As one of the world's most important crops, potatoes play an important role in maintaining the stability of the global food supply. Many countries, including China, believe that food supply security is a basic condition for maintaining national stability and development. Therefore, potatoes can not only solve the problem of international food shortage, but also promote the development of international trade. In recent years, with the continuous improvement of planting technology, the global production and trade volume of potatoes have also been continuously increasing. However, the development of traditional potato quality grading technology is relatively slow. Currently, it still relies on manual sorting in many countries and regions. Because workers can not keep their attention for a long time under huge work pressure and their understanding of grading standards is inconsistent, large amount of wrong potato grading often occurs. This result not only affects farmers' income, but also causes serious waste in the potato processing due to unqualified raw potatoes. In addition, with the continuous increase of manual wages, the cost of manual grading of potatoes has under challenge. Therefore, achieving automation of potato quality grading is imperative. Traditional grading system mainly uses cameras to capture potato color images, and achieves potato quality grading through color information analysis. This method can reach high success rate for certain defects detection, such as green skin, surface rot and mechanical damage. Due to the variety of shapes of potatoes growing underground, the appearance defects, such as bending, bump and hollow, are widely existing. These abnormal samples may fail to be detected and grade to wrong quality groups, the 3D appearance information cannot be fully perceived in 2D color images. In response to such issues, we have decided to build a machine vision system based on depth cameras, which can obtain depth images of potatoes with 3D shape information. Unlike each pixel in a color image that stores color information, each pixel in a depth image stores the distance from the target to the camera. Therefore, the potato 3D surface features can be sensed and used for bump and hollow defects detection. To capture high-quality depth images, we have constructed a specialized depth imaging system, and developed the image acquisition software based on OpenCV and OpenNI framework. Then, each potato surface features are analyzed and extracted for shape analysis, defect detection, and overall quality grading. In recent years, machine learning technology has developed rapidly and has been widely applied in fields such as object recognition and feature detection. Hence, we also apply machine learning technology to the field of potato quality grading. By developing a machine learning model based on convolutional neural networks, we can directly input potato depth images and get the corresponding quality level of the samples. The experiment achieved good grading results. Since color and depth images of potatoes are actually collected simultaneously in data collection step, a novel algorithm is developed for potato 3D model rebuilding. The method is based on Point Cloud Library and OpenGL technology, and it shows the advantage in solving the problem of data traceability, especially when users have objections to automatic quality classification results. This model not only displays 3D potato shape model, but also supports scaling and 360-degree rotation operations. Overall, we believe that with the development of machine learning and depth sensing, potato quality grading systems will become more intelligent, efficient and low-cost.
LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world
As one of the world's most important crops, potatoes play an important role in maintaining the stability of the global food supply. Many countries, including China, believe that food supply security is a basic condition for maintaining national stability and development. Therefore, potatoes can not only solve the problem of international food shortage, but also promote the development of international trade. In recent years, with the continuous improvement of planting technology, the global production and trade volume of potatoes have also been continuously increasing. However, the development of traditional potato quality grading technology is relatively slow. Currently, it still relies on manual sorting in many countries and regions. Because workers can not keep their attention for a long time under huge work pressure and their understanding of grading standards is inconsistent, large amount of wrong potato grading often occurs. This result not only affects farmers' income, but also causes serious waste in the potato processing due to unqualified raw potatoes. In addition, with the continuous increase of manual wages, the cost of manual grading of potatoes has under challenge. Therefore, achieving automation of potato quality grading is imperative. Traditional grading system mainly uses cameras to capture potato color images, and achieves potato quality grading through color information analysis. This method can reach high success rate for certain defects detection, such as green skin, surface rot and mechanical damage. Due to the variety of shapes of potatoes growing underground, the appearance defects, such as bending, bump and hollow, are widely existing. These abnormal samples may fail to be detected and grade to wrong quality groups, the 3D appearance information cannot be fully perceived in 2D color images. In response to such issues, we have decided to build a machine vision system based on depth cameras, which can obtain depth images of potatoes with 3D shape information. Unlike each pixel in a color image that stores color information, each pixel in a depth image stores the distance from the target to the camera. Therefore, the potato 3D surface features can be sensed and used for bump and hollow defects detection. To capture high-quality depth images, we have constructed a specialized depth imaging system, and developed the image acquisition software based on OpenCV and OpenNI framework. Then, each potato surface features are analyzed and extracted for shape analysis, defect detection, and overall quality grading. In recent years, machine learning technology has developed rapidly and has been widely applied in fields such as object recognition and feature detection. Hence, we also apply machine learning technology to the field of potato quality grading. By developing a machine learning model based on convolutional neural networks, we can directly input potato depth images and get the corresponding quality level of the samples. The experiment achieved good grading results. Since color and depth images of potatoes are actually collected simultaneously in data collection step, a novel algorithm is developed for potato 3D model rebuilding. The method is based on Point Cloud Library and OpenGL technology, and it shows the advantage in solving the problem of data traceability, especially when users have objections to automatic quality classification results. This model not only displays 3D potato shape model, but also supports scaling and 360-degree rotation operations. Overall, we believe that with the development of machine learning and depth sensing, potato quality grading systems will become more intelligent, efficient and low-cost.
LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world
Advanced Mechanics of Piezoelectricity" presents a comprehensive treatment of piezoelectric materials using linear electroelastic theory, symplectic models, and Hamiltonian systems. It summarizes the current state of practice and presents the most recent research findings in piezoelectricity. It is intended for researchers and graduate students in the fields of applied mechanics, material science and engineering, computational engineering, and aerospace engineering. Dr. Qinghua Qin is a professor at the School of Engineering, Australian National University, Australia.
An enormous number of burial objects have been unearthed from ancient tombs in archaeological excavations in China. These mingqi were made in all kinds of materials and in a broad range of forms, techniques and craftsmanship. In this book Quinghua Guo examines a particular type of mingqi -- pottery building. The striking realism of the pottery buildings suggests that they were modelled after actual buildings. They bring to life courtyard houses, manors, towers, granaries and pigsty-privies, as well as cooking ranges and well pavilions. These pottery buildings, previously little known, preserve knowledge of antiquity and demonstrate the architectural quality and structural variety of the period. The author identifies the typology of the pottery buildings they signify in terms of ontology and semiology, in order to provide a conceptual map for classification, and identifies building systems reflected by the mingqi to detect architectonic systems of the Han dynasty. Key features of this volume include: Cross-disciplinary research -- architectural study interlocking with archaeological study; architectural study interlocking with graphic study. The Han pottery buildings are important architectural models from the ancient world, and are contrasted with wooden houses of Middle-Kingdom Egypt and brick buildings of the Minor civilisation, Crete, allowing cross-cultural comparisons.
This book focuses on the transition of hundreds of rural households in ten villages in Zhejiang from 1986 to 2002, based on the theme of rural household sustainable development. Drawing on a large amount of first-hand data collected from fixed observation sites for 17 consecutive years, this book has depicted the changes in household behaviour in rural Zhejiang, and analysed the origins of such changes. The contents of the book contain examinations at household and village level. Chapters One to Seven describe the changes in rural behaviour at the household level from different aspects, such as land transfer, saving and borrowing, and rural tax burden. Chapters Eight to Ten analyse the transition concerning household behaviour based on the village level data. This book will help readers with a better understanding of rural China from the micro perspective of household behaviour. This book won the Monograph Award (ranked 1st out of 6 winners in total) in the Third China Rural Development Research Award (2008). It was also awarded theSecond Class Excellent Research Achievement in Humanities and Social Sciences by the Ministry of Education (2009). With the help of rich and detailed first-hand data collected from fixed observation sites in ten villages in Zhejiang for 17 consecutive years, this book gives a panoramic analysis of Chinese rural society in transition from the viewpoint of rural household behaviour. It starts the examination from individual rural households, before develops the investigation to the whole villages, so that the study could be carried out in a coherent, reliable and systematic way. Viewing the countryside from its micro perspective under the national political economy framework, the book steps out of the traditional way of inspecting exclusively the rural households and therefore is able to generalise its conclusion at a macro level. The award selection panel in 2008 believed it a unique monography for understanding the macro transition of the Chinese “agriculture, countryside and peasant (三农)” issues from the micro aspect of household behaviour.
THE FIRST PRACTICAL GUIDE FOR OPERATIONALIZING RESPONSIBLE AI ̃FROM MUL TI°LEVEL GOVERNANCE MECHANISMS TO CONCRETE DESIGN PATTERNS AND SOFTWARE ENGINEERING TECHNIQUES. AI is solving real-world challenges and transforming industries. Yet, there are serious concerns about its ability to behave and make decisions in a responsible way. Operationalizing responsible AI is about providing concrete guidelines to a wide range of decisionmakers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organizational, and team level; software engineering best practices; architecture styles and design patterns; system-level techniques connecting code with data and models; and trade-offs in design decisions. Responsible AI includes a set of practices that technologists (for example, technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure the AI systems they develop or adopt are trustworthy throughout the entire lifecycle and can be trusted by those who use them. The book offers guidelines and best practices not just for the AI part of a system, but also for the much larger software infrastructure that typically wraps around the AI. First book of its kind to cover the topic of operationalizing responsible AI from the perspective of the entire software development life cycle. Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system engineering techniques. Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering. Reduce the risks of AI adoption, accelerate AI adoption in responsible ways, and translate ethical principles into products, consultancy, and policy impact to support the AI industry. Online repository of patterns, techniques, examples, and playbooks kept up-to-date by the authors. Real world case studies to demonstrate responsible AI in practice. Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this defi nitive guide.
Eye, Ear, Nose and Throat Disorders include many of the most common conditions encountered in clinical practice, affecting women and men , children and the elderly equally. This book is designed primarily for overseas readers. It aims to provide real-life case studies and references for teachers and students of international TCM, acupuncture colleges, acupuncturists, and biomedical doctors who are interested in TCM and acupuncture. It is also geared to the general reader to familiarize them with the advantages of treating ophthalmic and otolaryngological disorders with TCM. This book is practical and is highly readability. It took more than two years to complete. During the writing process, there were many group discussions where the book was reviewed and revised. However, despite the authors’ best efforts, this book is still a work on progress. We invite the readers to send their comments, corrections and suggestions to supplement, amend, and improve when reprinted.
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