Abnormal Detection Algorithm Research Articles

Concept of Early Prediction and Identification of Truck Vehicle Failures Supported by In-Vehicle Telematics Platform Based on Abnormality Detection Algorithm

Concept of Early Prediction and Identification of Truck Vehicle Failures Supported by In-Vehicle Telematics Platform Based on Abnormality Detection Algorithm

Application of Video Abnormal Behavior Detection Algorithm in Evaluation of Track and Field Teaching and Training Effect

With the development of track and field, people pay more and more attention to the quality of classroom teaching of track and field technology, and the evaluation of teaching quality plays a key role in it. In today's educational reform, teaching evaluation plays an important role as an important method to test teachers' teaching and students' learning. With the rapid development of machine learning, especially deep learning, image-based individual abnormal behavior detection technology is becoming more and more mature, but there are still many difficulties to be solved in video-based group abnormal behavior detection technology. Therefore, it is necessary to study the detection algorithm. Based on machine vision theory, image processing theory and video analysis technology, this paper studies three key technologies involved in human abnormal behavior detection in video: moving target detection, moving target tracking and abnormal behavior detection.

Abnormal Behavior Recognition Based on 3D Dense Connections.

Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model's parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed.

YOLOv5s-MEE: A YOLOv5-based Algorithm for Abnormal Behavior Detection in Central Control Room

Aiming to quickly and accurately detect abnormal behaviors of workers in central control rooms, such as playing mobile phone and sleeping, an abnormal behavior detection algorithm based on improved YOLOv5 is proposed. The technique uses SRGAN to reconstruct the input image to improve the resolution and enhance the detailed information. Then, the MnasNet is introduced to replace the backbone feature extraction network of the original YOLOv5, which could achieve the lightweight of the model. Moreover, the detection accuracy of the whole network is enhanced by adding the ECA-Net attention mechanism into the feature fusion network structure of YOLOv5 and modifying the loss function as EIOU. The experimental results in the custom dataset show that compared with the original YOLOv5 algorithm, the algorithm proposed in this paper improves thedetection speed to 75.50 frames/s under the condition of high detection accuracy, which meets the requirements of real-time detection. Meanwhile, compared with other mainstream behavior detection algorithms, this algorithm also shows better detection performance.

Research on Employee Abnormal Behavior Detection Algorithm Based on Improved SSD

Research on Employee Abnormal Behavior Detection Algorithm Based on Improved SSD

Abnormal traffic detection system in SDN based on deep learning hybrid models

Software defined network (SDN) provides technical support for network construction of smart cities. However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection algorithms are complex and time-consuming, so it is difficult to find abnormalities in the network in time and unable to satisfy the requirements of abnormal traffic detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection method. Firstly, it completes the rough detection of abnormal traffic in the network according to the statistical information of switch ports and then uses wavelet transform and deep learning technology to extract multi-dimensional features of all traffic data flowing through suspicious switches, so as to realize the fine detection of abnormal traffic from the surface. The experimental results show that the proposed detection method based on port information can quickly locate the source of abnormal traffic. Compared with the traditional abnormal traffic detection method in SDN, the fine detection method based on multi-dimensional features improves the accuracy by 1.7 %, the recall rate by 1.6 %, and the false positive rate by 91.3 %.

Transmission line abnormal target detection algorithm based on improved YOLOX

Transmission line abnormal target detection algorithm based on improved YOLOX

EV charging load forecasting model mining algorithm based on hybrid intelligence

Precise EV charging load forecasting plays a critical role in optimizing resource allocation and facilitating economic operation and energy management of EV charging stations, as well as supporting the economic dispatch of the power grid for efficient and effective utilization of resources. Existing EV charging load forecasting models have highly restrictive load data requirements and thus have practical limitations in two fold: first, these models are a black box and cannot provide a quantitative reference for later analysis affecting EV charging load forecasting; and second, they fail in considering the impact of noisy data caused by uncontrollable factors (data collection failures, human errors, and network attacks, etc.) on the accuracy of EV charging load forecasting. To address the above issues, we propose a novel EV charging load forecasting model mining based on gene expression programming (CFMM-GEP) by fusing noisy load processing. This will tackle three-fold ideas: (1) the charging load dataset with abnormal data is reconstructed based on the Auto-Encoder, and the support vector machine-based abnormal detection algorithm is proposed; (2) a quantitative model for EV charging load forecasting based on gene expression programming is constructed. Extensive experiments are carried out on four open-source charging load datasets. Experimental results indicate that the superiority of our proposed CFMM-GEP model over 6 state-of-the-art models in terms of MAPE, RMSE, MAE, and R2.

A-CAVE: Network abnormal traffic detection algorithm based on variational autoencoder

A-CAVE: Network abnormal traffic detection algorithm based on variational autoencoder

Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning.

Studies relating to the right ventricle (RV) are inadequate, and specific diagnostic algorithms still need to be improved. This essay is designed to make exploration and verification on an algorithm of deep learning based on imaging and clinical data to detect RV abnormalities. The Automated Cardiac Diagnosis Challenge dataset includes 20 subjects with RV abnormalities (an RV cavity volume which is higher than 110mL/m2 or RV ejection fraction which is lower than 40%) and 20 normal subjects who suffered from both cardiac MRI. The subjects were separated into training and validation sets in a ratio of 7:3 and were modeled by utilizing a nerve net of deep-learning and six machine-learning algorithms. Eight MRI specialists from multiple centers independently determined whether each subject in the validation group had RV abnormalities. Model performance was evaluated based on the AUC, accuracy, recall, sensitivity and specificity. Furthermore, a preliminary assessment of patient disease risk was performed based on clinical information using a nomogram. The deep-learning neural network outperformed the other six machine-learning algorithms, with an AUC value of 1 (95% confidence interval: 1-1) on both training group and validation group. This algorithm surpassed most human experts (87.5%). In addition, the nomogram model could evaluate a population with a disease risk of 0.2-0.8. A deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm's ability to classify RV abnormalities by comparing it with human experts.

An EEG abnormality detection algorithm based on graphic attention network

An EEG abnormality detection algorithm based on graphic attention network

Abnormal behavior detection algorithm based on multi-branch convolutional fusion neural network

Abnormal behavior detection algorithm based on multi-branch convolutional fusion neural network

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.

Analysis of Collected Data and Establishment of an Abnormal Data Detection Algorithm Using Principal Component Analysis and K-Nearest Neighbors for Predictive Maintenance of Ship Propulsion Engine

Because ships are typically operated for more than 25 years after construction, they can be considered mobile factories that require economic maintenance before being scrapped. Therefore, for stable and efficient ship operation, continuous maintenance systems and processes are required. Ships cannot be operated when defects or failures occur in any of the numerous systems configured in them, and research is urgently needed to apply predictive maintenance to propulsion engines with high maintenance costs using machine learning. Therefore, this study analyzes the operation and control characteristics of the propulsion engine, acquires engine data from the alarm monitoring system of the ship in operation, and then preprocesses the data by constructing a data preprocessing algorithm that incorporates the engine control characteristics. In addition, principal component analysis and K-nearest neighbors were used to check whether preprocessing data were classified based on engine control characteristics, and an algorithm capable of detecting abnormal data was built and verified to lay the foundation for predictive maintenance of ship propulsion engines using machine learning.

Power system abnormal pattern detection for new energy big data

Abstract The energy crisis is a problem that countries all over the world pay more and more attention to, and a series of ecological problems caused by it have become increasingly prominent. It is difficult for traditional fossil fuels to maintain a healthy and coordinated sustainable development of society and economy. The establishment of a sustainable energy system has become the development trend of various countries to solve energy problems. Electric energy is a secondary energy that all primary energy can be converted into, and an irreplaceable consumable for all industrial technologies and people’s lives. Electric power data has the characteristics of large rate span, numerous data sources, complicated interaction methods, and various types of data. The existence of abnormal data in the power system will greatly reduce the accuracy of the system state estimation and the state estimation convergence rate. This paper introduces the power grid industrial control system, combines the data flow of power big data, and analyzes the abnormal information detection process in detail. It takes the data stream acquired by the acquisition unit PMU of the wide area measurement system as the research object. The rapid development of the Hadoop big data platform provides important technical support for the research of power grid big data. Based on the Hadoop platform, the clustering algorithm is used to complete the anomaly detection of real-time data. The LOF algorithm has poor performance when dealing with a large amount of high-dimensional data, and has high time and space complexity. In order to make up for the shortcomings of the LOF algorithm, this paper uses the K-means clustering algorithm to propose an improved algorithm K-LOF of the density-based local abnormal factor detection algorithm LOF, and optimizes the neighborhood query process. It is verified by experiments that the K-LOF algorithm can effectively reduce the time complexity of the anomaly detection algorithm and improve the detection accuracy by 2–4.2%.

Network Anomaly Traffic Detection Algorithm Based on RIC-SC-DeCN.

In the research of network abnormal traffic detection, in view of the characteristics of high dimensionality and redundancy in traffic data and the loss of original information caused by the pooling operation in the convolutional neural network, which leads to the problem of unsatisfactory detection effect, this paper proposes a network abnormal traffic detection algorithm based on RIC-SC-DeCN to improve the above problems. Firstly, a recursive information correlation (RIC) feature selection mechanism is proposed, which reduces data redundancy through the maximum information correlation feature selection algorithm and recursive feature elimination method. Secondly, a skip-connected deconvolutional neural network model (SC-DeCN) is proposed to reduce the information loss by reconstructing the input signal. Finally, the RIC mechanism and the SC-DeCN model are merged to form a network abnormal traffic detection algorithm based on RIC-SC-DeCN. The experimental results on the CIC-IDS-2017 dataset show that the RIC feature selection mechanism proposed in this paper has the highest accuracy when using MSCNN as the detection model compared to the other three, which can reach 96.22%. Compared with the other five models, the SC-DeCN model has the highest detection accuracy, while the model training time is moderate and can reach 96.55%. Compared with the SC-DeCN model, the RIC-SC-DeCN model reduces the overall training time by 45.50%, while the accuracy rate is increased to 97.68%. It shows that the algorithm proposed in this paper has a good detection effect in the detection of network abnormal traffic.

Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images

Currently, ground penetrating radar is the major technology for the detection of urban road collapses and disaster sources. Vehicle-mounted GPR collects tens of GB of data on site every day, but the present interpretation of abnormal regions detected by radar relies on manual interpretation with low process efficiency. The abnormal region images of GPR are different from the surrounding normal images. In terms of the features of abnormal regions in GPR images with an obvious brightness change and obvious directional characteristics, an abnormal region detection algorithm based on visual attention mechanism is proposed. Firstly, the complex background noise in the GPR images is suppressed by wavelet denoising and gamma transform, and the brightness and directional characteristics of the abnormal regions are enhanced. Secondly, by building a multi-scale image brightness and orientation feature pyramid model, the features of abnormal regions of interest are continuously enhanced, and the rapid screening of abnormal regions has been achieved. The effectiveness of the algorithm has been verified by actual tests on different types of abnormal radar image data.

Abnormal link detection algorithm based on semi-local structure

Abnormal link detection algorithm based on semi-local structure

The Relationship between the Mechanism of Sarcopenia and Exercise Based on Data Mining.

Due to the increasing prosperity of human life science and technology, many huge research results have been obtained, and the scientific research of molecular biology is developing rapidly. Therefore, the output of biological genome data has increased exponentially, which constitutes a huge amount of data analysis. The seemingly chaotic and massive amount of data information actually contains a large amount of data and information of great key scientific significance and value. Therefore, this kind of genomic data information not only contains the information content that describes the characteristics of human life but also contains the information content that can express the essence of the biological organism. It includes macroeconomic information that can reflect the basic structure and capabilities of living organisms and microinformation in related fields of molecular biology. This massive amount of genetic data is usually closely related to each other, can influence each other, and does not exist alone. In the article, the causes of uncertain data and the classification of uncertain data are introduced, and the basic concepts and related algorithms of data mining are explained. Focusing on the research and analysis of abnormal point detection and clustering algorithms in uncertain data mining technology, this paper solves the problem of how to obtain more diverse and accurate outlier detection and cluster analysis results in uncertain data. The results showed that whether it was related to obesity or not, the Lp(a) level of the sarcopenia group was significantly higher than that of the nonsarcopenia group. At the same time, the correlation analysis showed that ASM/height was negatively correlated with Lp(a). ASM/height is one of the criteria for diagnosing sarcoidosis, and it is also the core of the analysis. Among the 1956 tumor patients collected in this study, 432 had sarcopenia, accounting for 22.08%, and the incidence of sarcopenia in patients with gastrointestinal tumors increased.

Adaptive and Lightweight Abnormal Node Detection via Biological Immune Game in Mobile Multimedia Networks

Considering the contradiction between limited node resources and high detection costs in mobile multimedia networks, an adaptive and lightweight abnormal node detection algorithm based on artificial immunity and game theory is proposed in order to balance the trade-off between network security and detection overhead. The algorithm can adapt to the highly dynamic mobile multimedia networking environment with a large number of heterogeneous nodes and multi-source big data. Specifically, the heterogeneous problem of nodes is solved based on the non-specificity of an immune algorithm. A niche strategy is used to identify dangerous areas, and antibody division generates an antibody library that can be updated online, so as to realize the dynamic detection of the abnormal behavior of nodes. Moreover, the priority of node recovery for abnormal nodes is decided through a game between nodes without causing excessive resource consumption for security detection. The results of comparative experiments show that the proposed algorithm has a relatively high detection rate and a low false-positive rate, can effectively reduce consumption time, and has good level of adaptability under the condition of dynamic nodes.