311-325页

作者： Boghdady, Tarek A. Sweed, Islam A. Ibrahim, Doaa Khalil Cairo Univ Fac Engn Dept Elect Power Engn Giza 12613 Egypt

the challenging errand is enhancing the wind energy system (WES) performance to be more competitive and economically viable. One of the best ways to enhance the performance of the doubly-fed induction generator (DFIG)-based-WES is the optimization of the proportional-integral (PI) controllers for the variable frequency converter system. Many objectives with different optimization techniques have been used in literature to achieve optimal performance. Each choice has its advantages and disadvantages. This paper presents a new design approach for better performance of PI controllers and, hence DFIG over a wide range of operating conditions through two main themes. The first is by introducing a new multi-objective formulation, while the second is utilizing recent optimization techniques like Grey Wolf Optimizer and Whale optimization Algorithm. Four PI controllers are optimized using a traditional objective function and the proposed multi-objective formulation. Two are related to the Rotor Side Converter (RSC), named power regulator, and the main rotor side converter current regulator. The other two PI controllers related to Grid Side Converter (GSC) are the DC-link voltage regulator and the main grid-side converter current regulator. A performance comparison is held through normal and abnormal operating conditions on a simulation model of a 6 MW wind farm located in Jabal Alzayt along the Red Sea Coast in Egypt and directly connected to the grid. The results confirmed the effectiveness of the proposed approach to help the DFIG-based-WES to agree with the Egyptian Grid Code during disturbances compared with the traditional objective formulation.

关键词： Doubly-Fed Induction Generator (DFIG) Grid Side Converter optimization algorithms Rotor Side Converter Wind Energy System (WES)

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A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104743-104743页

作者： Ezugwu, Absalom E. Ikotun, Abiodun M. Oyelade, Olaide O. Abualigah, Laith Agushaka, Jeffery O. Eke, Christopher I. Akinyelu, Andronicus A. Univ KwaZulu Natal Sch Comp Sci Pietermaritzburg Campus ZA-3201 Pietermaritzburg South Africa Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia Fed Univ Lafia Fac Nat Sci Dept Comp Sci Lafia Nigeria Univ Free State Dept Comp Sci & Informat ZA-9301 Bloemfontein South Africa

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to desi

关键词： Clustering Clustering algorithms partitioning Data mining Hierarchical clustering Automatic clustering K-Means optimization algorithms Machine learning Unsupervised learning Supervised learning Clustering Clustering algorithms partitioning Data mining Hierarchical clustering Automatic clustering K-Means optimization algorithms Machine learning Unsupervised learning Supervised learning

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Thermodynamic study of interfacial tension between oil and aqueous phases composed of ionic liquids and brine

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JOURNAL OF THE INDIAN CHEMICAL SOCIETY 2022年第6期99卷

作者： Hashemi, Seyed Hossein Univ Mohaghegh Ardabili MSc Chem Engn Ardebil Iran Shiraz Branch Young Researchers & Elite Club Shiraz Iran

The first fundamental step in determining the physicochemical properties of an equilibrium system is to determine the activity coefficient of electrolyte and non-electrolyte ions. Based on understanding the importance of activity coefficient in thermodynamic systems in this study, in order to predict interfacial tension between oil and aqueous phases composed of ionic liquids and brine, a modified thermodynamic equation based on concentration and coefficient of activity of ionic liquids is defined. For this study, the Extended UNIQUAC model is desired and its adjustable parameters are optimized with Genetic + PSO algorithm. The modified model has practical features such as investigating the effect of concentrations of salts in the water of oil fields formation on the interfacial tension of the system, investigating the effect of concentrations of various organic compounds such as ionic liquids on the interfacial tension of the system and investigating the interaction energy between organic and inorganic ions. In this study, the optimization of the modified thermodynamic equation to predict the interfacial tension of solutions containing [C8Py][Cl], [C18Py][Cl], [C(12)mim][Cl] and [C18mim][Cl] with the presence of brine and distilled water is investigated. Also, the effect of ionic strength of the solution in 32 equilibrium systems on interfacial tension is investigated. According to the optimization results of this study, the design of a computer program can be considered to predict the interfacial tension with the presence of ionic liquids and salts.

关键词： Ionic liquids Interfacial tension Aqueous electrolyte solution Extended UNIQUAC model optimization algorithms

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A Genome-Scale Metabolic Model Accurately Predicts Fluxes in Central Carbon Metabolism under Stress Conditions

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PLANT PHYSIOLOGY 2010年第1期154卷 311-323页

作者： Williams, Thomas C. R. Poolman, Mark G. Howden, Andrew J. M. Schwarzlander, Markus Fell, David A. Ratcliffe, R. George Sweetlove, Lee J. Univ Oxford Dept Plant Sci Oxford OX1 3RB England Oxford Brookes Univ Sch Life Sci Oxford OX3 OBP England

Flux is a key measure of the metabolic phenotype. Recently, complete (genome-scale) metabolic network models have been established for Arabidopsis (Arabidopsis thaliana), and flux distributions have been predicted using constraints-based modeling and optimization algorithms such as linear programming. While these models are useful for investigating possible flux states under different metabolic scenarios, it is not clear how close the predicted flux distributions are to those occurring in vivo. To address this, fluxes were predicted for heterotrophic Arabidopsis cells and compared with fluxes estimated in parallel by (13) C-metabolic flux analysis (MFA). Reactions of the central carbon metabolic network (glycolysis, the oxidative pentose phosphate pathway, and the tricarboxylic acid [TCA] cycle) were independently analyzed by the two approaches. Net fluxes in glycolysis and the TCA cycle were predicted accurately from the genome-scale model, whereas the oxidative pentose phosphate pathway was poorly predicted. MFA showed that increased temperature and hyperosmotic stress, which altered cell growth, also affected the intracellular flux distribution. Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress. MFA also revealed a 3-fold reduction in carbon-use efficiency at the higher temperature. It is concluded that constraints-based genome-scale modeling can be used to predict flux changes in central carbon metabolism under stress conditions.

关键词： Pentose Phosphate Pathway Metabolic Networks scale models use efficiency carbon metabolism flux distribution multifibre agreement optimization algorithms Citric Acid Cycle glycolysis

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Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles

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SENSORS 2019年第8期19卷 1758.-1758.页

作者： Wu, Qing Shen, Xudong Jin, Yuanzhe Chen, Zeyu Li, Shuai Khan, Ameer Hamza Chen, Dechao Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310018 Zhejiang Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Kowloon Hong Kong 999077 Peoples R China

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

关键词： UAVs path planning obstacle avoidance MTS optimization algorithms

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A Multiform Many-objective Evolutionary Algorithm for Multi-label Feature Selection in Classification

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IEEE Transactions on Evolutionary Computation 2024年

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Burdur Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multi-label classification (MLC) deals with instances associated with multiple labels simultaneously and often includes high dimensional data with noisy, irrelevant, and redundant features. Feature selection for MLC is crucial for achieving successful classification performance. However, no unified metric exists for evaluating learning performance in MLC tasks. Instead, multiple metrics exist, each assessing a different aspect of the classification process, and these metrics are often inconsistent with one another. Consequently, multi-label feature selection (MLFS) becomes a many-objective optimization problem (MaOP) when optimizing three or more classification metrics and the number of selected features simultaneously. Evolutionary computation (EC) techniques have shown great promise in addressing many-objective tasks due to their ability to effectively explore large and complex search spaces. EC techniques can handle multiple objectives concurrently, making them well-suited for the challenges posed by MLFS. Despite this potential, research on EC-based MLFS methods remains limited, with few studies treating it as a MaOP. To address this gap, this paper proposes a new many-objective evolutionary algorithm within a multiform framework. The proposed algorithm leverages distinct sub-populations to address specific MLFS tasks, incorporates a strategy to exchange information between MLFS tasks, employs a local density-based selection mechanism to maintain diverse high-quality solutions, and uses an adaptive parameter scheme inspired by stochastic gradient descent to guide the search towards new regions in the objective space. Experimental results demonstrate the superiority of the proposed algorithm across a diverse range of high-dimensional datasets, outperforming both recently introduced many-objective and conventional MLFS algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

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