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Distributed Kalman filtering in presence of unknown outer network actuations
(Journal article; Peer reviewed, 2019)This letter presents a fully distributed approach for tracking state vector sequences over sensor networks in presence of unknown actuations. The problem arises in large-scale systems where modeling the full dynamics becomes ... -
Distributed Kalman Filtering with Privacy against Honest-but-Curious Adversaries
(Chapter, 2021)This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) to protect the private information of individual network agents from being acquired by honest-but-curious (HBC) adversaries. The proposed approach ... -
Distributed Kalman filtering: Consensus, diffusion, and mixed
(Chapter, 2018)A distributed Kalman filtering technique is developed for tracking state-space processes via sensor networks. Considering the optimal solution to multi-agent sequential filtering of linear Gaussian state-space processes, ... -
Distributed Knowledge in Case-Based Reasoning: Knowledge Sharing and Reuse within the Semantic Web
(Master thesis, 2006)The Semantic Web is an emerging framework for data reuse and sharing. By giving data clear semantics it allows for machine processing of this information. The Semantic Web technologies range from simple meta data to domain ... -
Distributed Learning and Estimation with Enhanced Privacy and Security
(Doctoral theses at NTNU;2023:59, Doctoral thesis, 2023)This thesis focuses on threat analysis and management in distributed learning scenarios intending to develop algorithms to mitigate the impact of adversaries in the network. The thesis begins with a threat analysis that ... -
Distributed learning for wind farm optimization with Gaussian processes
(Journal article; Peer reviewed, 2020) -
Distributed Learning over Networks with Non-Smooth Regularizers and Feature Partitioning
(Chapter, 2021)We develop a new algorithm for distributed learning with non-smooth regularizers and feature partitioning. To this end, we transform the underlying optimization problem into a suitable dual form and solve it using the ... -
Distributed Learning with Non-Smooth Objective Functions
(Chapter, 2020)We develop a new distributed algorithm to solve a learning problem with non-smooth objective functions when data are distributed over a multi-agent network. We employ a zeroth-order method to minimize the associated augmented ... -
Distributed Ledger and Decentralised Technology Adoption for Smart Digital Transition in Collaborative Enterprise
(Peer reviewed; Journal article, 2021)Digital transformation of Collaborative Enterprise (CE), both in terms of planning and implementation, relies on new business models and innovative technologies. One of such technologies is Distributed Ledger Technology ... -
Distributed Model Predictive Control of Interconnected Nonlinear Systems by Dynamic Dual Decomposition
(Chapter, 2014)A suboptimal approach to distributed Nonlinear Model Predictive Control (NMPC) for systems consisting of nonlinear subsystems with nonlinearly coupled dynamics subject to both state and input constraints is proposed. The ... -
Distributed motion sensing on ships
(Journal article; Peer reviewed, 2017)A current trend is autonomous transport of goods and people in the air, at land, and at sea. For safe and reliable operations, autonomous systems require sensors that replace, or even exceed, the senses of a human operator. ... -
Distributed MPC for Formation Path-Following of Multi-Vehicle Systems
(Peer reviewed; Journal article, 2022)The paper considers the problem of formation path-following of multiple vehicles and proposes a solution based on combining distributed model predictive control with parametrizations of the trajectories of the vehicles ... -
Distributed MPC for Peak Power Reduction in Smart Homes
(Master thesis, 2022)I de kommende årene anslås elektrisitetsetterspørselen og andelen ikke-kontrollerbar kraftproduksjon å øke. Forbedret forbrukerfleksibilitet er nødvendig for å imøtekomme økt produksjonsvolatilitet. I Norge i 2022 skal et ... -
Distributed NetFlow Processing Using the Map-Reduce Model
(Master thesis, 2010)We study the viability of using the map-reduce model and frameworks for NetFlow data processing. The map-reduce model is an approach to distributed processing that simplifies implementation work, and it can also help in ... -
Distributed Nonlinear Model Predictive Control by Sequential Linearization and Accelerated Gradient Method
(Journal article; Peer reviewed, 2016)A suboptimal approach to distributed NMPC for nonlinear interconnected systems subject to constraints is proposed. The objective is to develop a computationally efficient approach. The suggested method is based on a ... -
A distributed object-oriented simulator framework for marine power plants with weak power grids
(Peer reviewed; Journal article, 2022)In this work, we discuss and demonstrate how multi-engine marine power plants with weak power grids efficiently can be set up and simulated in a distributed co-simulation framework. To facilitate configuration switching ... -
Distributed Optimal Control of DAE Systems: Modeling, Algorithms, and Applications
(Doctoral theses at NTNU;2022:29, Doctoral thesis, 2022) -
Distributed Optimization Based Adaptive Underwater Communication Schemes
(Bachelor thesis, 2023)Denne bacheloroppgaven presenterer utviklingen av et kommunikasjonssystem for EvoLogics modemer som bruker standardprotokollen JANUS for undervannskommunikasjon. Det blir også integrert en optimaliseringsalgoritme for ... -
Distributed optimization using ADMM for Optimal Design of Thermal Energy Storage systems
(Master thesis, 2020)Hovedmålet vårt i begynnelsen av forskningsperioden har vært å benytte den alternerende retningsmetoden til multiplikatorer (ADMM) for å få en distribuert optimaliseringsalgoritme for å løse strukturerte ikke-lineære ... -
Distributed Personal Password Repository using Secret Sharing
(Journal article; Peer reviewed, 2018)Secret sharing based systems can provide both data secrecy and recoverability simultaneously. This is achieved by a special cryptographical splitting of the data, where the parts, called shares, are distributed among a ...