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Downscaling multilayer soil moisture using parameterized depth profiles associated with environmental factors

Journal of Hydrology 2025年 661卷

作者： Mo Zhang Yong Ge Yuxin Ma Yan Jin Yingying Chen Shaomin Liu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing 100101 China Jiangxi Provincial Key Laboratory of Ecological Intelligent Monitoring and Comprehensive Treatment of Watershed School of Geography and Environment Jiangxi Normal University Nanchang 330022 China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang 330022 China New South Wales Department of Climate Change Energy the Environment and Water Parramatta NSW 2150 Australia School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing 210023 China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing 100101 China State Key Laboratory of Earth Surface Processes and Hazards Risk Governance Beijing Normal University Beijing 100875 China

High-resolution multilayer soil moisture is valuable for water resources management, drought monitoring, and crop optimization. However, integrating process-based models into statistical downscaling methods to estimate multilayer soil moisture can increase both prediction uncertainty and computational complexity of parameterization. Here, three parameterized equations (Peak, Trough, and Balance) were proposed to quantify soil moisture profiles associated with environmental factors, and downscale multilayer soil moisture. Global (0–40 cm) and local (0–10, 10–20, and 20–40 cm) fitting strategies represented the vertical extent of environmental influences. Three machine learning models were employed to downscale satellite soil moisture and predict depth parameters, which were then used to generate sub-surface soil moisture. The results indicated that the local fitting strategy consistently outperformed the global strategy across all depths, reducing the mean absolute error (MAE) and root mean square error (RMSE) by 81.8 % and 80.4 %, and increasing the coefficient of determination (R 2 ) by 22.5 %. Furthermore, local fitting revealed distinct nonlinear and linear patterns within the 0–20 cm and 20–40 cm intervals, respectively. The prediction accuracy of sub-surface soil moisture was improved compared to surface results, with MAE and RMSE reduced by 30.2 %–60.7 % and 34.9 %–56.7 %, respectively. The modified local fitting strategy further enhanced prediction performance, with the Peak–Trough–Balance combination recommended as the optimal configuration. Future studies should account for variations in soil and vegetation cover, refine interval division by incorporating soil-forming processes, and adopt advanced uncertainty quantification methods to enhance its adaptability and robustness. This study provides a reference for multilayer soil moisture downscaling in large-scale regions with depth variability and environmental heterogeneity.

关键词： Soil moisture

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A Point Cloud Completion Method for Industrial Component Based on Multiple Encoders

A Point Cloud Completion Method for Industrial Component Bas...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lei Chen, Ran Huang, Wei Chen, Yiping Yin, Jun School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9798350303759

In real-world scenarios, laser-scanned point clouds often suffer from missing data points due to occlusion and other factors, significantly impacting subsequent tasks. We propose a novel point cloud completion method based on self-attention mechanisms and multi-layer perceptrons to address this issue, building upon the PCN model. Our experimental evaluations on the PCN dataset demonstrate notable enhancements in the completion effectiveness and evaluation metrics compared to the baseline model. Additionally, focusing on the point cloud completion context for industrial components, we introduce an approach for constructing an industrial component point cloud dataset. This method leverages 3D mesh models to swiftly generate complete point clouds with uniform distribution and devoid of noise while simulating the incomplete point clouds obtained from real-world scanning scenarios. The final results fully exhibit the feasibility of this scheme, which is a strong candidate for future industrial component point cloud completion. © 2023 IEEE.

关键词： Signal encoding

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Complex Substation Scene Segmentation Method Based on Optimized Straight Line Detection

Complex Substation Scene Segmentation Method Based on Optimi...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Hao Liu, Chuang Yin, Jun School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9798350303759

This paper proposes a scene segmentation method based on optimized line detection to address the difficulty of segmenting spatially independent equipment in complex substation scenes. The method first employs the RANSAC method to simulate the ground plane and extract the ground point cloud. Then, it projects the 3D point cloud scene onto a 2D plane to obtain a 2D image of the substation. Straight line segments of the power line are extracted from the image using the edge detection algorithm and an improved line detection algorithm. The straight line segments that do not belong to the power line are filtered out using the surrounding features of the 3D point corresponding to the 2D point on the line. The power line's straight line segment is extended using the region-growing algorithm. Finally, the power line clustering is completed using European clustering based on the three-dimensional coordinates of the two ends of the power line, thereby completing the scene segmentation. Experimental results show that the proposed method can effectively segment substation equipment. Compared to traditional segmentation methods, this method has an average power line identification accuracy of 14% higher. © 2023 IEEE.

关键词： Edge detection

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Robustness May be at Odds with Stability in Adversarial Training based Feature Selection?

Robustness May be at Odds with Stability in Adversarial Trai...

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IEEE International Conference on data Mining (ICDM)

作者： Yue Liu Yun Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

As an important stage in machine learning pipeline, feature selection techniques are mainly used to improve the generalization performance and training efficiency of machine learning model, but few works have focused on the robustness of machine learning models from the perspective of feature selection when dealing with adversarial attacks. In this paper, we propose the adversarial training (AT) based feature selection framework, i.e. AT based feature selection, to improve the robustness of machine learning model built on the feature selection result, which is inspired by using adversarial training to improve the robustness of deep learning model. AT based feature selection framework is the combination of adversarial training with some traditional feature selection algorithm, which can be divided into AT in-processing and AT post-processing feature selection. On the other hand, stability is also a very important property for feature selection. Then we experimentally analyze the relationship between robustness and stability of AT based feature selection, especially theoretically analyze the stability of $\ell_{2}$ regularized AT in-processing feature selection algorithm in two different adversarial training forms. Our experimental results on benchmark data sets show that AT based feature selection algorithm is effective to improve the robustness of machine learning model, however, obtain lower stability than corresponding feature selection model without AT.

关键词： Training Deep learning Machine learning algorithms Pipelines Benchmark testing Feature extraction Stability analysis

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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MDPFL:A Multiple Differential Privacy Protection Method based on Federated Learning

MDPFL:A Multiple Differential Privacy Protection Method base...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xinyu Liu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

With the continuous development of artificial intelligence technology, machine learning in distributed network systems, such as IoVflntemet of Vehicles), will inevitably lead to privacy leakage. At present, there are lots of problems in federated learning, differential privacy and other machine learning privacy protection schemes, such as high loss of availability and the need of a fully trusted third-party server. In order to solve these problems, we propose MDPFL(Multiple Differential Privacy based on Federated Learning) algorithm. The algorithm combines with the differential privacy model in each stage of federated learning to solve the problem of curious third-party data collectors obtaining users’ original data in the process of machine learning. Meanwhile, the algorithm does not adopt a decentralized machine learning scheme directly, but uses a double noise adding mode with the existence of the third-party data collectors. We use Laplacian mechanism in the central server and GRR mechanism in local clients to ensure the goal of stability of the effect of the machine ¡earning model. The algorithm is compared with FedAvg, CDP, LDP algorithm in accuracy and loss rate which based on EMNIST data sets. In different global sensitivities, the model training effect is consistent with FedAvg algorithm while comparing with LDP algorithm, the speed of model convergence is improved.

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Multi-Granularity Power Prediction for data Center Operations via Long Short-Term Memory Network

Multi-Granularity Power Prediction for Data Center Operation...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Ziyu Shen Xusheng Zhang Bin Xia Zheng Liu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The increasing numbers of the applications and requirement of cloud computing have made huge power consumption in data centers, which brings the problems of the high cost and resource waste. This problem attracts significant attention from academia and industry. A critical approach to solve this problem is constructing an intelligent energy management system for data centers. Furthermore, an efficient assessment and prediction module of power consumption in data centers is an essential part of the management system. It facilitates cloud service providers to perform workflow scheduling at the minimal cost and energy efficiency management with the requirement of QoS. Since the assessment and prediction of power consumption correlate, this paper presents a multi-granularity approach for power consumption prediction in data centers, which combines multi-task learning with the LSTM network. We first transfer a multi-granularity power prediction problem into a multi-task regression problem to assess and predict the power consumption of data center system maintenance and scheduling operations. Due to the time requirement for workflow and container scheduling, the prediction interval is 30 seconds. Then we propose an efficient long short-term memory network for the multigranularity prediction. The experimental results show our model outperforms other prediction models on the real datasets.

关键词： Power demand data centers Task analysis Predictive models data models Time series analysis Servers

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China 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 China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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Economical Electricity Supplement for Mobile Collectors in Large-Scale Multitask WSNs

Economical Electricity Supplement for Mobile Collectors in L...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Xin Zhai Lijie Xu Jialei Zhang Kun Wang Jia Xu Bei Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in much higher packet collision probability, especially for harsh environments. Mobile data collection is an efficient data acquisition way to prolong network lifetime for LSM-WSNs. However, the mobile collectors could suffer electricity shortage problem, since the limited battery capacity of any mobile collector could not afford the energy consumption of its long-distance movement and massive data collection in large-scale multitask scenarios. Deploying wireless chargers to supplement the energy of mobile collectors is a feasible solution to electricity shortage problem, but will incur extra charger deployment cost. In this paper, we focus on the problem that how to optimize such charger deployment cost, which is NP-hard. By transforming it into minimum-cost submodular cover problem, we devise an efficient approximation algorithm with a provable approximation ratio. The extensive simulation results reveal that our solution always outperforms the other solutions under whatever configurations.

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SSR-TA: Sequence to Sequence based expert recurrent recommendation for ticket automation

arXiv

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arXiv 2023年

作者： Cao, Chenhan Fang, Xiaoyu Luo, Bingqing Xia, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

The ticket automation provides crucial support for the normal operation of IT software systems. An essential task of ticket automation is to assign experts to solve upcoming tickets. However, facing thousands of tickets, inappropriate assignments will make tickets transfer frequently among experts, which causes time delays and wasted resources. Effectively and efficiently finding an appropriate expert in fewer steps is vital to ticket automation. In this paper, we proposed a sequence to sequence based translation model combined with a recurrent recommendation network to recommend appropriate experts for tickets. The sequence to sequence model transforms the ticket description into the corresponding resolution for capturing the potential and useful features of representing tickets. The recurrent recommendation network recommends the appropriate expert based on the assumption that the previous expert in the recommendation sequence cannot solve the expert. To evaluate the performance, we conducted experiments to compare several baselines with SSR-TA on two real-world datasets, and the experimental results show that our proposed model outperforms the baselines. The comparative experiment results also show that SSR-TA has a better performance of expert recommendations for user-generated tickets. Copyright © 2023, The Authors. All rights reserved.

关键词： Automation

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