Essays about: "Stochastic control"

Showing result 1 - 5 of 87 essays containing the words Stochastic control.

1. 1. Unleashing Profitability: Unraveling the Labor-R&D Nexus in SaaS Tech Firms : An Analysis of the Profitability Dynamics in SaaS Tech Firms through Stochastic Frontier

   University essay from Blekinge Tekniska Högskola/Institutionen för industriell ekonomi

   Author : prashant Atla; Noräs Salman; [2023]
   Keywords : Employee growth; SaaS Industries; Profitability; Technical efficiency; Stochastic Frontier Analysis; Marginal Product of Labor; Panel data Models;

   Abstract : Background: High-tech's rapid growth and prioritization of expansion over profitability can lead to vulnerability in economic downturns. The SaaS market, a part of the high-tech industry, offers affordable and flexible software solutions but is also susceptible to market volatility. READ MORE

3. 2. Characterization and Stabilization of Transverse Spatial Modes of Light in Few-Mode Optical Fibers

   University essay from Linköpings universitet/Informationskodning

   Author : Oscar Pihl; [2023]
   Keywords : spatial modes; Space division multiplexing; SDM; superpositions; LP-modes; few-mode fibers; quantum communication; quantum optics; adaptive optics; stochastic parallel gradient descent; SPGD; mode control; QKD; polarization controller; paddle controller; QRNG; quantum random number generator; spatial modes; perturbation effects;

   Abstract : With the growing need for secure and high-capacity communications, innovative solutions are needed to meet the demands of tomorrow. One such innovation is to make use of the still unutilized spatial dimension of light in communications, which has promising applications in both enabling higher data traffic as well as the security protocols of the future in quantum communications. READ MORE

4. 3. Investigating the Estimation of the infection rate and the fraction of infections leading to death in epidemiological simulation

   University essay from Uppsala universitet/Avdelningen för systemteknik

   Author : Jakob Gölén; [2023]
   Keywords : Epidemics; Compartmental Models; Parameter Inference; Synthetic Bootstrap; Infection Rate;

   Abstract : The main goal of this project is to investigate the behaviors of parameters used when modeling an epidemic. A stochastic SIHDRe model is used to simulate how an epidemic evolves over time. READ MORE

5. 4. Scalable Reinforcement Learning for Formation Control with Collision Avoidance : Localized policy gradient algorithm with continuous state and action space

   University essay from KTH/Skolan för teknikvetenskap (SCI); KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Andreu Matoses Gimenez; [2023]
   Keywords : Control theory; Multi-agent systems; Distributed systems; Formation control; Collision avoidance; Reinforcement learning; Teoria de control; Sistemes multiagent; Sistemes distribuïts; Control de formació; Prevenció de col·lisions; Reinforcement Learning; Reglerteknik; Multi-agent system; Distribuerade system; formationskontroll; Kollisionsundvikande; Reinforcement learning; Teoría de control; Sistemas multiagente; Sistemas distribuidos; Control de formación; Prevención de colisiones; Reinforcement Learning;

   Abstract : In the last decades, significant theoretical advances have been made on the field of distributed mulit-agent control theory. One of the most common systems that can be modelled as multi-agent systems are the so called formation control problems, in which a network of mobile agents is controlled to move towards a desired final formation. READ MORE

6. 5. LDPC DropConnect

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Xi Chen; [2023]
   Keywords : Bayesian approach; Machine learning; Coding theory; Measurement uncertainty; Algorithms; Bayesiansk metod; Maskininlärning; Kodningsteori; Mätosäkerhet; Algoritmer;

   Abstract : Machine learning is a popular topic that has become a scientific research tool in many fields. Overfitting is a common challenge in machine learning, where the model fits the training data too well and performs poorly on new data. READ MORE