Physics-based surface energy model optimization for water bodies in cold climates using visible and calibrated thermal infrared imagery

When tasked with accurately modeling a water body in a cold climate environment, the complexity of the system being simulated and the numerous parameters affecting the observable outcome pose an arduous task for any modeling effort. The task is increasingly complicated when the body of water is serving as a cooling pond for a power plant and can become partially frozen. The introduction of a heat effluent into the water creates a highly dynamic system whose physical state is not only reactionary to the surrounding environmental conditions, but the industrial facility\u27s operating parameters as well. Both calibrated thermal and visible imagery offer a powerful and unique source of validation data for these hydrodynamic modeling codes when trying to simulate these industrial processes in cold climates. This work presents an approach which uses an evolutionary optimization algorithm to drive the inputs of a hydrodynamic modeling code simulating a power plant cooling pond through imagery validation. The result of this process is an optimized set of functional parameters to the hydrodynamic model that best simulates the observed conditions. While applied to a hydrodynamic code for this work, the process created introduces a unique infrastructure for solving multi-dimensional, multi-system problem sets in a modular and evolutionary framework

All-in-one aerial image enhancement network for forest scenes

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.Award-winningPostprint (published version

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Segmentation of biological samples in cryo-electron microscopy images using machine learning methods

Zobrazovanie pomocou kryo-elektrónovej mikroskopie má svoje nezastúpiteľné miesto v analýze viacerých biologických štruktúr. Lokalizácia buniek kultivovaných na mriežke a ich segmentácia voči pozadiu alebo kontaminácii je základom. Spolu s vývojom viacerých metód hlbokého učenia sa podstatne zvýšila úspešnosť úloh sémantickej segmentácie. V tejto práci vyvinieme hlbokú konvolučnú neurónovú sieť pre úlohu sémantickej segmentácie buniek kultivovaných na mriežke. Dátový súbor pre túto prácu bol vytvorený pomocou dual-beam kryo-elektónového mikroskopu vyvinutého spoločnosťou Thermo Fisher Scientific Brno.Cryo-electron microscopy imaging has its irreplaceable position in analysis of various biological structures. Localization of the cells cultivated on grid and their segmentation towards background or contamination is essential. With the development of various deep learning methods, the performance of semantic segmentation tasks dramatically increased. In this thesis, we will develop a deep convolutional neural network for semantic segmentation of the cells cultivated on grid. Dataset for this thesis was created with dual-beam cryo-electron microscope developed by Thermo Fisher Scientific Brno.

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management