There is no excerpt because this is a protected post.
November 3 2023 (Friday) 9:00am -12:30pm
This Special TechTalk is the collaborative event with Department of Mechanical Engineering, The University of Hong Kong. The theme of the symposium is about Sustainable Environment for the Greater Bay Area. Experts in this field from various universities and institutes are joined together to share their insight.
November 2 2023 (Thursday) 4-5pm
The Hong Kong Harbour Area Treatment Scheme (HATS) serves a population of over 5 million. It ensures protection of the Tsuen Wan beaches and good water quality in Victoria Harbour. In the Stonecutters Island treatment works, 300 tonnes of 10 percent sodium hypochlorite solution (6 L/s) are dosed into a river of sewage (1.8 million m3/d) every day. In actual operation it is found that most of the chlorine is actually consumed without being used for disinfection. This talk presents an engineering innovation on how to mix the small chlorine dose with the large sewage flow, resulting in up to 30 percent reduction of chlorine demand – with significant savings of chemicals and reduction of carbon footprint of 1170 tonnes/year. The technology is generally applicable to chlorine disinfection of primary effluent in many developing countries.
October 18 2023 (Wednesday) 9:35-11:05 am & October 19 2023 (Thursday) 3:00-4:40 pm
This Special TechTalk is fully supported by GPPS Hong Kong23 (Global Power & Propulsion Society). All are welcome to join two sessions of plenary lectures with the topics of Advanced Computing and Future Energy on 18th October (Wednesday) and 19th October (Thursday) respectively.
October 17, 2023 (Tuesday) 4:30-5:30pm
Proximity sensing is a method of detecting the presence of objects without making physical contact. However, this concept has not been widely explored in the context of granular materials, which are materials composed of small particles like sand or gravel. This is because granular materials have complex properties and the sensing needs to work without the aid of vision. In this presentation, I will introduce a system called GRAINS (Granular Material-Embedded Autonomous Proximity Sensing). GRAINS is designed to sense objects buried within granular materials by utilizing fundamental principles related to the behavior of granules, such as how they flow like a fluid, how they can become jammed. GRAINS uses force signals to determine the proximity of buried objects. It achieves this by analyzing force anomalies that occur when granules become jammed due to their proximity to objects. These force anomalies are learned in real-time by the system using a mathematical technique called Gaussian process regression. To capture these patterns, a probe is moved along a spiral trajectory within the granular material. The results of our experiments demonstrate that GRAINS can adaptively adjust its parameters to effectively work with different types of granules. It can perceive objects in the nearby vicinity, approximately 0.5 to 7 cm ahead, without the need for direct contact with the buried obstacles.
(project page: https://sites.google.com/view/grains2/home)
October 12 2023 (Thursday) 3-4pm
To avoid catastrophic consequences of climate change, our current carbon-emitting energy infrastructure needs to be replaced with an energy system free from atmospheric carbon emissions. The enormous scale of this energy transition requires multiple energy sources to be developed, including carbon-free wind, solar, geothermal, and nuclear as well as fossil-fuel-based systems where the carbon dioxide from the waste stream is captured and stored securely in deep subsurface geologic formations, in a technology known as Carbon Capture and Storage, or CCS. Subsurface geologic formations are also likely to be used to provide short-term storage for energy-carrying fluids like hydrogen and natural gas, making the subsurface environment critical to the energy transition. In this talk, I will discuss practical computational approaches to analyze geological storage systems as well as economic and political issues associated with CCS. I will also briefly discuss basic climate change facts, as part of a proposed general curriculum for Environmental Literacy.
September 21 2023 (Thursday) 4:30-5:30pm
Can machines sense without cameras or sensors? Computer vision allows machines to “see,” but their perception capabilities based on cameras are fundamentally limited to a specific field of view and good lighting conditions – they cannot see through any occlusions or in the dark. In this talk, I will introduce Wireless AI Perception that opens a new sense for machine perception to decipher the physical world, even in absolute darkness and through walls and obstacles. To achieve this, Wireless AI leverages ambient wireless signals for sensing and turns any Wi-Fi devices from a pure communication medium into a ubiquitous all-in-one sensing platform. We will first introduce the concepts, principles, and grand challenges of Wi-Fi sensing, and then share our unique solution of Wireless AI, which has been commercialized and deployed as real-world products, such as motion sensing, sleep monitoring, fall detection, indoor tracking, just to name a few. We foresee that Wi-Fi Sensing will enter billions of devices and millions of homes, and today is just the beginning of this revolution.
September 19, 2023 (Tuesday) 4:30-5:30pm
Driven by the remarkable success of artificial intelligence (AI) and edge computing, the deployment of well-trained private AI models on third-party edge devices for mission-critical applications has become increasingly prevalent. Safeguarding these private models on untrusted devices, while simultaneously speeding up model serving (i.e., inference) through accelerators like GPUs, has escalated in urgency.
We introduce SOTER, a new AI serving system that, for the first time, achieves both high security and high performance. Harnessing the associativity property of AI operators, SOTER presents an innovative approach—transforming computationally expensive AI operators into parameter-morphed equivalents for secure execution on untrusted but fast GPUs, and losslessly restoring inference results within trusted execution environments (TEEs) in CPUs. Experimental results on six prevalent AI models in the three most popular categories show that, even with stronger model protection, SOTER achieves comparable performance with baselines while retaining the same high accuracy as insecure AI model inference.
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.
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.