Impulsive observer design for a class of switched nonlinear systems with unknown inputs

The final publication is available at Elsevier via https://doi.org/10.1016/j.jfranklin.2019.05.039. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper investigates hybrid observer design of a class of unknown input switched nonlinear systems. The distinguishing feature of the proposed method is that the stability of all subsystems of the error switched systems is not necessarily required. First, an output derivative-based method and time-varying coordinate transformation are considered to eliminate the unknown input. Then in order to maintain a satisfactory estimation performance, an impulsive full-order and switched reduced-order observer are developed with a pair of upper and lower dwell time bounds and constructing time-varying Lyapunov functions combined with convex combination technique. In addition, the time-varying Lyapunov functions method is also used to analyze the stability of a class of error switched nonlinear systems with stable subsystems. Finally, two examples are presented to demonstrate the effectiveness of the proposed method.This work is supported by National Natural Science Foundation of China (61473173 and 61703353), Major International (Regional) Joint Research Project of the National Natural Science Foundation of China (NSFC) (61320106011), Natural Sciences and Engineering Research Council of Canada (NSERC)

Pixelwise Complex-Valued Neural Network Based on 1D FFT of Hyperspectral Data to Improve Green Pepper Segmentation in Agriculture

It seems difficult to recognize an object from its background with similar color using conventional segmentation methods. An efficient way is to utilize hyperspectral images that contain more wave bands and richer information than only RGB components. Particularly in our task, we aim to separate a pepper from densely packed green leaves for automatic picking in agriculture. Given that hyperspectral imaging can be regarded as a kind of wave propagation process, we make a novel attempt of introducing a complex neural network tailored for wave-related problems. Due to the lack of hyperspectral data, pixelwise training is deployed, and 1D fast Fourier transform of the hyperspectral data is used for the construction of complex input. Experimental results have showcased that a complex neural network outperforms a real-valued one in terms of detection accuracy by 3.9% and F1 score by 1.33%. Moreover, it enables the ability to select frequency bands used such as low-frequency components to boost performance as well as prevent overfitting problems for learning more generalization features. Thus, we put forward a lightweight pixelwise complex model for hyperspectral-related problems and provide an efficient way for green pepper automatic picking in agriculture using small datasets

Study protocol for the Sino-Canadian Healthy Life Trajectories Initiative (SCHeLTI): a multicentre, cluster-randomised, parallel-group, superiority trial of a multifaceted community-family-mother-child intervention to prevent childhood overweight and obesity

Introduction Childhood overweight and obesity (OWO) is a primary global health challenge. Childhood OWO prevention is now a public health priority in China. The Sino-Canadian Healthy Life Trajectories Initiative (SCHeLTI), one of four trials being undertaken by the international HeLTI consortium, aims to evaluate the effectiveness of a multifaceted, community-family-mother-child intervention on childhood OWO and non-communicable diseases risk.Methods and analysis This is a multicentre, cluster-randomised, controlled trial conducted in Shanghai, China. The unit of randomisation is the service area of Maternal Child Health Units (N=36). We will recruit 4500 women/partners/families in maternity and district level hospitals. Participants in the intervention group will receive a multifaceted, integrated package of health promotion interventions beginning in preconception or in the first trimester of pregnancy, continuing into infancy and early childhood. The intervention, which is centred on a modified motivational interviewing approach, will target early-life maternal and child risk factors for adiposity. Through the development of a biological specimen bank, we will study potential mechanisms underlying the effects of the intervention. The primary outcome for the trial is childhood OWO (body mass index for age ≥85th percentile) at 5 years of age, based on WHO sex-specific standards. The study has a power of 0.8 (α=0.05) to detect a 30% risk reduction in the proportion of children with OWO at 5 years of age, from 24.4% in the control group to 17% in the intervention group. Recruitment was launched on 30 August 2018 for the pilot study and 10 January 2019 for the formal study.Ethics and dissemination The study has been approved by the Medical Research Ethics Committee of the International Peace Maternity and Child Health Hospital in Shanghai, China, and the Research Ethics Board of the Centre Intégré Universitaire de Santé et Services Sociaux de l’Estrie–CHUS in Sherbrooke, Canada. Data sharing policies are consistent with the governance policy of the HeLTI consortium and government legislation.Trial registration number ChiCTR1800017773.Protocol version November 11, 2020 (Version #5)