Wyniki wyszukiwania - BazTech

1

Reliable 5G waveform detection from an optical fibre transmitter system

Yaseen Zeyad T., Algriree Waleed

Opto-Electronics Review

|

2024

|

Vol. 32, No. 1

art. no. e149181

EN

The integration of optical fibre communication with multiple input multiple output-non-orthogonal multiple access (MIMO-NOMA) waveforms in cognitive radio (CR) systems is examined in this study. The proposed system leverages the advantages of optical fibre, including high bandwidth and immunity to electromagnetic interference to facilitate the transmission and reception of MIMO-NOMA signals in a CR environment. Moreover, MIMO-NOMA signal was detected and analysed by the hybrid-discrete cosine transform-Welch (H-DCT-W) method. Based on the modes results, a detection probability greater than 0.96%, a false alarm probability equal to 0.06, and a global system error probability equal to 0.09% were obtained with a signal-to-noise ratio (SNR) less than 0 dB, while maintaining a simple level of complexity. The results obtained in this paper indicate the potential of the optical fibre-based MIMO-NOMA system based on H-DCT-W technology in CR networks. Therefore, its suitability for practical CR applications is demonstrated by the improvements obtained in false alarms, detection probability, and error rates at low levels of SNR. This study contributes to the development of efficient and reliable wireless communication systems by linking cooperation and synergy concerning MIMO-NOMA, optical fibres, as well as the proposed detection technique (H-DCT-W).

2

Flexural response of FRP‑strengthened lightweight RC beams: hybrid bond efficiency of L-shape ribbed bars and NSM technique

Tahmouresi Behzad, Momeninejad Kasra, Mohseni Ehsan

Archives of Civil and Mechanical Engineering

|

2022

|

Vol. 22, no. 2

art. no. e95, 1--16

EN

This paper presents the results of an experimental study on employing near surface mounted (NSM) fiber-reinforced polymer (FRP) reinforcement technique, and L-shape ribbed bars, for flexural strengthening of lightweight reinforced concrete (RC) beams. 18 RC beams including 14 lightweight RC beams and four normal-weight concrete beams were designed. The beams were strengthened with glass fiber-reinforced polymer (GFRP) bars and carbon fiber-reinforced polymer (CFRP) laminate in bending tests. Test parameters included: (1) different FRP materials (glass bars and carbon sheets), (2) longitudinal steel reinforcement ratio, and (3) type of strengthening technique used (NSM reinforcement or hybrid). The ultimate tensile strength, deflection, compressive and tensile strain of concrete, and failure mode of the beams were examined under four-point flexural test. Results showed that the ultimate strength of all RC beams increased between 33 and 105% compared to the control beam. The ultimate strength of beams reinforced with CFRP in the mid-span region was 10% higher than that of beams strengthened at both ends, although the former exhibited 28% lower ultimate deflection. The ultimate strength and deflection of RC beams strengthened with combined steel reinforcing bars and GFRP bars were 10% and 108% higher, respectively, compared to those of RC beams strengthened with GFRP bars only. Hybrid L-shape ribbed bars beams showed a considerably higher ductility (up to 170% increase in the ultimate deflection) compared to other beams. The comparison of the experimental results of the ultimate strength of the beams with ACI440-2R guidelines indicated a reasonable and conservative prediction of the code expression.

3

Application of hybrid CFD/CAA technique for modeling pressure fluctuations in transonic flows

Dykas S., Wróblewski W., Rulik S.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

|

2013

|

Vol. 17, No 3-4

145--154

EN

Solving AeroAcoustics (CAA) problems by means of the Direct Numerical Simulation (DNS) or even the Large Eddy Simulation (LES) for a large computational domain is very time consuming and cannot be applied widely for engineering purposes. In this paper in-house CFD and CAA codes are presented. The in-house CFD code is based on the LES approach whereas the CAA code is an acoustic postprocessor solving non-linearized Euler equations for fluctuating (acoustic) variables. These codes are used to solve the pressure waves generated aerodynamically by a flow over a rectangular cavity and by the vortex street behind a turbine blade. The obtained results are discussed with respect to the application of the presented numerical techniques to pressure waves modeling in steam turbine stages.

4

Optimization of materials processing using a hybrid technique based on artificial neural networks

Kusiak J., Żmudzki A., Danielewska-Tułecka A.

Archives of Metallurgy and Materials

|

2005

|

Vol. 50, iss. 3

609-620

EN

The optimization problems in the field of material processing are very complex. They require a lot of computations, because most of processes are simulated on the base of the Finite Element Method (FEM). In classical optimization approach, every iteration requires time-consuming FEM calculation of the considered problem, which increases the computation time of the optimization procedures. The efficient optimization algorithms of such complex problems should minimize the computation time. The paper presents a new hybrid optimization technique based on the Artificial Neural Network (ANN) modelling. The search of the optimal value is performed not directly on the objective function, but on its values predicted by its ANN metamodel. Such approach does not require FEM recalculations of the whole analyzed problem for each optimization iteration. It allows decreasing the computation time of the optimization procedure. The paper presents the description of the proposed method, its algorithm and examples of application to test optimization problems and the inverse analysis of materials properties.

PL

W zagadnieniach dotyczących procesów obróbki materiałów, problemy optymalizacji są bardzo skomplikowane. Wymagają one bowiem wielu obliczeń numerycznych, co wynika z tego iż większość procesów modeluje się za pomocą Metody Elementów Skończonych (MES). W klasycznym podejściu, każda iteracja metody optymalizacji wymaga czasochłonnych symulacji MES rozważanego problemu, co znacząco zwiększa czas obliczeń. Cecha skutecznego algorytmu optymalizacji powinno byc zmniejszenie tego czasu. W artykule przedstawiono nowa hybrydowa technikę optymalizacji oparta o modelowanie z użyciem Sztucznych Sieci Neuronowych (SSN). Poszukiwanie wartości optymalnej nie jest przeprowadzone bezpośrednio za pomocą zdefiniowanej funkcji celu, ale za pomocą jej wartości wskazanej przez metamodel SSN. Zaprezentowane podejście nie wymaga ponownych obliczeń całego analizowanego problemu za pomocą MES w każdej iteracji algorytmu. Pozwala to na zmniejszenie czasu obliczeń procedury optymalizacji. W artykule zaprezentowano opis proponowanej metodyki, algorytm poszukiwania optimum, oraz przykłady zastosowania do optymalizacji funkcji testowych i do wyznaczania parametrów modelu materiałowego w analizie odwrotnej.