scholarly journals Latent-Insensitive Autoencoders for Anomaly Detection

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 112
Author(s):  
Muhammad S. Battikh ◽  
Artem A. Lenskiy
Keyword(s):  
Anomaly Detection ◽  
Qualitative Analysis ◽  
Performance Improvement ◽  
Transfer Learning ◽  
Loss Functions ◽  
Training Process ◽  
Theoretical Justification ◽  
Significant Performance ◽  
Ablation Study ◽  
Similar Domain

Reconstruction-based approaches to anomaly detection tend to fall short when applied to complex datasets with target classes that possess high inter-class variance. Similar to the idea of self-taught learning used in transfer learning, many domains are rich with similar unlabeled datasets that could be leveraged as a proxy for out-of-distribution samples. In this paper we introduce the latent-insensitive autoencoder (LIS-AE) where unlabeled data from a similar domain are utilized as negative examples to shape the latent layer (bottleneck) of a regular autoencoder such that it is only capable of reconstructing one task. We provide theoretical justification for the proposed training process and loss functions along with an extensive ablation study highlighting important aspects of our model. We test our model in multiple anomaly detection settings presenting quantitative and qualitative analysis showcasing the significant performance improvement of our model for anomaly detection tasks.

scholarly journals An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1590
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Clement Pang ◽  
George Oganesyan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning ◽  
Performance Monitoring ◽  
Nonlinear Problems ◽  
Time Series Forecasting ◽  
Stationary Time Series ◽  
Learning Gain ◽  
Application Performance ◽  
Cloud Applications

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

Spacecraft Time-Series Anomaly Detection Using Transfer Learning

2021 ◽  
Author(s):  
Sriram Baireddy ◽  
Sundip R. Desai ◽  
James L. Mathieson ◽  
Richard H. Foster ◽  
Moses W. Chan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning

Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

2021 ◽  
Author(s):  
Khalid Labib Alsamadony ◽  
Ertugrul Umut Yildirim ◽  
Guenther Glatz ◽  
Umair bin Waheed ◽  
Sherif M. Hanafy
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Transfer Learning ◽  
Temporal Resolution ◽  
Exposure Time ◽  
Ct Images ◽  
Loss Functions ◽  
High Quality ◽  
X Ray ◽  
Training Images

Abstract Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

Consideration of structural constraints in passive rotor blade design for improved performance

2015 ◽  
Vol 119 (1222) ◽  
pp. 1513-1539 ◽  
Author(s):  
J. W. Lim
Keyword(s):  
Performance Improvement ◽  
Rotor Blade ◽  
Parametric Design ◽  
Control Point ◽  
Bending Moment ◽  
Structural Constraints ◽  
Design Variables ◽  
Significant Performance ◽  
Structural Redesign ◽  
Improved Performance

AbstractThis design study applied parameterisation to rotor blade for improved performance. In the design, parametric equations were used to represent blade planform changes over the existing rotor blade model. Design variables included blade twist, sweep, dihedral, and radial control point. Updates to the blade structural properties with changes in the design variables allowed accurate evaluation of performance objectives and realistic structural constraints – blade stability, steady moments (flap bending, chord bending, and torsion), and the high g manoeuvring pitch link loads. Performance improvement was demonstrated with multiple parametric designs. Using a parametric design with advanced aerofoils, the predicted power reduction was 1·0% in hover, 10·0% at μ = 0·30, and 17·0% at μ = 0·40 relative to the baseline UH-60A rotor, but these were obtained with a 35% increase in the steady chord bending moment at μ = 0·30 and a 20% increase in the half peak-to-peak pitch link load during the UH-60A UTTAS manoeuvre Low vibration was maintained for this design. More rigorous design efforts, such as chord tapering and/or structural redesign of the blade cross section, would enlarge the feasible design space and likely provide significant performance improvement.

scholarly journals PERFORMANCE IMPROVEMENT DISTRICT BELINYU GUARD PT.TIMAH IN THE PRESENCE OF GIVING BONUS COMPENSATION

2019 ◽  
Vol 2 (2) ◽  
pp. 66-68
Author(s):  
EDWIN TRIGUNAWAN ◽  
ERWIN SUGIANTO
Keyword(s):  
Qualitative Analysis ◽  
Performance Improvement ◽  
Research Method ◽  
Security Guard

The purpose of this study is the first one to know is there an increase in the performance of the security guard with their bonuses PT.Timah compensation, the latter in order to give an idea of the performance of the security PT.Timah so as to provide input to the leading companies in making decisions regarding performance improvement and compensation in accordance with the expectations of the workers. This research method is descriptive and qualitative analysis. Resource persons from this study were drawn at random from among the guard PT.Timah, with the number of respondents as many as 10 people. The results obtained from this study is the performance of a high enough security PT.Timah although bonus compensation given by the company is not as expected the guards,

scholarly journals Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files

GigaScience ◽  
2018 ◽  
Vol 7 (6) ◽  
Author(s):  
Xiaobo Sun ◽  
Jingjing Gao ◽  
Peng Jin ◽  
Celeste Eng ◽  
Esteban G Burchard ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Performance Improvement ◽  
Significant Performance

Fiber nonlinearity compensation using optical phase conjugation in dispersion-managed coherent transmission systems

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jinqing Hao ◽  
Bingchen Han
Keyword(s):  
Performance Improvement ◽  
System Performance ◽  
Optical Transmission ◽  
Phase Conjugation ◽  
Fiber Amplifiers ◽  
Transmission Systems ◽  
Fiber Nonlinearity ◽  
Erbium Doped ◽  
Significant Performance ◽  
Transmission Link

Abstract In the discretely amplified transmission systems with erbium-doped fiber amplifiers, the system performance of nonlinearity-compensated optical transmission based on pre-dispersed spectral inversion (PSI) is investigated numerically. We find that PSI offers more significant performance improvement in dispersion-managed (DM) links than that in non-dispersion-managed (noDM) links. On the other hand, the DM link is more sensitive to the span offset from the center of the transmission link than noDM link. The performance difference between DM and noDM links is 1 dB if the span offset equals four spans in 20 × 90 km nonlinear transmission. Furthermore, we show that for the dispersion-managed transmission, in order to obtain the best system performance, the amount of pre-dispersion of the PSI, should be optimized over different dispersion maps.

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