arXiv subjects

Yubo Huang

Publications and source records attributed to Yubo Huang.

24 records · Page 2Linked to original sources

arXiv2024

Study of a Novel Capacitive Pressure Sensor Using Spiral Comb Electrodes

For traditional capacitive pressure sensors, high nonlinearity and poor sensitivity greatly limited their sensing applications. Hence, an innovative design of capacitors based on spiral comb electrodes is proposed for high-sensitivity pressure detection in this work. Compared to traditional capacitive pressure sensors with straight plate electrodes, the proposed sensor with the spiral electrodes increases the overlap areas of electrodes sufficiently, the pressure sensitivity can thus be greatly improved. Moreover, the capacitance variation of the proposed sensor is dominated by the change of the overlap area of the electrodes rather than the electrode's distance, the linearity can also thus be improved to higher than 0.99. Theoretical analysis and COMSOL-based finite element simulation have been implemented for principle verification and performance optimization. Simulation results show that the proposed design has a mechanical sensitivity of 1.5x10-4 m/Pa, capacitive sensitivity of 1.10 aF/Pa, and nonlinear error of 3.63%, respectively, at the pressure range from 0 to 30 kPa. An equivalent experiment has been further carried out for verification. Experimental results also show that both the sensitivity and linearity of capacitive pressure sensors with spiral electrodes are higher than those with straight electrodes. This work not only provides a new avenue for capacitor design, but also can be applied to high-sensitivity pressure detection.

physics.app-ph↗

arXiv2024

Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy

This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings. Medical robotics, particularly in vascular treatments, necessitates precise resource allocation and optimization due to the complex nature of robot and operator maintenance. Traditional heuristic methods, though intuitive, often fail to achieve global optimization. To address these challenges, this research introduces a novel strategy, combining mathematical modeling, a hybrid genetic algorithm, and ARIMA time series forecasting. Considering the dynamic healthcare environment, our approach includes a robust resource allocation model for robotic vessels and operators. We incorporate the unique requirements of the adaptive learning process for operators and the maintenance needs of robotic components. The hybrid genetic algorithm, integrating simulated annealing and greedy approaches, efficiently solves the optimization problem. Additionally, ARIMA time series forecasting predicts the demand for vascular robots, further enhancing the adaptability of our strategy. Experimental results demonstrate the superiority of our approach in terms of optimization, transparency, and convergence speed from other state-of-the-art methods.

cs.NE↗

arXiv2023

A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks. Very recently, Google released Gemini, its newest and most capable MLLM built from the ground up for multi-modality. In light of the superior reasoning capabilities, can Gemini challenge GPT-4V's leading position in multi-modal learning? In this paper, we present a preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities. We compare Gemini Pro with the state-of-the-art GPT-4V to evaluate its upper limits, along with the latest open-sourced MLLM, Sphinx, which reveals the gap between manual efforts and black-box systems. The qualitative samples indicate that, while GPT-4V and Gemini showcase different answering styles and preferences, they can exhibit comparable visual reasoning capabilities, and Sphinx still trails behind them concerning domain generalizability. Specifically, GPT-4V tends to elaborate detailed explanations and intermediate steps, and Gemini prefers to output a direct and concise answer. The quantitative evaluation on the popular MME benchmark also demonstrates the potential of Gemini to be a strong challenger to GPT-4V. Our early investigation of Gemini also observes some common issues of MLLMs, indicating that there still remains a considerable distance towards artificial general intelligence. Our project for tracking the progress of MLLM is released at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.

cs.CV↗

arXiv2020

Soft policy optimization using dual-track advantage estimator

In reinforcement learning (RL), we always expect the agent to explore as many states as possible in the initial stage of training and exploit the explored information in the subsequent stage to discover the most returnable trajectory. Based on this principle, in this paper, we soften the proximal policy optimization by introducing the entropy and dynamically setting the temperature coefficient to balance the opportunity of exploration and exploitation. While maximizing the expected reward, the agent will also seek other trajectories to avoid the local optimal policy. Nevertheless, the increase of randomness induced by entropy will reduce the train speed in the early stage. Integrating the temporal-difference (TD) method and the general advantage estimator (GAE), we propose the dual-track advantage estimator (DTAE) to accelerate the convergence of value functions and further enhance the performance of the algorithm. Compared with other on-policy RL algorithms on the Mujoco environment, the proposed method not only significantly speeds up the training but also achieves the most advanced results in cumulative return.

cs.LG↗

arXiv2020

Comprehensive Investigation and Isolation have Effectively Suppressed the Spread of COVID-19

The outbreak of COVID-19 since Dec. 2019 has caused severe life and economic damage worldwide, many countries are trapped by medical resource constraints or absence of targeted therapeutics, and therefore the implement of systemic policies to block this pandemic should be prioritized. Based on the transmission mechanisms and physicochemical properties of betacoronaviruses, we construct a fine-grained transmission dynamics model (ICRD) to forecast the crucial information of public concern, therein using dynamical coefficients to quantify the impact of the implement time and intensity of containment policies on the spread of epidemic. We find that the comprehensive investigation policy for susceptible population and the quarantine for suspected cases eminently contribute to reduce casualties during the phase of the dramatic increase of diagnosed cases. Statistic evidences strongly suggest that society should take such forceful public health interventions to cut the infection channels in the initial stage until the pandemic is interrupted.

q-bio.PE↗

arXiv2019

Small disturbances can trigger cascading failures in power grids

With the sharp increase of power demand, large-scale blackouts in power grids occur frequently around the world. Cascading failures are the main causes of network outages. Therefore, revealing the complicated cascade mechanism in grids is conducive to design the efficient policy to restrain the failures and further ensure a stable supply of power to users. Motivated by the recent advances of network dynamics, we proposed a framework based Lyapunov stability to analyze the dynamically induced cascading failures in complex networks. We abandoned the assumption that the network is stable in traditional static failure models and then detected that small disturbances actually can trigger cascading failures in unstable networks. What's worse, such failure usually accompanied the overload failure of lines during the equilibriums conversion process. Through the simulation analysis of the Spanish grid, we summarized that the features of this new failure mode include low incidence, large destructiveness, and fast propagation speed. And it usually tends to occur first in edge nodes and subsequently propagate to the central nodes. These features are consistent with the empirical observation of outages in practice.

eess.SY↗