الصفحة 17
الصفحة 17
Digital human modeling and applications in health, safety, ergonomics and risk management : Human communication, organization and work ; 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II
This two-volume set LNCS 12198 and 12199 constitutes the thoroughly refereed proceedings of the 11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, which was supposed to be held as part of the 22st HCI International Conference, HCII 2020, in Copenhagen, Denmark, in July 2020. The conference was held virtually due to the COVID-19 pandemic.
Digital Control of Electrical Drives
Digital Control of Electrical Drives offers insight into electric drives and their usage in motion control environment. It provides links among electrical machine and control theory, practical hardware aspects, programming issues, and application-specific problems. The book prepares the reader to understand the key elements of motion control systems, analyze and design discrete-time speed and position controllers, set adjustable feedback parameters, and evaluate closed-loop performances. Basic engineering principles are used to derive the controller structure in an intuitive manner, so that designs are easy to comprehend, modify, and extend.
Diagrammatic representation and inference ; 4th International conference, Diagrams 2006, Stanford, CA, USA, June 28-30, 2006, Proceedings
Proceedings of the 4th International Conference on Theory and Application of Diagrams, Stanford, CA, USA in June 2006. 13 revised full papers, 9 revised short papers, and 12 extended abstracts are presented together with 2 keynote papers and 2 tutorial papers. The papers are organized in topical sections on diagram comprehension by humans and machines, notations: history, design and formalization, diagrams and education, reasoning with diagrams by humans and machines, and psychological issues in comprehension, production and communication.
Development and evaluation of setup strategies in printed circuit board assembly
The last decade has seen a rapid extension of electronic control devices for various types of technical products. In modern electronics manufacturing, highly automated assembly systems are used to mount the electronic components onto the printed circuit boards (PCBs). Maintaining high production flexibility in order to meet the desired product variety and, at the same time, achieving high utilization rates of the capital-intensive production equipment can only be achieved by applying highly sophisticated planning and control strategies for the operation of modern placement machines. Ihsan Onur Yilmaz develops a novel group setup strategy which integrates multiple problems of the PCB assembly, especially in a medium-variety production environment. At the core of his principle approach are the identification of similarities between different types of PCBs and the generation of PCB clusters upon which group setup strategies are based. The developed setup strategies are also innovative in the sense that they integrate the optimization of detailed machine operations. This integration has not been achieved in the classical approaches which primarily rely on statistical clustering techniques.
Deterministic and statistical methods in Machine Learning ; 1st International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures
This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.
Designing Ubiquitous Information Environments : Socio-Technical Issues and Challenges; IFIP TC8 WG 8.2 International Working Conference, August 1-3, 2005, Cleveland, Ohio, U.S.A.
The book brings in diverse perspectives on ubiquitous information environments, from computer-supported collaborative work, institutional perspective, diffusion of innovation, management, sociology, individual cognition, and software engineering. It also covers a variety of technologies that make up ubiquitous information environments including RFID, wireless grid, GPS, mobile phones, and wireless local area network. The papers cover many contexts of ubiquitous computing including personal use, library, automobile, healthcare, police, professional knowledge work, remote diagnostics of machines, and marketing, attesting to the wide range of potential of ubiquitous information environments.
Designing machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Designing a human future with machines
What is human flourishing in an age of machine intelligence, when many claim that the world's most complex problems can be reduced to narrow technical questions? Does more computing make us more intelligent, or simply more computationally powerful? We need not always resist reduction; our ability to simplify helps us interpret complicated situations. The trick is to know when and how to do so. Against Reduction offers a collection of provocative and illuminating essays that consider different ways of recognizing and addressing the reduction in our approach to artificial intelligence, and ultimately to ourselves.
Design computing and cognition 06 ; 1st ed. ; Proceedings of the 2nd International conference on design computing and cognition
This is the second volume of the new conference series Design Computing and Cognition (DCC) that takes over from and subsumes the successful series Artificial Intelligence in Design (AID) published by Kluwer since 1992. The AID volumes have become standard reference texts for the field. It is expected that the DCC volumes will perform the same role.
Design and Analysis of Learning Classifier Systems : A Probabilistic Approach
This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Deep Learning-Based Face Analytics
Provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics : Techniques and Applications
Examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever.
Deep Learning to See : Towards New Foundations of Computer Vision
Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions.
Deep learning pipeline : Building a deep learning model with TensorFlow
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
Deep learning for computational problems in hardware security : Modeling attacks on strong physically unclonable function circuits
Discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
Deep learning approaches to cloud security
Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts.
Deep learning approach for text summarization
Machine learning and deep learning, as we know, have started ruling over almost every field in the computing industry and so, has revolutionized the process of text summarization too. Automatic text summarization is an advancing realm of the natural language processing research in which concise textual summaries are generated from lengthy input documents. Extensive research has been carried out on how automatic summarization can be prosecuted through various extractive and abstractive techniques. In this paper, we address all the approaches to text summarization and present the modus operandi of an Architecture called Encoder Decoder, under the machine learning approach.
