Coupling of Inference and Access Controls to Ensure Privacy Protection

This article is devoted to the topic of coupling access and inference controls into security policies. The coupling of these two mechanisms is necessary to strengthen the protection of the privacy of complex systems users. Although the PrivOrBAC access control model covers several privacy protection requirements, the risk of inferring sensitive data may exist. Indeed, the accumulation of several pieces of data to which access is authorized can create an inference. This work proposes an inference control mechanism implemented through multidimensional analysis. This analysis will take into account several elements such as the history of access to the data that may create an inference, as well as their influence on the inference. The idea is that this mechanism delivers metrics that reflect the level of risk. These measures will be considered in the access control rules and will participate in the refusal or authorization decision with or without obligation. This is how the coupling of access and inference controls will be applied. The implementation of this coupling will be done via the multidimensional OLAP databases which will be requested by the Policy Information Point, the gateway brick of XACML to the various external data sources, which will route the inference measurements to the decision-making point.

Advanced soft robot modeling in ChainQueen

We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

Multilevel Security Network Communication Model Based on Multidimensional Control

To solve the problems associated with the application of multilevel security to actual networks, such as flexibility, availability, security, and secure communication, this study proposes a multilevel security network communication model based on multidimensional control. In the model, access control is retained on the basis of security labels. In addition, relational restraints among protection domains, credibility degree restraints of subjects on security attributes, aggregation inference control restraints, and secure tunnel control restraints are introduced and applied. Thus, secure information exchange within a multilevel security network information system is ensured. Moreover, using this model, multilevel security virtual networks with logical and independent characteristics can be built to accomplish secure interconnection and communication between nonequivalent members, thereby reducing the probability of information leakage. Finally, the security of the model is confirmed by applying the nontransitive, noninterference theory, and the typical application of the model in actual networks is described.

Performance enhancement of grid-interfaced inverter using intelligent controller

The high-performance grid-interfaced inverters are in demand as they are rapidly used in renewable energy systems. The main objective of grid-interfaced inverters is to inject high-quality active and reactive power with sinusoidal current. Many control schemes have been proposed earlier in the literature, but the operation under parametric uncertainties has not been given much attention. In this article, an adaptive network–based fuzzy inference control algorithm for a three-phase grid-interfaced inverter under parametric uncertainties is proposed. The main purpose of the proposed technique is to enhance the response time, decrease the steady-state oscillation in the injected active and reactive power and enhance the power quality even with parametric uncertainties. For assessment and evaluation reason, the conventional proportional–integral control is compared with the proposed controller. For a fair comparison, the gain setting for the proportional–integral control is obtained by Particle swarm optimization algorithm. The suggested system is developed and simulated in MATLAB/Simulink. Simulation results demonstrate that both the controllers work well to regulate the powers to required values, even with parametric variations. However, the proposed control demonstrates superiority in comparison to conventional proportional–integral control in terms of speedy response, decreased steady-state fluctuations, better power quality and increased robustness. The rise time and fluctuations in the per-unit active and reactive power are much less with the proposed control. Total harmonic distortion of the injected current and grid current are significantly better than the conventional proportional–integral control.

New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes

The accident rate in the EU-28 region of the European Union showed a value of 2 fatal accidents per 100,000 people in 2019 that mainly affect construction (24%), manufacturing (19%) and logistics (19 %). To manage situations that affect occupational risk at work, a review of existing tools is first carried out taking into account three prevention, simultaneity and immediacy characteristics. As a result, a new dynamic methodology called Statistical Risk Control (SRC) based on Bayesian inference, control charts and analysis of the hidden Markov chain is presented. The objective is to detect a situation outside the limits early enough to allow corrective actions to reduce the risk before an accident occurs. A case is developed in a medium-density fiberboard (MDF) manufacturing plant, in which five inference models based on Poisson, exponential and Weibull distributions and risk parameters following gamma and normal distributions have been tested. The results show that the methodology offers all three characteristics, together with a better understanding of the evolution of the operators in the plant and the safety barriers in the scenario under study.