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学者姓名：韩国强
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Abstract ：
Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. Highlights: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm. © 2024 Wiley Periodicals LLC.
Keyword ：
adaptive sampling rate adaptive sampling rate atomic force microscopy (AFM) atomic force microscopy (AFM) back propagation neural network (BPNN) back propagation neural network (BPNN) block compressive sensing (BCS) block compressive sensing (BCS) continuous random scan continuous random scan
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| GB/T 7714 | Zhang, Y. , Chen, Y. , Wu, T. et al. Adaptive block imaging based on compressive sensing in AFM [J]. | Microscopy Research and Technique , 2024 , 87 (11) : 2555-2579 . |
| MLA | Zhang, Y. et al. "Adaptive block imaging based on compressive sensing in AFM" . | Microscopy Research and Technique 87 . 11 (2024) : 2555-2579 . |
| APA | Zhang, Y. , Chen, Y. , Wu, T. , Han, G. . Adaptive block imaging based on compressive sensing in AFM . | Microscopy Research and Technique , 2024 , 87 (11) , 2555-2579 . |
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Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing.& COPY; 2023 ISA. Published by Elsevier Ltd. All rights reserved.
Keyword ：
Bidirectional Long and Short-Term Memory Bidirectional Long and Short-Term Memory Channel attention module (CAM) Channel attention module (CAM) Convolutional Neural Network (CNN) Convolutional Neural Network (CNN) Electrocardiogram (ECG) Electrocardiogram (ECG) Heartbeat classification Heartbeat classification neural network (BLSTM) neural network (BLSTM)
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| GB/T 7714 | Liu, Fengqing , Li, Huaidong , Wu, Teng et al. Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM [J]. | ISA TRANSACTIONS , 2023 , 138 : 397-407 . |
| MLA | Liu, Fengqing et al. "Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM" . | ISA TRANSACTIONS 138 (2023) : 397-407 . |
| APA | Liu, Fengqing , Li, Huaidong , Wu, Teng , Lin, Hong , Lin, Chenyu , Han, Guoqiang . Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM . | ISA TRANSACTIONS , 2023 , 138 , 397-407 . |
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Abstract ：
Atomic force microscopy (AFM) is a kind of high-precision nanoscale instrument to measure the surface morphology of various samples. Nevertheless, the standard AFM scanning process takes a very long time to obtain high-resolution images. Compressive sensing (CS) can be used to achieve fast AFM imaging. But, the traditional CS-AFM imaging is difficult to balance the image quality of each local area, resulting in poor quality in the object area at low sampling rate. Therefore, a novel imaging scheme of adaptive CS-AFM is proposed. The fast scanning is first used to generate a low resolution image in a short time, and then bicubic interpolation is performed to obtain a high resolution image. Afterwards, an advanced detection algorithm is used to realize the accurate detection and positioning of the objects. Furthermore, the supplementary scanning is carried out to achieve adaptive sampling on the objects. After sampling, the measurement matrix corresponding to the measurement points is constructed. Finally, Total Variation Minimization by Augmented Lagrangian and Alternating Direction Algorithm (TVAL3) is used to reconstruct the whole AFM image. The imaging quality of the sample is analyzed and assessed by image evaluation metrics (PSNR and SSIM) and visual effect. Compared with two non adaptive imaging schemes, the proposed scheme is characterized by high automation, short time, and high quality.
Keyword ：
Adaptive sampling Adaptive sampling Atomic force microscopy (AFM) Atomic force microscopy (AFM) Compressive sensing (CS) Compressive sensing (CS) Object detection Object detection Reconstruction algorithm Reconstruction algorithm Supplementary scanning Supplementary scanning
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| GB/T 7714 | Han, Guoqiang , Chen, Yongjian , Wu, Teng et al. Adaptive AFM imaging based on object detection using compressive sensing [J]. | MICRON , 2022 , 154 . |
| MLA | Han, Guoqiang et al. "Adaptive AFM imaging based on object detection using compressive sensing" . | MICRON 154 (2022) . |
| APA | Han, Guoqiang , Chen, Yongjian , Wu, Teng , Li, Huaidong , Luo, Jian . Adaptive AFM imaging based on object detection using compressive sensing . | MICRON , 2022 , 154 . |
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Abstract ：
传统的光栅扫描法需要花费大量的时间来获得高分辨率图像,超分辨率算法可以用来提高原子力显微镜(AFM)图像的质量.但基于插值的方法容易产生图像伪影和边缘模糊,基于重构的图像处理方法也需要更好的先验知识和重构算法.为了尽可能获得AFM图像中更详细的纹理和特征信息,采用基于卷积神经网络的方法实现AFM超分辨率成像,并与传统的超分辨率方法进行对比,通过对重建图像的主客观评价验证了所提算法的可行性.
Keyword ：
卷积神经网络 卷积神经网络 原子力显微镜 原子力显微镜 超分辨率 超分辨率
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| GB/T 7714 | 林泓 , 林晨宇 , 吴腾 et al. 基于SRCNN的AFM超分辨率成像 [J]. | 机械制造与自动化 , 2022 , 51 (6) : 89-92 . |
| MLA | 林泓 et al. "基于SRCNN的AFM超分辨率成像" . | 机械制造与自动化 51 . 6 (2022) : 89-92 . |
| APA | 林泓 , 林晨宇 , 吴腾 , 韩国强 . 基于SRCNN的AFM超分辨率成像 . | 机械制造与自动化 , 2022 , 51 (6) , 89-92 . |
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Abstract ：
Atomic force microscope (AFM) is a powerful nanoscale instrument, which can obtain the true surface morphology of samples. There are strict requirements for the detailed features in AFM images, so it is necessary to mine the deep information in the images. Nevertheless, the standard AFM scanning process takes a very long time to obtain high-quality images. For most cases, the original images of AFM are with low resolution. In order to get the more detailed texture and feature information as much as possible, a super-resolution convolutional neural network algorithm based on enhanced data set is proposed in AFM imaging. By learning the mapping relationship between low-resolution images and high-resolution images from the image database, the high-resolution image is finally obtained. Aiming at the problem of long scanning time and too small training database of AFM image, adaptive histogram equalization is used to expand and enhance the training set of AFM images. Compared with the traditional super-resolution methods, the subjective and objective evaluation of the reconstructed image verifies the feasibility of the proposed algorithm. Aiming at the problem of long scanning time and too small training database of AFM image, a super-resolution convolutional neural network algorithm based on enhanced data set is proposed in AFM imaging. It can save a lot of time and hardware cost by using adaptive histogram equalization (AHE) and improve the generalization ability of deep learning model to further improve the reconstruction quality of AFM image.
Keyword ：
adaptive histogram equalization adaptive histogram equalization Atomic force microscope (AFM) Atomic force microscope (AFM) convolutional neural network (CNN) convolutional neural network (CNN) data set data set super-resolution (SR) super-resolution (SR)
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| GB/T 7714 | Han, Guoqiang , Wu, Teng , Lv, Luyao et al. Super-Resolution AFM Imaging Based on Enhanced Convolutional Neural Network [J]. | NANO , 2021 , 16 (12) . |
| MLA | Han, Guoqiang et al. "Super-Resolution AFM Imaging Based on Enhanced Convolutional Neural Network" . | NANO 16 . 12 (2021) . |
| APA | Han, Guoqiang , Wu, Teng , Lv, Luyao , Li, Huaidong , Lin, Hong , Lin, Chenyu et al. Super-Resolution AFM Imaging Based on Enhanced Convolutional Neural Network . | NANO , 2021 , 16 (12) . |
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Abstract ：
Atomic force microscope (AFM), which has nanoscale precision, is a widely used instrument in material science and biomedical science. Nevertheless, conventional AFM scanning is a time-consuming procedure. Compressive sensing (CS) and undersampling techniques have been introduced to accomplish fast AFM imaging in recent years. At present, existing undersampled scan patterns cannot simultaneously guarantee imaging efficiency and quality well. Therefore, a novel one called undersampled raster (USR) scan is put forward in this article. It has higher imaging efficiency than most existing scan patterns, which is drawn by calculating the scanning time with the proposed estimation formulas. Experimental results show that the imaging quality is obviously better than using other combinations of fast scan patterns and reconstruction algorithms when the regular form of the USR scan is employed with Total Variation Minimization by Augmented Lagrangian and Alternating Direction Algorithm (TVAL3). The universality of this imaging scheme is verified by using a variety of samples. In the end, the applications of regular USR scan or its variant for super-resolution AFM imaging and predicting appropriate sampling rates are proposed. In conclusion, applying regular USR scan and TVAL3 algorithm to CS-based AFMcan effectively realize fast and high-quality imaging.
Keyword ：
Atomic force microscope (AFM) Atomic force microscope (AFM) compressive sensing (CS) compressive sensing (CS) reconstruction algorithm reconstruction algorithm scanning time scanning time undersampled raster (USR) scan undersampled raster (USR) scan
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| GB/T 7714 | Niu, Yixiang , Han, Guoqiang . Fast AFM Imaging Based on Compressive Sensing Using Undersampled Raster Scan [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2021 , 70 . |
| MLA | Niu, Yixiang et al. "Fast AFM Imaging Based on Compressive Sensing Using Undersampled Raster Scan" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70 (2021) . |
| APA | Niu, Yixiang , Han, Guoqiang . Fast AFM Imaging Based on Compressive Sensing Using Undersampled Raster Scan . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2021 , 70 . |
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The synthesis of nanoscale arrays of magnetic particles, which may be used in magnetic recording devices, has attracted considerable interest recently. The coral-flake Co particles were electrodeposited into the pores of the porous NiAl matrix. Compared with the nonanodized samples, the anodized samples in H3PO4 solution formed thicker Al2O3 during the production of ordered Co particle arrays into the porous NiAl matrix. The deposition at a negative potential resulted in increased i(max) and decreased t(max). The diameter of Co particles increased with increasing deposition time. The interspace of Co was consistent with the interspace of pores distributed in the NiAl matrix. The coercivity (H-c) and the ratio of remanent magnetization to saturation magnetization (M-r/M-s) decreased with increased growth rate of the porous NiAl matrix at the same deposition time. H-c and M-r/M-s also decreased with the prolonged deposition time due to increasing Co size.
Keyword ：
Cobalt Cobalt Electrochemistry Electrochemistry Magnetic property Magnetic property Passivation Passivation
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| GB/T 7714 | Gao, Jianjun , Luo, Jian , Geng, Haibin et al. Coral-flake Co particles electrodeposited into the porous NiAl matrix [J]. | MATERIALS CHEMISTRY AND PHYSICS , 2020 , 244 . |
| MLA | Gao, Jianjun et al. "Coral-flake Co particles electrodeposited into the porous NiAl matrix" . | MATERIALS CHEMISTRY AND PHYSICS 244 (2020) . |
| APA | Gao, Jianjun , Luo, Jian , Geng, Haibin , Zhong, Jianhua , Han, Guoqiang , Cui, Kai et al. Coral-flake Co particles electrodeposited into the porous NiAl matrix . | MATERIALS CHEMISTRY AND PHYSICS , 2020 , 244 . |
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Abstract ：
Atomic force microscopy (AFM) is a powerful and ultra-precision instrument in nano-scale, which is widely used in many fields. It is a complex and time-consuming process for AFM imaging. Most of the original AFM images are with low resolution. For nano-scale measurement and imaging, it is very important to obtain super-resolution images. In most cases, super-resolution imaging takes a long time and the quality of imaging is unsatisfactory. In this regard, a novel super-resolution imaging method based on compressed sensing (CS) technology is proposed in AFM. In the experiment, six samples with different morphology were used to test the effect of super-resolution image reconstruction with different upscaling factors (2, 3 and 4). The quality of reconstructed image is analyzed and evaluated by image evaluation metrics (PSNR and SSIM). The relationship between the reconstruction quality of different images and the actual TV or TV/R of sample images is analyzed, which can provide a preliminary basis for predicting the imaging quality. Comparing with other super-resolution imaging methods, the proposed method has achieved better imaging effect both visually and quantitatively. In summary, super-resolution imaging method based on CS not only has high imaging quality but also has high speed.
Keyword ：
Atomic force microscopy (AFM) Atomic force microscopy (AFM) Compressed sensing (CS) Compressed sensing (CS) Measurement matrix Measurement matrix Super resolution (SR) Super resolution (SR)
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| GB/T 7714 | Han, Guoqiang , Lv, Luyao , Yang, Gaopeng et al. Super-resolution AFM imaging based on compressive sensing [J]. | APPLIED SURFACE SCIENCE , 2020 , 508 . |
| MLA | Han, Guoqiang et al. "Super-resolution AFM imaging based on compressive sensing" . | APPLIED SURFACE SCIENCE 508 (2020) . |
| APA | Han, Guoqiang , Lv, Luyao , Yang, Gaopeng , Niu, Yixiang . Super-resolution AFM imaging based on compressive sensing . | APPLIED SURFACE SCIENCE , 2020 , 508 . |
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Accurate and meticulous measurement is an important prerequisite to obtain the real surface information of samples in atomic force microscopy (AFM). A severe problem is the frequent occurrence of measurement errors, which are mainly caused by the nonlinearity of the probe driver, the temperature drift of the system and the tip characteristics. The measurement errors caused by probe tip are the main source of errors in AFM nanoscale measurements. The shape and state of AFM tip will distort the AFM image from the actual sample morphology. If the information about the probe is known, the measurement error caused by the probe tip can be greatly reduced. In order to obtain accurate AFM images, a new method based on geometric measurement model and blind tip reconstruction is proposed to eliminate tip-sample convolution in the measurement of grating samples. The static and dynamic characteristics of the AFM tip are described by four parameters: cone angle, curvature radius, scanning inclination angle and mounting inclination angle. Finally, the feasibility and effectiveness of the new calibration method are verified by evaluating the image reconstruction quality. In conclusion, the proposed method can effectively reconstruct accurate AFM images of the grating.
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| GB/T 7714 | Wu, Teng , Lv, Luyao , Zou, Yu et al. Image reconstruction of TGZ3 grating by eliminating tip-sample convolution effect in AFM [J]. | MICRO & NANO LETTERS , 2020 , 15 (15) : 1167-1172 . |
| MLA | Wu, Teng et al. "Image reconstruction of TGZ3 grating by eliminating tip-sample convolution effect in AFM" . | MICRO & NANO LETTERS 15 . 15 (2020) : 1167-1172 . |
| APA | Wu, Teng , Lv, Luyao , Zou, Yu , Han, Guoqiang . Image reconstruction of TGZ3 grating by eliminating tip-sample convolution effect in AFM . | MICRO & NANO LETTERS , 2020 , 15 (15) , 1167-1172 . |
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Atomic force microscope (AFM) is a high-precision instrument to research surface topography of various samples. Nevertheless, the standard AFM procedure takes excessively long time to acquire high-resolution images. Moreover, too much interaction between a probe tip and a specimen potentially leads to tip abrasion and sample damage. Compressive sensing (CS) has been employed to realize high-speed AFM (HS-AFM). However, a considerably large-sized image may be hard to be reconstructed through the common CS method with some ordinary algorithms, due to the high computational complexity and hardware costs. Thus, block-based compressive sensing (BCS) is adopted as an alternative means. In our work, three scanning patterns were utilized to generate undersampled AFM images at various sampling rates for five samples with different surface appearance. Each image block was reconstructed through one of six representative algorithms. Finally, an optimal measurement scheme for BCS-HS-AFM was put forward based on the analysis of experimental results, which consisted of sampling rate, scanning pattern, block mode and reconstruction algorithm. BCS was compared with other CS methods in terms of the computational complexity and image reconstruction effect to reveal its superior performance. In brief, BCS can be applied to effectively realize the high-resolution and low-damage HS-AFM imaging.
Keyword ：
Atomic force microscope (AFM) Atomic force microscope (AFM) Block-based compressive sensing (BCS) Block-based compressive sensing (BCS) Reconstruction algorithm Reconstruction algorithm Scanning pattern Scanning pattern Undersampling Undersampling
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| GB/T 7714 | Han, Guoqiang , Niu, Yixiang , Zou, Yu et al. Reconstruction of undersampled atomic force microscope images using block-based compressive sensing [J]. | APPLIED SURFACE SCIENCE , 2019 , 484 : 797-807 . |
| MLA | Han, Guoqiang et al. "Reconstruction of undersampled atomic force microscope images using block-based compressive sensing" . | APPLIED SURFACE SCIENCE 484 (2019) : 797-807 . |
| APA | Han, Guoqiang , Niu, Yixiang , Zou, Yu , Lin, Bo . Reconstruction of undersampled atomic force microscope images using block-based compressive sensing . | APPLIED SURFACE SCIENCE , 2019 , 484 , 797-807 . |
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