Robust and Efficient Swarm Communication Topologies for Hostile Environments

Swarm Intelligence-based optimization techniques combine systematic exploration of the search space with information available from neighbors and rely strongly on communication among agents. These algorithms are typically employed to solve problems where the function landscape is not adequately known and there are multiple local optima that could result in premature convergence for other algorithms. Applications of such algorithms can be found in communication systems involving design of networks for efficient information dissemination to a target group, targeted drug-delivery where drug molecules search for the affected site before diffusing, and high-value target localization with a network of drones. In several of such applications, the agents face a hostile environment that can result in loss of agents during the search. Such a loss changes the communication topology of the agents and hence the information available to agents, ultimately influencing the performance of the algorithm. In this paper, we present a study of the impact of loss of agents on the performance of such algorithms as a function of the initial network configuration. We use particle swarm optimization to optimize an objective function with multiple sub-optimal regions in a hostile environment and study its performance for a range of network topologies with loss of agents. The results reveal interesting trade-offs between efficiency, robustness, and performance for different topologies that are subsequently leveraged to discover general properties of networks that maximize performance. Moreover, networks with small-world properties are seen to maximize performance under hostile conditions

School-based mental health promotion in children and adolescents with StresSOS using online or face-to-face interventions: study protocol for a randomized controlled trial within the ProHEAD Consortium

Abstract Background Schools are an ideal setting in which to promote health. However, empirical data on the effectiveness of school-based mental health promotion programs are rare, and research on universal Internet-based prevention in schools is almost non-existent. Following the life skills approach, stress management training is an important component of health promotion. Mental health literacy is also associated with mental health status, and it facilitates formal help-seeking by children and adolescents (C&A). The main objectives of this study are (1) the development and evaluation of an Internet-based version of a universal school-based health promotion program called StresSOS and (2) demonstrating non-inferiority of the online setting compared to the face-to-face setting. StresSOS aims to improve stress management and mental health literacy in C&A. Methods/design A school-based sample of 15,000 C&A (grades 6–13 and older than 12 years) will be recruited in five regions of Germany within the ProHEAD Consortium. Those with a screening result at baseline indicating no mental health problems will be invited to participate in a randomized controlled trial comparing StresSOS online to an active online control condition (Study A). In addition, 420 adolescents recruited as a separate school-based sample will participate in the StresSOS face-to-face intervention. Participants in both intervention groups (online or face-to-face) will receive the same eight treatment modules to allow for the comparison of both methods of delivery (Study B). The primary outcome is the number of C&A with symptoms of mental health problems at a 12 months follow-up. Secondary outcomes are related to stress/coping (i.e., knowledge, symptoms of stress, coping resources), mental health literacy (knowledge and attitudes toward mental disorders and help-seeking), program usage patterns, cost-effectiveness, and acceptability of the intervention. Discussion This study represents the first adequately powered non-inferiority trial in the area of school-based mental health promotion. If online StresSOS proves efficacious and non-inferior to face-to-face delivery, this offers great potential for health promotion in youths, both in and outside the school environment. Trial registration German Clinical Trials Register, DRKS00014693 . Registered on 14 May 2018

Online Health Monitoring of the Polymer Electrolyte Membrane Fuel Cell

The Polymer Electrolyte Membrane (PEM) Fuel Cell is a widely researched fuel cell, and a very promising candidate for alternate power generation. However, technical issues such as cell flooding and drying prevent its deployment in many applications. Electrochemical Impedance Spectroscopy (EIS) is a very powerful technique used to isolate flooding and drying in a fuel cell. However, the time required to obtain measurements in EIS can sometimes be too large to cause irreparable damage to the cell, rendering it a mere post-mortem technique. This is because EIS perturbs a fuel cell with multiple cycles of a large number of sinusoidal signals at different frequencies. A new technique is proposed that uses the concept of EIS, but excites the cell with a chirp signal, allowing scanning a large range of frequencies in a relatively short time. his technique which we call Fast EIS, is computationally much faster than traditional EIS. Processing of data obtained with Fast EIS is done using two methods - the traditional Fourier Transform division method, and a new Wavelet Coherence method. Simulation results of Fast EIS with PEMFC models taken from literature are shown with performance comparable with that of traditional EIS. The information extracted from Fast EIS is also used for implementing a preliminary control technique to maintain the health of the fuel cell.by LayaM.Tech

Multivariate control loop performance assessment with Hurst exponent and Mahalanobis distance

by Laya Das and Babji Srinivasa

A novel approach for benchmarking and assessing the performance of state estimators

by Laya Das and Babji Srinivasa

Fiber interrogator for bragg grating sensors based on cavity ring-down technique

We report the results of simulations as well as experiments to study the performance of a fiber interrogator based on cavity ring-down principle. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.by Sandeep Chopra, Laya Das and Balaji Srinivasa

Neuralcompression: a machine learning approach to compress high frequency measurements in smart grid

by Laya Das, Dinesh Garg and Babji Srinivasa

A novel framework for integrating data mining with control loop performance assessment

Data driven control loop performance assessment techniques assume that the data being analysed correspond to single plant-controller configuration. However in an industrial setting where processes are affected due to the presence of feedstock variability and drifts, the plant-controller configuration changes with time. Also, user-defined benchmarking of control loops (common in industrial plants) requires that the data corresponding to optimal operation of the controller be known. However such information might not be available beforehand in which case it is necessary to extract the same from routine plant operating data. We propose a technique that addresses these fundamental requirements for ensuring reliable performance assessment. The proposed technique performs a recursive binary segmentation of the data and makes use of the fact that changes in controller settings translate to variations in plant output for identifying regions corresponding to single plant-controller configurations. The statistical properties of the data in each such window are then compared with the theoretically expected behaviour to extract the data corresponding to optimal configuration. This approach has been applied on: (i) raw plant output (ii) Hurst exponent and (iii) minimum variance index of the process data. Simulation examples demonstrate the applicability of proposed approach in industrial settings. A comparison of the three routes is provided with regard to the amount of data needed and the efficacy achieved. Key results are emphasised and a framework for applying this technique is described. This tool is of significance to industries interested in an automated analysis of large scale control loop data for multiple process variables that is otherwise left un-utilised. This article is protected by copyright. All rights reserved.by Laya Das, Babji Srinivasan and Raghunathan Rengaswam

Data driven approach for performance assessment of linear and nonlinear Kalman filters

by Laya Das, Babji Srinivasan and Raghunathan Rengaswam

On-line performance monitoring and control of the PEM fuel cell using a fast EIS approach

by Laya Das, Babji Srinivasan and Raghunathan Rengaswam