Device Anomaly Detection Algorithm Based on Enhanced Long Short-Term Memory Network

The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.

Short-term and imminent geomagnetic anomalies of the Wenchuan M_S8.0 earthquake and exploration on earthquake forecast

The diurnal variation of the geomagnetic vertical component is exhibited mainly by changes of phase and amplitude before strong earthquakes. Based on data recorded by the network of geomagnetic observatories in China for many years, the anomalous features of the appearance time of the minima of diurnal variations （i.e, low-point time） of the geo- magnetic vertical components and the variation of their spatial distribution （i.e, phenomena of low-point displacement） have been studied before the Wenchuan Ms8.0 earthquake. The strong aftershocks after two months＇ quiescence of M6 aftershocks of the Ms8.0 event were forecasted based on these studies. There are good correlativities between these geomagnetic anoma- lies and occurrences of earthquakes. It has been found that most earthquakes occur near the boundary line of sudden changes of the low-point time and generally within four days before or after the 27th or 41st day counting from the day of the appearance of the anomaly. In addition, the imminent anomalies in diurnal-variation amplitudes near the epicentral areas have also been studied before the Wenchuan earthquake.

POSSIBLE CONTRIBUTION OF A TROPICAL CYCLONE TO SHORT-TERM CLIMATE ANOMALIES IN EAST ASIA VIA SNOW COVER ON THE TIBETAN PLATEAU

Snow cover on the Tibetan Plateau(TP) has been shown to be essential for the East Asian summer monsoon.In this paper, we demonstrate that tropical cyclone(TC) 04B(1999) in the northern Indian Ocean, which made landfall during the autumn of 1999, may have contributed to climate anomalies over East Asia during the following spring and summer by increasing snow cover on the TP. Observations indicate that snow cover on the TP increased markedly after TC 04B(1999) made landfall in October of 1999. Sensitivity experiments, in which the TC was removed from a numerical model simulation of the initial field, verified that TC 04B(1999) affected the distribution as well as increased the amount of snow on the TP. In addition, the short-term numerical modeling of the climate over the region showed that the positive snow cover anomaly induced negative surface temperature, negative sensible heat flux, positive latent heat flux, and positive soil temperature anomalies over the central and southern TP during the following spring and summer. These climate anomalies over the TP were associated with positive(negative) summer precipitation anomalies over the Yangtze River valley(along the southeastern coast of China).

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.

An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network

The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.

An Efficient Correlation-Aware Anomaly Detection Framework in Cellular Network

Nowadays,the fifth-generation(5G)mobile communication system has obtained prosperous development and deployment,reshaping our daily lives.However,anomalies of cell outages and congestion in 5G critically influence the quality of experience and significantly increase operational expenditures.Although several big data and artificial intelligencebased anomaly detection methods have been proposed for wireless cellular systems,they change distributions of the data and ignore the relevance among user activities,causing anomaly detection ineffective for some cells.In this paper,we propose a highly effective and accurate anomaly detection framework by utilizing generative adversarial networks(GAN)and long short-term memory(LSTM)neural networks.The framework expands the original dataset while simultaneously keeping the distribution of data unchanged,and explores the relevance among user activities to further improve the system performance.The results demonstrate that our framework can achieve 97.16%accuracy and 2.30%false positive rate by utilizing the correlation of user activities and data expansion.

Anomalies of Precursory Group and Grouped Strong Earthquakes in the Sichuan-Yunnan Region

Three methods of extracting the information of anomalies of a precursory group are put forward, i.e., the mathematical analyses of the synthetic information of earthquake precursors (S), the inhomogeneous degree of precursory groups (ID) and the values of short-term and impending anomaly in near-source area (NS). Using these methods, we calculate the observational data of deformation, underground fluid and hydrochemical constituents obtained from different seismic stations in the Sichuan-Yunnan region and conclude that the synthetic precursory anomalies of a single strong earthquake with M S6.0 differ greatly from those of the grouped strong earthquakes, for the anomalous information of precursory groups are more abundant. The three methods of extracting the synthetic precursory anomaly and the related numerical results can be applied into the practice of prediction to the grouped strong earthquakes in the Sichuan-Yunnan region. Inhomogeneous degree (ID) of synthetic precursory anomaly can be identified automatically because it takes the threshold of distributive characteristics of the anomalies of precursory group as its criterion for anomaly.

The Anomaly Field of Subsurface Fluids and Its Formation and Evolution Mechanism Before Three Strong Earthquakes in the Northern North China Area

The data of pre-seismic subsurface fluid anomalies of such earthquakes as Datong-YanggaoM_s6.1 event on Oct.19,1989,western Baotou M_s6.4 event on May 3,1996 and Zhangbei-Shangyi M_s6.2 event on Jan.10,1998 are systematically collected and arranged.Then thefeatures of patterns,spatial distribution,time variation and time-spatial evolution of theseanomalies are compared and comprehensively analyzed.Then the formation and evolutionmechanism of medium-and short-term anomaly field of subsurface fluids in the northernNorth China area is proposed.The results show that the medium-term anomaly field is causedby regional tectonic activities,which further strengthen the local tectonic activities andpromote the formation and evolution of the seismic source body.The enhancement of loealtectonic activities causes the formation of anomaly field of short-term subsurface fluids,andthe evolution of source body engenders the source-precursor anomalies of subsurface fluids inthe epicenters at imminent stage.

Study on the Method of Short-Term Synthetic Earthquake Prediction in the North China Region

Based on the extraction and calculation of the short-term seismic precursory information magnitude from the 114 major precursory observations in the North China region, and together with consideration of factors such as geological structure, seismicity, crustal thickness, and in particular, the current geodynamics of the region, the authors studied the time-space evolution characteristics of the short-term earthquake precursory information magnitude and its relationship with earthquakes and proposed the index and method for the short-term synthetic prediction of earthquakes with M S≥5.0 in the North China region. The inspection through R-value shows that the method is effective to a certain extent for earthquake prediction.

Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

The operating conditions of wind turbines(WTs)in the same wind farm(WF)may share similarities due to their shared manufacturing process,control strategy,and operating environment.However,the similarities of WTs are seldom considered in WT anomaly detection,resulting in the disregard of useful information.This paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition(SCADA)data of multiple WTs in the same WF.First,a similarity assessment method based on a comparison of different observation time series is proposed,which objectively quantifies the similarities of WT operating conditions.Then,the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory(LSTM)algorithm.LSTM models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target WT.Finally,an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify anomalies.The effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

Real-time anomaly detection for very short-term load forecasting

Although the recent load information is critical to very short-term load forecasting(VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications.This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF.This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Finally, a general anomaly detection framework is proposed for the future research.

Anomalous Ionospheric foF2 Variations Observed Prior to the Dalbandin Earthquake in Pakistan

Ionosphereic foF2 variations are very sensitive to the seismic effect and results of ionospheric perturbations associated with earthquakes seem to very hopeful for short-term earthquake prediction. On January 18,2011 at 20: 23 UT a great earthquake( M = 7. 2)occurred in Dalbandin( 28. 73° N,63. 92° E),Pakistan. In this study,we have tried to find out the features of pre-earthquake ionospheric anomalies by using the hourly day time( 08. 00 a. m.- 05. 00 p. m.) data of critical frequency( foF2) obtained by three vertical sounding stations installed in Islamabad( 33. 78°N,73. 06°E),Multan( 32. 26°N,71. 51°E) and Karachi( 24. 89° N,67. 02° E), Pakistan. The results show the significant anomalies of foF2 in the earthquake preparation zone several days prior to the Dalbandin earthquake. It is also observed that the amplitude and frequency of foF2 anomalies are more prominent at the nearest station to the epicenter as compared to those stations near the outer margin of the earthquake preparation zone. The confidence level for ionospheric anomalies regarding the seismic signatures can be enhanced by adding the analysis of some other ionospheic parameters along with critical frequency of the layer F2.

Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous data.Furthermore,flight data with random noise pose a significant challenge for anomaly detection.This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data.First,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model.Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge.Then,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner.Finally,the method's effectiveness is validated on real UAV flight data.

The Beijing network of digital geomagnetic pulsation observatories

We present herein an introduction to the Beijing network of digital geomagnetic pulsation observatories, and describe its essential features, and important roles in earthquake prediction studies and other geomagnetic investigations. The network provides digitalized data of geomagnetic events, such as magnetic storms, magnetic disturbances, geomagnetic daily variations, and geomagnetic pulsations. The digitalized data, convenient for processing and analysis, contain very rich information because of high accuracy and wide dynamic range of the instruments.

Atmospheric Meteorological Parameters and Ionospheric F2 Layer Critical Frequency (foF2) Observation for 6^th December, 2016 Indonesia Earthquake (M 6.5): A Case Study

On 6^th December, 2016, an earthquake with M 6.5 occurred at the tectonic plate boundary, southwest of Sumatra, Indonesia (Latitude: 0.5897°S, Longitude: 101.3431°E). In this case, ionospheric critical frequency of F2 layer (foF2) variations and meteorological parameters, viz., air temperature, relative humidity, atmospheric pressure and wind speed variations were investigated so as to detect any anomalies. Data are obtained from different websites freely available for researchers. In the absence of real ionosonde foF2 data, IRI 2016 model data were used. For each parameter, anomaly window were defined when values fell beyond ± 6 ℃,< 70 %,± 4 mb and ± 3.5 km h-1 from the event day value and one third of total foF2 values broke the limits of the upper and lower bounds. Certain random anomalies in temperature, relative humidity, pressure, wind speed and foF2 frequencies were observed different days prior to occurrence of the quake but each parameter showed anomalies 12 days before the occurrence. Also, geomagnetic tranquility was justified through Kp and Dst indices. This study reveals that continuous monitoring of atmospheric meteorological parameters and regular ionospheric foF2 observations might help us to predict an earthquake about a week prior to the occurrence.

A Short Term Review of Operated Cases in the Plastic Surgery Unit at the Komfo Anokye Teaching Hospital, Kumasi, Ghana

Introduction: Surgical problems are of much disturbance to the world and should therefore be given serious attention. The prevalence of these surgical problems, has made plastic surgery become a broadly relevant and acceptable way for addressing problems like injuries, congenital anomalies, surgical infections and malignancies among others. Aim: This study is to quantify and characterize surgical procedures done in the plastic surgery theatre located in the new Accident and Emergency (A & E) Building of KATH. Materials and Methods: Data were obtained from the Operation Register/Theatre Books in the plastic surgery theatre at the A & E Centre on cases operated on from October 1, 2009 to September 30, 2012. Data entry, presentation and analysis were done using Statistical Package for the Social Sciences (SPSS) 20.0 version. Results: Adults formed the majority of patients who sought for plastic surgery with a percentage of 70.3%. The male patients also outnumbered the females recording (61.5%) out of the total number of patients. Most of the cases recorded were acquired cases (93.2%). Reconstructive surgery was the commonest operation performed (30%);in 53.8% cases general anaesthesia was used. Conclusion: Among all the procedures used reconstructive surgery was the commonest surgery performed in the unit and general anaesthesia was the most type of anaesthesia used for the operations.

Application Study on Correction Method for Lag of Water Level Response to Earth Tide and Atmospheric Pressure

The water level in a deep well instantly responds to the earth’s tide and atmospheric pressure, and varies accordingly, not only in terms of amplitude but also in the phase lag. Therefore, phase lag correction is used in analyzing digital groundwater observation data in eastern China. Calculation results presented by the authors in this paper show that the correction method is effective in the identification of anomalous changes for short-term seismic precursors. The correction method can also be applied to the processing of observed deformation and tilt data.

The Application of Wavelet Transform in Analysis of Digital Precursory Observational Data

Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and to discriminate between the trend anomalies and the short-term anomalies. This paper presents a method to separate the high frequency information from the low ones by using the wavelet transform to analyze the digital data of precursors, and illustrates with examples the train of thoughts of discriminating the short-term anomalies from trend anomalies by using the wavelet transform, thus provide a new effective approach for extracting the short-term and trend anomalies from the digital data of precursors.

Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model

Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding areas.As a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures.Nevertheless,analyzing DAS data to assess railroad health or detect potential damage is a challenging task.Due to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this data.This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring system.As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks.Extracting insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the railroad.Notably,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup.Moreover,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection rate.Applying a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition.Altogether,these proposed models exhibit promising potential for accurately identifying various railroad conditions,including anomalies and discrepancies that warrant thorough exploration.