Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and Automation Letter

Dual Attention on Pyramid Feature Maps for Image Captioning

Generating natural sentences from images is a fundamental learning task for visual-semantic understanding in multimedia. In this paper, we propose to apply dual attention on pyramid image feature maps to fully explore the visual-semantic correlations and improve the quality of generated sentences. Specifically, with the full consideration of the contextual information provided by the hidden state of the RNN controller, the pyramid attention can better localize the visually indicative and semantically consistent regions in images. On the other hand, the contextual information can help re-calibrate the importance of feature components by learning the channel-wise dependencies, to improve the discriminative power of visual features for better content description. We conducted comprehensive experiments on three well-known datasets: Flickr8K, Flickr30K and MS COCO, which achieved impressive results in generating descriptive and smooth natural sentences from images. Using either convolution visual features or more informative bottom-up attention features, our composite captioning model achieves very promising performance in a single-model mode. The proposed pyramid attention and dual attention methods are highly modular, which can be inserted into various image captioning modules to further improve the performance.Comment: in IEEE Transactions on Multimedia, 202

Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction

Masked Cross-image Encoding for Few-shot Segmentation

Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images. The key challenge in FSS is to classify the labels of query pixels using class prototypes learned from the few labeled support exemplars. Prior approaches to FSS have typically focused on learning class-wise descriptors independently from support images, thereby ignoring the rich contextual information and mutual dependencies among support-query features. To address this limitation, we propose a joint learning method termed Masked Cross-Image Encoding (MCE), which is designed to capture common visual properties that describe object details and to learn bidirectional inter-image dependencies that enhance feature interaction. MCE is more than a visual representation enrichment module; it also considers cross-image mutual dependencies and implicit guidance. Experiments on FSS benchmarks PASCAL-$5^i$ and COCO-$20^i$ demonstrate the advanced meta-learning ability of the proposed method.Comment: conferenc

Implementing intermittent current interruption into Li-ion cell modelling for improved battery diagnostics

A novel electroanalytical method, the intermittent current interruption (ICI) technique, has recently been promoted as a versatile tool for battery analysis and diagnostics. The technique enables frequent and continuous measurement of battery resistance, which then undergoes statistical analysis. Here, this method is implemented for commercial Li-ion cylindrical cells, and combined with a physics-based finite element model (FEM) of the battery to better interpret the measured resistances. Ageing phenomena such as solid electrolyte interphase (SEI) formation and metallic Li plating on the surface of the negative graphite particles are considered in the model. After validation, a long-term cycling simulation is conducted to mimic the ageing scenario of commercial cylindrical 21700 cells. The large number of internal resistance measurements obtained are subsequently visualized by creating a ‘resistance map’ as a function of both capacity and cycle numbers, providing a straight-forward image of their continuous evolution. By correlating the observed ageing scenarios with specific physical processes, the origins of ageing are investigated. The result shows that a decrease of the electrolyte volume fraction contributes significantly to the increase of internal resistance and affect the electrolyte diffusivity properties. Additionally, effects of porosity and particle radius of the different electrodes are investigated, providing valuable suggestions for battery design