Search Results (31)

As the development of oilfields in China enters its middle-to-late stage, the old oilfields still occupy a dominant position in the production structure. The seepage process of reservoirs in the high Water Content Period (WCP) presents significant nonlinear and non-homogeneous evolution characteristics, and the traditional seepage-modeling methods are facing the double challenges of accuracy and adaptability when dealing with complex dynamic scenarios. In recent years, Deep Learning technology has gradually become an important tool for reservoir seepage field prediction by virtue of its powerful feature extraction and nonlinear modeling capabilities. This paper systematically reviews the development history of seepage field prediction methods and focuses on the typical models and application paths of Deep Learning in this field, including FeedForward Neural networks, Convolutional Neural Networks, temporal networks, Graphical Neural Networks, and Physical Information Neural Networks (PINNs). Key processes based on Deep Learning, such as feature engineering, network structure design, and physical constraint integration mechanisms, are further explored. Based on the summary of the existing results, this paper proposes future development directions including real-time prediction and closed-loop optimization, multi-source data fusion, physical consistency modeling and interpretability enhancement, model migration, and online updating capability. The research aims to provide theoretical support and technical reference for the intelligent development of old oilfields, the construction of digital twin reservoirs, and the prediction of seepage behavior in complex reservoirs. Full article

Radar detection is a technology frequently used to detect objects and measure the range, angle, or velocity of those objects. Several studies have been performed to improve the accuracy and performance of detection methods, but they encountered a strong challenge, which was the minimization of false alarms and the distinguishing of real targets from false alarms, especially in nonhomogeneous environments. We propose a new detection method that uses time-frequency analysis tools to improve detection performance and maintain a low constant false alarm rate. Different from existing works, this paper combines the clutter map constant false alarm rate technique with the Gabor transform for accurate target detection in cluttered environments. We suggest the combination of a CFAR detector with a time-frequency method that enables us to tackle challenging scenarios involving near targets. The proposed method allows for locating the exact position of the target by reducing the impact of clutter and maintaining a low rate of false alarms, while the Gabor transform facilitates the extraction of pertinent target characteristics and improves differentiation from clutter. Through experiments and simulations in different scenarios and clutter models, we demonstrate that the method is efficient in measurements and performs well in cluttered environments. This research has a major impact on signal processing and significantly improves target detection in cluttered environments, allowing this method to be deeply developed and implemented. Full article

Encapsulated rejuvenators embedded in asphalt mixtures are a promising technology to extend the service life of asphalt pavements. However, their effects on the asphalt mixture’s performance still need to be properly understood. A recently developed three-dimensional discrete element method framework enables the evaluation of non-homogeneous distributions of the rejuvenator, closely resembling real conditions. This includes different scenarios involving capsule content and release efficiency. The presented numerical results show that the rejuvenator-to-mastic ratio and the number of rejuvenator-modified contacts influence the stiffness properties of asphalt mixtures. In cases where a homogeneous rejuvenator distribution is assumed, the three-dimensional DEM model predicts a significant reduction in the asphalt mixture’s stiffness that compromises the pavement’s performance. Simulations show that the diffusion effect needs to be considered for predicting the post-healed behavior of asphalt mixtures. For cases considering more suitable modified mastic amounts (less than 1.20 wt%), the effect on the asphalt mixture’s stiffness modulus is less pronounced, and the phase angle is not significantly affected. Additionally, the presented simulations suggest that the capsule content can be increased up to 0.75 wt%, and capsules with a release rate higher than 48% can be used without compromising the rheological performance of asphalt mixtures, possibly improving their self-healing properties. These numerical insights should be considered in future designs to achieve optimal post-healed behavior. Full article

The tunnel construction area poses significant challenges for the use of vision technology due to the presence of nonhomogeneous haze fields and low-contrast targets. However, existing dehazing algorithms display weak generalization, leading to dehazing failures, incomplete dehazing, or color distortion in this scenario. Therefore, an adversarial dual-branch convolutional neural network (ADN) is proposed in this paper to deal with the above challenges. The ADN utilizes two branches of the knowledge transfer sub-network and the multi-scale dense residual sub-network to process the hazy image and then aggregate the channels. This input is then passed through a discriminator to judge true and false, motivating the network to improve performance. Additionally, a tunnel haze field simulation dataset (Tunnel-HAZE) is established based on the characteristics of nonhomogeneous dust distribution and artificial light sources in the tunnel. Comparative experiments with existing advanced dehazing algorithms indicate an improvement in both PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity) by 4.07 dB and 0.032 dB, respectively. Furthermore, a binocular measurement experiment conducted in a simulated tunnel environment demonstrated a reduction in the relative error of measurement results by 50.5% when compared to the haze image. The results demonstrate the effectiveness and application potential of the proposed method in tunnel construction. Full article

In recent years, numerous single-image dehazing algorithms have made significant progress; however, dehazing still presents a challenge, particularly in complex real-world scenarios. In fact, single-image dehazing is an inherently ill-posed problem, as scene transmission relies on unknown and nonhomogeneous depth information. This study proposes a novel end-to-end single-image dehazing method called the Integrated Feature Extraction Network (IFE-Net). Instead of estimating the transmission matrix and atmospheric light separately, IFE-Net directly generates the clean image using a lightweight CNN. During the dehazing process, texture details are often lost. To address this issue, an attention mechanism module is introduced in IFE-Net to handle different information impartially. Additionally, a new nonlinear activation function is proposed in IFE-Net, known as a bilateral constrained rectifier linear unit (BCReLU). Extensive experiments were conducted to evaluate the performance of IFE-Net. The results demonstrate that IFE-Net outperforms other single-image haze removal algorithms in terms of both PSNR and SSIM. In the SOTS dataset, IFE-Net achieves a PSNR value of 24.63 and an SSIM value of 0.905. In the ITS dataset, the PSNR value is 25.62, and the SSIM value reaches 0.925. The quantitative results of the synthesized images are either superior to or comparable with those obtained via other advanced algorithms. Moreover, IFE-Net also exhibits significant subjective visual quality advantages. Full article

This numerical study investigates compression and flow field design effects on electrode behaviour in vanadium redox flow batteries (VRFBs). Through 3D simulations and analysis of various flow field designs, including conventional, serpentine, interdigitated, and parallel configurations, this study investigates three compression scenarios: uncompressed, non-homogeneously compressed, and homogeneously compressed electrodes. Hydrodynamic and electrochemical analyses reveal the impact on velocity, pressure, current density, overpotential, and charge–discharge performance. Interdigitated flow field is found to display the lowest charging potential and highest discharging potential among all flow fields under all three compression scenarios. Moreover, uncompressed electrode condition shows the conservative estimates of an average charging potential of 1.3647 V and average discharging potential of 1.3231 V in the case of interdigitated flow field, while compressed electrode condition and the non-homogeneously compressed electrode condition show an average charging potential of 1.3922 V and 1.3777 V, and an average discharging potential of 1.3019 V and 1.3224 V, respectively. Results highlight the significance of non-uniform compression while modelling and analysing the performance of VRFBs as it is a more realistic representation compared to the no-compression or homogeneous compression of the electrodes. The findings of this work provide insights for optimising VRFB performance by considering compression and flow field design. Full article

An innovative non-homogeneous dynamic model is presented for the recovery of temperature during the industrial laser welding process of Al-Si $5\%$ alloy plates. It considers that, metallurgically, during welding, the alloy melts with the presence of solid/liquid phases until total melt is achieved, and afterwards it resolidifies with the reverse process. Further, a polynomial substitute thermal capacity of the alloy is chosen based on experimental evidence so that the volumetric solid-state fraction is identifiable. Moreover, to the usual radiative/convective boundary conditions, the contribution due to the positioning of the plates on the workbench is considered (endowing the model with Cauchy–Stefan–Boltzmann boundary conditions). Having verified the well-posedness of the problem, a Galerkin-FEM approach is implemented to recover the temperature maps, obtained by modeling the laser heat sources with formulations depending on the laser sliding speed. The results achieved show good adherence to the experimental evidence, opening up interesting future scenarios for technology transfer. Full article

Global warming and climate variations are expected to alter hydrologic conditions and exacerbate flooding, primarily through increasingly frequent and intense storm events and sea-level rise. The interactions between coastlines and their inhabitants around the world are highly diverse, making them challenging to model due to the non-homogeneous, nonlinear, and complex nature of human decision-making. Agent-based modeling has proven valuable in various fields, enabling researchers to explore various social phenomena and emergent patterns under different institutional frameworks, including climate change scenarios and policy decisions, particularly at local scales. This approach is particularly useful in providing insights into possible outcomes and feedback resulting from the convergence of individual- and community-level adaptation decisions, and it has increasingly been used to model coastal systems. However, there are a limited number of studies that examine the effects of climate adaptation decisions on coastal tourism systems. This paper aims to address this gap by first providing an overview of the current state of agent-based modeling literature that explores coastal community adaptation responses to climate change. Subsequently, the paper argues for the application of these methods to simulate the effects of adaptation on coastal tourism dynamics. To better capture the interactions within subsystems and potential redistributed effects inherent in multi-scale and multi-stakeholder decision-making processes within these systems, we propose integrating agent-based modeling with a novel system of socio-environmental systems (SoSES) approach. This integration aims to assist city planners, policymakers, stakeholders, and attraction managers in effectively assessing adaptation options to safeguard their communities from the multifaceted impacts of climate change. Full article

The current study examines how different types of passengers (elders, travelers with luggage, travelers without luggage, and mixed population) affect the evacuation process in railway tunnels after a fire accident based on Fractional Effective Dose (FED) index values. A 20 MW diesel pool fire in an immobilized train located inside a straight, rectangular railroad tunnel that is ventilated by a longitudinal jet fan ventilation system is the scenario under consideration. Two fire scenarios were examined, one with and one without ventilation, combined with four evacuation scenarios. The numerical simulation of the fire and the evacuation process is conducted with the Fire Dynamics Simulator and Evacuation code (FDS + Evac) which is a Large Eddy Simulator (LES) for low-Mach thermally driven flows. The results (evacuation times, walking speeds, and mean and max FED values) are compared for each passenger type. It is found that during the evacuation from a railway tunnel fire accident, the most affected population are the elderly because of their lower movement speed, and travelers with luggage because of their increased dimensions. It is also shown that a non-homogenous population has increased uptake of combustion products and longer evacuation times than a homogenous population with similar geometrical characteristics. Full article

In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault detection filter (FDF) implemented in a semi-reliable communication network, it is important to consider the variation in time of the network parameters, by assuming the more accurate scenario provided by a nonhomogeneous jump system. Such a premise can be properly taken into account within the linear parameter varying (LPV) framework. In this sense, this paper proposes a new design method of ${\mathcal{H}}_{\infty }$ gain-scheduled FDF for Markov jump linear systems under the assumption of a nonhomogeneous MC. To illustrate the applicability of the theoretical solution, a numerical simulation is presented. Full article

The evaluation of cell elasticity is becoming increasingly significant, since it is now known that it impacts physiological mechanisms, such as stem cell differentiation and embryogenesis, as well as pathological processes, such as cancer invasiveness and endothelial senescence. However, the results of single-cell mechanical measurements vary considerably, not only due to systematic instrumental errors but also due to the dynamic and non-homogenous nature of the sample. In this work, relying on Chiaro nanoindenter (Optics11Life), we characterized in depth the nanoindentation experimental procedure, in order to highlight whether and how experimental conditions could affect measurements of living cell stiffness. We demonstrated that the procedure can be quite insensitive to technical replicates and that several biological conditions, such as cell confluency, starvation and passage, significantly impact the results. Experiments should be designed to maximally avoid inhomogeneous scenarios to avoid divergences in the measured phenotype. Full article

If baryosynthesis is strongly nonhomogeneous, macroscopic regions with antibaryon excess can be created in the same process from which the baryonic matter is originated. This exotic possibility can become real, if the hints to the existence of antihelium component in cosmic rays are confirmed in the AMS02 experiment, indicating the existence of primordial antimatter objects in our Galaxy. Possible forms of such objects depend on the parameters of models of baryosynthesis and evolution of antimatter domains. We elaborate the formalism of analysis of evolution of antibaryon domain with the account for baryon-antibaryon annihilation at the domain borders and possible “Swiss cheese” structure of the domain structure. We pay special attention to evolution of various forms of high, very high and ultrahigh density antibaryon domains and deduce equations of their evolution in the expanding Universe. The proposed formalism will provide the creation of evolutionary scenarios, linking the possible forms and properties of antimatter bodies in our Galaxy to the mechanisms of nonhomogeneous baryosynthesis. Full article

The main purpose of this research is to investigate the effect of kerbside parking demand and provision on short-term parking (STP) and freight activity space (FAS) as a benchmark for infrastructure planning, considering the impacts of expected future growth and capacity changes. In this study, we adopted a mixed-methods approach of quantitative analysis including a spatial view of parking using manual and video-captured camera data from the majority of STP and FAS parking bays covering a diverse range of loads/tasks with different levels of elasticity and substitutes, as well as simulation of current demand influenced by various factors, as a basis for the development of strategies and prioritisation of the allocation of limited kerbside spaces in Parramatta, a rapidly transforming/growing CBD city centre environment. Parking demand consisted of a diverse range of FAS and STP categories. Spatial analysis showed a non-homogeneous distribution of parking demand and loads across several sections of the city. A large proportion of short-term parking spaces is attributed to two peak periods during the day and increased traffic volumes at peak times. Comparatively lower average parking times in the northern and western regions compared to those in the city centre indicate the potential to reduce peak parking periods and therefore traffic congestion in the city centre by changing parking limits. The presented simulation model can be used as a reliable benchmarking model for the simulation of future impact scenarios and to make recommendations with respect to infrastructure planning and to develop travel demand management strategies. This research is based on a case study and is therefore subject to limitations in its applications in other contexts. Extension of the baseline simulation with future impact scenarios is planned for the next stage of this research. A simulation model is presented and illustrated as a reliable benchmarking tool for the simulation of future impact scenarios through a case study of a rapidly changing city environment. Full article

Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an a priori known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of a priori known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of a priori known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates. Full article

The current study examines the propagation of surface waves in an asymmetric rotating doubly coated nonhomogeneous half space. The coating layers are assumed to be made of different homogeneous isotropic materials, while the overlaying nonhomogeneous half space layer is considered to be of exponentially varying material properties. The consequential exact vibrational displacements and dispersion relation are determined analytically, in addition to the approximate validation of the dispersion relation via the application of an asymptotic procedure within the long wave limit. Two cases of unloaded and loaded end surface scenarios are analyzed by examining the posed fundamental modes. More precisely, an elastic Winkler foundation was considered in the case of a mechanically loaded end surface condition and was found to proliferate the transition between having a fundamental mode over the frequency axis to the wave number axis as the angular velocity increased. Moreover, the rotational effect was found to have a direct impact on the surface wave propagation with a long wave and low frequency. Aside from that, an increase in the nonhomogeneity parameter resulted in propagation with a relatively long frequency. Full article