Search

Tropical cyclones (TC) are often generated from pre-existing ``seed" vortices. Seeds with higher persistence might have a higher chance to undergo TC genesis. What controls seed persistence remains unclear. This study proposes that planetary Rossby wave drag is a key factor that affects seed persistence. Using recently developed theory for the response of a vortex to the planetary vorticity gradient, a new parameter given by the ratio of the maximum wind speed ($V_{max}$) to the Rhines speed at the radius of maximum wind ($R_{max}$), here termed ``vortex compactness" ($C_v$), is introduced to characterize the vortex weakening by planetary Rossby wave drag. The relationship between vortex compactness and weakening rate is tested via barotropic $\beta$-plane experiments. The vortex's initial $C_v$ is varied by systematically varying their initial $V_{max}$ and $R_{max}$ in idealized wind profile models. Experiments using vorticity distributions from real-world seeds possessing are also taken from reanalysis. The weakening rate depends strongly on the vortex's initial $C_v$ across both idealized and real-world experiments, and the asymmetry introduces minor differences. Experiments doubling the size of seed vortices cause them to weaken more rapidly in line with other experiment sets. The dependence of the weakening rate on initial compactness can be predicted from a simple theory, which is more robust for more compact vortices. Our results suggest that a seed's structure strongly modulates how long it can persist in the presence of a planetary vorticity gradient. Connections to real seeds on Earth are discussed., Comment: 15 pages, 6 figures

Ultrasonic welding machines play a critical role in the lithium battery industry, facilitating the bonding of batteries with conductors. Ensuring high-quality welding is vital, making tool condition monitoring systems essential for early-stage quality control. However, existing monitoring methods face challenges in cost, downtime, and adaptability. In this paper, we present WeldMon, an affordable ultrasonic welding machine condition monitoring system that utilizes a custom data acquisition system and a data analysis pipeline designed for real-time analysis. Our classification algorithm combines auto-generated features and hand-crafted features, achieving superior cross-validation accuracy (95.8% on average over all testing tasks) compared to the state-of-the-art method (92.5%) in condition classification tasks. Our data augmentation approach alleviates the concept drift problem, enhancing tool condition classification accuracy by 8.3%. All algorithms run locally, requiring only 385 milliseconds to process data for each welding cycle. We deploy WeldMon and a commercial system on an actual ultrasonic welding machine, performing a comprehensive comparison. Our findings highlight the potential for developing cost-effective, high-performance, and reliable tool condition monitoring systems., Comment: 9 pages, 5 figures

Accurate detection of human presence in indoor environments is important for various applications, such as energy management and security. In this paper, we propose a novel system for human presence detection using the channel state information (CSI) of WiFi signals. Our system named attention-enhanced deep learning for presence detection (ALPD) employs an attention mechanism to automatically select informative subcarriers from the CSI data and a bidirectional long short-term memory (LSTM) network to capture temporal dependencies in CSI. Additionally, we utilize a static feature to improve the accuracy of human presence detection in static states. We evaluate the proposed ALPD system by deploying a pair of WiFi access points (APs) for collecting CSI dataset, which is further compared with several benchmarks. The results demonstrate that our ALPD system outperforms the benchmarks in terms of accuracy, especially in the presence of interference. Moreover, bidirectional transmission data is beneficial to training improving stability and accuracy, as well as reducing the costs of data collection for training. To elaborate a little further, we have also evaluated the potential of ALPD for detecting more challenging human activities in multi-rooms. Overall, our proposed ALPD system shows promising results for human presence detection using WiFi CSI signals., Comment: Accepted by IEEE Sensors Journal

Recent work found evidence using aquaplanet experiments that tropical cyclone size on Earth is limited by the Rhines scale, which depends on the planetary vorticity gradient, $\beta$. This study aims to examine how the Rhines scale limits the size of an individual tropical cyclone. The traditional Rhines scale is first re-expressed as a vortex Rhines scale and Rhines speed to characterize how wave effects vary with radius in a vortex whose wind profile is known. Experiments are performed using a simple barotropic model on a $\beta$-plane initialized with a TC-like axisymmetric vortex defined using a recently-developed theoretical model for the tropical cyclone wind profile. $\beta$ and initial vortex size are each systematically varied to investigate the detailed responses of the TC-like vortex to $\beta$. Results show that the vortex shrinks towards an equilibrium size that closely follows the vortex Rhines scale. Physically, this scale divides the vortex into a vortex-dominant region at small radii, where the axisymmetric circulation is steady, and a wave-dominant region at larger radii, where the circulation stimulates Rossby waves and dissipates. A larger initial vortex relative to its vortex Rhines scale will shrink faster, and the shrinking timescale is well described by the vortex Rhines timescale, which is defined as the overturning timescale of the circulation at the vortex Rhines scale and is shown to be directly related to the Rossby wave group velocity. The relationship between our idealized results and the real Earth is discussed., Comment: 17 pages, 10 figures and 1 table

This paper presents a novel preconditioning strategy for the classic 8-point algorithm (8-PA) for estimating an essential matrix from 360-FoV images (i.e., equirectangular images) in spherical projection. To alleviate the effect of uneven key-feature distributions and outlier correspondences, which can potentially decrease the accuracy of an essential matrix, our method optimizes a non-rigid transformation to deform a spherical camera into a new spatial domain, defining a new constraint and a more robust and accurate solution for an essential matrix. Through several experiments using random synthetic points, 360-FoV, and fish-eye images, we demonstrate that our normalization can increase the camera pose accuracy by about 20% without significantly overhead the computation time. In addition, we present further benefits of our method through both a constant weighted least-square optimization that improves further the well known Gold Standard Method (GSM) (i.e., the non-linear optimization by using epipolar errors); and a relaxation of the number of RANSAC iterations, both showing that our normalization outcomes a more reliable, robust, and accurate solution., Comment: Accepted to ICRA 2021