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| 1 | Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN显示文摘Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.However,deep learning entails extensive data for training,and it may be challenging to collect plant datasets.Even though plant datasets can be collected,they may be uneven in quantity.As a result,the problem of classification model overfitting arises.This study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available data.First,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image quality.Then,a gradient penalty mechanism was added to the loss function to improve the training stability of the model.Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset.On AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the experiment.Moreover,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original dataset.Furthermore,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this approach.The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases. | Huaxin Zhou Ziying Fang Yilin Wang Mengjun Tong | 2023 | Computers, Materials & Continua2023，，7： | 1 |
| 2 | Erratum to:Efficient flexible perovskite solar cells and modules using a stable SnO_(2)-nanocrystal isopropanol dispersion显示文摘Erratum to Nano Research,2024,17(4):2704-2711 http://gffzzd3cc09b8251d45dfsoq9vo96v9bfv6q6b.ffgz.tsg.suse.edu.cn/10.1007/s12274-023-6115-y(1)In the article,the table of contents(TOC)image was unfortunately mispresented. | Zhiwei Su Jing Li Ruixuan Jiang Shujie Zhang Chengkai Jin Feng Ye Bingcan Ke Mengjun Zhou Jinhui Tong Hyesung Park Fuzhi Huang Yi-Bing Cheng Tongle Bu | 2024 | Nano Research2024，17，5： | 0 |
| 3 | Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis显示文摘Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA. | LIANG GuiZhao CHEN ZeCong YANG ShanBin MEI Hu ZHOU Yuan YANG Li ZHOU Peng YANG ShengXi SHU Mao LIAO ChunYang WU ShiRong LI GenRong HE Liu GAO JianKun Gan MengYu LI DeJing CHEN GuoPing WANG GuiXue LONG Sha JING JuHua ZHENG XiaoLin ZENG Hui ZHANG QiaoXia ZHANG MengJun YANG Qi TIAN FeiFei TONG JianBo WANG JiaoNa LIU YongHong LI Bo QIU LiangJia CAI ShaoXi ZHAO Na YANG Yan SU XiaLi SONG Jian CHEN MeiXia ZHANG XueJiao SUN JiaYing LI JingWei CHEN GuoHua CHEN Gang DENG Jie PENG ChuanYou ZHU WanPing XU LuoNan WU YuQuan LIAO LiMin LI Zhi LI Jun LU DaJun SU QinLiang HUANG ZhengHu ZHOU Ping LI ZhiLiang | 2008 | Science China Chemistry2008，51，2： | 0 |
| 4 | Efficient flexible perovskite solar cells and modules using a stable SnO_(2)-nanocrystal isopropanol dispersion显示文摘The outstanding advantages of lightweight and flexibility enable flexible perovskite solar cells(PSCs)to have great application potential in mobile energy devices.Due to the low cost,low-temperature processibility,and high electron mobility,SnO_(2) nanocrystals have been widely employed as the electron transport layer in flexible PSCs.To prepare high-quality SnO_(2) layers,a monodispersed nanocrystal solution is normally used.However,the SnO_(2) nanocrystals can easily aggregate,especially after long periods of storage.Herein,we develop a green and cost-effective strategy for the synthesis of high-quality SnO_(2) nanocrystals at low temperatures by introducing small molecules of glycerol,obtaining a stable and well-dispersed SnO_(2)-nanocrystal isopropanol dispersion successfully.Due to the enhanced dispersity and super wettability of this alcohol-based SnO_(2)-nanocrystal solution,large-area smooth and dense SnO_(2) films are easily deposited on the plastic conductive substrate.Furthermore,this contributes to effective charge transfer and suppressed non-radiative recombination at the interface between the SnO_(2) and perovskite layers.As a result,a greatly enhanced power conversion efficiency(PCE)of 21.8%from 19.2%is achieved for small-area flexible PSCs.A large-area 5 cm×5 cm flexible perovskite solar mini-module with a champion PCE of 16.5%and good stability is also demonstrated via this glycerol-modified SnO_(2)-nanocrystal isopropanol dispersion approach. | Zhiwei Su Jing Li Ruixuan Jiang Shujie Zhang Chengkai Jin Feng Ye Bingcan Ke Mengjun Zhou Jinhui Tong Hyesung Park Fuzhi Huang Yi-Bing Cheng Tongle Bu | 2024 | Nano Research2024，17，4： | 0 |
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