Projects – Toward Smart CitiesData analytics for smart citiesData are overwhelming in our era, catalyzing the growth of data analytics. However, the escalating volumes and complexity of data have introduced substantial challenges in the collection, management, processing, analysis, and interpretation of the massive data. Reduced-dimensional dynamic data analytics:An intriguing observation is that the information, particularly the dynamics, inherent in many high-dimensional data sets, can be accommodated by a low-dimensional subspace. For example, numerous sensors may be embedded to monitor an autonomous system or an industrial process, and the dynamics of this system or process remain independent of the number of sensors. This fact has sparked a hot topic on the extraction of reduced-dimensional dynamics from high-dimensional data.
Decision-making for smart citiesDecision-making in the context of smart cities primarily involves the strategic allocation of resources. The intricacy of the objectives, the combinatorial nature of decision variables, the real-time requirements, and other complexities of decision-making have posed significant challenges to researchers and engineers. Partial order programming (POP) with majorization-ordered objective valuesReal-world scenarios often require the simultaneous evaluation of multiple criteria and constraints. Therefore, partial order programming POP is introduced to formulate and solve problems whose objective values may not be comparable. Majorization has emerged as a significant partial order to compare two vectors whose components are all nonnegative real numbers. The usefulness of majorization has been elaborated in the monograph written by Marshall, Olkin, and Arnold. We have derived several interesting results on majorization-related POP. Real-life applications include electric vehicle charging, portfolio optimization, multi-agent cooperation, and privacy and security.
Indivisible resource allocation via integer matrix completionIndivisible resource allocation motivates the study of integer matrix completion problems with prescribed row/column sums and preassigned zeros. Several results have been derived for particular (0,1)-matrix and (-1,0,1)-matrix completion problems for electrical energy allocation in smart grids.
Online decision-makingMany resource allocation problems necessitate a real-time implementation where the decisions should be made without future information. The competitive ratios metric can be used to evaluate and guide online decision-making, compared to the ideal but unrealistic ones under a clairvoyant setting or in hindsight. We have developed several competitive online algorithms for reducing the peak procurement of a large-load customer from the grid using energy storage.
Emerging lifestyles for smart citiesThriving within smart cities, our lifestyles evolve due to the demand-centered new services and the dynamic low-altitude economy. Demand-centered service designNew technologies have facilitated the participation of consumers in smart grids and thus the demand side can contribute more to the supply-demand balance in power systems than before. Accordingly, innovative electricity services should be designed and analyzed to elicit and exploit load flexibility.
Low-altitude economyLow-altitude economy refers to a spectrum of economic activities occurring within low-altitude airspace, including a digital platform for urban drone delivery.
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