In general,members of all different cultures are concerned about the cosmological query“What it is all about”.In the literature,many commentaries are available to us on the question.Nevertheless,a comprehensive,brie...In general,members of all different cultures are concerned about the cosmological query“What it is all about”.In the literature,many commentaries are available to us on the question.Nevertheless,a comprehensive,brief summary,as below,of the state of this inquiry on the subject may be helpful.展开更多
Artificial intelligence technology, mainly refers to strengthening the artificial way, so as to combinecomputer technology with product design. Firstly, the auxiliary innovation of bathroomproducts based on artificial...Artificial intelligence technology, mainly refers to strengthening the artificial way, so as to combinecomputer technology with product design. Firstly, the auxiliary innovation of bathroomproducts based on artificial intelligence technology is proposed, then the user characteristicsare analysed, and the auxiliary design framework of bathroom products is designed. Finally, theinnovation model is established to realise the auxiliary innovation design of bathroom products.Experimental results show that the proposed method can effectively improve the interactionefficiency and response time, and reduce the false response.展开更多
Wittgenstein is widely viewed as a potential critic of a key philosophical assumption of the Strong Artificial Intelligence （AI） thesis, namely, that it is in principle possible to build a programmed machine which c...Wittgenstein is widely viewed as a potential critic of a key philosophical assumption of the Strong Artificial Intelligence （AI） thesis, namely, that it is in principle possible to build a programmed machine which can achieve real intelligence. Smart Shanker has provided the most systematic reconstruction of the Wittgensteinian argument against AI, building on Wittgenstein＇s own statements, the ＂rule-following＂ feature of language-games, and the putative alliance between AI and psychologism. This article will attempt to refute this reconstruction and its constituent arguments, thereby paving the way for a new and amicable rather than agonistic conception of the Wittgensteinian position on AI.展开更多
Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is needed.The study aimed to establish a normal fetal lung gestational age(G...Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is needed.The study aimed to establish a normal fetal lung gestational age(GA)grading model based on deep learning(DL)algorithms,validate the effectiveness of the model,and explore the potential value of DL algorithms in assessing FLM.Methods:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41+6 weeks were analyzed in this study.There were no pregnancy-related complications that affected fetal lung development,and all infants were born without neonatal respiratory diseases.The images were divided into three classes based on the gestational week:class I:20 to 29+6 weeks,class II:30 to 36+6 weeks,and class III:37 to 41+6 weeks.There were 3323,2142,and 1548 images in each class,respectively.First,we performed a pre-processing algorithm to remove irrelevant information from each image.Then,a convolutional neural network was designed to identify different categories of fetal lung ultrasound images.Finally,we used ten-fold cross-validation to validate the performance of our model.This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA.This was used to establish a grading model.The performance of the grading model was assessed using accuracy,sensitivity,specificity,and receiver operating characteristic curves.Results:A normal fetal lung GA grading model was established and validated.The sensitivity of each class in the independent test set was 91.7%,69.8%,and 86.4%,respectively.The specificity of each class in the independent test set was 76.8%,90.0%,and 83.1%,respectively.The total accuracy was 83.8%.The area under the curve(AUC)of each class was 0.982,0.907,and 0.960,respectively.The micro-average AUC was 0.957,and the macro-average AUC was 0.949.Conclusions:The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs,which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy.The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.展开更多
文摘In general,members of all different cultures are concerned about the cosmological query“What it is all about”.In the literature,many commentaries are available to us on the question.Nevertheless,a comprehensive,brief summary,as below,of the state of this inquiry on the subject may be helpful.
基金the 2022 first phase of the supply and demand docking employment education project of the Ministry of Education College Students Division,project number:20220104052,project name:Research and Practice on Talent Training Model for New Engineering Design Professionals Based on Interdisciplinary and Integration of Industry and EducationThe second batch of industryuniversity cooperative education projects in 2021 of the Higher Education Department of the Ministry of Education,project number:202102321010,project name:Exploration and practice of new engineering product design specialty construction based on multidisciplinary intersection andindustry-education integration.
文摘Artificial intelligence technology, mainly refers to strengthening the artificial way, so as to combinecomputer technology with product design. Firstly, the auxiliary innovation of bathroomproducts based on artificial intelligence technology is proposed, then the user characteristicsare analysed, and the auxiliary design framework of bathroom products is designed. Finally, theinnovation model is established to realise the auxiliary innovation design of bathroom products.Experimental results show that the proposed method can effectively improve the interactionefficiency and response time, and reduce the false response.
文摘Wittgenstein is widely viewed as a potential critic of a key philosophical assumption of the Strong Artificial Intelligence （AI） thesis, namely, that it is in principle possible to build a programmed machine which can achieve real intelligence. Smart Shanker has provided the most systematic reconstruction of the Wittgensteinian argument against AI, building on Wittgenstein＇s own statements, the ＂rule-following＂ feature of language-games, and the putative alliance between AI and psychologism. This article will attempt to refute this reconstruction and its constituent arguments, thereby paving the way for a new and amicable rather than agonistic conception of the Wittgensteinian position on AI.
基金a grant from the National Key Research and Development Program of China(No.2016YFC1000104).
文摘Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is needed.The study aimed to establish a normal fetal lung gestational age(GA)grading model based on deep learning(DL)algorithms,validate the effectiveness of the model,and explore the potential value of DL algorithms in assessing FLM.Methods:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41+6 weeks were analyzed in this study.There were no pregnancy-related complications that affected fetal lung development,and all infants were born without neonatal respiratory diseases.The images were divided into three classes based on the gestational week:class I:20 to 29+6 weeks,class II:30 to 36+6 weeks,and class III:37 to 41+6 weeks.There were 3323,2142,and 1548 images in each class,respectively.First,we performed a pre-processing algorithm to remove irrelevant information from each image.Then,a convolutional neural network was designed to identify different categories of fetal lung ultrasound images.Finally,we used ten-fold cross-validation to validate the performance of our model.This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA.This was used to establish a grading model.The performance of the grading model was assessed using accuracy,sensitivity,specificity,and receiver operating characteristic curves.Results:A normal fetal lung GA grading model was established and validated.The sensitivity of each class in the independent test set was 91.7%,69.8%,and 86.4%,respectively.The specificity of each class in the independent test set was 76.8%,90.0%,and 83.1%,respectively.The total accuracy was 83.8%.The area under the curve(AUC)of each class was 0.982,0.907,and 0.960,respectively.The micro-average AUC was 0.957,and the macro-average AUC was 0.949.Conclusions:The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs,which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy.The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.
