The speedy raise of residential buildings' carbon emissions is a hindrance to achieving China's 2030 carbon peak goal. This study constructs an assessment framework for comprehensive consideration of 30 Chinese provinces' socioeconomic circumstances, energy demand, and emissions reduction technology to meet the consistent coupling degree of equity and efficiency (CDEE). This study is the first to propose an allocation scheme for equilibrating provincial carbon increments for rural and urban residential buildings in 2030 under carbon peaking constraints. The relevant results are fourfold. (1) Residential building's floor area per capita and energy carbon emissions coefficients are the soliddest drivers to facilitate and inhibit the raise of carbon emissions during 2010–2020. (2) Through dynamic Monte Carlo simulation from 2021 to 2030, we demonstrate that provinces with the most gamey carbon emissions in urban and rural areas include Shandong, at 121.52 (± 5.50) Mt. and Hebei, at 61.34 (± 3.08) Mt. in 2030, respectively. (3) A CDEE of 52.3% (biased equity) in urban areas and 34.5% (biased efficiency) in rural areas indicates equilibrated allocation of provincial carbon increment. (4) In the final 2030 allocation scheme, the greatest carbon mitigation pressures are in Beijing (11.34 Mt) and Heilongjiang (3.23 Mt), and the provinces with the largest carbon increment in urban areas include Hebei, Henan, and Guangdong, while the largest carbon increments in rural areas are in Hebei, Henan, and Guangdong. Overall, this study furnishes a targeted and valuable decision making reference for the government to determine provincial carbon peak goals for Chinese residential buildings.
Photometric stereo is a well-established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency. EventPS seamlessly integrates with both optimization-based and deep-learning-based photometric stereo techniques to offer a robust solution for non-Lambertian surfaces. Extensive experiments validate the effectiveness and efficiency of EventPS compared to frame-based counterparts. Our algorithm runs at over 30 fps in real-world scenarios unleashing the potential of EventPS in time-sensitive and high-speed downstream applications.
Deep neural networks can be employed for estimating the direction of arrival (DOA) of individual sound sources from audio signals. Existing methods mostly focus on estimating the DOA of each source on individual frames, without utilizing the motion information of the sources. This paper proposes a method for estimating trajectories of sources, leveraging the differential of trajectories across different time scales. Additionally, a neural network is employed for enhancing the trajectories wrongly estimated especially for sound sources with low-energy. Experimental evaluations conducted on simulated dataset validate that the proposed method achieves more precise localization and tracking performance and encounters less interference when the sound source energy is low.