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Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods
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作者 Yang Yang 《Journal of Forestry Research》 SCIE EI CAS CSCD 2024年第5期216-222,共7页
As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan ba... As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards. 展开更多
关键词 Sound quality Wood vibration performance Paulownia wood Machine learning methods
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A Deep Learning Approach to Shape Optimization Problems for Flexoelectric Materials Using the Isogeometric Finite Element Method
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作者 Yu Cheng Yajun Huang +3 位作者 Shuai Li Zhongbin Zhou Xiaohui Yuan Yanming Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1935-1960,共26页
A new approach for flexoelectricmaterial shape optimization is proposed in this study.In this work,a proxymodel based on artificial neural network(ANN)is used to solve the parameter optimization and shape optimization... A new approach for flexoelectricmaterial shape optimization is proposed in this study.In this work,a proxymodel based on artificial neural network(ANN)is used to solve the parameter optimization and shape optimization problems.To improve the fitting ability of the neural network,we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training.The isogeometric analysis-finite element method(IGA-FEM)is used to discretize the flexural theoretical formulas and obtain samples,which helps ANN to build a proxy model from the model shape to the target value.The effectiveness of the proposed method is verified through two numerical examples of parameter optimization and one numerical example of shape optimization. 展开更多
关键词 Shape optimization deep learning flexoelectric structure finite element method isogeometric
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Expediting carbon dots synthesis by the active adaptive method with machine learning and applications in dental diagnosis and treatment
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作者 Yaoyao Tang Quan Xu +3 位作者 Xinyao Zhang Rongye Zhu Nuo Zhao Juncheng Wang 《Nano Research》 SCIE EI CSCD 2024年第11期10109-10118,共10页
Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaus... Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co^(2+) and Fe^(3+), with a concentration range of 0–120 and 0–150 µM, and their detection limits are 1.17 and 0.06 µM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction. 展开更多
关键词 machine learning simulated annealing active adaptive method carbon dots Ions detection dental diagnosis and treatment
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Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model
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作者 Qin Qian Mingjing Lu +3 位作者 Anhai Zhong Feng Yang Wenjun He Min Li 《Energy Engineering》 EI 2024年第8期2167-2190,共24页
The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinea... The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets. 展开更多
关键词 Shale oil production capacity data-driven model model-driven method machine learning
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A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation
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作者 Mohamed Karray Nesrine Triki Mohamed Ksantini 《Computers, Materials & Continua》 SCIE EI 2024年第7期119-138,共20页
Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recogn... Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology. 展开更多
关键词 Driving automation advanced driver assistance systems(ADAS) traffic sign recognition(TSR) artificial intelligence ensemble learning belief functions voting method
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A Study on Effective Methods of English Vocabulary Learning
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作者 Siyu Pang 《Journal of Contemporary Educational Research》 2024年第9期139-144,共6页
English is a key subject in high school that troubles many students,especially in the aspect of vocabulary learning.Only by laying a good vocabulary foundation can students better complete the learning tasks such as r... English is a key subject in high school that troubles many students,especially in the aspect of vocabulary learning.Only by laying a good vocabulary foundation can students better complete the learning tasks such as reading,writing,listening,and speaking training.This paper aims to explain the importance of improving the efficiency of English vocabulary learning and discuss the effective methods of English vocabulary learning in high school,in order to help more students find their own learning methods,improve vocabulary memory and application skills,and lay a solid foundation for follow-up learning,examination,and even work. 展开更多
关键词 High school English Vocabulary learning methodS
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The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities
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作者 Jiankun Feng 《Journal of Contemporary Educational Research》 2024年第4期290-295,共6页
In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and s... In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching. 展开更多
关键词 Group cooperative learning method Colleges and universities Badminton teaching Effective development
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Model Change Active Learning in Graph-Based Semi-supervised Learning
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作者 Kevin S.Miller Andrea L.Bertozzi 《Communications on Applied Mathematics and Computation》 EI 2024年第2期1270-1298,共29页
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to bes... Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art. 展开更多
关键词 Active learning Graph-based methods Semi-supervised learning(SSL) Graph Laplacian
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A systematic machine learning method for reservoir identification and production prediction 被引量:2
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作者 Wei Liu Zhangxin Chen +1 位作者 Yuan Hu Liuyang Xu 《Petroleum Science》 SCIE EI CAS CSCD 2023年第1期295-308,共14页
Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been appl... Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness. 展开更多
关键词 Reservoir identification Production prediction Machine learning Ensemble method
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A Real-time Prediction System for Molecular-level Information of Heavy Oil Based on Machine Learning
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作者 Yuan Zhuang Wang Yuan +8 位作者 Zhang Zhibo Yuan Yibo Yang Zhe Xu Wei Lin Yang Yan Hao Zhou Xin Zhao Hui Yang Chaohe 《China Petroleum Processing & Petrochemical Technology》 SCIE CAS CSCD 2024年第2期121-134,共14页
Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data predic... Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses. 展开更多
关键词 heavy distillate oil molecular composition deep learning SHAP interpretation method
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Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning
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作者 Yue Mei Jianwei Deng +4 位作者 Dongmei Zhao Changjiang Xiao Tianhang Wang Li Dong Xuefeng Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期911-935,共25页
Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induce... Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine. 展开更多
关键词 Nonhomogeneous elastic property distribution reconstruction deep learning finite element method inverse problem ELASTOGRAPHY conditional generative adversarial network
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A new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning
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作者 Wendi Chen Qinglai Wei 《Journal of Automation and Intelligence》 2024年第1期34-39,共6页
In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied sy... In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods.To achieve optimal control,RL algorithm based on critic–actor architecture is considered for the nonlinear system.Due to the significant security risks of network transmission,the system is vulnerable to deception attacks,which can make all the system state unavailable.By using the attacked states to design coordinate transformation,the harm brought by unknown deception attacks has been overcome.The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded.Finally,the simulation experiment is shown to prove the effectiveness of the strategy. 展开更多
关键词 Nonlinear systems Reinforcement learning Optimal control Backstepping method
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Exploring the Application Effect of Flipped Classroom Combined with Problem-Based Learning Teaching Method in Clinical Skills Teaching of Standardized Training for Resident Doctors of Traditional Chinese Medicine 被引量:1
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作者 Jingjing Tang 《Journal of Biosciences and Medicines》 CAS 2023年第2期169-176,共8页
Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese M... Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn. 展开更多
关键词 Standardized Training for Resident Doctors of Traditional Chinese Medicine Clinical Skills Teaching Flipped Classroom Problem-Based learning Teaching method
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Constitutive modelling of idealised granular materials using machine learning method 被引量:1
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作者 Mengmeng Wu Zhangqi Xia Jianfeng Wang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第4期1038-1051,共14页
Predicting the constitutive response of granular soils is a fundamental goal in geomechanics.This paper presents a machine learning(ML)framework for the prediction of the stress-strain behaviour and shearinduced conta... Predicting the constitutive response of granular soils is a fundamental goal in geomechanics.This paper presents a machine learning(ML)framework for the prediction of the stress-strain behaviour and shearinduced contact fabric evolution of an idealised granular material subject to triaxial shearing.The MLbased framework is comprised of a set of mini-triaxial tests which provide a benchmark for the setup and validation of the discrete element method(DEM)model of the granular materials,a parametric DEM simulation programme of virtual triaxial tests which provides datasets of micro-and macro-mechanical information,as well as a multi-layer perceptron(MLP)neural network which is trained and tested using the DEM-based datasets.The ML model only requires the initial void ratio of the granular sample as the input for predicting its constitutive response.The excellent agreement between the ML model prediction and experimental test and DEM simulation results indicates that the MLebased modelling approach is capable of capturing accurately the effects of initial void ratio on the constitutive behaviour of idealised granular materials,bypassing the need to incorporate the complex micromechanics underlying the macroscopic mechanical behaviour of granular materials.Lastly,a detailed comparison between the used MLP model and long short-term memory(LSTM)model was made from the perspective of technical algorithm,prediction accuracy,and computational efficiency. 展开更多
关键词 Machine learning(ML) Multi-layer perceptron(MLP) Contact fabric Granular material Discrete element method(DEM)
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Research and Discussion on Flipped Classroom Combined with Case-Based Learning in Pharmacoeconomics Teaching
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作者 Xingwen Zhou Zilong Dang +4 位作者 Xingdong Wang Chen Chen Zhi Rao Ting Wei Yanping Wang 《Journal of Contemporary Educational Research》 2024年第4期120-125,共6页
Objective:To explore the application effect of flipped classroom combined with case-based learning teaching methods in pharmacoeconomics teaching.Methods:The students majoring in clinical pharmacy in 2019 were selecte... Objective:To explore the application effect of flipped classroom combined with case-based learning teaching methods in pharmacoeconomics teaching.Methods:The students majoring in clinical pharmacy in 2019 were selected as the study subjects,and the cost-effectiveness analysis of different dosage forms of Yinzhihuang in the treatment of neonatal jaundice was selected as the teaching case.The flipped classroom combined with case-based learning teaching method was used to carry out theoretical teaching to the students.After the course,questionnaires were distributed through the Sojump platform to evaluate the teaching effect.Results:The results of the questionnaire showed that 85.71%of the students believed that the flipped classroom combined with case-based learning teaching method was helpful in mobilizing the learning enthusiasm and initiative,and improving the comprehensive application ability of the knowledge of pharmacoeconomics.92.86%of the students think that it is conducive to the understanding and memorization of learning content,as well as the cultivation of teamwork,communication,etc.Conclusion:Flipped classroom combined with case-based learning teaching method can improve students’knowledge mastery,thinking skills,and practical application skills,as well as optimize and improve teachers’teaching levels. 展开更多
关键词 Flipped classroom Case-based learning teaching method PHARMACOECONOMICS Teaching methods
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基于改进Q-learning算法的移动机器人路径规划
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作者 井征淼 刘宏杰 周永录 《火力与指挥控制》 CSCD 北大核心 2024年第3期135-141,共7页
针对传统Q-learning算法应用在路径规划中存在收敛速度慢、运行时间长、学习效率差等问题,提出一种将人工势场法和传统Q-learning算法结合的改进Q-learning算法。该算法引入人工势场法的引力函数与斥力函数,通过对比引力函数动态选择奖... 针对传统Q-learning算法应用在路径规划中存在收敛速度慢、运行时间长、学习效率差等问题,提出一种将人工势场法和传统Q-learning算法结合的改进Q-learning算法。该算法引入人工势场法的引力函数与斥力函数,通过对比引力函数动态选择奖励值,以及对比斥力函数计算姿值,动态更新Q值,使移动机器人具有目的性的探索,并且优先选择离障碍物较远的位置移动。通过仿真实验证明,与传统Q-learning算法、引入引力场算法对比,改进Q-learning算法加快了收敛速度,缩短了运行时间,提高了学习效率,降低了与障碍物相撞的概率,使移动机器人能够快速地找到一条无碰撞通路。 展开更多
关键词 移动机器人 路径规划 改进的Q-learning 人工势场法 强化学习
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Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals
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作者 Jiali Wang Bing Li +7 位作者 Chengyu Qiu Xinyun Zhang Yuting Cheng Peihua Wang Ta Zhou Hong Ge Yuanpeng Zhang Jing Cai 《Computers, Materials & Continua》 SCIE EI 2023年第6期4843-4866,共24页
Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-ti... Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR. 展开更多
关键词 multi-view learning transfer learning least squares regression EPILEPSY EEG signals
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Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method
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作者 Mao Zhang GaoPeng Lu +5 位作者 HaiLiang Huang ZhengWei Cheng YaZhou Chen Steven A.Cummer JiaYi Zheng JiuHou Lei 《Earth and Planetary Physics》 EI CSCD 2023年第3期407-413,共7页
Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere wavegu... Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long distances.In this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning method.The differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),respectively.Moreover,the automatic method is capable of recognizing higher harmonic tweek sferics.The evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere. 展开更多
关键词 machine learning method tweek atmospherics reflection height D-region ionosphere
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Design of N-11-Azaartemisinins Potentially Active against Plasmodium falciparum by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods
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作者 Jeferson Stiver Oliveira de Castro José Ciríaco Pinheiro +5 位作者 Sílvia Simone dos Santos de Morais Heriberto Rodrigues Bitencourt Antonio Florêncio de Figueiredo Marcos Antonio Barros dos Santos Fábio dos Santos Gil Ana Cecília Barbosa Pinheiro 《Journal of Biophysical Chemistry》 CAS 2023年第1期1-29,共29页
N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning m... N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties ε<sub>LUMO+1</sub> (one level above lowest unoccupied molecular orbital energy), d(C<sub>6</sub>-C<sub>5</sub>) (distance between C<sub>6</sub> and C<sub>5</sub> atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation. 展开更多
关键词 Antimalarial Design MEP Ligand-Receptor Interaction Supervised Machine learning methods Models Built with Supervised Machine learning methods
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Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques
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作者 Mohamed Abouhawwash S.Sridevi +3 位作者 Suma Christal Mary Sundararajan Rohit Pachlor Faten Khalid Karim Doaa Sami Khafaga 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期239-253,共15页
One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrom... One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms. 展开更多
关键词 Deep learning automatic detection polycystic ovarian syndrome tri-stage wrapper method mutual information RELIEF CHI-SQUARE
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