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Industrial Pose Detection System Based on FastDTW and MediaPipe 2nd

Industrial Pose Detection System Based on FastDTW and MediaP...

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2nd International conference on the Frontiers of Robotics and software engineering, FRSE 2024

作者： Huang, Baoyi Liao, Jiaxing Dai, Huawei Wang, Yuxuan School of Computer Science and Engineering Huizhou University Huizhou China Han’s Laser Technology Industry Group Co. Ltd. Shenzhen China

ISBN: (纸本)9789819631605

The posture and mobility of workers in industrial production directly affect productivity, safety, and product quality. However, existing posture monitoring technologies for large-scale applications and real-time monitoring are *** utilize an improved FastDTW and a gesture-to-video matching algorithm that normalizes feature vectors. An industrial posture detection system was developed to enhance the efficiency and quality of the production line, with a prototype based on the Orange Pi 3B. Our algorithm was compared to advanced gesture recognition algorithms such as OpenPose, DeepLabCut, and TensorFlow. The results show that our method exhibits higher accuracy and faster processing speed due to its efficient feature extraction and optimized similarity calculation. The optimized hardware design and operating system support enable rapid processing and analysis of real-time data streams, combining the advantages of geometric feature extraction and FastDTW. Compared to other components, our system surpasses existing gesture recognition methods in many ways, opening new possibilities for real-time applications. The accuracy rate in the test data reaches 90%, which is comparable to other algorithms but less computationally intensive. This automated posture detection system improves productivity and quality, bringing significant technological innovation to industrial assembly lines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital storage

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Deep Reinforcement Learning-Based Dependent Task Offloading for QoS Optimization in Satellite Edge Computing 25th

Deep Reinforcement Learning-Based Dependent Task Offloading ...

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25th International conference on Web Information systems engineering, WISE 2024

作者： Tian, Di Chen, Juan Chen, Peng Zhong, Jie Li, Xi Xiong, Ling School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9789819605699

Satellite edge computing (SatEC) is an emerging computing paradigm for remote and disaster-affected areas with scarce computing resources. It is crucial to make the appropriate offloading strategy to maximize quality of service (QoS) due to the scarcity of satellite computing resources. However, existing researches mostly focus on the offloading of independent tasks without considering tasks with dependencies. To address this problem, we first design a satellite-aerial task offloading (SATO) framework. In the SATO, unmanned aerial vehicles (UAVs) collect ground tasks and upload them to high-altitude platform (HAP), while HAP provides local computing, and low earth orbit (LEO) satellites provide edge computing. Furthermore, we propose SatEC directed acyclic graph (DAG) task offloading (SECDTO) algorithm to investigate the task offloading problem. Specifically, we model tasks with dependencies as a DAG, then we propose an encoder-decoder model with attention based on deep reinforcement learning (DRL) to translate graph vector information into offloading strategies. In order to maximize QoS, we use proximal policy optimization (PPO) to train the model. Simulation results show that our method converges quickly and stably during the training process and outperforms several baseline algorithms in terms of QoS@. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Ensemble Classifier for EEG-Based Stress Classification: An Empirical Study on Stacking Classifiers 3rd

Ensemble Classifier for EEG-Based Stress Classification: An ...

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3rd International conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Software Engineering Delhi Technological University Delhi India Department of Electronics and Communication Delhi Technological University Delhi India

ISBN: (纸本)9789819719426

In the last few years, combining multiple algorithms to improve the performance of machine learning models has been a common practice. However, its application to stress detection still needs to be explored. This paper uses a stacking ensemble technique to introduce a novel stress classification approach using electroencephalogram (EEG) signals. Specifically, the study evaluates the effectiveness of various classifiers, including Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Adaboost (AB), individually and in combination with RF as the base model, for stress classification tasks. The paper uses the SAM40 Dataset consisting of 32-channel EEG data to extract features in time and frequency domains for stress classification. The results show that the stacked classifiers outperform single classifiers, with RF + KNN providing the highest accuracy of 98.55%. The findings suggest that a stacked classifier is a promising approach for stress classification, as it can leverage the strengths of different algorithms and improve generalization performance. It holds promising future applicability in personalized stress management, healthcare interventions, and addressing societal concerns related to mental well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline 18th

Profinder: Towards Professionals Recognition on Mobile Devi...

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18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Research on the Application of Embedded Real-Time software in computer

Research on the Application of Embedded Real-Time Software i...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Yemei Qi Tianjin University of Technology Tianjin China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

The wide application of computer technology has been deeply integrated into the field of scientific research, engineering design, and across the military technology, diversified industries, and even business, culture, art and entertainment industry. While profoundly affecting People's Daily life, it shows strong vitality and broad application prospects. Therefore, this paper will deeply discuss the embedded real-time software testing, energy consumption research and application practice in the field of computing software design, and reveal its specific advantages in the application process through systematic analysis.

关键词： software testing Energy consumption Technological innovation Systematics Real-time systems User experience Stability analysis Thermal stability System analysis and design Embedded software

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Truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive Applications in Edge Clouds 18th

Truthful Double Auction-Based Resource Allocation Mechanisms...

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18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Wu, Dongkuo Wang, Xueyi Wang, Xingwei Huang, Min Wang, Zhitong College of Computer Science and Engineering Northeastern University Liaoning Shenyang China College of Software Northeastern University Liaoning Shenyang China College of Information Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9783031714696

With the rapid advancement of wireless networks, edge computing has emerged as a promising paradigm for providing computing services to nearby latency-sensitive applications. Toward this trend, resource trading markets among mobile users (MUs) and edge clouds are emerging which need well-designed allocation and pricing mechanisms. In this paper, we propose a double auction-based multi-market resource trading framework (DAMRT), which integrates the dynamic programming and padding-based double auction methods, aiming to achieve approximate social welfare maximization and guarantee the properties of truthfulness and budget balance in the constrained edge computing system. To be specific, for admission control among different base station (BS) central markets, a dynamic programming-based method is proposed to obtain the optimal BS-MU association set. For each market, a truthful padding-based double auction resource allocation mechanism (TPDA) is proposed, which uses a linear programming-based padding method and a binary search algorithm to obtain the near-optimal allocation solution, and leverages a critical-value and a VCG-based pricing strategy for winning MUs and service providers. Our theoretical analysis proves that TPDA achieves truthfulness, individual rationality, and budget balance. Furthermore, simulation results verify the effectiveness of DAMRT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Enabling Cursor Control Through Eye Movement Using Hidden Markov Model 2nd

Enabling Cursor Control Through Eye Movement Using Hidden Ma...

引用

2nd International conference on Data Science and Network engineering, ICDSNE 2024

作者： Tanusha, G. Havirbhavi, P. Ashwini, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783359

First and foremost, we present a novel scheme for controlling the cursor based on eye movement. Other strategies rely on physical items like touchpads or a mouse and place strict requirements on people with motor handicaps. Our solution connects digital interaction with human visual attention through eye-tracking technology. We examine the nuanced gaze patterns that develop, allowing for cursor control while also developing a sense of interaction context. Gaze data is transformed by our method into the motion of a cursor. It is highly accurate and adjustable. Cursor motions are generated based on the gaze movement pattern, making the interface more dynamically accurate and more tailored to individual behaviors. The context-aware interface maximizes efficiency through its adaptability as it learns and adopts the behaviors. In addition, our research introduces a novel scheme through the use of software tools to offer an eye-controlled mouse system. This system accurately recognizes facial features and interprets eye movements through image processing techniques. The system enables intuitive cursor control by interpreting observable eye movements. Transforming eye motions into a cursor's actions, it enhances user accessibility and hands-free interaction;in other words, its performance is captured in the experimental analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eye movements

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Leveraging Predictive Maintenance for Photovoltaic systems 9th

Leveraging Predictive Maintenance for Photovoltaic Systems

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9th International conference on Smart City Applications, SCA 2024

作者： Ledmaoui, Younes Raif, Mourad El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston K7K 7B4 ON Canada

ISBN: (纸本)9783031886522

This paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. Through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. The seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Edge device Energy management system Energy monitoring Predictive maintenance Renewable energy Solar energy

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Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners 22

Debiasing Architectural Decision-Making: An Experiment With ...

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22nd IEEE International conference on software Architecture, ICSA 2025

作者： Borowa, Klara De Almeida, Rodrigo Reboucas Wiese, Marion Warsaw University of Technology Institute of Control and Computation Engineering Warsaw Poland Federal University of Paraíba Department of Exact Sciences PB Rio Tinto Brazil Universität Hamburg Department of Informatics Hamburg Germany

ISBN: (纸本)9798331520908

Cognitive biases are predictable, systematic errors in human reasoning. They influence decision-making in various areas, including architectural decision-making, where architects face many choices. For example, anchoring can cause architects to unconsciously prefer the first architectural solution that they came up with, without considering any solution alternatives. Prior research suggests that training individuals in debiasing techniques during a practical workshop can help reduce the impact of biases. The goal of this study was to design and evaluate a debiasing workshop with individuals at various stages of their professional careers. To test the workshop's effectiveness, we performed an experiment with 16 students and 20 practitioners, split into control and workshop group pairs. We recorded and analyzed their think-aloud discussions about improving the architectures of systems they collaborated on. The workshop improved the participants' argumentation when discussing architectural decisions and increased the use of debiasing techniques taught during the workshop. This led to the successful reduction of the researched biases' occurrences. In particular, anchoring and optimism bias occurrences decreased significantly. We also found that practitioners were more susceptible to cognitive biases than students, so the workshop had a more substantial impact on practitioners. We assume that the practitioners' attachment to their systems may be the cause of their susceptibility to biases. Finally, we identified factors that may reduce the effectiveness of the debiasing workshop. On that basis, we prepared a set of teaching suggestions for educators. Overall, we recommend using this workshop to educate both students and experienced practitioners about the typical harmful influences of cognitive bias on architectural decisions and how to avoid them. © 2025 IEEE.

关键词： Students

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Experimental Evaluation of Road-Crossing Decisions by Autonomous Wheelchairs Against Environmental Factors 8th

Experimental Evaluation of Road-Crossing Decisions by Auto...

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8th International conference on Intelligent Transport systems, INTSYS 2024

作者： Corradini, Franca Grigioni, Carlo Antonucci, Alessandro Guzzi, Jérôme Flammini, Francesco University of Applied Sciences and Arts of Southern Switzerland IDSIA Lugano Switzerland University of Florence Department of Mathematics and Computer Science "Ulisse Dini" Florence Italy

ISBN: (纸本)9783031863691

Safe road crossing by autonomous wheelchairs can be affected by several environmental factors such as adverse weather conditions influencing the accuracy of sensors based on artificial vision. Previous studies have addressed experimental evaluation of multi-sensor information fusion to support road-crossing decisions in autonomous wheelchairs. In this study, we focus on the experimental evaluation of its tracking performance against outdoor environmental factors such as fog, rain, darkness, etc. It is rather intuitive that those factors can negatively affect the tracking performance;therefore our aim is to quantify through a set of metrics how the performance of the single sensors and their information fusion changes when such external factors are present. This is a first step in designing warning strategies in a novel framework based on the MAPE-k feedback loop established for the sensor system. System reconfiguration to reduce the reputation of less accurate sensors can then be set, thus improving overall safety. The problem is analysed within the context of the European project REXASI-PRO which aims to design a trustworthy autonomous wheelchairs supported by drones in which security, safety, ethics, and explainability are entangled to improve autonomy for people with reduced mobility. Results have been achieved by using an available laboratory dataset realised for a simplified framework in a road-crossing scenario and by applying appropriate software filters to simulate different environmental conditions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Data fusion

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