Innovation Wing Two

TechTalk – Simulation, Optimization and Artificial Intelligence for On-demand Ride Service Operations

Innovation Wing Two, TechTalk / By

September 14, 2023 (Thursday) 4:30-5:30pm
On-demand ride services or ride-sourcing services, offered by transportation network companies like Uber, Lyft and Didi, have been experiencing fast development and steadily reshaping the way people travel in the past decade. Various mathematical models and optimization algorithms, including reinforcement learning approaches, have been developed in the literature to help ride-sourcing platforms design better operational strategies to achieve higher operational efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will thus be very important for both researchers and industrial practitioners to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models/algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents (including drivers and passengers) on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. Evaluated by experiments based on real-world datasets, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.

Young Scholar TechTalk – Modeling Uncertainty of Connected Vehicle Penetration Rate: Theory and Application

Innovation Wing Two, TechTalk / By

September 12, 2023 (Tuesday) 4:30-5:30pm
The rapid development of communication technologies enables real-time information exchange between vehicles, thus being virtually connected. However, the full connected vehicle (CV) deployment will take a long time and may never be achievable, due to privacy, security, and willingness. Knowledge of the CV penetration rate is thus crucial for realizing numerous beneficial applications during the prolonged transition period. Although several novel models have been proposed for CV penetration rate estimation, they are solely point estimators. Direct use of these point estimators without considering their variability can lead to biased models or suboptimal solutions. To bridge this research gap, this study proposes a series of analytical models to accurately estimate the variability of CV penetration rate. Comprehensive VISSIM simulations, real-world applications, and a simple CV-based adaptive signal control scheme demonstrate the readiness of the models for use in real-world situations and the potential of the models to improve system optimizations.

Ultrastrong and multifunctional aerogels with hyperconnective network of composite polymeric nanofiber

Innovation Wing Two, Third exhibition / By

Aerogels are lightweight materials with extensive microscale pores, which could be used in thermal insulation, energy devices, aerospace structures, as well as emerging technologies of flexible electronics. However, traditional aerogels based on ceramics tend to be brittle, which limits their performance in load-bearing structures. Due to restrictions posed by their building blocks, recently developed classes of polymeric aerogels can only achieve high mechanical strength by sacrificing their structural porosity or lightweight characteristics.

TechTalk – Quantitative Predictive Theories through Integration of Quantum, Statistical, and Irreversible Thermodynamics

Innovation Wing Two, TechTalk / By

August 28, 2023 (Monday) 2-3pm
Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable. Its derivatives to natural variables give fundamental physico-chemical properties of the system. It is historically divided into four categories: equilibrium thermodynamics by Gibbs, statistical thermodynamics by Gibbs and Landau, irreversible thermodynamics by Onsager and Prigogine, and quantum mechanics. The development of density function theory (DFT) enabled the quantitative prediction of properties of the ground state of a system from quantum mechanics. Their integration into predictive theories will be discussed in this presentation along with future perspectives. It will be shown that the zentropy theory combines the bottom-up DFT predictions with the revised top-down statistical thermodynamics, while the theory of cross phenomena keeps the entropy production due to irreversible processes in the combine law of thermodynamics to revise the Onsager flux equations. The zentropy theory is capable of quantitatively predicting free energy landscape, singularity and emergent divergences of properties at critical point free of parameters, while the theory of cross phenomena can predict the coefficients of internal processes between non-conjugate variables.

DeepPhase: Periodic Autoencoders for Learning Motion Phases Manifolds

Innovation Wing Two, Third exhibition / By

Professor Komura’s research focuses on creating diverse human movements for computer animation, games, and virtual environments. His research team designed a new type of neural network called the Periodic Autoencoder that can identify repeating patterns in large sets of motion data without any additional information. This allows us to create unique movements in various styles, such as dance motions synchronized to music or dribbling movements in soccer. Additionally, the system can help find similar motions in large database, produce natural movements between a small number of key frame poses and estimate human movements even when the body is partially obscured in videos.

From Hospital-centric to Human-centric: “PERfECT” Wearables for Digital Health

Innovation Wing Two, Third exhibition / By

“HKU PERfECT” is the first wearable platform that can simultaneously acquire the following three merits.

1. Highly sensitive: By combing electrochemical technology with microelectronic technology, the highest sensitivity is reached.

2. Smallest and lightest: By using the smallest possible electronic units and marrying emerging stretchable bioelectronic technologies, coin-sized and light (0.5 grams) PERfECT wearables have been used for diagnosis and treatment of various diseases, and rehabilitation.

3. Energy efficient: By using interdisciplinary research strategies spanning analytical chemistry, low-power microelectronics, and low-power wireless communication, PERfECT achieves the highest accuracy with the lowest power consumption, ideal for long-term using.

A Simulation Platform for Shared Mobility Services

Innovation Wing Two, Second exhibition / By

This is a large-scale simulation platform for managing and controlling Hong Kong taxis. The simulator platform can be used to simulate the movements and trajectories of taxis for idle cruising, picking up passengers, and delivering passengers on a large-scale transportation network. The simulation platform is calibrated by a real dataset of Hong Kong taxis to ensure that the simulation well approximates the reality. This simulator is jointly developed by the teams of Dr. Jintao Ke at HKU and Prof. Hai Yang at HKUST. The simulation platform will be open for public use in the near future.