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26th International Joint Conference on industrial engineering and Operations Management, IJCIEOM 2020

作者： Francisco, Rosemary de Freitas Rocha Loures, Eduardo Santos, Eduardo Alves Portela Deschamps, Fernando UNISINOS University São LeopoldoRS Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná CuritibaPR Brazil

ISBN: (纸本)9783030569198

Industry 4.0 is creating a radically more dynamic work environment, which calls for considerable changes in the role of industry professionals. As industries need to respond to evolving trends quickly, as well professionals need to be able to work and constantly learning in this new work environment. Besides, due to the continuous advances in technology and the adoption of these technologies in the industries processes, the level of complexity in the workplace is increasing, which results in the need for a new professional role. However, the main challenge is how to prepare and qualify these professionals to acquire these competences. In order to provide a better understanding of the new professional role and their relationship with the industry 4.0 working environment, this paper introduces an ontology for Competence Management in the Fourth industrial Revolution. Considering the recent scientific and business developments for preparing the industries and workers for this new working era, competences and skills needed for professionals in industry 4.0 were reviewed, as well as challenges and opportunities. Based on a systematic literature review in scientific sources and grey literature, this work identified what technologies are being adopted and what the professional competences are for working in this working environment. Since Industry 4.0 is spreading all over the world, this study contributes to this understanding and also provides a starting point for further research regarding workforce competence management for Industry 4.0. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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A Method to Gaze Following Detection by Computer Vision Applied to Production Environments 17th

A Method to Gaze Following Detection by Computer Vision Appl...

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17th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2020

作者： Cenzi, Emannuell Dartora Rudek, Marcelo Pontifícia Universidade Católica do Paraná PUCPR Industrial and Systems Engineering Graduate Program – PPGEPS CuritibaPR80215-901 Brazil

ISBN: (纸本)9783030628062

The humans have the natural ability of following objects with the head and eyes and identify the relationship between those objects. This daily activity represents a challenge for computer vision systems. The procedure to identify the relationship between human eye gaze and the trackable objects is complex and demands several details. In this current paper we proposed a review of the main gazing following methods, identified the respective performance of them and also proposed an AI based method to estimate the gaze from 2D images based on head pose estimation. The main important details to be recovered from images are scene depth, head position and alignment and ocular rotation. In this approach we perform a track estimation of the gaze direction without the use of the eye position, and also, the face partial occlusion is considered in the analysis. The proposed approach allows low cost in processing with considerable accuracy at low complexity sceneries, because we don’t need to extract the facial features. Gaze tracking is important to evaluate employees’ attention to specific tasks in order to prevent accidents and improve work quality. The presented method improves the current knowing workflow by applying the head pose estimation instead face detection for training and inference. The promisors results are presented and open points are also discussed. © 2020, IFIP International Federation for Information Processing.

关键词： Neural networks

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Intelligent Phase Contrast Meta-Microscope System

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Nano Letters 2023年第24期23卷 11630-11637页

作者： Chu, Cheng Hung Chia, Yu-Hsin Hsu, Hung-Chuan Vyas, Sunil Tsai, Chen-Ming Yamaguchi, Takeshi Tanaka, Takuo Chen, Huei-Wen Luo, Yuan Yang, Pan-Chyr Tsai, Din Ping YongLin Institute of Health National Taiwan University Taipei10672 Taiwan Institute of Medical Device and Imaging National Taiwan University Taipei10051 Taiwan Department of Biomedical Engineering National Taiwan University Taipei10051 Taiwan Department of Mechanical Engineering National Taiwan University Taipei10617 Taiwan Innovative Photon Manipulation Research Team RIKEN Center for Advanced Photonics Saitama351-0198 Japan Graduate Institute of Toxicology College of Medicine National Taiwan University Taipei100 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei100 Taiwan Program for Precision Health and Intelligent Medicine National Taiwan University Taipei106319 Taiwan Department of Internal Medicine National Taiwan University Hospital National Taiwan University Taipei10002 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei11529 Taiwan Department of Electrical Engineering City University of Hong Kong Kowloon999077 Hong Kong Centre for Biosystems Neuroscience and Nanotechnology City University of Hong Kong Kowloon999077 Hong Kong The State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Kowloon99907 Hong Kong

Phase contrast imaging techniques enable the visualization of disparities in the refractive index among various materials. However, these techniques usually come with a cost: the need for bulky, inflexible, and complicated configurations. Here, we propose and experimentally demonstrate an ultracompact meta-microscope, a novel imaging platform designed to accomplish both optical and digital phase contrast imaging. The optical phase contrast imaging system is composed of a pair of metalenses and an intermediate spiral phase metasurface located at the Fourier plane. The performance of the system in generating edge-enhanced images is validated by imaging a variety of human cells, including lung cell lines BEAS-2B, CLY1, and H1299 and other types. Additionally, we integrate the ResNet deep learning model into the meta-microscope to transform bright-field images into edge-enhanced images with high contrast accuracy. This technology promises to aid in the development of innovative miniature optical systems for biomedical and clinical applications. © 2023 American Chemical Society.

关键词： Microscopes

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A Study on Giant Magnetostrictive Actuator for Interior Sound Control System of the Ultra-Compact Mobility: Fundamental Consideration on Formation of the Giant Magnetostrictive Phases Depending on Heat Treatment Condition

A Study on Giant Magnetostrictive Actuator for Interior Soun...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Taro Kato Ryusei Naganuma Koki Bando Kentaro Sawada Wu Wenbao Ikkei Kobayashi Jumpei Kuroda Daigo Uchino Kazuki Ogawa Keigo Ikeda Ayato Endo Hideaki Kato Takayoshi Narita Mitusaki Furui Department of Mechanical Engineering Tokyo University of Technology Hachioji-shi Tokyo Japan Sustainable Engineering Program Graduate School of Engineering Tokyo University of Technology Hachioji-shi Tokyo Japan Course of Mechanical Engineering Tokai University Hiratsuka-shi Kanagawa Japan Course of Science and Technology Tokai University Hiratsuka-shi Kanagawa Japan Department of Mechanical Engineering National Institute of Technology (KOSEN) Numazu College Numazu-shi Shizuoka Japan Department of Electronic Robotics Aichi University of Technology Gamagori-shi Aichi Japan Department of Mechanical Engineering Hokkaido University of Science Sapporo Hokkaido Japan Department of Electrical Engineering Fukuoka Institute of Technology Fukuoka-shi Fukuoka Japan Department of Mechanical System Engineering Tokai University Hiratsuka-shi Kanagawa Japan

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

An active noise control system using giant magnetostrictive actuator has been proposed as a countermeasure for interior noise of the ultra-compact mobilities, which is a new transportation tools. In this paper, as basic research toward the development of the proposed giant magnetostrictive actuator for active noise control, we conduct experiments on the material properties when terbium, dysprosium, and iron powder, which make up the giant magnetostrictive material, are mixed and the heat treatment conditions are changed. In this study, the heat treatment temperature was varied from $800^{\circ}\mathrm{C}$ to $1100^{\circ}\mathrm{C}$ in $100^{\circ}\mathrm{C}$ incrementsbased on the equilibrium state temperature of each material, and the heat treatment time was set in 30-minute increments from 1 hour to 3 hours. As a result, TbFe2 was confirmed after heat treatment at an electric furnace temperature of $1000^{\circ}\mathrm{C}$ for 3 hours, although its strength was insufficient.

关键词： Actuators Heat treatment Powders Temperature Magnetostriction Noise Terbium Iron Resistance heating Material properties

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Attribute estimation using pedestrian clothing and autonomous mobile robot navigation based on it 2

Attribute estimation using pedestrian clothing and autonomou...

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2nd International Workshop on Intelligent systems, IWIS 2022

作者： Takahashi, Hidenao Takino, Nami Hashimoto, Hiroki Chugo, Daisuke Muramatsu, Satoshi Yokota, Sho She, Jin-Hua Hashimoto, Hiroshi Graduate School of Sceince and Technology Kwansei Gakuin University Hyogo Sanda Japan School of Sceince and Technology Kwansei Gakuin University Hyogo Sanda Japan School of Information Science and Technology Tokai University Kanagawa Hiratsuka Japan Toyo University Faculty of Science and Engineering Saitama Kawagoe Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

ISBN: (数字)9798350398939

ISBN: (纸本)9798350398939

This paper proposes a new method for estimating the attributes of a pedestrian, e.g. an office worker or a student, from the pedestrian's clothing and, based on the estimation results, a method for efficiently navigating an autonomous mobile robot while safely avoiding pedestrians. The robot can estimate the organization the pedestrians belong to by learning in advance the clothing of pedestrians that frequently appear in the city in which the is travelling, e.g. the uniforms of certain high school students. Furthermore, the robot is given the direction in which the person in that organization is likely to be heading at the current time, so it uses this information to estimate the future walking trajectory of that pedestrian and design a running path that is less likely to prevent that pedestrian from walking. We use the artificial potential method to design the robot's running trajectory. Even if pedestrians with different attributes are detected in the robot's running path, our proposed robot can efficiently reach its destination while avoiding those pedestrians appropriately in real time. The effectiveness of the proposed method was confirmed by computer simulations and subjects' experiments with our prototype robot. © 2022 IEEE.

关键词： Machine design

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Nonlinear Self-Calibrated Spectrometer with Single GeSe-InSe Heterojunction Device

arXiv

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arXiv 2023年

作者： Darweesh, Rana Yadav, Rajesh Kumar Adler, Elior Poplinger, Michal Levi, Adi Lee, Jea-Jung Leshem, Amir Ramasubramaniam, Ashwin Xia, Fengnian Naveh, Doron Faculty of Engineering Bar-Ilan University Ramat-Gan52900 Israel Institue of Nanotechnology and Advanced Materials Bar-Ilan University Ramat-Gan52900 Israel Department of Electrical Engineering Yale University New HavenCT United States Department of Mechanical and Industrial Engineering University of Massachusetts AmherstMA01003 United States Materials Science and Engineering Graduate Program University of Massachusetts AmherstMA01003 United States

Optical spectroscopy – the measurement of electromagnetic spectra – is fundamental to various scientific domains and serves as the building block of numerous technologies.1–3 Computational spectrometry is an emerging field that employs an array of photodetectors with different spectral responses or a single photodetector device with tunable spectral response, in conjunction with numerical algorithms, for spectroscopic measurements.4–6 Compact single photodetectors made from layered materials are particularly attractive, 7–9 since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance.7,10 However, compact tunable photodetectors are typically nonlinear devices and this adds complexity to extracting optical spectra from the device response. Here, we report on the training of an artificial neural network (ANN) to recover the full nonlinear spectral photoresponse - a nonlinear problem of high dimensionality - of a single GeSe-InSe p-n heterojunction device. We demonstrate the functionality of a calibrated spectrometer in the spectral range of 400-1100 nm, with a small device footprint of ~25 × 25 μm, and we achieve a mean reconstruction error of 2 × 10-4 for the power-spectrum at a spectral resolution of 0.35 nm. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical *** Codes Nonlinear Self-Calibrated Spectrometer, Artificial neural network, Optical imaging © 2023, CC BY.

关键词： Spectrometers

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Deep Learning Approach for EEG Classification in Lower-Limb Movement Phases: Towards Enhanced Brain-Computer Interface Control

Deep Learning Approach for EEG Classification in Lower-Limb ...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Cristian Felipe Blanco-Diaz Cristian David Guerrero-Mendez Guilherme Rangel Furtado Teodiano Bastos-Filho Rafhael Milanezi de Andrade Dept. of Excellence and AI The BioRobotics Institute Scuola Superiore Sant’Anna Pisa Italy Dept. Electronics and Biomedical Engineering Universidade Estadual de Campinas Campinas SP Brazil Dept. of Electrical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Posgraduate Program in Electrical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Graduate Program in Mechanical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Embedded Brain-Computer Interfaces (BCI) have emerged as an alternative for rehabilitation/assistance of people with neuromotor impairments. Data-driven methods are usually implemented to design controllers to (de)activate these systems using electroencephalography (EEG), which may limit usability and degrees of freedom. Thus, the development of more adaptive classifiers remains a challenge. This study focuses on the implementation of a Deep Learning (DL) method to classify EEG signals during different phases of gait and pedaling. To address this, public and in-house databases were used, which contain information on EEG and kinematic data during walking on a treadmill and pedaling on a minibike. Kinematic labels of the phases were extracted using k-means and classified by a Convolutional Neural Network (CNN), which demonstrated accuracy above 60%. Furthermore, a negative correlation was found between velocities and classifier performance metrics, suggesting that faster movements can hinder EEG decoding. Our proposal is a starting point to identify current states of lower-limb movement, which can be used for designing state machine-based controllers of robotic BCIs with rehabilitation or assistance purposes, such as motorized mini exercise bikes or leg exoskeletons.

关键词： Legged locomotion Deep learning Mechatronics Kinematics Electroencephalography Brain-computer interfaces Convolutional neural networks Proposals Usability Robots

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Improve industrial performance based on systematic analyses of manufacturing data

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IFAC-PapersOnLine 2021年第1期54卷 709-716页

作者： M. Lisboa E. Jesus R. Seixas P. Valle F. Deschamps C. Strobel Institute of Applied Technology Solutions (InSTA) Manufacturing Engineering Graduate Program (PPGEM) Department of Mechanical Engineering (DEMEC) Federal University of Paraná (UFPR) Curitiba Brazil

This Article was written based on a systematic review of the literature considering three reference axes, Industry 4.0, Data Analytics and Predictive Maintenance, including specific combination of search terms to ensure a reasonable quantity of articles keeping adherence to the topic that is decision making based on collection and analysis of relevant data on B2B (Business to Business). The study focuses on the area of predictive maintenance, which has been strongly highlighted by the fourth industrial revolution, due to the mechanical and mainly the electronic embedded systems complexity increases in the last decades, as well as network connectivity possibilities for equipment’s data acquisition, enabling technologies for predictive maintenance as a key factor for competitiveness, reducing costs, increasing equipment’s availability, or as a servitization strategy. The article is divided into five parts, starting with the research model and selection of articles, including the proposal for a data analysis framework and the application of systematic analysis of these data, concluding with the opening of a discussion and the indication of future directions.

关键词： Industry 4.0 industrial Internet of things (IIOT) Data Analytics Predictive Maintenance

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Mass Flow Prediction in a Refrigeration Machine Using Artificial Neural Networks

SSRN

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SSRN 2022年

作者： Fonseca, Vinícius David Maia, Antônio Augusto Torres Duarte, Willian Moreira Machado, Luiz Graduate Program in Mechanical Engineering Universidade Federal de Minas Gerais Avenida Antônio Carlos 6627 Pampulha – Minas Gerais Belo Horizonte31270901 Brazil

Measuring mass flow rate in refrigeration systems with flow meters can be expensive when taking into account the cost of the equipment itself and the costs related to installation and maintenance. A model based on Artificial Neural Networks (ANNs) can be used to predict the value of the mass flow, at low cost, through easily observed and measured parameters, like temperatures. Additionally, well-known correlations to calculate parameters that directly influence the mass flow rate can be used as input data for the ANN to improve its accuracy. Within this context, the present study aims to develop a Multilayer Perceptrons (MLP) model to predict the mass flow rate of a refrigeration systems. Later, it is presented an alternative mass flow rate meter, using an ANN model programmed in a microcontrolled circuit with only three temperatures as inputs. To develop the ANN model, experimental data were collected in a refrigeration machine in several operating points. Step disturbances were introduced in the mass flow rate to produce transient data. Two different data set were considered in the training process. The first data set contained only steady-state data and in the second data set there were steady-state plus transient data. The mass flow rate estimated through the ANN presented a maximum error of 0.69% when considering steady-state and transient data in the training process, and 0.75% when considering only steady-state data in the training procedure. The microcontrolled circuit successfully estimated the mass flow rate with an average error of 0.32%. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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