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1,191 results on '"Wang, Ranran"'

2. Some study of the approximate orthogonality connected with integral orthogonalities

Author

Wang, Ranran, Liu, Qi, Xia, Jinyu, and Hu, Yongmo

Subjects
Mathematics - Functional Analysis, 46B20, 46C05
Abstract

In this paper, we investigate a novel form of approximate orthogonality that is based on integral orthogonality. Additionally, we establish the fundamental properties of this new approximate orthogonality and examine its capability to preserve mappings of orthogonality. Moreover, we explore the relationship between this new approximate orthogonality and other forms of approximate orthogonality.

Published
2024

12. Development of portable electrochemical sensor for heavy metals and application of detection of heavy metals lead and cadmium in rice

Author

XU Haoyang, WANG Ranran, YAO Bangben, CHEN Wei, DENG Shanggui, and YAO Li

Subjects
rices, heavy metal residue, screen printed electrode, electrochemical detection, Food processing and manufacture, TP368-456
Abstract

[Objective] To develop an efficient electrochemical method for the rapid detection of cadmium and lead ions （Cd2+ and Pb2+） in rice samples. [Methods] Designed a portable electrochemical sensor utilizing a disposable screen printed electrode and anodic stripping voltammetry with an isoplated bismuth electrode. This setup was further optimized through square wave voltammetry （SWV）, cyclic voltammetry （CV）, and chronocurrent methods （I-T）. [Results] The proposed approach exhibited outstanding performance with a linear detection range of 20 to 80 μg/L for Cd2+ and 10 to 80 μg/L for Pb2+. The LOD of the cadmium ion were 1.201 0 μg/L, the LOD of the lead ion were 0.963 9 μg/L. [Conclusion] Our newly established electrochemical sensor for heavy metal detection in rice offers high sensitivity, ease of operation, rapid analysis, and aligns with environmentally friendly practices.

Published
2024
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13. SRSA-Net: Separable ResUnit and Self-attention Optimized Network for Simultaneous Nuclei Segmentation and Classification in Histology Images

Author

Wang, Ranran, Qiu, Yusong, Zhang, Yong, Xu, Hongming, Magjarević, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Wang, Guangzhi, editor, Yao, Dezhong, editor, Gu, Zhongze, editor, Peng, Yi, editor, Tong, Shanbao, editor, and Liu, Chengyu, editor

Published
2024
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14. Dual-Stream Context-Aware Neural Network for Survival Prediction from Whole Slide Images

Author

Gao, Junxiu, Jin, Shan, Wang, Ranran, Wang, Mingkang, Wang, Tong, Xu, Hongming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
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16. Astragalus polysaccharide/carboxymethyl chitosan/sodium alginate based electroconductive hydrogels for diabetic wound healing and muscle function assessment

Author

Tang, Letian, Xie, Shuyang, Wang, Danyang, Wei, Yiying, Ji, Xiaopu, Wang, Yicheng, Zhao, Nana, Mou, Zonglei, Li, Baoping, Sun, Wan Ru, Wang, Ping Yu, Basmadji, Nicola Paccione, Pedraz, José Luis, Vairo, Claudia, Lafuente, Eusebio Gainza, Ramalingam, Murugan, Xiao, Xiaofei, and Wang, Ranran

Published
2025
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24. Single-layer outer edge waterflooding development model of complex fault block reservoirs with high water cut

Author

YAO Xiutian, XU Lei, WANG Ranran, YAN Sen, WANG Mingpeng, and YANG Qinwen

Subjects
single-layer outer edge waterflooding, development model, periodic waterflooding, numerical simulation, complex fault block reservoir, Chemical technology, TP1-1185, Petroleum refining. Petroleum products, TP690-692.5, Geology, QE1-996.5
Abstract

In the late stage of development， the complex fault block reservoir faces a series of problems， such as high comprehensive water cut， lack of formation energy， complex remaining oil distribution， and poor well pattern suitability. In this paper， based on the oil-water distribution status of the waterflooding reservoir， such as water-in-oil and oil-in-water， the development mode of single-layer outer edge waterflooding was studied through theoretical model derivation and numerical simulation methods by taking Ken71 fault block reservoir in the middle of Zhanhua Sag of Jiyang Depression as an example. The results show that the reasonable water injection volume， fluid production volume， and spacing between injection and production wells during single-layer development can be obtained through the force analysis of the fluid production end and the water injection end. The key factors affecting the remaining oil saturation and the water saturation are reasonable water injection pressure difference， water injection volume， and fluid production volume of single-layer development systems. It is essential in controlling the reasonable formation pressure level and the secondary enrichment of the remaining oil. The spacing between injection and production wells increases to the periphery of the oil-water boundary， and 2.9 times the width of the oil-bearing strip is the critical value of reasonable spacing between injection and production wells. An asymmetric periodic injection and production method with short injection and long standing is adopted to establish a single-layer outer edge waterflooding development model， which is beneficial to maintain the stability of the pressure system and increase the sweep efficiency of the remaining oil of the single-layer， so as to improve the oil recovery. Field application shows that after applying the single-layer outer edge waterflooding development model， the daily production of Ken71 fault block increases to 254 t， the water cut is controlled to 93.7%， the recovery increases to 31.53%， and the oil recovery rate is increased by 4.9%， which shows an excellent yield-increasing effect. The single-layer outer edge waterflooding model solves the problems in the development of complex fault block reservoirs with high water cut， such as dispersed remaining oil distribution， low waterflooding sweep efficiency， and poor well pattern suitability and can provide guidance and reference for complex fault block reservoir development.

Published
2024
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37. RODE-Net: Learning Ordinary Differential Equations with Randomness from Data

Author

Liu, Junyu, Long, Zichao, Wang, Ranran, Sun, Jie, and Dong, Bin

Subjects
Mathematics - Numerical Analysis, Computer Science - Machine Learning, Physics - Computational Physics, Statistics - Machine Learning
Abstract

Random ordinary differential equations (RODEs), i.e. ODEs with random parameters, are often used to model complex dynamics. Most existing methods to identify unknown governing RODEs from observed data often rely on strong prior knowledge. Extracting the governing equations from data with less prior knowledge remains a great challenge. In this paper, we propose a deep neural network, called RODE-Net, to tackle such challenge by fitting a symbolic expression of the differential equation and the distribution of parameters simultaneously. To train the RODE-Net, we first estimate the parameters of the unknown RODE using the symbolic networks \cite{long2019pde} by solving a set of deterministic inverse problems based on the measured data, and use a generative adversarial network (GAN) to estimate the true distribution of the RODE's parameters. Then, we use the trained GAN as a regularization to further improve the estimation of the ODE's parameters. The two steps are operated alternatively. Numerical results show that the proposed RODE-Net can well estimate the distribution of model parameters using simulated data and can make reliable predictions. It is worth noting that, GAN serves as a data driven regularization in RODE-Net and is more effective than the $\ell_1$ based regularization that is often used in system identifications., Comment: 11 pages, 6 figures, 3 tables

Published
2020

38. Enhancing Certified Robustness via Smoothed Weighted Ensembling

Author

Liu, Chizhou, Feng, Yunzhen, Wang, Ranran, and Dong, Bin

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Statistics - Machine Learning
Abstract

Randomized smoothing has achieved state-of-the-art certified robustness against $l_2$-norm adversarial attacks. However, it is not wholly resolved on how to find the optimal base classifier for randomized smoothing. In this work, we employ a Smoothed WEighted ENsembling (SWEEN) scheme to improve the performance of randomized smoothed classifiers. We show the ensembling generality that SWEEN can help achieve optimal certified robustness. Furthermore, theoretical analysis proves that the optimal SWEEN model can be obtained from training under mild assumptions. We also develop an adaptive prediction algorithm to reduce the prediction and certification cost of SWEEN models. Extensive experiments show that SWEEN models outperform the upper envelope of their corresponding candidate models by a large margin. Moreover, SWEEN models constructed using a few small models can achieve comparable performance to a single large model with a notable reduction in training time.

Published
2020

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