Rayleigh scattering spectra of high-index {730} elongated tetrahexahedral gold nanoparticles and low-index {100}, {110}, and {111} gold nanorods were collected in real time in the reduction of 4-nitrophenol. The high-index facets are capable of accepting electrons seven times faster and emitting electrons two-and-a-half times faster than low-index facets.
In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep learning methods have recently been applied. In traffic networks with complex spatiotemporal relationships, deep neural networks (DNNs) often perform well because they are capable of automatically extracting the most important features and patterns. In this study, we survey recent STTP studies applying deep networks from four perspectives. 1) We summarize input data representation methods according to the number and type of spatial and temporal dependencies involved. 2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks. 3) We summarize previous STTP studies in terms of the type of DNN techniques, application area, dataset and code availability, and the type of the represented spatiotemporal dependencies. 4) We compile public traffic datasets that are popular and can be used as the standard benchmarks. Finally, we suggest challenging issues and possible future research directions in STTP.INDEX TERMS Artificial intelligence, deep neural network (DNN), intelligent transportation systems (ITS), neural networks, prediction algorithms, short-term traffic prediction (STTP), traffic forecasting
In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep learning methods have recently been applied. In traffic networks with complex spatiotemporal relationships, deep neural networks (DNNs) often perform well because they are capable of automatically extracting the most important features and patterns. In this study, we survey recent STTP studies applying deep networks from four perspectives. 1) We summarize input data representation methods according to the number and type of spatial and temporal dependencies involved. 2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and metalearning networks. 3) We summarize previous STTP studies in terms of the type of DNN techniques, application area, dataset and code availability, and the type of the represented spatiotemporal dependencies. 4) We compile public traffic datasets that are popular and can be used as the standard benchmarks. Finally, we suggest challenging issues and possible future research directions in STTP.
Multiple gas detection in mixed-gas environments is a challenging issue in many engineering industries because some of the gases can raise defect rates and reduce production efficiency. For chemoresistive gas sensors, a precise estimation can be challenging because of the measurement variance and non-linear nature of the gas sensors, especially in a low concentration environment. A simple application of the deep learning models, however, does not yield sufficiently accurate predictions of the concentrations of multiple gases in gas mixtures; thus, it is essential to develop basic strategies for enhancing the accuracy in all possible ways. In this study, we develop a deep learning framework for achieving high accuracy of gas concentration prediction by studying the essential pre-processing techniques, learning task design, and architecture design. For the pre-processing, we study several aspects of processing time-series sensor data and identify the key techniques for complementing deep learning models' limitations. We utilize the mixedgas nature for the learning task design and show that multi-task learning can generate a synergistic effect. Additionally, we show that a further improvement is possible by considering on-off classification as a part of the hybrid learning task. Concerning architecture design, we investigate Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) models after applying the identified pre-processing techniques. CNN outperformed other models in a joint analysis with the learning task. The effectiveness of our framework is confirmed with the UCI gas mixture dataset acquired using a chemical detection platform where 16 chemical sensors are exposed to ethylene, CO, and methane gases. Using the dataset, we study the basic techniques that can be effective to mixed-gas prediction. For the UCI dataset, our deep learning framework achieves a significant improvement in estimation accuracy when compared to the previous studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.