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Design of robotic workstation for laser welding of pressure vessels 3

Design of robotic workstation for laser welding of pressure ...

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3rd International conference on Robotics, Automation and Intelligent control, ICRAIC 2023

作者： Gui, Wenjun Zhang, Liangan Xu, Xiangrong Fan, Jianwei Zhong, Weiming Anhui University of Technology Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet University of Technology Maanshan China Anhui University of Technology School of Mechanical Engineering Maanshan China

ISBN: (纸本)9798350360813

Oriented to the stable and efficient welding requirements of pressure vessels, a laser welding robot workstation that can realize fast clamping and automatic position change is designed. The workstation consists of Mitsubishi PLC, six-axis industrial robot, ground rail, welding positioner, touch screen, etc. Through the touch screen to detect the information and set the parameters, it is able to realize the multi-station, multi-seam welding processing of the product. The design of the workstation includes the overall layout design, the design of the positioning jigs and fixtures with single and double-axis and the design of the PLC-based intelligent control system. The welding process parameters were optimized by using the uniform design method, and the welding performance test was conducted. The results show that compared with manual welding, this workstation can complete the pressure vessel welding work more efficiently and stably, the weld seam is smooth, the productivity is significantly improved, the welding error rate is as low as 1.5%, and the optimal welding parameters can be found quickly to meet the needs of industrial production. © 2023 ieee.

关键词： Machine design

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Network Traffic Prediction using Deep Learning Models: Reducing Latency in High-Demand Scenarios

Network Traffic Prediction using Deep Learning Models: Reduc...

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2025 International conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Sagar, B.J. Job Karuna Divya, M.V. Kale, Namita Anand, Neetu Gaurav, Kumar Shashidhara, Shrividya Joginpally B.R Engineering College Department of Cse Telangana Hyderabad India East West Institute of Technology Department of Computer Science Bangalore India MET'S Institute Of Engineering Department of Information Technology Nashik India Maharaja Surajmal Institute Department of Computer Applications Delhi India Bms Institute of Technology and Management Department of Cse Bangalore India

ISBN: (纸本)9798331538538

The availability of the internet and enhanced applications, more specifically, the utilization of more devices in networks, and improved computational applications has created complexity in the control of network traffic. Network traffic forecasting is an essential component in low-latency networks, as well as in real-time applications that deserve excellent service quality and efficient resource allocation. This study therefore focuses on identifying whether deep learning models can further improve the accuracy in forecasting traffic flows in a network. Therefore, with the help of LSTM networks and Convolutional Neural Networks, the work will build temporal dependencies and spatial correlations of network traffic data. There are major and minor adaptations to some classic methodologies that conform the proposed framework;moreover, it is applied to two real datasets to highlight the capacity of the model in anticipating traffic congestion with a higher level of accuracy than the traditional methodologies. Evaluation of models in dynamic network conditions is done through common parameters such as prediction accuracy, lowered latency, and scalability. Saved models show that deep learning-based solutions reduce latency at high loads much more effectively, which will help network administrators in finding efficient solutions. This study demonstrate how deep learning can aid in the prediction of traffics, and the manner in which it is changing the efficiency and effectiveness of modern-day network systems. It makes the foundation for improved understanding of traffic patterns in order to apply it to optimize future generation of communication networks. © 2025 ieee.

关键词： Convolutional neural networks

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Hydrogel-Based Polymer Fiber Strain Sensor for Human Body Monitoring

Hydrogel-Based Polymer Fiber Strain Sensor for Human Body Mo...

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2024 Academic conference of China Instrument and control Society, ACCIS 2024

作者： Wang, Qianao Zhang, Yiyang Du, Mingchang Song, Aiguo Tan, Yuegang Li, Tianliang The School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China The School of Instrument Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350350326

Hydrogels are crosslinked networks of hydrophilic polymers, widely recognized for their excellent physical properties and good biocompatibility. This paper develops a hydrogel-based polymer fiber strain (HPFS) sensor, which connects plastic optical fibers with hydrogel fibers through a simple three-step preparation method to create the HPFS sensor. Tested for up to 100% stretchability, it operates based on the Beer-Lambert law for sensing. The static characteristics of the HPFS sensor were explored through tensile calibration experiments. The experimental results indicate that the sensor has a sensitivity of -0.6845 mV/%, a hysteresis error of 3.786%, and a repeatability error of 2.324%, demonstrating good sensing performance. Moreover, attaching the HPFS sensor to different parts of the human body can detect varying degrees of strain. The experiments show that the sensor has excellent stretch strain sensing capabilities and is expected to find further applications in the field of smart medical wearable devices. © 2024 ieee.

关键词： Plastic optical fibers

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IoT-Based Passenger Overload Monitoring and Reporting System 8

IoT-Based Passenger Overload Monitoring and Reporting System

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Natarajan, Balakrishnan Naveena, N. Ranjitha, R. Sowmiya, M. Sona College of Technology Department of Master of Computer Applications Salem India

ISBN: (纸本)9798350376425

This research study addresses the critical issues of passenger overcrowding and safety in public transportation. The objective is to tackle overloading by employing sensors to count passengers and detect obstacles, complemented by automated doors for efficient entry and exit control. Additionally, an alcohol sensor ensures the bus driver's sobriety. Notably, the project integrates with an open-source cloud platform, utilized to transmit real-time data about overloading and the driver's sobriety. This integration enhances monitoring and protection by providing a central hub for data storage and analysis. When the passenger count exceeds the preset limit, an automated alert is sent to the nearby Regional Transport Office for swift intervention and safety compliance. By harnessing cutting-edge technologies, the project aims to significantly enhance safety and reduce the likelihood of accidents in public transportation. © 2024 ieee.

关键词： Mass transportation

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applications on Deep Reinforcement Learning in Traffic Signal control 4

Applications on Deep Reinforcement Learning in Traffic Signa...

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4th ieee International conference on Civil Aviation Safety and Information technology, ICCASIT 2022

作者： Sun, Haolun Sun, Yilong Yu, Boyang Beihang University Entrepreneurship Management Training College Beijing China Dalian University of Technology Leicester Institution Dalian China Southwest Minzu University College of Electronic and Information Chengdu China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

Under the transportation resources and infrastructures that exist today, artificial intelligence is used as an efficient and intelligent solution to traffic congestion in strategic decision-making and trajectory planning control. This paper will introduce one of the methods - Deep Reinforcement Learning (DRL), and the application of DRL in intelligent traffic signal control. This paper would introduce the basic knowledge of DRL, explain a traffic signal control model application based on Markov Decision Process and present the relating knowledge of MDP, introduce the signal decision-making model based on Q reinforcement learning and compare the pros and cons of both models. This paper compares different solutions of traffic signal controls of varying learning modes with the standard of efficiency, implementation difficulty, advantages, and disadvantages. © 2022 ieee.

关键词： Reinforcement learning

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Improved control Strategy for Grid-connected Converters Based on PLL-free control 17

Improved Control Strategy for Grid-connected Converters Base...

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17th ieee conference on Industrial Electronics and applications, ICIEA 2022

作者： Shuxin, Yang Zhen, Xie Peihua, Hu Hefei University of Technology School of Electrical Engineering and Automation Hefei China

ISBN: (纸本)9781665409841

With the increase of renewable energy penetration, the ability of grid-connected converters to operate under weak grid conditions has gradually become a challenge. Under weak grid conditions, due to the instability of grid voltage, the coupling between phase-locked loop (PLL) and grid impedance will affect the work of the converter or even become unstable. Aiming at the weak grid control method of grid-side converters, this paper uses the inherent dynamic characteristics of DC voltage to realize voltage-controlled autonomous synchronous control, instead of the traditional PLL control strategy, and adopts a control structure without inner loop. The improvement of DC voltage stabilization control is proposed, and the dynamic response process of DC voltage is optimized. In this paper, by establishing a DC voltage small signal model, the influence of control parameters and grid strength on DC voltage control is analyzed in detail. Through the simulation, it is verified that the stability of the DC voltage control is optimized after the improvement, and it has a better weak grid operation capability. © 2022 ieee.

关键词： Phase locked loops

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Research on uniform interface technology of radiotherapy equipment based on edge computing 2

Research on uniform interface technology of radiotherapy equ...

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2nd ieee International conference on Power, Electronics and Computer applications, ICPECA 2022

作者： Gong, Yanjie Xie, Sufen Zhao, Yanling Network and Sensor Control Center Instrumentation Technology and Economy Institute Beijing China

ISBN: (纸本)9781665442763

This article starts with the introduction of data integration problems in the current radiotherapy process, analyzes the composition of the various systems involved in the radiotherapy process, provides a method for constructing a radiotherapy equipment data dictionary and a unified information model for the radiotherapy process, and provides a common semantic specification for data integration during the radiotherapy process. This research is helpful to realize the real-time collection and transmission of key information of radiotherapy equipment based on edge computing, realize the sharing of medical information on a larger scale, realize the comprehensive utilization of data in the process of radiotherapy to a greater extent, and improve the digitalized and networked level in the overall treatment process of radiotherapy. © 2022 ieee.

关键词： Edge computing

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Comparison of Classic and Recent Multi-Agent Path Finding Methods via MAPFame 20

Comparison of Classic and Recent Multi-Agent Path Finding Me...

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20th ieee International conference on Networking, Sensing and control, ICNSC 2023

作者： Huang, Jiaqi Zhou, Yangming Zhou, MengChu Liu, Wei Macau University of Science and Technology Faculty of Innovation Engineering China Shanghai Jiao Tong University Sino-US Global Logistics Institute Shanghai China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou China

ISBN: (纸本)9798350369502

A Multi-Agent Path Finding (MAPF) problem aims to plan paths for multiple agents given a prescribed map and ensure they do not conflict with each other and travel the shortest distance or lowest cost in the minimal time. MAPF is useful in many practical applications, e.g., automated warehouses and intelligent factories. It has been widely-studied in the past decade. Existing MAPF algorithms have evolved from those solving single-agent path finding problems. When realized in different programming languages, they tend to deliver varying results regarding execution time and solution quality. Many kinds of simulated maps are used but some of them are not directly related to actual application environment. In this paper, we experimentally compare existing MAPF algorithms based on an open-source simulation platform called Multi-Agent Path Finding based on Advanced Methods and Evaluation (MAPFame). We analyze and test the effects of obstacle density, different maps, and agents counts on their performance indices. Hence, our research outcomes can be used by practitioners to select a right method for their particular applications. © 2023 ieee.

关键词： Simulation platform

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Informative Path Planning Algorithm for the Exploration of Unknown 2D Environments 14

Informative Path Planning Algorithm for the Exploration of U...

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14th ieee Annual Ubiquitous Computing, Electronics and Mobile Communication conference, UEMCON 2023

作者： Macias, Geronimo Lee, Kooktae New Mexico Institute of Mining and Technology Department of Mechanical Engineering SocorroNM United States

ISBN: (纸本)9798350304138

This paper addresses the Informative Path Planning (IPP) algorithm for autonomous robots to explore unknown 2D environments for mapping purposes. IPP can be beneficial to many applications such as search and rescue and cave exploration, where mapping an unknown environment is necessary. Autonomous robots' limited operation time due to their finite battery necessitates an efficient IPP algorithm, however, it is challenging because autonomous robots may not have any information about the environment. In this paper, we formulate a mathematical structure of the IPP problem along with the derivation of the optimal control input. Then, a discretized model for the IPP algorithm is presented as a solution for exploring an unknown environment. The proposed approach provides relatively fast computation time while being applicable to broad robot and sensor platforms. Various simulation results are provided to show the performance of the proposed IPP algorithm. © 2023 ieee.

关键词： Mapping

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Software Defect Prediction Model Based on Deep Learning

Software Defect Prediction Model Based on Deep Learning

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2024 International conference on Power, Electrical Engineering, Electronics and control, PEEEC 2024

作者： Zhang, Yajie Harbin Far East Institute of Technology Heilongjiang Harbin150000 China

ISBN: (纸本)9798350378917

At present, under the background of the vigorous development of Internet technology and the era of the Internet of Everything, the diversification and complexity of software application scenarios are growing at an unprecedented rate. Although this trend has greatly enriched the user experience, it is also accompanied by the problem of frequent software failures. Software defects, as the key factor to weaken software reliability, are increasingly attracting extensive attention from the industry and academia. The software defect prediction model based on deep learning (DL) proposed in this article is a cutting-edge exploration in this field. This model fully utilizes the powerful ability of DL in processing large-scale and high-dimensional data, and can automatically learn complex features and patterns from historical data of software projects, achieving accurate prediction of software defects. The experimental results show that compared with traditional prediction methods, this model exhibits significant advantages in key indicators such as prediction accuracy and recall rate, effectively improving the comprehensiveness and accuracy of software defect prediction, and providing strong technical support for software testing and quality assurance work. © 2024 ieee.

关键词： Prediction models

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