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检索条件"任意字段=9th IEEE International Enterprise Distributed Object Computing Conference"

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the Evolutionary Logic from DIKW to DIKWP, and then to Platform Economy DIKICRWPIP 25

The Evolutionary Logic from DIKW to DIKWP, and then to Platf...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Dou, Erxiang Cheng, Ailiu Duan, Yucong Mei, Yingtian Peking University Beijing China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350330014

DIKW can be regarded as the overall model for human social cognition and transformation of the world. In terms of improving cognitive efficiency and improving transformation accuracy, the DIKWP model is an evolution. the difference between any animal lies in its original purpose. the clearer the purpose P, the more significant the results of cognition and transformation, and the greater the progress of human society. this evolution is also reflected in the evolution of information carriers, as exemplified by the prosperity of the platform economy. the new platform economy has improved the completeness of the platform on the basis of information carrier technology, especially in terms of the three commonalities of the platform, namely fairness, dialectics, and openness. the new platform is more credible. the modern enterprise system will transition to a 'platform + company' system, which is conducive to the formation of a unified domestic and international market and emerging value. © 2023 ieee.

关键词： international trade

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Enhancing YOLOv3 object Detection: An In-Depth Analysis of C3 Module Integrated Architecture 9

Enhancing YOLOv3 Object Detection: An In-Depth Analysis of C...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Quan, Jiatao Deng, Yancong University of Washington Seattle United States University of California San DiegoCA United States

ISBN: (纸本)9798350385991

object detection has been a cornerstone in the field of computer vision with pivotal applications into autonomous driving, surveillance to drone navigation, industrial automation. the YOLO (You Only Look Once) series of models have brought the greater contributions to this domain more than other myriad models because it provides real-time detection as well as detection with high accuracy. this paper investigates how the C3 module has been integrated with this model, as an adaptation that enhances the performance of object detection. We analyze the C3 module in detail, consider its structure and operational mechanics to understand how they can affect the efficiency of the YOLOv3 model. We then assessed the effect of the C3 module on several performance indicators, including Average Precision (AP), F1 Score, Recall, and Precision through extensive experimentation and performance analysis. From the findings, there are slight improvements brought about by the integration of the C3 module in improving AP and F1 Score performance in some training epochs, but it is not all rosy since Precision has a slight dip. this further underscores the need of cautious parameter tuning and optimization in harnessing, the full capability of the C3 module. the paper makes a timorous contribution to this continued development of computerized vision technology, giving understanding towards their complexity in tracking and integrating new modules into well-established models like YOLOv3. Our work stresses subtlety in terms of balancing the need to refine models and opens doors for further exploration on how to optimize deep learning architectures for object detection. © 2024 ieee.

关键词： object tracking

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Radar System Design for Static object Detection for Indian Road Conditions 9

Radar System Design for Static Object Detection for Indian R...

引用

9th ieee international conference on Electronics, computing and Communication Technologies, CONECCT 2023

作者： Aparajith, B.S.M. Srikanth, Aadithya Ali, Arbaz Chandar, T.S. Pes University Dept. of Ece Bengaluru India

ISBN: (纸本)9798350334395

there is an increase in the adoption of radars into automobiles as it is robust to weather changes and provides direct range and velocity measurements. Several low-height static targets such as humps and potholes are difficult to detect in regular vehicles, especially when covered with water. Hence, it is necessary to study their detection based on how they interact with radar waves which are characterized by their radar cross-section (RCS). In this work, a detailed methodology for estimating the RCS of humps and potholes has been presented, and its detection is studied in two different radar waveforms, frequency-modulated continuous waveform (FMCW) and orthogonal frequency division multiplexing waveform (OFDM). © 2023 ieee.

关键词： Radar cross section

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Hand Sign Recognition to Structured Sentences 9

Hand Sign Recognition to Structured Sentences

引用

9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Harchandani, Rohan Preethi, N. George, Jossy P Pune Lavasa India

ISBN: (纸本)9798350397376

Computer vision is not just a concept of deep learning;it has wide applications such as motion recognition, object recognition, video indexing, video media understanding, and recognition-based intelligence. -However, vision-based systems are a challenging field for research and accurate results. Recent areas of interest are human action recognition or human hands gesture recognition techniques using video data set, still, an image data set, spatiotemporal methods, features in RGB, deep learning methods. Hand action recognition has applications such as communication systems to shorten the bridge gap for people with speech disabilities by using a vision-based system to recognize hand sign language and convert it to text, forming structured sentences which will be easy to understand and communicate. © 2023 ieee.

关键词： object recognition

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Spatiotemporal Anomaly object Detection Using Edge Cloud Collaborative for Real-Time Surveillance Video 9

Spatiotemporal Anomaly Object Detection Using Edge Cloud Col...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Aarthy, R. Viranth, D. Ramya, S. Rajagopal, S. Divyashri, S. National Engineering College B. Tech Information Technology Tamilnadu Kovilpatti India National Engineering College Tamilnadu Kovilpatti India

ISBN: (纸本)9798350397376

Video object Detection is a method that enables computer systems to examine video and identify items in the scene. object detection is computer vision technology that tries to find items in images or videos. the objective of Video object Detection is to enable the system to detect, identify, and categorize items in the frame. In normal closed-circuit television footage, only the videos are captured and recorded. So, in order to trigger the detection of events automatically, an Internet of things-enabled camera system was used. In the Internet of things applications, the videos 0are stored and processed in a centralized server which causes time latency. In order to overcome this problem, a simple hybrid instantaneous online video object detection system was proposed. In this system, an edge and cloud collaborative technology was used. the current video object detection methodology uses Deep Neural Network to detect objects present in the scene. For real-time streaming video, the object could not be detected accurately using Deep Convolutional Neural Network because of low end-to-end latency, and also it was not applicable for latency-sensitive Internet of things applications. the proposed work focuses on detecting vehicles traveling in the wrong lane of the road by analyzing real-time surveillance video. For detecting this anomaly, an edge cloud computing technology was proposed. In this technology, data are analyzed using the Kalman filter algorithm and with an entry-exit algorithm, and the result was stored in the cloud. By implementing the above technologies, both processing and communication time are reduced and instantaneous anomaly detection was achieved. © 2023 ieee.

关键词： Internet of things

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Proceedings of the 2024 ieee international conference on Progress in Informatics and computing, PIC 2024

Proceedings of the 2024 IEEE International Conference on Pro...

引用

9th ieee international conference on Progress in Informatics and computing, PIC 2024

ISBN: (纸本)9798350363203

the proceedings contain 46 papers. the topics discussed include: a UAV edge computing task offloading method based on reinforcement learning and the underwater lobster optimization algorithm;effects of weighting schemes on retrieval precision;expanding the relevant judgment list based on classified documents;constructing multi-granular knowledge graph with structure similarity and hierarchical clustering;audio-to-score conversion model based on whisper methodology;evaluating tourism service quality using large language models: a sentiment analysis approach;pre-welding defect detection in hull construction based on dual-branch attention feature enhancement;enhancing semantic segmentation through a targeted approach to hard-to-classify examples;and area mapping via pseudo-label discovery for weakly supervised object detection.

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Human-Led Robotic Transportation of Elastic objects with Adaptive Control 9

Human-Led Robotic Transportation of Elastic Objects with Ada...

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9th international conference on Robotics and Automation Engineering, ICRAE 2024

作者： Cui, Zhenxi Lai, Jiewen Lu, Bo Guo, Yi Chu, Henry K. University of West London School of Computing and Engineering London United Kingdom Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Soochow University School of Mechanical and Electrical Engineering Suzhou China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States The Hong Kong Polytechnic University Department of Mechanical Engineering Hong Kong

ISBN: (纸本)9798331518301

the perception and transportation of soft objects are critical tasks in daily life, and recent sensor advancements enable robots to safely perform these tasks in human-shared environments. this study advances elastic linear object transportation by introducing a collaborative framework where a human leads, providing task-specific instructions to guide the robot. Unlike conventional methods focused on fixed object manipulation, our approach dynamically controls and converges feature points of the elastic object into desired configurations during transportation. We developed an online model estimation technique using a least-squares optimization algorithm, with an exponential forgetting mechanism to adaptively update the model under disturbances from sensors and human interaction. Validated in experiments with a 6-DOF robot and depth camera, our method demonstrated robustness across varied scenarios, achieving faster convergence and reduced positional fluctuation compared to conventional gradient descent approaches. © 2024 ieee.

关键词： Adaptive control systems

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A Combined Dispatch Model of Power Load Satisfaction and Comfort Based on Genetic Algorithm 9

A Combined Dispatch Model of Power Load Satisfaction and Com...

引用

9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Fan He, Shan Lin, Xiaoming Zhao, Yuming Tang, Jianlin Li, Yan Electric Power Research Institute of Csg Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering Enterprise of Power Grid Guangzhou China Shenzhen Power Supply Co. Ltd. Shenzhen China

ISBN: (纸本)9798350376548

In recent years, the demand for electricity of the whole society has been increasing rapidly, and the power suppliers have been unable to meet the power consumption demand of all users. In order to effectively balance the relationship between the power suppliers and users, and ensure the satisfaction of suppliers and the experience of users, a load optimization model is proposed. Based on Time-of-use tariff, the model comprehensively considers the income satisfaction of the power supplier, the satisfaction of the consumer's electricity expenditure and the satisfaction of electricity consumption, and takes Time-of-use tariff and elastic load as decision variables. Finally, the multi-objective function of residential user price response under Time-of-use tariff mechanism is constructed, and the genetic algorithm is used to optimize the model under various constraints. the experimental results show that the load optimization algorithm can effectively reduce the load curve of residential electricity, and can carry out peak cutting and valley filling on the premise of ensuring the satisfaction of users and power supplier. © 2024 ieee.

关键词： Demand response

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Incremental Learning for object Detection Based on Self-Supervised Learning 9

Incremental Learning for Object Detection Based on Self-Supe...

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9th ieee international conference on Progress in Informatics and computing, PIC 2024

作者： Sheng, Haihong Zhang, Jiapeng Wan, Lu Huang, Zheng Wu, Pengzuo Wang, Qiang Zhejiang Huayun Information Technology Co. Ltd. Information Technology Department Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350363203

Incremental learning, as an approach well-suited for handling applications characterized by evolving data streams, aligns effectively with the objectives of object detection. However, existing methods exhibit suboptimal performance in addressing issues related to catastrophic forgetting and few-shot learning of new classes. through the enhancement of existing models, we propose a novel method that integrates self-supervised learning. By employing a feature-prediction collaborative mechanism and gradient stopping techniques, the method significantly enhances the model's generalization capability for new category features while also mitigating the forgetting of old category knowledge during the incremental learning process to some extent. Experimental results demonstrate that the proposed method maintains detection performance for old categories throughout multiple incremental learning phases while simultaneously improving the learning capacity for new categories. © 2024 ieee.

关键词： Self-supervised learning

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Research on Fast Adaptive Transmission Models for international Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

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10th ieee international conference on Cyber Security and Cloud computing and 9th ieee international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics international Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. the proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua international Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 ieee.

关键词： Resource allocation

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