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CO2 Laser Fabrication of a Passive continuous-flow T-shaped Polymethyl Methacrylate (PMMA) Micromixer

Lasers in Manufacturing and Materials Processing 2023年第3期10卷 373-388页

作者： Okello, Job Lazarus El-Bab, Ahmed M. Fath Yoshino, Masahiko El-Hofy, Hassan A. Industrial and Manufacturing Engineering Department University of Science and Technology New Borg El-Arab City Egypt Mechatronics and Robotics Engineering Department University of Science and Technology New Borg El-Arab City Egypt Department of Mechanical Engineering Tokyo Institute of Technology Tokyo Japan On leave from Department of Production Engineering Faculty of Engineering Alexandria University Alexandria Egypt

Since the emergence of microfluidics, the demand for microfluidic devices is ever increasing. Polymethyl methacrylate (PMMA) is extensively used to manufacture microfluidic devices because it is cheap, disposable, and biocompatible. CO2 laser micromachining of PMMA is cheap, fast, and efficient. However, thermal defects affect the process. In this work, a passive continuous-flow T-shaped micromixer was fabricated using a low-power CO2 laser. Microchannels were fabricated on PMMA substrates coated with a 1-µm layer of 99.95% pure aluminium. The power and speed of the laser varied from 1.5 to 7.5 W and 5 to 17.5 mm/s respectively. The effects of the deposited energy on surface roughness, heat-affected zone, and volumetric removal rate were examined. The microchannels were characterized to get the suitable dimensions for the micromixer’s microchannel which was fabricated at 3.0 W and 17.5 mm/s. The micromixer was bonded using the microwave welding technique and tested for leakage using purple dye by varying the flow pressure at intervals of 100 kPa for 20 min. Up to 1.5 MPa, no leakage occurred. Beyond 1.5 MPa, any increase in pressure resulted in leakage from the inlet tubes. Coating PMMA substrates with 99.95% pure aluminium before fabrication reduces thermal defects and improves efficiency. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Carbon dioxide lasers

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification 26

Analysis of fast prototyping of microcontroller-based ML sof...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Filip Marciniak, Wojciech Marciniak, Tomasz Poznan University of Technology Faculty of Computing and Telecommunications Poznań60-965 Poland Poznan University of Technology DECYBEL Science Club Poznań60-965 Poland Poznan University of Technology Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznań60-965 Poland

ISBN: (纸本)9798350304985

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations. © 2023 Division of Signal Processing and Electronic Systems, Poznan University of technology (DSPES PUT).

关键词： Microcontrollers

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection Computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Person Re-Identification Based on Spatial Feature Learning and Multi-Granularity Feature Fusion

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Journal of Shanghai Jiaotong University (science) 2025年第2期30卷 363-374页

作者： Diao, Zijian Cao, Shuai Li, Wenwei Liang, Jianan Wen, Guilin Huang, Weixi Zhang, Shouming Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Robotic Laboratory South China Robotics Innovation Research Institute Foshan528300 Guangdong China Foshan Zhiyouren Technology Co. Ltd. Foshan528300 Guangdong China Robotic Laboratory Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou510070 China

In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestrian re-identification tasks, a person re-identification method combining spatial feature learning and multi-granularity feature fusion was proposed. First, an attention spatial transformation network (A-STN) is proposed to learn spatial features and solve the problem of misalignment of pedestrian spatial features. Then the network was divided into a global branch, a local coarse-grained fusion branch, and a local fine-grained fusion branch to extract pedestrian global features, coarse-grained fusion features, and fine-grained fusion features, respectively. Among them, the global branch enriches the global features by fusing different pooling features. The local coarse-grained fusion branch uses an overlay pooling to enhance each local feature while learning the correlation relationship between multi-granularity features. The local fine-grained fusion branch uses a differential pooling to obtain the differential features that were fused with global features to learn the relationship between pedestrian local features and pedestrian global features. Finally, the proposed method was compared on three public datasets: Market1501, DukeMTMC-ReID and CUHK03. The experimental results were better than those of the comparative methods, which verifies the effectiveness of the proposed method. © Shanghai Jiao Tong University 2023.

关键词： Convolutional neural networks

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Smart Anti-Pinch Window Simulation Using H_/H_(∞)Criterion and MOPSO

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Computers, Materials & Continua 2022年第7期72卷 215-226页

作者： Maedeh Mohammadi Azni Mohammad Ali Sadrnia Shahab S.Band Zulkefli Bin Mansor Department of Electrical and Robotics Engineering Shahrood University of TechnologySemnanIran Future Technology Research Center College of FutureNational Yunlin University of Science and Technology 123 University RoadSection 3DouliouYunlin64002Taiwan Faculty of Information Science and Technology Universiti Kebangsan Malaysia43600UKM BangiSelangorMalaysia

Automobile power windows are mechanisms that can be opened and shut with the press of a *** these windows can comfort the effort of occupancy to move the window,failure to recognize the person’s body part at the right time will result in damage and in some cases,loss of that *** anti-pinch mechanism is an excellent choice to solve this problem,which detects the obstacle in the glass path immediately and moves it *** this paper,an optimal solution H_/H_(∞)is presented for fault detection of the anti-pinch window *** anti-pinch makes it possible to detect an obstacle and prevent damages through sampling parameters such as current consumption,the speed and the position of DC *** this research,a speed-based method is used to detect the *** order to secure the anti-pinch window,an optimal algorithm based on a fault detection observer is *** the residual design,the proposed fault detection algorithm uses theDCmotor angular velocity *** against disturbances and sensitivity to the faults are considered as an optimization problem based on Multi-Objective Particle Swarm Optimization ***,an optimal filter for solving the fault problem is designed using the H_/H_(∞)*** results show that the simulated anti-pinch window is pretty sensitive to the fault,in the sense that it can detect the obstacle in 50 ms after the fault occurrence.

关键词： H_/H_(∞) anti-pinch residual fault detection multi-objective particle swarm optimization(MOPSO) multi-objective optimization automotive power windows electric windows

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Fault Detection Robotic System: Controlling and Experimental Validation of Overhead Crane Climbing Robot

Fault Detection Robotic System: Controlling and Experimental...

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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Arun Kumar Yadav Janusz Szpytko Faculty of Mechanical & Robotics Engineering AGH University of Science &Technology Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

The involvement of robots has increased rapidly in the last few years in many applications. A variety of robot performs numerous type of task in a hazardous environment. In order to prevent server damage to the infrastructures of overhead cranes in an industrial environment, a Fault Detection System (FDS) and Structural Health Monitoring (SHM) has a significant role. Mainly enhancing the safety, reliability, and availability of industrial overhead cranes requires timely continuous monitoring. This research proposes a wide approach for inspection that applies to various types of physical machines by mainly focusing on overhead crane bridges. The study of fault detection using a robotic system by NDT & SHM is relatively new, as only a few types of research has been performed. To be more specific, this paper provides a detailed insight into an autonomous robot for crack detection in overhead cranes bridges and girder using an array of GMR (Giant magneto resistive) sensors and magnetic climbing mechanisms. Firstly, we focused on the challenges and issues in the monitoring of the overhead cranes and the possibility of cracks and faults. Concerning these challenges and issues, we have proposed a new approach applicable for overhead cranes bridges crack detection. The intention of conducting this research is to make the inspection process cost-effective, reliable, and safe. This paper provides detailed insight into crack detection robots and sensor systems by considering all attributes.

关键词： Bridges Cranes Service robots Magnetic resonance imaging Inspection Robot sensing systems Structural beams

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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Comparison of Object Detection Technique for Applications in ETAT Automation Training Set 7

Comparison of Object Detection Technique for Applications in...

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7th International Conference on Information technology, InCIT 2023

作者： Kosiyanurak, Karin Singsri, Pongpat Seangsri, Soontaree Promput, Sommart Rajamangala University of Technology Tawan-ok School of Engineering and Innovation Department of Mechatronics and Robotics Engineering Chonburi Thailand Rajamangala University of Technology Tawan-ok Faculty of Science and Technology Department of Information and Communication Technology Chonburi Thailand

ISBN: (纸本)9798350358698

Object detection is a type of artificial intelligence (AI) that is currently being developed and used widely. It can classify and detect objects in images or videos. This research aims to investigate object detection that is suitable and accurate for detecting the color of the workpieces in the training set. This study will experiment comparison of three object detection models: 1. OpenCV Python code via the Thonny software. Using the BGR color separation principle, the range of color values must be specified so that the model can separate different colors. 2. V4-tiny and 3. V7-tiny are models that use model training principles in the CiRA-CORE Platform. In this study, 1,271 images of workpieces processed with the Augmentation Technique were utilized to train the model. and 240 images for testing the model. To analyze of find a suitable and accurate model for detecting the color of workpieces Because light is important in visual or object detection in real-time this study divided the experiment into two conditions: LED Lighting and without LED Lighting. The results showed that V7-tiny is an object detection model that can detect the color of the target in both lighting conditions. The V7-tiny model has an accuracy of 97.92%. © 2023 IEEE.

关键词： Object detection

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The Application of Machine Learning in Sentiment Analysis of Social Media and E-commerce: A Review of Methods and Techniques

The Application of Machine Learning in Sentiment Analysis of...

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International Carpathian Control Conference (ICCC)

作者： Agata Nawrocka Marcin Nawrocki Andrzej Kot Marcin Słychan Department of Process Control Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland Departament of Mechinery Engineering and Transport Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

The paper describes the application of machine learning for sentiment analysis in the context of social media and e-commerce. It presents a literature review and an overview of available sentiment analysis methods, such as sentiment classification, the use of lexicons, machine learning methods (e.g., SVM, decision trees), and word embedding techniques. Both traditional approaches and modern solutions based on deep learning are thoroughly *** the practical part of the thesis, sentiment analysis was conducted on data collected from social media platforms such as Twitter and Reddit. Various machine learning algorithms were utilized, and the model's behavior was analyzed based on different activation functions.

关键词： Deep learning Support vector machines Sentiment analysis Analytical models Recurrent neural networks Social networking (online) Text categorization Electronic commerce Systematic literature review Sorting

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Unleashing Artificial Cognition: Integrating Multiple AI Systems

arXiv

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arXiv 2024年

作者： Adnan, Muntasir Gamage, Buddhi Xu, Zhiwei Herath, Damith Kuhn, Carlos C.N. Open Source Institute Faculty of Science and Technology University of Canberra Australia Collaborative Robotics Lab Faculty of Science and Technology University of Canberra Australia

In this study, we present an innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence. The introduced open-source AI system seamlessly integrates a Chess engine with a language model, enabling it to predict moves and provide strategic explanations. Leveraging a vector database to achieve retrievable answer generation, our AI system elucidates its decision-making process, bridging the gap between raw computation and human-like understanding. Our choice of Chess as the demonstration environment underscores the versatility of our approach. Beyond Chess, our system holds promise for diverse applications, from medical diagnostics to financial forecasting. Our AI system is available at https://***/TheOpenSI/***. © 2024, CC BY.

关键词： Structured Query Language

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