A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models. [PDF]
Sci Rep, 2023 The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random ...
Zhou Z, Qiu C, Zhang Y.
europepmc +2 more sources
Soft Sensor Design via Switching Observers. [PDF]
Sensors (Basel), 2023 The goal of the paper is the design of soft sensors for single input single output (SISO) nonlinear processes. This goal is of essential importance for process monitoring, fault detection and fault isolation. The observer-based technique, being a fruitful direction in soft sensor design, is followed to develop soft sensors for nonlinear processes with ...
Koumboulis FN +3 more
europepmc +5 more sources
A Miniature Soft Sensor with Origami-Inspired Self-Folding Parallel Mechanism [PDF]
Micromachines, 2022 Miniature soft sensors are crucial for the perception of soft robots. Although centimeter-scale sensors have been well developed, very few works addressed millimeter-scale, three-dimensional-shaped soft sensors capable of measuring multi-axis forces.
Yongqi Shi +8 more
doaj +2 more sources
Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning [PDF]
Sensors, 2023 Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the
Na Lu, Bo Wang, Xianglin Zhu
doaj +2 more sources
A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions. [PDF]
Sensors (Basel), 2022 Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured ...
Andrade P +5 more
europepmc +2 more sources
Chlorophyll soft-sensor based on machine learning models for algal bloom predictions. [PDF]
Sci Rep, 2022 Harmful algal blooms (HABs) are a growing concern to public health and aquatic ecosystems. Long-term water monitoring conducted by hand poses several limitations to the proper implementation of water safety plans.
Mozo A +7 more
europepmc +2 more sources
Soft Sensor Transferability: A Survey [PDF]
Applied Sciences, 2021 Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of process hard-to-measure variables based on their relation with easily accessible ones. They allow implementation of real-time control and monitoring of the plants and present other advantages in terms of costs and efforts.
Francesco Curreri +2 more
openaire +2 more sources
Frequency-Dependent Ultrasonic Stimulation of Poly(N-Isopropylacrylamide) Microgels in Water
Gels, 2022 As a novel stimulus, we use high-frequency ultrasonic waves to provide the required energy for breaking hydrogen bonds between Poly(N-isopropylacrylamide) (PNIPAM) and water molecules while the solution temperature is maintained below the volume phase ...
Atieh Razavi +5 more
doaj +1 more source
An Overview on Recent Advances in Biomimetic Sensors for the Detection of Perfluoroalkyl Substances
Sensors, 2023 Per- and polyfluoroalkyl substances (PFAS) are a class of materials that have been widely used in the industrial production of a wide range of products.
Fatemeh Ahmadi Tabar +8 more
doaj +1 more source
Hittite Journal of Science and Engineering, 2022 In soft robotics, a recent challenge is to decrease the number of rigid components used tocreate entirely soft robots. A common rigid component used in soft robots is the rigid encoder, which should be replaced with a soft counterpart if possible.
Mert Ali İhsan Kalın +3 more
doaj +1 more source