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Incentive-driven advanced attacks have become a major concern to cyber-security. Traditional defense techniques that adopt a passive and static approach by assuming a fixed attack type are insufficient in the face of highly adaptive and stealthy attacks. In particular, a passive defense approach often creates information asymmetry where the attacker knows more about the defender. To this end, moving...
The paper analyzes the existing methods and approaches to correcting bases of fuzzy rules of decision support systems (DSS). There was developed and implemented a two-stage method of fuzzy rule base correction for hierarchically-organized DSS with discrete logic output at variable structure of input vector. The appropriate method interactively provides reduction of database rules structure and correction...
Increasingly, trust has played a crucial role in the security of an IoT system from its inception to the end of its lifecycle. A device has to earn some level of trust even before it is authenticated for admission to the system. Furthermore, once the device is admitted to the system, it may behave maliciously over time; hence its behavior must be evaluated constantly in the form of trust to ensure...
The paper considers the problem of decision support systems constructing for solving the problems of modeling and estimating selected types of risks with the possibility for application of alternative data processing techniques, modeling and estimation of parameters and states for the processes under study. The system proposed has a modular architecture that provides a possibility for easy extension...
This paper explores the problem of path planning under uncertainty. Specifically, we consider online receding horizon based planners that need to operate in a latent environment where the latent information can be modelled via Gaussian Processes. Online path planning in latent environments is challenging since the robot needs to explore the environment to get a more accurate model of latent information...
Iterative learning control (ILC) has evolved with the time after Arimoto's first paper, ILC was designed to cope the problems associated with the processes which involve repetitions. ILC involves dynamics along two dimensions i.e., iteration domain and time domain, which makes it different from other control techniques. In this paper, a comprehensive insight is given for the evolution of different...
Fault diagnosis methods ensure safe operation of industrial plants. Steadily increasing appearance of larger and interconnected systems and the necessity to take process uncertainties into account drives the need for reliable diagnosis procedures. Set-based frameworks for model-based fault diagnosis allow to handle these challenges, albeit at a high cost of computations. We propose a method to reduce...
Soft sensors are used to infer the quality variable from easy-to-measure process variables. The conventional static soft sensor is incapable of handling the dynamic of processes. For data-based soft sensor development, with abundance of the raw sensor data, the problem of variable correlations and large number of sample are encountered. This work presents a latent variable model (LVM) based active...
This work addresses the problem of motion planning among obstacles for quadrotor platforms under external disturbances and with model uncertainty. A novel Nonlinear Model Predictive Control (NMPC) optimization technique is proposed which incorporates specified uncertainties into the planned trajectories. At the core of the procedure lies the propagation of model parameter uncertainty and initial state...
Recognition of sequential human activities, such as “sitting down” and “standing up”, is a common but challenging problem in human-robot interaction, which requires modeling their underlying temporal patterns. Although previous sequence modeling methods, such as Hidden Conditional Random Fields (HCRFs), demonstrated satisfactory recognition accuracy, they do not explicitly model the uncertainty in...
In fast changing assembly scenarios, it is required to adapt the task execution to the current state of the setup without extensive calibration routines. Therefore, it is important to estimate the geometric uncertainties and contact states during the assembly execution. We use a sequential Monte Carlo (SMC) method to track the relative poses between workpieces during a robotic assembly based on joint...
The exoskeleton robots because of its potential applications in rehabilitation engineering, assistive robotics, and power augmentation are getting more attention in the field of robotics. Besides kinematics model and dynamic model, three type controllers are applied in position control mode, PID control, computed torque control and time delay control, respectively. For the sake of the difficulty in...
The performance of optical filters with resonant waveguide gratings is predicted numerically, assuming random fluctuations of various design variables. Specifically, we derive stochastic models based on polynomial-chaos expansions (PCEs), by employing a stochastic collocation (SC) approach that exploits the rigorous coupled-wave analysis (RCWA) deterministic solver. The statistical moments of the...
A biologically-inspired intelligent controller based on a computational model of emotional learning in mammal's brain is employed for flocking control of Multi-Agent Systems (MAS). The methodology, known as Brain Emotional Learning Based Intelligent Controller (BELBIC), is implemented in this application for the first time, enhancing the flocking strategy with multi-objective properties. The learning...
In the paper, we present a software toolbox for rigorous analysis and design of nonlinear continuous and discrete-continuous (digital) control systems based on the reduction method and sublinear vector Lyapunov functions. For these systems, the toolbox provides solution of the following problems: verification of dynamic properties of dissipativity, asymptotic and practical stability; computation of...
Among the modern requirements for complex object control simulators is to provide the properties of adaptability, flexibility, extensibility, reliability, security and reconfigurability. The use of semantic description as one of the ways to solve the problem of increasing adaptability and efficiency (productivity, reliability, security, reconfigurability) in the development of simulators with human-machine...
Microstructural information plays a key role in governing the dominant physics for various applications involving fracture networks. Resolving the interactions of thousands of interconnected sub-micron scale fractures is computationally intensive, and is intractable with current technologies. Coarsening of the domain and simplification of the physics are two commonly used workarounds, but these methods...
We present a convex model describing risk-averse strategies for electricity producers in congested electricity networks. Extending prior work on Cournot-Bertrand equilibria in Poolco-style spot markets with locational marginal pricing, we propose a formulation which integrates uncertainty through robust convex optimization. We find that producers uniformly benefit from robust strategies under small...
High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on...
Uncertain data visualization plays a fundamental role in many applications such as weather forecast and analysis of fluid flows. Exploring scalar uncertain data modeled as probability distribution fields is a challenging task because the underlying features are often more complex, and the data associated with each grid point are high dimensional. In this work, we present a compact and effective representation,...
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