The Study of Sustainable Energy at UB

Sustainable energy includes solar energy, wind energy, hydropower, geothermal energy, and biomass. It has environmental, health and cost advantages over fossil fuels. Therefore, it attracts more and more attention on the energy harvesting, conversion, and distributed storage. In this paper, the facility and current research topics in sustainable energy are presented. In the mean time, the energy-related courses are introduced for the training of the next generation of engineers

Multiple-Periods Locally-Facet-Based MIP Formulations for the Unit Commitment Problem

The thermal unit commitment (UC) problem has historically been formulated as a mixed integer quadratic programming (MIQP), which is difficult to solve efficiently, especially for large-scale systems. The tighter characteristic reduces the search space, therefore, as a natural consequence, significantly reduces the computational burden. In literatures, many tightened formulations for a single unit with parts of constraints were reported without presenting explicitly how they were derived. In this paper, a systematic approach is developed to formulate tight formulations. The idea is to use more binary variables to represent the state of the unit so as to obtain the tightest upper bound of power generation limits and ramping constraints for a single unit. In this way, we propose a multi-period formulation based on sliding windows which may have different sizes for each unit in the system. Furthermore, a multi-period model taking historical status into consideration is obtained. Besides, sufficient and necessary conditions for the facets of single-unit constraints polytope are provided and redundant inequalities are eliminated. The proposed models and three other state-of-the-art models are tested on 73 instances with a scheduling time of 24 hours. The number of generators in the test systems ranges from 10 to 1080. The simulation results show that our proposed multi-period formulations are tighter than the other three state-of-the-art models when the window size of the multi-period formulation is greater than 2.Comment: 76 pages, 18 figures, 10 tables. This work has been published in IEEE Transactions on Power System

Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph

Due to the ubiquity of graph data on the web, web graph mining has become a hot research spot. Nonetheless, the prevalence of large-scale web graphs in real applications poses significant challenges to storage, computational capacity and graph model design. Despite numerous studies to enhance the scalability of graph models, a noticeable gap remains between academic research and practical web graph mining applications. One major cause is that in most industrial scenarios, only a small part of nodes in a web graph are actually required to be analyzed, where we term these nodes as target nodes, while others as background nodes. In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle the obstacles fundamentally. To this end, we make the first attempt to study the problem of massive background nodes compression for target nodes classification. Through extensive experiments, we reveal two critical roles played by the background nodes in target node classification: enhancing structural connectivity between target nodes, and feature correlation with target nodes. Followingthis, we propose a novel Graph-Skeleton1 model, which properly fetches the background nodes, and further condenses the semantic and topological information of background nodes within similar target-background local structures. Extensive experiments on various web graph datasets demonstrate the effectiveness and efficiency of the proposed method. In particular, for MAG240M dataset with 0.24 billion nodes, our generated skeleton graph achieves highly comparable performance while only containing 1.8% nodes of the original graph.Comment: 21 pages, 11 figures, In Proceedings of the ACM Web Conference 2024 (WWW'24

Classification on Boundary-Equilibria and Singular Continuums of Continuous Piecewise Linear Systems

In this paper, we show that any switching hypersurface of n -dimensional continuous piecewise linear systems is an (n−1) -dimensional hyperplane. For two-dimensional continuous piecewise linear systems, we present local phase portraits and indices near the boundary equilibria (i.e. equilibria at the switching line) and singular continuum (i.e. continuum of nonisolated equilibria) between two parallel switching lines. The index of singular continuum is defined. Then we show that boundary-equilibria and singular continuums can appear with many parallel switching lines

Design of Residential Hydrogen Fueling System in UB Bodine Hall

Pollution and emission from the burning fossil fuel already became a serious environmental issue. To solve this problem, more and more green energy or renewable energy has been used into and impact onto modern society. For instance, solar energy, hydrogen, and wind turbine gradually play an important role in manufactory industry. Using hydrogen fueling system for the residential is good for environments, because the students whose live in dormitory have their own cars. If those cars are hydrogen car, they can use the fueling system to supply the hydrogen to the cars. They do not have to go the hydrogen gas station to gas the hydrogen. And using hydrogen is more eco friendly than using the burning fossil. This design is to produce hydrogen for the hydrogen cars belonged to the residents of Bodine Hall, a dormitory at the UB, through a solar energy powered system. In this design, a Proton-Exchange-Membrane (PEM) eletrolyzer is used as a hydrogen generator, solar panels are used to convert solar energy to electricity for electrolyzer, and a hydrogen compressor system is used to compress hydrogen and store it

Conditional Mutual Information Constrained Deep Learning for Classification

The concepts of conditional mutual information (CMI) and normalized conditional mutual information (NCMI) are introduced to measure the concentration and separation performance of a classification deep neural network (DNN) in the output probability distribution space of the DNN, where CMI and the ratio between CMI and NCMI represent the intra-class concentration and inter-class separation of the DNN, respectively. By using NCMI to evaluate popular DNNs pretrained over ImageNet in the literature, it is shown that their validation accuracies over ImageNet validation data set are more or less inversely proportional to their NCMI values. Based on this observation, the standard deep learning (DL) framework is further modified to minimize the standard cross entropy function subject to an NCMI constraint, yielding CMI constrained deep learning (CMIC-DL). A novel alternating learning algorithm is proposed to solve such a constrained optimization problem. Extensive experiment results show that DNNs trained within CMIC-DL outperform the state-of-the-art models trained within the standard DL and other loss functions in the literature in terms of both accuracy and robustness against adversarial attacks. In addition, visualizing the evolution of learning process through the lens of CMI and NCMI is also advocated

Epi-illumination SPIM for volumetric imaging with high spatial-temporal resolution.

We designed an epi-illumination SPIM system that uses a single objective and has a sample interface identical to that of an inverted fluorescence microscope with no additional reflection elements. It achieves subcellular resolution and single-molecule sensitivity, and is compatible with common biological sample holders, including multi-well plates. We demonstrated multicolor fast volumetric imaging, single-molecule localization microscopy, parallel imaging of 16 cell lines and parallel recording of cellular responses to perturbations