Sensorless Control of Surface-Mount Permanent-Magnet Synchronous Motors Based on a Nonlinear Observer

International audienceA nonlinear observer for surface-mount permanent-magnet synchronous motors (SPMSMs) was recently proposed by Ortega et al.(LSS, Gif-sur-Yvette Cedex, France, LSS Internal Rep., Jan. 2009). The nonlinear observer generates the position estimate hat(theta) via the estimates of sin theta and cos theta. In contrast to Luenberger-type observers, it does not require speed information, thus eliminating the complexity associated with speed estimation errors. Further, it is simple to implement. In this study, the nonlinear observer performance is verified experimentally. To obtain speed estimates from the position information, a proportional-integral (PI) tracking controller speed estimator was utilized. The results are good with and without loads, above 10 r/min

Out-of-school STEM Program for Students with Visual Impairments: Adaptations and Outcomes During the COVID-19 Pandemic

Although previous research exists on making adaptations for students with visual impairments in online settings, there is limited research on the teaching and learning dynamics of students with visual impairments during the COVID-19 pandemic. Since responses to the pandemic made it difficult for students with visual impairments to participate in educational opportunities that require hands-on experiences, gaps have been identified in gaining access to educational opportunities. The current project was originally planned with programs based on in-person modes, aimed at increasing motivation and awareness of science, technology, engineering, and math of students with visual impairments. Due to limitations of in-person participation, substantial modifications and adaptations were required for the programs to be meaningful and effective when delivered in online environments. It was found that proficiency in the use of technology options, specific instruction and guidance for access of information, and program planning in advance were the three key elements for successful implementation of the programs. This article documents 1) existing research on the impacts of the pandemic, 2) meaningful adaptations and modifications, 3) essential elements for developing online programs in STEM, and 4) identified strategies in program transition for students with visual impairments. Some skills may not be most efficiently taught through online interactions, however participation of family members, careful modifications of existing activities, and sufficient level of technology support allows many skills to be acquired through online learning. Most importantly, strong collaboration of participating team members makes it possible for students with visual impairments to participate equitably in online environments

Long-term efficacy, safety and immunogenicity in patients with rheumatoid arthritis continuing on an etanercept biosimilar (LBEC0101) or switching from reference etanercept to LBEC0101: an open-label extension of a phase III multicentre, randomised, double-blind, parallel-group study

Background To evaluate the long-term efficacy, safety and immunogenicity of continuing LBEC0101; the etanercept (ETN) biosimilar; or switching from the ETN reference product (RP) to LBEC0101 in patients with rheumatoid arthritis (RA). Methods This multicentre, single-arm, open-label extension study enrolled patients who had completed a 52-week randomised, double-blind, parallel phase III trial of LBEC0101 vs ETN-RP. Patients treated with ETN-RP during the randomised controlled trial switched to LBEC0101; those treated with LBEC0101 continued to receive LBEC0101 in this study. LBEC0101 (50 mg) was administered subcutaneously once per week for 48 weeks with a stable dose of methotrexate. Efficacy, safety and immunogenicity of LBEC0101 were assessed up to week 100. Results A total of 148 patients entered this extension study (70 in the maintenance group and 78 in the switch group). The 28-joint disease activity scores (DAS28)-erythrocyte sedimentation rate (ESR) were maintained in both groups from week 52 to week 100 (from 3.068 to 3.103 in the maintenance group vs. from 3.161 to 3.079 in the switch group). ACR response rates at week 100 for the maintenance vs. switch groups were 79.7% vs. 83.3% for ACR20, 65.2% vs. 66.7% for ACR50 and 44.9% vs. 42.3% for ACR70. The incidence of adverse events and the proportion of patients with newly developed antidrug antibodies were similar in the maintenance and switch groups (70.0% and 70.5%, 1.4% and 1.3%, respectively). Conclusions Administration of LBEC0101 showed sustained efficacy and acceptable safety in patients with RA after continued therapy or after switching from ETN-RP to LBEC0101. Trial registration ClinicalTrials.gov, NCT02715908. Registered 22 March 2016.This extension study was funded by LG Chem, Ltd. (formerly, LG Life Sciences, Ltd), Mochida Pharmaceutical Co., Ltd. and Korea Health Industry Development Institute

Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index

Load forecasting techniques can be an essential method to save energy and shave peak loads in order to improve energy efficiency and maintain the stability of a power grid. To achieve this goal, machine learning-based approaches have been proposed recently. Before moving toward the long-term and ultimate solution such as machine learning, we propose a simple and efficient method to forecast electricity usage patterns and the duration of maximum electrical load using a small data set. The proposed algorithm can forecast maximum electrical load duration using random sampling and a cumulative slope index. To verify the algorithm, we utilized electricity data (from 2015.11 to 2016.12) obtained from a building with a constant lifestyle and electricity pattern. The performance of the algorithm was evaluated using electricity bills, the discharging condition of an energy storage system, and the cumulative slope index. It was found that the proposed algorithm could provide electricity cost savings of 0.62–2.28% compared with other, conventional electricity prediction techniques, such as the moving average method and exponential smoothing. In near future research, it is expected that this algorithm could be applied to electrical big data to handle real-time data processing

Soil Phosphorus Landscape Models for Precision Soil Conservation

Phosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a precision soil conservation approach combining finescale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment