Thermal Image Generation for Robust Face Recognition

This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.

Service Interface Translation. An Interoperability Approach

Interoperability plays an important role in Industry 4.0. Interoperability in the engineering process allows the automation of the engineering phase, reducing the human effort involved and the associated engineering costs. It improves the quality of the engineering process and its overall efficiency. Nevertheless, the diversity of available standards, devices, and systems leads to great levels of heterogeneity and makes it difficult to achieve the aforementioned interoperability. As the lack of interoperability increases, a generic solution to the problem is increasingly demanded by the industry. This paper approaches the interoperability problem from a service interface perspective. A novel approach is presented to address service interface heterogeneity. The proposed solution is based on service interface translation, which is achieved via the generation of service interfaces. A new system, the consumer interface generator system, has been designed and implemented to generate interface instances to solve the interoperability mismatches between service consumers and providers at runtime. In this paper, the autonomous consumer interface generation process, the system architecture, and the generated interface instance are described. The proposed approach has been validated through practical experimentation, including the implementation of a system prototype and a testbed.

Dynamic Reliability Evaluation of Diesel Generator System of One Chinese 1000MWe NPP Considering Temporal Failure Effects

Loss of power supply from the diesel generator system (DGS) after loss of offsite power (LOOP) will pose great threat to the safety of GEN-II pressurized water reactors (PWR). Therefore, it is very desirable to evaluate the DGS’s reliability. The traditional analyzing tools are limited to static approaches neglecting the dynamic sequence failure behaviors, such as reliability block diagram (RBD), static fault tree (SFT). Static reliability modeling techniques are not capable of capturing the dynamic sequence-dependent failure behaviors typically existing in NPP safety systems such as DGS, and thus often overestimate the unreliability of systems. In this paper, motivated to study the effects of sequence failure behaviors, dynamic fault tree (DFT) is applied to evaluate the reliability of the DGS of one Chinese 1000MWe Nuclear Power Plant (NPP), and an integrated two-phased Markov Chain model is also developed, which can be considered as a contribution of this article. Comparative study of DGS reliability between DFT and SFT is carried out. The results indicate that compared with the result derived from the DFT model, the unreliability of DGS calculated by SFT is greatly overestimated by about one to two orders of magnitude. Therefore, DFT has a potential to improve the economy of NPP by relaxing the overestimated unreliability of nuclear power systems.

Turbine-generator system with a load state observer

Objective: To generate a mathematical model of a gas turbine engine state observer for a GT1h series gas turbine locomotive. Methods: Calculations and modeling of processes were performed using software packages for mathematical modeling of complex electromechanical systems with implementation in Matlab, while data processing and graph plotting were performed using Microsoft Excel. Results: It has been shown that the use of the mathematical model of the state observer in the automatic control system allows providing conditions for the formation of an optimal power load trajectory of the Gas Turbine Engine — Traction Generator system when regulating the power, taking into account the limitations associated with the physical processes occurring in the gas turbine engine. Practical importance: The use of the mathematical model of the state observer makes it possible to generate rational gas turbine load trajectories in the entire range of its use.