A Power Data Anomaly Detection Model Based on Deep Learning with Adaptive Feature Fusion

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.

Turbo Message Passing Based Burst Interference Cancellation for Data Detection in Massive MIMO-OFDM Systems

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

Prediction of Lung Cancer Stage Using Tumor Gene Expression Data

Lung cancer remains a significant global health challenge and identifying lung cancer at an early stage is essential for enhancing patient outcomes. The study focuses on developing and optimizing gene expression-based models for classifying cancer types using machine learning techniques. By applying Log2 normalization to gene expression data and conducting Wilcoxon rank sum tests, the researchers employed various classifiers and Incremental Feature Selection (IFS) strategies. The study culminated in two optimized models using the XGBoost classifier, comprising 10 and 74 genes respectively. The 10-gene model, due to its simplicity, is proposed for easier clinical implementation, whereas the 74-gene model exhibited superior performance in terms of Specificity, AUC (Area Under the Curve), and Precision. These models were evaluated based on their sensitivity, AUC, and specificity, aiming to achieve high sensitivity and AUC while maintaining reasonable specificity.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

Anomaly Detection Algorithm for Stay Cable Monitoring Data Based on Data Fusion

In order to improve the accuracy and consistency of data in health monitoring system,an anomaly detection algorithm for stay cables based on data fusion is proposed.The monitoring data of Nanjing No.3 Yangtze River Bridge is used as the basis of study.Firstly,an adaptive processing framework with feedback control is established based on the concept of data fusion.The data processing contains four steps:data specification,data cleaning,data conversion and data fusion.Data processing information offers feedback to the original data system,which further gives guidance for the sensor maintenance or replacement.Subsequently,the algorithm steps based on the continuous data distortion is investigated,which integrates the inspection data and the distribution test method.Finally,a group of cable force data is utilized as an example to verify the established framework and algorithm.Experimental results show that the proposed algorithm can achieve high detection accuracy,providing a valuable reference for other monitoring data processing.

Joint MAP channel estimation and data detection for OFDM in presence of phase noise from free running and phase locked loop oscillator

This paper addresses a computationally compact and statistically optimal joint Maximum a Posteriori(MAP)algorithm for channel estimation and data detection in the presence of Phase Noise(PHN)in iterative Orthogonal Frequency Division Multiplexing(OFDM)receivers used for high speed and high spectral efficient wireless communication systems.The MAP cost function for joint estimation and detection is derived and optimized further with the proposed cyclic gradient descent optimization algorithm.The proposed joint estimation and detection algorithm relaxes the restriction of small PHN assumptions and utilizes the prior statistical knowledge of PHN spectral components to produce a statistically optimal solution.The frequency-domain estimation of Channel Transfer Function(CTF)in frequency selective fading makes the method simpler,compared with the estimation of Channel Impulse Response(CIR)in the time domain.Two different time-varying PHN models,produced by Free Running Oscillator(FRO)and Phase-Locked Loop(PLL)oscillator,are presented and compared for performance difference with proposed OFDM receiver.Simulation results for joint MAP channel estimation are compared with Cramer-Rao Lower Bound(CRLB),and the simulation results for joint MAP data detection are compared with“NO PHN"performance to demonstrate that the proposed joint MAP estimation and detection algorithm achieve near-optimum performance even under multipath channel fading.

Antarctic ice-sheet near-surface snowmelt detection based on the synergy of SSM/I data and QuikSCAT data

Microwave radiometer SSM/I data and scatterometer QuikSCAT data have been widely used for the icesheet near-surface snowmelt detection based on their sensitivity to liquid water present in snow. In order to improve the Antarctic ice-sheet near-surface snowmelt detection accuracy, a new Antarctic icesheet near-surface snowmelt synergistic detection method was proposed based on the principle of complementary advantages of SSM/I data(high reliability) and QuikSCAT data(high sensitivity) by the use of edge detection model to automatically extract the edge information to get the distribution of Antarctic snowmelt onset date, snowmelt duration and snowmelt end date. The verification result shows that the proposed snowmelt synergistic detection method improves the detection accuracy from about 75% to 86% based on AWS(Automatic Weather Stations) Butler Island and Larsen Ice Shelf. The algorithm can also be applied to other regions, which provides methodological support and supplement for the global snowmelt detection.

Anomaly Detection Based on Data-Mining for Routing Attacks in Wireless Sensor Networks

With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wireless sensor networks.The schemeuses the Apriori algorithm to extract traffic patternsfrom both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model.Through thecombination of these two algorithms,routing attackscan be detected effectively and automatically.Themain advantage of the proposed approach is that it isable to detect new attacks that have not previouslybeen seen.Moreover,the proposed detection schemeis based on no priori knowledge and then can beapplied to a wide range of different sensor networksfor a variety of routing attacks.

An Efficient Outlier Detection Approach on Weighted Data Stream Based on Minimal Rare Pattern Mining

The distance-based outlier detection method detects the implied outliers by calculating the distance of the points in the dataset, but the computational complexity is particularly high when processing multidimensional datasets. In addition, the traditional outlier detection method does not consider the frequency of subsets occurrence, thus, the detected outliers do not fit the definition of outliers (i.e., rarely appearing). The pattern mining-based outlier detection approaches have solved this problem, but the importance of each pattern is not taken into account in outlier detection process, so the detected outliers cannot truly reflect some actual situation. Aimed at these problems, a two-phase minimal weighted rare pattern mining-based outlier detection approach, called MWRPM-Outlier, is proposed to effectively detect outliers on the weight data stream. In particular, a method called MWRPM is proposed in the pattern mining phase to fast mine the minimal weighted rare patterns, and then two deviation factors are defined in outlier detection phase to measure the abnormal degree of each transaction on the weight data stream. Experimental results show that the proposed MWRPM-Outlier approach has excellent performance in outlier detection and MWRPM approach outperforms in weighted rare pattern mining.

Fault detection observer design for networked control system with long time-delays and data packet dropout

Focusing on the networked control system with long time-delays and data packet dropout,the problem of observerbased fault detection of the system is studied.According to conditions of data arrival of the controller,the state observers of the system are designed to detect faults when they occur in the system.When the system is normal,the observers system is modeled as an uncertain switched system.Based on the model,stability condition of the whole system is given.When conditions are satisfied,the system is asymptotically stable.When a fault occurs,the observers residual can change rapidly to detect the fault.A numerical example shows the effectiveness of the proposed method.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

Detecting network intrusions by data mining and variable-length sequence pattern matching

Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.

Sequence detection data fusion with distributed multisensor

This Paper presents a data fusion method with distributed sequence detection for on hypothasis testingtheory including the data fusion algorithm of sequence detection based on least error probability rule, the decision ruleand the calcation formula of the detction times and the simulation result of system performance as well.

MA-IDS: A Distributed Intrusion Detection System Based on Data Mining

Aiming at the shortcomings in intrusion detection systems (IDSs) used incommercial and research fields, we propose the MA-IDS system, a distributed intrusion detectionsystem based on data mining. In this model, misuse intrusion detection system CM1DS) and anomalyintrusion de-lection system (AIDS) are combined. Data mining is applied to raise detectionperformance, and distributed mechanism is employed to increase the scalability and efficiency. Host-and network-based mining algorithms employ an improved. Bayes-ian decision theorem that suits forreal security environment to minimize the risks incurred by false decisions. We describe the overallarchitecture of the MA-IDS system, and discuss specific design and implementation issue.

Anomaly Detection of Store Cash Register Data Based on Improved LOF Algorithm

As the cash register system gradually prevailed in shopping malls, detecting the abnormal status of the cash register system has gradually become a hotspot issue. This paper analyzes the transaction data of a shopping mall. When calculating the degree of data difference, the coefficient of variation is used as the attribute weight;the weighted Euclidean distance is used to calculate the degree of difference;and k-means clustering is used to classify different time periods. It applies the LOF algorithm to detect the outlier degree of transaction data at each time period, sets the initial threshold to detect outliers, deletes the outliers, and then performs SAX detection on the data set. If it does not pass the test, then it will gradually expand the outlying domain and repeat the above process to optimize the outlier threshold to improve the sensitivity of detection algorithm and reduce false positives.

Constructing Three-Dimension Space Graph for Outlier Detection Algorithms in Data Mining

Outlier detection has very important applied value in data mining literature. Different outlier detection algorithms based on distinct theories have different definitions and mining processes. The three-dimensional space graph for constructing applied algorithms and an improved GridOf algorithm were proposed in terms of analyzing the existing outlier detection algorithms from criterion and theory. Key words outlier - detection - three-dimensional space graph - data mining CLC number TP 311. 13 - TP 391 Foundation item: Supported by the National Natural Science Foundation of China (70371015)Biography: ZHANG Jing (1975-), female, Ph. D, lecturer, research direction: data mining and knowledge discovery.

An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.