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An Alternative Algorithm for the Symmetry Classification of Ordinary Differential Equations

Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

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Segmenting vegetation from UAV images via spectral reconstruction in complex field environments

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植物表型组学（英文） 2025年第1期7卷 212-224页

作者： Zhixun Pei Xingcai Wu Xue Wu Yuanyuan Xiao Peijia Yu Zhenran Gao Qi Wang Wei Guo State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China State Key Laboratory of Green Pesticide Guizhou UniversityGuiyang 550025China New Rural Development Research Institute Guizhou UniversityGuiyang550025China Graduate School of Agricultural and Life Sciences The University of TokyoTokyo188-0002Japan

Segmentation of vegetation remote sensing images can minimize the interference of background,thus achieving efficient monitoring and analysis for vegetation *** segmentation of vegetation poses a significant challenge due to the inherently complex environmental ***,there is a growing trend of using spectral sensing combined with deep learning for field vegetation segmentation to cope with complex ***,two major constraints remain:the high cost of equipment required for field spectral data collection;the availability of field datasets is limited and data annotation is time-consuming and *** address these challenges,we propose a weakly supervised approach for field vegetation segmentation by using spectral reconstruction(SR)techniques as the foundation and drawing on the theory of vegetation index(Ⅵ).Specifically,to reduce the cost of data acquisition,we propose SRCNet and SRANet based on convolution and attention structure to reconstruct multispectral images of fields,***,borrowing from the Ⅵprinciple,we aggregate the reconstructed data to establish the connection of spectral bands,obtaining more salient vegetation ***,we employ the adaptation strategy to segment the fused feature map using a weakly supervised method,which does not require manual labeling to obtain a field vegetation segmentation *** segmentation method can achieve a Mean Intersection over Union(MIoU)of 0.853 on real field datasets,which outperforms the existing *** addition,we have open-sourced a dataset of unmanned aerial vehicle(UAV)RGB-multispectral images,comprising 2358 pairs of samples,to improve the richness of remote sensing agricultural *** code and data are available at egment_SR,and .

关键词： Segmentation Spectral reconstruction UAV field images

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A Survey on Evolutionary Computation Based Drug Discovery

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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A Unified Framework Integrating Knowledge and data for Collaborative Root Cause Identification

A Unified Framework Integrating Knowledge and Data for Colla...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Jiefei Yu Zicheng Cao Siyi He Zuyi Gu Yingcheng Xu Kai Zhong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Anhui China Data Science and Big Data Technology School of Big Data and Statistics Anhui University Anhui China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller.

关键词： Fault diagnosis Knowledge engineering Reactive power Analytical models Delay effects Redundancy Collaboration Cause effect analysis Vectors Safety

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Efficient Remote Memory Paging for Disaggregated Memory Systems 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Wang, Tao Liu, Haikun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Memory disaggregation has attracted increasing attention in recent years because it is a cost-efficient approach to scale memory capacity for applications in a data center. However, the latency of remote memory access is a major concern in disaggregated memory systems. This paper presents VANDI, a virtual memory paging mechanism that allows applications to use remote memory pools transparently. VANDI enables effective data caching and prefetching mechanisms to address the problem of high access latency in disaggregated memory systems. VANDI exploits a low-complexity cache replacement strategy to optimize the asynchronous staging queue so that the remote write latency can be significantly reduced. VANDI can also prefetch data in multi-granularity from a remote memory pool in an adaptive manner, and thus further improves the hit rate of the local cache to reduce the read latency of remote memory. Our extensive experiments using micro-benchmarks show that VANDI can improve the performance of typical remote paging system–Infiniswap by up to 15 × –102 ×. VANDI can also improve the performance of state-of-the-art disaggregated memory system–Valet by 1.2 × –2.7 ×. For typical machine learning workloads, VANDI can achieve 20% to 80% performance improvement compared with the state-of-the-art Valet. © 2023, Springer Nature Switzerland AG.

关键词： Paging systems

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An effective compressed image authentication scheme based on N-variant AMBTC

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Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

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Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

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Research in Astronomy and Astrophysics 2023年第6期23卷 86-97页

作者： Zhen-Hong Shang Si-Yu Mu Kai-Fan Ji Zhen-Ping Qiang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650216China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on *** method transforms the transverse velocity field measurements into an optical flow field prediction *** evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope *** experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning *** the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 *** optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar *** code implementing the proposed method is available on https://***/lygmsy123/transverse-velocity-field-measurement.

关键词： methods:data analysis techniques:image processing Sun:fundamental parameters

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Hierarchical Clustering Combination Method Using Intuitionistic Fuzzy Similarity in Software Module Clustering

Hierarchical Clustering Combination Method Using Intuitionis...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Xia, Hong Wang, Bo Zhang, Yongkang Chen, Yanping School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9798350327380

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture. © 2023 IEEE.

关键词： Fuzzy set theory

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Research on Prediction Method of Auto Insurance Claim Amount Based on Graph Deep Learning 3

Research on Prediction Method of Auto Insurance Claim Amount...

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2023 3rd International Conference on big data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Lu, Jianguang Liu, Pengfei Xing, Bin Chongqing Industrial Big Data Innovation Center Co. Ltd. Chongqing400707 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang550025 China

ISBN: (纸本)9798400716478

The number of autos in China has surpassed 312 million, and automotive insurance is the most important part of property insurance. A win-win scenario for policyholders and property insurance firms may be achieved by precisely predicting the amount of auto insurance claims. This will also assist to measure premiums and successfully minimize auto insurance fraud. The existing approach of estimating the amount of auto insurance claims suffers from issues with a simple feature extraction method and a lack of focus on feature correlations, which leads to low prediction accuracy. This research suggests a strategy for forecasting the auto insurance claims amount based on graph deep learning to address these issues. The approach starts by automatically extracting features using convolutional neural networks, generates a feature connection graph, applies graph neural networks to learn and represent the feature relationship graph, and then properly forecasts the amount of auto insurance claims. The experimental results demonstrate that, when compared with Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), the approach described in this work decreases the root mean square error by 3.10% and 2.15%, and the mean absolute error by 3.98% and 1.14%, respectively. This article offers a fresh perspective on the estimation of vehicle insurance claims, as well as implications for other numerical regression prediction disciplines. © 2023 ACM.

关键词： Feature extraction

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An Asynchronous Transmission for Inter-Stage Communication in Spark Via Time-Varying Network Bandwidth Utilization

SSRN

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SSRN 2024年

作者： Zhu, Changpeng Wang, Xi Han, Bo Zhou, Tian Department of Data Science and Big Data Chongqing University of Technology China School of Cyber Science and Engineering Xi’an Jiaotong University China

Spark has garnered significant attention owing to its superior capability in processing vast amounts of data. Its executor-based model necessitates that sufficient resources are allocated to executors, thus facilitating the executions of diverse tasks. These tasks often exhibit different resource consumption patterns, resulting in time-varying resource consumption concerning CPU, network resources, etc. Some research endeavors have aimed to optimize the performance of Spark applications with time-varying CPU consumption, overlooking another crucial time-varying resource, namely, network bandwidth *** address this issue, this paper proposes a novel asynchronous data transmission approach to optimize inter-stage communication in Spark. The fundamental idea of this approach leverages idle network bandwidth during the runtime of a map stage to transmit output of map tasks upon completion, rather than waiting to transmit until corresponding reduce tasks begin to execute. Consequently, these reduce tasks' execution times are considerablely decreased as most of their input data have already been in local nodes due to the asynchronous transmission. This leads to an overall enhancement in the performance of Spark applications. Our approach makes significant modifications to Spark's internal working mechanism to achieve the asynchronous transmission, including extensions of the Spark \emph{task scheduler} and \emph{executor}. Our experiments conducted utilizing the \emph{HiBench} benchmark demonstrate that our approach can boost Spark applications' performance by up to 31.3\%, in comparison to the native Spark. © 2024, The Authors. All rights reserved.

关键词： Bandwidth

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