Misjudgment Rate Research Articles

Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with Cost-Sensitive Multi-Feature Extraction

An arc fault is a potential hazard in power systems, capable of causing serious safety accidents such as fires. Therefore, the timely detection of arc faults and implementation of circuit-breaking measures are crucial for ensuring safety, preventing fires, and maintaining the stable operation of power systems. Although existing studies have made progress in improving the accuracy of their detection, most methods have not proposed effective solutions that address the cost-sensitive problem of feature selection. Thus, a multi-feature method is proposed by combining time-domain, frequency-domain, energy, and spatial features, which are integrated into a CBN (conditional batch normalization) convolutional neural network for detection. The experimental results show that the proposed method outperforms traditional models in terms of its accuracy and misjudgment rate while maintaining a lower computational cost, demonstrating its superior detection performance. This provides an effective improvement for arc fault detection.

An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features

Pipeline feature recognition is crucial for the reliability and safety of long-distance natural gas pipelines. Utilizing manual or machine learning methods to recognize pipeline features is not only inefficient, but also has problems such as high misjudgment rate and poor robustness. To overcome the above problems, this paper proposes a pipeline feature recognition method based on Multi-scale Attention Convolutional Neural Network (MACNN) and Gated_Twins_Transformer. MACNN is used to extract multi-scale information of pipeline features, and then the attention mechanism in it to focus on the more important feature information and suppress the less important feature information. It is then transmitted to the Gated_Twins_Transformer model, which uses the gated mechanism and the twins encoder module to determine the importance of the data length and input dimensions, focusing on the feature information of both with different weights, and the Transformer enhances the extraction of global features. Finally, the measured pipeline bending strain data are used as model input, trained and tested, and compared with other advanced models, the superiority of the proposed model in this paper is verified by comparing the metrics of Accuracy, Precision, Recall and F1-score.

A Multipath Mitigation Method for Significant Environmental Changes in Deformation Monitoring

Significant environmental changes around monitoring antennas, caused by activities like earthmoving, protective structure installation, and vegetation removal, are common in GNSS deformation monitoring. Traditional multipath mitigation models fail to effectively suppress these multipath effects. To address this, we propose an Abnormal multipath Detection and Re-Mitigating (ADRM) method based on grid precision checks. The analysis results indicated that the traditional Multipath Hemispherical Model (MHM) inadequately mitigated multipath errors after environmental changes. In contrast, ADRM provided accurate detection of abnormal multipath areas. In the tested grid strategies, employing denser grids has proven more effective in mitigating multipath errors. The strategy utilizing 1°×1° grids achieved a detection accuracy exceeding 90%, with a misjudgment rate under 10%. This strategy is therefore recommended. Further mitigation of multipath errors after environmental changes using ADRM has significantly enhanced positioning precision, with improvements of 76.47%, 89.17%, and 80.83% in the east, north, and vertical directions, respectively.

Development of PIND system based on resonant sensors and research on material identification methods for redundant particles

Abstract Aiming at the problems of the existing particle impact noise detection (PIND) system, such as low detection accuracy, high rate of misjudgment and missed judgment, and the inability to judge the material of internal redundant particles in real time, a method for automatic detection and material identification of redundant particles based on neural network recognition is proposed, and an automatic detection system for redundant particles of sealed electronic devices is developed. The system is equipped with multi-channel piezoelectric resonant sensors, which convert the redundant particle impact noise signal into a voltage signal, and then transmit the signal to the host using a high-speed data acquisition system. The upper computer uses a short-term energy threshold detection method to extract signal pulses; Then, a variety of time and frequency domain features are extracted and combined with wavelet domain features; Finally, by training the multi output neural network prediction model, it can automatically judge whether there are redundant particles in the tested parts, and automatically give the material information of the redundant particles. Experiments show that the accuracy of the system for material recognition of redundant particles with a mass greater than 0.1 mg can reach 88.6%.

A research on resistance spot welding quality judgment of stainless steel sheets based on revised quantum genetic algorithm and hidden markov model

In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment.Firstly, by RSW test to obtain the main influencing factors of quality, and combining with expert experience to realize the preliminary judgment of it, on this basis, HMM was established. Next, to address the susceptibility of HMM to initial model, quantum genetic algorithm (QGA) was utilized to search for the optimal initial model. Moreover, in view of the limitations of QGA (“coding length disaster” and fixed rotation angle), a corresponding improvement was also presented. At last, the established model was applied to the RSW process of stainless steel sheets railway carriage roof. Compared with the other five models, it took the least time to complete parameter training of HMM in four quality states, less than 15s; when judging the quality, its log-likelihood probability value was −47.8418, which was close to the maximum value of this state; during the classification test, the average classification accuracy of the four quality states were 90.8 %, 92.2 %, 90.8 % and 92.0 %, respectively, which was higher than the other five models. Consequently, the model established in this paper was effective.

Text classification for distribution substation inspection based on BERT-TextRCNN model

With the advancement of source-load interaction in the new power systems, data-driven approaches have provided a foundational support for aggregating and interacting between sources and loads. However, with the widespread integration of distributed energy resources, fine-grained perception of intelligent sensing devices, and the inherent stochasticity of source-load dynamics, a massive amount of raw data is being recorded and accumulated in the data center. Valuable information is often dispersed across different paragraphs of the raw data, making it challenging to extract effectively. Distribution substation inspection plays a crucial role in ensuring the safe operation of the power system. Traditional methods for inspection report text classification typically rely on manual judgment and accumulated experience, resulting in low efficiency and a significant misjudgment rate. Therefore, this paper proposes a text classification method for inspection reports based on the pre-trained BERT-TextRCNN model. By utilizing the dense connection between the BERT embedding layer and the neural network, the proposed method improves the accuracy of matching long texts. This article collected 2,831 maintenance data for the first quarter of 2023 from the distribution room, including approximately 58 environmental testing data, 738 environmental box testing data, approximately 672 distribution room testing data, and approximately 1,363 box type substation testing data. A text corpus was constructed for experiments. Experimental results demonstrate that the proposed model automatically classifies a large volume of manually recorded inspection report data based on time, location, and faults, achieving a classification accuracy of 94.7%, precision of 92%, recall of 92%, and F1 score of 90.3%.

Detection and prediction of anomalous behaviors of enterprise’s employees based on data-mining and optimization algorithm

Internal employees have always been at the core of organizational security management challenges. Once an employee exhibits behaviors that threaten the organization, the resulting damage can be profound. Therefore, analyzing reasonably stored behavioral data can equip managers with effective threat monitoring and warning solutions. Through data-mining, a knowledge graph for internal threat data is deduced, and models for detecting anomalous behaviors and predicting resignations are developed. Initially, data-mining is employed to model the knowledge ontology of internal threats and construct the knowledge graph; subsequently, using the behavioral characteristics, the BP neural network is optimized with the Sparrow Search Algorithm (SSA), establishing a detection model for anomalous behaviors (SBP); additionally, behavioral sequences are processed through data feature vectorization. Utilizing SBP, the LSTM network is further optimized, creating a predictive model for employee behaviors (SLSTM); ultimately, SBP detects anomalous behaviors, while SLSTM predicts resignation intentions, thus enhancing detection strategies for at-risk employees. The integration of these models forms a comprehensive threat detection technology within the organization. The efficacy and practicality of detecting anomalous behaviors and predicting resignations using SBP and SLSTM are demonstrated, comparing them with other algorithms and analyzing potential causes of misjudgment. This work has enhanced the detection efficiency and update speed of abnormal employee behaviors, lowered the misjudgment rate, and significantly mitigated the impact of internal threats on the organization.

Identification Method for Spring Dust Intensity Levels Based on Multiple Remote Sensing Parameters

The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have attracted considerable attention. However, gridded observation data of dust intensity levels are not collected in current dust monitoring and forecasting operations. Based on the Himawari 9 geostationary satellite data, this study establishes a new method to identify spring dust events. This method integrates the brightness temperature difference method and the multiple infrared dust index, taking into account the response discrepancies of the multiple infrared dust index under various underlying surfaces. Furthermore, by obtaining dynamic background brightness temperature values eight times a day, threshold statistics are applied to analyze the correlation between the infrared difference dust index and ground-observed dust level, so as to establish a satellite-based near-surface dust intensity level identification algorithm. This algorithm aims to improve dust detection accuracy, and to provide more effective gridded observation support for dust forecasting and monitoring operations. The test results indicate that the algorithm can effectively identify the presence or absence of dust, with a misjudgment rate of less than 3%. With regard to dust intensity, the identification of blowing sand and floating dust aligns relatively well with ground-based observations, but notable uncertainties exist in determining a dust intensity of sand-storm level or above. Among these uncertainties, the differences between ground-based observations and satellite identification caused by non-grounded dust in the upper air, and the selection of dust identification thresholds, are two important error sources in the dust identification results of this study.

Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).

The aggressiveness of prostate cancer (PCa) is crucial in determining treatment method. The purpose of this study was to establish a 2.5-dimensional (2.5D) deep transfer learning (DTL) detection model for the automatic detection of clinically significant PCa (csPCa) based on bi-parametric magnetic resonance imaging (bp-MRI). A total of 231 patients, including 181 with csPCa and 50 with non-clinically significant PCa (non-csPCa), were enrolled. Stratified random sampling was then employed to divide all participants into a training set [185] and a test set [46]. The DTL model was obtained through image acquisition, image segmentation, and model construction. Finally, the diagnostic performance of the 2.5D and 2-dimensional (2D) models in predicting the aggressiveness of PCa was evaluated and compared using receiver operating characteristic (ROC) curves. DTL models based on 2D and 2.5D segmentation were established and validated to assess the aggressiveness of PCa. The results demonstrated that the diagnostic efficiency of the DTL model based on 2.5D was superior to that of the 2D model, regardless of whether in a single or combined sequence. Particularly, the 2.5D combined model outperformed other models in differentiating csPCa from non-csPCa. The area under the curve (AUC) values for the 2.5D combined model in the training and test sets were 0.960 and 0.949, respectively. Furthermore, the T2-weighted imaging (T2WI) model showed superiority over the apparent diffusion coefficient (ADC) model, but was not as effective as the combined model, whether based on 2.5D or 2D. A DTL model based on 2.5D segmentation was developed to automatically evaluate PCa aggressiveness on bp-MRI, improving the diagnostic performance of the 2D model. The results indicated that the continuous information between adjacent layers can enhance the detection rate of lesions and reduce the misjudgment rate based on the DTL model.

Visual defect inspection of touch screens using multi-angle filtering in curvelet domain

Touch screens are widely used in smartphones and tablets. These screens exhibit a pattern of directional, regular lines on their surface. The intricate texture of this background, which quickly causes interference, poses a significant challenge in detecting surface defects. Surface defects can be mainly classified into two types: linear and planar. Existing methods cannot effectively detect both types of defects. This study proposes a curvelet transform-based multi-angle filtering method. It can effectively attenuate regular patterns from panel images with textural backgrounds and preserve fine linear and planar defects in the reconstructed image. Curvelet transform is a multi-scale directional transformation that can capture the curved edges of objects well. The filtered curvelet coefficients are then reconstructed into the spatial domain and binarized using a threshold based on the interval estimation skill. The results of the trial show that the suggested approach can precisely locate and identify defects in touch panels. The rate of defect detection (1-β) stands at 93.33 %. The rate of defect misjudgment (α) is at a low of 1.26 %. The correct classification rate (CR) is impressively high at 98.69 %, indicating that the proposed method provides fine-grained segmentation results over existing methods for detecting surface defects on touch panels.

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly.

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.

A falls recognition framework based on faster R-CNN and temporal action sequences that can be deployed on home service robots

With the popularity of home service robots (e.g. floor sweepers), robots should be considered to have more features for older adult care. Compared to fixed home monitors with a limited field of view, fall detection with service robots is an ideal solution to keep older adults and disabled people within sight. However, the user’s actions, such as lying on the bed to sleep or slumping on the sofa to rest, cause the traditional fall detection system to generate false alarms, which disrupts the user’s family life. The present work proposed an enhanced faster R-convolutional neural network (CNN) network by incorporating temporal action sequences and fall acceleration computation, demonstrating a reduced misjudgment rate on the service robot platform. Firstly, motion images were captured to obtain the target’s motion area description and action timing at the input stage. Then, the faster R-CNN algorithm was implemented to check the suspected falls further based on the falling acceleration of the detected actions during the training phase. Finally, the proposed temporal action sequences module eliminated the action mistaken for falling. Network training and robotic platform testing demonstrated that the proposed approach distinguished between falls and false alarms, which mitigated the occurrence of false positives. On the service robot platform, experimental results showed that the FAR was 8.19 and processing time was 0.79 s.

Data mining algorithm in the identification of accounting fraud by smart city information technology

The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study.

Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC. To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC. This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR). Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%. Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.

Exploration on the Practice of Translation Teaching for English Majors Based on the Internet of Things and Multimedia Assistance

In this paper, an automatic judgment algorithm of English text is proposed, which is split and filtered first, then extracted and optimized, and finally interactively fused, and a BP neural machine judgment system is designed. After machine judgment and teachers' self-judgment of the same English sentence sample, the test results show that the ETSS system has excellent performance, improves the reliability and accuracy of judgment, and reduces the degree of human intervention and misjudgment rate in English translation judgment work. The results of the model translation of the Generalized Maximum Probability Ratio Detection (GLR) algorithm and its recognition include nested data points so that accuracy cannot be effectively guaranteed. In the GLR based comprehensive evaluation of content recognition acquisition improvement, the ID accuracy rate exceeded 95%, and the total result was 92.3%.

Intelligent supervision of PIVAS drug dispensing based on image recognition technology.

Pharmacy Intravenous Admixture Services (PIVAS) are places dedicated to the centralized dispensing of intravenous drugs, usually managed and operated by professional pharmacists and pharmacy technicians, and are an integral part of modern healthcare. However, the workflow of PIVAS has some problems, such as low efficiency and error-prone. This study aims to improve the efficiency of drug dispensing, reduce the rate of manual misjudgment, and minimize drug errors by conducting an in-depth study of the entire workflow of PIVAS and applying image recognition technology to the drug checking and dispensing process. Firstly, through experimental comparison, a target detection model suitable for drug category recognition is selected in the drug-checking process of PIVAS, and it is improved to improve the recognition accuracy and speed of intravenous drug categories. Secondly, a corner detection model for drug dosage recognition was studied in the drug dispensing stage to further increase drug dispensing accuracy. Then the PIVAS drug category recognition system and PIVAS drug dosage recognition system were designed and implemented.

A Method for Identifying Gross Errors in Dam Monitoring Data

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

Transformer fault diagnosis method based on SMOTE and NGO-GBDT

In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, the Synthetic Minority Over-sampling Technique (SMOTE) was used to expand the minority samples. Secondly, the non-coding ratio method was used to construct multi-dimensional feature parameters, and the Light Gradient Boosting Machine (LightGBM) feature optimization strategy was introduced to screen the optimal feature subset. Finally, Northern Goshawk Optimization (NGO) algorithm was used to optimize the parameters of Gradient Boosting Decision Tree (GBDT), and then the transformer fault diagnosis was realized. The results show that the proposed method can reduce the misjudgment of minority samples. Compared with other integrated models, the proposed method has high fault identification accuracy, low misjudgment rate and stable performance.

Experimental study on quantitative characterization of cavitation internal structure based on the distribution of void fraction in pressure relief valve

Accurately quantify the complex structure of cavitation in pressure relief valve by experiment remains a challenging task. In this research, a quantitative method for assessing the vapor volume fraction in cavitation images by void fraction experimentally is proposed, this method is less affected by environmental interference and could achieve rapid and accurate characterization of cavitation. The results show that void fraction effectively represents changes in vapor volume fraction under different conditions. The influence of the grid division numbers on the vapor volume fraction is investigated, the optimal number of grid division is 93 in X direction, with a misjudgment number of 130 and a misjudgment rate of 1.62 %. The effects of grid numbers on vapor volume fraction under different cavitation image pixels is also discussed, the optimal number of grid division is 112 in flow direction, with a misjudgment number of 144 and a misjudgment rate of 1.21 %. Through processing the vapor volume fraction image using the Proper Orthogonal Decomposition (POD) method under different cavitation numbers, the results show that the cavitation number has an important influence on the proportion of re-entrant jet area and cavitation collapse area, when the cavitation number decreases to 0.047, the proportion of re-entrant jet area and cavitation collapse area achieve the maximum that 6.41 % and 8.68 %, respectively. Such quantitative analysis of the cavitation degrees under various working conditions can contribute to a better understanding of the impact of cavitation on valve dynamics mechanisms.

A novel multi-scale standardized index analyzing monthly to sub-seasonal drought-flood abrupt alternation events in the Yangtze River basin

Drought-flood abrupt alternation (DFAA) events are often cause severe disasters that affect economic growth and ecological security. Since the current monthly to sub-seasonal drought-flood abrupt alternation index (DFAI) has the limitations of misjudgments, omissions and neglecting alternation speed, this study proposes a novel multi-scale standardized DFAI index (MSDFAI) based on the definition of the misjudgment and omission rates as well as alternation speed. A comparative test and spatial–temporal characterization analysis in the Yangtze River Basin (YRB) are conducted. The results show that the misjudgment rate of DFAI in the YRB varies between 3 %–181 %, and its omission rate varies between 0.5 %–45 %, which is unable to find a suitable point that will satisfy both misjudgment and omission at the same time. The alternation speed has a significant impact on the level of DFAA events, and there are 30 underestimations of DFAA events in the YRB during 1962–2022 when only considering the alternation intensity. The MSDFAI index makes a conditional judgment on whether a drought or flood event occurs based on the standardized precipitation index (SPI) value, which can effectively avoid these misjudgments and omissions. Meanwhile, the MSDFAI index describes the DFAA events in a more comprehensive manner by introducing the speed factor. The DFAA has a spatial pattern with relatively low frequency in the Poyang Lake basin and the middle YRB, and high frequency in the upper YRB, the Han River, and the lower YRB. The inter decadal variability of DFAA events in the YRB is characterized by a turning point in the 1990 s, when DFAA events are increased after the 1990 s. The proposed MSDFAI index can monitor DFAA events much comprehensively and effectively in the YRB, and provide technique support for identification and prevention of DFAA in the changing environment.