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U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies 被引量:1
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作者 Shuangying Du Rong-Hua Zhang 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1403-1416,共14页
El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been develope... El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies. 展开更多
关键词 U-Net models wind stress anomalies ICM integration of AI and physical components
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Preliminary geological interpretation of long-wavelength magnetic anomalies over China and surrounding regions
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作者 Jie Wang YanYan Yang +4 位作者 ZhiMa Zeren Jian Wang Xin Wang YuXin Luo XuHui Shen 《Earth and Planetary Physics》 EI CAS CSCD 2024年第3期445-458,共14页
Long-wavelength(>500 km)magnetic anomalies originating in the lithosphere were first found in satellite magnetic surveys.Compared to the striking magnetic anomalies around the world,the long-wavelength magnetic ano... Long-wavelength(>500 km)magnetic anomalies originating in the lithosphere were first found in satellite magnetic surveys.Compared to the striking magnetic anomalies around the world,the long-wavelength magnetic anomalies in China and surrounding regions are relatively weak.Specialized research on each of these anomalies has been quite inadequate;their geological origins remain unclear,in particular their connection to tectonic activity in the Chinese and surrounding regions.We focus on six magnetic high anomalies over the(1)Tarim Basin,(2)Sichuan Basin(3)Great Xing’an Range,(4)Barmer Basin,(5)Central Myanmar Basin,and(6)Sunda and Banda Arcs,and a striking magnetic low anomaly along the southern part of the Himalayan-Tibetan Plateau.We have analyzed their geological origins by reviewing related research and by detailed comparison with geological results.The tectonic backgrounds for these anomalies belong to two cases:either ancient basin basement,or subduction-collision zone.However,the geological origins of large-scale regional magnetic anomalies are always subject to dispute,mainly because of limited surface exposure of sources,later tectonic destruction,and superposition of multi-phase events. 展开更多
关键词 long-wavelength magnetic anomaly lithospheric magnetic anomaly lithospheric magnetic field model satellite magnetic survey CSES
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Research on the Extraction of OLR Anomaly Prior to Ms 7.5 Sand Point,Alaska Earthquake based on IPI Method
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作者 FENG Maoning ZHANG Yongxian +3 位作者 XIONG Pan LIU Yue TIAN Weixi JU Changhui 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2024年第S01期24-26,共3页
Based on the understanding that the seismic fault system is a nonlinear complex system,Rundle(1995)introduced the nonlinear threshold system used in meteorology to analyze the ocean-atmosphere interface and the El Ni?... Based on the understanding that the seismic fault system is a nonlinear complex system,Rundle(1995)introduced the nonlinear threshold system used in meteorology to analyze the ocean-atmosphere interface and the El Ni?o Southern Oscillation into the study of seismic activity changes,and then proposed the PI method(Rundle et al.,2000a,b).Wu et al.(2011)modified the Pattern Informatics Method named MPI to extract the ionospheric anomaly by using data from DEMETER satellites which is suitable for 1–3 months short-term prediction. 展开更多
关键词 IPI method OLR anomaly prior to earthquake Characteristics of OLR anomalies prior to earthquake Quartile method NOAA satellite data
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Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention
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作者 Yifan Gao Jieming Zhang +1 位作者 Zhanchen Chen Xianchao Chen 《Computers, Materials & Continua》 SCIE EI 2024年第4期493-507,共15页
In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower s... In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection. 展开更多
关键词 anomaly detection TRANSFORMER graph structure
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A VGGNet-based correction for satellite altimetry-derived gravity anomalies to improve the accuracy of bathymetry to depths of 6500 m
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作者 Xiaolun Chen Xiaowen Luo +6 位作者 Ziyin Wu Xiaoming Qin Jihong Shang Huajun Xu Bin Li Mingwei Wang Hongyang Wan 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2024年第1期112-122,共11页
Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the... Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results. 展开更多
关键词 gravity anomaly bathymetry inversion VGGNet multibeam sonar satellite altimetry
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Prevalence of Dental Anomalies among Patients Who Report to the Komfo Anokye Teaching Hospital’s Orthodontic Clinic
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作者 Ama Agyeibea Amuasi Daniel K. Sabbah +1 位作者 Alexander Oti-Achempong Rahman N. Mamah 《Open Journal of Stomatology》 2024年第2期103-117,共15页
BACKGROUND: Dental anomalies are variations from the established well-known general anatomy and morphology of the tooth as a result of disturbances during tooth formation. They can be developmental, congenital, or acq... BACKGROUND: Dental anomalies are variations from the established well-known general anatomy and morphology of the tooth as a result of disturbances during tooth formation. They can be developmental, congenital, or acquired and may be localized to a single tooth or involve systemic conditions. AIM: To evaluate the prevalence of dental anomalies in patients who report to the Komfo Anokye Teaching Hospital (KATH) dental clinics. METHOD: A descriptive cross-sectional design was used with a sample size of 92 patients aged 18 or older, obtained through convenience sampling. Data analysis was performed using SPSS version 26.0. RESULTS: The study included 92 patients aged 18 to 72 years, with 47.8% males and 52.2% females. Dental anomalies were observed in 51.1% of participants, with a higher prevalence in females (55.3%). The most common anomalies were diastema (48.3%), impacted teeth (22.0%), dilaceration (11.9%), and peg-shaped lateral teeth (6.8%). CONCLUSION: This study highlights the importance of conducting thorough dental examinations to identify and address dental anomalies, which may have implications for treatment. Early detection and correction of these anomalies are crucial to prevent future complications. 展开更多
关键词 Dental Anomalies PREVALENCE Impacted Teeth Dilaceration
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PUNet:A Semi-Supervised Anomaly Detection Model for Network Anomaly Detection Based on Positive Unlabeled Data
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作者 Gang Long Zhaoxin Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第10期327-343,共17页
Network anomaly detection plays a vital role in safeguarding network security.However,the existing network anomaly detection task is typically based on the one-class zero-positive scenario.This approach is susceptible... Network anomaly detection plays a vital role in safeguarding network security.However,the existing network anomaly detection task is typically based on the one-class zero-positive scenario.This approach is susceptible to overfitting during the training process due to discrepancies in data distribution between the training set and the test set.This phenomenon is known as prediction drift.Additionally,the rarity of anomaly data,often masked by normal data,further complicates network anomaly detection.To address these challenges,we propose the PUNet network,which ingeniously combines the strengths of traditional machine learning and deep learning techniques for anomaly detection.Specifically,PUNet employs a reconstruction-based autoencoder to pre-train normal data,enabling the network to capture potential features and correlations within the data.Subsequently,PUNet integrates a sampling algorithm to construct a pseudo-label candidate set among the outliers based on the reconstruction loss of the samples.This approach effectively mitigates the prediction drift problem by incorporating abnormal samples.Furthermore,PUNet utilizes the CatBoost classifier for anomaly detection to tackle potential data imbalance issues within the candidate set.Extensive experimental evaluations demonstrate that PUNet effectively resolves the prediction drift and data imbalance problems,significantly outperforming competing methods. 展开更多
关键词 Network anomaly detection representation learning candidate set CatBoost
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An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data
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作者 R.Rajakumar S.Sathiya Devi 《China Communications》 SCIE CSCD 2024年第5期249-260,共12页
Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL... Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets. 展开更多
关键词 anomaly detection deep learning hyperparameter optimization OVERSAMPLING SMOTE streaming data
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Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things
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作者 Mengmeng Zhao Haipeng Peng +1 位作者 Lixiang Li Yeqing Ren 《Computers, Materials & Continua》 SCIE EI 2024年第8期2815-2837,共23页
In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.A... In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods. 展开更多
关键词 Multivariate time series anomaly detection spatial-temporal network TRANSFORMER
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Federated Network Intelligence Orchestration for Scalable and Automated FL-Based Anomaly Detection in B5G Networks
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作者 Pablo Fernández Saura José M.Bernabé Murcia +3 位作者 Emilio García de la Calera Molina Alejandro Molina Zarca Jorge Bernal Bernabé Antonio F.Skarmeta Gómez 《Computers, Materials & Continua》 SCIE EI 2024年第7期163-193,共31页
The management of network intelligence in Beyond 5G(B5G)networks encompasses the complex challenges of scalability,dynamicity,interoperability,privacy,and security.These are essential steps towards achieving the reali... The management of network intelligence in Beyond 5G(B5G)networks encompasses the complex challenges of scalability,dynamicity,interoperability,privacy,and security.These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence(AI)-based analytics,empowering seamless integration across the entire Continuum(Edge,Fog,Core,Cloud).This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning(FL)-based anomaly detection in B5Gnetworks.By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm,which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize normal behavior,the systemorchestrates network intelligence to detect and prevent cyber-attacks.Integrated into a B5G Zero-touch Service Management(ZSM)aligned Security Framework,the proposal utilizes multi-domain and multi-tenant orchestration to automate and scale the deployment of FL-agents and AI-based anomaly detectors,enhancing reaction capabilities against cyber-attacks.The proposed FL architecture can be dynamically deployed across the B5G Continuum,utilizing a hierarchy of Network Intelligence orchestrators for real-time anomaly and security threat handling.Implementation includes FL enforcement operations for interoperability and extensibility,enabling dynamic deployment,configuration,and reconfiguration on demand.Performance validation of the proposed solution was conducted through dynamic orchestration,FL,and real-time anomaly detection processes using a practical test environment.Analysis of key performance metrics,leveraging the 5G-NIDD dataset,demonstrates the system’s capability for automatic and near real-time handling of anomalies and attacks,including real-time network monitoring and countermeasure implementation for mitigation. 展开更多
关键词 Federated learning 6G ORCHESTRATION anomaly detection security policy
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Challenging situation of coronary artery anomaly associated with ischemia and/or risk of sudden death
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作者 Shigenori Ito 《World Journal of Cardiology》 2024年第4期173-176,共4页
Coronary artery anomaly is known as one of the causes of angina pectoris and sudden death and is an important clinical entity that cannot be overlooked.The incidence of coronary artery anomalies is as low as 1%-2%of t... Coronary artery anomaly is known as one of the causes of angina pectoris and sudden death and is an important clinical entity that cannot be overlooked.The incidence of coronary artery anomalies is as low as 1%-2%of the general population,even when the various types are combined.Coronary anomalies are practically challenging when the left and right coronary ostium are not found around their normal positions during coronary angiography with a catheter.If there is atherosclerotic stenosis of the coronary artery with an anomaly and percutaneous coronary intervention(PCI)is required,the suitability of the guiding catheter at the entrance and the adequate back up force of the guiding catheter are issues.The level of PCI risk itself should also be considered on a caseby-case basis.In this case,emission computed tomography in the R-1 subtype single coronary artery proved that ischemia occurred in an area where the coronary artery was not visible to the naked eye.Meticulous follow-up would be crucial,because sudden death may occur in single coronary arteries.To prevent atherosclerosis with full efforts is also important,as the authors indicated admirably. 展开更多
关键词 Coronary artery anomaly Single coronary artery ISCHEMIA Sudden death Percutaneous coronary intervention Coronary vessel anomalies Myocardial ischemia Sudden cardiac death
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Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction
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作者 Min-Gyu Kim Hwankuk Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期585-607,共23页
In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly d... In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific nodes.This study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node information.The proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet metadata.This makes it well-suited for direct application in IoT environments due to its low resource consumption.In experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based features.Additionally,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and OneClassSVM.Furthermore,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling methods.This study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods. 展开更多
关键词 One-class anomaly detection feature extraction auto-encoder encrypted traffic CICIoT2023
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A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region
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作者 Yuhua Yin Zhiliang Liu +1 位作者 Bin Guo Mingjian Zuo 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第1期229-241,共13页
Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance o... Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions. 展开更多
关键词 Bearing condition monitoring anomaly detection Safe region Support vector data description
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LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes
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作者 Brij B.Gupta Akshat Gaurav +3 位作者 Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期2689-2706,共18页
This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It ... This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring. 展开更多
关键词 LSTM neural networks anomaly detection smart home health-care elderly fall prevention
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A Power Data Anomaly Detection Model Based on Deep Learning with Adaptive Feature Fusion
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作者 Xiu Liu Liang Gu +3 位作者 Xin Gong Long An Xurui Gao Juying Wu 《Computers, Materials & Continua》 SCIE EI 2024年第6期4045-4061,共17页
With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve suffi... With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed. 展开更多
关键词 Data alignment dimension reduction feature fusion data anomaly detection deep learning
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Tectonic framework of Qilian orogen: reveal from an aeromagnetic anomaly feature
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作者 Yang Hai Xiong Shengqing +7 位作者 Liu Qiankun Zhou Daoqing Yang Xue Fan Zhengguo Jia Zhiye Li fang Gao xiuhe Liu fuxiang 《Applied Geophysics》 SCIE CSCD 2024年第3期528-548,618,619,共23页
The Qilian Orogenic belt is one of the typical orogenic belts globally and a natural laboratory for studying plate tectonics.Many researchers have studied the ophiolite and high pressure and ultra-high pressure metamo... The Qilian Orogenic belt is one of the typical orogenic belts globally and a natural laboratory for studying plate tectonics.Many researchers have studied the ophiolite and high pressure and ultra-high pressure metamorphic rocks in the Qilian orogen and obtained valuable achievements.However,a hot debate exists on the basement property,the distribution of ophiolite,and the boundaries of tectonic units.Large-scale high-precision aeromagnetic surveys have recently been conducted in the Qilian Orogenic belt and adjacent areas.In this study,we are trying to analysis the tectonic framework of the Qilian Orogen using 1:500,000 aeromagnetic data.The results provide geophysical perspectives for studying the structural framework and deformation of this area.According to the aeromagnetic∆T anomaly map,the central and Southern Qilian have the same magnetic anomaly feature that noticeably differs from the North Qilian Orogenic belt and the Qaidam Block.This result indicates that the central and Southern Qilian have a unified magnetic basement and differ from the North Qilian orogenic belt and Qaidam Block.The map shows the distribution of ophiolite in the North Qilian orogenic belt.Linear magnetic anomalies represent the ophiolites because the mafic–ultramafic rocks usually have high magnetic susceptibility.The ophiolite belts are continuously distributed in the western part of North Qilian orogenic belt and have a large scale.However,the scale of the ophiolite belt and the outcropping of mafic–ultramafic rocks reduces when they pass through Qilian County to the east.The results indicate differences in the evolution process between the eastern and western parts of North Qilian,with Qilian County as the transition zone.This study also systematically defines the geophysical boundaries of the Qaidam Block,Qilian Block,North Qilian Orogenic belt,and Alxa block.It is proposed that the sinistral displacement of the Altun Fault is adjusted and absorbed by the series of NE-trending faults in the Qilian orogen and merge into the Longshoushan–Gushi Fault.The extension of the North Qilian Orogenic belt is strengthened by the neotectonics movement along the shearing direction,which separated the North Qilian Orogenic belt into several segments and formed a series of northeast-trending faults. 展开更多
关键词 Qilian orogen tectonic framework aeromagnetic anomaly feature mafic–ultramafic
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A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks
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作者 Bing Liu Zhe Zhang +3 位作者 Shengrong Hu Song Sun Dapeng Liu Zhenyu Qiu 《Computers, Materials & Continua》 SCIE EI 2024年第3期4049-4069,共21页
Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misr... Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS. 展开更多
关键词 Network security Internet of Things data-tampering attack anomaly detection
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An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data
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作者 Jun Xu Jinsong Du Changqing Yuan 《Geodesy and Geodynamics》 EI CSCD 2024年第5期465-476,共12页
Due to environmental noise and human factors,magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation.Empirical Mo... Due to environmental noise and human factors,magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation.Empirical Mode Decomposition(EMD),an adaptive multiscale analysis method for nonlinear and non-stationary signals,is widely used in geophysical and geodetic data processing.Compared with traditional EMD,Improved Complete Ensemble EMD with Adaptive Noise(ICEEMDAN)is more effective in addressing the problem of mode mixing.Based on the principles of 1D ICEEMDAN,this paper presents an alternative algorithm for 2D ICEEMDAN,extending its application to two-dimensional scenarios.The effectiveness of the proposed approach is demonstrated through synthetic signal experiments,which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD(BEMD)method.Furthermore,to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components,an improved soft thresholding technique is introduced.Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods.To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN,it is applied to ground magnetic survey data in the Yandun area of Xinjiang.The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data.In particular,practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD.In conclusion,as a potential tool for multi-scale decomposition,the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data. 展开更多
关键词 2D ICEEMDAN Mode mixing effect Magnetic anomaly DENOISING Improved soft thresholding
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Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel
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作者 Qing Ai Hao Tian +4 位作者 Hui Wang Qing Lang Xingchun Huang Xinghong Jiang Qiang Jing 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1797-1827,共31页
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient... Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance. 展开更多
关键词 anomaly detection dynamic predictive model structural health monitoring immersed tunnel LSTM ARIMA
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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks
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作者 Muaadh A.Alsoufi Maheyzah Md Siraj +4 位作者 Fuad A.Ghaleb Muna Al-Razgan Mahfoudh Saeed Al-Asaly Taha Alfakih Faisal Saeed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期823-845,共23页
The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected world.However,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly int... The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected world.However,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion attacks.In addition,IoT devices generate a high volume of unstructured data.Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data sources.Given the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack efficiency.Across numerous research domains,deep learning techniques have demonstrated their capability to precisely detect anomalies.This study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT networks.Firstly,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error.Secondly,the Convolutional Neural Network(CNN)technique is employed to create a binary classification approach.The proposed SAE-CNN approach is validated using the Bot-IoT dataset.The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of 0.9992.In addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better. 展开更多
关键词 IOT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network
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