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Analysis of entrepreneurship education in colleges and based on improved decision tree algorithm and fuzzy mathematics

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期40卷 2095-2107页

作者： Mao, Li Zhang, Wanhong China Univ Min & Technol Xuzhou Jiangsu Peoples R China Jiangsu Agrianim Husb Vocat Coll Taizhou Peoples R China

In order to improve the performance of entrepreneurship and innovation education in colleges and universities, this study attempts to build an evaluation system and model of innovation and entrepreneurship in colleges and universities to provide a complete and practical tool for government education authorities and universities to evaluate the implementation of innovation and entrepreneurship education. In this research, decision tree and fuzzy mathematics are used as the basis of the model algorithm, and the algorithm is improved based on the analysis of traditional algorithms. Moreover, based on the improved decision tree algorithm, an evaluation index system for university innovation and entrepreneurship education is constructed. After determining the evaluation indicators of innovation and entrepreneurship education in colleges and universities, this study uses several universities as examples to analyze and define the definitions of various indicators. In addition, this study statistically analyzes the results of entrepreneurship and innovation education in colleges and universities through simulation. The research shows that the model proposed in this paper has a certain practical effect, and based on the simulation results, this study makes several suggestions.

关键词： Neural network improved algorithm path ranking network education knowledge recommendation

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Logistics demand forecasting model based on improved neural network algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期40卷 6385-6395页

作者： Ma, Hongjiang Luo, Xu Chongqing Municipal Res Inst Design Chongqing Peoples R China Chongqing Jiaotong Univ Chongqing Peoples R China

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

关键词： Neural network improved algorithm logistics demand forecasting model

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An improved Simulated Annealing algorithm in Dataset Domain for Optimizing Robust Workpiece Fixture Layout

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ADVANCED THEORY AND SIMULATIONS 2023年第9期6卷

作者： Ding, Heng Dong, Yude Gao, Haoyu Cheng, Hui Yang, Zhongshu Liang, Yulei Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China Inst Engn Res Got High Tech Hefei 230000 Anhui Peoples R China

The robust layout of the fixture on the workpiece solves the problem of workpiece fixing well. Aiming at the problem of workpiece position error caused by fixture source error on workpiece surface, an improved simulated annealing algorithm based on dataset domain is adopted to reduce the workpiece position error caused by fixture locator deviation as much as possible. To find a better robust layout way, a point differentiation method is proposed to discretize continuous workpieces to obtain the dataset domain. Meanwhile, two objective functions considering curvature and non-curvature are used to evaluate the placement scheme of locators. A point differentiation method is proposed to discretize continuous workpieces to obtain the dataset domains. Meanwhile, two objective functions considering curvature and non-curvature are used to evaluate locator layout schemes. To improve the performance of the algorithm, an AHP-based multi-attribute decision-making method is used to find a set of optimal parameters to configure the algorithm. The benchmark example and practical verification show that the locator obtained by the algorithm proposed is less discrete and denser, which solves the problem of fixture positioning very well, and the position error of the workpiece is effectively reduced.

关键词： workpiece analysis source error point differentiation improved algorithm robust layout

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Multi-USV Collaborative Obstacle Avoidance Based on improved Velocity Obstacle Method

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING 2024年第1期10卷

作者： Du, Zhe Li, Weifeng Shi, Guoyou Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

Cooperative obstacle avoidance of multiple unmanned surface vessels (multi-USV) is an important issue in unmanned vessel safety. This study aims to solve dynamic safety collision avoidance problems, especially decision oscillations, which exist in the traditional velocity obstacle method for multi-USV cooperative obstacle avoidance. To cope with these problems, we analyze the principle of the reciprocal velocity obstacle method based on multiple constraints, such as the dynamics constraint and International Regulations for Preventing Collisions at Sea (COLREGS) constraint of unmanned vessels, and propose an improved velocity obstacle method under multiple constraints. The method introduces the uncertainty factor of the velocity obstacle to obtain the optimal velocity of the USV when performing obstacle avoidance actions on the water surface and then achieves the replanning of the trajectory. Through simulation experimental verification, we demonstrate that this method can effectively resolve different encounter conflict situations, achieve coordinated motion control among multiple USVs, and improve the safety of USV obstacle avoidance. In addition, the method meets the dynamic obstacle avoidance requirements of surface unmanned vessels under multiple constraints in actual navigation. This research is of great significance in improving unmanned vessels' autonomous obstacle avoidance capability and provides strong support for the practical application of unmanned vessels. Future research can further explore the applicability of the method.

关键词： Unmanned vessel Velocity obstacle method improved algorithm Dynamic obstacle avoidance

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Design of educational method classification model based on improved multi-label transfer learning model

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SOFT COMPUTING 2023年 1-11页

作者： Zeng, Chanjuan Zhao, Chunhui South China Agr Univ Coll Humanities Zhujiang Coll Guangzhou 510900 Guangdong Peoples R China

In order to study a reliable classification model of educational methods, this paper uses improved multi-label transfer learning as the basic algorithm to construct an educational method classification model based on improved multi-label transfer learning. Moreover, this paper restructures the data set into a multi-category data set by coupling other labels to reduce the ratio of unbalanced labels and uses a binary classifier to predict each single label and combines the multi-class classifier and the binary classifier to output the label prediction of the multi-label data. Considering the importance of label information in multi-label data, this paper adds metric migration learning on the basis of transfer learning and proposes a multi-label metric migration learning model MLMTL. With the aid of metric learning, the model effectively retains the inherent geometric information of the feature space and label space. The experimental results demonstrate that the model constructed in this paper is highly effective, with a low distortion rate and high classification accuracy. This indicates that the system is capable of accurately identifying and classifying music education data and provides a reliable and efficient platform for students and teachers to access and manage educational materials.

关键词： Transfer learning Machine learning improved algorithm Education method Classification

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Investigation of fault diagnosis model of rotary kiln based on improved algorithm of Bayesian

Investigation of fault diagnosis model of rotary kiln based ...

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2015 Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

作者： Hao-Ran Liu Xiao-He Lv Xuan Li Shi-Zhao Li Yong-Hong Shi Information Science and Engineering College of YanShan University Hebei Province Key Laboratory of Special Optical Fiber and Optical Fiber Sensing Qinhuangdao066004 Information Science and Engineering College of YanShan University Qinhuangdao066004

ISBN: (纸本)9781467377249

Bayesian Network is one of the most efficient and reliable method in data mining, and Bayesian Network structure learning is a key link in the process of Bayesian Network research. Aiming at the problem of the classic Hill-Climbing algorithm is easy to fall into local optimum and low in efficiency, establishing the Most Weight Supported Tree by calculating the mutual information, and combining the Most Weight Supported Tree and the simplified Hill-Climbing algorithm, proposes a new improved Bayesian Network structure learning algorithm. Comparing with the classic Hill-Climbing algorithm and K2 algorithm, the simulation experiments shown that the improved algorithm not only can obtain a high accuracy rate model, but improve the efficiency of building model. Based on the improved algorithm and combined with JiDong cement's cement rotary kiln operating data, we can establish the fault diagnosis model of cement rotary kiln and realize a precise and rapid fault diagnosis.

关键词： Most Weight Supported Tree improved algorithm Bayesian Network Structure Learning Cement Rotary Kiln Fault Diagnosis Model

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An information extraction method based on improved mixed text density web pages

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EXPERT SYSTEMS 2024年第6期41卷 e13267-e13267页

作者： Zhou, Yuan Yin, Xiaojun Yan, Jingchen Minjiang Teachers Coll Sch Digital Informat Engn Fuzhou 350108 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

To improve the effect of web page information extraction, this paper proposes an improved information extraction method of mixed text density grids. Under various power constraints of the relay node itself, this paper proposes a design scheme of joint beamforming and artificial noise based on safety and rate maximisation. Furthermore, with the help of semidefinite relaxation techniques and first-order approximations, it can be approximated as a semidefinite programming problem that is easy to solve. In addition, this paper uses an iterative algorithm based on a continuous convex approximation to process data to improve the accuracy of web page data extraction. The experimental results show that the information extraction method based on improved mixed text density webpages proposed in this paper has a good information extraction effect.

关键词： improved algorithm information extraction mixed text density web page

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RETRACTED: Research on athlete training behavior based on improved support vector algorithm and target image detection (Retracted article. See vol. 41, pg. 5771, 2021)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5725-5736页

作者： Min, Jiang Jiangsu Agrianim Husb Vocat Coll Taizhou 225300 Peoples R China

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.

关键词： Neural network improved algorithm athlete feature recognition training model

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Extracting Individual Tree Positions in Closed-Canopy Stands Using a Multi-Source Local Maxima Method

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FORESTS 2025年第2期16卷 262-262页

作者： Lai, Guozhen Cao, Meng Zhou, Chengchuan Liu, Liting Zhong, Xun Guo, Zhiwen Ouyang, Xunzhi Jiangxi Agr Univ Coll Forestry Nanchang 330045 Peoples R China Jiangxi Acad Forestry Nanchang 330032 Peoples R China Jiangxi Forestry Resources Monitoring Ctr Nanchang 330045 Peoples R China

The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management.

关键词： individual tree position local maxima method improved algorithm multi source closed-canopy stands

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improved K-means algorithm for clustering non-spherical data

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EXPERT SYSTEMS 2022年第9期39卷 e13062-e13062页

作者： He, Honglei He, Yuxuan Wang, Fang Zhu, Wenming Lianyungang Tech Coll Sch Informat Engn Lianyungang Peoples R China Xuzhou Univ Technol Sch Math & Stat Xuzhou Jiangsu Peoples R China Jiangsu Union Tech Inst Lianyungang TCM Branch Lianyungang Peoples R China Shenzhen Inst Informat Technol Dept Publ Courses Shenzhen 518172 Peoples R China

As one of the commonly used data mining algorithms, K-means has the advantage of fast clustering speed, but the disadvantage is that it is less effective for clustering non-spherical data. An improved K-means algorithm (IK-means) is proposed to enhance clustering efficiency for non-spherical data. The original dataset is clustered into a relatively larger number of high-density sub-clusters, and the final result is obtained by merging connected sub-clusters respectively. The connectivity among sub-clusters is evaluated by the sub-clusters density and the nearest distance class between sub-clusters. By testing on University of California, Irvine(UCI) datasets and several other artificial simulation datasets, the comparison of proposed IK-means algorithm against DBSCAN, KGFCM shows its clustering capability for data of arbitrary shape. The clustering Adjusted Rand Index (ARI) value for 72,000 sizes data is 24% higher than DBSCAN, and 95.2% higher than KGFCM. For larger datasets, the IK-means algorithm is faster than DBSCAN and KGFCM.

关键词： arbitrary shape clustering improved algorithm K-means non-spherical

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