Results 151 to 160 of about 102,050 (305)

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

open access: yesAdvanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu   +8 more
wiley   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

open access: yesAdvanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding   +5 more
wiley   +1 more source

Benchmarking Coaxial and Angular Optical Emission Spectroscopy With Recommendations for Reliable Compositional In Situ Monitoring During Laser Powder Bed Fusion

open access: yesAdvanced Materials Technologies, EarlyView.
ABSTRACT Real‐time insight into local chemistry is critical for reliable part quality in additive manufacturing, especially laser powder bed fusion (PBF‑LB/M), where rapid thermal cycles and localized evaporation can undermine part performance. Optical emission spectroscopy (OES) offers non‑intrusive, in situ plume monitoring, but detection geometry ...
Philipp Gabriel   +4 more
wiley   +1 more source

Evaluating reinforcement learning for game theory application learning to price airline seats under competition

open access: yes, 2009
Applied Game Theory has been criticised for not being able to model real decision making situations. A game's sensitive nature and the difficultly in determining the utility payoff functions make it hard for a decision maker to rely upon any game ...
Collins, Andrew
core  

The Probably Approximately Correct Learning Model in Computational Learning Theory

open access: yesCoRR
This survey paper gives an overview of various known results on learning classes of Boolean functions in Valiant's Probably Approximately Correct (PAC) learning model and its commonly studied variants.
openaire   +2 more sources

Recent Advances of Slip Sensors for Smart Robotics

open access: yesAdvanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang   +8 more
wiley   +1 more source

Multimodal Haptic Perception Through Synergistic Nanocomposite Sensor Arrays

open access: yesAdvanced Materials Technologies, EarlyView.
Multi‐modal fingertip haptics are advanced through a bioinspired &vertical‐via' electronic skin architecture. A confined PDMS/MWCNT/NiNP nanocomposite, sitting at the percolation threshold, enables tactile, thermal, and magnetic sensing. A unique via‐density gradient and dedicated &Un‐Touch' reference nodes provide robust spatial resolution and signal ...
Amos Bardea, Fernando Patolsky
wiley   +1 more source

Computational models of the development of perceptual expertise

open access: yes, 2007
In a recent article, Palmeri, Wong and Gauthier have argued that computational models may help direct hypotheses about the development of perceptual expertise.
Lane, PCR, Campitelli, G, Gobet, F
core  

Data‐Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning

open access: yesAdvanced Optical Materials, EarlyView.
Dissipative relaxation transfer learning (DIRTL) enables data‐efficient training of electromagnetic surrogate solvers by pretraining data generated with artificial material loss before fine‐tuning on target lossless data. The framework suppresses resonant outlier effects during early training, allowing effective adaptation to high‐amplitude resonances ...
Sunghyun Nam   +2 more
wiley   +1 more source

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