Real-time Domain Research Articles

Driving Authority Allocation Strategy Based on Driving Authority Real-Time Allocation Domain

In the switching process of driving authority for man-machine cooperative driving, it should be ensured that the driver can take over the vehicle safely, stably and efficiently. This paper proposes a driving authority allocation strategy based on real-time allocation domain (RAD), which mainly includes the establishment of RAD and the dynamic optimization. The RAD is a comprehensive dynamic allocation internal, whose range is determined by the corresponding allocation values of driver’s cognitive state, driver’s muscle state and environment state. The dynamic optimization is to search for the optimal driving authority in RAD at any time, and its optimization objectives and constraints all contain time-varying parameters. This work proposes an improved simulated annealing algorithm to solve this problem. Simulation results show that the proposed driving authority allocation strategy can effectively allocate the driving authority according to the current condition and driver’s state, and ensure the safety, stability and efficiency of the take-over process.

Anisotropic carrier dynamics in a laser-excited Fe1/(MgO)3(001) heterostructure from real-time time-dependent density functional theory

The interaction of a femtosecond optical pulse with a Fe$_{1}$/(MgO)$_{3}$(001) metal/oxide heterostructure is investigated using time-dependent density functional theory (TDDFT) calculations in the real-time domain. We systematically study electronic excitations as a function of laser frequency, peak power density and polarization direction. While spin-orbit coupling is found to result in only a small time-dependent reduction of magnetization (less than 10%), we find a marked anisotropy in the response to in-plane and out-of-plane polarized light, which changes its character qualitatively depending on the excitation energy: the Fe-layer is efficiently addressed at low frequencies by in-plane polarized light, whereas for frequencies higher than the MgO band gap, we find a particularly strong response of the central MgO-layer for cross-plane polarized light. For laser excitations between the charge transfer gap and the MgO band gap, the interface plays the most important role, as it mediates concerted transitions from the valence band of MgO into the $3d$ states of Fe closely above the Fermi level and from the Fe-states below the Fermi level into the conduction band of MgO. As these transitions can occur simultaneously altering charge balance of the layers, they could potentially lead to an efficient transfer of excited carriers into the MgO bulk, where the corresponding electron and hole states can be separated by an energy which is significantly larger than the photon energy.

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. In this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

Memory Response on the Elasto-Thermodiffusive Interaction Subjected to Harmonically Varying Heat Source

Enlightened by the Caputo type of fractional derivative, this research paper is the analysis of a mathematical model of generalized thermoelastic diffusion with memory-dependent derivative (MDD) for an isotropic infinite medium with a cylindrical cavity in the context of dual-phase lag model. The surface of the cavity is traction free and subjected to harmonically heat sources with constant angular frequency of thermal vibration. Laplace transform technique has been used to solve the problem. Later eigenvalue approach is used to obtain the analytical expressions for different physical fields in the transformed domain. Finally to obtain the solutions in the real-time domain, the Riemann-sum approximation method is used. According to the graphical representations corresponding to the numerical results, the effect of heat source speed on temperature, displacement, stress, mass concentration and chemical potential are studied. The effect of memory-dependent parameters, as well as in the absence of MDD on physical quantities are also demonstrated.

Smoothed finite element approach for viscoelastic behaviors of general shell structures

This study focuses on the viscoelastic analysis of laminated composite shell structures under long-term creep mechanical loading. An advanced finite element technique named as cell/element-based smoothed discrete shear gap method (CS-DSG3) is employed to obtain the numerical solutions for both elastic and viscoelastic problems owing to its accuracy and rapid convergence. In the CS-DSG3 shell element, each triangular shell element is divided into three DSG3 subtriangles to avoid transverse shear locking. Subsequently, the total element strain is computed from the three partial strains of the subtriangles using a smoothing technique. To overcome the difficulty in the constitutive equations in integral forms for a viscoelastic material, all formulations are transformed into the Laplace domain using the convolution theorem. Finally, time-dependent deformations are obtained and converted back to the real-time domain using inverse Laplace techniques. For validation, various numerical examples of a pinched cylinder, clamped hyperbolic paraboloid, and hemispherical shell formed of isotropic elastic, isotropic viscoelastic, and anisotropic composite materials are selected to investigate creep behavior under mechanical loading. The present study extends the finite element simulation of anisotropic viscoelastic shell structures to achieve high accuracy and efficiency based on the Laplace transform and CS-DSG3.

The optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source function

The main objective of this study is to develop the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source function. The available semi-analytical infinite-conductivity models (ICMs) for horizontal well under rectangular bounded reservoir in literature were developed by applying superposition of pressures in space (SPS). A new instantaneous source function (i.e., instantaneous uniform-flux segmentary source function under bounded reservoir) is derived to be used instead of SPS to develop the optimal semi-analytical ICM. The new semi-analytical ICM is verified with ICM of Schlumberger [1] and with previous semi-analytical ICMs in terms of bottom hole pressure (BHP) profile and inflow rate distribution along the wellbore. The model is also validated with real horizontal wells in terms of inflow rate distribution along the wellbore. The results show that the developed model gives the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir. Besides that, high computational-efficiency and high-resolution of wellbore discretization have been achieved (i.e., wellbore segment number could be tens of hundreds depending on solution requirement). The results also show that at pseudo-steady state (PSS) flow regime, inflow rate distribution along the wellbore by previous semi-analytical ICMs is stabilized U-shaped as performance of inflow rate distribution at late radial flow regime. Therefore, the previous semi-analytical ICMs are incorrectly modeling inflow rate distribution at PSS flow regime due to the negative influence of applying SPS. The optimal semi-analytical ICM is in a general form and real time domain, and can be applicable for 3D horizontal well and 2D vertical fracture well under infinite and rectangular bounded reservoirs, of uniform-flux and infinite-conductivity wellbore conditions at any time of well life.The novelties in this study are as follows:1. At PSS flow regime:(1) Inflow rate distribution along the wellbore is stabilized uniform-flux which was verified mathematically.(2) Primary pressure derivative (PPD) (i.e., PPD = ∂PDt/∂tDA) is equal to (2π/mt) for any well and reservoir configurations and depends only on half-length wellbore segments number (mt).2. The new ICM gives different trend of Bourdet derivative for the first three flow regimes (i.e., early radial, early linear, late radial) and gives the same trend of Bourdet derivative for PSS flow regime, to their counterparts by uniform-flux model (UFM). The trend of pressure derivatives by UFM for any flow regime is well studied in literature, while the counterparts by ICM are new and need detailed study.

Comparison of Repeatability and Stability of Residual Magnetic Field for Stress Characterization in Elastic and Plastic Ranges of Silicon Steels

Deep insights into microstructures and domain wall behaviors in the evaluation of different material statuses under elastic and plastic stress ranges have essential implications for magnetic sensing and nondestructive testing and evaluation (NDT&E). This paper investigates the repeatability and stability of residual magnetic field (RMF) signals using a magneto-optical Kerr effect microscope for the stress characterization of silicon steel sheets beyond their elastic limit. Real-time domain motion is used for RMF characterization, while both the repeatability under plastic ranges after the cyclic stress rounds and stability during relaxation time are studied in detail. The distinction between elastic and plastic materials is discussed in terms of their spatio-temporal properties for further residual stress measurement since both ranges are mixed. During the relaxation time, the RMF of the plastic material shows a two-stage change with apparent recovery, which is contrasted with the one-stage change in the elastic material. Results show that the grain boundary affects the temporal recovery of the RMF. These findings concerning the spatio-temporal properties of different RMFs in plastic and elastic materials can be applied to the design and development of magnetic NDT&E for (residual) stress measurement and material status estimation.

A deep analysis of the image and video processing techniques using nanoscale quantum-dots cellular automata

The advancement of small electrical components into the nanoscale range opens up opportunities for massively parallel computing systems. Quantum-dot Cellular Automata (QCA) is a potential novel nanoelectronics technique that fits image processing requirements well. QCA circuits are typically highly stable, quick, and utilize little energy. Because studies about QCA logic image processing are starting up, the suggested contribution will initiate a new line of inquiry in the real-time video and image processing domain. Noise reduction, face identification, feature extraction, segmentation, skeletonizing, thickening, thinning, object detection, form extraction, and tracking are all examples of image processing. This article describes how to use QCA technology for image processing in various ways. The fundamentals of image processing approaches and the function of QCA in them are covered. The findings demonstrate that a more effective layout based on QCA technology would reduce power consumption and computational complexity in the long run. We anticipate that this compendium will give readers an overview of the advances made in quantum image processing and pique their curiosity in doing a more complex investigation in the field.

Metal-Ion Specific Recognition with Amplified Transcription from Subnanometer to Submillimeter or Real-Time Domain by Self-Assembled Vanadyl Quartets.

Vanadyl(V) complexes 1 and 2 bearing a nematic liquid crystal (LC) like a p-heptoxyphenyl group or a fluorous-tag p-nonafluoroheptoxyphenyl (NFH) group at the C5 position of the N-salicylidene template were designed and synthesized. Each complex was subjected to MVO3-induced self-assembly to form metal-ion, encapsulated quartet clusters 3-M and 3'-M. The Na+ in cluster complex 3-Na or 3'-Na can be readily replaced by Rb+, Ag+, or Hg2+ in an aqueous layer to form cluster complexes by ion swapping at the H2O/CDCl3 bilayer interface. Selectivity profiles were examined with alkali-metal ions, Ag+, and Hg2+ through metal-ion competition experiments. The 3'-Na has an exclusive selectivity for Hg2+ in the presence of Zn2+ and Cd2+. Cluster complexes 3-M were utilized as chiral dopants to nematic LC materials. The effects of the encapsulated metal ions within the alkali family and Ag+ on Cano's line widths and helical pitch changes were viewed in wedge cells under a polarized microscope. Their correlations with the ionic radius were identified. The subnano information of the metal ions can thus be asymmetrically amplified to Cano's line spacings of the submilimeter domain. Conversely, the effects of the encapsulated alkali metal ions and Hg2+ in 3'-M on the interactions of their NFH tails toward fluorous silica gel (FSG) were performed via HPLC analyses. Their retention times became longer as the sizes of encapsulated, alkali metal ions increased. The increasing ion size from Na+ to Cs+ caused the four lower rim NFH tags of the cluster to be closer due to reduced cone angles. Their interactions among NFH tail groups on FSG became larger, thus leading to distinctive separations with tR from 7.36 to 10.27 min. The retention time difference between 3'-Na and 3'-Hg on HPLC was ∼3.6 min, resulting in discernible separation. The individual ion size differences on the subnano scale can thus be amplified and unambiguously established in the real time domain.

An online identification approach for ship domain model based on AIS data.

As an important basis of navigation safety decisions, ship domains have always been a pilot concern. In the past, model parameters were usually obtained from statistics of massive historical cumulative data, but the results were mostly historical analysis and static data, which obviously could not meet the needs of pilots who wish to master the ship domain in real time. To obtain and update the ship domain parameter online in time and meet the real-time needs of maritime applications, this paper obtains CRI as the weight coefficient-based PSO-LSSVM method and proposes to use short-term AIS data accumulation through the risk-weighted least squares method online rolling identification method, which can filter nonhazardous targets and improve the identification accuracy and real-time performance of nonlinear models in the ship domain. The experimental examples show that the method can generate the ship domain dynamically in real time. At the same time, the method can be used to study the dynamic evolution characteristics of the ship domain over the course of navigation, which provides a reference for navigation safety decisions and the analysis of ship navigation behavior.

Semi-analytical solution for reactive contaminant transport in a filled-fractured system with intervening rock matrices: Case examples of tritium and uranium

While fractures are commonly filled in the subsurface, they have received little attention in terms of flow and solute transport. This study presents a semi-analytical solution for a reactive contaminant in a five-layer fracture-matrix groundwater system characterized by a thin rock matrix separating the fractures, which are taken to be filled with sediments. Under conditions of steady groundwater flow using the Laplace transform for two scenarios of two-dimensional unilateral and three-dimensional radial flows, the solutions have obtained that account for longitudinal advective transport in the mobile portion of fractures and transverse diffusion in the adjacent matrices. The mathematical models in the real-time domain are derived by numerically inverting the solutions from the Laplace domain and validated against both the numerical and existing semi-analytical solutions. Examples of the solution behavior are presented, which demonstrate an increase in the fracture-mobile/fracture-immobile porosity ratio tends to increase the solute concentration in the adjacent matrices, particularly under unilateral flowrate, because in addition to the groundwater velocity, Peclet number, and hydrodynamic dispersion coefficient, the ratio of retardation factor to hydrodynamic dispersion coefficient also plays a significant role in solute transport. Compared to the fracture, the solute behavior in the matrices, particularly under the unilateral flow case, is more sensitive to changes in the parameters. The porosity of the rock matrix has a twofold effect since as porosity is increased, the retardation factor decreases while storage capacity increases. A thinner middle matrix in addition to a lower peak in tritium and uranium concentrations inside the whole system over long-time periods, is further accompanied by a quick overlapping the concentration penetration depth of adjacent fractures. The diffusion process in the rock matrix has been further identified as important as advection in the fracture for those repositories that are planned to safely dispose wastes for a long-time. The idea of combining multiple fractures as a single one with a double aperture is found to be only reasonable for the conservative solute under the steady-state or long-time conditions while it is not to be useful for the cases of sorptive solutes under the unsteady-state solute transport. The findings of this study can assist for simulating tracer tests as well as fate, transport, and remediation of groundwater contaminants in fractured rocks to better evaluate the safety degree of deep reservoirs in regard to disposal of nuclear and chemical wastes.

A Semi-Analytical Mathematical Model for Off-Center Fractured Vertical Wells With Asymmetrical Fractures in Columnar Coalbed Methane

Coalbed methane (CBM) is one of the important unconventional oil and gas resources. Since the permeability of the CBM reservoir is very low, CBM is usually developed by hydraulic fracturing technology to enhance gas productivity. In fact, most of the wells are not at the center of the columnar CBM reservoirs, which leads to different wellbore pressure responses. It is meaningful to analyze the wellbore pressure response of the off-center fractured vertical wells with asymmetrical fracture. The adsorption-desorption, fracture asymmetry are considered and a semi-analytical mathematical model is established. The pressure wellbore solution of the Laplace domain can be obtained by Laplace transformation, model coupling, and Gauss elimination. Then, the Stehfest numerical inversion is employed to obtain the pressure solution and rate distribution in the real-time domain. This semi-analytical solution is verified; the results show that the wellbore pressure includes six flow regimes, which include bilinear flow, linear flow, elliptic flow, radial flow, arc boundary reflection, and boundary dominated flow regime. The influence of fracture angle, dimensionless conductivity, and off-center distance on wellbore pressure and rate distribution are discussed in detail.

An analytical model for pressure behavior in multilayered radially composite reservoir with formation crossflow

The present work proposes an analytical model to calculate the pressure response for multilayered radially composite reservoirs considering the presence of formation crossflow along with all layers. The behavior of pressure transient during production tests in radially composite multilayered reservoirs with non-communicating layers has been widely studied, however there is still a lack of studies considering communicating layers, i.e., formation crossflow in such a model. In that sense, the main purpose of this work is to develop a novel analytical solution that can be used to accurately analyze production tests conducted in multilayered radially composite reservoirs considering communicating layers and multiple regions that can have different radii. An infinite reservoir is considered and the properties for each layer, such as permeability, oil viscosity, and porosity may be different. Moreover, the mathematical formulation for this model is presented in the Laplace domain and the Stehfest Algorithm is used to convert the pressure variation into the real-time domain. The results presented in this work show a close agreement when compared to the responses provided by a commercial flow simulator based on finite differences.

Computing exact WCRT for typed DAG tasks on heterogeneous multi-core processors

Heterogeneous multi-core architectures achieve high performance and energy efficiency in real-time domain towards various applications. Most real-time parallel applications on heterogeneous multi-cores can be modeled as a typed directed acyclic graph (DAG) task, where the workload of each vertex is only allowed to execute on a particular type of core. Traditional worst-case response time (WCRT) analysis methods for these applications modeled as DAG are very pessimistic and impractical, i.e., there is a big gap between existing WCRT bounds and the exact WCRT. In this paper, we propose a satisfiability modulo theories-based method to exactly analyze the WCRT of the typed DAG task scheduled upon heterogeneous multi-cores. Experimental results show that our method can significantly improve the precision of the WCRT, and thus, dramatically increase the acceptance rate in the schedulability analysis.

Task-Relevant Encoding of Domain Knowledge in Dynamics Modeling: Application to Furnace Forecasting From Video

Waste incineration plants are complex dynamical systems that rely on expert human operators to maintain steady combustion, by observing real-time in-chamber video feeds. Real-time plant forecasting provides vital operational support in decision making, and applying machine learning to automatically learn dynamics forecast models from video feeds is an attractive means to realise this. However, learning complex dynamics in systems that requires cost-efficiency remains an open research problem. Specifically, modelling plant dynamics in real-time is challenging due to uncertainties caused by inhomogeneous waste inputs, requiring complex learning that impedes real-time modelling. To address this, this paper presents a real-time data-driven framework for generating video forecasts, by incorporating <i>task-relevant domain-knowledge</i>, during learning. Specifically, this method combines dynamics modelling and forecasting using <i>dynamic mode decomposition</i>, with <i>Fourier transformations</i> informed by expert operator heuristic knowledge for encoding task-relevant frequency information inside the learning process. Experiments in this paper demonstrate that the proposed framework captures intuitive physical aspects of the underlying physiochemical process, with a greatly reduced computational runtime in comparison to standard approaches, allowing for application in real-time domains. Forecasted video predictions are accurate over short time horizons, and capture important system characteristics over longer time periods.

Cybernetic Data Augmentation for Neural Network Classification of Control Skills

Mathematical human controller (HC) models are widely used in tuning manual control systems and for understanding human performance. Typically, quasi-linear HC models are used, which can accurately capture the linear portion of HCs’ behavior, averaged over a long measurement window. This paper presents a deep learning HC skill-level evaluation method that works on short windows of raw HC time signals, and accounts for both the linear and non-linear portions of HC behavior. This deep learning approach is applied to data from a previous skill training experiment performed in the SIMONA Research Simulator at TU Delft. Additional human control data is generated using cybernetic HC model simulations. The results indicate that the deep learning evaluation method is successful in predicting HC skill level with 85-90% validation accuracy, but that training the classifier solely on simulated HC data reduces this accuracy by 15-25%. Inspection of the results especially shows a strong sensitivity of the classifier to the presence of remnant in the simulated training data. In conclusion, these results reveal that current quasi-linear HC model simulations, and in particular the remnant portion, do not adequately capture real time-domain HC behavior to allow effective training-data augmentation.

Towards functional safety compliance of matrix–matrix multiplication for machine learning-based autonomous systems

Autonomous systems execute complex tasks to perceive the environment and take self-aware decisions with limited human interaction. This autonomy is commonly achieved with the support of machine learning algorithms. The nature of these algorithms, that need to process large data volumes, poses high-performance demands on the underlying hardware. As a result, the embedded critical real-time domain is adopting increasingly powerful processors that combine multi-core processors with accelerators such as GPUs. The resulting hardware and software complexity makes it difficult to demonstrate that the system will run safely and reliably. This is the main objective of functional safety standards, such as IEC 61508 or ISO 26262, that deal with the avoidance, detection and control of hardware or software errors. In this paper, we adopt those measures for the safe inference of machine learning libraries on multi-core devices, two topics that are not explicitly covered in the current version of standards. To this end, we adapt the matrix-matrix multiplication function, a central element of existing machine learning libraries, according to the recommendations of functional safety standards. The paper makes the following contributions: (i) adoption of recommended programming practices for the avoidance of programming errors in the matrix-matrix multiplication, (ii) inclusion of diagnostic mechanisms based on widely used checksums to control runtime errors, and (iii) evaluation of the impact of previous measures in terms of performance and a quantification of the achieved diagnostic coverage. For this purpose, we implement the diagnostic mechanisms on one of the ARM R5 cores of a Zynq UltraScale+ multi-processor system-on-chip and we then adapt them to an Intel i7 processor with native code employing vectorization for the sake of performance.

Temporal Relation Extraction in Clinical Texts

Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages.

Analytical solutions for predicting and optimizing geothermal energy extraction from an enhanced geothermal system with a multiple hydraulically fractured horizontal-well doublet

In this study, novel analytical solutions are presented for predicting and optimizing energy (heat and power) extraction from an idealized single hot-dry rock (HDR) or hot-wet rock (HWR) through a multistage hydraulically fractured horizontal well doublet (MHFWD). The solutions are based on a two-dimensional analytical model that accounts for heat transfer mechanisms through an MHFWD completed in a homogeneous nearly impermeable matrix of the HDR/HWR system. The method of Laplace transformation is used to obtain the solutions. The solutions are inverted numerically and analytically to compute the fracture water outlet temperature and the temperature inside the matrix in the real-time domain. The analytical solutions presented in the study have not been presented elsewhere. The fluid outlet and matrix temperature distributions computed from the analytical model were validated against the results of a commercial simulator. The derived analytical solutions were integrated to convert thermal energy to electric power, and a sensitivity study was performed to investigate the effect of certain parameters, such as fracture height, number, half spacing, injection rate, and injection temperature. In addition, the study provides insights and guidelines to predict, design, and optimize heat recovery and electricity production from an EGS system stimulated by an MHFWD.

Experimental Real-Time Heuristic Search Results in a Video Game

In real-time domains such as video games, a planning algo- rithm has a strictly bounded time before it must return the next action for the agent to execute. We introduce a realistic video game benchmark domain that is useful for evaluating real-time heuristic search algorithms. Unlike previous bench- marks such as grid pathfinding and the sliding tile puzzle, this new domain includes dynamics and induces a directed graph. Using both the previous and new domains, we investigate sev- eral enhancements to a leading real-time search algorithm, LSS-LRTA*. We show experimentally that 1) it is not dif- ficult to outperform A* when optimizing goal achievement time, 2) it is better to plan after each action than to commit to multiple actions or to use a dynamically sized lookahead, 3) A*-based lookahead can cause undesirable actions to be selected, and 4) on-line de-biasing of the heuristic can lead to improved performance. We hope that this new domain and results will stimulate further research on applying real-time search to dynamic real-time domains.