Peng Wang – författare

Civil Aircraft Electrical Power System Safety Assessment: Issues and Practices provides guidelines and methods for conducting a safety assessment process on civil airborne systems and equipment. As civil aircraft electrical systems become more complicated, electrical wiring failures have become a huge concern in industry and government-especially on aging platforms. There have been several accidents (most recently battery problems on the Boeing 777) with some of these having a relationship to wiring and power generation.

Featuring a case study on the continuous safety assessment process of the civil airborne electrical power system, this book addresses problems, issues and troubleshooting techniques such as single event effects (SEE), the failure effects of electrical wiring interconnection systems (EWIS), formal theories and safety analysis methods in civil aircrafts.

Advanced Structural Textile Composites Forming: Characterization, Modeling, and Simulation comprehensively describes the influence of fiber/fabric architectures and properties on composites forming, along with their deformability and structural optimization, covering the latest advances in the composites forming field. Part one reviews textile reinforcement architectures and discusses the forming behaviors of important 2D and 3D fabrics. Part two discusses numerical models to conduct simulation analysis of different structural composites forming at mesoscopic and macroscopic scales, in particular, 3D preforms with through-the-thickness yarns.Part three looks at the latest developments in the relationship between forming and other steps in composite manufacturing, such as resin injection, and automated fiber placement (AFP) and the effects on certain mechanical properties, such as structural damage and impact resistance. The book will be an essential reference for academic researchers, industrial engineers and materials scientists working with the manufacture and design of fiber-reinforced composite materials.

Cadmium Contamination in Soils and Plants presents a rigorous, up-to-date account of how this toxic metal enters agricultural landscapes, persists in soils, interacts with living systems, and ultimately affects food safety and human health. The volume synthesizes cutting-edge findings on the processes that drive Cd movement from soil to crops and clarifies the biological pathways by which plants and microorganisms respond. It translates scientific insight into practical solutions for safer agricultural production systems, drawing on recent advances in measurement, modeling, and remediation.

This book is intended for researchers, graduate students, and professionals across environmental science, agronomy, and public health who need an authoritative, research-grounded reference to inform decisions in cultivation-related settings.

Subpixel mapping is a technology that generates a fine resolution land cover map from coarse resolution fractional images by predicting the spatial locations of different land cover classes at the subpixel scale. This book provides readers with a complete overview of subpixel image processing methods, basic principles, and different subpixel mapping techniques based on single or multi-shift remote sensing images. Step-by-step procedures, experimental contents, and result analyses are explained clearly at the end of each chapter. Real-life applications are a great resource for understanding how and where to use subpixel mapping when dealing with different remote sensing imaging data.

This book will be of interest to undergraduate and graduate students, majoring in remote sensing, surveying, mapping, and signal and information processing in universities and colleges, and it can also be used by professionals and researchers at different levels in related fields.

Subpixel mapping is a technology that generates a fine resolution land cover map from coarse resolution fractional images by predicting the spatial locations of different land cover classes at the subpixel scale. This book provides readers with a complete overview of subpixel image processing methods, basic principles, and different subpixel mapping techniques based on single or multi-shift remote sensing images. Step-by-step procedures, experimental contents, and result analyses are explained clearly at the end of each chapter. Real-life applications are a great resource for understanding how and where to use subpixel mapping when dealing with different remote sensing imaging data.

This book will be of interest to undergraduate and graduate students, majoring in remote sensing, surveying, mapping, and signal and information processing in universities and colleges, and it can also be used by professionals and researchers at different levels in related fields.

Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans.

Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator’s unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning.

This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.

Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans.

Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator’s unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning.

This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.

Multilingual Artificial Intelligence is a guide for non-computer science specialists and learners looking to explore the implementation of AI technologies to solve real-life problems involving language data.

Focusing on multilingual, multicultural, pre-trained large language models and their practical use through fine-tuning and prompt engineering, Wang and Smith demonstrate how to apply this new technology in areas such as information retrieval, semantic webs, and retrieval augmented generation, to improve both human productivity and machine intelligence. Finally, they discuss the human impact of language technologies in the cultural context, and provide an AI competence framework for users to design their own learning journey.

This innovative text is essential reading for all students, professionals, and researchers in language, linguistics, and related areas looking to understand how to integrate multilingual and multicultural artificial intelligence technology into their research and practice.

Multilingual Artificial Intelligence is a guide for non-computer science specialists and learners looking to explore the implementation of AI technologies to solve real-life problems involving language data.

Focusing on multilingual, multicultural, pre-trained large language models and their practical use through fine-tuning and prompt engineering, Wang and Smith demonstrate how to apply this new technology in areas such as information retrieval, semantic webs, and retrieval augmented generation, to improve both human productivity and machine intelligence. Finally, they discuss the human impact of language technologies in the cultural context, and provide an AI competence framework for users to design their own learning journey.

This innovative text is essential reading for all students, professionals, and researchers in language, linguistics, and related areas looking to understand how to integrate multilingual and multicultural artificial intelligence technology into their research and practice.

This book presents recent advances in data-driven global optimization methods, combining theoretical foundations with real-world applications to address complex engineering optimization challenges.

The book begins with an overview of the state of the art, key technologies and standard benchmark problems in the field. It then delves into several innovative approaches: space reduction-based, hybrid surrogate model-based and multi-surrogate model-based global optimization, followed by surrogate-assisted constrained global optimization, discrete global optimization and high-dimensional global optimization. These methods represent a variety of optimization techniques that excel in both optimization capability and efficiency, making them ideal choices for complex engineering optimization problems. Through benchmark test problems and real-world engineering applications, the book illustrates the practical implementation of these methods, linking established theories with cutting-edge research in industrial and engineering optimization.

Both a professional book and an academic reference, this title will provide valuable insights for researchers, students, engineers and practitioners in a variety of fields, including optimization methods and algorithms, engineering design and manufacturing and artificial intelligence and machine learning.