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共 20108 条记录，以下是81-90 订阅

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Exploring Ethical Issues and Challenges of Autonomous Vehicles in Different Countries and Cultures 59

Exploring Ethical Issues and Challenges of Autonomous Vehicl...

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59th Annual conference on Information Sciences and systems, CISS 2025

作者： Shah, Kruti Guven, Erhan Johns Hopkins University Whiting School of Engineering Ep Program LaurelMD United States

ISBN: (纸本)9798331513269

Autonomous vehicles (AVs) promise a revolutionary future of transportation, but ethical considerations pose significant challenges to widespread adoption. this paper delves into the intricate ethical challenges surrounding AVs, encompassing the perplexing "trolley problem"of unavoidable accidents, the thorny issue of liability attribution, the paramount concern of data privacy, the chilling prospect of hacking vulnerabilities, and the potential for widespread job displacement. We embark on a global exploration, examining how these ethical dilemmas manifest differently across the United States, Europe, Australia, China, and developing *** dissecting the ethical landscape in each region, we highlight the diverse cultural, legal, and societal contexts that influence the perception and management of these challenges. Each region offers a unique tapestry of approaches and solutions, from implementing robust regulatory frameworks and cultivating cutting-edge technological advancements to fostering community engagement and prioritizing educational initiatives. this analysis unveils the profound impact of cultural perspectives, highlighting how deeply ingrained values, such as individualism versus collectivism and risk tolerance, shape the understanding of ethical dilemmas and the formulation of ***, the paper investigates the possibility of creating a universally compliant self-driving car. We acknowledge the formidable challenges posed by the complex and challenging nature of international traffic laws, the heterogeneity of safety standards, and the disparities in infrastructure across the globe. the paper recognizes the crucial need for global collaboration and developing culturally sensitive solutions to ensure a future for autonomous vehicles grounded in safety, ethical principles, and responsible implementation. through this comprehensive exploration, the paper strives to deepen our understanding of the moral dimensions of autonomous driving

关键词： Autonomous vehicles

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Experimental Investigations of Pressure Loss Characterizations for Coal-Water Mixture Transport through Pipe Fittings

Experimental Investigations of Pressure Loss Characterizatio...

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9th international conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Bhayana, Mohit Kumar, Sanjeev Kumar, Satish Ratha, Dwarikanath Mechanical Engineering Department Chandigarh India National Institute of Technology Mechanical Engineering Department Jharkhand Jamshedpur India Thapar University Department of Civil Engineering Punjab Patiala India

ISBN: (纸本)9798331509262

the pipeline method of coal transportation has numerous benefits, such as a cost-effective solution to transport high volumes of material, continuous operation, environmental benefits, reduced labour costs compared to traditional modes of transport like rail or truck. Moreover, coal slurry transport is crucial in many industrial applications, such as coal-fired power plants, steel manufacturing, coal liquefaction plants, mining operations, etc. Understanding the pressure drop characteristics of coal slurry behaviour inside the pipeline is vital for efficient system design and operation. In this experimental study, pilot-level pipe loop experiments were conducted to analyse the coal slurry flow behaviour inside a pilot plant test loop having sudden contractions, and bends. Experimental setup was fabricated to transport coal slurry, mixture of coal and water, through pipeline transitions. the coal particles characteristics were examined using various characterization techniques such as scanning electron microscope, thermal analyzer etc. the pressure drops were examined for a flow rate ranging from 10 m3/hr. to 40 m3/hr. and coal concentration (Cw) of 3%, 7%, and 10% over the bend and sudden contraction pipeline transitions. the results revealed complex relationships between pressure drop, slurry properties, flow conditions, and pipeline geometry. the understanding of key flow regimes, assessment of the relationship between particle size distribution and pressure loss, and the development of correlations developed empirically for pressure drop prediction over pipeline transitions were amongst the key findings. the present study provides valuable insights for optimizing coal slurry transport systems and contributed to the broader understanding of multiphase flow dynamics in complex geometries. © 2025 ieee.

关键词： Coal water mixtures

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th international conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

the rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. this paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. they also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Traffic Speed Prediction Based on Dynamic Graph Convolutional Networks with Multi-Head Attention Mechanism 24

Traffic Speed Prediction Based on Dynamic Graph Convolutiona...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Li, Ru Wang, Li Li, Haolong He, Liming School of Information Engineering Chang'an University Shaanxi Xi'an China

ISBN: (纸本)9798400709999

Accurate traffic speed prediction is crucial for accelerating smart transportation infrastructure and promoting the development of intelligent transportation systems. However, traffic networks exhibit complex spatial structures and dynamically random temporal characteristics, and many existing graph convolutional network models overlook the dynamic spatiotemporal changes in traffic flow data, failing to accurately capture their hidden correlations. To further explore the dynamic spatiotemporal features hidden in the data and improve prediction accuracy, this paper proposes a traffic speed prediction model called ST-MAGCN, based on a multi-head attention mechanism and dynamic graph convolutional networks. the model combines graph convolutional networks to capture spatial features of the traffic network while introducing a temporal convolutional network with a gated mechanism and dilated causal convolution to capture temporal features across multiple time domains. Multi-head attention mechanisms are integrated between spatiotemporal convolutional blocks to enhance the representation of spatiotemporal correlations, and residual connections are used to improve the model's generalization ability. the proposed method was tested on the PeMSD7 dataset for 15, 30, and 45-minute traffic speed predictions. Using three evaluation methods, including root mean square error, the experimental results demonstrate that the ST-MAGCN model excels in feature extraction and prediction performance for traffic speed sequences compared to most existing benchmark models. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Traffic control

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Video Based Car Parking Management and Monitoring Using computer Vision and Machine Learning

Video Based Car Parking Management and Monitoring Using Comp...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sujitha, Bulla Ponraj, Anitha Chakkarapani, V. Parabrahmachari, Sriram Lakshmi, T.V. Hyma Annamani, T. V. R. Siddhartha Engineering College Department of Ece AP Vijayawada India Kalasalingam Academy of Research and Education Kalasalingam University Department of Computer Science and Engineering Tamil Nadu Krishnankoil India School of Electrical and Electronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Guru Nanak institutions Department of Cse Technical Campus Telangana Ibrahimpatnam India S.R.K.R. Engineering College Department of Ece A.P Bhimavaram India Anurag University Department of Ece Telangana Hyderabad India

ISBN: (纸本)9798331509828

Each day, countless individuals around the globe are affected by the extensive and complex problem of traffic congestion. this raising challenge is becoming increasingly substantial across the country, driven by factors such as rising population numbers, speedy urban development, insufficient infrastructure, and changing socioeconomic requirements. this paper depicts a novel global video-based parking approach designed to boost the supervising and management of parking spaces using real-time aerial statistics. Utilizing advanced image processing and computer vision procedures, the system essentially detects and examines the occupancy position of parking slots. In our realization, we assessed a total of 48 parking spaces, positively identifying 7 as unused while 41 were occupied. the application of Python, OpenCV, and machine learning assisted real-time monitoring, allowing precise counting and expose of parking availability. the procedure contained critical procedures, including Gaussian blur filtering to minimize noise and improve edge detection, followed by binary thresholding to describe vehicles and parking spots from the background. Furthermore, a median filter was utilized to boost the clarity of vehicle contours. these image enhancement methods significantly expanded detection accuracy and facilitated precise recognition of the Region of Interest (ROI). the results show the system's ability to provide reliable, real-time data for dynamic parking administration, thereby focusing the challenges of urban parking. © 2025 ieee.

关键词： Traffic congestion

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Humanoid Robot Control: A Mixed-Signal Footstep Planning SoC with ZMP Gait Scheduler and Neural Inverse Kinematics 25

Humanoid Robot Control: A Mixed-Signal Footstep Planning SoC...

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30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Cao, Qiankai Li, Yiqi Oh, Juin Chuen Gu, Jie Department of Electrical and Computer Engineering Northwestern University Evanston United States

ISBN: (纸本)9798400706356

With the rapid expansion of autonomous robotic systems in recent years, humanoid robots are also gaining considerable attention. However, their motion control presents more complex challenges than wheeled mobile robots. For the first time, this work presents a complete footstep planning SoC chip for humanoid robots. It includes a novel time-domain graph search engine for 3D footstep planning, along with a mixed-signal zero-moment point (ZMP) gait scheduler enhanced by neural inverse kinematics for efficient motion control. this work is demonstrated in-situ on a fully assembled robot using the 65nm system-on-chip (SoC), achieving the best-in-class performance and power including an 18.4x enhancement on energy efficiency for motion control and a 2.7x energy saving in graph search compared to previous works. the demo video is provided in https://***/kBe-aRnzmG4. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： System-on-chip

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Classification of endoscopic capsule pathologies using Multiple Instance Learning methods 16

Classification of endoscopic capsule pathologies using Multi...

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16th international conference on ENTERprise Information systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Moreira, Vitor Machado, Eduardo Barbosa, Dalila Salgado, Marta Braz, Geraldo Cunha, António University of Trás-os-Montes e Alto Douro Vila Real5001-801 Portugal INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Centro Hospitalar Universitário de Santo António Porto Portugal MA São Luís Brazil

this article presents an investigation into the classification of endoscopic capsule pathologies using Multiple Instance Learning (MIL) methods in conjunction with deep neural network architectures. the primary problem addressed in this study is the accurate and efficient detection of gastrointestinal pathologies, a significant challenge in medical diagnostics that can have a profound impact on patient outcomes. the use of endoscopic capsules is particularly important as they provide a minimally invasive method to capture comprehensive images of the gastrointestinal tract, facilitating early detection of conditions such as ulcers, polyps, bleeding, and Crohn's disease. Specifically, we explore three variants of MIL-Max, Mean, and Attention-for analysing sets of images captured by the endoscopic capsule. MIL was employed because it effectively handles scenarios where individual image instances are not explicitly labelled but are grouped in bags with known labels, making it suitable for the complex nature of endoscopic data. Furthermore, MIL has not yet been extensively applied in this modality, highlighting the innovative aspect of our approach. In addition, we evaluated the performance of three convolutional neural network architectures-VGG16, ResNet50, and DenseNet121-in the classification task. the results indicate that the combination of MIL methods and deep neural network architectures offers a promising approach to the detection and classification of gastrointestinal pathologies, with significant improvements in diagnostic accuracy and efficiency. © 2025 the Author(s).

关键词： Endoscopy

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Suitability of Blockchain Smart Contracts for Securing the Internet of things 5th

Suitability of Blockchain Smart Contracts for Securing the I...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Khichi, Deepika Sharma, Ajay Kumar Patel, Mayank Rathore, Narendra Singh Department of Computer Science Engineering GITS Udaipur India GITS Udaipur India

ISBN: (纸本)9789819761050

Due to its core characteristics, blockchain research has gained attention (Alrubei et al, in Ethereum blockchain for securing the Internet of things: practical implementation and performance evaluation. ieee, 2019 [1]). this is because it provides anonymity, security and integrity and is independent from the reliable 3rd party organizations. Blockchain technology could initially only be employed with currency. However, applications outside of crypto currencies are already being created and investigated thanks to the emergence of Ethereum, which enables the development and how smart contracts are executed (miniature computer programmes that operate on blockchain technology). the fact is that an enormous progress has been made for the improvement of the technology of blockchain and stress is given on smart contracts (Zhaofeng et al, in ieee Trans Ind Inform 16(3):2013–2021, 2019 [2]). It was found that few descriptive studies on the application of smart contracts for security of IoT and Internet are present. the purpose of this research project is to locate and evaluate scholarly works on smart contracts based on blockchain for securing the Internet. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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Ensemble Learning and Event-Based Feature Mining for Li-Ion Battery State Prediction 22

Ensemble Learning and Event-Based Feature Mining for Li-Ion ...

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22nd international Learning and Technology conference, L and T 2025

作者： Qaisar, Saeed Mian Subasi, Abdulhamit Effat University Electrical and Computer Engineering Department Jeddah21478 Saudi Arabia Institute of Biomedicine Faculty of Medicine University of Turku Turku20520 Finland College of Engineering Effat University Department of Computer Science Jeddah21478 Saudi Arabia

ISBN: (纸本)9798331532215

Contemporary power solutions are revolutionizing the existing trend in residential and transportation services. Mobile realizations, in particular, are advancing based on innovative mobile power supply solutions. Batteries are essential to several key applications, such as electric vehicles (EVs), satellites, and mobile phones. Additionally, batteries are elementary parts of the energy storage for distributed renewable microgrids. the are just a few applications based on the batteries-based power supply. Li-ion batteries are frequently used because of their exceptional qualities, which include endurance, high power density, and compact size. Besides these notable benefits the Li-Ion batteries are expensive. To effectively compensate for this high initial cost, the "battery management systems"(BMSs) are used. these solutions are effective in maximizing the Li-Ion battery life and also guarantee a safer operation. Based on the advantages of modular design, the contemporary Li-Ion batteries can be comprised of thousands of individual cells. It results in complex and sophisticated BMSs and can render a notable overhead in terms of power consumption. the focus of this research is on enhancing the existing Li-Ion BMSs by incorporating novel event-based feature mining and machine/ensemble learning techniques. the event-based feature extractor mines pertinent information and promises significant real-time compression. In order to predict the capacity of Li-Ion cells, the produced feature set is then processed utilizing robust machine/ensemble learning algorithms, and the performance of considered regressors is compared. A real Li-Ion battery dataset is used to demonstrate the applicability. For the case of a bagging-based ensemble regressor, the method obtained a correlation coefficient of 0.9996. © 2025 ieee.

关键词： Lithium-ion batteries

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Simulating Application Driven Mobile D2D underlay Networks using Gym API 17

Simulating Application Driven Mobile D2D underlay Networks u...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Batabyal, Suvadip National Institute of Technology Department of Computer Science & Engineering Durgapur India

ISBN: (纸本)9798331531195

Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short and low-power links between the devices. For this, the D2D paradigm is considered to be an integral part of all kinds of future networks and is an active area of research at different levels. this requires a simple, configurable, scalable, and realistic evaluation of the protocols proposed for the D2D networking paradigm. Although there exists a few platform to evaluate a D2D underlay network, they have one or more of the following drawbacks: (i) long development time and complex environment, (ii) limited support for mobility, (iii) no support for application specific development and evaluation. In this work, we developed a framework for simulating an application-driven mobile D2D underlay network using the Gym API, where D2D connections are made spontaneously based on the application requirements. the proposed framework includes modules for generating application-specific traffic, buffer for transmission and caching of application data, and mobility management. this complements several earlier works on application (or utility) driven resource allocation in D2D underlay network. the framework is used to evaluate a D2D network using different mobility models and performance of cache based video streaming in a D2D underlay network. Finally, the framework is compared with ns-3 and OMNET++ simulators and is observed to have lower CPU utilization and run-time under the same network settings. © 2025 ieee.

关键词： Resource allocation

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