The use of thermoelements is a commonly applied method in industry and engineering. It provides a wide measurement range of temperature, a direct voltage signal from the transducer, low cost of the thermoelement, and its resistance to many unfavorable factors which occur in an industrial environment. Unfortunately, thermoelements may not be resistant to interferences of a strong electromagnetic field because of the nature and design of a transducer. Induction heating is the most commonly used type of heating, at present, for metals. In order to guarantee the correctness of the carried out heating process, it is essential to control the temperature of the heated element. The impact of a strong electromagnetic field upon the thermocouple temperature measurement of the inductively heated elements has been analyzed in this paper. The experiment includes dozens of measurements where the following parameters have been varied: frequency of the current which feeds the heating inductor, power supplied to the heating system, geometry of heat inductor, and the charge material and its geometrical dimensions. Interferences of the power-line frequency have been eliminated in part of the carried out measurements.

Next-generation cloud radio access networks (C-RANs) are anticipated to provide multi-Gbps data rate transmission and ultra-high bandwidth capacity, which is one of the key performance indicators for future mobile networks. The integral layout of fiber optics and radio network manages the capabilities of the C-RAN, but needs to be optimized in terms of cost, reliability and further scalibility. For C-RAN architectures, Radio over Fiber (RoF) transport-based fronthaul is a promising candidate but the associated issues of distortions due to nonlinear impairments (NLIs) from power amplifier, linear distortions (LDs) due to modulating lasers and high peak to average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals need to be addressed. This work investigates these performance limiting factors and presents a DSP receiver-based solution to mitigate the effects of NLIs, LDs and high PAPR. Simulations are performed by applying a various range of transmission input powers, different quadrature amplitude modulation (QAM) formats for the OFDM signal, optimized filtering at the receiver end and varying channel spacing among the optical WDM channels to analyze the performance of the proposed receiver under different conditions. The simulations and theoretical model of the proposed case studies verify that the presented solution for the RoF transport utilize less power, performs better for longer transmission distances, supports higher modulation formats and transports large number of WDM channels in the presence of NLIs and DLs as compared to the conventional RoF approach. With compensation of NLIs and LDs, transmission distance up to 10 km is investigated using 16 WDM channels with aggregate data rate of 100 Gb/s which shows that the proposed receiver can be used for future C-RAN fronthaul networks.

Induction contour hardening of gear wheels belongs to effective heat treatment technologies especially recommended for high-tech applications in machinery, automotive and aerospace industries. In comparison with long term, energy consuming conventional heat treatment (carburizing and consequent quenching), its main positive features are characterized by high total efficiency, short duration and relatively low energy consumption. However, modeling of the process is relatively complicated. The numerical model should contain both multi-physic and non-linear formulation of the problem. The paper concentrates on the modeling of rapid induction heating being the first stage of the contour induction hardening process which is the time consuming part of the computations. It is taken into consideration that critical temperatures and consequently the hardening temperature are dependent on the velocity of the induction heating. Numerical modeling of coupled non-linear electromagnetic and temperature fields are shortly presented. Investigations are provided for gear wheels made of a special quality steel AISI 300M. In order to evaluate the accuracy of the proposed approach, exemplary computations of the full induction contour hardening process are provided. The exemplary results are compared with the measurements and a satisfactory accordance between them is achieved.

Reducing the energy consumption of a data centre while maintaining the requirements of the compute resources is a challenging problem that requires intelligent system design. It even becomes more challenging when dealing with an operating data centre. To achieve that goal without compromising the working conditions of the compute resources, a temperature model is needed that estimates the temperature within the hot corridor of the cooling system based on the properties of the external weather conditions and internal conditions such as server energy consumption, and cooling system state. In this paper, we discuss the dataset creation process as well as the process of evaluating a model for forecasting the temperature in the warm corridor of the data centre. The proposed solution compares two new neural network architectures, namely Time-Series Dense Encoder (TiDE) and Time-Series Mixer (TSMixer) with classical methods such as Random Forest and XGBoost and AutoARIMA. The obtained results indicate that the lowest prediction error was achieved by the TiDE model allowing to achieve 0.1270 of N-RMSE followed by the XGBoost model with 0.1275 of N-RMSE. The additional analysis indicates a limitation of the use of the XGBoost model which tends to underestimate temperature as it approaches higher values, which is particularly important in avoiding safety conditions violations of the compute units.