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SSRN 2024年

作者： Sun, Weiwei Huang, Ke Yang, Gang Fu, Bolin Chen, Chao Meng, Xiangchao Feng, Tian Ge, Yong Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Zhejiang Collaborative Innovation Center Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences & Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Geomatics and Geoinformation Guilin University of Technology Guilin541006 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China

Mangroves are declining and degrading under the influence of human activities and natural factors, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices based on the spectral characteristics of multispectral images have some limitations in recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring. © 2024, The Authors. All rights reserved.

关键词： Water levels

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Managing interdisciplinary collaboration in product-service systems engineering: Insights from a mixed-method analysis of a student project

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International Journal of Product Development 2020年第4期24卷 320-345页

作者： Hüer, Lucas Hagen, Simon Pfisterer, Hans-Jürgen Thomas, Oliver Faculty of Engineering and Computer Science University of Applied Sciences Osnabrueck Albrechtstraße 30 Osnabrueck49076 Germany Smart Enterprise Engineering DFKI Affiliation Parkstr 40 Osnabrueck49080 Germany Smart Enterprise Engineering DFKI Affiliation Parkstr. 40 Osnabrueck49080 Germany Information Management and Information System University of Osnabrueck Parkstraße 40 Osnabrueck49080 Germany

In recent years, a growing number of companies recognised that a large proportion of customers neglect the "classic" purchase of products and, instead, favour the acquisition of functions or solutions. As a result, multiple companies offer a mix of products and services designed to provide individual solutions. However, due to the complexity of integrated bundles of products and services, new approaches for developing such systems are required and a framework for companies for an efficient interdisciplinary collaboration is needed. By observing and analysing an interdisciplinary student project in which a product-service system was developed, insights regarding interdisciplinary work are obtained and guidelines for collaboration are derived. The results will serve as a basis for organising collaboration in other interdisciplinary projects. In order to obtain knowledge about relevant factors in the development of product-service systems, various methods of empirically quantitative and qualitative research were selected and integrated within a mixed method approach. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Project management

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Adaptive neural output constrained control for an unmanned surface vessel with coupled input constraint

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Ocean engineering 2025年 335卷

作者： Yuncheng Ouyang Yang Bai Linghuan Kong Xuerao Wang School of Artificial Intelligence the Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Anhui University Hefei China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macao China

In this paper, we focus on the tracking control problem of an uncertain unmanned surface vessel (USV) system with coupled input constraint and output constraint. Considering the issue of coupled input constraint, a transformation matrix is proposed to decouple the constrained inputs. Concerning output constraint occurring in a limited time interval (OCOLT) of the USV system, a novel shifting function with a new barrier function is introduced to transform the constrained system into an unconstrained one. Furthermore, neural network technology is employed to tackle the problems of system uncertainty. Through the Lyapunov stability theory, the stability analysis of the USV system is given to demonstrate the feasibility of the proposed control method. Finally, effectiveness of the proposed control method is verified by simulations.

关键词： Adaptive control systems

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Correction: Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam

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BMC medical imaging 2024年第1期24卷 195页

作者： Yogesh Kumaran S J Jospin Jeya Mahesh T R Surbhi Bhatia Khan Saeed Alzahrani Mohammed Alojail Department of Computer Science & Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bengaluru 562112 India. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India. Department of Computer Science & Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bengaluru 562112 India. trmahesh.1978@***. School of science engineering and environment University of Salford Manchester UK. Management Information System Department College of Business Administration King Saud University Riyadh Saudi Arabia.

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HILBERT-KAMKE EQUATIONS AND GEOMETRIC DESIGNS OF DEGREE FIVE FOR CLASSICAL ORTHOGONAL POLYNOMIALS

arXiv

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arXiv 2025年

作者： Mishima, Teruyuki Lu, Xiao-Nan Sawa, Masanori Uchida, Yukihiro Accenture Japan Ltd 2-2-2 Nakanoshima Kita-ku Osaka530-0005 Japan Department of Electrical Electronics and Computer Engineering Faculty of Engineering Gifu University 1-1 Yanagido Gifu-shi Gifu501-1193 Japan Graduate School of System Informatics Kobe University 1-1 Rokkodai Nada Kobe Hyogo657-8501 Japan Department of Mathematical Sciences Graduate School of Science Tokyo Metropolitan University 1-1 Minami-Osawa Hachioji Tokyo192-0397 Japan

In this paper we elucidate the advantage of examining the connections between Hilbert-Kamke equations and geometric designs, or Chebyshev-type quadrature, for classical orthogonal polynomials. We first establish a classification theorem for such 5-designs with 6 points. The proof is based on an elementary polynomial identity and some advanced techniques on the computation of the genus of a certain irreducible curve. We then prove a necessary and sufficient condition for the existence of 5-designs with rational points, especially for the Chebyshev measure of the first kind. It is noteworthy that this result presents a completely explicit construction of rational designs. Moreover, we create novel connections among Hilbert-Kamke equations, geometric designs and the Prouhet-Tarry-Escott (PTE) problem. For example, we establish that the 5-designs with 6 points for the Chebyshev measure appear in the famous parametric solution for the PTE problem found by Borwein (2002).MSC Codes 33C45, 05E99, 65D32 (Primary) 12E10, 11E76 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Exploring diabetes through the lens of AI and computer vision: Methods and future prospects

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computers in Biology and Medicine 2025年 185卷 109537-109537页

作者： Chundi, Ramesh G., Sasikala Basivi, Praveen Kumar Tippana, Anitha Hulipalled, Vishwanath R. N., Prabakaran Simha, Jay B. Kim, Chang Woo Kakani, Vijay Pasupuleti, Visweswara Rao School of Computer Applications Dayananda Sagar University Bangalore India School of Computer Science and Applications REVA University Rukmini Knowledge Park Bangalore560064 India Pukyong National University Industry-University Cooperation Foundation Pukyong National University Busan48513 Korea Republic of Department of Biotechnology School of Applied Sciences REVA University Rukmini Knowledge Park Bangalore560064 India School of Computing and Information Technology REVA University Rukmini Knowledge Park Bangalore560064 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Abiba Systems CTO and RACE Labs REVA University Rukmini Knowledge Park Bangalore560064 India Department of Nanotechnology Engineering College of Engineering Pukyong National University Busan48513 Korea Republic of Integrated System Engineering Inha University 100 Inha-ro Nam-gu Incheon22212 Korea Republic of School of Biosciences Taylor's University Lakeside Campus Selangor Subang Jaya47500 Malaysia Faculty of Earth Sciences Universiti Malaysia Kelantan Campus Jeli Jeli Kelantan17600 Malaysia

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals’ well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects. © 2024 Elsevier Ltd

关键词： Artificial intelligence computer vision Diabetes Deep learning Machine learning

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Investigation of the long-term adhesion and barrier properties of a PDMS-Parylene stack with PECVD ceramic interlayers for the conformal encapsulation of neural implants 23

Investigation of the long-term adhesion and barrier properti...

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23rd European Microelectronics and Packaging Conference and Exhibition, EMPC 2021

作者： Babaroud, Nasim Bakhshaee Dekker, Ronald Holk, Ole Tiringer, Ursa Taheri, Peyman Horvath, Domonkos Nanasi, Tibor Ulbert, Istvan Serdijn, Wouter Giagka, Vasiliki Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Micro-Integration IZM Berlin Germany Delft University of Technology Delft Netherlands Institute of Cognitive Neuroscience and Psychology Research Center for Natural Science Budapest Hungary Philips Research Eindhoven Netherlands Rotterdam Netherlands

ISBN: (纸本)9780956808677

In this paper, we investigate the long-term adhesion strength and barrier property of our recently proposed encapsulation stack that includes PDMS-Parylene C and PECVD interlayers (SiO2 and SiC) for adhesion improvement. To evaluate the adhesion strength of our proposed stack, the sample preparation consisted in depositing approximately 25 nm of SiC and 25 nm of SiO2 on half wafers, previously coated with Parylene C. Next, 50 µm PDMS was spin-coated on top. Finally, the samples were detached from the Si wafer and soaked in a PBS solution at 67 °C to accelerate the aging process. Two samples were also implanted, subcutaneously, on the left and right subscapular regions of a rat. The optical inspection and peel tests performed after two months confirmed our preliminary findings and showed a significant improvement of the adhesion in our proposed encapsulation stack compared to the case of PDMS on Parylene C alone. In addition, the X-ray photoelectron spectroscopy (XPS) analysis at the interface between SiC and Parylene C showed different peaks for the interface compared to the reference spectra, which could be an indication of a chemical bond. Finally, water vapor transmission rate (WVTR) tests were performed to investigate the barrier property of our proposed encapsulation stack against water vapor transmission. The results demonstrated that the proposed stack acts as a significantly (two orders of magnitude) higher barrier against moisture compared to only Parylene C and PDMS encapsulation layers. The proposed method yields a fully transparent encapsulation stack over a broad wavelength spectrum that can be used for the conformal encapsulation of flexible devices and thus, making them compatible with techniques such as optical imaging and optogenetics. © 2021 2021 23rd European Microelectronics and Packaging Conference and Exhibition, EMPC 2021. All rights reserved.

关键词： Adhesion

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Advancing agriculture with machine learning:a new frontier in weed management

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Frontiers of Agricultural science and engineering 2025年第2期 288-307页

作者： Mohammad MEHDIZADEH Duraid K.A.AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Department of Agronomy and Plant Breeding Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili Ilam Science and Technology Park Department of Horticulture College of Agriculture University of Al-Qasim Green Department of GIS and Remote Sensing Faculty of Geography University of Tehran Medical Laboratories Techniques Department Al-Mustaqbal University College Erbil polytechnic university Technical engineering college information system engineering department Department of Fundamental Economics Tashkent State University of Economics Manipur International University Department of Computer Science Nawroz University

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental *** have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed *** learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate *** learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.

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Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

Fused 3-Stage Image Segmentation for Pleural Effusion Cell C...

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International Conference on Pattern Recognition

作者： Sike Ma Meng Zhao Hao Wang Fan Shi Xuguo Sun Shengyong Chen Hong-Ning Dai Ministry of Education Key Laboratory of Computer Vision and System School of Computer Science and Engineering Tianjin University of Technology Tianjin China Norwegian University of Science and Technology School of Medical Laboratory Tianjin Medical University Faculty of Information Technology Macau University of Science and Technology Macau

The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%.

关键词： Image segmentation Clustering algorithms Lung Fluorescence Feature extraction Pattern recognition Metastasis

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