In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%. Flying ad-hoc network (FANET) usually undertakes important information collection and transmission tasks in the battlefield environment. Thus, it has high requirements for communication quality. The battlefield environment is expected to become more complex in the future, and the communication between unmanned aerial vehicles (UAVs) occurs in a relatively open environment, which makes it more susceptible to malicious interference. Thus, the accuracy of information transmission and the security rates of communication systems are reduced. Considering the above problems, it is of great research significance to reduce the interference’s influence on the communication quality.To solve the interference influence in open environments, some researchers have proposed advanced interference suppression methods. Lin proposed methods based on beamforming ,2,3 and Traditional interference recognition methods mainly include decision tree and support vector machine (SVM) methods. The method based on decision trees extracts distinguishing features. Then, it sets the threshold of the features and classifies the interference signals layer by layer. Luo  extracteIn recent years, deep neural networks (DNNs) have been widely used in the fields of image and speech processing and have achieved good results, so many scholars have also applied them to the field of communication interference recognition and research. Zhao  improvedYang  used theHowever, the above methods do not pay specific attention to the suppression and processing of noise in the signal, and the network models are simple with few layers, so the accuracy is still unsatisfactory under a low JNR. Moreover, the above methods do not consider that in complex battlefield environments, UAVs are likely to be attacked by unknown interference with the modification of attack methods.Motivated by the above problems, a network model based on the improved deep residual shrinkage network is proposed, which is called AFUCR-SNRSN, for known and unknown recognition. The structure of the model is shown in (1)We modify the soft thresholding function in the deep residual shrinkage network to avoid eliminating useful features, and a simple non-local module is introduced into the network to evaluate the threshold from both the spatial dimension and channel dimension.(2)We introduce the LIP strategy into the downsampling process to suppress noise. We propose a joint loss function to allow different features to be “separable between classes and compact within classes”, which can improve the recognition accuracy in low JNR situations.(3)Focusing on unknown interference recognition in an open set, we propose an acceptance factor based on the confidence distribution difference between known and unknown interference. Using the acceptance factor, unknown interference recognition could be converted into a one-class classification problem and solved.(4)According to various simulation experiments, it is verified that our method not only can recognize interference under a high JNR, but also retains relatively high accuracy in low JNR situations.The main contributions of this research are as follows:(1)SNRSN: Based on the deep residual shrinkage network (DRSN) , the sof(2)AFUCR: Through analyzing the confidence distribution difference output from the softmax layer between known and unknown class interference, this method proposes an acceptance factor. Unknown interference recognition can be converted into a one-class classification problem and solved by calculating samples’ acceptance factors.In the study, the proposed model is composed of two parts, SNRSN and AFUCR, which are shown in Based on DRSN, the SNCS module is used to replace the residual shrinkage module in DRSN. SNRSN also introduces the LIP strategy and constructs a joint loss function. The structure of SNRSN is shown in x represents the input feature and A soft threshold is widely used in the field of traditional signal denoising, such as wavelet denoising. Wavelet denosing mainly consists of three steps: wavelet decomposition, soft thresholding and wavelet reconstruction. Wavelet decomposition involves transforming the original signal into a decomposable scale. Under this scale, coefficients close to 0 are considered unimportant and may be seen as noise. Soft thresholding is used to compress this signal to 0. Its formula is shown in (1), where The usage of soft thresholding is to set coefficients that are less than the threshold to 0 (filter noise) and shrink the others. A soft threshold can avoid the discontinuity problem of a hard threshold function. From (1), the derivative of the soft threshold function can be calculated as in (2).However, the traditional method of threshold selection requires multiple attempts and a lot of expertise in signal processing. In 2020, Zhao proposed the DRSN  model fo(1)Threshold CorrectionThe DRSN shrinkage module is shown in As shown in (3), soft thresholding in each channel directly zeros all features below the threshold, which may eliminate a few useful features other than noise, thereby reducing the signal recognition accuracy . To solv(2)Simple Non-Local ModuleEquations (3) and (4) show that the modified threshold function does not completely zero the feature when the absolute value is smaller than the threshold, and some useful signal information is still retained. While the noise information is greatly suppressed, it is not completely eliminated. The derivative is always between 0 and 1, which can prevent gradient disappearance and explosion problems during the training process.DRSN is similar to the channel attention module in threshold evaluation, focusing only on channel dimension information and ignoring information in the spatial dimension. Therefore, a simple non-local module  is introThe simple non-local module refers to the idea of non-local means denoising. It is a module based on the spatial attention mechanism. The features that have a large distance from each other can be related by calculating the correlation of their positions on the feature map. The structure of the simple non-local module is shown in x represents the input and This module first compresses the input feature map with dimension Replacing the global average pooling in the DRSN shrinkage module with the simple non-local module can fully utilize the global context information. Combined with spatial information to obtain the long-term dependency between features, it can evaluate the threshold more concretely. Then, replacing the soft threshold function with the proposed correction threshold function, the simple non-local correction shrinkage (SNCS) module is proposed. The structure of the SNCS module is shown in Assume that the input dimension is After feature extraction by convolution operations, the quantity of parameters is usually large. Thus, it is necessary to pass through the pooling layer for downsampling, which is a lossy process. The method adopted in classical neural networks is to use average pooling or max pooling. Average pooling assumes that features in all positions are equally important. It does not distinguish features belonging to useful signals or noise. Max pooling assumes that the max value is the most representative one. Thus, it retains the max value but abandons others. These two assumptions are not always correct. It may cause useless noise features to be retained and useful signal features to be abandoned.A new pooling strategy, LIP , is intrI and the kernel index set C with a relative sampling position of I.Given the input feature map g. To make the importance weights non-negative and easy to optimize, the g, which means that LIP generates an importance graph by using a learnable fully convolutional network Due to the strong randomness of the frequency of interference signals and the different noise content in the same category in complex environments, the characteristics of similar signals have a large gap within the class. Therefore, it is worth considering that in the feature space, the interference signals are not only separable between classes but also compact within the class. This modification can obtain a more robust judgment result when the range of similar signals is large. In this case, the center loss function  and the i-th sample and i.Center loss can constrain the distances between sample features in the feature space so that the distance between each sample feature and the average features of the same class is small enough. It can achieve compactness within the class. The calculation process is as shown in (10), where m represents the number of samples, i-th category and i-th category.The cross-entropy loss function is calculated as in (11), where n represents the number of categories, The joint loss calculation is shown in (12), where In the −8 dB JNR situation, By observing the confidence distribution prediction in In the training process, only training samples of known class signals can be accessed, so the problem is converted into a one-class classification problem, which means that a hypersphere is trained in the training phase to contain all training samples. Meanwhile, in the testing phase, the samples inside the hypersphere are recognized as known classes, and the outside samples are marked as unknown classes. The simulation of the samples’ distribution in the hypersphere is shown in x is the train samples that come from the acceptance factors output from the softmax layer in the SNRSN train process, and According to the above characteristic, one-class vector machine (OCSVM), a classical class of support vector machines, is selected as the classification training model. Using a kernel function, the origin point can be defined as the only error sample. Then, the unknown interference recognition problem can be converted into a classical SVM problem. The classification hyperplane’s representation is shown in (14), where l is the number of the samples and v is the penalty parameter. Moreover, To maximize the distance between outliers and the classification hyperplane, the solution of SVM can be transformed into a quadratic programming problem as in (15), where Based on (15), the Lagrange function can be constructed as in (16).According to the Lagrange function’s duality, the dual problem of (16) is (17).Calculating the partial derivative of (16) and letting the partial derivative be equal to zero results in (18).Substituting (18) into (16) leads to (19).In this situation, the dual problem of (15) can be represented as in (20), where After solving (20) and obtaining the optimal solution Substitute the solved result in (21) into (14), and the classification hypersphere can be constructed and the OCSVM problem is solved. AFUCR consists of the calculation of the acceptance factor and OCSVM. Combining AFUCR and SNRSN, the complete AFUCR-SNRSN model is constructed.We use the 1-D CNN to transform the input signal’s type into 2-D and extract the basic features of the signal. Then, the LIP layer is used for downsampling according to (9) to decrease the quantity of the parameters and retain the useful features more precisely.After LIP, features are processed by 6 SNCS modules. The operation of a single SNCS module can be mainly described in the following two steps: (1) using a spatial attention mechanism with multi-channel information fusion and SNM to calculate the correlation between each feature in different positions according to (5); (2) calculating the threshold according to (6) using the correlation from the last step. Then, we adopt the threshold in the correction soft threshold function (3) to filter noise and useless features.Outputs of SNCS modules are processed by LIP and softmax to generate a temporary recognition result. According to (13), the sample’s acceptance factors can be calculated using the output from softmax. The OCSVM is then trained using the set of acceptance factors.The test samples are fed into the trained SNRSN to obtain the confidence distribution and classification results of the softmax layer. According to the output results of the softmax layer, the acceptance factor is calculated and input into the trained OCSVM model. If the output category is “+1”, the previously obtained classification result is directly output; otherwise, the sample is marked as an unknown class and added to the unknown sample library.The training and test process of the algorithm is shown in The experimental dataset is generated by Matlab2022, the sampling frequency of the interference signal The training and testing of the network model involve a large number of network parameters, and the main parameters and initial settings are as follows: the batch size is initially set to 128, the number of iterations is set to 50 and the learning rate mainly affects the learning speed in the network, which is usually initially set to a small value of 0.001 and adaptively adjusted using the Adam optimization algorithm. Differences in the dimensions and values of the variables may cause incomparability between different signals. To solve this problem, the signal amplitude is normalized, which can avoid unnecessary interference caused by the signal strength. The normalization method is shown in (22), where Through linear transformation, whole data are transformed into the distribution whose mean is 0. At this time, the value of the data represents the data’s off-center degree. The centralization method is shown in (23).To evaluate the accuracy and potential of the proposed AFUCR-SNRSN model, an ablation experiment is applied to SNRSN to check each module’s performance in recognizing interference. Then, the classic SVM, 1D-CNN in , LRN in As mentioned in (a) In order to test the impact of the introduction of the SNCS module and LIP pooling strategy on the network recognition rate, the cross-entropy loss function is uniformly adopted. The network with maximum pooling and the original residual shrinkage module is recorded as the basic network. The pooling policy in the basic network replaced with the LIP is recorded as the LIP network. The residual shrinkage module in the basic network replaced by the SNCS module is recorded as the SNCS network. The proposed network is denoted as the LIP-SNCS network. The recognition accuracy at different JNRs is shown in As can be seen from Specifically, compared with the base network, the accuracy of the LIP network is improved by approximately 2% in the range of −16–0 dB. This indicates that the maximum pooling replaced by the LIP can suppress noise and strengthen the discriminating features during the downsampling process, so that useful information can be extracted under a low JNR.The SNCS network improves the accuracy by approximately 5% compared to the basic network in the range of −16–0 dB. This indicates that the SNCS module can pay more attention to the global information compared with the original residual shrinkage module, obtain long-range dependencies and select the threshold more accurately. The modified threshold function also avoids the filtering of useful features, further improving the recognition accuracy. The SNCS network has higher recognition accuracy than the LIP network, because LIP pooling only occurs twice in the whole model. Moreover, the first downsampling occurs at a shallow network level where feature extraction is not sufficient, while the SNCS module in the network suppresses noise and pays attention to global information in the deeper network layer. Thus, the recognition accuracy is higher.The LIP-SNCS network replaces the original residual shrinkage module with the SNCS module and the pooling strategy with LIP, which has the best overall performance. The accuracy is improved by approximately 7%, and, when at −4 dB, the recognition accuracy reaches more than 99%.(b) In order to analyze the impact of the loss function on the network performance, the network using the cross-entropy loss function is recorded as the classified network, the network using the center loss function is recorded as the center network and the network using the joint loss function is recorded as the joint network. The recognition accuracy under different JNRs is shown in It can be seen from Compared with the classification network and center network, the recognition of joint network accuracy improvement is approximately 4%, indicating that the joint guidance can improve the performance of the network.First, the model is trained by a known class training set. The recognition accuracy and loss in training process are shown in The proposed SNRSN network model is compared with the traditional SVM, 1D-CNN, LRN and Res-soft on the known interference test set. The results are shown in It can be seen from In particular, the SNRSN network generally outperforms the other three methods in recognizing interference signals under the condition of each JNR. In the strong noise environment of −16 dB, the SNRSN network model has recognition accuracy of more than 55% for the four interference signals. As for the time-domain Gaussian pulse interference, the recognition accuracy is more than 80%, because it behaves very differently from the other three signals in the time-domain waveform. Multi-tone interference is superimposed by multiple single-tone interference so the waveform is more complex and the recognition accuracy is slightly lower than that of other interference signals. When the JNR reaches −6 dB, the SNRSN recognition accuracy for all four types of interference reaches 99%.The SVM method relies on manual feature extraction. Thus, in low JNR situations, its accuracy is around 40%. Because 1D-CNN and LRN only consist of two Conv Layers, which leads to low feature extraction ability, they cannot extract the features of interference signals in low JNR situations. Their accuracies are close and relatively low. Res-soft fully utilizes the data of multiple nodes to evaluate and uses soft fusion to judge. Thus, its recognition accuracy is higher compared to CNN and LRN. However, it does not focus on noise processing and suppression. The extracted features include many redundant and wrong features, which leads to relatively low accuracy in low JNR situations.The proposed SNRSN executes a denoising process during feature extraction. Compared with Res-soft, its recognition accuracy increases by approximately 2–10% in the JNR range of −16 to −6 dB. This proves that SNRSN focuses more on useful features in the signal and suppresses noise to make the network perform more stably. SNRSN has robustness and noise immunity. Each model’s recognition accuracy on each type of interference is shown in To analyze the classification results on known interference, the confusion matrix under a −16 dB and −8 dB JNR is adopted, as shown in In order to test the scalability of each method for interference signal testing, the known interference signal set and the unknown class interference signal set are combined to form an open test set. The CNN, LRN, TF-LSTM, SNRSN and AFUCR-SNRSN models are tested, and the model results are evaluated using two indicators, which are well-accepted metrics in intelligent recognition to compare model performance ,27: the The experimental results are shown in To improve the recognition performance of communication interference signals in a strong noise environment, one SNCS model that embeds the modified threshold function and a simple non-local module into the residual shrinking network is proposed. The LIP pooling strategy and the joint loss function are also introduced to obtain better performance. The SNRSN model can obtain an approximately 4–9% increase in recognition accuracy compared to other methods; moreover, the accuracy in recognizing four known types of interference signals under the JNR of −6 dB reaches 99%.Based on the analysis of the confidence distribution of known and unknown class signals in softmax, the acceptance factor is proposed to distinguish between known and unknown classes. The AFUCR-SNRSN model combined with AFUCR and SNRSN reduces the FPR to 0.09, which means that it has the capacity for both known class recognition and new class rejection.Furthermore, future work will focus on using existing models for transfer learning to recognize more interference signals.


In 2020, the Chinese government developed and implemented an innovative case-based payment method under the regional global budget called the diagnosis-intervention packet (DIP) payment to pay for inpatient care. This study aims to assess the changes to inpatient care provision in hospitals after the DIP payment reform was implemented.This study used inpatient medical costs per case, the proportion of the out-of-pocket (OOP) expenditure in inpatient medical costs, and the average length of stay (LOS) of inpatient care as outcome variables, and conducted an interrupted time series analysis to evaluate changes after the DIP payment reform. January 2021 was taken as the intervention point when a national pilot city of the DIP payment reform in the Shandong province began using the DIP payment to pay for inpatient care of secondary and tertiary hospitals. The data used in this study were obtained from the aggregated monthly claim data of inpatient care of secondary and tertiary hospitals.p < 0.001). The average LOS of inpatient care in secondary hospital significantly increased after the intervention, and it immediately increase 0.44 day after intervention (p = 0.211). Moreover, the change of average LOS of inpatient care in secondary hospital after intervention was opposite to that in tertiary hospital, it had no statistical difference (p = 0.269).Compared to the pre-intervention trend, the inpatient medical costs per case, the proportion of the OOP expenditure in inpatient medical costs both in tertiary and secondary hospitals significantly decreased after the intervention. After the intervention, the reduction in the inpatient medical costs per case, the proportion of the OOP expenditure in inpatient medical costs in tertiary hospital were both higher than those in secondary hospital (In the short term, the DIP payment reform could not only effectively regulate provider behavior of inpatient care in hospitals, but also improves the rational allocation of the regional healthcare resources. However, the long-term effects of the DIP payment reform need to be investigated in the future. The growth of health expenditure has become a critical issue for healthcare policymakers and governments in many countries. To remedy this, countries have sought to improve the efficiency of healthcare delivery and reduce the growth rate of health expenditure by implementing healthcare provider payment reforms . Among tOver past decades, total health expenditure (THE) in China has rapidly increased . This isHowever, as the DIP payment is a recent initiative. There are few studies about the effect evaluation of the DIP payment, which leads that the current studies have not been able to adequately demonstrate its impact on changes to inpatient care provision until now. Among the few related studies, Lai et al.  discoverAs with the DRG payment system, the DIP payment includes a series of classifications of DIP disease groups. Based on the same major diagnosis principles and similar treatment procedures, the National Healthcare Security Administration firstly enacted the uniform score catalogue and classification standards of the DIP disease groups, containing 11,553 core disease groups and 2,499 comprehensive disease groups. Subsequently, according to the uniform classification standards of the DIP disease groups, the 71 pilot cities of the DIP payment reform throughout China were required to enact the catalog of core disease groups and comprehensive disease groups. Meanwhile, the pilot cities needed to formulate the average score of each local DIP disease group by using the average medical cost of all inpatient care cases in the last 3 years.Although the DIP payment has several differences with the DRG payment , howeverReimbursements to hospitals are further adjusted based on several indicators of performance. These include, quality of medical records, readmission rate, length of stay (LOS) and mortality. These adjustments are made in the form of organization level ‘fines’ .Through this design, the policymakers hope that the DIP payment can effectively incentivize individual hospitals to improve the efficiency and quality of their services, while promoting the allocative efficiency of regional healthcare resources.per capita is 156.90 CNY which is the highest in Shandong province  design was used to assess whether the DIP payment reform produced significant changes to inpatient care provision in secondary and tertiary hospitals.Fortunately, City A is the only one in Shandong province that did not have a COVID-19 pandemic before May 2022. All the residents’ healthcare consumption always can be satisfied in local hospitals. Therefore, we collected and aggregated the monthly medical claim data of inpatient care for secondary and tertiary hospitals in City A during January 2019 to April 2022, which was obtained from the Healthcare Security Administration in City A.To accurately quantify the change to inpatient care provision in hospitals, several variables were selected for measurement during the study observation, including inpatient medical costs per case, proportion of the out-of-pocket (OOP) expenditure in inpatient medical costs, and average LOS of inpatient care. All data in the study was adjusted using the related consumer price index, with 2019 serving as the base year .The ITS analysis was used to assess the changes to inpatient care provision. In the ITS analysis, a segmented OLS regression model with a Newey-West test was used to assess the impacts of the DIP payment reform on the change to inpatient care provision in hospitals . The segtY is the dependent variable we measured at every monthly point t, tT is the time series variable representing the time in months since the start of observation until to the time t, tX is a dummy variable representing the intervention point (pre-intervention period is 0 and the post-intervention period is 1), t XtT is an interaction term of the time and intervention, and tε is the residual term representing the unknown variation component of the regression model. 0β represents the baseline level; 1β represents the baseline trend prior to intervention; 2β represents the immediate level of change after the intervention compared to the pre-intervention; 3β represents the trend change after the intervention compared to the pre-intervention; and β1+ 3β represents the trend after the intervention  (p = 0.307). However, the inpatient medical costs per case in tertiary hospitals keep decreasing by 0.07 CNY per month, which is opposite to that in secondary hospitals (p < 0.05) (< 0.001) . Moreove < 0.05) .p < 0.001) (p < 0.01). Furthermore, the reduction in the proportion of the OOP expenditure in inpatient medical costs in tertiary hospital was more pronounced than that in secondary hospital (p < 0.001)  . The prohospital , decreas< 0.001) .p = 0.211). However, the change in average LOS of inpatient care in secondary and tertiary hospitals were opposite. Compared to the pre-intervention trend, the average LOS of inpatient care in tertiary hospital decreased by 0.05 day (p < 0.001), and the average LOS of inpatient care in secondary hospital increased 0.04 day (p < 0.001)  .The rapid growth of the inpatient health expenditure poses a serious challenge for the Chinese government . Faced wIn this study, we found that the inpatient medical costs per case decreased after the implementation of the DIP payment reform, which was aligned to the related studies . ConsequPrevious studies highlight that the single healthcare provider payment reforms occasionally result in the increase in the proportion of the OOP expenditure or other medical costs of healthcare services , 28. In It is worth noting that the average LOS in secondary hospital experienced an obvious increase after the DIP payment reform was implemented, which was opposite to that in tertiary hospital. We consider that this opposite result is a positive reaction to the implementation of the DIP payment. In order to better reflect the service capacity of hospitals, the reimbursements of hospitals in the DIP payment are relevant to the severity degree of inpatient cases and the status of hospitals. It not only promoted hospitals have more initiatives to treatment the sicker cases, but also achieved that the hospitals under the DIP payment have no necessary to strictly control the LOS of inpatient care due to the cost containment. Accordingly, the reasons we conclude behind the change of the average LOS in this study was that the implementation of the DIP payment enhanced the enthusiasm of inpatient care provision in secondary hospitals, which made some patients who can be treated in secondary hospitals accept more treatments. Additionally, the number of secondary hospitals is more than that of tertiary hospitals, and the copayment ratio of inpatient care in City A is increased with the status of hospitals increases. In this case, the patients got more inpatient care in secondary hospitals also led to the LOS in tertiary hospitals decreasing. Furthermore, the reduction of the proportion of the OOP expenditure in inpatient medical costs in tertiary hospitals compared to the pre-intervention trend is more obvious than that in secondary hospitals after the DIP payment reform was implemented, which further illustrates that the DIP payment reform can effectively alleviate the cost-control pressure of inpatient care in tertiary hospitals. Therefore, combined with the change in the proportion of OOP expenditure in inpatient medical costs and average LOS in secondary and tertiary hospitals, we believe that the DIP payment reform not only alleviated the provision pressure of inpatient care in tertiary hospitals but also improved the rational allocation of the regional healthcare resources. However, Vuagnat et al. outlined that case-based payments, such as the DRG payment, could indirectly lead hospitals to increase the average LOS and readmission rates of inpatient care for the sake of getting more premium . FurtherThis study has several limitations that needed to be addressed: First, this study did not introduce a control group which means that we cannot rule out the possibility that the estimated changes are due to the impact of other phenomena, that changed patient and hospital behavior. Second, due to its recent introduction, we were only able to assess the short-term effects of the DIP payment reform, yet it is possible that there may be a lag in realizing the full effect of policy changes as patients and providers adapt to the new incentives. Third, this study did not involve patient outcomes, such as patient satisfaction or readmission rate, which could indicate a potential impact of the DIP payment on quality of inpatient care.The DIP payment could effectively regulate provider behavior of inpatient care in hospitals in the short term, both of which are clearly reflected in the decrease of inpatient medical costs per case and the proportion of the OOP expenditure in inpatient medical costs in secondary and tertiary hospitals. Meanwhile, the implementation of the DIP payment could improve the rational allocation of the regional healthcare resources, which is reflected in the significant increase of the average LOS of inpatient care in secondary hospital and the decrease of average LOS of inpatient care in tertiary hospital. However, the long-term effects of the DIP payment reform still need to be evaluated in the future. In summary, we suggest the countries that are using the case-based payment or ongoing provider payment reform of inpatient care refer to the design elements of the DIP payment to further improve the design of case-based payments.201920724@mail.sdu.edu.cn.The data analyzed in this study is subject to the following licenses/restrictions: the data that support the findings of this study are available from the Healthcare Security Administrations but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Requests to access these datasets should be directed to YD, YD, CZ, and QS: conceptualization. JY, SD, and QS: project administration and supervision. YD and CZ: data curation, methodology, and software. YD: writing—original draft. YD, SD, and QS: writing—review and editing. All authors contributed to the article and approved the submitted version.This work was undertaken as part of the UK China Health and Economy Partnership, a higher education programme funded by the GlaxoSmithKline (GSK) and supported by the British Council through an unrestricted grant. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


System logs are a crucial component of system maintainability, as they record the status of the system and essential events for troubleshooting and maintenance when necessary. Therefore, anomaly detection of system logs is crucial. Recent research has focused on extracting semantic information from unstructured log messages for log anomaly detection tasks. Since BERT models work well in natural language processing, this paper proposes an approach called CLDTLog, which introduces contrastive learning and dual-objective tasks in a BERT pre-trained model and performs anomaly detection on system logs through a fully connected layer. This approach does not require log parsing and thus can avoid the uncertainty caused by log parsing. We trained the CLDTLog model on two log datasets (HDFS and BGL) and achieved F1 scores of 0.9971 and 0.9999 on the HDFS and BGL datasets, respectively, which performed better than all known methods. In addition, when using only 1% of the BGL dataset as training data, CLDTLog still achieves an F1 score of 0.9993, showing excellent generalization performance with a significant reduction of the training cost. One of the critical features of modern large-scale systems is their need for high availability and reliability . Due to For software-intensive systems, console logs are typically generated to record system status and critical events. These logs provide engineers with detailed information about the system status and help them detect anomalies and determine the cause of failures . HoweverWith the advent of the Big Data era, deep learning has widely penetrated various fields . Due to Existing approaches usually require parsing logs to process semi-structured log data. In this process, the parser removes the variable part from the log information and keeps the constant part for obtaining log events. However, Le et al.  showed tAlthough existing system log detection methods based on log semantics ,15 have Therefore, this study aims to address these challenges and improve the performance and generalization of anomaly detection. To this end, we propose a method called CLDTLog, which combines contrastive learning and dual-objective tasks, and illustrate the effectiveness of CLDTLog in terms of the following five research questions:(1) How effective is CLDTLog in anomaly detection? We will evaluate the anomaly detection performance of CLDTLog on different datasets and compare it with existing methods to verify its effectiveness. (2) Does contrastive learning have a significant impact on CLDTLog? We will explore the role of contrastive learning techniques in CLDTLog and analyze its improvement and implications for anomaly detection performance. (3) What is the generalization performance of CLDTLog? We will evaluate the dependence of CLDTLog on labeled data by examining its performance when using training datasets of different sizes and verify its generalization ability in real-world applications. (4) How to choose the appropriate hyperparameters to combine the two target tasks? We will explore how to select the proper hyperparameters to balance between the two target tasks to achieve the best anomaly detection performance. (5) How does the log sequence length affect CLDTLog? We will investigate the impact of log sequence length on CLDTLog performance and analyze its performance on data.By answering these five key questions, we aim to gain a deeper understanding of the strengths, limitations, and applicability of the CLDTLog approach and provide valuable insights for further research and practice in the area of log anomaly detection.In CLDTlog, firstly, we group all log messages by timestamps or block_id and then use a pre-trained BERT model that has been fine-tuned by contrastive learning and dual-objective tasks to extract semantic vectors of the entire log sequence. These vectors capture the semantic information embedded in the log sequence and the relationship between each log message. Next, we employ a fully connected layer as a classifier with these semantic vectors as input. Our BERT model is fine-tuned to learn contextual information from log sequences and reduce the similarity between normal and abnormal log sequences, which can be effectively used for log-based anomaly detection tasks.(1)We propose CLDTLog, which treats log sequences as natural language sequences, fine-tunes the BERT model  using th(2)The contrastive learning we use increases the semantic distance between the semantic features of normal and abnormal log sequences, which makes it easier for the model to distinguish abnormal log sequences from regular log sequences, thus improving the accuracy of abnormality detection.(3)Our proposed method achieves an F1 score of 0.9993 on the BGL dataset using only 1% of the dataset for training, showing powerful generalization performance while reducing the training cost in practical applications.We evaluate the proposed method using two public datasets by answering the above five questions. The experimental results show that CLDTlog can deeply understand the semantics of log data. The method achieves high F1 scores (both close to 1) for anomaly detection, outperforming existing log anomaly detection methods. Our contributions:The accuracy of log parsing has a relatively significant impact on the performance of log mining . Log parIn recent years, with the emergence of new log types , new logIn recent years, most log-based anomaly detection methods have used log parsers to represent log messages as log events. However, deep learning-based models have become a hot research topic. DeepLog , proposeLogAnomaly  and SwisDue to the limitations of existing methods, the semantics of log messages may not be captured and produce incorrect results. Log2Vec  is a metTo address these limitations, we propose an anomaly detection method called CLDTLog. It fine-tunes the BERT model using contrastive learning and dual-objective tasks to understand the semantics of raw log messages and capture contextual information without using a log parser. Combined with a fully connected neural network, CLDTLog achieves optimal anomaly detection accuracy. In addition, we only use the content part of the log messages from the target system without any auxiliary data, which can further improve the utility.The full name of BERT is Bidirectional Encoder Representations from Transformers, which uses a bidirectional encoder based on the Transformer architecture to learn language knowledge through a large-scale unsupervised pre-training task, which is then fine-tuned by a supervised task to adapt to specific downstream natural language processing tasks. The pre-training tasks for BERT models typically include two approaches: Masked Language Model (MLM) and Next Sentence Prediction (NSP). In the MLM task, the BERT model masks some words in the input text and then predicts what those masked words are. In the NSP task, the BERT model determines whether two input texts are adjacent sentences and predicts whether they are semantically related. Both tasks help the BERT model to learn a more comprehensive and rich linguistic representation.(1)The BERT model employs a bidirectional encoder, which can effectively capture contextual information in the input text.(2)The BERT model learns language knowledge through large-scale unsupervised learning, which can reduce the reliance on large amounts of labeled data.(3)The BERT model is highly scalable and can be applied to various natural language processing tasks, such as text classification, named entity recognition, language generation, etc.(4)The BERT model has achieved state-of-the-art performance on several natural language processing tasks, proving its solid practical value in natural language processing.The BERT model has several advantages:Therefore, we use BERT as our feature extractor.Focal Loss  is a losSpecifically, Focal loss introduces a modifier  Formula .(1)LFL=p is the probability of the model prediction, and y represents the actual label of the input sample, where 1 indicates a positive class, and 0 indicates a negative class. When Among them,  Formula .(2)pt=pIn text classification, Focal loss can reduce the loss of common categories that are easy to classify, thus improving the classification accuracy of rare categories or complex samples. This is particularly effective for dealing with text data with long-tail distribution . Focal lIn our method, the different number of samples in different categories in the datasets may lead to poor recognition of the model for the classes with fewer samples, so we use Focal loss as the first objective task to solve the problem of category imbalance in the datasets.The Triplet loss  is a widSpecifically, the Triplet loss is defined by constructing triplet data; each triplet data consists of an anchor sample, a positive sample, and a negative sample. The anchor sample and positive sample belong to the same category, while the negative sample belongs to a different category from the anchor sample. Triplet loss tries to make the distance between the anchor sample and the positive sample as small as possible while driving the distance between the anchor sample and the negative sample as large as possible. In addition, to keep the features of the samples from aggregating into a tiny space, it is required that the negative and positive samples should be at least farther apart than the two positive samples.Therefore, it is hoped that Formulas  and 4)  4) will T represents the entire feature space. Then, according to Formulas ,,f(xia) iFormulas .(5)LTL=In face recognition, Triplet loss is used to learn discriminative feature representations of faces. Triplet loss can drive models to learn feature spaces that make the same person’s image features closer and those of different people more dispersed. Such feature representation can improve the accuracy and robustness of face recognition systems . TripletContrastive learning is a deep learning idea, the core idea of which is to learn to make feature representations more similar between samples of the same category and more different between samples of the different categories. To achieve this goal, we use the Triplet loss to implement contrastive learning as the second objective task to learn high-quality feature representations.Building the model requires first preprocessing the logs into a form of data that can be fed into the model. Therefore, log preprocessing is the first step. For both BGL (BlueGene/L) and HDFS (HDFS distributed file system) datasets, we first partition the original log datasets we collected to obtain the content part of the log messages. Then, the log message content is processed. First, the log message is split into words using the separator as a marker. Then, to avoid the effect of upper and lower case letters on the log message, each upper case letter is converted to lower case. All special characters that are not partitioned characters are removed from the log statement content. These non-characters include operators, punctuation marks, and numbers. This type of token is removed because it usually indicates a variable in the log message and has no information. For the BGL log dataset, log sequences are obtained by grouping logs according to different window sizes after processing. In the log preprocessing phase, we obtain the log sequence, and in the log sequence representation phase, we need to tokenize the sequence using BERT’s word splitter. BERT uses a WordPiece tokenizer , which cNext, as shown in The BERT model has been trained with a large-scale corpus to learn the general features of the language. To further reduce the training time, we only tune the last three encoders of the BERT pre-trained model. This approach not only improves training efficiency but also achieves excellent results. There are two target tasks in our approach.The purpose of the first task is to reduce the similarity between the semantic features of normal and abnormal log sequences so that the semantic features generated by normal and abnormal log sequences are farther apart in the semantic space . CompletThe second task is to make the actual prediction result close to the desired output. Here, we add a fully connected layer as a classifier after the BERT model, and we use Focal loss as the loss function of the classifier to improve the classification accuracy. The second task is used to reduce the weight of easy-to-classify samples and enhance the importance of hard-to-classify samples by introducing a moderator. This improves the recognition ability of the model for hard-to-classify samples. This loss function has been used in many practical tasks to achieve significant optimization results.(1)Improving the robustness of the model: The first task of CLDTLog helps the model to learn the difference between normal and anomaly log sequences better, thus improving the accuracy and recall of detection.(2)Solving the sample imbalance problem: In system log anomaly detection, the number of normal logs is often much larger than the number of anomaly logs, which leads to the sample imbalance problem. The second objective task of CLDTLog can balance the number of normal and abnormal samples by assigning higher weights to anomalous samples. Likewise, it can alleviate the sample imbalance problem by reducing the consequences of easy-to-classify samples, so the model focuses more on classifying complex samples. Thus, the performance of anomaly detection is improved.(3)Adaptability: CLDTLog can be applied to different types of models and tasks with high adaptability.System logs are a crucial component of system maintainability. In CLDTLog, the final loss function is constructed by calculating the weighted sum of Triplet loss and Focal loss to improve the performance and reliability of detection. Specifically, their advantages include the following:In summary, CLDTLog can solve the sample imbalance problem, improve the robustness and reliability of the model, and is highly adaptable.The training and testing phases of CLDTlog are shown in We only use the second task in the testing phase to obtain the final classification results. First, we process the logs into log sequences, then input the log sequences into the fine-tuned BERT model, and input the log sequence representations obtained from the BERT model into the linear classifier to obtain the final prediction results.In this section, we answer the five research questions posed in the introduction to fully evaluate and analyze the performance and effectiveness of our approach in anomaly detection tasks. These research questions include:RQ 1: How effective is CLDTLog in anomaly detection?RQ 2: Does contrast learning have a significant impact on CLDTLog?RQ 3: What is the generalization performance of CLDTLog?RQ 4: How to choose the appropriate hyperparameters to combine the two target tasks?RQ 5: How does the log sequence length affect the CLDTLog?We will conduct experiments in the order of these research questions and provide detailed experimental designs, method descriptions, and analysis of results to answer these questions and demonstrate the performance and advantages of our method.In this paper, we evaluate the performance of CLDTLog on two public datasets, HDFS and BGL (Blue Gene/L). The HDFS (Hadoop Distributed File System)  dataset To evaluate the effectiveness of CLDTLog in anomaly detection, we use precision, recall, and F1-score metrics.TP (True Positives): True cases, where the prediction is positive, and the case is positive.FP : False Positives, predicted to be positive but negative.FN : False negative cases, predicted to be negative but positive.Precision: The percentage of correctly detected abnormal log sequences by pattern to all detected anomaly log sequences. The Precision is calculated as shown in Formula .(6)PrecRecall: The percentage of log sequences correctly identified as anomalous to all true anomalies. The recall is calculated as shown in Formula .(7)RecaF1-score: The summed average of precision and recall. The F1-score is calculated as shown in Formula .(8)F1-sOur research question 1 is to evaluate the effectiveness of CTDTLog on two public log datasets. After preprocessing the raw logs, we found that most of the log messages in both datasets are less than ten characters. For the BGL dataset, the data are labeled according to the log messages. Therefore, we use a single log message for anomaly detection. So, we set the maximum length of the input sequence to 10. To construct log sequences on the HDFS dataset, we associate log messages with the same block_id. Since the dataset is labeled according to the block_id, and most of the log sequences have less than 30 log messages, we set the input sequence length to 300 to ensure that the model can cover most of the log message content.After that, we separate normal and abnormal log sequences and then extract 80% of the log data from each log sequence as the training set. We divide the log data according to the timestamps of the logs, using the first 80% of the log sequences as the training set and the remaining 20% as the test set. This design ensures that no data leakage occurs.We compared the results of our proposed approach with seven existing approaches, traditional approaches such as SVM , which cHowever, we can also clearly see the superiority of CLDTLog, as seen from The experimental results on the BGL dataset in After the experiments, we found that the F1 score of CLDTLog is exceptionally close to 1 on the BGL dataset, which is 0.0028 higher than that on the HDFS dataset. The main reason is that CLDTLog pays more attention to the semantic information of log messages. In the HDFS dataset, most of the log sequences have more than 20 log messages, and if one log message in the log sequence is an anomaly, this log sequence is considered an anomaly. In contrast, one log message in BGL is a log sequence. Therefore, on the BGL dataset, CLDTLog can detect anomalies more accurately compared to the HDFS dataset. This is why CLDTLog can improve the F1 score to 0.9999 on the BGL dataset. In addition, the experimental results in RQ5 also verify this description.To evaluate the impact of comparative learning on the performance of CLDTLog, we choose the log sequence partitioning method and dataset segmentation method for HDFS and BGL datasets in RQ1 to evaluate CLDTLog and CLDTLog_uncl.CLDTlog_uncl: Without contrastive learning, the loss function only uses Focal loss.CLDTLog: Contrastive learning is implemented through the Triplet loss function, using the weighted sum of Focal loss and Triplet loss as the loss function.As seen from In RQ3, we evaluated the models CLDTLog and CLDTog_uncl on the BGL dataset, using different proportions of training data for evaluation. The statistics of the BGL dataset are shown in In the BGL dataset, except for the training set, the remaining data were used as the test set for model evaluation. As shown in the figure, the solid line indicates the performance of CLDTLog on the three evaluation metrics, and the dashed line indicates the performance of CLDTLog_uncl on the three evaluation metrics. From In this RQ, our goal is to investigate how to combine two target tasks to maximize the performance of CLDTLog. The approach we adopt involves a loss function. As shown in Formula .(9)L=α*The We used the grid search method to explore the optimal weight The results show that the precision of CLDTLog gradually increases as the The combined analysis shows that CLDTLog performs best at an alpha value of 0.2, with high precision, recall, and F1 score. Therefore, we recommend choosing this In the fifth RQ, we only experiment with the BGL dataset. We set the input sequence length of the BERT model (log sequence length of 1) to 10. We truncate the log sequence length when it is larger than the input sequence length, and we fill it when the log sequence length is smaller than the input sequence length. When we change the log sequence length, we increase the input sequence length of the BERT model accordingly so that the model can cover the same size of data. This is done to exclude the effect of truncating too much log content since the input sequence of the BERT pre-trained model is 512 at maximum. Therefore, when the log sequence length is 100, the input sequence can only be set to 512. It is clear from In this research problem, by comparing the performance of CLDTLog with different log sequence lengths, it is found that CLDTLog performs best when the log sequence length is 1. Therefore, CLDTLog with the log sequence length of 1 was selected as the optimal model for evaluation in the above research problem on the BGL dataset.This paper proposes a CLDTLog method based on contrastive learning and dual-objective tasks for automatically detecting log anomalies. Compared with previous LSTM and CNN-based methods, CLDTLog can better capture semantic information from raw logs. In addition, by learning semantic relations extracted from log sequences comparatively, CLDTLog increases the difference between normal and abnormal log sequences compared to the BERT pre-trained-based approach, while using a Focal loss objective function to focus optimization targets on hard-to-classify samples, allowing the model to detect abnormalities in log sequences more easily. We evaluate our proposed method on two log datasets. The experimental results show that CLDTLog outperforms existing Syslog-based anomaly detection methods and has strong generalization performance to reduce the cost of data collection and processing. As a result, when faced with increasingly large log files, our CLDTLog can quickly and accurately filter out anomalous logs, identify potential problems or security threats, and take necessary measures promptly. It also avoids the workload of manually checking line by line.In future research, we will explore using unsupervised methods for anomaly detection in system logs. Although our approach CLDTLog can achieve efficient anomaly detection performance when using limited samples, it still relies on labeled training data. In practical applications, we may face data labeling limitations or cost challenges. Therefore, we will develop more autonomous and unsupervised techniques to reduce the dependence on labeled data and improve the pervasiveness and scalability of anomaly detection. This will help better adapt to various environments and application scenarios and address data labeling issues that may be encountered in practice.


Pumped-storage hydroelectricity (PSH) is a facility that stores energy in the form of the gravitational potential energy of water by pumping water from a lower to a higher elevation reservoir in a hydroelectric power plant. The operation of PSH can be divided into two states: the turbine state, during which electric energy is generated, and the pump state, during which this generated electric energy is stored as potential energy. Additionally, the condition monitoring of PSH is generally challenging because the hydropower turbine, which is one of the primary components of PSH, is immersed in water and continuously rotates. This study presents a method that automatically detects new abnormal conditions in target structures without the intervention of experts. The proposed method automatically updates and optimizes existing abnormal condition classification models to accommodate new abnormal conditions. The performance of the proposed method was evaluated with sensor data obtained from on-site PSH. The test results show that the proposed method detects new abnormal PSH conditions with an 85.89% accuracy using fewer than three datapoints and classifies each condition with a 99.73% accuracy on average. Pumped-storage hydroelectricity (PSH) is a hydroelectric energy storage method used for load balancing in hydroelectric power plants ,2. When However, PSH is a complex and nonstationary system, in which many components influence and interact with other components. A hydropower turbine, one of the primary power-generating components of PSH, experiences various time-varying loads during its operation. These loads can induce local, cyclic, and thermal stresses in the rotating system in operation, which frequently cause abnormal conditions in main facilities. As listed in Monitoring the accidental occurrence of these abnormal conditions is usually challenging because the primary components of PSH, such as a hydropower turbine and generator, are submerged in water during operation. To tackle this issue, long-term condition-monitoring systems have been constructed with several types of sensors attached to the exterior surfaces of the primary components. By collecting long-term physical responses and analyzing the potential abnormalities of the components, these monitoring systems allow for targeted maintenance and emergency planning . Due to A series of studies have been conducted on the abnormal condition monitoring of PSH systems, and one of the most popular and important topics is vibration signal analyses of hydraulic turbines ,8,10. ThTo overcome these limitations, machine learning techniques such as clustering , supportIn this study, a condition-monitoring method is proposed for the end-to-end intelligent diagnosis of PSH. The proposed method employs two algorithms: (1) a new class recognition algorithm that detects a novel type of abnormal condition that is not trained with a dataset, and (2) a continuous learning algorithm that trains and optimizes a previously trained model without human involvement when detecting a new type of abnormal condition. Using these two algorithms, the proposed method provides a high level of accuracy in the recognition of new types of abnormal conditions and the classification of automatically trained models.The proposed method offers the following advantages: (1) monitoring and classifying the overall state of the structure using various sensor data, (2) detecting unknown abnormal conditions using less than three datapoints without retraining or modifying the trained model, and (3) automatically optimizing the architecture of the classification model for a new abnormal class.The remainder of this paper is organized as follows. To classify the conditions of a target structure, the ACDN builds a one-dimensional Fully Connected Network (FCN), as shown in In a typical neural network classifier, the activation vector computed from the final fully connected layer is first applied as an input to a SoftMax activation function. Afterward, the network is trained to minimize a loss function such as the cross-entropy on the outputs of the SoftMax layer. In Algorithm 1, the activation vector Consider that The average intraclass distance of all The interclass distance The network is trained with stochastic gradient descent with backpropagation to minimize the loss function After the network training is complete, During testing, an outlier score function Because the network is trained to project the class members as close as possible to the mean of the class, the further the projection After identifying an appropriate outlier score for the existing classes, the threshold value of the outlier score is determined to distinguish a new condition. Even under normal conditions, outlier data can be generated intermittently in PSH because of measurement or signal transmission errors. On the feature map in the ACDN, the distance between the outlier data and If Algorithm 1 identifies a new abnormal condition, Algorithm 2 optimizes the ACDN using a continuous learning method without human involvement. Consider that the number of conditions  increases from The ACDN utilizes most of the knowledge obtained from the previous tasks and dynamically extends its capacity when the accumulated knowledge is insufficient to explain the new task. The naivest approach for continuous learning would be to retrain the entire model whenever a new abnormal condition is detected. However, such retraining can be prohibitively expensive for deep neural networks. To address this issue, this study proposes an automatic selective retraining of the model, which retrains only the weights affected by the addition of a new abnormal condition to the dataset. When the first abnormal condition occurs : base deep neural networks (DNN) are manually trained for each task separately. MTL indicates “Manually Task Learning.” Without class incrementation, this is the most conventional machine learning model for classification. This model is optimized to have the highest classification accuracy for the data of this study.DNN-fine—same architecture as DNN-MTL model, trained for initial tasks and the fine-tuning of the last layer under an increasing number of conditions.INN—increment neural network (INN) for each task consistently, based on incremental learning. The most widely used machine learning model for increasing the number of classes.ACDN—the proposed model.ACDN-1st—base ACDN, applies Algorithm 2 at only the first layer. The computing cost and time are lower than those of the ACDN.To validate the proposed method, four conventional machine learning models were used. All the models employed a three-layer network with batch normalization and ReLU activations. The models are summarized as:Except for DNN-MTL, all the models were continuously optimized based on pretrained models using the data from the two classes. Furthermore, ACDN and ACDN-1st automatically detected new classes. Because INN and DNN-fine could not recognize a new class automatically, these models were informed of the class increase in the training section. In contrast, the most commonly used classification model, DNN-MTL, was optimized for all the classes so that it could be used as a reference to compare the performances of other models, displaying the best performance on the test dataset.All the models were coded in the Python programming language with the Pytorch framework . All theFor the model optimization, the Adam optimizer  was usedF1 score was used as an indicator to evaluate the performance of the proposed model. The precision and recall were calculated for each model and the F1 score was the harmonic mean of the precision and recall. Fault detection systems are precise in detecting faults and determining whether they are an actual fault, implying that accuracy is measured only for the result wherein the fault detection system determines it to be a fault. Recall is a measure of how well the defect detection system detects actual failure data as a failure without omission. The precision, recall, and F1 score were calculated as follows: The F1 score is mostly utilized in applications such as condition monitoring, where the value of false detection is significant.The The classification and new class detection performance were evaluated using unknown classes. A sample was randomly selected from an abnormal condition that was not used during the training to create the unknown classes.To evaluate the new class detection performance for an unknown abnormal class, the proposed ACDN was compared to two conventional methods, (1) SoftMax and (2) Openmax , to evalF1 score was approximately 99% for all the tests of the total datapoints. This was approximately 23% higher than the SoftMax method with a 0.7 threshold value and 6% higher than the Openmax method. Nevertheless, even with only three data points, the ACDN had an F1 score of more than 95%. This was more than a 10% improvement over the Openmax result. The results summarized in After identifying a new class, the suggested model dynamically adjusted the architecture to cover an increasing number of classes. F1 score of 100% in two classes. Subsequently, whenever the class of the input data increased, the previously trained model was continuously optimized without the guidance of an expert. This process was repeated until the total number of classes reached five. The precision slightly decreased as each class was added. However, all the results consistently demonstrated that the proposed method showed a high performance of > 99% in its precision. Here, all the values of the recall were identically 100%, regardless of the number of classes, because the ACDN did not misdiagnose the normal condition as an abnormal condition. This advantage proved the high reliability of the condition-monitoring performance of the ACDN. The computing time of the ACDN was 0.0076 s per one datapoint of the five-class dataset. Therefore, the developed system is sufficiently applicable for real-time monitoring because it can process sensor data at a field frequency of > 120 Hz of sampling frequency.The classification performance of the automatically optimized architecture of the proposed model utilizing algorithm 2 was validated. A few significant metrics for evaluating the average per-class classification of the proposed ACDN are listed in F1 score of 95.89%. Additionally, the F1 score of the classification could achieve 99.73% with the auto-optimized ACDN, and the computing time for the single input data was only 0.0076 s.In this study, a PSH condition-monitoring method called the auto-learning and class detection network model (ACDN) was proposed. Unlike previous machine-learning-based structural condition-monitoring methods, the proposed ACDN could detect new abnormal conditions that had not been trained and update and optimize the model itself without human intervention. The proposed ACDN was based on a continuous learning algorithm, which continuously learnt when a new class was detected. By applying actual condition-monitoring data from an actual PSH system, the authors verified that the proposed ACDN detected new abnormal conditions with less than three datapoints for each new abnormal condition, with an In this study, detection and optimization were accomplished only for a single unknown abnormal condition in the target PSH system. If more than two unknown abnormal conditions occurred, the proposed method detected only a single abnormal condition. Currently, we are focusing on developing a model that can generalize the correlation between the monitoring data from similar structures and abnormal conditions. Additionally, we plan to build an extended machine learning model to support the maintenance of the target PSH system by deriving the problem part from an analysis of the correlation with the abnormal state accumulated from similar structures, such as traditional hydroelectricity, nuclear power plants, and wind power plants, by combining the proposed ACDN with the ongoing research model.


Our findings clearly confirm that the collective motion is essentially driven by fluid flow turbulence, with the canopy having in this respect a fully-passive behavior. Instead, some features pertaining to the structural response turn out to manifest in the motion of the individual canopy elements when focusing, in particular, on the spanwise oscillation and/or on sufficiently small Cauchy numbers.Flexible filamentous beds interacting with a turbulent flow represent a fundamental setting for many environmental phenomena, e.g., aquatic canopies in marine current. Exploiting direct numerical simulations at high Reynolds number where the canopy stems are modelled individually, we provide evidence on the essential features of the  Such intriguing features indeed manifest in a vast number of problems pertaining to various fields, ranging from aforementioned environmental processes 9 to engineering applications 11 and, in a broader sense, biological systems 14.The coherent waving motion of seagrass meadows in marine currents or plant crops under the action of wind is a fascinating example of the complex interaction between fluid flows and a multitude of deformable structures, namely hairy surfaces or filamentous beds. More generally, the emergence of large-scale collective motion . Both issues, on the other hand, can be overcome by the support of computational studies based on direct or large-eddy numerical simulations26, with more recent advancements based on fully-resolved approaches where the stems/elements of the canopy are resolved individually and not modelled, e.g., as a continuous porous medium29.Environmental phenomena arguably represent the most classical example where collective motion can be observed and numerous studies have been devoted to understand the key features of the mutual coupling between the fluid and structural motionIn this work, we aim at deciphering the highlighted issues by means of a high-fidelity computational approach to accurately and comprehensively access both the flow physics and canopy collective dynamics, as well as the motion of the individual stems. Overall, we aim at establishing whether the fluid and structural dynamics act competitively or not, and which of the two has a dominant role. Moreover, we explore the connection between the individual motion of the elements  and the collective coherent motion.28 and individually clamped at the bottom boundary, as shown in Fig. To investigate the essential features of the FSI between a turbulent flow and a flexible canopy, we have performed high-fidelity, direct numerical simulations (DNS) where the canopy elements are modelled individually and where the two-way coupling between the fluid and the solid dynamics is realized by means of an immersed boundary method (IBM). The considered setup is that of an open channel at constant flow rate with the flexible-canopy elements randomly distributedU is the imposed bulk flow velocity, H the height of the open channel and h/H equal to 0.25  and with canopy solidity d being the stem diameter and 30. The selected solid-to-fluid density ratio is We fix the hydrodynamic parameters to a reference configuration with bulk Reynolds number 25 as well as collective motion 31. Note that the analysis can also be expressed in terms of a variation of the reduced velocity We have performed a set of simulations varying the Cauchy number 27). To give a qualitative indication, snapshots from the representative case at For the chosen configurations, the (average) canopy height expressed in wall units is overall mentclass2pt{minimY. The first insight is that a peak can be observed for all cases, and specifically also at the smallest Cauchy number, i.e., 31. Secondly, the location of the peak is typically at O(H), both in the streamwise and spanwise wavelength component, and does not appear to appreciably vary within the investigated range. Increasing 17.Figure entclass1pt{minimay. Similarly, Fig. H observed for the wall-normal and spanwise velocity components at 5. Considering the content in the spanwise wavelength, shown in Fig. U, in order to better show the large-scale peak. An alternative normalization, using the local friction velocity to better visualize the flow structures in the intra-canopy region, is provided in Fig. To better characterize the relevant lengthscales of the turbulent flow that are involved in the honami/monami phenomenon, Fig. 16.A further confirmation of the marginal role of the Cauchy number on the characterization of the honami/monami comes from the joint probability density function (JPDF) of the streamwise and wall-normal velocity fluctuations components, computed on a plane parallel to the wall at the average canopy tip. As Fig. Y. A low-frequency , large-wavelength peak can be generally observed at a very similar location for both the flow and canopy motion. Remarkably, the frequency of the filament tip motion does not appear to agree with the natural frequency , we choose as a representative configuration the one at H/U (right panels). In fact, it can be noticed that the latter works better for the streamwise component, while for the spanwise oscillation the dominant frequency scales well with the natural one. This qualitative difference (to the authors’ knowledge so far not reported) can be explained by the presence of a net convective transport of the turbulent fluctuations in the streamwise direction. However, it can be observed that when decreasing the Cauchy number the dominant frequency eventually becomes comparable with the natural frequency, and it can be consequently argued that a qualitative change occurs at 34. The resulting power spectra associated with the filament oscillation can thus be linked to that of the turbulent forcing by means of a response function dependent on the structural parameters, yielding for the investigated cases the emergence of the two power-law slopes reported in Fig. Lastly, we contrast the observation on the canopy collective dynamics by focusing on the motion of the individual filaments composing the canopy. Figure With the goal of unravelling the essential mechanisms in the mutual interaction between a turbulent flow and a dense hairy surface made of flexible filaments, we have performed high-fidelity direct numerical simulations over a range of the Cauchy number  in order to isolate the effects related to the canopy flexibility and fluid vs structural characteristic timescales.Our results show that the so-called honami/monami instability and consequent coherent motion is always observed, also at the small Cauchy numbers to which it is commonly attributed the absence of such physical mechanism. When decreasing the Cauchy number, the deformation of the canopy clearly gets smaller as approaching the rigid limit. Yet, the signature of the large-scale flow structures over the canopy collective motion is still present, therefore suggesting that there is not a particular threshold between different dynamical regimes .35. Here, passive means that the coherent waving of the canopy does not manifest the characteristic structural response of the individual canopy elements and is fully driven by the flow. Conversely, certain features of the individual filament motion may (or may not) show the fingerprint of the structural response . This is typically the case for the individual spanwise oscillation, while for the streamwise oscillation it can be argued only for sufficiently rigid stems. Nevertheless, our conclusions on the fully-passive nature of the honami/monami collective dynamics are not affected by the particular choice of Cauchy number.The absence of a clear variation in the main features of the flow while varying the Cauchy number similarly supports the idea that the collective dynamics phenomena  are essentially driven by the resulting turbulent flow, with the flexible canopy having always a passive dynamical behavior in this respectIn conclusion, our findings provide novel physical insight on the nature of the fascinating collective dynamics that can be observed in a multitude of environmental settings as well as engineered textured surfaces with relevant applications to, e.g., drag reduction, mixing and flow control. Further work aiming at an extension of our results may incorporate the effect of gravity or consider the role of other parameters such as inertia or canopy geometry, although the main driving mechanism of the collective motion can be expected to remain those highlighted in the present investigation.Fujin (https://groups.oist.jp/cffu/code). The fluid flow is assumed to be governed by the incompressible Navier-Stokes equations,x and z), while the no-slip and free-slip conditions are imposed at the bottom .Equation  are solvs and time t; 36. However, after extensive testing on different kinds of collision models and their calibration parameters, the influence of the filament-to-filament collision term was found to be very weak, whereas the filament-to-wall interaction model turned out to be necessary only for sufficiently large 37 with the difference that the bending term is treated implicitly to allow for a larger timestep40.The flexible stems composing the canopy are modelled as elastic inextensible filaments by the set of equationsy-direction in order to better describe the sharper gradients in the proximity of the canopy top. The canopy is made of 28. In the rigid canopy case (i.e., In the present study, for all cases the fluid flow is described using 45 and, in particular, the employed solver for the filament dynamics has been validated in the past with several test cases46. Results for the representative FSI problem of a flapping filament in laminar flow are reported in Fig. 47. Results showing good agreement in both the mean velocity profile and Reynolds shear stress distribution are reported in Fig. The numerical method has extensively been tested in a variety of problemsSupplementary Information.


Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars. The reliable and accurate tracking of targets is an important problem in civil, industrial, and military applications. Most existing tracking systems employ active radar (or LIDAR or sonar) to obtain remote measurements on a target which are then fed into a tracking system. Active sensors have several undesirable properties, such as using spectral resources, creating emissions which can interfere with other systems, and providing emitter location information which can be exploited by an enemy. To overcome the disadvantages of active (cooperative) emissions, there is a rapidly increasing interest in using ambient (non-cooperative) environmental radiation in so-called passive radar to locate and track targets. The idea is very straightforward. Target reflections from transmissions such as AM and FM radio, digital free to air TV, cell phone base station transmissions (GSM etc.), emissions associated with navigation satellite systems, and a host of other sources ,2,3 are Passive radar is considerably less expensive than active radar because it does not use any high-power transmitter hardware. It is also spectrally efficient and capable of covert operation because it produces no emissions. However, the signal processing for high-performance detection and tracking can be challenging ,5 becausTarget tracking is a broad term encompassing a set of algorithms designed to detect and track moving objects based on sequential measurements obtained from sensors. It involves several critical processes including track initiation and termination , data association , and track smoothing (fitting feasible track paths through sequences of noisy sensor measurements associated with a particular track). Sensor measurements can exhibit linear or nonlinear relationships with track kinematic variables, and they are susceptible to noise, false measurements, and missed true measurements. Track kinematic variables can evolve linearly or non-linearly, often comprising linear sections and maneuvers.Early tracking systems employed M-out-of-N sequential detectors for track initiation and termination, nearest-neighbor (NN) algorithms and gating for data association, as well as recursive least-squares alpha-beta filters with maneuver detection gates for track smoothing. Although these methods performed well in many scenarios, they faced limitations in dense multiple target scenarios, environments with heavy clutter, highly maneuvering targets, and strongly nonlinear sensor models. Consequently, research efforts over the past 50 years have aimed to overcome these limitations and integrate these processes  into computationally feasible recursive algorithms capable of accurate tracking under various sensor models and track evolution scenarios.The integration of these tracking processes began in 1964 with Sittler’s work . The PDAIn this paper, we study target tracking in a Cartesian framework using only range and range-rate information derived from a bistatic passive radar. The relationship between the target state and the measurements is non-linear, which further complicates the tracking problem. Standard tracking filters based on the extended Kalman filter (EKF) perform poorly in such circumstances, resulting in unstable and biased track estimates, even when bearing measurements are available, although less so ,19,20. TImproved tracking accuracy by eliminating bias in 3D Cartesian coordinates using range and range-rate measurements without angular information.Development of a framework for utilizing the extended Kalman filter (EKF) in passive radar systems without requiring knowledge of bearing angles.Development of a framework for applying the unscented Kalman filter (UKF) in passive radar environments without the need for bearing angle information.Integration of probabilistic data association (PDA) with the unscented Kalman filter (UKF) for low bias tracking in cluttered bistatic passive radar environments, considering a probability of detection less than one and accommodating occasional missed detections.Performance analysis of the proposed system design and framework, including the integration of UKF and PDA.The main contributions of this paper are:To the best of the author’s knowledge, there exist no solutions for integrating an EKF and UKF, and with the inclusion of PDA for passive bistatic radar-based tracking and hence, we consider the work presented here to be quite novel.When there is a nonlinear relationship between the state and the measurement, the standard and commonly used algorithm for target tracking and localization is the extended Kalman filter (EKF) ,22, whicA particle filter  uses theUKF methods have proven to be superior to EKF filters in a wide range of applications . UKF useApplication of an unscented Kalman filter in any system is not trivial. In , the autAnother challenge of tracking in passive radar is the measurement model. A traditional radar system assumes the bearing information is available in the measurement model ,33,34. SWhen tracking targets that have a probability of detection below one in the presence of false alarms , it is crucial to make informed decisions about which measurements to use for updating each track. Many algorithms have been created to tackle this issue, as noted in sources such as ,37,38. AThe strongest-neighbor filter (SNF) selects the validated measurement with the highest intensity within the validation gate for track updating, discarding the rest of the measurements. On the other hand, the nearest-neighbor filter (NNF) chooses the measurement closest to the predicted measurement to update the track. Although these simple algorithms can work well in low-clutter situations, they become less effective as the probability of false alarms increases, such as in the case of low observable maneuvering targets ,40. An aIn , the autWe consider a constant velocity (CV), constant heading target model in three-dimensional Cartesian space. For the CV model, the target’s location is Assume a stationary receiver and a stationary illuminator are located at h is the nonlinear transformation of the state vector to the measurement vector. The mathematical expression of the nonlinear transformation can be written as,f is the illuminator’s carrier frequency and c is the speed of light.The relationship between the elements of state vector  seen in  and 8)..xt and mIn this paper, we develop and simulate an UKF-based tracker incorporating probabilistic data association (PDA) to handle clutter. In In this section, we present the two methods that we study for tracking a target with a bistatic passive radar, given measurements are in the form of range and range-rate. The tracking methods are extended Kalman Filter (EKF) and the unscented Kalman Filter (UKF).h is the nonlinear function of the state space mapping it to the measurement space. The Jacobian of h is calculated at the predicted state In the extended Kalman filter, the nonlinear predicted measurement The measurement covariance h mentioned in ,,h mentioy-axis and z-axis can be calculated in the same manner as above.The derivatives of the bistatic range and range rate measurements w.r.t An unscented Kalman filter (UKF)  is implen is the dimension of the state vector. The scaling parameter A set of ith column of the matrix square root of Furthermore, f.The sigma points are propagated through the transition function The next step is to compute the predicted state mean The subsequent step involves the transformation of sigma points into measurement space. If Computation of the filter gain The unscented Kalman filter is susceptible to numerical instability, and a common issue arises when the noise covariance is small. In such cases, rounding errors may lead to the computation of a small positive eigenvalue as a negative number, causing the numerical representation of the state covariance matrix to become indefinite, whereas its true form is positive and definite. Positive-definite matrices can be efficiently computed using the Cholesky factorization algorithm. It is crucial to maintain the covariance matrix in this form to prevent it from acquiring a negative diagonal or becoming asymmetric.Another instability occurs while computing and updating the state co-variance matrix. The covariance matrix is expected to be symmetric, positive, and definite, but for some updates it fails to be so; hence, it is required to approximate it to the nearest positive-definite matrix. This problem can be solved by using nearest symmetric positive semi-definite matrix given in . To the In this section, we incorporate probabilistic data association (PDA)  into them is the number of valid measurements at time The PDA algorithm is executed in three main steps which are measurement validation/gating phase, data association and state estimation. Validation of measurements is performed by the gating process. The gate is formed around the predicted measurement. Only the measurements within the gate are considered for association with the track. The size of the gate determines the probability of measurements falling within the gate which is called gate probability. The measurement validation phase is illustrated in Di(t+1)2=c is the dimension of measurement vector, In the above equation, fined in , Di(t+1)fined in , PD is tOnce PDA measurement A combined effect of all the original and clutter measurements expressed in the form of given in  is used given in  with (32In this section, the performance of the proposed technique incorporating PDA with UKF is presented. The proposed system model was simulated in Matlab using a Monte Carlo approach based on the parametric values identified below.The simulation scenario consists of a single stationary receiver and a single illuminator. The receiver is located at the origin of the coordinate system, and the illuminator is placed at To start the tracking process, an initial estimate of the target’s position and velocity is required. However, the track initialization is not the focus of this research, so we initiate the tracks by adding small errors to the true position and velocity of the target. The errors are Once the simulation scenario is established, the subsequent step involves tracking the target via a traditional EKF. The actual and tracked measurements of the target are illustrated in In order to transform the tracked measurements from the range and range-rate domain to the three-dimensional Cartesian domain, which is the actual requirement of the tracking system, the linearization of the measurements is conducted by using the Jacobian in . Once thAdditionally, the tracking results of the UKF algorithm in the range and range-rate domain are shown in Further, in this study, the performance of an UKF in the presence of clutter is analyzed through simulations. The measurements from clutter and the measurements from the target are both used as input for the PDA algorithm that was discussed in It can be seen from The effectiveness of the UKF-PDA framework in handling cluttered environments is further emphasized in In our simulation studies, we also employed real-time measurements obtained from one of the largest ADS-B networks worldwide, comprising over 35,000 connected receivers . Our stuIn summary, this study demonstrates the implementation of EKF and UKF algorithms in a passive bistatic radar system that lacks bearing angle information. The target is tracked in three-dimensional Cartesian space utilizing bistatic range and range rate measurements. The results indicate that the UKF algorithm outperforms the EKF in terms of tracking accuracy. The numerical issues encountered in the UKF are mitigated by utilizing approximations. Furthermore, the integration of the PDA algorithm demonstrates the UKF’s ability to work effectively in a cluttered environment. Despite numerical instability challenges, the successful implementation of the UKF-PDA framework significantly advances tracking techniques in passive bistatic radar, particularly in situations where reliable bearing information is unavailable or limited.