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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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Sensor Placement Optimization and Rapid Deployment of a Class of Visual Sensor Networks 42

Sensor Placement Optimization and Rapid Deployment of a Clas...

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42nd Chinese Control Conference, CCC 2023

作者： Deng, Heng Zhou, Jiahui Zhao, Zhiyao Zhang, Liguo Beijing University of Technology Key Laboratory of Computational Intelligence and Intelligent Systems Faculty of Information Technology Beijing100124 China China National Light Industry Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9789887581543

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks (VSNs), this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as possible. In the proposed method, some necessary models are established as the mathematical basis of the optimization process. Then, a multi-objective optimization function is designed to balance the coverage and reconstruction error, followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Additionally, a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance, which makes the search process simpler and increases execution speed. Finally, comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multiobjective optimization

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A hybrid modeling approach incorporating physical modeling and deep learning for multiphase flow simulation 84

A hybrid modeling approach incorporating physical modeling a...

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84th EAGE Annual Conference and Exhibition

作者： Wang, Y. Zhang, J. Du, S. Yan, B. Song, H. Wang, J. Xie, C. China Texas A&M University United States University of Science and Technology Beijing China National & Local Joint Engineering Laboratory for Big-Data Analysis and Computing Technology King Abdullah University of Science and Technology Saudi Arabia Computer Network Information Center Chinese Academy of Sciences China

ISBN: (纸本)9781713884156

Multiphase flows are of great importance for the sustainable utilization of geological resources and ecological protection. Numerical simulation, as a general and powerful approach for multiphase flow modeling, faces great computational challenge for large scale and heterogeneous problems. This study explores the possibility of a hybrid modeling architecture for nonlinear multiphase flow, which integrates physical modeling and deep learning approaches. By breaking down the coupled system into pressure and saturation equations, we construct novel solvers using a physics-guided U-net and Fourier Neural Operator instead of the traditional time-consuming implicit pressure solver. The coupling of our carefully designed solver for pressure and explicit solver for saturation provides a new perspective for modeling large scale complex multiphase flows. Our results demonstrate that this hybrid modeling architecture exhibits superior predictive performance for pressure and saturation, with relative error about 0.2% and 0.6%, respectively. More importantly, our hybrid solver can improve computational efficiency by several orders of magnitude compared to traditional numerical simulation workflows. Our work showcases that the proposed hybrid modeling approach incorporating physical modeling and deep learning is promising to provide a more flexible and powerful alternative for multiphase flow modeling in geological formations. © 2023 84th EAGE Annual Conference and Exhibition. All rights reserved.

关键词： Multiphase flow

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SRNDIFF: SHORT-TERM RAINFALL NOWCASTING WITH CONDITION DIFFUSION MODEL

arXiv

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arXiv 2024年

作者： Ling, XuDong Li, ChaoRong Qin, FengQing Yang, Peng Huang, Yuanyuan Faculty of Artificial Intelligence and Big Data Chongqing University of Technology Yibin University Yibin644000 China Faculty of Artificial Intelligence and Big Data Yibin University Yibin644000 China Chengdu University of Information Technology Chengdu610225 China

Diffusion models are widely used in image generation because they can generate high-quality and realistic samples. This is in contrast to generative adversarial networks (GANs) and variational autoencoders (VAEs), which have some limitations in terms of image *** introduce the diffusion model to the precipitation forecasting task and propose a short-term precipitation nowcasting with condition diffusion model based on historical observational data, which is referred to as SRNDiff. By incorporating an additional conditional decoder module in the denoising process, SRNDiff achieves end-to-end conditional rainfall prediction. SRNDiff is composed of two networks: a denoising network and a conditional Encoder network. The conditional network is composed of multiple independent UNet networks. These networks extract conditional feature maps at different resolutions, providing accurate conditional information that guides the diffusion model for conditional *** surpasses GANs in terms of prediction accuracy, although it requires more computational *** SRNDiff model exhibits higher stability and efficiency during training than GANs-based approaches, and generates high-quality precipitation distribution samples that better reflect future actual precipitation conditions. This fully validates the advantages and potential of diffusion models in precipitation forecasting, providing new insights for enhancing rainfall prediction. Copyright © 2024, The Authors. All rights reserved.

关键词： Rain

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Integrating hydrologic modeling and satellite remote sensing to assess the performance of sprinkler irrigation

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地球空间信息科学学报（英文版） 2024年第3期27卷 934-952页

作者： Chaolei Zheng Li Jia Massimo Menenti Guangcheng Hu Jing Lu Qiting Chen Min Jiang Marco Mancini Chiara Corbari Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina Faculty of Civil Engineering and Earth Sciences Delft University of TechnologyDelftNetherlands Department of Civil and Environmental Engineering Politecnico di MilanoMilanoItaly

Improving irrigation water management is a key concern for the agricultural sector,and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and *** study presents a novel methodology to explicitly estimate daily gross and net crop water requirements,actual crop water use,and irrigation efficiency ofcenter pivot irrigation systems,by mainly utilizing the Sentinel-2 MultiSpectral Instrument(MSI)imagery at the farm *** model is adapted to estimate actual water use(as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil)at daily/10-m resolution,benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm *** gross irrigation water requirement is estimated from the net crop water requirement and the water loss,including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception *** method was applied to a pilot farmland with two major crops(wheat and potato)in the Inner Mongolia Autonomous Region of China,where modern equipment and appropriate irrigation methods are deployed for efficient water *** estimated actual crop water use showed good agreement with the ground observations,*** determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56mm/day to 1.24mm/day for wheat and potato when comparing the estimated evapo-transpiration with the measurement by the eddy covariance *** also showed that the losses of total irrigated volume were 25.4％for wheat and 23.7％for potato,respectively,and found that the water allocation was insufficient to meet the water requirement in this irrigated *** suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system,which imply the necessit

关键词： Net irrigation water requirement crop water requirement consumptive water use gross irrigation water use irrigation performance center pivot irrigation systems ETMonitor

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Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter

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Chinese Physics B 2023年第7期32卷 558-563页

作者：倪黄晶杜若瑜梁磊花玲玲朱丽华秦姣龙 School of Geographic and Biologic Information Nanjing University of Posts and TelecommunicationsNanjing 210003China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing 210003China Department of Psychiatry the Affiliated Brain Hospital of Nanjing Medical UniversityNanjing 210029China Jiangsu Health Vocational College Nanjing 211800China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain ***,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain *** this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices *** network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these *** results demonstrate a decrease in the NNSEs of gray matter with *** with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate *** addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral *** study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.

关键词： two-dimensional horizontal visibility graph brain aging structural magnetic resonance imaging network structure entropy

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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data Preprocessing technology in Network Traffic Anomaly Detection

Data Preprocessing Technology in Network Traffic Anomaly Det...

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作者： Duan, Xueyuan Fu, Yu Wang, Kun Department of Information Security Naval University of Engineering Wuhan430033 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang464000 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China

Computer network plays a very important role in today’s world. With the development of network technology and the increasing popularity of network topology, network management is facing huge challenges. Among them, finding network traffic anomalies is one of the most important tasks in network monitoring, and is an important technology in network intrusion, security monitoring, and performance maintenance. This paper aims to study data preprocessing techniques in network traffic anomaly detection. This paper firstly introduces the network traffic measurement characteristics completely, and then selects and applies the data preprocessing algorithm, constructs a complete data preprocessing framework, and conducts experiments for the abnormal detection of network multi-dimensional characteristic traffic and the abnormal detection of unit-level characteristic traffic. The superiority of the data preprocessing technology in this paper, by reducing the data, determines the data preprocessing threshold of this paper to ensure that the data preprocessing is maximized. Experiments show that the data preprocessing technology constructed in this paper has about 20% more data filtering than traditional data filtering, which shortens the amount of detection data. The threshold is around 0.6, and data preprocessing can maximize the reduction of data without destroying the integrity of the attacked data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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An Improvement of Whale Optimization Algorithm with Rao Algorithm for Optimization Problems 15

An Improvement of Whale Optimization Algorithm with Rao Algo...

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15th International Conference on information technology and Electrical Engineering, ICITEE 2023

作者： Pravesjit, Sakkayaphop Thammano, Arit Kantawong, Krittika Jitkongchuen, Duangjai Longpradit, Panchit Pengchata, Rattasak School of Information and Communication Technology University of Phayao Phayao Thailand King Mongkut's Institute of Technology Ladkrabang Facluty of Information Technology Ladkrabang Thailand Government Big Data Instutite Digital Economy Promotion Agency Bangkok Thailand Mahidol University Faculty of Social Sciences and Humanities Nakhon Pathom Thailand

ISBN: (纸本)9798350304466

This paper proposes an improvement of whale optimization algorithm for optimization problems. In this study, the Rao algorithm was improved by means of procedures of spiral updating position. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), intersection mutation differential evolution (IMDE) algorithm, and whale optimization algorithm (WOA). The computation results illustrated that the proposed algorithm can produce optimal solutions for three out of six functions. Comparing to the other three algorithms, the proposed algorithm has provided the best results. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function, and the discontinuous step function. © 2023 IEEE.

关键词： Evolutionary algorithms

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MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

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