Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification...Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.展开更多
Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings...Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective judgment.However,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased evaluations.Against thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture posture.First,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation algorithm.Second,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned humanscores.The correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human scores.Under ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 points.Finally,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition algorithms.The fusion of multiple models resulted in an overall average correlation improvement of 0.0226.展开更多
文摘Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.
基金Supported by the Open Fund of Key Laboratory of Anhui Higher Education Institutes(CS2021-07)the National Natural Science Foundation of China(61701004)the Outstanding Young Talents Support Program of Anhui Province(gxyq2021178)。
文摘Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective judgment.However,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased evaluations.Against thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture posture.First,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation algorithm.Second,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned humanscores.The correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human scores.Under ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 points.Finally,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition algorithms.The fusion of multiple models resulted in an overall average correlation improvement of 0.0226.
