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data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

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Science China(Technological Sciences) 2023年第5期66卷 1304-1316页

作者： YANG Lei LI ShaoBo LI ChuanJiang ZHU CaiChao ZHANG AnSi LIANG GuoQiang School of Mechanical Engineering Guizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China State Key Laboratory of Mechanical Transmission Chongqing UniversityChongqing400044China

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from ***-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous ***,flight data with random noise pose a significant challenge for anomaly *** work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight ***,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the ***-based feature subset selection is subsequently performed to reduce the reliance on expert ***,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised ***,the method's effectiveness is validated on real UAV flight data.

关键词： unmanned aerial vehicle(UAV) anomaly detection spatiotemporal correlation based on long short-term memory and autoencoder(STC-LSTM-AE) Savitzky-Golay feature selection

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Complex patterns of precipitation and extreme events during 1951-2011 in Sichuan Basin, Southwestern China

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Journal of Mountain Science 2018年第2期15卷 340-356页

作者： ZHOU Xiang-yang LEI Wen-juan Guizhou Provincial Key Laboratory of Public Big Data Guizhou University State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan University College of Architecture & Environment Sichuan University

Sichuan Basin is located in southwestern China and affected by a complex water vapor （WV） sources. Here, the spatial and temporal patterns of precipitation and extreme events are investigated by six indices of World Meteorology Organization Commission, including annual precipitation total （AP）, maximum daily precipitation （Maxld）, intensity of rainfall over 1 mm/d （IR1）, maximum and mean consecutive dry days （Max CDD, Mean CDD） and coefficient of variance. Based on 24 daily precipitation time series from 1951 to 2o11, Mann-Kendall test is employed to quantify the significant level of these indices, from which the classification of precipitation change and its spatial patterns are obtained. Meanwhile, the probability distributions of these indices are identified by L-moment analysis and the Goodness-of-fit test, and the corresponding values are calculated by theoretical model at different return periods. The results reveal that the western basin displays normal drought： less AP and precipitation intensity while longer drought. The southern basin shows normal increase： larger AP and precipitation intensity but shorter CDD. However, in hilly region of the central basin and the transition zone between basin and mountains, precipitation changes abnormally： increasing both drought （one or both of Mean CDD and MaxCDD） and precipitation intensity （one or both of Maxld and trend of AP is. Probability IR1） no matter what the distribution models also demonstrate the complex patterns： a negative correlation between Maxld and Max CDD in the west （R2≥0.61） while a positive correlation in the east （R2≥0.41） at all return periods. These patterns are induced by the changes in WV sources and the layout of local terrain. The increase of WV in summer and decrease in spring leads to the heavier rainfall and longer drought respectively. The large heat island effect of the basin contributes to a lower temperature in transition zones and more precipitation in the downwind area. These resul

关键词： Extreme precipitation events Climatechange Sichuan Basin Trend analysis Probability distribution

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Mining Bytecode Features of Smart Contracts to Detect PonziScheme on Blockchain

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Computer Modeling in engineering & Sciences 2021年第6期127卷 1069-1085页

作者： Xiajiong Shen Shuaimin Jiang Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China

The emergence of smart contracts has increased the attention of industry and academia to blockchain technology,which is tamper-proofing,decentralized,autonomous,and enables decentralized applications to operate in untrustworthy ***,these features of this technology are also easily exploited by unscrupulous individuals,a typical example of which is the Ponzi scheme in *** negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been *** solve this problem,we propose a detection model for detecting Ponzi schemes in smart contracts using *** this model,our innovation is shown in two aspects:We first propose to use two bytes as one characteristic,which can quickly transform the bytecode into a high-dimensional matrix,and this matrix contains all the implied characteristics in the ***,We innovatively transformed the Ponzi schemes detection into an anomaly detection ***,an anomaly detection algorithm is used to identify Ponzi schemes in smart *** results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme ***,the F1-score of this model can reach 0.88,which is far better than those of other traditional detection models.

关键词： Ponzi scheme blockchain security smart contracts anomaly detection

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Phase spectrum based automatic ship detection in synthetic aperture radar images

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Journal of Ocean engineering and Science 2021年第2期6卷 185-195页

作者： Miaohui Zhang Baojun Qiao Ming Xin Bo Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina Institute of Data and Knowledge Engineering Henan UniversityKaifengChina School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

关键词： Ship detection saliency detection phase spectrum sea-land segmentation

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Leaving None Behind: data-Free Domain Incremental Learning for Major Depressive Disorder Detection

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IEEE Transactions on Affective Computing 2024年第02期16卷 758-770页

作者： Chen, Tao Guo, Yanrong Hao, Shijie Hong, Richang Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Hefei University of Technology Ministry of Education and School of Computer Science and Information Engineering Hefei230009 China

While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some efforts have resorted to aggregating data from different domains to expand the data volume. However, their effectiveness is currently limited by the domain gap and data privacy. Additionally, the class imbalance issue is particularly severe in our application, leading to biased classifying performance accordingly. To address these challenges, we propose data-Free Domain Incremental Learning for the MDD detection (DIL-MDD) task, accommodating multiple feature distributions by only accessing well-trained models from previous domains and the data in the current domain. Specifically, DIL-MDD consists of two key modules: Adaptive Class-tailored Threshold Learning (ACTL) and data-Free Domain Alignment (DFDA). The first module measures the discrepancy between the outputs of two sequential domains, based on which we learn a class-tailored threshold adaptively. Building on this, we differentiate between samples that either exhibit similarities or dissimilarities with the previous domain, where this similar sample set is identified to investigate the feature distribution of the historical data. The second module imposes an alignment constraint to narrow the gap between these two sample sets, thereby exploring the expertise of the previous domain. To validate the effectiveness of the proposed method, we conduct extensive experiments on the public MDD datasets, i.e., DAIC-WOZ, MODMA, and CMDC. We also apply our method to another mental health condition, Autism Spectrum Disorder (ASD), to further demonstrate its applicability. Finally, the ablation studies validate the superiority of the proposed modules. © 2010-2012 IEEE.

关键词： Adversarial machine learning

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Segment-Adaptive Spread Spectrum Audio Watermarking Using NSGA-Ⅱ

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Chinese Journal of Electronics 2021年第4期30卷 727-735页

作者： SU Zhaopin ZHANG Guofu ZHOU Xianxian LI Wangwang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology

In the field of robust audio watermarking,how to seek a good trade-off between robustness and imperceptibility is challenging. The existing studies use the same embedding parameter for each part of the audio signal, which ignores that different parts may have different requirements for embedding parameters. In this work, the constraints on imperceptibility are first ***, we present a segment multi-objective optimization model of the scaling parameter under the constrained Signal-to-noise ratio(SNR) in Spread spectrum(SS)audio watermarking. Additionally, we adopt the Nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) to solve the proposed model. Finally, we compare our algorithm(called SS-SNR-NSGA-Ⅱ) with the existing methods. The experimental results show that the proposed SS-SNRNSGA-Ⅱ not only provides flexible choices for different application demands but also achieves more and better trade-offs between imperceptibility and robustness.

关键词： Spread spectrum audio watermarking Scaling parameter Segment Constrained SNR NSGA-Ⅱ

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Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1293-1304页

作者： Zhu, Yi Wu, Xindong Li, Yun Qiang, Jipeng Yuan, Yunhao Yangzhou University School of Information Engineering Yangzhou225012 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei230002 China

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source- and target-domain datasets, most of the existing imbalanced domain adaptation models fix the imbalanced parameters, which cannot adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this article, we propose a self-adaptive imbalanced domain adaptation method via a deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross-entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods. © 2020 IEEE.

关键词： Noise abatement

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knowledge Graph for China's Genealogy1

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IEEE Transactions on knowledge and data engineering 2023年第1期35卷 634-646页

作者： Wu, Xindong Jiang, Tingting Zhu, Yi Bu, Chenyang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei230009 China Hefei University of Technology Research Institute of Big Knowledge Anhui Hefei230009 China Mininglamp Technology Mininglamp Academy of Sciences Beijing100102 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230009 China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230009 China

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model. © 1989-2012 IEEE.

关键词： History

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An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes

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Case Studies in Thermal engineering 2025年 72卷

作者： Qian Yi Xin Wu Junkang Zhuo Congbo Li Chuanjiang Li Huajun Cao College of Mechanical and Vehicle Engineering Chongqing University Chongqing 400044 PR China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang 550025 PR China

In manufacturing enterprises, the variety of heat treatment processes, insufficient data in the early stages of production, and the continuous increase of samples over time make it difficult to predict and optimize carbon emissions dynamically. Therefore, this paper proposes a low-carbon optimization method for heat treatment processes based on incremental data-driven approaches. Using life cycle assessment (LCA) theory, carbon emission sources are accurately analyzed and quantified, and a full life cycle carbon emission model is established. The key process parameters affecting part performance and carbon emission were screened through mechanism analysis, and the incremental data were fused by the Elasticity Weight Consolidation (EWC) algorithm to establish an EWC-BPNN heat treatment carbon emission prediction model. Then the model is solved by the Multi-objective Seagull Optimization Algorithm (MOSOA), and the result achieves a good balance between the carbon emission and hardness of the heat treatment process. Finally, using real data collected in enterprises for four consecutive years, it is verified that the predictive model can be effectively applied to the working conditions of multi-variety, small batch, and incremental heat treatment data.

关键词： Incremental data Heat treatment Carbon consumption prediction Process optimization

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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