AIMC Topic: Plastics

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Deep learning for detecting macroplastic litter in water bodies: A review.

Water research
Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate t...

A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges.

International journal of environmental research and public health
Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelli...

A strategy to formulate data-driven constitutive models from random multiaxial experiments.

Scientific reports
We present a test technique and an accompanying computational framework to obtain data-driven, surrogate constitutive models that capture the response of isotropic, elastic-plastic materials loaded in-plane stress by combined normal and shear stresse...

Deep learning based approach for automated characterization of large marine microplastic particles.

Marine environmental research
The rapidly growing concern of marine microplastic pollution has drawn attentions globally. Microplastic particles are normally subjected to visual characterization prior to more sophisticated chemical analyses. However, the misidentification rate of...

Multimodal collective swimming of magnetically articulated modular nanocomposite robots.

Nature communications
Magnetically responsive composites can impart maneuverability to miniaturized robots. However, collective actuation of these composite robots has rarely been achieved, although conducting cooperative tasks is a promising strategy for accomplishing di...

Identifying plastics with photoluminescence spectroscopy and machine learning.

Scientific reports
A quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data cha...

Neural representational geometry underlies few-shot concept learning.

Proceedings of the National Academy of Sciences of the United States of America
Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and...

Deep-Learning-Based Character Recognition from Handwriting Motion Data Captured Using IMU and Force Sensors.

Sensors (Basel, Switzerland)
Digitizing handwriting is mostly performed using either image-based methods, such as optical character recognition, or utilizing two or more devices, such as a special stylus and a smart pad. The high-cost nature of this approach necessitates a cheap...

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.

The Analyst
Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medical masks, have a short effective protection time. Their misdisposition and subsequent natural degrad...

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence.

Computational intelligence and neuroscience
Through the research and development of the regression prediction function of support vector machine, this paper applies it to the prediction of drilling fluid performance parameters and the formulation design of drilling fluid. The research in this ...