Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1

Anomaly detection in ICS datasets with machine learning algorithms

An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed. The machine learning algorithms have been performed with labeled output for prediction classification. The activity traffic between ICS components is analyzed and packet inspection of the dataset is performed for the ICS network. The features of flow-based network traffic are extracted for behavior analysis with port-wise profiling based on the data baseline, and anomaly detection classification and prediction using machine learning algorithms are performed

Expansion of perturbation theory applied to shim rotation automation of the Advanced Test Reactor

textIn 2007, the Department of Energy (DOE) declared the Advanced Test Reactor (ATR) a National Scientific User Facility (NSUF). This declaration expanded the focus of the ATR to include diversified classes of academic and industrial experiments. An essential part of the new suite of more accurate and flexible codes being deployed to support the NSUF is their ability to predict reactor behavior at startup, particularly the position of the outer shim control cylinders (OSCC). The current method used for calculating the OSCC positions during a cycle startup utilizes a heuristic trial and error approach that is impractical with the computationally intensive reactor physics tools, such as NEWT. It is therefore desirable that shim rotation prediction for startup be automated. Shim rotation prediction with perturbation theory was chosen to be investigated as one method for use with startup calculation automation. A modified form of first order perturbation theory, called phase space interpolated perturbation theory, was developed to more accurately model shim rotation prediction. Shim rotation prediction is just one application for this new modified form of perturbation theory. Phase space interpolated perturbation theory can be used on any application where the range of change to the system is known a priori, but the magnitude of change is not known. A cubic regression method was also developed to automate shim rotation prediction by using only forward solutions to the transport equation.Mechanical Engineerin

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

Thin-wall parts are common in the aeronautical sector. However, their machining presents serious challenges such as vibrations and part deflections. To deal with these challenges, di erent approaches have been followed in recent years. This work presents the state of the art of thin-wall light-alloy machining, analyzing the problems related to each type of thin-wall parts, exposing the causes of both instability and deformation through analytical models, summarizing the computational techniques used, and presenting the solutions proposed by di erent authors from an industrial point of view. Finally, some further research lines are proposed

Eco-efficient process based on conventional machining as an alternative technology to chemical milling of aeronautical metal skin panels

El fresado químico es un proceso diseñado para la reducción de peso de pieles metálicas que, a pesar de los problemas medioambientales asociados, se utiliza en la industria aeronáutica desde los años 50. Entre sus ventajas figuran el cumplimiento de las estrictas tolerancias de diseño de piezas aeroespaciales y que pese a ser un proceso de mecanizado, no induce tensiones residuales. Sin embargo, el fresado químico es una tecnología contaminante y costosa que tiende a ser sustituida. Gracias a los avances realizados en el mecanizado, la tecnología de fresado convencional permite alcanzar las tolerancias requeridas siempre y cuando se consigan evitar las vibraciones y la flexión de la pieza, ambas relacionadas con los parámetros del proceso y con los sistemas de utillaje empleados. Esta tesis analiza las causas de la inestabilidad del corte y la deformación de las piezas a través de una revisión bibliográfica que cubre los modelos analíticos, las técnicas computacionales y las soluciones industriales en estudio actualmente. En ella, se aprecia cómo los modelos analíticos y las soluciones computacionales y de simulación se centran principalmente en la predicción off-line de vibraciones y de posibles flexiones de la pieza. Sin embargo, un enfoque más industrial ha llevado al diseño de sistemas de fijación, utillajes, amortiguadores basados en actuadores, sistemas de rigidez y controles adaptativos apoyados en simulaciones o en la selección estadística de parámetros. Además se han desarrollado distintas soluciones CAM basadas en la aplicación de gemelos virtuales. En la revisión bibliográfica se han encontrado pocos documentos relativos a pieles y suelos delgados por lo que se ha estudiado experimentalmente el efecto de los parámetros de corte en su mecanizado. Este conjunto de experimentos ha demostrado que, pese a usar un sistema que aseguraba la rigidez de la pieza, las pieles se comportaban de forma diferente a un sólido rígido en términos de fuerzas de mecanizado cuando se utilizaban velocidades de corte cercanas a la alta velocidad. También se ha verificado que todas las muestras mecanizadas entraban dentro de tolerancia en cuanto a la rugosidad de la pieza. Paralelamente, se ha comprobado que la correcta selección de parámetros de mecanizado puede reducir las fuerzas de corte y las tolerancias del proceso hasta un 20% y un 40%, respectivamente. Estos datos pueden tener aplicación industrial en la simplificación de los sistemas de amarre o en el incremento de la eficiencia del proceso. Este proceso también puede mejorarse incrementando la vida de la herramienta al utilizar fluidos de corte. Una correcta lubricación puede reducir la temperatura del proceso y las tensiones residuales inducidas a la pieza. Con este objetivo, se han desarrollado diferentes lubricantes, basados en el uso de líquidos iónicos (IL) y se han comparado con el comportamiento tribológico del par de contacto en seco y con una taladrina comercial. Los resultados obtenidos utilizando 1 wt% de los líquidos iónicos en un tribómetro tipo pin-on-disk demuestran que el IL no halogenado reduce significativamente el desgaste y la fricción entre el aluminio, material a mecanizar, y el carburo de tungsteno, material de la herramienta, eliminando casi toda la adhesión del aluminio sobre el pin, lo que puede incrementar considerablemente la vida de la herramienta.Chemical milling is a process designed to reduce the weight of metals skin panels. This process has been used since 1950s in the aerospace industry despite its environmental concern. Among its advantages, chemical milling does not induce residual stress and parts meet the required tolerances. However, this process is a pollutant and costly technology. Thanks to the last advances in conventional milling, machining processes can achieve similar quality results meanwhile vibration and part deflection are avoided. Both problems are usually related to the cutting parameters and the workholding. This thesis analyses the causes of the cutting instability and part deformation through a literature review that covers analytical models, computational techniques and industrial solutions. Analytics and computational solutions are mainly focused on chatter and deflection prediction and industrial approaches are focused on the design of workholdings, fixtures, damping actuators, stiffening devices, adaptive control systems based on simulations and the statistical parameters selection, and CAM solutions combined with the use of virtual twins applications. In this literature review, few research works about thin-plates and thin-floors is found so the effect of the cutting parameters is also studied experimentally. These experiments confirm that even using rigid workholdings, the behavior of the part is different to a rigid body at high speed machining. On the one hand, roughness values meet the required tolerances under every set of the tested parameters. On the other hand, a proper parameter selection reduces the cutting forces and process tolerances by up to 20% and 40%, respectively. This fact can be industrially used to simplify workholding and increase the machine efficiency. Another way to improve the process efficiency is to increase tool life by using cutting fluids. Their use can also decrease the temperature of the process and the induced stresses. For this purpose, different water-based lubricants containing three types of Ionic Liquids (IL) are compared to dry and commercial cutting fluid conditions by studying their tribological behavior. Pin on disk tests prove that just 1wt% of one of the halogen-free ILs significantly reduces wear and friction between both materials, aluminum and tungsten carbide. In fact, no wear scar is noticed on the ball when one of the ILs is used, which, therefore, could considerably increase tool life