A Computational Model of the Short-Cut Rule for 2D Shape Decomposition

We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition. The model we proposed generates a set of cut hypotheses passing through the points on the silhouette which represent the negative minima of curvature. We then show that most part-cut hypotheses can be eliminated by analysis of local properties of each. Finally, the remaining hypotheses are evaluated in ascending length order, which guarantees that of any pair of conflicting cuts only the shortest will be accepted. We demonstrate that, compared with state-of-the-art shape decomposition methods, the proposed approach achieves decomposition results which better correspond to human intuition as revealed in psychological experiments.Comment: 11 page

Thermodynamic Analysis Of Steam Ejector Refrigeration Cycle

Steam ejectors are capable of drawing large volumes of vapor within a relatively small space and at a low cost. In this study, the compressor is replaced by a constant-area mixing ejector to reduce the energy consumption in refrigeration cycle. The influence of various parameters on the performance of the system is obtained by an iterative program and reasons are analyzed in this paper. The effect of pressure difference, the difference of evaporation pressure and primary nozzle outlet pressure, on the COP and the exergy loss of every component in system is considered. Finally the key points to optimize the ejector cycle and the minimum exergy loss location to optimize the ejector design are obtained by theoretical research. A better understanding for the real industrial application is provided by this theoretical analysis on the steam ejector refrigeration system and a foundation for the simulation and experimental reach is laid

Visual Instruction Tuning

Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.Comment: NeurIPS 2023 Oral; project page: https://llava-vl.github.io

Shear thickening effects of drag-reducing nanofluids for low permeability reservoir

Drag-reducing nanofluids are complex non-Newtonian fluids. Their constitutive characteristics are the basis of flow mechanism analysis in porous media. However, the rheological effects of drag-reducing nanofluids have not been thoroughly studied. In the present work, rheological properties of several nanofluids were measured, and the shear thickening mechanism was investigated experimentally. The results show that all the nanofluids examined have complex characteristics and critical shear rates. The viscosity exhibits a slow linear increase with the shear rate below the critical shear rate, while the shear thickening power-law fluid behaviour appears above the critical shear rate. The critical shear rate increases with the increase of particle concentration, which indicates the injection rate needs to be controlled to avoid significant increase of nanofluids viscosity. The rheological curve of increasing shear rate nearly coincides with that of decreasing shear rate, which indicates that the shear thickening of nanofluids studied in this work is transient and reversible. A constant index constitutive equation with an exponent of 0.5 is obtained from test results by the fixed index method, and its coefficient k(c) is a linear function of the concentration, which can replace a set of conventional constitutive equations with different concentrations. The constant index constitutive equation also clarifies the coefficient dimension. Similar results have been obtained by analysing several other nanofluids using the fixed index method, which validates the new effective method for constructing the constitutive equations of non-Newtonian nanofluids.Cited as: Gu, C., Qiu, R., Liu, S., You, Z., Qin, R. Shear thickening effects of drag-reducing nanofluids for low permeability reservoir. Advances in Geo-Energy Research, 2020, 4(3): 317-325, doi: 10.46690/ager.2020.03.0