@article{joshi_capezza_alhaji_chow_2023, title={Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems}, volume={10}, ISSN={["2329-9274"]}, DOI={10.1109/JAS.2023.123657}, abstractNote={In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid. The artificial intelligence field has the potential to address the problems in EMS and can provide resilient, efficient, reliable, and scalable solutions. This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids. We analyze EMS methods for centralized, decentralized, and distributed microgrids separately. Then, we summarize machine learning techniques such as ANNs, federated learning, LSTMs, RNNs, and reinforcement learning for EMS objectives such as economic dispatch, optimal power flow, and scheduling. With the incorporation of AI, microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources. However, challenges such as data privacy, security, scalability, explainability, etc., need to be addressed. To conclude, the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.}, number={7}, journal={IEEE-CAA JOURNAL OF AUTOMATICA SINICA}, author={Joshi, Aditya and Capezza, Skieler and Alhaji, Ahmad and Chow, Mo-Yuen}, year={2023}, month={Jul}, pages={1513–1529} }
 @article{joshi_nandi_chester_brown_muller_2018, title={Study of Poly(N-isopropylacrylamide-co-acrylic acid) (pNIPAM) Microgel Particle Induced Deformations of Tissue-Mimicking Phantom by Ultrasound Stimulation}, volume={34}, ISSN={["0743-7463"]}, DOI={10.1021/acs.langmuir.7b02801}, abstractNote={Poly(N-isopropylacrylamide) (pNIPAm) microgels (microgels) are colloidal particles that have been used extensively for biomedical applications. Typically, these particles are synthesized in the presence of an exogenous cross-linker, such as N,N'-methylenebis(acrylamide) (BIS); however, recent studies have demonstrated that pNIPAm microgels can be synthesized in the absence of an exogenous cross-linker, resulting in the formation of ultralow cross-linked (ULC) particles, which are highly deformable. Microgel deformability has been linked in certain cases to enhanced bioactivity when ULC microgels are used for the creation of biomimetic particles. We hypothesized that ultrasound stimulation of microgels would enhance particle deformation and that the degree of enhancement would negatively correlate with the degree of particle cross-linking. Here, we demonstrate in tissue-mimicking phantoms that using ultrasound insonification causes deformations of ULC microgel particles. Furthermore, the amount of deformation depends on the ultrasound excitation frequency and amplitude and on the concentration of ULC microgel particles. We observed that the amplitude of deformation increases with increasing ULC microgel particle concentration up to 2.5 mg/100 mL, but concentrations higher than 2.5 mg/100 mL result in reduced amount of deformation. In addition, we observed that the amplitude of deformation was significantly higher at 1 MHz insonification frequency. We also report that increasing the degree of microgel cross-linking reduces the magnitude of the deformation and increases the optimal concentration required to achieve the largest amount of deformation. Stimulated ULC microgel particle deformation has numerous potential biomedical applications, including enhancement of localized drug delivery and biomimetic activity. These results demonstrate the potential of ultrasound stimulation for such applications.}, number={4}, journal={LANGMUIR}, author={Joshi, Aditya and Nandi, Seema and Chester, Daniel and Brown, Ashley C. and Muller, Marie}, year={2018}, month={Jan}, pages={1457–1465} }