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Xiangshan Li,Yong Zhu,Dachuan Xia,Zhanke Wang,Guangxu Zhang 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.127 No.-
A novel amino-anchored sulfonic acid functional heteropolyacid ionic liquid ([NHSO]3PW12O40) wasdesigned, synthesized and used as a catalyst in the Baeyer-Villiger (BV) oxidation of cyclohexanone tosynthetic e-caprolactone (e-CL). The catalyst was well characterized by FT-IR, XRD, 1H NMR, XPS, TGDTAand SEM analysis methods, which showed that the tungsten phosphate anion (PW12O403) was successfullymodified from the sulphonate-functionalised IL precursor NHSO and formed a new hydrogenbond between the introduced –NH2 group and PW12O403. The combined effect of ionic bonds and hydrogenbonds contributed to the good stability of the catalyst. Catalyst activity evaluation experiments confirmedthat [NHSO]3PW12O40 exhibited superior performances such as a cyclohexanone conversion of86% and e-CL yield and selectivity of 82% and 95%, respectively. Furthermore, [NHSO]3PW12O40 couldbe recovered by simple treatment and the activity of the catalyst did not decrease significantly after fivereplicae experiments. In addition, the reaction kinetics and mechanism of the catalyst were investigatedand a simple validation was given in conjunction with the bond energy changes of the catalyst during thereaction. The innovative of the green, stable and high-performance functionalised heteropolyacid IL catalystprovides a new solution for the efficient production of e-CL.
Classification of CTC on Fluorescence Image Based on Improved AlexNet
Kohei Kisanuki,Li Guangxu,Tohru Kamiya 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
These days, cancer has been the most primary cause of death in Japan. Cancer often progresses by repeating metastasis, so early detection and early treatment are important. Analysis of Circulating Tumor Cells (CTCs) has come to gather attention as a new biomarker that CTCs can detect primary cancer in human body. However, the number of CTCs in a billion blood cells is only a few, and detecting CTCs is very hard. Accordingly, we propose an automatic detection method of CTCs from fluorescence microscopy images to enable quantitative analysis by computer. This method consists of two parts. The first part, we use some series of filtering to the images and, new dividing method some overlapping nucleus then, from the images cut out the region of interest (ROI). The second part is distinguishing images by using CNN. We applied the proposed method to 5040 images of 6 samples. As a result, we obtained TPR:94.59%, FPR:6.544% by using AlexNet based model.
Environment Recognition from Spherical Camera Images Based on Multi-Attention DeepLab
Yuta Nishida,Li Guangxu,Huimin Lu,Tohru Kamiya 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Electric wheelchair is an easy-to-operate means of transportation that does not require physical strength. With the number of electric wheelchair users increasing in recent years, the increase in traffic accidents becomes a problem. Therefore, by developing an autonomous electric wheelchair, it is expected that the risk of accidents will be reduced and the convenience of the electric wheelchair will be improved. Environment recognition is indispensable for the development of autonomous electric wheelchairs. We propose a semantic segmentation method for recognizing 16 objects in traffic environment. This paper examines the improvement of problems such as the high price of autonomous electric wheelchairs due to the increase in the number of sensors used, which has been a concern in related research. Therefore, we use panoramic images acquired by a spherical camera as input data, and extern the Multi-Attention Deep Lab algorithms fitting for the recognition of distorted images. A new CNN model is constructed sing Deep Lab v3+, scSE Block, Pairwise Self-Attention, and Joint Pyramid Up-sampling. We conducted a recognition experiment using images taken on campus and verified its effectiveness. (Comparing to DeepLab v3+, IoU and Dice showed a 3.5% and 3.6% improvement in accuracy, respectively.)
Incorporating Ghost Module into RCANfor Super-Resolution of Satellite Images
Hiromu Ikeda,Guangxu Li,Tohru Kamiya 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
With the explosion of amount of low cost satellites, satellite images have been widely used for many non-military applications, such as agriculture, landscape, and recognition of environment. Improving the image resolution to mine useful information becomes one of the immediate problems. Therefore, it is expected to improve the recognition accuracy by increasing the resolution of satellite images. Recently, deep learning technique has been proposed to increase the resolution of images. However it requires a large number of learning parameters, which results in huge computational cost. To overcome this problem, we develop a new deep learning model based on ghost module to reduce the parameters while maintaining the quality of results. We utilized Google Earth Pro satellite imagery for the network training and testing. Comparing to the classical convolutional neural network module based methods, the number of parameters used in our model was reduced 49.31% but keeping the same level of Peak Signal - to - Noise Ratio (24.1578) and Structural Similarity (0.7174).
Denoising on Low-Dose CT Image Using Deep CNN
Yuta Sadamatsu,Seiichi Murakami,Guangxu Li,Tohru Kamiya 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Computed Tomography (CT) scans are widely used in Japan, and they contribute to public health. On the other hand, there is also a risk of radiation exposure. To solve this problem, attempts are being made to reduce the radiation dose during imaging. However, reducing the radiation dose causes noise and degrades image quality. In this paper, we propose an image analysis method that efficiently removes noise by changing the activation function of Deep Convolutional Neural Network (Deep CNN). Experimental tests using full-body slice CT images of pigs and phantom CT images of lungs with Poisson noise show that the proposed method is helpful by comparing them with normal-dose CT images and evaluating image quality using peak signal-to-noise ratio (PSNR).
Xi Chen,Jian-yang Xue,Hechao Li,Guangxu Tu,Xin Lu 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.10
Experimental and theoretical research on the rotational behavior of Multi-cell shaped concrete-filled steel tubular (MCFST) connection under low-period cyclic load is performed. The experimental results showed that the design parameters had significant effects on the elastic rotational stiffness of the connection, and affected the sequence and position of the plastic hinges appearance at the column and beam ends. Furthermore, the damage mechanism of the plastic hinge lines is obtained, and the simultaneous yielding for the plastic hinges results in the rapid increase of the beam end rotation. In order to handle the problem with irregular geometry boundary and uneven distribution of tightening-ring stresses a differential element of an unified material is proposed considering the effective tightening-ring stresses of special-shaped steel tube on confined concrete, which can be used to analyse the whole mechanical and damage performance of the connection. Combining unified design theory and experimental results, the calculation for the elastic bending rigidity of the connection is obtained. Finally, the moment-rotation relation model of the connection is proposed by regression analysis on experimental results, and the theoretical results are observed to agree well with the experimental results.