In the digital era,the education and teaching of private undergraduate universities in China are undergoing innovative reform.This article uses the literature review method to conduct a survey and research on the qual...In the digital era,the education and teaching of private undergraduate universities in China are undergoing innovative reform.This article uses the literature review method to conduct a survey and research on the quality of teaching resources,teaching platforms,and literature reserves,and proposes a series of strategies and innovative paths to improve the teaching quality of private undergraduate universities.The research results show that the rational use of digital technology can improve the quality of education and teaching,and achieve innovative development in education and teaching.The research results have certain theoretical value and guiding significance for the teaching reform of private undergraduate universities under the background of digital teaching.展开更多
AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize anno...AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effectiveness of ALFA-Mix+.展开更多
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potent...Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.展开更多
A low phase noise millimeter-wave(MMW) signal generator is proposed and experimentally demonstrated with a C-band passively Fabry-Pérot(F-P) quantum dot mode-locked laser. A novel method is proposed to generate l...A low phase noise millimeter-wave(MMW) signal generator is proposed and experimentally demonstrated with a C-band passively Fabry-Pérot(F-P) quantum dot mode-locked laser. A novel method is proposed to generate low phase noise MMW signal, which is simply based on a commercial off-the-shelf dual-driven Li Nb O3 Mach-Zehnder modulator and a passively F-P quantum dot mode-locked laser. MMW signal with the frequency of 30 GHz, 45 GHz and 90 GHz respectively is obtained experimentally. Single-sideband phase noise of the 30 GHz and 45 GHz MMW signal is-112 d Bc/Hz and-106 d Bc/Hz at an offset of 1 k Hz, respectively. The linewidth of the 30 GHz and 45 GHz MMW signal is about from 225 Hz and 239 Hz. This is considered a very simple MMW generator with a quasi-tunable broadband and ultra-low phase noise.展开更多
Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt t...Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt to changes in assembly situations.To address these issues,a collaborative assembly is proposed.Based on the requirements of collaborative assembly,a colored Petri net(CPN)model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly.Also,an artificial potential field based planning algorithm(AFPA)is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs.Then an adaptive quantum genetic algorithm(AQGA)is developed to optimize the assembly process.Lastly,taking a two-pole circuit-breaker controller with leakage protection(TPCLP)as an assembly instance,comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly.The distribution of resources can also be optimized in the assembly.The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.展开更多
基金The 2023 Research Project of Zhejiang Private Education Association“Research on Digital Assistance in Teaching and Management of Private Schools”(Project number:ZMX2023B071)The 2023 Research Project of Zhejiang Private Education Association“Research on Improving the Quality of Education and Teaching in Private Schools”(Project number:ZMX2023B074)。
文摘In the digital era,the education and teaching of private undergraduate universities in China are undergoing innovative reform.This article uses the literature review method to conduct a survey and research on the quality of teaching resources,teaching platforms,and literature reserves,and proposes a series of strategies and innovative paths to improve the teaching quality of private undergraduate universities.The research results show that the rational use of digital technology can improve the quality of education and teaching,and achieve innovative development in education and teaching.The research results have certain theoretical value and guiding significance for the teaching reform of private undergraduate universities under the background of digital teaching.
基金Supported by the National Natural Science Foundation of China(No.61906066)the Zhejiang Provincial Philosophy and Social Science Planning Project(No.21NDJC021Z)+4 种基金Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202011015)Shenzhen Science and Technology Planning Project(No.KCXFZ20211020163813019)the Natural Science Foundation of Ningbo City(No.202003N4072)the Postgraduate Research and Innovation Project of Huzhou University(No.2023KYCX52)。
文摘AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effectiveness of ALFA-Mix+.
基金Supported by National Natural Science Foundation of China(No.61906066)Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202250196)+4 种基金Zhejiang Provincial Philosophy and Social Science Planning Project(No.21NDJC021Z)Natural Science Foundation of Ningbo City(No.202003N4072)Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202011015)Shenzhen Fundamental Research Program(No.JCYJ20220818103207015).
文摘Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.
基金supported by the Humanity and Social Science Foundation of Chinese Ministry of Education (No.19YJC880053)the Natural Science Foundation of Zhejiang Province (No.LQ18F010008)+3 种基金the Philosophy and Social Science Planning Project of Zhejiang Province (No.19NDJC0103YB)the Natural Science Foundation of Ningbo (No.2018A610092)the Research Fund Project of Ningbo Institute of Finance&Economics (No.1320171002)the Education and Teaching Reform Program of Ningbo Institute of Finance&Economics (No.20jyyb16)。
文摘A low phase noise millimeter-wave(MMW) signal generator is proposed and experimentally demonstrated with a C-band passively Fabry-Pérot(F-P) quantum dot mode-locked laser. A novel method is proposed to generate low phase noise MMW signal, which is simply based on a commercial off-the-shelf dual-driven Li Nb O3 Mach-Zehnder modulator and a passively F-P quantum dot mode-locked laser. MMW signal with the frequency of 30 GHz, 45 GHz and 90 GHz respectively is obtained experimentally. Single-sideband phase noise of the 30 GHz and 45 GHz MMW signal is-112 d Bc/Hz and-106 d Bc/Hz at an offset of 1 k Hz, respectively. The linewidth of the 30 GHz and 45 GHz MMW signal is about from 225 Hz and 239 Hz. This is considered a very simple MMW generator with a quasi-tunable broadband and ultra-low phase noise.
基金supported by the National Natural Science Foundation of China(No.52175124)the Zhejiang Provincial Natural Science Foundation of China(No.LZ21E050003)the Fundamental Research Funds for Zhejiang Universities,China(No.RF-C2020004)。
文摘Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt to changes in assembly situations.To address these issues,a collaborative assembly is proposed.Based on the requirements of collaborative assembly,a colored Petri net(CPN)model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly.Also,an artificial potential field based planning algorithm(AFPA)is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs.Then an adaptive quantum genetic algorithm(AQGA)is developed to optimize the assembly process.Lastly,taking a two-pole circuit-breaker controller with leakage protection(TPCLP)as an assembly instance,comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly.The distribution of resources can also be optimized in the assembly.The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.
