no code implementations • 25 Apr 2024 • Davide Bianchi, Florian Bossmann, Wenlong Wang, Mingming Liu
We present a hybrid method that combines deep learning with iterated graph Laplacian and show its application in acoustic impedance inversion which is a routine procedure in seismic explorations.
no code implementations • 26 Mar 2024 • Yue Ding, Sen Yan, Maqsood Hussain Shah, Hongyuan Fang, Ji Li, Mingming Liu
Furthermore, we provide a comprehensive analysis of energy consumption modelling based on the dataset using a set of representative machine learning algorithms and compare their performance against the contemporary mathematical models as a baseline.
no code implementations • 12 Mar 2024 • Maqsood Hussain Shah, Yue Ding, Shaoshu Zhu, Yingqi Gu, Mingming Liu
In response to the escalating global challenge of increasing emissions and pollution in transportation, shared electric mobility services, encompassing e-cars, e-bikes, and e-scooters, have emerged as a popular strategy.
1 code implementation • 12 Dec 2023 • Sen Yan, Hongyuan Fang, Ji Li, Tomas Ward, Noel O'Connor, Mingming Liu
Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy.
no code implementations • 26 Sep 2023 • Sen Yan, Maqsood Hussain Shah, Ji Li, Noel O'Connor, Mingming Liu
E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector.
no code implementations • 2 Jun 2023 • Hongde Wu, Sen Yan, Mingming Liu
The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks.
1 code implementation • COLING 2022 • Mengting Hu, Hang Gao, Yinhao Bai, Mingming Liu
Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers.
no code implementations • 6 Sep 2022 • Hoa X. Nguyen, Shaoshu Zhu, Mingming Liu
Microservice-based architecture has become prevalent for cloud-native applications.
no code implementations • 2 Jul 2022 • Hongde Wu, Mingming Liu
In this paper, we focus on the detection of traffic flow anomaly due to drivers' lane change intention on the highway traffic networks after a VSL system.
no code implementations • 28 Jun 2022 • Alan F. Smeaton, Aparajita Dey-Plissonneau, Hyowon Lee, Mingming Liu, Michael Scriney
Second language learning can be enabled by tandem collaboration where students are grouped into video conference calls while learning the native language of other student(s) on the calls.
no code implementations • 30 May 2022 • Carlos Muli, Sangyoung Park, Mingming Liu
Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature.
no code implementations • 5 Aug 2021 • Mingming Liu
Recently, there has been an increasing interest in the roll-out of electric vehicles (EVs) in the global automotive market.
no code implementations • 21 Apr 2021 • Zhengyong Chen, Hongde Wu, Noel E. O'Connor, Mingming Liu
Accurately forecasting transportation demand is crucial for efficient urban traffic guidance, control and management.
no code implementations • 15 Apr 2021 • Hongde Wu, Noel E. O'Connor, Jennifer Bruton, Mingming Liu
In this paper, we investigate a key problem of Internet of Things (IoT) applications in practice.
no code implementations • 15 Jan 2021 • Hyowon Lee, Mingming Liu, Hamza Riaz, Navaneethan Rajasekaren, Michael Scriney, Alan F. Smeaton
We can also factor in other criteria into video summary generation such as parts where the student was not paying attention while others in the class were, and parts of the video that other students have replayed extensively which a given student has not.
no code implementations • 10 Oct 2020 • Beiran Chen, Yi Zhang, George Iosifidis, Mingming Liu
This paper models this dynamic computational resource allocation problem into a Markov Decision Process (MDP) and designs a model-based reinforcement-learning agent to optimise the dynamic resource allocation of the CPU usage.