Search

Showing total 9 results
Start Over Topic artificial intelligence Topic machine learning Publication Type Electronic Resources
9 results

1. Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2020.

Author

Beyerer, Jürgen, Beyerer, Jürgen, Maier, Alexander, and Niggemann, Oliver

Subjects
Communications engineering / telecommunications, Computer networking & communications, Electrical engineering, Artificial Intelligence, Cognitive Robotics, Communications Engineering, Networks, Computational intelligence, Computer Engineering and Networks, Computer Systems Organization and Communication Networks, Computer-based algorithms, Cyber-Physical Systems, Cyber-physical systems, IoT, Cybernetics & systems theory, Industry 4.0, Internet of Things, Machine Learning, Open Access, Smart grid
Abstract

Summary: This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

2. Visual and Camera Sensors.

Author

Park, Kang Ryoung, Kim, Euntai, Lee, Sangyoun, and Park, Kang Ryoung

Subjects
Information technology industries, 3D point clouds, BIM, CNN, DLT, DeblurGAN, DoF-dependent, Hough-space, OCD4M, PnP, VSLAM-photogrammetric algorithm, accuracy analysis, artificial intelligence, automatic online calibration, autonomous landing, blur image restoration, camera calibration, camera networks, classification, close range photogrammetry, computer vision, computer-aided diagnosis, convolutional auto-encoders, convolutional filter, convolutional neural network, covariance, deep-learning-based motion deblurring and marker detection, distortion partition, drone, ensemble learning, facial expression recognition system, flying object detection, image processing, in vivo endoscopy, lens distortion, local image patch, low-cost device, machine learning, multi-scale featured local binary pattern, multi-sensor fusion, multimodal human recognition, network slimming, open-pit mine slope monitoring, optimum deployment, pathological site classification, plank recognition, point pair feature, portable mobile mapping system, pruning model, robotic grasping, robustness, self-assembly device, semi-direct SLAM, side-rear-view monitoring system, three-dimensional reconstruction, uncertainty, unmanned aerial vehicle, unsharp masking, vision measurement, visual-inertial, weighted DLT
Abstract

Summary: This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors.

3. The Impact of Digital Technologies on Public Health in Developed and Developing Countries. 18th International Conference, ICOST 2020, Hammamet, Tunisia, June 24-26, 2020, Proceedings.

Author

Jmaiel, Mohamed, Abdulrazak, Bessam, Aloulou, Hamdi, Jmaiel, Mohamed, Kallel, Slim, and Mokhtari, Mounir

Subjects
Artificial intelligence, Expert systems / knowledge-based systems, Information retrieval, Network hardware, Systems analysis & design, User interface design & usability, Artificial Intelligence, Computer Communication Networks, Computer System Implementation, Computer and Information Systems Applications, Information Systems Applications (incl. Internet), Internet of Things, Internet searching, IoT, Special Purpose and Application-Based Systems, User Interfaces and Human Computer Interaction, artificial intelligence, communication systems, computer vision, databases, hci, human-computer interaction, image processing, machine learning, network protocols, open access, sensors, signal processing, software architecture, software design, telecommunication networks, telecommunication systems, user interfaces, wireless telecommunication systems
Abstract

Summary: This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic.

4. Artificial Intelligence and Ambient Intelligence.

Author

Gams, Matjaz, Gams, Matjaz, and Gjoreski, Martin

Subjects
Information technology industries, CSI, Doppler spectrum, PUT-Hand, Wi-Fi, affective computing, agent, ambient intelligence, artificial intelligence, biometrics, cognitive load, control, crowd counting, effective computing, electronics, emotion system, emotional machine, grasping, human-machine interface, identification, information society, machine learning, manipulation, mechatronics, multi-modal fusion, n/a, one-dimensional depth sensor, perception, perception for grasping, psychophysiology, robotic hand, robotics, supervised learning, tactile sensing, underactuated
Abstract

Summary: This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on "Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules", presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots' ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science.

5. Original portfolio of compositions

Author

Rhodes, Christopher, Climent, Ricardo, and Allmendinger, Richard

Subjects
AR, Mixed Reality, MR, Extended Reality, XR, Oculus Quest, Electromyographic, Performance, Guitar, Violin, Myo, Piano, Augmented Reality, Oculus Quest 2, VR, Music, Interactive Composition, Composition, Unreal Engine, Unity, EMG, Biometrics, Game engines, ML, Machine Learning, Interactive Music, Virtual Reality, AI, Artificial Intelligence
Abstract

This portfolio of compositions uses novel biometrics from gestural interfaces called Myo armbands to compose five pieces of electroacoustic music within a continuum of spatial environments, ranging from the real to the virtual. The portfolio achieves this by processing biometric data from the Myo armbands within developed software (using Max 8), applying machine learning to such data (via Wekinator) and mapping predictive outputs to audiovisual materials across the spatial continuum. The results of this approach show how unique compositional affordances can be created when using biometrics within different spatial environments and how implementing machine learning methods can help to create such opportunities. The outcomes are presented via five composed portfolio works, three published papers within the scientific community, and numerous music performances navigating the overlapping areas of music composition and computer science. By using novel biometrics to compose music within a continuum of space, this interdisciplinary research topic contributes original knowledge to both the fields of music composition and computer science (human-computer interaction).

Published
2022

6. Numerical and Evolutionary Optimization 2021.

Author

Quiroz, Marcela, Lara, Adriana, Quiroz, Marcela, Schütze, Oliver, Trujillo, Leonardo, and de la Fraga, Luis Gerardo

Subjects
Computer science, Information technology industries, ANFIS, ANFIS (FFA), ANFIS (GA), ANFIS (PSO), ASD, AutoML, BCI, CFD, DVT, EEG, Grouping Genetic Algorithm, IPOPT solver engine, KPI, Large-Scale Constrained Optimization, Mallows model, Markov chain, OR in health services, OpenFOAM, XGBoost classifiers, aerodynamic analysis, ambulance response time, artificial intelligence, attention, autism, bin packing, decision-support system, differential privacy, distributor's pallet loading problem, early diagnosis, edge computing, edge device, embedded system, emergency medical services, emergency medicine, erosion, estimation of distribution algorithm, fault severity assessment, feature selection, features, first-passage time, gearboxes, geo-indistinguishability, heuristics, input perturbation, job shop scheduling problem, largest small polygons (LSP), learning activities, machine learning, machine-learning, mathematica model development environment, modal analysis, moth-flame algorithm, n/a, nonlinear programming, numerical optimization results and regression model for estimating {A(n)}, optimal area sequence {A(n)}, pre-hospital emergency care, privacy-preserving machine learning, quay crane scheduling problem, queueing theory, real-life instances, revised LSP model, simulation, smart system, variable decomposition, wind energy, wind turbine blades, {LSP(n)} model-class
Abstract

Summary: This reprint was established after the 9th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.

7. Low Back Pain (LBP)

Author

Denaro, Vincenzo, Denaro, Vincenzo, Iavicoli, Sergio, Russo, Fabrizio, and Vadalà, Gianluca

Subjects
Environmental science, engineering & technology, Technology: general issues, African American, Australian method, CT, EMG, Google Trends, Latino, Low Back Pain Scale, MRI, Neurac, ROC curve, Wikipedia, X-ray, absenteeism, anthropometry, artificial intelligence, back pain, back problems, bone cancer, bone metastasis, canal stenosis, chronic, cognitive behavioral therapy, computer aided diagnosis, computer vision, core stability, cross-sectional studies, decision support systems, deep learning, depression, digital image processing, disability, disk herniation, epidemiology, epidural steroid injections, ergonomics, fear of pain, fear-avoidance beliefs, gender difference, handling task, hip-knee line, infodemiology, insurance, kinesiophobia, knowledge, low back pain, low back pain (LBP), low-back pain, lower extremity, lumbalgia, lumbar decompression, lumbar fusion, lumbar instability, lumbar rotation, lumbar translation, lumbosacral radicular pain, lung cancer, machine learning, manual therapy, maximum voluntary isometric contraction, meta-analysis, mindfulness-based stress reduction, musculoskeletal disorders, musculoskeletal pain, non-radiological lumbar instability, occupational back-support exoskeleton, occupational disorder, occupational health, occupational safety and health, older adults, orthopaedics, pain, patient care, pelvic incidence, physical activity, population attributable fraction, postural stability, prevention, prostate cancer, public health, radiography, radiomics, rehabilitation nurses, reliability, return to work, review, risk assessment, satisfaction, screening tool, seasonality, sedentary behaviour, sensitivity, signature, specificity, spinal load, standing position, sub-threshold lumbar instability, systematic review, underserved, unemployment, unexpected external postural perturbations, wearable assistive device, work ability, workers, workload, workplace, workplace interventions
Abstract

Summary: Low back pain (LBP) is a major public health problem, being the most commonly reported musculoskeletal disorder (MSD) and the leading cause of compromised quality of life and work absenteeism. Indeed, LBP is the leading worldwide cause of years lost to disability, and its burden is growing alongside the increasing and aging population. The etiology, pathogenesis, and occupational risk factors of LBP are still not fully understood. It is crucial to give a stronger focus to reducing the consequences of LBP, as well as preventing its onset. Primary prevention at the occupational level remains important for highly exposed groups. Therefore, it is essential to identify which treatment options and workplace-based intervention strategies are effective in increasing participation at work and encouraging early return-to-work to reduce the consequences of LBP. The present Special Issue offers a unique opportunity to update many of the recent advances and perspectives of this health problem. A number of topics will be covered in order to attract high-quality research papers, including the following major areas: prevalence and epidemiological data, etiology, prevention, assessment and treatment approaches, and health promotion strategies for LBP. We have received a wide range of submissions, including research on the physical, psychosocial, environmental, and occupational perspectives, also focused on workplace interventions.

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

Author

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

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

9. Extending probabilistic programming systems and applying them to real-world simulators

Author

Gram-Hansen, Bradley, Rainforth, Thomas, Günes Baydin, Atilim, Torr, Philip, and Teh, Yee

Subjects
006.3, Statistics, Machine learning, Inference, Artificial intelligence
Abstract

Probabilistic programming is a paradigm that enables us to efficiently write probabilistic models as program code that we can sample, infer underlying parameters and predict outcomes based on complete or incomplete observations. Naturally, stochastic simulators, a special sub-class of simulators containing random variables, internal inference procedures, and the simulation of observations, are structurally rich probabilistic models. However, most simulators are not written in the probabilistic programming paradigm, as they are written in arbitrary programming code. This means that it is challenging to automatically update the variables in these simulators to account for observations from conducted experiments, which limits the simulators' use. Furthermore, there are two components to a probabilistic programming system i) the language and compilation procedure, ii) the inference procedures. These components can limit our ability to compile particular classes of probabilistic models, such as models that contain mixtures of parameter types, due to restrictions in the expressiveness of the language. Restrictions in the expressivity of the language can also inhibit our ability to generate efficient inferences, as this naturally influences the design of the probabilistic programming system and the set of available inference backends. Creating probabilistic programming systems that are expressive enough for different probabilistic models leads to the creation of many different probabilistic programming systems, which is inefficient - it would be more efficient if we could repurpose existing probabilistic programming systems. In this thesis, we develop three pieces of original work through four papers. The first piece of work describes how to extend differentiable first-order probabilistic programming systems to perform statistically correct and computationally efficient inference on models with mixtures of continuous and non-continuous parameters, without having to modify the underlying language, or develop an entirely new probabilistic programming system. The second describes how to translate real-world stochastic simulators written in arbitrary program languages to probabilistic programming systems. And finally, in the third piece of work, we develop two new Bayesian inference schemes to make inference more computationally and statistically efficient in nested models, models that contain probabilistic programs, within probabilistic programs, which arise in many real-world stochastic simulators.

Published
2021