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EN
The paper presents some considerations on the performance of various objective function minimization methods in the process of GNSS antenna PCV determination. It is particulary important in the case of structural health monitoring and diagnostics. PCV are used as an additional feature to improve the GNSS positioning accuracy. The process of PCV derivation is complex and involves fitting spherical harmonics into a set of observables. The paper compares computing performance and accuracy of few methods used in the fitting process.
EN
When designing structures, it is often necessary to re-analyse a structure that is different in some parts from the original one. As real structures are often complex, their analysis is therefore very challenging. In such cases, reanalysis methods are advantageously used. The aim of this paper is to approach the problem of solving the constructions using reanalysis method in which the time taken in solving algebraic equations is reduced. In particular, the purpose of this work is to demonstrate on a chosen system the time savings and the advantages of the chosen direct efficient reanalysis method for a given design problem. A basic condition for meeting these criteria is the modernization of computational procedures in the mechanics of compliant solids.
EN
Purpose: The purpose of the current study was to develop and validate a finite element (FE) pedestrian model with high computational efficiency and stability using a novel modeling approach. Methods: Firstly, a novel modeling approach of using hollow structures (HS) to simulate the mechanical properties of soft tissues under impact loading was proposed and evaluated. Then, an FE pedestrian model was developed, employing this modeling approach based on the Total Human Model for Safety (THUMS) pedestrian model, named as THUMS-HS model. Finally, the biofidelity of the THUMS-HS model was validated against cadaver test data at both segment and full-body level. Results: The results show that the proposed hollow structures can simulate the mechanical properties of soft tissues and the predictions of the THUMS-HS model show good agreement with the cadaver test data under impact loading. Simulations also prove that the THUMS-HS model has high computational efficiency and stability. Conclusions: The proposed modeling approach of using hollow structures to simulate the mechanical properties of soft tissues is plausible and the THUMS-HS model could be used as a valid, efficient and robust numerical tool for analysis of pedestrian safety in vehicle collisions.
4
Content available remote Computing Duals of Finite Gödel Algebras
EN
We introduce an algorithm that computes and counts the duals of finite G\"odel-Dummett algebras of k ≥ 1 elements. The computational cost of our algorithm depends on the factorization of k, nevertheless a Python implementation is sufficiently fast to compute the results for very large values of k.
5
Content available remote Generalisation and improvement of the compact gravity inversion method
EN
Compact gravity inversion (CGI) is widely used to invert gravity data following the principle of minimising the volume of the causative body due to its simplicity, high efciency, and sharp-boundary inversion results. In this study, the compactness weighting function is generalised and the depth weighting function is introduced to CGI to obtain the reweighted CGI (RCGI) method. Although RCGI exhibits better fexibility than CGI, selecting an appropriate compactness factor α and depth weighting function β is difcult, and we design a parameter selection rule to search the proper α and β quantitively. Furthermore, we improve RCGI for boasting superior computational efciency by gradually eliminating the model blocks that reach the designated boundaries in the iterative algorithm of inversion. This approach is termed the reweighted and element-elimination CGI (REECGI) method. The inversion results show that both RCGI and REECGI result in better inversion accuracy than CGI, and REECGI has higher computational efciency than RCGI and CGI, which increases with the number of iterations.
EN
Thread mapping is one of the techniques which allow for efficient exploiting of the potential of modern multicore architectures. The aim of this paper is to study the impact of thread mapping on the computing performance, the scalability, and the energy consumption for parallel dense linear algebra kernels on hierarchical shared memory multicore systems. We consider the basic application, namely a matrix-matrix product (GEMM), and two parallel matrix decompositions (LU and WZ). Both factorizations exploit parallel BLAS (basic linear algebra subprograms) operations, among others GEMM. We compare differences between various thread mapping strategies for these applications. Our results show that the choice of thread mapping has the measurable impact on the performance, the scalability, and energy consumption of the GEMM and two matrix factorizations.
PL
W artykule przedstawiono porównanie wydajności obliczeniowej mikrokontrolerów z rdzeniem ARM Cortex M na przykładzie rodziny STM32. Omówiono różnice pomiędzy poszczególnymi rdzeniami Cortex M. Przedstawiono metody badania wydajności obliczeniowej stałoprzecinkowej oraz zmiennoprzecinkowej. Przedstawiono wyniki badań wydajności na przykładzie 4 mikrokontrolerów różniących się zastosowanym rdzeniem. Porównano skuteczność mechanizmów zwiększających wydajność, takich jak pamięć Cache. Zbadano przepustowość magistrali pamięci dynamicznej DRAM, porównano wydajność algorytmów automatycznej optymalizacji kodu oraz wydajność jednostki zmiennoprzecinkowej FPU.
EN
The paper presents computational performance comparison of ARM Cortex M microcontrollers using STM32 family as an example. Differences between Cortex M cores have been described. The article also presents methods of performance analysis for integer and floating point arithmetics. Measurement results have been given for 4 microcontrollers with different cores as well as DRAM memory bandwidth, automatic optimization efficiency and FPU throughput. Performance boosting modules efficiency such as Cache memory was also investigated.
PL
W artykule przedstawiono wyniki badań dotyczące pracy 8/16-bitowego mikrokontrolera ATXmega64A3 w warunkach kriogenicznych (w temperaturze 77K). W przeprowadzonych eksperymentach obserwowano zachowanie wewnętrznych oscylatorów 2 MHz (oscylator RC) i 32 MHz (oscylator pierścieniowy). Potwierdzono zdolność do prawidłowego działania w tak niskiej temperaturze, a także możliwość zwielokrotnienia sygnału zegarowego z wykorzystaniem pętli PLL, porównano wydajność obliczeniową układu dla różnych warunków pracy.
EN
In this paper the results of experiments with an 8/16-bit ATXmega64A3 microcontroller (ATMEL) at low temperature are presented. The examined devices were immersed in a Dewar flask with liquid nitrogen (Fig. 1). First of all we focused on internal oscillators. There are four types of oscillators inside a microcontroller and we tested two of them: 2 MHz RC and 32 MHz ring oscillator. The results at 77K (liquid nitrogen) were compared to the results at 300K (room temperature). The frequency as a function of the supply voltage for 300K and 77K is shown in Fig. 2 and Fig. 3, respectively. According to the theory of silicon semiconductors, the activity of carriers increases in low temperatures, so there was expected increase in the oscillation frequency. For the ring oscillator (32 MHz) our expectations of the growth of the frequency were confirmed. Due to PLL there was a possibility to increase the frequency, and we reached 64 MHz. Figs. 4 and 5 show the frequency as a function of the supply voltage at 77K in detail. The power consumption was also measured (Fig. 6). With increase in the frequency the increase in the power consumption was obtained (Fig. 7). The value of the power depends on the supply voltage strongly, while on the temperature less, so the results at 300K and 77K do not differ too much (Fig. 8). We also calculated the energy efficiency for the microcontroller under various conditions. The frequency to power coefficient was defined, and the results are shown in Figs.9 and 10. The energy consumption for the example task was estimated (Tab.1) and verified by experiments.
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