Search

Your search keyword 'LN cat08778a' showing total 19 results
19 results on 'LN cat08778a'

1. Situation Awareness for Smart Distribution Systems.

Author

Ge, Leijiao, Yan, Jun, Sun, Yonghui, Wang, Zhongguan, and Ge, Leijiao

Subjects
Technology: general issues, History of engineering & technology, community integrated energy system, energy management, user dominated demand side response, conditional value-at-risk, electric heating, load forecasting, thermal comfort, attention mechanism, LSTM neural network, smart distribution network, situation awareness, high-quality operation and maintenance, critical technology, comprehensive framework, distributionally robust optimization (DRO), integrated energy system (IES), joint chance constraints, linear decision rules (LDRs), Wasserstein distance, load disaggregation, denoising auto-encoder, REDD dataset, TraceBase dataset, machine learning, secondary equipment, CNN, short text classification, electric vehicle, short-term load forecasting, convolutional neural network, temporal convolutional network, climate factors, correlation analysis, sustainable wind-PV-hydrogen-storage microgrid, power-to-hydrogen, receding horizon optimization, storage, photovoltaic (PV) system, DC series arc fault, power spectrum estimation, attentional mechanism, lightweight convolutional neural network, capacity configuration, wind-photovoltaic-thermal power system, carbon emission, multi-objective optimization, inertia security region, n/a
Abstract

Summary: In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas.

2. Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking.

Author

Echterhof, Thomas, Ohno, Ko-Ichiro, Visuri, Ville-Valtteri, and Echterhof, Thomas

Subjects
Technology: general issues, History of engineering & technology, liquid metals, bubble generation, bubble size distribution, porous plugs, bubble deformation, drag force, lift force, mathematical modelling, computational fluid dynamic, slag heat recovery, heat exchanger, drying, slag energy content, heat recovery technology, RecHeat, rotary kiln, reduction process, numerical simulation, pre-reduction, scrap preheating, electric arc furnace, continuous charging, turbulence modelling, RANS/LES/DNS, inflow condition, model validation, model application, energy demand, regression, artificial neural network, Gaussian process regression, Köhle formula, evaluation model, quantitative relationship, scrap melting, mass transfer coefficient, steelmaking process, direct current, arc impingement, arc gap, gas density, electric arc, magneto hydrodynamics, computational fluid dynamics, real-time model, estimation, model predictive control, steel refining, mathematical modeling, carbon composite briquette, blast furnace ironmaking, reaction kinetics, coke saving, direct reduction, Midrex, HYL, Rist diagram, energy consumption, profile optimization, modelling, machine learning, steelmaking, fuzzy modelling, evolving modelling, continuous casting, near net shape casting, twin roll (Bessemer) casting, horizontal single belt casting, n/a
Abstract

Summary: In recent years, improving the sustainability of the steel industry and reducing its CO2 emissions has become a global focus. To achieve this goal, further process optimization in terms of energy and resource efficiency and the development of new processes and process routes are necessary. Modeling and simulation have established themselves as invaluable sources of information for otherwise unknown process parameters and as an alternative to plant trials that involves lower costs, risks, and time. Models also open up new possibilities for model-based control of metallurgical processes. This Special Issue focuses on recent advances in the modeling and simulation of unit processes in iron and steelmaking. It includes reviews on the fundamentals of modeling and simulation of metallurgical processes, as well as contributions from the areas of iron reduction/ironmaking, steelmaking via the primary and secondary route, and continuous casting.

3. Advances in Asphalt Pavement Technologies and Practices.

Author

Tabakovic, Amir, Valentin, Jan, He, Liang, and Tabakovic, Amir

Subjects
Technology: general issues, History of engineering & technology, n/a, steel slag aggregate, warm mix asphalt, RSM, stiffness modulus, dynamic creep, moisture sensitivity, pavement, porous asphalt mixture, low temperature performance, orthogonal experimental design, porosity, cigarette filters, recycled cigarette butts, asphalt concrete, SMA, fibers, Trinidad lake asphalt, aged asphalt, hot-mix recycled asphalt mixture, reclaimed asphalt pavement, high-modulus asphalt mixture, Grouted Semi-flexible Pavement, influential factor, evaluation method, performance, laboratory test, hot mix asphalt, compaction quality control, artificial neural networks, degree of compaction, vibration, reflection crack, numerical analysis, extended finite element method, J-integral, stress intensity factors, multiple aging and rejuvenation cycles, SBS modified asphalt, morphological properties, DSR, FTIR, AFM, moisture damage, random forest, machine learning, factor importance, prediction, high and low temperature performance, infrared spectroscopy, additives, orthogonal test, bituminous mixtures, neural network, Bayesian optimization, asphalt mixture, master curve, characteristic parameter, relaxation characteristics, cold recycling, foamed asphalt, emulsified asphalt, asphalt pavements, self-healing, hybrid self-healing system, induction heating, indirect tensile strength, water sensitivity, wheel tracking rutting resistance, International Roughness Index, pavement condition evaluation, correlation, Saudi Arabia, flexible pavement, pavement maintenance, management system, smartphone sensors, Google Earth, resilient modulus, pavement design, unbound granular material (UGM), cyclic triaxial test (CTT), non-destructive testing, bearing capacity, falling weight deflectometer (FWD), base layers, subbase layers, rutting performance prediction model, long-term observation data, full-scale pavement structure, model accuracy evaluation, local correction coefficient, rutting prediction, moisture, interface cracking, digital image correlation (DIC), molecular dynamics, crumb rubber asphalt (CR), bitumen, coarse-grained, force field, asphalt pavement, construction control, toposable set theory, comprehensive evaluation
Abstract

Summary: Unlike other construction materials, road materials have developed minimally over the past 100 years. However, since the 1970s, the focus has been on more sustainable road construction materials such as recycled asphalt pavements. Recycling asphalt involves removing old asphalt and mixing it with new (fresh) aggregates, binders, and/or rejuvenators. Similarly, there are various efforts to use alternative modifiers and technical solutions such as crumb rubber, plastics, or various types of fibres. For the past two decades, researchers have been developing novel materials and technologies, such as self-healing materials, in order to improve road design, construction, and maintenance efficiency and reduce the financial and environmental burden of road construction. This Special Issue on "Advances in Asphalt Pavement Technologies and Practices" curates advanced/novel work on asphalt pavement design, construction, and maintenance. The Special Issue comprises 19 papers describing unique works that address the current challenges that the asphalt industry and road owners face.

4. AI Applications to Power Systems.

Author

Tjing Lie, Tek and Tjing Lie, Tek

Subjects
Technology: general issues, History of engineering & technology, self-healing grid, machine-learning, feature extraction, event detection, optimization techniques, manta ray foraging optimization algorithm, multi-objective function, radial networks, optimal power flow, automatic P2P energy trading, Markov decision process, deep reinforcement learning, deep Q-network, long short-term delayed reward, inter-area oscillations, modal analysis, reduced order modeling, dynamic mode decomposition, machine learning, artificial neural networks, steady-state security assessment, situation awareness, cellular computational networks, load flow prediction, contingency, fuzzy system, change detection, data analytics, data mining, filtering, optimization, power quality, signal processing, total variation smoothing, n/a
Abstract

Summary: Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

5. Machine Learning in Insurance.

Author

Nielsen, Jens Perch, Asimit, Alexandru, Kyriakou, Ioannis, and Nielsen, Jens Perch

Subjects
History of engineering & technology, deposit insurance, implied volatility, static arbitrage, parameterization, machine learning, calibration, dichotomous response, predictive model, tree boosting, GLM, validation, generalised linear modelling, zero-inflated poisson model, telematics, benchmark, cross-validation, prediction, stock return volatility, long-term forecasts, overlapping returns, autocorrelation, chain ladder, Bornhuetter-Ferguson, maximum likelihood, exponential families, canonical parameters, prior knowledge, accelerated failure time model, chain-ladder method, local linear kernel estimation, non-life reserving, operational time, zero-inflation, overdispersion, automobile insurance, risk classification, risk selection, least-squares monte carlo method, proxy modeling, life insurance, Solvency II, claims prediction, export credit insurance, semiparametric modeling, VaR estimation, analyzing financial data, n/a
Abstract

Summary: Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' "preferred methods" were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

6. New Frontiers in Sustainable Geotechnics.

Author

Jastrzębska, Małgorzata, Kazimierowicz-Frankowska, Krystyna, Chiaro, Gabriele, Rybak, Jaroslaw, and Jastrzębska, Małgorzata

Subjects
Technology: general issues, History of engineering & technology, cement bound base course, unbound base course, compaction technology, Proctor test, vibrating hammer test, recycled rubber, geotechnical properties, diesel, contaminated soil, clay, environmental geotechnics, impact monitoring, geotechnical influence, ground improvement, soil nailing system, soil characteristics, soil slope, foundation pressure, nailed soil, end-of-life tires, recycling, gravel-rubber mixtures, sustainable geotechnical applications, foundation systems, soil freezing, probability, return period, reliability, correction coefficient, depth of ground freezing, pullout test, retaining structures, gabions, bearing resistance, welded steel grid, woven steel grid, reinforcement of embankments, safety factor, Neogene clay elevation, Warsaw Slope, finite element method, electrical resistivity tomography, biomineralization, Bacillus pasteurii, marine clay, MICP, soil improvement, acidic soil, soil treatment, ammonia-soda residue, compressibility, constant rate of strain, drained, shear strength, triaxial test, calcium carbonate precipitation induced by microorganisms, silt, void ratio, fine particle content, unconfined compressive strength, permeability coefficient, XRD, SEM, stepped spillways, flow 3D, energy dissipations, pressure profiles, cavitation, incremental equations, non-cohesive soil, partial saturation, liquefaction, Taiwan mudstone, engineering compaction, cement paste, engineering material improvement, machine learning, decision trees, random forests, penetrometer, MWD, rigid inclusions, soil stabilization, chitosan, non-traditional additives, soil shear modulus, temporary geotechnical structures, calcium carbonate, HPMC, surface strength, ammonia retention rate, wind erosion resistance, reinforced soil walls, experimental tests, facing displacements, analytical methods, n/a
Abstract

Summary: With increasing awareness about environmental sustainability issues, the geotechnical engineering community is in search of sustainable materials, innovative applications and ground improvement technologies which are resilient to climate change, involve low carbon emissions, are ecologically friendly and affordable, yet effective. As such, this reprint brings together 18 novel contributions providing the state-of-the-art in "Sustainable Geotechnics" with special focus on the reuse and recycling of waste materials in geotechnical applications as well as sustainable design, construction, and monitoring of the performance of geostructures. This reprint may be of particular interest to geotechnical professional engineers and researchers who are facing the challenging task of increasing the sustainability and resiliency of geotechnical assets and infrastructures.

7. Artificial Intelligence for Multisource Geospatial Information.

Author

Bordogna, Gloria, Fugazza, Cristiano, and Bordogna, Gloria

Subjects
Technology: general issues, History of engineering & technology, geosemantics, implicit semantics, formal semantics, powerful semantics, satellite image, semantic segmentation, encoder-decoder, CNN, TH-1, cloud and snow detection, label quality, street crime, people on the street, streetscape, Baidu Street View image, spatial lag negative binomial regression, deep learning, convolutional neural network, backbone network, target detection, remote sensing images, segmentation, high resolution, transformer, OLAP, fuzzy SOLAP-based framework, fuzzy spatiotemporal queries, fuzzy spatiotemporal predictive query, fuzzy query visualization, machine learning, classification, LiDAR, 3D point cloud, urban trees, image feature vector, clustering, Siamese Network, automatic classification of tourist photos, deep learning model, Arabic tweets, COVID-19 pandemic, sentiment analysis, social data mining, spatio-temporal correlation, off-line integration of geo-tagged data sets, data sets about public places, soft integration methodology, effective soft integration through a stand-along tool, n/a
Abstract

Summary: This reprint collects 10 original research contributions published in the Special Issue entitled "Artificial Intelligence for Multisource Geospatial Information" of the ISPRS International Journal of Geo-Information. The focus is on different methods of Geospatial Artificial Intelligence (GeoAI) based on deep learning using different network architectures, clustering, soft computing, and semantic approaches. They are proposed to deal with a variety of Geospatial Big Data (GBD), such as georeferenced texts and photos in social networks, remote sensing images, cartographic maps, multidimensional geo databases, metadata in spatial data infrastructures, and for different tasks, such as for multisource georeferenced text integration and geodata flexible querying, for social sensing by applying sentiment analysis, clustering and geo analysis, for segmentation of roads, clouds and snow, and for detection of small targets and people on the streets.

8. Non-destructive Testing in Civil Engineering.

Author

Hoła, Jerzy, Sadowski, Łukasz, and Hoła, Jerzy

Subjects
History of engineering & technology, conveyor belt, climatic factors, thermal shocks, mechanical properties, applied geophysics, urban geophysics, Eleftheria Square, ERT, probability-based ERT inversion (PERTI) method, GPR, EMI, impact-echo, tooth gear impactor, contact duration, delamination, rapid scanning, non-destructive testing, concrete slab, building defects, building diagnosis, building envelope, building inspection system, urgency of repair, historic buildings, brick walls, nondestructive testing, artificial neural networks, Nearly Zero Energy Buildings (NZEB), passive house (PH), heat flux meter (HFM), quantitative infrared thermography (QIRT), building thermal performance, U-value, Mediterranean climate, electrical resistivity, electrical impedance, concrete, mortar, measurement accuracy, ultrasonic elastography, underground detection, soil inspection, underwater acoustics, non-destructive test, monitoring, housing, buildings, façade, thermal transmittance, HFM method, infrared thermography, solar loading thermography, lock-in thermography, passive thermography, thermal thickness, thermal effusivity, infrastructure, NDT, onsite X-ray bridge inspection, 950 keV/3.95 MeV X-ray sources, PC bridge, unfilled grout, quantitative evaluation of stage of unfilled grout, step-heating thermography, active thermography, linear effusivity fit, ground-penetrating radar, signal features, material moisture, classification, machine learning, moisture measurements, building floors, civil engineering, crack measurement, image processing, high resolution, working distance, crack width, visual inspection, structure state assessment, ground sample distance (GSD), sonic resonant method, impulse excitation technique, resonant frequency, steel fiber reinforced concrete, fiber orientation, micro-computed tomography, ultrasound, spectral induced polarization, small force, load, instrumental signal, two-point bending test, strain gauge, calibration equation, n/a
Abstract

Summary: This Special Issue, entitled "Non-Destructive Testing in Civil Engineering", aims to present to interested researchers and engineers the latest achievements in the field of new research methods, as well as the original results of scientific research carried out with their use-not only in laboratory conditions but also in selected case studies. The articles published in this Special Issue are theoretical-experimental and experimental, and also show the practical nature of the research. They are grouped by topic, and the main content of each article is briefly discussed for your convenience. These articles extend the knowledge in the field of non-destructive testing in civil engineering with regard to new and improved non-destructive testing (NDT) methods, their complementary application, and also the analysis of their results-including the use of sophisticated mathematical algorithms and artificial intelligence, as well as the diagnostics of materials, components, structures, entire buildings, and interesting case studies.

9. Systems Engineering: Availability and Reliability.

Author

Antosz, Katarzyna, Machado, Jose, Mazurkiewicz, Dariusz, Antonelli, Dario, Soares, Filomena, and Antosz, Katarzyna

Subjects
Technology: general issues, History of engineering & technology, Quality check, bike frame, mathematical model, graphical user interface, risk management, safety assurance, medical parallel robot, robotic assisted cancer treatment, risk performance reasoning, hidden Markov model, Handy bauxite carrier, process safety, performance evaluation, safety, coupling correlation, risk assessment, multi-dimensional theory, precision steel tape, tape transportation, roller-tape interactions, roller-tape contact pair, reliability, truck unloading system, petroleum equipment, preventive maintenance, cork-rubber composites, compression, apparent compression modulus, Young's modulus, bonded condition, importance measure, cost, inventory systems, air compression system, nitrogen generation system, utility module, availability, sensitivity analysis, predictive maintenance, Industry 4.0, Internet of Things, artificial intelligence, machine learning, maintenance, predictive scheduling, flow shop, job shop, ant colony optimisation, on-line monitoring, collaborative robots, human robot collaboration, time between failure (TBF), common beta hypothesis (CBH) test, meta-analysis, level of heterogeneity, mean time between failure (MTBF), text mining, network-based distributed manufacturing systems, moth flame optimization algorithm, support vector machines, Naive Bayes, random forest, decision trees, supplier classification, machining centre, DSM, Copula function, fault propagation intensity, fault propagation behaviour, lubricity, gear oil, wear, operational reliability, n/a
Abstract

Summary: Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE'2020 conference. This conference and journal's Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling.

10. Wireless Sensing and Networking for the Internet of Things.

Author

Lin, Zihuai, Xiang, Wei, and Lin, Zihuai

Subjects
Technology: general issues, History of engineering & technology, Energy industries & utilities, lawful interception, hybrid SDN, intercept access point, minimum vertex cover, text detection, natural scene, feature fusion, soil water content, sensor networks, distributed sensing, IoT measurements, Precision Agriculture, moisture sensor, wireless communication, LoRa, LoRaWAN™, indoor localisation, fingerprinting, landmark, wearable device, inertial measurement device, motion mode detection, body shadowing compensation, nearest neighbour, label-free biosensor, machine learning, support vector machine, artificial neural network, principal component analysis, Green IoT, IIoT, edge computing, AI, edge AI, sustainability, digital transition, digital circular economy, Industry 5.0, direction-of-arrival estimation, geometric algebra, ESPRIT algorithm, electromagnetic vector-sensor array, hardware security, electromagnetic sensing, real time, cybersecurity, anomaly detection, the Internet of Things, deep learning, blockchain, 5G/6G, URLLC, tactile Internet, industrial IoT, network emulator, robotic simulator, virtual testbed, wireless power transfer, energy harvesting, power conversion efficiency, single diode rectifier, voltage doubler, harmonic balance method, autonomous sensor node, wireless sensor network, multi-tone signal, full-wave simulations of PCB, millimeter wave, new radio, unmanned aerial vehicles, LoS blockage, closed-from approximation, rooftop deployments, coverage path planning, unmanned aerial vehicle, cell decomposition, decomposition methods, energy-aware approaches, energy optimal path, multi-robot systems, multi-UAV, smart cities, Internet of Things (IoT), sensors, 6G, wireless communications, resilience, climate change, connectivity, data, wireless systems, mobile sensors, D2D, technological development, Internet of things, LoRaWAN, reliability, downlink, safety, IoT, LPWAN, proximal soil sensor device, conventional communication methods, ultralow power consumption, long-distance transmission, economic value, inventoried sensor devices, digital twin, LSTM model, n/a
Abstract

Summary: In recent years, we have been witnessing the exponential proliferation of the Internet of Things (IoT), networks of physical devices, vehicles, appliances and other items embedded with electronics, software, sensors, actuators and connectivity that enables these objects to connect and exchange data. Enabling the introduction of highly efficient IoT, wireless sensing and network technologies will reduce the need for traditional processes that are currently be carried out manually, thus freeing up the precious resources of dwindling working staff, to do more meaningful and human-centered work. This reprint aims to bring together innovative developments in areas related to IoT, wireless sensing and networking. The aspects covered include software-defined network (SDN)-based IoT networks, artificial intelligence (AI) for IoT, industrial IoT, smart sensors, optimization of energy efficiency for IoT, and wireless sensor networks, IoT applications for agriculture, smart cities, healthcare, localization and environment monitoring.

11. Advanced Signal Processing in Wearable Sensors for Health Monitoring.

Author

Abbod, Maysam, Shieh, Jiann-Shing, and Abbod, Maysam

Subjects
Technology: general issues, History of engineering & technology, automated dietary monitoring, eating detection, eating timing error analysis, biomedical signal processing, smart eyeglasses, wearable health monitoring, artificial neural network, joint moment prediction, extreme learning machine, Hill muscle model, online input variables, Review, ECG, Signal Processing, Machine Learning, Cardiovascular Disease, Anomaly Detection, photoplethysmography, motion artifact, independent component analysis, multi-wavelength, continuous arterial blood pressure, systolic blood pressure, diastolic blood pressure, deep convolutional autoencoder, genetic algorithm, electrocardiography, vectorcardiography, myocardial infarction, long short-term memory, spline, multilayer perceptron, pain detection, stress detection, wearable sensor, physiological signals, behavioral signals, non-invasive system, hemodynamics, arterial blood pressure, central venous pressure, pulmonary arterial pressure, intracranial pressure, heart rate measurement, remote HR, remote PPG, remote BCG, blind source separation, drowsiness detection, EEG, frequency-domain features, multicriteria optimization, machine learning, n/a
Abstract

Summary: Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods.

12. Integration and Control of Distributed Renewable Energy Resources.

Author

Nazaripouya, Hamidreza and Nazaripouya, Hamidreza

Subjects
Technology: general issues, History of engineering & technology, distribution system, microgrids, power quality, power system management, power system reliability, smart grids, distribution networks, Monte Carlo simulations, PV hosting capacity, photovoltaics, green communities, energy independence, HOMER, wind turbines, power losses, power system optimization, PV curves, DG, TSA/SCA, solar-powered electric vehicle parking lots, different PV technologies, PLO's profit, uncertainties, smart grid paradigm, distributed generation, model-based predictive control, robustness, worst-case scenario, min-max optimisation, intraday forecasting, Gaussian process regression, machine learning, off-grid system, composite control strategy, solar photovoltaic panel, wind turbine, diesel generator, energy storage system (ESS), synchronous machine (SM), permanent magnet brushless DC machine (PMBLDCM), power quality improvement, n/a
Abstract

Summary: The deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories-regulatory issues, social issues, and economic issues. This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges.

13. Advances in Intelligent Vehicle Control.

Author

Cabrera, Juan A. and Cabrera, Juan A.

Subjects
Technology: general issues, History of engineering & technology, nonlinear height control, active air suspension, output constraints, random road excitation, disturbance observer design, electric vehicles, in-vehicle network, controller area network, cybersecurity, intrusion detection, deep learning, transfer learning, model-based control, vehicle dynamic potential, tyre thermodynamics, tyre wear, weather influence, vehicle safety, double lane change, safety optimization, noninverting buck-boost converter, high efficiency, wide bandwidth control, discrete-time sliding-mode current control (DSMCC), electric vehicle (EV), driver vehicle system, energy management, vehicle localization, GNSS receivers, RTK corrections, sensor redundancy, VMS, machine learning, ADAS, image processing, environment perception, semantics, 3D multiple object detection, multiple object tracking, dynamic SLAM, roll angle estimator, Kalman filter, LQR controller, inertial sensors, motorcycle lean angle, electrical vehicles, EV charging scheduling, binary linear programming, binary quadratic programming, vehicle control, reinforcement learning, curriculum learning, sim-to-real world, intelligent mobility, heterogeneous vehicular communication, Internet of connected vehicles, vehicular ad hoc networks, heterogeneous networking, Internet of Things, n/a
Abstract

Summary: This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems.

14. Environmental Technology Applications in the Retrofitting of Residential Buildings.

Author

Luther, Mark B., Martek, Igor, Amirkhani, Mehdi, Zucker, Gerhard, and Luther, Mark B.

Subjects
Technology: general issues, History of engineering & technology, automatically generated heating ventilating and air conditioning (HVAC) controllers, Building Information Modelling (BIM), Industry Foundation Classes (IFC), advanced building controls, HVAC control, occupancy prediction, energy consumption, thermal comfort, smart building, smart city, Bayesian networks, elevator control algorithm, intelligent elevator system, decision theory, decision support systems, solar PV, battery, costs, CO2 emissions, electric vehicle, high-density residential district, pedestrian wind environment, computational fluid dynamics, enclosure boundary, lift-up design, bushfire, retrofit, ember attack, pressure differential, urban fringe, rural housing, pyro-tornadogenesis, backdraft, self-consumption, solar photovoltaics, heat pumps, thermal storage, space heating, space cooling, residential construction, retrofitting, sustainable construction, literature review, research trends, scientometric review, carbon emission, Green Building-Rating System (GBS), cross laminated timber (CLT), hybrid structure system, radiant system, conditioning system, residential building, BIM, IFC, architecture model enrichment, machine learning, IfcSpace, n/a
Abstract

Summary: The impact of buildings on the environment is nothing short of devastating. In recent years, much attention has been given to creating an environmentally friendly built environment. Nonetheless, it has been levied on new buildings. Residential buildings make up at least 80% of the built environment, most of which were built before any energy efficiency guidelines or regulations were introduced. Retrofitting existing residential buildings is a key yet neglected priority in effecting the transition to an environmentally friendly, sustainable built environment. It is pivotal to reducing a building's energy consumption while simultaneously improving indoor environmental quality and minimizing harmful emissions. This Special Issue showcases studies investigating applications of environmental technology that is tailored to enhance the sustainable performance of existing residential buildings. It helps to better understand the innovations that have been taking place in retrofitting residential buildings, as well as highlighting many opportunities for future research in this field.

15. Future Powertrain Technologies.

Author

Rinderknecht, Stephan, Jardin, Philippe, Esser, Arved, and Rinderknecht, Stephan

Subjects
History of engineering & technology, degree of hybridization, energy management, hybrid propulsion, proton exchange membrane fuel cell, simulink, supercapacitor, fleet transition, optimization, life-cycle assessment, greenhouse gas, global warming potential, vehicle powertrain concepts, dedicated hybrid transmission, benchmarking, hybrid electric vehicle, efficiency, topology optimization, drive train optimization, powertrain concepts, structural reliability, uncertainties, ensemble learning, fault diagnosis, VFS, GA, input feedforward, fault observation, pressure sensor, aftermarket hybridization kit, emissions mitigation, local driving cycle, plug-in hybrid electric vehicles, vehicle efficiency, plug-in hybrid electric vehicle, electromechanical coupling, electrified mechanical transmission, multi-purpose vehicle, machine learning, powertrain control, automatic re-training, hybrid electric vehicles, dynamic programming, transmission, vehicle emissions, particle measurement programme (PMP), portable emissions measurement systems (PEMS), volatile removal efficiency, non-volatiles, solid particle number, catalytic stripper, evaporation tube, artefact, E-Mobility, powertrain design, high-speed, electric machine design, transmission design, gearbox, electric vehicles, range extenders, zinc-air battery, lithium-ion battery, electric vehicle transition, Arrhenius model, losses, mission profile, inverter, powertrain, Rainflow algorithm, reliability, thermal network, electric vehicle, n/a
Abstract

Summary: Among the various factors greatly influencing the development process of future powertrain technologies, the trends in climate change and digitalization are of huge public interest. To handle these trends, new disruptive technologies are integrated into the development process. They open up space for diverse research which is distributed over the entire vehicle design process. This book contains recent research articles which incorporate results for selecting and designing powertrain topology in consideration of the vehicle operating strategy as well as results for handling the reliability of new powertrain components. The field of investigation spans from the identification of ecologically optimal transformation of the existent vehicle fleet to the development of machine learning-based operating strategies and the comparison of complex hybrid electric vehicle topologies to reduce CO2 emissions.

16. Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools.

Author

Puigjaner, Luis, Espuña Camarasa, Antonio, Muñoz Mata, Edrisi, Capón García, Elisabet, and Puigjaner, Luis

Subjects
Technology: general issues, History of engineering & technology, manufacturing process, additive manufacturing, IoT, computer systems and networks, 3D printing, digital twin, Industry 4.0, pharmaceutical manufacturing, biopharmaceutical manufacturing, process modeling, mixed-integer linear programming, bin-packing problem, material requirements planning, agri-supply chain management, variable production rate, optimal resources, imperfect production, eco-efficient production, closed-loop scheduling, scheduling stability, optimal control and scheduling, fouling, heat exchanger networks, continuous manufacturing, bioprocessing, process systems engineering, single-use technology, AGV-Automated Guided Vehicles, DES-Discrete Event Simulation, FMS-Flexible Manufacturing Systems, OEE-Overall Equipment Efficiency, WCLcWorld Class Logistic, abrasive water jet, cutting, surface quality, quality prediction, 3D simulation modeling and analysis, model implementation, bottleneck analysis, production costs, resource conservation, smart manufacturing, edge computing, machine learning, blockchain, Industrial Internet of Things, inventory management, supply chain, multi-echelon, stochastic programming, reinforcement learning, digitalisation, model-based, computational engineering, process simulation, LNG terminal, operational optimization, BOG compressor, MINLP, enterprise process architecture, new technologies integration, process intelligence, real-time monitoring, fault detection, predictive process adjustment, vacuum gripper, sensor data, Tri-X Intelligence, cyber-physical systems, human-cyber systems, intelligent systems, intelligent manufacturing, multi-parametric programming, explicit MPC, enterprise-wide optimisation, set-point tracking, algebraic geometry, continuous pharmaceutical manufacturing, model predictive control, state estimation, quality-by-control (QbC), glidant effects, plant-model mismatch, regional logistics, low-carbon economy, cloud model, comprehensive evaluation, Beijing-Tianjin-Hebei region, pharmaceutical manufacture, uncertainty, operational flexibility, operational envelopes, modeling, n/a
Abstract

Summary: This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems.

17. Intelligent Sensors for Human Motion Analysis.

Author

Krzeszowski, Tomasz, Świtoński, Adam, Kepski, Michal, Calafate, Carlos Tavares, and Krzeszowski, Tomasz

Subjects
Technology: general issues, History of engineering & technology, gait recognition, biometrics, regularized discriminant analysis, particle swarm optimization, grey wolf optimization, whale optimization algorithm, FMCW, vital sign, XGBoost, MFCC, COVID-19, 3D human pose estimation, deep learning, generalization, optical sensing principle, modular sensing unit, plantar pressure measurement, gait parameters, 3D human mesh reconstruction, deep neural network, motion capture, neural networks, reconstruction, gap filling, FFNN, LSTM, BILSTM, GRU, pose estimation, movement tracking, computer vision, artificial intelligence, markerless motion capture, assessment, kinematics, development, machine learning, human action recognition, features fusion, features selection, recognition, fall risk detection, balance, Berg Balance Scale, human tracking, elderly, telemedicine, diagnosis, precedence indicator, knowledge measure, fuzzy inference, rule induction, posture detection, aggregation function, markerless, human motion analysis, gait analysis, data augmentation, skeletal data, time series classification, EMG, pattern recognition, robot, cyber-physical systems, RGB-D sensors, human motion modelling, F-Formation, Kinect v2, Azure Kinect, Zed 2i, socially occupied space, facial expression recognition, facial landmarks, action units, convolutional neural networks, graph convolutional networks, artifact classification, artifact detection, anomaly detection, 3D multi-person pose estimation, absolute poses, camera-centric coordinates, deep-learning, n/a
Abstract

Summary: The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems.

18. Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems.

Author

Calvo, Roque, Yagüe-Fabra, José A., Tosello, Guido, and Calvo, Roque

Subjects
Technology: general issues, History of engineering & technology, Industry 4.0, continuous improvement, lean manufacturing, lean production systems, lean 4.0, systematic literature review, tiny RFID, metallic electromagnetic isolation, analog manometer, Ansys HFSS simulator, passive digitization, ontology, production simulation, multi-agent, digital twin, augmented reality, industry 4.0, quality 4.0, metrology, assembly, turbine blades, re-manufacturing, uncertainty, robust scheduling, case-based reasoning, failure mode, effect and criticality analysis, knowledge-based system, nearest neighbor, reliability-centered maintenance, virtual reality, cyber physical system, OPC UA, CAD, industrial IoT, prototyping, retrofitting solutions, embedded solutions, low-cost, value chain, additive manufacturing, subtractive manufacturing, cost comparison, plant simulation, technology comparison, hybrid dependability modelling, production scheduling, dynamic failure rate, discrete event simulation, time-driven simulation, machine learning, manufacturing, artificial intelligence, smart manufacturing, digitization, smart surface, friction force field, under-actuation, feeding, simulation, material flow handling, intralogistics, robotic bin-picking, simulation model, ADAMS, pick-point determination, MATLAB/Simulink, 2-F robotic gripper, performance analysis, plastic injection molding, design of experiments, process optimization, facility layout problem, evolutionary algorithms, numerical modeling, multi-objective optimization, n/a
Abstract

Summary: The book is the reprint of the Special Issue 'Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems' published in the journal Applied Science (MDPI). It contains 17 articles, including 1 Editorial, 14 Research Papers, and 2 Reviews.

19. Machine Learning in Tribology.

Author

Tremmel, Stephan, Marian, Max, and Tremmel, Stephan

Subjects
Technology: general issues, History of engineering & technology, artificial intelligence, machine learning, artificial neural networks, tribology, condition monitoring, semi-supervised learning, random forest classifier, self-lubricating journal bearings, reduced order modelling, dynamic friction, rubber seal applications, tensor decomposition, laser surface texturing, texturing during moulding, digital twin, PINN, reynolds equation, triboinformatics, databases, data mining, meta-modeling, monitoring, analysis, prediction, optimization, fault data generation, Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), bearing fault diagnosis, unbalanced datasets, tribo-testing, tribo-informatics, natural language processing, tribAIn, BERT, amorphous carbon coatings, UHWMPE, total knee replacement, Gaussian processes, rolling bearing dynamics, cage instability, regression, neural networks, random forest, gradient boosting, evolutionary algorithms, rolling bearings, remaining useful life, feature engineering, structure-borne sound, n/a
Abstract

Summary: Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

Catalog

Books, media, physical & digital resources
See catalog results