Water level data obtained from telemetry stations typically contains large number of outliers. Anomaly detection and a data imputation are necessary steps in a data monitoring system. Anomaly data can be detected if its values lie outside of a normal pattern distribution. We developed a median-based statistical outlier detection approach using a sliding window technique. In order to fill anomalies, various interpolation techniques were considered. Our proposed framework exhibited promising results after evaluating with F1-score and root mean square error (RMSE) based on our artificially induced data points. The present system can also be easily applied to various patterns of hydrological time series with diverse choices of internal methods and fine-tuned parameters. Specifically, the Spline interpolation method yielded a superior performance on non-cyclical data while the long short-term memory (LSTM) outperformed other interpolation methods on a distinct tidal data pattern.

Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series

In application verticals that rely on mission-critical control, such as cooperative intelligent transport systems (C-ITS), 5G-Advanced networks must be able to provide dynamic positioning with accuracy down to the centimeter level.To achieve this level of precision, technology enablers, such as massive multiple-input multiple-output (mMIMO), millimeter waves (mmWave), machine learning and cooperation are of paramount importance.In this paper, we propose a cooperative deep learning (DL)-based positioning methodology that combines these key technologies into a new promising solution for precise 5G positioning.Sparse channel impulse response (CIR) data are used by the positioning infrastructure to extract position-dependent features.We model the problem as a joint task composed of nonline-of-sight (NLOS) identification and position estimation which permits to suitably handle geometrical location measurements and channel fingerprints.The network of base stations (BSs) automatically steers between egocentric (in case of NLOS) and cooperative (for LOS) positioning mode.We perform extensive standard-compliant simulations in a 5G urban micro (UMi) vehicular scenario obtained by ray-tracing and simulation of urban mobility (SUMO) software.Results show that the proposed cooperative DL architecture is able to outperform conventional geometrical positioning algorithms operating in LOS by 47%, achieving a median error of 71 cm on unseen trajectories. 

Cooperative Deep-Learning Positioning in mmWave 5G-Advanced Networks

The widespread adoption of the fifth generation (5G) of cellular networks has brought new opportunities for the development of localization-based services. High-accuracy positioning use cases and functionalities defined by the standards are drawing the interest of vertical industries. In the transition towards the deployment, this paper aims to provide an in-depth tutorial on 5G positioning, summarizing the evolutionary path that led to the standardization of cellular-based positioning, describing the localization elements in current and forthcoming releases of the Third Generation Partnership Project (3GPP) standard, and the major research trends. By providing fundamental notions on wireless localization, comprehensive definitions of measurements and architectures, examples of algorithms, and details on simulation approaches, this paper is intended to represent an exhaustive guide for researchers and practitioners. Our approach aims to merge practical aspects of enabled use cases and related requirements with theoretical methodologies and fundamental bounds, allowing to understand the trade-off between system complexity and achievable, i.e., tangible, benefits of 5G positioning services. We analyze the performance of 3GPP Rel-16 positioning by standard-compliant simulations in realistic outdoor and indoor propagation environments, investigating the impact of the system configuration and the limitations to be resolved for delivering accurate positioning solutions.

A Tutorial on 5G Positioning

Nowadays, the anomaly detection of aluminum electrolysis cell is a big problem in the aluminum electrolysis industry. The problem of unbalanced time series samples is common in industrial applications. The number of samples under normal conditions is much larger than that under abnormal conditions. In the electrolytic aluminum industry, this problem is even more serious, it is very difficult to find abnormal samples in industrial production because experts do not have a clear criterion to judge abnormalities. In traditional machine learning algorithms, such as support vector machine (SVM) and convolutional neural network (CNN), it is difficult to obtain high classification accuracy on the problem of class imbalance, and these methods tend to be more biased towards positive samples. In recent years, generative adversarial network (GAN) has become more and more popular in the field of anomaly detection. However, these methods need to find the best mapping from the actual space to the latent space in the anomaly detection stage, and the optimization process may bring new errors and take a long time. In this article, we use the ability of GAN to model complex high-dimensional image distribution, and propose a self-adaption AAE-GAN network based on adaptive changes of input samples. This time series anomaly detection method converts multi-dimensional time series data into a two-dimensional matrix, and only normal samples are needed in the training process, which effectively solves the above problems. The method we proposed is to use encoder and decoder to constitute a generator and a discriminator. During the training process, the generator and the discriminator are trained jointly and confrontationally, so that the mapping ability of the encoder can be fully reflected. In the anomaly detection stage, we determine whether the sample is abnormal according to the size of the reconstruction difference. Experimental results show that the detection accuracy and speed of this method are very high.

/pdf/self-adaption-aae-gan-for-aluminum-electrolytic-cell-anomaly-46zq94q13f.pdf

Self-Adaption AAE-GAN for Aluminum Electrolytic Cell Anomaly Detection

RADS: a real-time anomaly detection model for software-defined networks using machine learning

Fifth generation (5G) new radio (NR) signals present unique opportunities for users and devices localization due to their higher bandwidth and more antenna elements. We propose an efficient deep learning-based end-to-end algorithm for 5G or beyond 5G (B5G) positioning. Both the channel geometric parameters and complex impulse response are utilized to exploit the connection between propagation channel and environment. A new multifeature fusion network architecture is proposed. Simulation results and complexity analysis are provided to demonstrate the efficacy of the proposed algorithm.

A Deep Learning-Based End-to-End Algorithm for 5G Positioning

Images registration for an optical and synthetic aperture radar (SAR) is a key of multisensor images analysis. Also, parameters selection affects the final result of the registration algorithms for optical and SAR images. For images obtained by different sensors, how to choose an appropriate parameter for accurate registration is a key problem. In this letter, a parameter adaptive registration algorithm between optical and SAR images based on scale-invariant feature transform (SIFT) is proposed. Because of the different imaging mechanisms of these two kinds of images, the algorithm uses the multiscale Sobel operator to calculate the gradient for the optical image, while for the SAR image, a new adaptive operator based on the neighborhood pixel value is proposed to calculate the gradient. In the feature extraction, the adaptive value estimated by constant false alarm rate (CFAR) detection is used instead of the fixed threshold. Finally, a matching method constrained by image size scaling [scale-constrained fast sample consensus (FSC)] is proposed. The evaluation was designed in two aspects: feature extraction and image registration. The algorithm we proposed shows excellent performances in the aspects of repeatability, correct matching rate (CMR), and root mean square error (RMSE). The experimental results show that our method maintains the performance of the original algorithm and has some optimization and breakthrough.

Adaptive Registration for Optical and SAR Images With a Scale-Constrained Matching Method

As a major form of data, the collection and analysis of time series have been widely used in many fields. In practice, wireless sensor network is a popular mechanism for data collection and it is suitable for time series. However, due to the diversity deployment area for a wireless sensor network, the external environmental factors pose a great challenge on the quality of data collected by a sensor node. Besides, the accuracy and reliability of collected data are also affected by internal factors of a sensor node and the performance of the whole wireless sensor network. Common internal factors are resource limitation such as battery capacity, computing ability, and memory size. Moreover, a wireless sensor network deployed for production use is very likely to suffer from malicious attacks from both inside and outside. Therefore, anomaly is inevitable for time series collected by a wireless sensor network. In the paper, we make an extensive review about two types of anomaly detection methods in time series for wireless sensor networks: outlier detection and pattern anomaly detection. For outlier detection, six categories of detection methods are elaborated. For pattern anomaly detection, two main ideas are discussed. Finally, we present the main drawbacks of existing approaches to outlier detection and pattern anomaly detection for time series. The possible future research directions are also summarized.

Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks

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A Deep Learning-Based End-to-End Algorithm for 5G Positioning

Adaptive Registration for Optical and SAR Images With a Scale-Constrained Matching Method

Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks