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Development of an Optimization Algorithm for Internet Data Traffic
Published 2020“…In this paper, an optimization algorithm is proposed to reduce net data traffic, which works at Internet layer in the TCP/IP reference model. …”
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Optimal Operation and Planning of Power System Integrated with Reverse Osmosis Water Desalination
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Tensile Test Optimization Using the Design of Experiment and Soft Computing
Published 2023“…This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). …”
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Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations
Published 2025“…A comparative evaluation of three FL algorithms (FedAvg, FedProx, Mime-lite) identifies the most suitable aggregation strategy. …”
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Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
Published 2022“…Then, a minimum distortion optimization algorithm iteratively computes all the possible resource allocations to find the optimal allocation based on the minimum distortion. …”
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Optimized 3D Deployment of UAV-Mounted Cloudlets to Support Latency-Sensitive Services in IoT Networks
Published 2019“…We formulate the problem as a mixed integer program, and propose an efficient meta-heuristic solution based on the ions motion optimization algorithm. The performance of the meta-heuristic solution is evaluated and compared to the optimal solution as a function of various system parameters and for different application use cases. …”
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Efficient Approximate Conformance Checking Using Trie Data Structures
Published 2021“…By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. We show how our algorithm supports the definition of a budget for alignment computation and also augment it with strategies for meta-heuristic optimization and pruning of the search space. …”
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms
Published 2025“…Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.…”
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Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids
Published 2017Get full text
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Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose
Published 2020“…These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. …”
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Morphology for Planar Hexagonal Modular Self-Reconfigurable Robotic Systems
Published 2009Get full text
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FoGMatch
Published 2019“…Our solution consists of (1) two optimization problems, one for the IoT devices and one for the fog nodes, (2) preference functions for both the IoT and fog layers to help them rank each other on the basis of several criteria such latency and resource utilization, and (3) centralized and distributed intelligent scheduling algorithms that consider the preferences of both the fog and IoT layers to improve the performance of the overall IoT ecosystem. …”
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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side
Published 2024“…Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. …”
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Estimation of power grid topology parameters through pilot signals
Published 2016“…The proposed topology estimation method relies on injected pilot signals through generators feeding in power. …”
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Leveraging UAVs for Coverage in Cell-Free Vehicular Networks
Published 2020“…Then, we leverage deep reinforcement learning to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. …”
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On-demand deployment of multiple aerial base stations for traffic offloading and network recovery
Published 2019“…We present performance results for the proposed algorithm as a function of various system parameters and demonstrate its effectiveness compared to the close-to-optimal greedy approach and its superiority compared to recent related work from the literature.…”
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