205 research outputs found
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
A framework for smart production-logistics systems based on CPS and industrial IoT
Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems
Therblig-embedded value stream mapping method for lean energy machining
To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12% , and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning
An investigation into reducing the spindle acceleration energy consumption of machine tools
Machine tools are widely used in the manufacturing industry, and consume large amount of energy. Spindle acceleration appears frequently while machine tools are working. It produces power peak which is highly energy intensive. As a result, a considerable amount of energy is consumed by this acceleration during the use phase of machine tools. However, there is still a lack of understanding of the energy consumption of spindle acceleration. Therefore, this research aims to model the spindle acceleration energy consumption of computer numerical control (CNC) lathes, and to investigate potential approaches to reduce this part of consumption. The proposed model is based on the principle of spindle motor control and includes the calculation of moment of inertia for spindle drive system. Experiments are carried out based on a CNC lathe to validate the proposed model. The approaches for reducing the spindle acceleration energy consumption were developed. On the machine level, the approaches include avoiding unnecessary stopping and restarting of the spindle, shortening the acceleration time, lightweight design, proper use and maintenance of the spindle. On the system level, a machine tool selection criterion is developed for energy saving. Results show that the energy can be reduced by 10.6% to more than 50% using these approaches, most of which are practical and easy to implement
Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor
Offshore tethered platform springing response statistics
This paper demonstrates the validity of the Naess–Gadai method for extrapolating extreme value statistics of second-order Volterra series processes through application on a representative model of a deep water small size tension leg platform (TLP), with specific focus on wave sum frequency effects affecting restrained modes: heave, roll and pitch. The wave loading was estimated from a second order diffraction code WAMIT, and the stochastic TLP structural response in a random sea state was calculated exactly using Volterra series representation of the TLP corner vertical displacement, chosen as a response process. Although the wave loading was assumed to be a second order (non-linear) process, the dynamic system was modelled as a linear damped mass-spring system. Next, the mean up-crossing rate based extrapolation method (Naess–Gaidai method) was applied to calculate response levels at low probability levels. Since exact solution was available via Volterra series representation, both predictions were compared in this study, namely the exact Volterra and the approximate one. The latter gave a consistent way to estimate efficiency and accuracy of Naess–Gaidai extrapolation method. Therefore the main goal of this study was to validate Naess–Gaidai extrapolation method by available analytical-based exact solution. Moreover, this paper highlights limitations of mean up-crossing rate based extrapolation methods for the case of narrow band effects, such as clustering, typically included in the springing type of response.publishedVersio
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras
We propose DiffuStereo, a novel system using only sparse cameras (8 in this
work) for high-quality 3D human reconstruction. At its core is a novel
diffusion-based stereo module, which introduces diffusion models, a type of
powerful generative models, into the iterative stereo matching network. To this
end, we design a new diffusion kernel and additional stereo constraints to
facilitate stereo matching and depth estimation in the network. We further
present a multi-level stereo network architecture to handle high-resolution (up
to 4k) inputs without requiring unaffordable memory footprint. Given a set of
sparse-view color images of a human, the proposed multi-level diffusion-based
stereo network can produce highly accurate depth maps, which are then converted
into a high-quality 3D human model through an efficient multi-view fusion
strategy. Overall, our method enables automatic reconstruction of human models
with quality on par to high-end dense-view camera rigs, and this is achieved
using a much more light-weight hardware setup. Experiments show that our method
outperforms state-of-the-art methods by a large margin both qualitatively and
quantitatively.Comment: Accepted by ECCV202
Does urbanization have spatial spillover effect on poverty reduction: empirical evidence from rural China
In light of a scarcity of research on the spatial effects of urbanization
on poverty reduction, this study uses panel data on 30 provinces
in China from 2009 to 2019 to construct a system of indices
to assess poverty that spans the four dimensions of the economy,
education, health, and living. We use the spatial autocorrelation
test and the spatial Durbin model (SDM) to analyze the spatial
effects of urbanization on poverty reduction in these different
dimensions. The main conclusions are as follows: (a) China’s
urbanization has the characteristics of spatial aggregation and a
spatial spillover effect. (b) Different dimensions of poverty had
the attributes of spatial agglomeration, and Moran’s index of a
reduction in economic poverty was the highest. Under the SDM,
the different dimensions of poverty also showed a significant
positive spatial correlation. (c) Urbanization has a significant effect
on poverty reduction along the dimensions of the economy, education,
and living, but has little effect on reducing health poverty.
It has a spatial spillover effect on poverty reduction in economic
and living contexts. (d) There were spatial differences in the effect
of urbanization on relieving economic and living-related poverty
Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D
imagery. Towards fine-grained control over facial attributes, recent efforts
incorporate 3D Morphable Face Model (3DMM) to describe deformation in
generative radiance fields either explicitly or implicitly. Explicit methods
provide fine-grained expression control but cannot handle topological changes
caused by hair and accessories, while implicit ones can model varied topologies
but have limited generalization caused by the unconstrained deformation fields.
We propose a novel 3D GAN framework for unsupervised learning of generative,
high-quality and 3D-consistent facial avatars from unstructured 2D images. To
achieve both deformation accuracy and topological flexibility, we propose a 3D
representation called Generative Texture-Rasterized Tri-planes. The proposed
representation learns Generative Neural Textures on top of parametric mesh
templates and then projects them into three orthogonal-viewed feature planes
through rasterization, forming a tri-plane feature representation for volume
rendering. In this way, we combine both fine-grained expression control of
mesh-guided explicit deformation and the flexibility of implicit volumetric
representation. We further propose specific modules for modeling mouth interior
which is not taken into account by 3DMM. Our method demonstrates
state-of-the-art 3D-aware synthesis quality and animation ability through
extensive experiments. Furthermore, serving as 3D prior, our animatable 3D
representation boosts multiple applications including one-shot facial avatars
and 3D-aware stylization.Comment: Project page: https://mrtornado24.github.io/Next3D
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