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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Tuaniai Archevapanich Woranidtha Krungseanmuang Vasutorn Chaowalittawin Posathip Sathaporn Punyisa Chaowalittawin Boonchana Purahong Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Indusstrail Physics School of Science Bangkok Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

关键词： Uniform resource locators Image processing Metals Wheels Systems architecture User interfaces Mobile applications

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Low-power wireless sensor network system for early diagnostic of subacute rumen acidosis in cows

Low-power wireless sensor network system for early diagnosti...

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IEEE Workshop on Advances in Information, Electronic and Electrical engineering (AIEEE)

作者： Anatolijs Zabasta Nadezda Kunicina Kristaps Vitols Ilmars Duritis Uldis Grunde Janis Judvaitis Modris Greitans Ilga Sematovica Aija Malniece Ilja Galkins Institute of Electrical Engineering and Electronics EEF Riga Technical University Riga Latvia Faculty of Veterinary Medicine Latvia University of Life Sciences and Technologies Jelgava Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia

High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of productivity and reduced longevity. The aim of this work is to develop a reticulo-ruminal long-acting cyber-physical system for monitoring rumen parameters. Three scientific institutions in cooperation with two farmers conduct the research in order to create a prototype of a low-power wireless sensor network system for early diagnostic of subacute rumen acidosis of dairy cows. The new system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes a base station for data collection from boluses, an MQTT broker, a web server and a database. Data communication solution has been developed and tested in the laboratory, and micro-controllers have been selected and adapted for data processing. In addition, research is under way to create an autonomous long-term power supply system. Work shall be conducted in two directions: (a) stand-alone battery-powered electricity supply system; (b) an autonomous power supply system based on the generation of an electrostatic generator. The results of the initial stage of the research are discussed in this paper.

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Retinal Disorder Diagnosis Based on Hybrid Deep Learning Models

SSRN

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SSRN 2022年

作者： Sedik, Ahmed El-Hag, Noha A. El-Hoseny, Heba M. El-Banby, Ghada M. Khalaf, Ashraf A.M. Abd El-Samie, Fathi E. El-Shafai, Walid Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Egypt Menoufia University Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Deptartment of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified for anomaly *** MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, braintumors are represented as areas composed of abnormal cells that need to be detected. In addition, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

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Deep Transfer Learning-Based Demagnetization Analysis for Linear Oscillating Actuator Considering Circumferential Segmented Structure

Deep Transfer Learning-Based Demagnetization Analysis for Li...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Ji-Hyeon Lee Soo-Hwan Park Du-Ha Park Jae-Hoon Jeong Myung-Seop Lim Department of Automotive Engineering (Automotive-Computer Convergence) Hanyang University Seoul Republic of Korea Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul Republic of Korea H&A Fundamental Technology R&D Lab LG Electronics Seoul Republic of Korea

The linear oscillating actuator (LOA) achieves high efficiency and features a simple mechanical structure because it doesn’t require the conversion of rotational motion into linear motion. Therefore, the LOA is an appealing option for devices such as compressors, linear pump and automobile active suspension due to its high efficiency and power density. The stability of permanent magnets (PMs) can be impacted by different factors such as temperature, electromagnetic fields, and other external influences. In more severe cases, these factors can result in the occurrence of irreversible demagnetization, causing permanent damage to the magnetic properties of the PM. The irreversible demagnetization of permanent magnets impacts the electromagnetic functionality of the LOA, making it necessary to account for it during the design stage. However, the intricate configuration of the LOA such as divided outer stator and PMs diminishes accuracy of the 2-D finite element analysis (FEA). Despite its high computational cost for calculating accurate demagnetization ratio (DR), 3-D FEA is essential. Thus, we propose a demagnetization analysis based on transfer learning (TL) to reduce the computational cost associated with accurately calculating the 3-D FEA-based DR. This approach takes into account the permeance in the stator core and circumferential leakage flux. With TL, the parameters of pre-trained models learned from a source dataset in different but similar domains are transferred to learn the target dataset and effectively enhance the performance of neural network. The TL is conducted with a substantial dataset from 2-D FEA-based demagnetization ratio (DR) anda limited dataset from 3-D FEA-based DR. TL is a cognitive learning approach that utilizes knowledge acquired from a source task to enhance learning in a related but different target task were compared.

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On Motion Blur and Deblurring in Visual Place Recognition

arXiv

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arXiv 2024年

作者： Ismagilov, Timur Ferrarini, Bruno Milford, Michael Nguyen, Tan Viet Tuyen Ramchurn, S.D. Ehsan, Shoaib The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom MyWay srl Via Osti 6 PC Vernasca29010 Italy The QUT Centre for Robotics School of Electrical Engineering and Robotics BrisbaneQLD4000 Australia The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This paper bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the paper proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios. © 2024, CC BY.

关键词： Mobile robots

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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DynaPP: A Dynamic Resolution Model with Patch Packing for Fast Online Video Detection

DynaPP: A Dynamic Resolution Model with Patch Packing for Fa...

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International Joint Conference on Neural Networks (IJCNN)

作者： Changrok So Simon S. Woo Jong Hwan Ko Advanced Robotics Lab CTO Division LG Electronics Seoul South Korea Department of Artificial Intelligence Sungkyunkwan University Suwon South Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Online video detection becomes more challenging with higher resolution as computational costs increase proportionally with increasing resolution. To address this issue, we present a novel approach, DynaPP, which arranges object candidate regions into a compact form. DynaPP performs resource-intensive whole-image inference only on sparse key frames, employing reduced resolutions for inference on other frames. Additionally, we propose transforming a 1-stage detector into a dynamic resolution model to facilitate frame inference at reduced resolutions. Here, the dynamic resolution model signifies a model capable of inferring all resolutions, distinguishing itself from typical models by not having restricted inferable resolutions. Unlike prior studies introducing new model structures for multi-resolution models, our work demonstrates that slight modifications to existing models can convert them to dynamic resolution models. DynaPP showcases substantial acceleration in video detection across four representative video datasets: AU-AIR (5.5×), UAVDT (3.67×), VisDrone (2.73×), and ImageNet VID (3.69×), while maintaining a mean average precision with a small loss (≤2.2). Furthermore, we observed that our method achieves a detection acceleration of up to 8.84×, depending on the video clip.

关键词： Image resolution Neural networks Detectors Computational efficiency

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Parameter identification of a linear permanent magnet motor using particle swarm optimization

Parameter identification of a linear permanent magnet motor ...

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7th Annual International Conference on Electrical engineering/electronics, computer, Telecommunications and Information Technology, ECTI-CON 2010

作者： Therdbankerd, Tithiwat Sanposh, Peerayot Chayopitak, Nattapon Fujita, Hideaki Department of Electrical Engineering Intelligent Mechatronics Automation Robotics and Control Laboratory Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Industrial Control and Automation Laboratory National Electronics and Computer Technology Center 112 Thailand Science Park Phahonyothin Rd. Klong 1 Klong Luang Pathumthani 12120 Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology S3-18 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan

ISBN: (纸本)9789746724913

Accurate and effective parameter identification is an important engineering task in high performance control system design. One emerging approach to effectively identify such nonlinear or dynamic unknown parameters is to use Particle Swarm Optimization (PSO) algorithm. Linear Permanent Magnet (LPM) motor is a high performance actuator employed in many applications that require direct linear motion without mechanical transmission for high acceleration and accurate positioning. Therefore, accurate motor parameters are necessary to effectively control the LPM motors. This paper proposes a simple PSO based method with chirp inputs to identify the LPM motor's parameters. The simulations and experiments are conducted to verify the results and determine the effectiveness of the proposed method.

关键词： Parameter estimation

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Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems

arXiv

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arXiv 2021年

作者： Grelewicz, Patryk Khuat, Thanh Tung Czeczot, Jacek Nowak, Pawel Klopot, Tomasz Gabrys, Bogdan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Gliwice44100 Poland University of Technology Sydney Faculty of Engineering and IT Advanced Analytics Institute NSW2007 Australia

In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed loop performance. This approach allows for determining the best (but also robust and practically achievable) closed loop performance based on very popular and intuitive closed loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different machine learning based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution *** Codes 93 © 2021, CC BY-NC-SA.

关键词： Three term control systems

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