Síntesis de interpolación de los controladores para un sistema de accionamiento eléctrico multimotor que contiene un elemento enlazado elásticamente

Partial differential equations, integral, differential, or other equations describe multi-motor automatic electric drive systems containing elastic conveyor belts. Because of the elastic and distributive nature of the system parameters, the transfer function describing them is often a complex expression, containing not only the arguments as a linear system but also the inertial and transcendental components. This makes the precise control of tension and speed synchronously much more complicated than the centralized parameter system. A promising numerical solution based on the real interpolation method will simplify the procedure for synthesizing control loops while preserving the characteristic properties of objects with distributed parameters. The objective of the study is to propose a feasible solution for synthesizing the regulators based on the real interpolation method; it allows direct operation with the original transfer function containing the inertial and transcendental components. In this paper, we proposed an approach to synthesize the control system for objects with distributed parameters using the real interpolation method to reduce computational capacity and synthesis error while preserving the properties of this object class. Building an experimental model of the two-motor electric drive system containing an elastic conveyor to verify the effectiveness of the proposed algorithm. The simulation and experimental results indicate that the control system with the received regulators operating stably and meets the required quality criteria. It proves the efficiency of the synthesis algorithm based on the real interpolation method.Introducción: los sistemas de accionamiento eléctrico multimotor que incluyen transportadores elásticos son un ejemplo de sistemas típicos con parámetros distribuidos descritos por ecuaciones complejas. Debido a la naturaleza elástica y distributiva de los parámetros del sistema, la función de transferencia que los describe suele ser una expresión compleja que contiene los componentes inercial y trascendental. Problema: la naturaleza elástica y distributiva de los parámetros del sistema hace que el control preciso de la tensión y la velocidad sincrónicamente sea mucho más complicado que el sistema de parámetros centralizados. Metodología: se propone una solución numérica para sintetizar los reguladores basada en el método de interpolación real para reducir la capacidad computacional y el error de síntesis preservando las propiedades características de los objetos con parámetros distribuidos. Conclusión: la eficacia del algoritmo propuesto se verifica mediante un modelo experimental del sistema de accionamiento eléctrico de dos motores que contiene un transportador elástico. Los resultados de simulación y experimentales indican que el sistema de control con los reguladores recibidos opera de manera estable y cumple con los criterios de calidad requeridos. Originalidad: los resultados de la investigación se pueden aplicar en el desarrollo de sistemas centrales de control y monitoreo para líneas de producción automáticas con sistemas de accionamiento multimotor que incluyen transportadores

Consolidation of a WSN and Minimax Method to Rapidly Neutralise Intruders in Strategic Installations

Due to the sensitive international situation caused by still-recent terrorist attacks, there is a common need to protect the safety of large spaces such as government buildings, airports and power stations. To address this problem, developments in several research fields, such as video and cognitive audio, decision support systems, human interface, computer architecture, communications networks and communications security, should be integrated with the goal of achieving advanced security systems capable of checking all of the specified requirements and spanning the gap that presently exists in the current market. This paper describes the implementation of a decision system for crisis management in infrastructural building security. Specifically, it describes the implementation of a decision system in the management of building intrusions. The positions of the unidentified persons are reported with the help of a Wireless Sensor Network (WSN). The goal is to achieve an intelligent system capable of making the best decision in real time in order to quickly neutralise one or more intruders who threaten strategic installations. It is assumed that the intruders’ behaviour is inferred through sequences of sensors’ activations and their fusion. This article presents a general approach to selecting the optimum operation from the available neutralisation strategies based on a Minimax algorithm. The distances among different scenario elements will be used to measure the risk of the scene, so a path planning technique will be integrated in order to attain a good performance. Different actions to be executed over the elements of the scene such as moving a guard, blocking a door or turning on an alarm will be used to neutralise the crisis. This set of actions executed to stop the crisis is known as the neutralisation strategy. Finally, the system has been tested in simulations of real situations, and the results have been evaluated according to the final state of the intruders. In 86.5% of the cases, the system achieved the capture of the intruders, and in 59.25% of the cases, they were intercepted before they reached their objective

Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications

Networked control systems for intelligent transportation systems and industrial automation

This thesis presents a study of two different applications of Networked Control Systems. The first is: Ethernet Networked Control System On-board of Train-wagons. An Ethernet backbone is shared between control and entertainment. The wagon contains a dedicated control server and a dedicated entertainment server, which act as fault-tolerant machines for one another. In the event of a server failure, the remaining machine can serve both entertainment and/or control. The study aims at enhancing system design in order to maximize the tolerable entertainment load in the event of a control/entertainment server failure, while not causing any control violations. This fault-tolerant system is mathematically analyzed using a performability model to relate failure rates, enhancements and rewards. The model is taken further to test two identical wagons, with a total of four fault-tolerant servers. All possible failure sequences are simulated and a different communication philosophy is tested to further minimize the degradation of the entertainment load supported during the failure of up to three of the four servers. The system is shown to be capable of operating with minimal degradation with one out of four servers. The second is: Wireless Networked Control Systems (WNCS) for Industrial Automation. A WNCS using standard 802.11 and 802.3 protocols for communication is presented. Wireless Interface for Sensors and Actuators (WISA) by ABB is used as a benchmark for comparison. The basic unit is a single workcell, however, there is a need to cascade several cells along a production line. Simulations are conducted and a nontraditional allocation scheme is used to ensure correct operation under the effect of co-channel interference and network congestion. Next, fault-tolerance at the controller level is investigated due to the importance of minimizing downtime resulting from controller failure. Two different techniques of interconnecting neighboring cells are investigated. The study models both a two and three-cell scenario, and all systems show that fault-tolerance is achievable. This is mathematically studied using a performability analysis to relate failure rates with rewards at each failure state. All simulations are conducted on OPNET Network Modeler and results are subjected to a 95% confidence analysis

Intelligent Novel Methods for Identifying Critical Components and Their Combinations for Hypothesized Cyber-physical Attacks Against Electric Power Grids

As a revolutionary change to the traditional power grid, the smart grid is expected to introduce a myriad of noteworthy benefits by integrating the advanced information and communication technologies in terms of system costs, reliability, environmental impacts, operational flexibility, etc. However, the wider deployment of cyber networks in the power grid will bring about important issues on power system cyber security. Meanwhile, the power grid is becoming more vulnerable to various physical attacks due to vandalism and probable terrorist attacks. In an envisioned smart grid environment, attackers have more entry points to various parts of the power grid for launching a well-planned and highly destructive attack in a coordinated manner. Thus, it is important to address the smart grid cyber-physical security issues in order to strengthen the robustness and resiliency of the smart grid in the face of various adverse events. One key step of this research topic is to efficiently identify the vulnerable parts of the smart grid. In this thesis, from the perspective of smart grid cyber-physical security, three critical component combination identification methods are proposed to reveal the potential vulnerability of the smart grid. First, two performance indices based critical component combination recognition methods are proposed for more effectively identifying the critical component combinations in the multi-component attack scenarios. The optimal selection of critical components is determined according to the criticality of the components, which can be modeled by various performance indices. Further, the space-pruning based enumerative search strategy is investigated to comprehensively and effectively identify critical combinations of multiple same or different types of components. The pruned search space is generated based on the criticality of potential target component which is obtained from low-order enumeration data. Specifically, the combinatorial line-generator attack strategy is investigated by exploring the strategy for attacking multiple different types of components. Finally, an effective, novel approach is proposed for identifying critical component combinations, which is termed search space conversion and reduction strategy based intelligent search method (SCRIS). The conversion and reduction of the search space is achieved based on the criticality of the components which is obtained from an efficient sampling method. The classic intelligent search algorithm, Particle Swarm Optimization (PSO), is improved and deployed for more effectively identifying critical component combinations. MATLAB is used as the simulation platform in this study. The IEEE 30, 39, 118 and Polish 2383-bus systems are adopted for verifying the effectiveness of the proposed attack strategies. According to the simulation results, the proposed attack strategies turn out to be effective and computationally efficient. This thesis can provide some useful insight into vulnerability identification in a smart grid environment, and defensive strategies can be developed in view of this work to prevent malicious coordinated multi-component attacks which may initiate cascading failures in a cyber-physical environment

An efficient approach to online bot detection based on a reinforcement learning technique

In recent years, Botnets have been adopted as a popular method used to carry and spread many malicious codes on the Internet. These codes pave the way to conducting many fraudulent activities, including spam mail, distributed denial of service attacks (DDoS) and click fraud. While many Botnets are set up using a centralized communication architecture such as Internet Relay Chat (IRC) and Hypertext Transfer Protocol (HTTP), peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control (C&C) messages, which is a more resilient and robust communication channel infrastructure. Without a centralized point for C&C servers, P2P Botnets are more flexible to defeat countermeasures and detection procedures than traditional centralized Botnets. Several Botnet detection techniques have been proposed, but Botnet detection is still a very challenging task for the Internet security community because Botnets execute attacks stealthily in the dramatically growing volumes of network traffic. However, current Botnet detection schemes face significant problem of efficiency and adaptability. The present study combined a traffic reduction approach with reinforcement learning (RL) method in order to create an online Bot detection system. The proposed framework adopts the idea of RL to improve the system dynamically over time. In addition, the traffic reduction method is used to set up a lightweight and fast online detection method. Moreover, a host feature based on traffic at the connection-level was designed, which can identify Bot host behaviour. Therefore, the proposed technique can potentially be applied to any encrypted network traffic since it depends only on the information obtained from packets header. Therefore, it does not require Deep Packet Inspection (DPI) and cannot be confused with payload encryption techniques. The network traffic reduction technique reduces packets input to the detection system, but the proposed solution achieves good a detection rate of 98.3% as well as a low false positive rate (FPR) of 0.012% in the online evaluation. Comparison with other techniques on the same dataset shows that our strategy outperforms existing methods. The proposed solution was evaluated and tested using real network traffic datasets to increase the validity of the solution

Quantitative analysis of the release order of defensive mechanisms

PhD ThesisDependency on information technology (IT) and computer and information security (CIS) has become a critical concern for many organizations. This concern has essentially centred on protecting secrecy, confidentiality, integrity and availability of information. To overcome this concern, defensive mechanisms, which encompass a variety of services and protections, have been proposed to protect system resources from misuse. Most of these defensive mechanisms, such as CAPTCHAs and spam filters, rely in the first instance on a single algorithm as a defensive mechanism. Attackers would eventually break each mechanism. So, each algorithm would ultimately become useless and the system no longer protected. Although this broken algorithm will be replaced by a new algorithm, no one shed light on a set of algorithms as a defensive mechanism. This thesis looks at a set of algorithms as a holistic defensive mechanism. Our hypothesis is that the order in which a set of defensive algorithms is released has a significant impact on the time taken by attackers to break the combined set of algorithms. The rationale behind this hypothesis is that attackers learn from their attempts, and that the release schedule of defensive mechanisms can be adjusted so as to impair the learning process. To demonstrate the correctness of our hypothesis, an experimental study involving forty participants was conducted to evaluate the effect of algorithms’ order on the time taken to break them. In addition, this experiment explores how the learning process of attackers could be observed. The results showed that the order in which algorithms are released has a statistically significant impact on the time attackers take to break all algorithms. Based on these results, a model has been constructed using Stochastic Petri Nets, which facilitate theoretical analysis of the release order of a set of algorithms approach. Moreover, a tailored optimization algorithm is proposed using a Markov Decision Process model in order to obtain efficiently the optimal release strategy for any given model by maximizing the time taken to break a set of algorithms. As our hypothesis is based on the learning acquisition ability of attackers while interacting with the system, the Attacker Learning Curve (ALC) concept is developed. Based on empirical results of the ALC, an attack strategy detection approach is introduced and evaluated, which has achieved a detection success rate higher than 70%. The empirical findings in this detection approach provide a new understanding of not only how to detect the attack strategy used, but also how to track the attack strategy through the probabilities of classifying results that may provide an advantage for optimising the release order of defensive mechanisms