The Artificial Intelligence of Things (AIoT) is the combination of Artificial intelligence (AI) with the Internet of things (IoT) to enable autonomous decision-making, data analytics, and system optimization. AIoT for smart cities allows the collection of enormous sensor data for a better understanding of the environment, human behaviors, and city operations, which leads to more efficient resource management and promotes a sustainable and healthier society. The Smart Water Auditing project aims to use IoT and machine learning to provide insights into how water is being used in the households of Hong Kong to reduce the consumption of water and raise awareness of people’s water consumption habits. Our talk will present our design workflow, IoT infrastructures, machine learning algorithms, and experimentation for water end-use disaggregation in Hong Kong.
Intersections are essential road infrastructures for traffic in modern metropolises; however, they can also be the bottleneck of traffic flows due to traffic incidents or the absence of traffic coordination mechanisms such as traffic lights. Thus, various control and coordination mechanisms that are beyond traditional control methods have been proposed to improve the efficiency of intersection traffic. Amongst these methods, the control of foreseeable hybrid traffic that consists of human-driven vehicles (HVs) and robot vehicles (RVs) has recently emerged. We propose a decentralized reinforcement learning approach for the control and coordination of hybrid traffic at real-world, complex intersections–a topic that has not been previously explored. Comprehensive experiments are conducted to show the effectiveness of our approach. We show that using 5% RVs, we can prevent congestion formation inside the intersection under the actual traffic demand of 700 vehicles per hour. When there exist more than 50% RVs in traffic, our method starts to outperform traffic signals on the average waiting time of all vehicles at the intersection.
Global scarcity and contamination of freshwater pose a significant threat to sustainable development. To address this crisis, reverse osmosis (RO) technology has been playing a pivotal role in desalination and water reuse for freshwater production. The effectiveness of the RO membrane filtration is highly dependent on its surface functional rejection layer. My research focuses on shaping this rejection layer to be a voids-bearing structure, resembling blowing bubbles within the layer. This technique will result in a thinner rejection layer with a larger surface area, favoring water transport. On this basis, shaping branch bubbles to resemble a tree or coral can potentially achieve an exponential increase in water filtration efficiency, resulting in faster production of freshwater with significantly lower energy consumption.
Climate change increases the frequency and intensity of extreme rainfall events and magnifies the threat of rainfall-induced slope failure. The consequences of these failures can be dramatic and devastating if flow slides are triggered. While considerable efforts have been made in the past decades to understand the failure mechanisms and develop techniques to mitigate the hazards, the complexity of interplays of various factors causes it to remain an area of uncertainty and difficulty in geotechnical engineering. This talk will briefly review and discuss the main factors affecting rainfall-induced slope failures from a perspective integrating the geotechnical, hydrological, and climatological aspects. The two deadly landslides in Sau Mau Ping, Hong Kong, in June 1972 and August 1976, which caused 165 casualties, are revisited. We raise an intriguing question that has long been overlooked: why were the slopes able to withstand the 1972 rainfall but failed in the 1976 rainfall event, given that the rainfall intensity of the latter event was only half of the former. We explore the roles of geological and hydrological settings and the rainfall characteristics to look into the causes and mechanisms of these failures. Implications of the new findings for practice will also be discussed.
Soils are vital for several sectors of the economy: transportation, energy, water, food security, historical heritage. Soils deteriorate over time, in response to cyclic processes (seasonal effects) and extreme events (from heatwaves to heavy rainfall). Mitigation is frequently based on intrusive and heavy engineering solutions. In this Tech Talk, Dr. Sérgio Lourenço will focus on how soil properties can be controlled or tuned as needed. Recent advances which borrow on ideas from allied fields, will be presented, from bioengineering to surfaces and interfaces. The potential of adaptable, sensing and self-healing soils as the way forward, will be discussed.
Metals usually exist in form of polycrystalline solids, in which the networks of disordered grain boundaries tend to get eliminated through grain coarsening upon heating or straining, or to transform into metastable amorphous states when the grains are small enough. This is why nano-grained structures in metals are much more unstable relative to their coarse-grained counterparts. Through experiments and molecular dynamic simulations, we recently discovered a novel metastable structure in metals with grains of few nanometers in size, namely Schwarz crystal structure. The GB-network of the metal is characterized by 3D minimal interfaces structure with a zero-mean-curvature constrained by twin boundaries. The unique structure is thermally stable against grain coarsening at temperatures close to the equilibrium melting point and exhibits a hardness in vicinity of the theoretical value. The across-boundary diffusion is so effectively suppressed that the diffusion-controlled processes such as intermetallic precipitation are inhibited. In this presentation, Professor Ke Lu will introduce the formation process, structure characteristics, and some properties of the Schwarz crystal structures in a number of pure metals and alloys.
This lecture presents a rational procedure for the seismic analysis of underground tunnels using recorded free-field earthquakes based on the 2.5D finite/infinite element approach. The near and far fields of the half space are modeled by finite and infinite elements, respectively. Using the 1D wave theory, the nodal force and displacement on the near-field boundary are computed for each spectral frequency of the earthquake. Then, equivalent seismic forces are computed for the near-field boundary for the imposition of earthquake spectrum. By assuming the soil-tunnel system to be uniform along the tunnel axis, the 2.5D approach adopted can duly account for the wave transmission along the tunnel axis, which reduces to the 2D case for infinite transmission velocity. The horizontal and vertical components of the 1999 Chi-Chi Earthquake (TCU068) are adopted as the free-field motions in the numerical analysis. The maximal stresses and distribution patterns of the tunnel section under the P- and SV-waves are thoroughly studied by the 2.5D and 2D approaches, which should prove useful to the design of underground tunnels. Comments on the idea to extend the present approach to include the effect of overlying water, such as the case for the sites below reservoirs, rivers, or sea, will also be pointed out.
Deep neural networks are a powerful tool for the characterization of quantum states. Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized. In this talk, Mr. Yan Zhu, from Department of Computer Science, will introduce a model of network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the states in the fiducial set. With little guidance of quantum physics, the network builds its own data-driven representation of quantum states, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representation produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter.
Photonic platforms with multiplexing capabilities are of profound importance for high-dimensional information processing. In this talk, Professor Nicholas X. Fang will present their recent effort on advancing scalable nanoprinting methods compatible with nanophotonic computing platforms. In the first part, Professor Nicholas X. Fang will discuss an efficient and cost-effective grayscale stencil lithography method to achieve material deposition with spatial thickness variation, for spatially resolved amplitude and phase modulation suitable for flat optics and metasurfaces. The design of stencil shadow masks and deposition strategy offers arbitrarily 2D thickness patterning with low surface roughness. The method is applied to fabricate multispectral reflective filter arrays based on lossy Fabry–Perot-type optical stacks with dielectric layers of variable thickness, which generate a wide color spectrum with high customizability. Grayscale stencil lithography offers a feasible and efficient solution to overcome the thickness-step and material limitations in fabricating spatially thickness-varying structures. In the second part, they show that selective ion doping of oxide electrolyte with electronegative metals shows promise to reproducible resistive switching that are critical for reliable hardware neuromorphic circuits. Based on density functional theory calculations, the underlying mechanism is hypothesized to be the ease of creating oxygen vacancies in the vicinity of electronegative dopants due to the capture of the associated electrons by dopant midgap states and the weakening of Al-O bonds. These oxygen vacancies and vacancy clusters also bind significantly to the dopant, thereby serving as preferential sites and building blocks in the formation of conducting paths. They validate this theory experimentally by implanting different dopants over a range of electronegativities in devices made of multiple alternating layers of alumina and WN and find superior repeatability and yield with highly electronegative metals, Au, Pt, and Pd. These devices also exhibit a gradual SET transition, enabling multibit switching that is desirable for analog computing.