Energy Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.Comment: 7 pages, 6 figure

Robust H∞ filter design for uncertain linear systems over network with network-induced delays and output quantization

This paper investigates a convex optimization approach to the problem of robust H∞ filtering for uncertain linear systems connected over a common digital communication network. We consider the case where quantizers are static and the parameter uncertainties are norm bounded. Firstly, we propose a new model to investigate the effect of both the output quantization levels and the network conditions. Secondly, by introducing a descriptor technique, using Lyapunov- Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) for the existence of the desired network-based quantized filters with simultaneous consideration of network induced delays and measurement quantization. The explicit expression of the filters is derived to satisfy both asymptotic stability and a prescribed level of disturbance attenuation for all admissible norm bounded uncertainties. © 2009 Norwegian Society of Automatic Control

A Semiactive Vibration Control Design for Suspension Systems with Mr Dampers

Published version of a chapter published in the book: Vibration Analysis and Control - New Trends and Developments. Also available from the publisher at: http://www.intechopen.com/source/pdfs/17688/InTech-A_semiactive_vibration_control_design_for_suspension_systems_with_mr_dampers.pdf. O

SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning

This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, more than 22\% in F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to effectively handle these complexities. Taken together, these findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGP

Bond graph modeling and simulation of wind turbine systems

This paper addresses the problem of bond graph methodology as a graphical approach for the modeling of wind turbine generating systems. The purpose of this paper is to show some of the benefits the bond graph approach has, in contributing a model for wind turbine systems. We will present a nonlinear model of a wind turbine generating system, containing blade pitch, drive train, tower motion and generator. All which will be modeled by means of bond graph. We will especially focus on the drive train, and show the difference between modeling with a classical mechanical method and by using bond graph. The model consists of realistic parameters, but we are not trying to validate a specific wind turbine generating system. Simulations are carried out in the bond graph simulation software 20-sim