525 research outputs found

    A stochastic surrogate model for time-variant reliability analysis of flexible multibody system

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    The dynamic model of the flexible multibody systems (FMS) is usually the differential equations with time-variant, high nonlinear and strong coupling characteristics. The traditional reliability models are inefficient to solve these problems. And the reliability model is poor in accuracy and computational efficiency. Based on this point, a new stochastic surrogate model for time-variant reliability analysis of FMS is proposed. Combined model order reduction with generalized polynomial chaos, the stochastic surrogate model is established and the statistical characteristics of system responses are obtained. The calculation method of kinematic time-variant reliability is given. Finally, the effectiveness of the method is verified by a rotating flexible beam. The results show that this method has high computational accuracy compared with Monte Carlo method

    CHEMICAL REACTIVITY OF SIZE-SELECTED SUPPORTED CLUSTERS: COMBINED TPD/XPS STUDIES AND APPARATUS DEVELOPMENT

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    There has been a sustained research interest in the unique reactivities of ultra-small metal and metal oxide clusters. Numerous studies have highlighted the capability of the cluster deposition method to explore the catalytic reactivity of ultra-small clusters with different sizes, stoichiometries, and substrates. In this thesis, two beam line coupled surface analytical apparatus are introduced. The newly constructed one is an upgraded version of the previous one with better mass selection capability and improved geometries for combined temperature-programmed desorption (TPD) and X-ray photoelectron spectroscopy (XPS) analysis. Two combined TPD and XPS studies, which were performed on the previous apparatus, are presented, including ligation and decomposition of 1,6-hexanedithiol, and decomposition of dimethyl methylphosphonate (DMMP) by size selected copper and copper oxide clusters. Several crucial troubleshooting processes for the new apparatus are discussed, and some preliminary experimental results are presented

    DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual Design

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    We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailored for visual design scenarios. Recent T2I models like DALL-E 3 and others, have demonstrated remarkable capabilities in generating photorealistic images that align closely with textual inputs. While the allure of creating visually captivating images is undeniable, our emphasis extends beyond mere aesthetic pleasure. We aim to investigate the potential of using these powerful models in authentic design contexts. In pursuit of this goal, we develop DEsignBench, which incorporates test samples designed to assess T2I models on both "design technical capability" and "design application scenario." Each of these two dimensions is supported by a diverse set of specific design categories. We explore DALL-E 3 together with other leading T2I models on DEsignBench, resulting in a comprehensive visual gallery for side-by-side comparisons. For DEsignBench benchmarking, we perform human evaluations on generated images in DEsignBench gallery, against the criteria of image-text alignment, visual aesthetic, and design creativity. Our evaluation also considers other specialized design capabilities, including text rendering, layout composition, color harmony, 3D design, and medium style. In addition to human evaluations, we introduce the first automatic image generation evaluator powered by GPT-4V. This evaluator provides ratings that align well with human judgments, while being easily replicable and cost-efficient. A high-resolution version is available at https://github.com/design-bench/design-bench.github.io/raw/main/designbench.pdf?download=Comment: Project page at https://design-bench.github.io

    Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

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    Despite the promising progress in multi-modal tasks, current large multi-modal models (LMMs) are prone to hallucinating inconsistent descriptions with respect to the associated image and human instructions. This paper addresses this issue by introducing the first large and diverse visual instruction tuning dataset, named Large-scale Robust Visual (LRV)-Instruction. Our dataset comprises 400k visual instructions generated by GPT4, covering 16 vision-and-language tasks with open-ended instructions and answers. Unlike existing studies that primarily focus on positive instruction samples, we design LRV-Instruction to include both positive and negative instructions for more robust visual instruction tuning. Our negative instructions are designed at three semantic levels: (i) Nonexistent Object Manipulation, (ii) Existent Object Manipulation and (iii) Knowledge Manipulation. To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts. GAVIE does not require human-annotated groundtruth answers and can adapt to diverse instruction formats. We conduct comprehensive experiments to investigate the hallucination of LMMs. Our results demonstrate existing LMMs exhibit significant hallucinations when presented with our negative instructions, particularly Existent Object and Knowledge Manipulation instructions. Moreover, we successfully mitigate hallucination by finetuning MiniGPT4 and mPLUG-Owl on LRV-Instruction while improving performance on several public datasets compared to state-of-the-art methods. Additionally, we observed that a balanced ratio of positive and negative instances in the training data leads to a more robust model.Comment: 40 pages, 32 figures. Under Revie

    Preliminary study of a customised total knee implant with musculoskeletal and dynamic squatting simulation

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    Customised total knee replacement could be the future therapy for knee joint osteoarthritis. A preliminary design of a customised total knee implant based on knee anatomy was studied in this article. To evaluate its biomechanical performance, a dynamic finite element model based on the Oxford knee rig was created to simulate a squatting motion. Unlike previous research, this dynamic model was simulated with patient-specific muscle and joint loads that were calculated from an OpenSim musculoskeletal model. The dynamic response of the customised total knee implant was simulated under three cruciate ligament scenarios: both cruciate ligaments retained, only anterior cruciate ligament removed and both cruciate ligaments removed. In addition, an off-the-shelf symmetric total knee implant with retained cruciate ligaments was simulated for comparison analysis. The customised total knee implant with both cruciate ligaments retained showed larger ranges of femoral external rotation and posterior translation than the symmetric total knee implant. The motion of the customised total knee implant was also in good agreement with a healthy knee. There were no big differences in the tibiofemoral compressive forces in the customised total knee implant model under the three scenarios. These forces were generally consistent with other experimental and simulation results. However, the customised total knee implant design resulted in larger tibiofemoral compressive force than the symmetric total knee implant after 50° knee flexion, which was caused by the larger tibiofemoral relative motion
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