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Conventional thermal power generating plants reject a large amount of energy every year. If this rejected heat were to be used through district heating networks, given prior energy valorisation, there would be a noticeable decrease in the amount of fossil fuels imported for heating. As a consequence, benefits would be experienced in the form of an increase in energy efficiency, an improvement in energy security, and a minimisation of emitted greenhouse gases. Given that heat demand is not expected to decrease significantly in the medium term, district heating networks show the greatest potential for the development of cogeneration. Due to their cost competitiveness, flexibility in terms of the ability to use renewable energy resources (such as geothermal or solar thermal) and fossil fuels (more specifically the residual heat from combustion), and the fact that, in some cases, losses to a country/region’s energy balance can be easily integrated into district heating networks (which would not be the case in a “fully electric” future), district heating (and cooling) networks and cogeneration could become a key element for a future with greater energy security, while being more sustainable, if appropriate measures were implemented. This book therefore seeks to propose an energy strategy for a number of cities/regions/countries by proposing appropriate measures supported by detailed case studies.

district heating --- 4th generation district heating --- data mining algorithms --- energy system modeling --- neural networks --- baseline model --- hydronic pavement system --- biomass district heating for rural locations --- CO2 emissions abatement --- low temperature networks --- ultralow-temperature district heating --- domestic --- optimization --- energy efficiency --- sustainable energy --- big data frameworks --- verification --- energy prediction --- parameter analysis --- greenhouse gas emissions --- time delay --- heat pumps --- primary energy use --- retrofit --- energy consumption forecast --- district heating (DH) network --- low-temperature district heating --- thermal inertia --- variable-temperature district heating --- data streams analysis --- Computational Fluid Dynamics --- energy management in renovated building --- Scotland --- heat reuse --- thermally activated cooling --- district cooling --- space cooling --- Gulf Cooperation Council --- biomass --- TRNSYS --- hot climate --- optimal control --- air-conditioning --- machine learning --- low temperature district heating system --- data center --- twin-pipe --- residential --- prediction algorithm --- CFD model --- nZEB --- thermal-hydraulic performance

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Extremely popular for statistical inference, Bayesian methods are also becoming popular in machine learning and artificial intelligence problems. Bayesian estimators are often implemented by Monte Carlo methods, such as the Metropolis–Hastings algorithm of the Gibbs sampler. These algorithms target the exact posterior distribution. However, many of the modern models in statistics are simply too complex to use such methodologies. In machine learning, the volume of the data used in practice makes Monte Carlo methods too slow to be useful. On the other hand, these applications often do not require an exact knowledge of the posterior. This has motivated the development of a new generation of algorithms that are fast enough to handle huge datasets but that often target an approximation of the posterior. This book gathers 18 research papers written by Approximate Bayesian Inference specialists and provides an overview of the recent advances in these algorithms. This includes optimization-based methods (such as variational approximations) and simulation-based methods (such as ABC or Monte Carlo algorithms). The theoretical aspects of Approximate Bayesian Inference are covered, specifically the PAC–Bayes bounds and regret analysis. Applications for challenging computational problems in astrophysics, finance, medical data analysis, and computer vision area also presented.

Research & information: general --- Mathematics & science --- bifurcation --- dynamical systems --- Edward–Sokal coupling --- mean-field --- Kullback–Leibler divergence --- variational inference --- Bayesian statistics --- machine learning --- variational approximations --- PAC-Bayes --- expectation-propagation --- Markov chain Monte Carlo --- Langevin Monte Carlo --- sequential Monte Carlo --- Laplace approximations --- approximate Bayesian computation --- Gibbs posterior --- MCMC --- stochastic gradients --- neural networks --- Approximate Bayesian Computation --- differential evolution --- Markov kernels --- discrete state space --- ergodicity --- Markov chain --- probably approximately correct --- variational Bayes --- Bayesian inference --- Markov Chain Monte Carlo --- Sequential Monte Carlo --- Riemann Manifold Hamiltonian Monte Carlo --- integrated nested laplace approximation --- fixed-form variational Bayes --- stochastic volatility --- network modeling --- network variability --- Stiefel manifold --- MCMC-SAEM --- data imputation --- Bethe free energy --- factor graphs --- message passing --- variational free energy --- variational message passing --- approximate Bayesian computation (ABC) --- differential privacy (DP) --- sparse vector technique (SVT) --- Gaussian --- particle flow --- variable flow --- Langevin dynamics --- Hamilton Monte Carlo --- non-reversible dynamics --- control variates --- thinning --- meta-learning --- hyperparameters --- priors --- online learning --- online optimization --- gradient descent --- statistical learning theory --- PAC–Bayes theory --- deep learning --- generalisation bounds --- Bayesian sampling --- Monte Carlo integration --- PAC-Bayes theory --- no free lunch theorems --- sequential learning --- principal curves --- data streams --- regret bounds --- greedy algorithm --- sleeping experts --- entropy --- robustness --- statistical mechanics --- complex systems

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Sensors are the eyes or/and ears of an intelligent system, such as UAV, AGV and robots. With the development of material, signal processing, and multidisciplinary interactions, more and more smart sensors are proposed and fabricated under increasing demands for homes, the industry, and military fields. Networks of sensors will be able to enhance the ability to obtain huge amounts of information (big data) and improve precision, which also mirrors the developmental tendency of modern sensors. Moreover, artificial intelligence is a novel impetus for sensors and networks, which gets sensors to learn and think and feed more efficient results back. This book includes new research results from academia and industry, on the subject of “Smart Sensors and Networks”, especially sensing technologies utilizing Artificial Intelligence. The topics include: smart sensors biosensors sensor network sensor data fusion artificial intelligence deep learning mechatronics devices for sensors applications of sensors for robotics and mechatronics devices

microelectromechanical systems --- inertial measurement unit --- long short term memory recurrent neural networks --- artificial intelligence --- deep learning --- CNN --- LSTM --- CO2 welding --- molten pool --- online monitoring --- mechanical sensor --- self-adaptiveness --- ankle-foot exoskeleton --- walking assistance --- visual tracking --- correlation filter --- color histogram --- adaptive hedge algorithm --- scenario generation --- autonomous vehicle --- smart sensor and device --- wireless sensor networks --- task assignment --- distributed --- reliable --- energy-efficient --- audification --- sensor --- visualization --- speech to text --- text to speech --- HF-OTH radar --- AIS --- radar tracking --- data fusion --- fuzzy functional dependencies --- maritime surveillance --- surgical robot end-effector --- clamping force estimation --- joint torque disturbance observer --- PSO-BPNN --- cable tension measurement --- queue length --- roadside sensor --- vehicle detection --- adverse weather --- roadside LiDAR --- data processing --- air pollution --- atmospheric data --- IoT --- machine learning --- RNN --- Sensors --- smart cities --- traffic flow --- traffic forecasting --- wireless sensor network --- fruit condition monitoring --- artificial neural network --- ethylene gas --- banana ripening --- unidimensional ACGAN --- signal recognition --- data augmentation --- link establishment behaviors --- DenseNet --- short-wave radio station --- landing gear --- adaptive landing --- vehicle classification --- FBG --- smart sensors --- outlier detection --- local outlier factor --- data streams --- air quality monitoring --- n/a --- evacuation path --- multi-story multi-exit building --- temperature sensors --- multi-time-slots planning --- optimization

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Sensors are the eyes or/and ears of an intelligent system, such as UAV, AGV and robots. With the development of material, signal processing, and multidisciplinary interactions, more and more smart sensors are proposed and fabricated under increasing demands for homes, the industry, and military fields. Networks of sensors will be able to enhance the ability to obtain huge amounts of information (big data) and improve precision, which also mirrors the developmental tendency of modern sensors. Moreover, artificial intelligence is a novel impetus for sensors and networks, which gets sensors to learn and think and feed more efficient results back. This book includes new research results from academia and industry, on the subject of “Smart Sensors and Networks”, especially sensing technologies utilizing Artificial Intelligence. The topics include: smart sensors biosensors sensor network sensor data fusion artificial intelligence deep learning mechatronics devices for sensors applications of sensors for robotics and mechatronics devices

History of engineering & technology --- microelectromechanical systems --- inertial measurement unit --- long short term memory recurrent neural networks --- artificial intelligence --- deep learning --- CNN --- LSTM --- CO2 welding --- molten pool --- online monitoring --- mechanical sensor --- self-adaptiveness --- ankle-foot exoskeleton --- walking assistance --- visual tracking --- correlation filter --- color histogram --- adaptive hedge algorithm --- scenario generation --- autonomous vehicle --- smart sensor and device --- wireless sensor networks --- task assignment --- distributed --- reliable --- energy-efficient --- audification --- sensor --- visualization --- speech to text --- text to speech --- HF-OTH radar --- AIS --- radar tracking --- data fusion --- fuzzy functional dependencies --- maritime surveillance --- surgical robot end-effector --- clamping force estimation --- joint torque disturbance observer --- PSO-BPNN --- cable tension measurement --- queue length --- roadside sensor --- vehicle detection --- adverse weather --- roadside LiDAR --- data processing --- air pollution --- atmospheric data --- IoT --- machine learning --- RNN --- Sensors --- smart cities --- traffic flow --- traffic forecasting --- wireless sensor network --- fruit condition monitoring --- artificial neural network --- ethylene gas --- banana ripening --- unidimensional ACGAN --- signal recognition --- data augmentation --- link establishment behaviors --- DenseNet --- short-wave radio station --- landing gear --- adaptive landing --- vehicle classification --- FBG --- smart sensors --- outlier detection --- local outlier factor --- data streams --- air quality monitoring --- evacuation path --- multi-story multi-exit building --- temperature sensors --- multi-time-slots planning --- optimization