Digital twins are virtual representations of real-world objects, which offer a range of possibilities and potential benefits, especially in the case of industry, where such information can be used in a variety of ways. Simulation, optimization, or maintenance are among the most significant. In this study, we focus on an infrastructure maintenance model that may include a deep neural network model for detecting defects on metal surfaces, which are ubiquitous in the industry. Since digital twins must process data in real time and are often equipped with many different modules, it is important to prepare appropriately lightweight architectures. For this purpose, we present a lightweight neural network for fault detection, which is tested on the special case of welding faults. In our model, we used a bilateral architecture, equipping it with an attention module, thanks to which we managed to improve the performance of the bilateral network while maintaining a small number of parameters.

In this article we present a model of adjustable moisture control for historical buildings. Proposed system is developed in a form of flexible IoT infrastructure in which a complex system of sensors is set to measure inside conditions of humidity and compare result to levels of groundwaters, rain and wind speed to manage drying system. Developed control model is using type-2 fuzzy logic reasoning to flexibly adjust decisions to the intensity of water absorption. In this way proposed model makes an innovative intelligent system for control of interior conditions in historical buildings. Developed system was installed and examined in an old brewery building. Research results show efficiency in dehumidification at lowest cost.

Based on sensor networks within a dynamic and real-time environment, a novel parking-lot navigation method is proposed based on diffusion equation and Poisson equation to achieve convenient and efficient navigation process with edge computing mind to aid information query and navigation. From the perspective of theoretical proof, is presented parallel method mainly for ordinary differential equations (ODEs) by partitioning the time domain. In this article, our new method is combined by parallelization for linear heat equations. The model problem is decoupled into several sub-problems in space-time sub-domains. We prove the super-linear convergence when the time interval is bounded. Numerical experiments testify our theoretical analysis. Simultaneously, the proposed method holds the lower constraint condition and some improper navigation routes can be updated. Mathematical analysis and simulations show that the method is accurately and efficiently enabled to solve typical sensor network configuration information navigation problem.

Advanced models of Artificial Intelligence enable systems of IoT to work with great flexibility to the needs of users. In this article we present our developed IoT system for driving support by the use of type-2 fuzzy logic control module. We have developed the IoT system to collect the data about driving conditions and evaluate them adjusting to the needs of user. Applied module of fuzzy logic of the second type was used in analysis of accelerometers signals to flexibly adjust to uncertainty of evaluation of driving expectations of each driver. Our developed system was tested in different cars by driving on various roads and results show excellent efficiency.

Intelligent Manufacturing 5.0 of multipulse rectifier systems requires them to be optimized for a variety of use in transportation and factories producing hearty touch technology. The research presented in this article show advances of using heuristic models to set 12-pulse and 24-pulse rectifiers to work under low- and high-voltage load. As a result of heuristic optimization electric systems increase efficiency and reduce energy consumption by efficiency benefits in adopting artificial intelligence. Applied heuristic models helped in computer simulations to optimize system settings in a short time. Results show that optimized models are more efficient and our proposed approach is reducing voltage pulsation. As a result optimized system improves electromagnetic compatibility for beneficial use in modern industry and sensible human–machine cooperation.

In this article, Polar Bear Optimization Algorithm (PBO) is parallelized to solve the problem of computed tomography (CT) with incomplete data. It is vary hard to model correctly 2D and 3D objects by using CT scanners when information is incomplete. Our approach is to use PBO to reduce recovery time and simplify specificity of the phenomenon. Results from our research show that proposed approach is enabling fast and accurate reconstruction of objects modeled in projection space.

The paper proposes a model of regulation for transformer secondary voltages in order to improve quality of rectified voltage. For this purpose, a simulation model for an 18-pulse rectifier system was presented. We have defined optimization task by using proposed parallel version of the Polar Bear Optimization (PBO) algorithm. For the developed method simulation results are presented and discussed. Research show that proposed parallelization enabled efficient optimization of the large number of parameters and, at the same time, shortens simulation time.

Technological development increases capacity of information systems, which with development of faster data transfer will be able to host variety of new devices. In this article we present electronic modules, infrastructure and fuzzy rules control model with implemented software for new generation home environment. The system is developed for the next IoT level based on 6G network communication standards. Proposed control model is efficient in water flow management, wind shield control, security aspects and carbon dioxide limitation via adaptive ventilation. Developed infrastructure is ready for new 6G communication standard, which will additionally improve efficiency and data flow at end-user devices and local area level.

The fusion of Internet of Things (IoTs) and computational intelligence makes it possible to increase energetic efficiency of our homes. Connected devices can be optimally adjusted to the needs of a family. In this article, we present our developed IoT convection installation for a small house with the developed remote platform control system. The control module is gartering readings from sensors and information from users about conditions in the house and, by the use of computational intelligence, optimizes parameters to adjust the developed IoT convection system for better comfort of a family. We have done a full convection installation, both in practical and theoretical models, together with remote control system and the proposed security model. Optimization results show increased comfort of use with lower changes in the temperature inside. The system after optimization shows significant improvement in lower changes of the temperature and lower consumption.