Thermally responsive liquid crystal elastomers (LCEs) hold great promise in applications of soft robots and actuators because of the induced size and shape change with temperature. Experiments have successfully demons...
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Thermally responsive liquid crystal elastomers (LCEs) hold great promise in applications of soft robots and actuators because of the induced size and shape change with temperature. Experiments have successfully demonstrated that the LCE based bimorphs can be effective soft robots once integrated with soft sensors and thermal actuators. Here, we present an analytical transient thermo-mechanical model for a bimorph structure based soft robot, which consists of a strip of LCE and a thermal inert polymer actuated by an ultra-thin stretchable open-mesh shaped heater to mimic the unique locomotion behaviors of an inchworm. The coupled mechanical and thermal analysis based on the thermo-mechanical theory is carried out to underpin the transient bending behavior, and a systematic understanding is therefore achieved. The key analytical results reveal that the thickness and the modulus ratio of the LCE and the inert polymer layer dominate the transient bending deformation. The analytical results will not only render fundamental understanding of the actuation of bimorph structures, but also facilitate the rational design of soft robotics.
The energy transition, shifting the authors’ primary supply sources evermore from fossil to renewable, leads the energy supply system to face major challenges. In order to prevent the impending overload of the networ...
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The energy transition, shifting the authors’ primary supply sources evermore from fossil to renewable, leads the energy supply system to face major challenges. In order to prevent the impending overload of the networks, the activation of decentralised flexibility is needed. This includes the integration of multi-energy infrastructures as well as smart demand-side management (DSM). The implementation of this paradigm requires new methods for planning and operation. In this study, a suitable modelling and optimisation framework for decentralised sector-coupled energy systems is presented. This framework is extended by a decentralised DSM concept, which is currently being tested for performance and reliability in the bi-national Future Smart Energy project. The tests are carried out in a Smart Grid laboratory and include the integration of hardware and software. This study outlines the DSM concept integration into the modelling framework and presents the first results of the concept's performance testing.
Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intellige...
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Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r...
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnificati...
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In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage...
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Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that parti...
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computervision is widely recognized as an influential technology in the field of precision management of animals. Emerging studies have demonstrated the potential to improve pig health and welfare through animal surv...
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computervision is widely recognized as an influential technology in the field of precision management of animals. Emerging studies have demonstrated the potential to improve pig health and welfare through animal surveillance systems and computervision (CV) algorithms. However, the lack of benchmark datasets and robust fundamental algorithms restrict CV applications for the commercial use. This study aims to bridge the gap between technology development and commercial applications in pig farming scenarios by introducing a general-purpose dataset ( PigLife ), comparing benchmark performances of foundational CV algorithms and model development workflows. The PigLife dataset contains video clips and images (38 short video clips, 2K image frames, 22K pig instances) across most pig production phases in a typical commercial pig farm: Breeding and Gestation, Farrow to Wean, Weaning & Nursery, and Growth to Finish. Three detection algorithms ( Faster R-CNN , RetinaNet , TridentNet ) and three segmentation algorithms ( Mask R-CNN , MViTv2 , Point-Rend ) were trained on the PigLife dataset from scratch. Fine-tuning of pre-trained models ( YOLO8-m , Faster-RCNN-r50 ) and no-training from zero-shot models ( CLIP-SAM , Grouddino-HQSAM ) were also evaluated to suggest faster CV development workflows for commercial applications in pig farming. This study emphasizes the necessity of a benchmark dataset for evaluating the robustness of algorithms and identifying the remaining difficulties and challenges across various algorithms. Furthermore, developing CV models from pre-trained algorithms or zero-shot models showed better performance and a faster process, which could reduce barriers when developing high-performance CV products in pig production industry.
Minimizing Gaussian curvature of meshes is fundamentally important for obtaining smooth and developable surfaces. However, there is a lack of computationally efficient and robust Gaussian curvature optimization method...
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Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In...
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