Vibration and stability of internally damped rotating composite Timoshenko shaft

The mechanical model for the dynamic behavior of an internally damped rotating composite shaft is derived using a refined variational asymptotic method and the principle of virtual work. The composite shaft is considered as an anisotropic thin-walled Timoshenko beam. The internal damping of composite shaft is modelled by adopting the multi-scale damping analysis method. Galerkin’s method is employed to discretize and solve the equations of motion. The effect of design parameters including fiber orientation, length aspect ratio, stacking sequences and boundary conditions on the free vibration and stability of composite shaft is investigated

Primary resonance of a rotating composite shaft with geometrical nonlineary

The primary resonance of a simply supported rotating composite shafts with geometrical nonlineary is studied. The composite shaft is modeled as a thin-walled Euler-Bernoulli beam. A variational-asymptotical method (VAM) applied to anisotropic thin-walled closed-cross-sectional beams is used to describe the displacement and strain fields of the composite shafts. The geometrical nonlineary is considered in the relationships of strain and displacement of the shaft. The nonlinear extensional-bending-torsional equations of motion for the composite shaft are derived by using the Hamilton principle. In order to emphatically study nonlinear transverse bending vibration, the effects of extensional and torsional deformations are ignored. By means of the method of multiple scales the approximation solution of primary resonance of transverse bending vibration is obtained. The Galerkin method is employed to reduce the governing equations to the ordinary differential equations. By using fourth-order Runge-Kutta method the time histories, phase diagrams and power spectrums are plotted. The study shows the effect of the external damping, ply angle, eccentricity, ratios of length over radius, ratios of radius over thickness and rotating speed on nonlinear dynamic behavior of the shaft. Specifically, the numerical simulation results show that the shaft exhibits the complex dynamic behavior including periodic, quasi-periodic and chaotic motion

Observer Design for a Core Circadian Rhythm Network

The paper investigates the observer design for a core circadian rhythm network in Drosophila and Neurospora. Based on the constructed highly nonlinear differential equation model and the recently proposed graphical approach, we design a rather simple observer for the circadian rhythm oscillator, which can well track the state of the original system for various input signals. Numerical simulations show the effectiveness of the designed observer. Potential applications of the related investigations include the real-world control and experimental design of the related biological networks

Simulation and Prediction of Storm Surges and Waves Driven by Hurricanes and Assessment of Coastal Flooding and Inundation

River, Estuarine and Coastal Dynamic

Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation

Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent works explore vision-language pre-training to handle this task, but suffer from unrealistic assumptions in practical scenarios, i.e., low-quality textual category names. For example, this paradigm assumes that new textual categories will be accurately and completely provided, and exist in lexicons during pre-training. However, exceptions often happen when meet with ambiguity for brief or incomplete names, new words that are not present in the pre-trained lexicons, and difficult-to-describe categories for users. To address these issues, this work proposes a novel decomposition-aggregation framework, inspired by human cognition in understanding new concepts. Specifically, in the decomposition stage, we decouple class names into diverse attribute descriptions to enrich semantic contexts. Two attribute construction strategies are designed: using large language models for common categories, and involving manually labelling for human-invented categories. In the aggregation stage, we group diverse attributes into an integrated global description, to form a discriminative classifier that distinguishes the target object from others. One hierarchical aggregation is further designed to achieve multi-level alignment and deep fusion between vision and text. The final result is obtained by computing the embedding similarity between aggregated attributes and images. To evaluate the effectiveness, we annotate three datasets with attribute descriptions, and conduct extensive experiments and ablation studies. The results show the superior performance of attribute decomposition-aggregation

Transforming the Interactive Segmentation for Medical Imaging

The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a novel Transformer-based architecture for Interactive Segmentation (TIS), that treats the refinement task as a procedure for grouping pixels with similar features to those clicks given by the end users. Our proposed architecture is composed of Transformer Decoder variants, which naturally fulfills feature comparison with the attention mechanisms. In contrast to existing approaches, our proposed TIS is not limited to binary segmentations, and allows the user to edit masks for arbitrary number of categories. To validate the proposed approach, we conduct extensive experiments on three challenging datasets and demonstrate superior performance over the existing state-of-the-art methods. The project page is: https://wtliu7.github.io/tis/.Comment: Accepted to MICCAI 202

Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models

When trained at a sufficient scale, self-supervised learning has exhibited a notable ability to solve a wide range of visual or language understanding tasks. In this paper, we investigate simple, yet effective approaches for adapting the pre-trained foundation models to the downstream task of interest, namely, open-vocabulary semantic segmentation. To this end, we make the following contributions: (i) we introduce Fusioner, with a lightweight, transformer-based fusion module, that pairs the frozen visual representation with language concept through a handful of image segmentation data. As a consequence, the model gains the capability of zero-shot transfer to segment novel categories; (ii) without loss of generality, we experiment on a broad range of self-supervised models that have been pre-trained with different schemes, e.g. visual-only models (MoCo v3, DINO), language-only models (BERT), visual-language model (CLIP), and show that, the proposed fusion approach is effective to any pair of visual and language models, even those pre-trained on a corpus of uni-modal data; (iii) we conduct thorough ablation studies to analyze the critical components in our proposed Fusioner, while evaluating on standard benchmarks, e.g. PASCAL-5i and COCO-20i , it surpasses existing state-of-the-art models by a large margin, despite only being trained on frozen visual and language features; (iv) to measure the model's robustness on learning visual-language correspondence, we further evaluate on synthetic dataset, named Mosaic-4, where images are constructed by mosaicking the samples from FSS-1000. Fusioner demonstrates superior performance over previous models.Comment: BMVC 2022 Ora

Surface Albedo Variation and Its Influencing Factors over Dongkemadi Glacier, Central Tibetan Plateau

Glacier albedo plays a critical role in surface-atmosphere energy exchange, the variability of which influences glacier mass balance as well as water resources. Dongkemadi glacier in central Tibetan Plateau was selected as study area; this research used field measurements to verify Landsat TM-derived albedo and MOD10A1 albedo product and then analyzed the spatiotemporal variability of albedo over the glacier according to them, as well as its influence factors and the relationship with glacier mass balance. The spatial distribution of glacier albedo in winter did not vary with altitude and was determined by terrain shield, whereas, in summer, albedo increased with altitude and was only influenced by terrain shield at accumulation zone. During 2000–2009, albedo in summer decreased at a rate of 0.0052 per year and was influenced by air temperature and precipitation levels, whereas albedo in winter increased at a rate of 0.0045 per year, influenced by the level and frequency of precipitation. The annual variation of albedo in summer during 2000–2012 has the high relative to that of glacier mass balance measurement, which indicates that glacier albedo in the ablation period can be considered as a proxy for glacier mass balance

Evaluation of Ecosystem Services in Strategic Environmental Assessment for River Basin Planning

Water Resources Planning and Managemen