In this paper, we study distributed and centralized approaches of Q-learning for multi-objective optimization of binary problems and investigate their characteristics and performance on complex epistatic problems usin...
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There is a need to design a system to ensure that parties involved in crop production, distribution and consumption have a transparent supply chain management system that guarantees the products' openness, neutral...
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AΕSΕH is one of the evolutionary algorithms used for many-objective optimization. It uses Ε-dominance during survival selection to sample from a large set of non-dominated solutions to reduce it to the required pop...
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Power system optimization problems are solved in a centralized manner with approximations or relaxations in power flow equations. However, applying the centralized algorithm for power system optimization has become ch...
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As the automotive industry shifts towards enabling self-driving vehicles, real-time situational awareness is becoming a crucial requirement. This paper introduces a novel information-sharing mechanism to opportunistic...
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This paper presents a low-cost, scalable approach for monitoring photovoltaic (PV) installations, catering to the growing demand for effective monitoring frameworks in renewable energy. The system incorporates sensors...
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Data centres are emerging as the essential backbone infrastructure for the booming information age and are becoming a sizable consumer of the energy system. Green and modular data centre are a new class of data centre...
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High-resolution (HR) simulations in cosmology, in particular when including baryons, can take millions of CPU hours. On the other hand, low-resolution (LR) dark matter simulations of the same cosmological volume use m...
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High-resolution (HR) simulations in cosmology, in particular when including baryons, can take millions of CPU hours. On the other hand, low-resolution (LR) dark matter simulations of the same cosmological volume use minimal computing resources. We develop a denoising diffusion superresolution emulator for large cosmological simulation volumes. Our approach is based on the image-to-image Palette diffusion model, which we modify to 3 dimensions. Our superresolution emulator is trained to perform outpainting, and can thus upgrade very large cosmological volumes from LR to HR using an iterative outpainting procedure. As an application, we generate a simulation box with 8 times the volume of the Illustris TNG300 training data, constructed with over 9000 outpainting iterations, and quantify its accuracy using various summary statistics.
Multi-robot task allocation has many applications in the real world. Robots often have noisy or local sensor readings, making their workspace partially observable. This paper proposes a partially observable spatial ta...
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This paper presents the compact and low-profile textile monopole antenna for WBAN applications. The antenna structure consists of an octagonal patch connected with microstrip feed line. The ground plane is an opposite...
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