Fifth‐generation (5G) communication technologies and artificial intelligence enable the design and deployment of sophisticated solutions for enhanced user experience and superior network‐based service delivery. However, the performance of the systems offering 5G‐based services depends on various factors. In this paper, we consider the case of the online railway ticketing system in China that serves the needs of hundreds of millions of people daily. This system’s online access rates vary over time, and fluctuations are experienced, affecting its overall dependability and service quality. We use long short‐term memory network, particle swarm optimization, and differential evolution to construct DP‐LSTM—a hybridly optimized model to predict network flow for dependable and quality‐enhanced service delivery. We evaluate the proposed model using real data collected over six months from the “12306 online ticketing” system. We compare the performance of the proposed model with mainstream network traffic prediction models. We use mean absolute percentage error, mean absolute error, and root mean square error for performance evaluation. Experimental results show the superiority of the proposed model.

Abstract In this paper, we propose a novel integrated method for effective modeling and realistic enhancement of scale‐sensitive fluid simulation details. The core of our method is the organic of multi‐layer depth image regression analysis and fluid implicit particle fluid simulation of which the regression analysis induces the criterion where the fluid details should be produced. First, we capture the depth buffer of the fluid surface dynamically from the top of scene. Second, we employ depth peeling technique to decompose the target fluid volume into multiple depth layers and conduct time‐space analysis over surface layers. Third, we propose a logistic regression‐based model to rigorously pinpoint the complex interacting regions, wherein multiple detail‐relevant factors are taken into account based on the captured multiple depth layers. Finally, details are enhanced by animating extra diffuse materials and augmenting the air‐fluid mixing phenomenon. It is evident that, with depth peeling technology, we can afford rigorous analysis not only across surface layers at different fluid depth but along the depth direction as well. After integrating the analysis results from these two sources, we are capable of performing detail enhancement both on the fluid surface and inside the fluid to obtain a great visual effect, even when large occlusion exists. Directly benefiting from the flexibility of image‐space‐dominant processing, our unified framework can be entirely implemented on graphics processing units and thus achieves interactive performance. For various fluid phenomena with different diffuse materials (e.g., spray, foam, and bubble), comprehensive experiments and evaluations have demonstrated its superiority in high‐fidelity fluid detail enhancement and its interaction with surrounding environment.

As the feature size of the semiconductor process is scaling down to 10nm and below, it is possible to assemble systems with high performance processors that can theoretically provide computational power of up to tens of PLOPS. However, the power consumption of these systems is also rocketing up to tens of millions watts, and the actual performance is only around 60% of the theoretical performance. Today, power efficiency and sustained performance have become the main foci of processor designers. Traditional computing architecture such as superscalar and GPGPU are proven to be power inefficient, and there is a big gap between the actual and peak performance. In this paper, we present the MaPU architecture, a novel architecture which is suitable for data-intensive computing with great power efficiency and sustained computation throughput. To achieve this goal, MaPU attempts to optimize the application from a system perspective, including the hardware, algorithm and corresponding program model. It uses an innovative multi-granularity parallel memory system with intrinsic shuffle ability, cascading pipelines with wide SIMD data paths and a state-machine-based program model. When executing typical signal processing algorithms, a single MaPU core implemented with a 40nm process exhibits a sustained performance of 134 GLOPS while consuming only 2.8 W in power, which increases the actual power efficiency by an order of magnitude comparable with the traditional CPU and GPGPU.

This paper advocates a novel integrated method to tightly couple simulation with analysis for the effective modeling and enhancement of scale-aware fluid details. It brings forth a suite of innovations in a unified framework, including depth-image-based space analysis for multi-scale detail detection, time-space analysis based on the logistic regression model that integrates both geometry and physics criteria, and depth-image-based sampling for quality-efficiency tradeoff. Our method contains an intertwined two-level processing architecture at its core. At the analysis level, we propose a rigorous time-space analysis model to pinpoint complex interacting regions, which can take into account multiple detail-relevant factors based on the depth-image sequence captured from FLIP-driven simulation sequence. At the simulation level, details are enhanced by animating extra diffuse materials, and augmenting the air-fluid mixing phenomenon. Directly benefitting from the flexibility of image-space-dominant processing, our unified framework can be entirely implemented on GPU, hence interactive performance could be guaranteed. Comprehensive experiments and evaluations on various diffuse phenomena (e.g., spray, foam, and bubble) have demonstrated its superiority in high-fidelity detail enhancement during fluid simulation and its interaction with surrounding environment for VR applications.

Abstract This paper presents a novel hybrid particle‐grid method that tightly couples Lagrangian particle approach with Eulerian grid approach to simulate multi‐scale diffuse materials varying from disperse droplets to dissipating spray and their natural mixture and transition, originated from a violent (high‐speed) liquid stream. Despite the fact that Lagrangian particles are widely employed for representing individual droplets and Eulerian grid‐based method is ideal for volumetric spray modeling, using either one alone has encountered tremendous difficulties when effectively simulating droplet/spray mixture phenomena with high fidelity. To ameliorate, we propose a new hybrid model to tackle such challenges with many novel technical elements. At the geometric level, we employ the particle and density field to represent droplet and spray respectively, modeling their creation from liquid as well as their seamless transition. At the physical level, we introduce a drag force model to couple droplets and spray, and specifically, we employ Eulerian method to model the interaction among droplets and marry it with the widely‐used Lagrangian model. Moreover, we implement our entire hybrid model on CUDA to guarantee the interactive performance for high‐effective physics‐based graphics applications. The comprehensive experiments have shown that our hybrid approach takes advantages of both particle and grid methods, with convincing graphics effects for disperse droplets and spray simulation.

Abstract In this paper, we present a novel method to couple Smoothed Particle Hydrodynamics (SPH) and nonlinear FEM to animate the interaction of fluids and deformable solids in real time. To accurately model the coupling, we generate proxy particles over the boundary of deformable solids to facilitate the interaction with fluid particles, and develop an efficient method to distribute the coupling forces of proxy particles to FEM nodal points. Specifically, we employ the Total Lagrangian Explicit Dynamics (TLED) finite element algorithm for nonlinear FEM because of many of its attractive properties such as supporting massive parallelism, avoiding dynamic update of stiffness matrix computation, and efficient solver. Based on a predictor‐corrector scheme for both velocity and position, different normal and tangential conditions can be realized even for shell‐like thin solids. Our coupling method is entirely implemented on modern GPUs using CUDA. We demonstrate the advantage of our two‐way coupling method in computer animation via various virtual scenarios.