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1. A Truncated Fourier Based Analytical Model for SRMs with Higher Number of Rotor Poles

   https://doi.org/10.1109/ITEC48692.2020.9161708  

   Jin, Zichao; Jia, Yijiang; Salameh, Mohamad; Bilgin, Berker; Li, Shuwang; Krishnamurthy, Mahesh (June 2020, 2020 IEEE Transportation Electrification Conference & Expo (ITEC))

   Switched reluctance motors (SRM) have been seen as a potential candidate for automotive, aerospace as well as domestic applications and High-Rotor pole SRM (HR-SRM) present a significant advancement in this area. This machine configuration offers most of the the benefits offered by conventional SRMs and has shown significant benefits in efficiency and torque quality. However, HR-SRM has a narrower inductance profile with a lower saliency ratio as compared to a conventional SRM with an identical stator. This can make it inherently challenging to directly adopt mathematical models and sensorless control approaches currently in use. This paper presents a time-efficient analytical model for the characterization of a 6/10 SRM using an inductance model utilizing truncated Fourier series as well as multi-order polynomial curve-fitting algorithm. The inductance model is extended to accurately predict back-EMF and electromagnetic torque response towards obtaining a comprehensive model for every operating point of the machine during dynamic operation. The effectiveness of the proposed concept has analyzed for a prototype machine and verified using Finite Element Analysis (FEA). 

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2. A Hybrid Time-Efficient Modeling Approach for Acoustic Noise Prediction in SRMs

   https://doi.org/10.1109/ITEC51675.2021.9490075  

   Zhang, Ziyan; Jin, Zichao; Chen, Chengxiu; Yaman, Selin; Krishnamurthy, Mahesh (June 2021, 2021 IEEE Transportation Electrification Conference & Expo (ITEC), 2021)

   null (Ed.)

   This study presents a computationally cost-effective modeling approach for a switched reluctance machine (SRM) towards predicting vibration and acoustic noise. In the proposed approach, the SRM is modeled using Finite Element (FE) software for capturing magnetic snapshots from static simulations. Using an advanced field reconstruction method (FRM), these snapshots are used to develop basis functions to estimate magnetic fields under any arbitrary stator excitation and at any desired rotor position. This method includes magnetic properties of the machine and can estimate flux density at once instead of partially predicting it. The vibration model is built in FE software while the acoustic noise is predicted using the analytical method. The proposed study can significantly reduce the computational time for vibration and noise analysis with decent accuracy. Dynamic simulation by finite-element analysis (FEA) software and experimental verification have been carried out to verify the effectiveness of the proposed hybrid model. 

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3. Soybean prediction using computationally efficient Bayesian spatial regression models and satellite imagery

   https://doi.org/10.1002/agj2.21670  

   Fischer, Richard_J; Rekabdarkolaee, Hossein_Moradi; Joshi, Deepak_R; Clay, David_E; Clay, Sharon_A (September 2024, Agronomy Journal)

   Abstract Preharvest yield estimates can be used for harvest planning, marketing, and prescribing in‐season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non‐Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced‐based blue, green, red, and near‐infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP;R²‐testing = 0.485) model, which captures the short‐range correlation, outperformed the (BLR;R²‐testing = 0.02), Bayesian spatial regression (SRM;R²‐testing = 0.087), and Bayesian skewed spatial regression (sSRM;R²‐testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within‐field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lowerR²values and higher execution times than the random forest model. 

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4. Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

   https://doi.org/10.1109/TVCG.2023.3326939  

   Floricel, Carla; Wentzel, Andrew; Mohamed, Abdallah; Fuller, CDavid; Canahuate, Guadalupe; Marai, GElisabeta (January 2024, IEEE Transactions on Visualization and Computer Graphics)

   Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research. 

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5. Performance-oriented multistage design for multi-principal element alloys with low cost yet high efficiency

   https://doi.org/10.1039/D1MH01912K  

   Li, Jia; Xie, Baobin; Li, Li; Liu, Bin; Liu, Yong; Shaysultanov, Dmitry; Fang, Qihong; Stepanov, Nikita; Liaw, Peter K. (May 2022, Materials Horizons)

   Multi-principal element alloys (MPEAs) with remarkable performances possess great potential as structural, functional, and smart materials. However, their efficient performance-orientated design in a wide range of compositions and types is an extremely challenging issue, because of properties strongly dependent upon the composition and composition-dominated microstructure. Here, we propose a multistage-design approach integrating machine learning, physical laws and a mathematical model for developing the desired-property MPEAs in a very time-efficient way. Compared to the existing physical model- or machine-learning-assisted material development, the forward-and-inverse problems, including identifying the target property and unearthing the optimal composition, can be tackled with better efficiency and higher accuracy using our proposed avenue, which defeats the one-step component-performance design strategy by multistage-design coupling constraints. Furthermore, we developed a new multi-phase MPEA at the minimal time and cost, whose high strength-ductility synergy exceeded those of its system and subsystem reported so far by searching for the optimal combination of phase fraction and composition. The present work suggests that the property-guided composition and microstructure are precisely tailored through the newly built approach with significant reductions of the development period and cost, which is readily extendable to other multi-principal element materials. 

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