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Register a new userModel Optimization for A Dynamic Rail Transport System on an Asymmetric Multi-Core System
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Added by
Anas Al-Oraiqat Dr.
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The problem of optimization of the rolling dynamics model is considered. That providing safe movement at high frequency when interacting with the railway. Moreover, allowing to evaluate the dynamic parameters when designing new and modernizing existing locomotives. The object of this research is a rail transport dynamic system model. The articles purpose is to increase the efficiency of the digital hardware in the rolling stock loop model by optimizing the organization of the computing process. The mathematical model analysis of the object made it possible to attribute it to the class of hard real-time systems. The computation of the model phase variables with different frequencies is necessary to optimize the simulation time of the train movements and is performed by splitting the original algorithm into parallel threads. The developed planning algorithm and the cyclic schedule implementation for the model of a dynamic real-time object consider microarchitecture solutions of symmetric multiprocessor systems with shared memory and methods for optimizing software tools. The experiments confirmed the operability of the optimized model. Also, allow us to recommend it for practical use in studying objects and determine the dynamic force of trolley structural elements during operation. Analysis of the optimized model simulation results, using cyclic schedules shows the correspondence of the obtained simulation results to the standard. The main advantage of the model is the increase in productivity when performing data processing by reducing the processor time. The optimized cyclic schedule algorithm of the semi-natural modeling platform is used for the subsequent development of the control system in real and accelerated time scales.
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This paper summarises a successful application of functional programming within a commercial environment. We report on experience at Accentures Financial Services Solution Centre in London with simulating an object-oriented financial system in order to assist analysis and design. The work was part of a large IT project for an international investment bank and provides a pragmatic case study.
Recently, there has been increasing interest in transparency and interpretability in Deep Reinforcement Learning (DRL) systems. Verbal explanations, as the most natural way of communication in our daily life, deserve more attention, since they allow users to gain a better understanding of the system which ultimately could lead to a high level of trust and smooth collaboration. This paper reports a novel work in generating verbal explanations for DRL behaviors agent. A rule-based model is designed to construct explanations using a series of rules which are predefined with prior knowledge. A learning model is then proposed to expand the implicit logic of generating verbal explanation to general situations by employing rule-based explanations as training data. The learning model is shown to have better flexibility and generalizability than the static rule-based model. The performance of both models is evaluated quantitatively through objective metrics. The results show that verbal explanation generated by both models improve subjective satisfaction of users towards the interpretability of DRL systems. Additionally, seven variants of the learning model are designed to illustrate the contribution of input channels, attention mechanism, and proposed encoder in improving the quality of verbal explanation.
Linear models are regularly used for mapping cores to tiles in a chip. System-on-Chip (SoC) design requires integration of functional units with varying sizes, but conventional models only account for identical-sized cores. Linear models cannot calculate the varying areas of cores in SoCs directly and must rely on approximations. We propose using non-linear models: Semi-definite programming (SDP) allows easy model definitions and achieves approximately 20% reduced area and up to 80% reduced white space. As computational time is similar to linear models, they can be applied, practically.
Spectral imaging is a method in medical x-ray imaging to extract information about the object constituents by the material-specific energy dependence of x-ray attenuation. Contrast-enhanced spectral imaging has been thoroughly investigated, but unenhanced imaging may be more useful because it comes as a bonus to the conventional non-energy-resolved absorption image at screening; there is no additional radiation dose and no need for contrast medium. We have used a previously developed theoretical framework and system model that include quantum and anatomical noise to characterize the performance of a photon-counting spectral mammography system with two energy bins for unenhanced imaging. The theoretical framework was validated with synthesized images. Optimal combination of the energy-resolved images for detecting large unenhanced tumors corresponded closely, but not exactly, to minimization of the anatomical noise, which is commonly referred to as energy subtraction. In that case, an ideal-observer detectability index could be improved close to 50% compared to absorption imaging. Optimization with respect to the signal-to-quantum-noise ratio, commonly referred to as energy weighting, deteriorated detectability. For small microcalcifications or tumors on uniform backgrounds, however, energy subtraction was suboptimal whereas energy weighting provided a minute improvement. The performance was largely independent of beam quality, detector energy resolution, and bin count fraction. It is clear that inclusion of anatomical noise and imaging task in spectral optimization may yield completely different results than an analysis based solely on quantum noise.
The effectiveness of rapid rail transit system is analyzed using tools of complex network for the first time. We evaluated the effectiveness of the system in Beijing quantitatively from different perspectives, including descriptive statistics analysis, bridging property, centrality property, ability of connecting different part of the system and ability of disease spreading. The results showed that the public transport of Beijing does benefit from the rapid rail transit lines, but there is still room to improve. The paper concluded with some policy suggestions regarding how to promote the system. This study offered significant insight that can help understand the public transportation better. The methodology can be easily applied to analyze other urban public systems, such as electricity grid, water system, to develop more livable cities.
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