Dynamic Re-Optimization of a MEMS Controller in Presence of Unmodeled Uncertainties

Online trained neural networks have become popular in recent years in designing robust and adaptive controllers for dynamic systems with uncertainties in their system equations because of their universal function approximation property. This paper discusses a technique that dynamically reoptimizes a Single Network Adaptive Critic (SNAC) based optimal controller in the presence of unmodeled uncertainties. The controller design is carried out in two steps: (i) synthesis of a set of online neural networks that capture the uncertainties in the plant equations on-line (ii) re-optimization of the existing optimal controller to drive the states of the plant to a desired reference by minimizing a predefined cost function. The neural network weight update rule for the online networks has been derived using Lyapunov theory that guarantees stability of the error dynamics as well as boundedness of the weights. This approach has been applied in the online reoptimization of a micro-electromechanical device controller and numerical results from simulation studies are presented here

Diminishing activity of recent solar cycles (22–24) and their impact on geospace

This study examines the variation of different energies linked with the Sun and the Earth’s magnetosphere-ionosphere systems for solar cycles (SCs) 22–24 for which the gradual decrease in the solar activity is noticed. Firstly, we investigated the variation of solar magnetic energy density (SMED) for SCs 21–24 and its relation to the solar activity. We observed distinct double peak structures in SMED for the past four SCs, 21–24. This feature is consistent with noticeable asymmetry in their two peaks. For SCs 22–24 a significant decrease is observed in the integrated SMED of each SC. This reduction is 37% from SCs 22 to 23 and 51% from SCs 23 to 24, which indicates substantial weakening of Sun’s magnetic field for SC 24. Also, the magnetic, kinetic, and thermal energy densities at the Earth’s bow-shock nose are found to be considerably low for the SC 24. We examined the solar wind Alfven speed, magnetosonic Mach number, solar wind-magnetosphere energy coupling parameter (ε), and the Chapman-Ferraro magnetopause distance (LCF) for the SCs 22–24. The estimated maximum stand-off magnetopause distance is larger for SC 24 (LCF ≤ 10.6 RE) as compared to SC 23 (LCF ≤ 10.2 RE) and SC 22 (LCF ≤ 9.8 RE). The solar wind Alfven speeds during SCs 22 and 23 are in the same range and do not exceed ≈73 km/s whereas, it is below 57 km/s for SC 24. A lower bound of solar wind magnetosonic Mach number for SC 24 is larger (M ≥ 6.9) as compared to SC 22 (M ≥ 5.9) and SC 23 (M ≥ 6). We noticed weakening in the energy coupling parameter for SC 24, which resulted in substantial (15%–38%) decrease in average strength of high latitude ionospheric (AE), low latitude magnetospheric (Dst) and equatorial ionospheric (EEJ) current systems in comparison with SC 23. Subsequently, a reduction of ≈30% is manifested in the high latitude Joule heating for SC 24. Overall this study indicates the significant step down in various energies at Sun, Earth’s bow-shock, and near Earth environment for current SC 24, which will have important implication on our Earth’s atmosphere-ionosphere-magnetosphere system

SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP

Preface

[No abstract available

STOCHASTIC COLOURED PETRINET BASED HEALTHCARE INFRASTRUCTURE INTERDEPENDENCY MODEL

The Healthcare Critical Infrastructure (HCI) protects all sectors of the society from hazards such as terrorism, infectious disease outbreaks, and natural disasters. HCI plays a significant role in response and recovery across all other sectors in the event of a natural or manmade disaster. However, for its continuity of operations and service delivery HCI is dependent on other interdependent Critical Infrastructures (CI) such as Communications, Electric Supply, Emergency Services, Transportation Systems, and Water Supply System. During a mass casualty due to disasters such as floods, a major challenge that arises for the HCI is to respond to the crisis in a timely manner in an uncertain and variable environment. To address this issue the HCI should be disaster prepared, by fully understanding the complexities and interdependencies that exist in a hospital, emergency department or emergency response event. Modelling and simulation of a disaster scenario with these complexities would help in training and providing an opportunity for all the stakeholders to work together in a coordinated response to a disaster. The paper would present interdependencies related to HCI based on Stochastic Coloured Petri Nets (SCPN) modelling and simulation approach, given a flood scenario as the disaster which would disrupt the infrastructure nodes. The entire model would be integrated with Geographic information based decision support system to visualize the dynamic behaviour of the interdependency of the Healthcare and related CI network in a geographically based environment

New Nonlinear Observer Design with Application to Electrostatic Micro-Actuators

In many practical applications it is not possible to measure all the states required to control the system. In such instances observer/filter is used to give a good estimate of the states of the system. The objective of the observer is to estimate the states such that the error between the actual and computed measurements goes to zero and obtain the best estimates of the states of a given system. In the current study a new nonlinear observer structure is proposed. The development of the observer is based on optimal control theory. A cost function is defined in terms of the measurement residual and the magnitude of correction term. The observer gains are obtained by minimizing the cost function with respect to the magnitude of corrections. The proposed observer is used to estimate the states of a one-dimensional electrostatic micro-actuator. The states of the actuator dynamics are, charge on the capacitor plates, the distance between the plates and the relative velocity between the plates. The regulation of the actuator states to desired trajectories is achieved through optimal control based state feedback. However in practice it is very difficult to measure the relative position and velocity of the plates. In this paper optimal feedback control based on the state estimates provided by the observer is used to regulate the actuator states to the desired location

3D Object Detection in LiDAR Point Clouds using Graph Neural Networks

LiDAR (Light Detection and Ranging) is an advanced active remote sensing technique working on the principle of time of travel (ToT) for capturing highly accurate 3D information of the surroundings. LiDAR has gained wide attention in research and development with the LiDAR industry expected to reach 2.8 billion $ by 2025. Although the LiDAR dataset is of rich density and high spatial resolution, it is challenging to process LiDAR data due to its inherent 3D geometry and massive volume. But such a high-resolution dataset possesses immense potential in many applications and has great potential in 3D object detection and recognition. In this research we propose Graph Neural Network (GNN) based framework to learn and identify the objects in the 3D LiDAR point clouds. GNNs are class of deep learning which learns the patterns and objects based on the principle of graph learning which have shown success in various 3D computer vision tasks.Comment: Errors in the results section. Experiments are carried out to rectify the result