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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Identifying Traffic Congestion Through Vehicle Counting and Motion Estimation 7th

Identifying Traffic Congestion Through Vehicle Counting and ...

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7th International Conference on Green Technology and Sustainable Development

作者： Minh-Thien Duong Thanh-Danh Phan Van-Binh Nguyen My-Ha Le HCMC Univ Technol & Educ Intelligent Syst Lab Ho Chi Minh City Vietnam Soongsil Univ Seoul South Korea Chungbuk Natl Univ Cheongju South Korea Thu Dau Mot Univ Thu Dau Mot City Binh Duong Prov Vietnam

ISBN: (纸本)9783031761966;9783031761973

A series of arterial roads and major intersections in most major cities in the world often fall into traffic congestion, slowing socioeconomic growth and negatively impacting the environment. Therefore, this article presents a solution to identify traffic congestion by leveraging the view of many cameras at intersections in the city to recognize the amount and volume of vehicles moving and whether or not traffic congestion will occur. Total Vehicles and Traffic Velocity are two essential features considered as an evaluation index of traffic conditions. Our proposed solution encompasses three main stages: vehicle detection, motion estimation, and identification. To begin with, we introduce the improved YOLOv4-CSP model and OC-SORT tracking algorithm to detect and count total vehicles with the training dataset derived from the HCMAI Challenge competition. For the motion estimation, the feature-matching method extracts the keypoints of detected vehicles. Subsequently, we calculate the Euclidean distance of the corresponding keypoints in the next frame to estimate traffic velocity. Finally, we combine the two preceding stages and abide by the following rule to identify the traffic state. A traffic state will be normal if the total number of vehicles is dense but moving steadily. On the contrary, if the vehicle density is prolonged but the vehicles move very slowly, this traffic state is identified as traffic congestion. Experimental results demonstrate that our study obtained satisfactory results and suggest that it is prospective for practical applications.

关键词： Traffic Congestion Vehicle Counting Traffic Velocity Motion Estimation Fusion Method

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Work-in-Progress: Technology-Driven Introductory Phase in Engineering Sciences for Sustainable Individual Support of Students' Academic Success 21st

Work-in-Progress: Technology-Driven Introductory Phase in En...

引用

21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Block, Brit-Maren Dethmann, Jannis Haus, Benedikt Leuphana Univ Luneburg Inst Prod Technol & Syst D-21335 Luneburg Germany

ISBN: (纸本)9783031618901;9783031618918

The work-in-progress (WiP) paper comprises the conception, implementation, and evaluation project of an integrated introductory phase in engineering sciences with the aim of providing personalized support to students before commencing their studies and during their initial two semesters. The didactic innovation lies in designing a digitally supported comprehensive introductory phase that effectively accompanies the transition from school to university and significantly contributes to enhancing students' academic abilities during their initial semesters. As a specific contribution to the ongoing research discussion on learning with and about technologies in engineering education, this paper aims to provide theoretical and practical insights into the following aspects: how to design an enhanced introductory phase based on theoretical foundations, how to agilely address forthcoming developments and assist freshmen using technologies in engineering educational environments, and how to conduct evaluations for quality assurance and validation processes.

关键词： Engineering Education of the Future Virtual Environments Learning Analytics Undergraduate Mobile and Micro Learning

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Continuous Action Recognition in Manufacturing Contexts by Deep Graph Convolutional networks 1

引用

Intelligent systems Conference (IntelliSys)

作者： Maselli, M. V. Marani, R. Cicirelli, G. D'Orazio, T. CNR Inst Intelligent Ind Syst & Technol Adv Mfg STIIM Bari Italy

ISBN: (数字)9783031477188

ISBN: (纸本)9783031477171;9783031477188

Human action recognition is an active topic of research in computer vision and machine learning. Its application in the industrial domain is even more challenging since workers can handle multiple objects and follow different assembly sequences, and only a few datasets are target-oriented. However, the availability of low-cost cameras capable of extracting high-level information about human posture and movement opens up new possibilities. This work compares four state-of-the-art graph neural networks working with skeletal data to recognize the actions in the HA4M dataset, where subjects perform an assembly task. Videos are divided into clips of consecutive frames that form the input skeletal graphs of the networks. Then, an algorithm for action segmentation is proposed to assess each action's exact starting and ending instants. Results show that the best performance is achieved by a twostream Adaptive Graph Convolutional Network trained with input clips 77 frames long.

关键词： Smart manufacturing Action recognition Skeleton data Graph convolutional network

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PharmaSys: Toward Preventing Prescription Misuse Using a HIPAA-Compliant Blockchain Protocol 9th

PharmaSys: Toward Preventing Prescription Misuse Using a HIP...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Nguyen, Aaron H. Lewtschuk, Alex Durham, James Lucas Lundquist, Peyton Dagher, Gaby G. San Jose State Univ San Jose CA 95192 USA Marshall Univ Huntington WV 25755 USA Boise State Univ Boise ID 83725 USA

ISBN: (纸本)9789819732883;9789819732890

Prescription drug misuse has become a major health crisis across the United States with prescription drug abuse skyrocketing during the twenty-first century resulting in classification as an epidemic. The rise in prescription misuse has resulted in negative consequences including heightened instances of overdose deaths, increased crime rate, and escalated healthcare expenditures. Although illegal sales contribute to the issue, a notable portion of prescription drugs stems from licensed medical professionals who engage in over-prescribing medications. To help combat this issue we propose PharmaSys, a novel multi-party blockchain system with a quorum-based consensus protocol that aims to prevent prescription misuse. PharmaSys achieves this by introducing and establishing a checks and balances system for prescription transactions, all while maintaining compliance with HIPAA regulations. PharmaSys consists of three distinct node types: prescribers, pharmacists, and patients;each of which executes unique functions within the system. We have implemented PharmaSys to illustrate our system's scalability and security.

关键词： Blockchain Prescription tracking Healthcare HIPAA compliance Consensus protocol Drug abuse Proof of sharding

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Sentiment Analysis of Product Reviews Using Deep Learning and Transformer Models: A Comparative Study 1

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Kusal, Sheetal Patil, Shruti Gupta, Aashna Saple, Harsh Jaiswal, Devashish Deshpande, Vaishnavi Kotecha, Ketan Symbiosis Int Deemed Univ Symbiosis Inst Technol Pune 412115 Maharashtra India Symbiosis Int Deemed Univ Symbiosis Ctr Appl Artificial Intelligence SCAAI Symbiosis Inst Technol Pune 412115 Maharashtra India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

In recent times, the WWW has changed the way people interact with one another, express their opinions, provide feedback on websites, and share feelings on social media on different platforms. In the age of AI, it becomes necessary to understand the sentiments from peoples' opinions, feedback, and interactions. Sentiment analysis has recently drawn a lot of attention. Sentiment analysis has been extensively used and applied in a range of domains, including business and e-commerce. Mainly in the e-commerce sector, where product reviews and service reviews are one of the most important criteria for improving products and services, so analyzing the sentiments behind customer reviews or feedback becomes crucial. On the other hand, sentiment analysis helps customers in decision-making. The authors explored the Amazon reviews dataset, using state-of-the-art natural language-based transformer models to analyze product review sentiment. This paper presents a comparative investigation of different techniques used for sentiment analysis in product reviews to discover which AI-based technique works pre-eminent for review datasets.

关键词： Transformer Pre-trained models Deep learning E-commerce Sentiment analysis Natural language processing Amazon review dataset

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Inverse Dynamics for Feedforward Control of an Underactuated 6-DOF Gantry Crane 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Bettega, Jason Fabris, Francesco Richiedei, Dario Tamellin, Iacopo Trevisani, Alberto Univ Padua Dept Management & Engn DTG Stradella San Nicola 3 I-36100 Vicenza Italy

ISBN: (数字)9783031704659

ISBN: (纸本)9783031704642;9783031704659

Inverse dynamics in underactuated, non-minimum phase multibody systems is addressed in this paper by means of the challenging example of a gantry crane driving a spatial double pendulum. The latter consists of a pendulum plus a spatial rigid body attached to the pendulum terminal mass. The reference to be tracked is imposed to the two planar Cartesian coordinates of the rigid-body tip, while the actuated coordinates are those of the trolley. Because of underactuation and non-flat property of the system, inverse dynamics relies on a mixed algebraic and differential scheme. Additionally, this input-output configuration leads to a system with an unstable internal dynamic, whose integration cannot be done unless stabilization is performed. By exploiting the concept of output redefinition, i.e., by assuming a fictitious output in the internal dynamics equations, stabilization is performed, while ensuring excellent tracking performances. Numerical simulations proof the method effectiveness.

关键词： Inverse dynamics Feedforward control Underactuated multibody systems Internal dynamics Gantry crane

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Iterative Algorithm for the Generalized Segmental Continuous Cutting Problem with Optimization Time Constraint

Iterative Algorithm for the Generalized Segmental Continuous...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Petunin, Aleksandr Ukolov, Stanislav Ural Fed Univ 19 Mira St Ekaterinburg 620002 Russia IMM UB Ras 16 S Kovalevskaya St Ekaterinburg 620108 Russia

ISBN: (纸本)9783031671944;9783031671951

The paper proposes a new algorithm for solving one class of the tool path problems for CNC sheet cutting machines (the generalized segmental continuous cutting problem, GSCCP) with an additional parameter limited the calculation time for finding an optimal solution. As input data in the GSCCP, 2D layout of nested parts and a finite set of subtasks for optimizing the cutting path are used. Each subtask contains a set of so-called basic cutting segments, which determine the trajectories of tool movement between the points for material piercing and the points for switching tool off. Each subtask can be solved independently within the specified calculation time. The best solution found for the all subtasks is a solution to the GSCCP problem. The proposed iterative algorithm involves quantizing the total computation time. Moreover, within each time quant, all subtasks are also solved sequentially by calculation the upper and lower bounds. Initial upper bounds can, for example, be obtained using any fast heuristic based on effective combinatorial optimization methods. Next, for each of them, the procedure for searching for the lower boundary is launched. The next iteration of the solution process is performed taking into account the adjusted bounds. The solution procedures are interrupted when the specified time quant is reached, or upon obtaining a guaranteed exact solution. The effectiveness of the algorithm is illustrated by practical examples. It is also shown that for generate a set of finite set of subtasks for GSCCP, it is advisable to use neural networks.

关键词： Cutting path Tool Path Problem Segment Cutting Optimization time Iterative algorithm Time Quantizing Lower and upper bounds Classification Problem

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Detecting Credit Card Fraud Using 1D Convolutional Neural Network: An Efficient Approach for Enhanced Security 4th

Detecting Credit Card Fraud Using 1D Convolutional Neural Ne...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： El Hlouli, Fatima Zohra Riffi, Jamal Mahraz, Mohamed Adnane Yahyaouy, Ali El Fazazy, Khalid Tairi, Hamid Univ Sidi Mohamed Ben Abdellah Fac Sci LISAC Lab Fes Morocco

ISBN: (纸本)9783031686597;9783031686603

The COVID-19 pandemic promotes the increasing tendency of internet buying even more. Credit cards are frequently utilized in Internet shopping. Credit card fraud detection is becoming more pressing due to the growing damages caused by fraudulent activity, necessitating sophisticated detection approaches. This study uses the one-dimensional Convolutional Neural Network (CNN) to identify fraudulent credit card transactions. Our approach requires careful preparation of transactional data and the development of a specific 1D-CNN architecture designed to identify complex temporal patterns that indicate fraudulent activity. By conducting rigorous experiments on two real-world datasets, our technique significantly improves accuracy for detecting fraud, obtaining 99.77% and 98.89%, respectively. Furthermore, the model demonstrates interpretability and computational efficiency, making it well-suited for easy integration into fraud detection systems. This study presents an adaptable and robust approach utilizing advanced deep learning methods, particularly 1D-CNN, to enhance the security of credit card transactions, effectively solving a critical issue in financial protection.

关键词： Credit Card Fraud SMOTE Correlation matrix Feature importance 1D-CNN MLP

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Self-organized Locomotion with Multiple Stepping Frequencies in an Insect-Like Robot Under Decentralized Adaptive Neural Control 27th

Self-organized Locomotion with Multiple Stepping Frequencies...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Chuthong, Thirawat Manoonpong, Poramate Vidyasirimedhi Inst Sci & Technol Rayong Thailand

ISBN: (纸本)9783031707216;9783031707223

While walking robots typically employ a homogeneous stepping frequency across all legs to establish interlimb coordination, certain insects, such as locusts, interestingly exhibit adaptive interlimb coordination by employing heterogeneous stepping frequencies for different leg pairs. This adaptation arises from the distinct structures and lengths of their front, middle, and hind legs. Inspired by this biological phenomenon, this paper proposes decentralized adaptive neural control with multiple central pattern generators capable of realizing the adaptive interlimb coordination observed in insect walking. This neural control system can automatically generate gaits from heterogeneous stepping frequencies without the need for predefined interlimb coordination. Our preliminary results demonstrate that this neural control approach enables an insect-like robot, equipped with distinctly heterogeneous leg lengths, to achieve stable gaits autonomously and rapidly under various combinations of leg-stepping frequencies.

关键词： Central pattern generators Legged robots Motor control

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The Impact of Modes of Locomotion in a Virtual Reality Environment on the Human Being 19th

The Impact of Modes of Locomotion in a Virtual Reality Envir...

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19th International Conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Woda, Marek Michalski, Jakub Wroclaw Univ Technol Dept Comp Engn Janiszewskiego 11-17 PL-50372 Wroclaw Poland

ISBN: (纸本)9783031618567;9783031618574

Virtual reality technology has evolved to the point where users are no longer confined to a single location but can explore virtual worlds created by developers. However, the use of virtual reality often leads to the experience of virtual reality sickness, which poses a limitation to its widespread adoption. A recent study aimed to identify the factors that contribute to virtual reality sickness by examining the different locomotion techniques used in virtual reality and their impact on users. The study employed a simulator sickness questionnaire that was modified to obtain the necessary data. By analyzing these responses, researchers hope to gain insights into the elements of locomotion that are responsible for virtual reality sickness and their effects on human users. Understanding these factors can help improve the design and implementation of virtual reality experiences, making them more enjoyable and reducing the occurrence of virtual reality sickness. The aim of this study is to investigate the impact of locomotion and its elements on humans in virtual reality. This will allow us to determine which elements of locomotion and which types have negative effects. The thesis will present the results of a study to determine which elements of locomotion are most responsible for the occurrence of virtual reality sickness. Once this has been established, it will be possible to determine which elements should be avoided and which can be used to reduce the incidence of virtual reality sickness.

关键词： virtual reality simulator sickness questionnaire locomotion virtual reality sickness

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