An official website of the United States government
Spatio-temporal deep learning has drawn a lot of attention since many downstream real-world applications can benefit from accurate predictions. For example, accurate prediction of heavy rainfall events is essential for effective urban water usage, flooding warning, and mitigation. In this paper, we propose a strategy to leverage spatially connected real-world features to enhance prediction accuracy. Specifically, we leverage spatially connected real-world climate data to predict heavy rainfall risks in a broad range in our case study. We experimentally ascertain that our Trans-Graph Convolutional Network (TGCN) accurately predicts heavy rainfall risks and real estate trends, demonstrating the advantage of incorporating external spatially-connected real-world data to improve model performance, and it shows that this proposed study has a significant potential to enhance spatio-temporal prediction accuracy, aiding in efficient urban water usage, flooding risk warning, and fair housing in real estate.
more »
« less
Ultrasonic Bubble Cleaner as a Sustainable Solution
We aim to develop a floor-cleaning design by exploiting oscillating bubbles combined with ambient pressure waves to clean various surfaces. Previous studies of this method in lab settings have proven its efficacy, but practical applications, especially concerning real-world conditions like dirt surfaces, remain largely unprobed. Our findings indicate that, excluding a configuration with a heavy mass bottom transducer, all tested configurations achieved approximately 60–70% cleaning performance. A slight improvement in cleaning performance was observed with the introduction of microbubbles, although it was within the error margin. Particularly noteworthy is the substantial reduction in water consumption in configurations with a water pocket, decreasing from 280 mL to a mere 3 mL, marking a significant step toward more environmentally sustainable cleaning practices, such as reduced water usage. This research provides implications for real-world cleaning applications, promising an eco-friendly and efficient cleaning alternative that reduces water usage and handles a variety of materials without causing damage.
more »
« less
- Award ID(s):
- 2002714
- PAR ID:
- 10520035
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Fluids
- Volume:
- 8
- Issue:
- 11
- ISSN:
- 2311-5521
- Page Range / eLocation ID:
- 291
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Among promising applications of metal‐halide perovskite, the most research progress is made for perovskite solar cells (PSCs). Data from myriads of research work enables leveraging machine learning (ML) to significantly expedite material and device optimization as well as potentially design novel configurations. This paper represents one of the first efforts in providing open‐source ML tools developed utilizing the Perovskite Database Project (PDP), the most comprehensive open‐source PSC database to date with over 43 000 entries from published literature. Three ML model architectures with short‐circuit current density (Jsc) as a target are trained exploiting the PDP. Using the XGBoost architecture, a root mean squared error (RMSE) of 3.58 , R2of 0.35 and a mean absolute percentage error (MAPE) of 9.49% are achieved. This performance is comparable to results reported in literature, and through further investigation can likely be improved. To overcome challenges with manual database creation, an open‐source data cleaning pipeline is created for PDP data. Through the creation of these tools, which have been published on GitHub, this research aims to make ML available to aid the design for PSC while showing the already promising performance achieved. The tools can be adapted for other applications, such as perovskite light‐emitting diodes (PeLEDs), if a sufficient database is available.more » « less
-
Chen, Guohua; Khan, Faisal (Ed.)Artificial intelligence (AI) and machine learning (ML) are novel techniques to detect hidden patterns in environmental data. Despite their capabilities, these novel technologies have not been seriously used for real-world problems, such as real-time environmental monitoring. This survey established a framework to advance the novel applications of AI and ML techniques such as Tiny Machine Learning (TinyML) in water environments. The survey covered deep learning models and their advantages over classical ML models. The deep learning algorithms are the heart of TinyML models and are of paramount importance for practical uses in water environments. This survey highlighted the capabilities and discussed the possible applications of the TinyML models in water environments. This study indicated that the TinyML models on microcontrollers are useful for a number of cutting-edge problems in water environments, especially for monitoring purposes. The TinyML models on microcontrollers allow for in situ real-time environmental monitoring without transferring data to the cloud. It is concluded that monitoring systems based on TinyML models offer cheap tools to autonomously track pollutants in water and can replace traditional monitoring methods.more » « less
-
null (Ed.)Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss critical opportunities to leverage the statistical nature of ML. In this paper, we present WILLUMP, an optimizer for ML inference that introduces two statistically-motivated optimizations targeting ML applications whose performance bottleneck is feature computation. First, WILLUMP automatically cascades feature computation for classification queries: WILLUMP classifies most data inputs using only high-value, low-cost features selected through empirical observations of ML model performance, improving query performance by up to 5× without statistically significant accuracy loss. Second, WILLUMP accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model and then ranking the remainder with a more powerful model, improving query performance by up to 10× with minimal accuracy loss. WILLUMP automatically tunes these optimizations’ parameters to maximize query performance while meeting an accuracy target. Moreover, WILLUMP complements these statistical optimizations with compiler optimizations to automatically generate fast inference code for ML applications. We show that WILLUMP improves the end-to-end performance of real-world ML inference pipelines curated from major data science competitions by up to 16× without statistically significant loss of accuracy.more » « less
-
Photocatalytic self-cleaning coatings with a high surface area are important for a wide range of applications, including optical coatings, solar panels, mirrors, etc. Here, we designed a highly porous TiO2 coating with photoinduced self-cleaning characteristics and very high hydrophilicity. This was achieved using the swelling-assisted sequential infiltration synthesis (SIS) of a block copolymer (BCP) template, which was followed by polymer removal via oxidative thermal annealing. The quartz crystal microbalance (QCM) was employed to optimize the infiltration process by estimating the mass of material infiltrated into the polymer template as a function of the number of SIS cycles. This adopted swelling-assisted SIS approach resulted in a smooth uniform TiO2 film with an interconnected network of pores. The synthesized film exhibited good crystallinity in the anatase phase. The resulting nanoporous TiO2 coatings were tested for their functional characteristics. Exposure to UV irradiation for 1 h induced an improvement in the hydrophilicity of coatings with wetting angle reducing to unmeasurable values upon contact with water droplets. Furthermore, their self-cleaning characteristics were tested by measuring the photocatalytic degradation of methylene blue (MB). The synthesized porous TiO2 nanostructures displayed promising photocatalytic activity, demonstrating the degradation of approximately 92% of MB after 180 min under ultraviolet (UV) light irradiation. Thus, the level of performance was comparable to the photoactivity of commercial anatase TiO2 nanoparticles of the same quantity. Our results highlight a new robust approach for designing hydrophilic self-cleaning coatings with controlled porosity and composition.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/fluids8110291
