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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Analysis of the Perception of Innovations Adoption in Public Health Establishments in Morocco: Crossed Perspectives of Healthcare Professionals 4th

Analysis of the Perception of Innovations Adoption in Public...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Hantem, Aziz Soussi, Houssine Emzi, Khadija Emzi, Kawtar Ibn Zohr Univ Lab MADILOG Agadir Morocco Ibn Zohr Univ Natl Sch Business & Management Dakhla Agadir Morocco Ibn Zohr Univ Natl Sch Business & Management Agadir Agadir Morocco

ISBN: (纸本)9783031686498;9783031686504

The aim of this study is to identify and analyze healthcare professionals' perceptions of the adoption of innovations within public health establishments in Morocco. To answer our research question "What is the perception of innovations among healthcare professionals in Morocco?" and to test our hypotheses, we adopted a quantitative methodological approach based on hypothetico-deductive reasoning. The empirical results show that only two objectives positively favor the acceleration of innovation adoption in healthcare facilities. However, the explanatory variable "Perception of innovations" and the explanatory variables "Professional category" and "Age of healthcare professionals" are independent.

关键词： Perception Innovations Healthcare professionals Healthcare sector Morocco

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The Concept of Digital Transformation of SMEs Through the Implementation of ERP and MES systems and Lean Tools 14th

The Concept of Digital Transformation of SMEs Through the Im...

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14th International Conference on Learning Factories (CLF)

作者： Saravanja, Luka Stojkic, Zeljko Bosnjak, Igor Veza, Ivica Univ Mostar Fac Mech Engn Comp & Elect Engn Matice Hrvatske Bb Mostar 88000 Bosnia & Herceg Univ Split Fac Elect Engn Mech Engn & Naval Architecture Rudera Bogkovica 32 Split 21000 Croatia

ISBN: (纸本)9783031653995;9783031654008

Industry 4.0 and digital transformations shape business today more than ever, with the aim of achieving better results and performance. The introduction of digital technologies enables the creation of an agile and flexible business that is ready to respond to the demands of today's production. As a basic prerequisite for the implementation of Industry 4.0, modern information system are needed, which are the backbone for networking, collecting and processing data in real time. This especially applies to MES systems. However, in order for information systems as well as other digital technologies to realise their full potential on the path of Industry 4.0, knowledge and use of Lean manufacturing for the organisation is necessary, to achieve efficient production and business processes without losses. The goal of this paper is to contribute to a better understanding of the possibilities of digital technologies in the field of information systems in production, as well as to present the concept of digital transformation in SMEs with the support of Lean manufacturing, and all with the support of the learning factory. The paper presents an example of the implementation of digital transformation in a local company through the implementation of information systems as well as Lean manufacturing. Also, the paper describes previous research in the field of digital transformation of SMEs, achieved through the work and developed infrastructure of the learning factory, which gave a broader picture of the current state of enterprises in the environment on the way to digital transformation and Industry 4.0.

关键词： Industry 4.0 Digital Transformation MES systems Lean Manufacturing Learning Factory

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Design a Touch Sensor Using Conductive Spandex Fabric 7th

Design a Touch Sensor Using Conductive Spandex Fabric

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7th International Conference on Green Technology and Sustainable Development

作者： Minh Ky Tran Thi Tuyet Nga Ly Bao Hy Nguyen Chi Cuong Vu HCMC Univ Technol & Educ Dept Facil Elect & Elect Engn 01 Vo Ngan St Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Recent advancements in sensor technology have propelled the e-textile industry to the forefront of innovation. E-textiles, also known as smart fabrics, possess physical and dimensional flexibility, making them suitable for wearable sensor technology. These technologies are developed to cater to the needs of advanced daily life, which aligns with the IoT development trend. This paper introduces an applied resistive sensor that uses an electronic fabric made from elastic fabric and carbon nanotubes (CNTs) fabric as the electrical layer. The sensor achieves high sensitivity (2.6241 N-1 with force less than 1 N), thickness (similar to 3 mm), and versatility make it ideal for load performance and fast resistance switching without waiting for capacitor charging and discharging as capacitive sensors, facilitating faster interaction with e-touch. The study provides an overview of the sensor's characteristics and future directions, emphasizing the creation of soft sensors based on pyralux for medical devices and health monitoring applications in the future.

关键词： Touch sensor Carbon nanotubes (CNTs) Conductive spandex fabric Force-sensing resistor (FSR)

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Finite Element Investigation of Torque Ripple in Roller-Cycloidal Contact: Examining Gear Ratio, Mesh Density, and Transmitted Power Dependencies 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Fraccaroli, Lorenzo Maccioni, Lorenzo Concli, Franco Blagojevic, Mirko Rotini, Federico Free Univ Bozen Bolzano Fac Engn I-39100 Bolzano Italy Univ Kragujevac Fac Engn Kragujevac 34000 Serbia Univ Firenze Dept Ind Engn I-50139 Florence Italy

ISBN: (数字)9783031704628

ISBN: (纸本)9783031704611;9783031704628

The low backlash of cycloidal gearboxes is undoubtedly a distinctive feature. However, due to their architecture, cycloidal drives exhibit torque oscillations, i.e. Torque Ripple (TR), caused by variations in system stiffness as contact points change over time. Such oscillations can be critical in position control applications. One of the most common approaches to analyze torque ripple, namely finite element analysis, is exploited and discussed in this paper. Specifically, the aim is to investigate how modeling parameters such as mesh density, in different geometries and operating torques, impact torque ripple estimation. Preliminary results from 12 simulations, conducted at two gear ratios, two levels of transmissible power, and three mesh densities (coarse, medium, fine), demonstrate that with an equal number of elements per cycloidal disk lobe, models with smaller GR exhibit larger TR. It is observed that changes in Mesh density significantly affect TR, without a corresponding significant change in maximum stress levels. Additionally, lower applied torques result in higher TR, though this effect diminishes with denser meshes. These findings highlight the critical need for thorough mesh sensitivity analysis in TR studies and the necessity to establish a contact zone discretization approach that is independent of GR, to accurately assess and mitigate TR in cycloidal drives.

关键词： FEM FEA Cycloidal Drive Dynamic high-power density

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U-Net/ResNet-50 Network with Transfer Learning for Semantic Segmentation in Search and Rescue 6th

U-Net/ResNet-50 Network with Transfer Learning for Semantic ...

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6th Iberian Robotics Conference (Robot)

作者： Salas-Espinales, Andres Velez-Chavez, Elian Vazquez-Martin, Ricardo Garcia-Cerezo, Alfonso Mandow, Anthony Univ Malaga Inst Mechatron Engn & Cyber Phys Syst C Dr Ortiz Ramos S-N Malaga 29071 Andalucia Spain Univ Tecn Manabi Fac Ciencias Matemat Fis & Quim Dept Elect Elect & Automatizac C Ave Urbina & Che Guevara Portoviejo 130105 Manabi Ecuador

ISBN: (纸本)9783031591662;9783031591679

Semantic segmentation has been successfully adopted for scenarios such as indoor, outdoor, urban scenes, and synthetic scenes, but applications with scarce labeled data such as search-and-rescue (SAR), have not been addressed. In this work, we propose a transfer learning approach where the U-Net convolutional neural network incorporates ResNet-50 as an encoder for the segmentation of objects in SAR situations. First, the proposed model is trained and validated with 19 classes of the CityScapes dataset. Then we test the proposed approach by i) training the model with a set of 14 Cityscapes classes with relevant similarities to SAR classes, and ii) using transfer learning with the self-developed dataset in SAR scenarios, which has 349 semantic labeled SAR images. The results indicate good recognition in classes with significant presence on the training images.

关键词： Deep Learning Semantic segmentation Transfer Learning Search and rescue dataset

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An Insightful Analysis of Preprocessing Methods Used in EEG Signals for Computer-Assisted Cognitive Domain 1

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4th Congress on Intelligent systems (CIS)

作者： Kaur, Gagandeep Aggarwal, Himanshu Goel, Neelam Punjabi Univ Dept Comp Sci & Engn Patiala Punjab India Panjab Univ Dept Informat Technol Chandigarh India

ISBN: (数字)9789819990436

ISBN: (纸本)9789819990429;9789819990436

The most popular non-intrusive approach for analyzing electrical brain activity is electroencephalography (EEG), which is employed comprehensively in mental neuroscience and medical diagnostics. EEG readings are frequently contaminated with various types of noise and artifacts, which can conceal the relevant brain activity. It has been proven that preprocessing strategies are necessary to improve data quality, reduce noise or artifacts, and prepare the data for proper analysis and interpretation. Therefore, substantial research over the last 20 years has concentrated on discovering methods for dealing with these artifacts during the preprocessing step. Nevertheless, it remains a focus of present research because no particular recognized artifact removal method is comprehensive or ubiquitous. The paper describes a qualitative study of current state-of-the-art of substantial preprocessing pipeline stages for EEG signal including filtering, sampling rate conversion, artifacts removal, channel elimination, and re-referencing. Subsequently, the finite impulse response (FIR) filter with rectangular, hamming, and Blackman windows are compared based on their average side lobe peak in frequency domain and black window outperformed among the all. It is considerable that this study provides the researchers with a contemporary perspective on preprocessing pipeline of EEG signals and acts as a basic guideline in general.

关键词： Electroencephalography Preprocessing Finite impulse response filter ICA Artifacts

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Artificial Intelligence in Prostate Cancer Diagnosis: "Synergy-Net" in Campania FESR-POR (European Fund of Regional Development-Regional Operative Program) Research Project 9th

Artificial Intelligence in Prostate Cancer Diagnosis: "Syner...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Parmeggiani, Domenico De Sio, Marco Moccia, Giancarlo Luongo, Pasquale Miele, Francesco Allaria, Alfredo Torelli, Francesco Marrone, Stefano Gravina, Michela Sansone, Carlo Bollino, Ruggiero Bassi, Paola Sciarra, Antonella Arcaniolo, Davide Donnarumma, Maddalena Claudia Colonnese, Chiara Romano, Lorenzo Colapietra, Federica Di Domenico, Marina Docimo, Ludovico Agresti, Massimo Univ Campania Luigi Vanvitelli Dept Adv Med & Surg Sci Naples Italy Univ Campania Luigi Vanvitelli Dept Woman Child & Gen & Specialized Surg Naples Italy Univ Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy CINI ITEM Lab C Savy Via Cintia 21 Naples Italy Bollino It SpA Via Ustrie 31 Naples Italy Univ Naples Federico II Dept Neurosci Reprod Sci & Odontostomatol Federic Naples Italy Univ Campania Luigi Vanvitelli Dept Precis Med Naples Italy

ISBN: (纸本)9789819745807;9789819745814

Background: The diagnosis of prostate cancer can only be obtained following the analysis of the tissue taken by means of a biopsy. Given the position of the organ, the biopsy is typically assisted by ultrasound images and the procedure consists of taking different portions of tissue from different areas, according to a map well-defined by international standards. Given the invasiveness of the procedure, the objective set within the Synergy-Net project is to analyze biomedical images in order to guide the operator on identifying the most suspicious tissues. Results: The dataset acquired by the Synergy-Net Platform at the "Vanvitelli" Urology Operating Unit is made up of a total of 350 outpatient services from which the diagnosis emerged on ultrasound, elastosonography, RNM, and biopsy of 50 prostate carcinomas which were then operated on. In the context of the Synergy-Net project, a new convolutional architecture was therefore created based on the U-Net paradigm, designed to perform a slice-by-slice segmentation in DCE-MRI of the prostate. The data processing with CNNs was carried out on a dataset of 37 patients, selected from the initial 50 for completeness and uniformity of the data, all affected by k-prostatic disease, using a tenfold cross-validation in order to obtain a statistically more significant estimate of the goodness of the results obtained. The performance metric used was the DICE coefficient. Conclusion: The results present a low intra-subject variability and a high inter-subject variability, with DICE values ranging between a minimum of 5.8% and a maximum of 60.3%. On average, a value of 35% is reported, considering the arithmetic mean of the dice achieved on all folds (macro-average).

关键词： Prostate cancer Elastography RNM Artificial intelligence (AI) Convolutional neural networks (CNNs)

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Forecasting Market Clearing Prices in Electricity Markets with Time Series Based Machine Learning Models

Forecasting Market Clearing Prices in Electricity Markets wi...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Yagmur, Mehmet Bora Turhan, Kagan Kaya, Tolga Istanbul Tech Univ Dept Management Engn TR-34367 Istanbul Turkiye

ISBN: (纸本)9783031671913;9783031671920

The Turkish Electricity Market has experienced various procedural transformations over time. These changes have led to the establishment of a system in the electricity market that allows stakeholders to secure hourly energy through next-day sales and purchases. This system is known as the pre-day market and the price set within this framework is referred to as the market clearing price. This study was designed to predict the electricity price for the next 24 time units within the next 24 h in Turkey. Predictions of the market clearing price were conducted using numerous machine learning models. Time series data of clearing prices in Turkey were used in the analysis. Exogenous variables such as production amount and holiday dummies were also incorporated. The data period was from January 2021 to December 2023. The study utilized two lagged market clearing price features with 19 independent lagged and unlagged additional variables. Various machine learning models were tested for their efficacy in forecasting the market clearing price, to identify the most effective one. To benefit from the various advantages of different models, the three models with the best performance, lightGBM, OMP, and STLF were blended to obtain a new model.

关键词： Electricity markets market clearing price electricity price forecasting machine learning lightGBM

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Optimizing Training for Human-Robot Collaboration in Learning Factories: An Employee-Centered Perspective 14th

Optimizing Training for Human-Robot Collaboration in Learnin...

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14th International Conference on Learning Factories (CLF)

作者： Gil, Raquel Salcedo Ulfert, Anna-Sophie Rispens, Sonja Le Blanc, Pascale Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9783031654107;9783031654114

With the introduction of robots in manufacturing, the imperative of continuous workplace learning emerges as a cornerstone for acquiring new knowledge and skills. While technical competence development plays a crucial role in understanding how the robot works and mastering skills like (re)programming the robot, this is not enough to create effective Human-Robot Collaboration (HRC). Organizational change frameworks emphasize the importance of preparing employees for change by increasing their readiness. However, current training practices frequently overlook this step. By combining different literature streams, we develop three propositions to guide the design of employee-centered HRC training. We propose that, in addition to training technical competencies (e.g., knowledge about robots), incorporating employee-centered components (e.g., fostering employee readiness) and promoting HRC skills (e.g., coordination) are essential for effective HRC training. Further, the implementation of robots in the workplace is a process in which training should be provided to employees throughout the different phases. This challenges current practices in which training is seen as a one-time event. The present paper aims to advance our understanding of HRC training and encourages the integration of technical and employee-centered elements when designing training for HRC. This will help design successful training initiatives and, thereby, support employee adaptation and organizational functioning when introducing robots in the workplace.

关键词： Human-Robot Collaboration Employee-centered Training Design

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Modeling and Simulation of a Hybrid Electrical Grid for Reliability and Power Quality Enchantment 2nd

Modeling and Simulation of a Hybrid Electrical Grid for Reli...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： AL-Mashhadani, Yahya Mohammed Jasim Ucan, Osman Nuri Altinbas Univ Elect & Comp Engn Istanbul Turkiye

ISBN: (纸本)9783031628801;9783031628818

In this paper, we focus on using a hybrid system of grid-connected photovoltaic (PV) panels to reduce the use of fossil fuels in producing electrical power to preserve the environment and compensate for reactive power in the solar energy system by installing a fixed capacitor bank at load for improving the power quality. The system that is suggested is simulated and evaluated Using the software program MATLAB Simulink, the suggested structure has been examined in different operational scenarios, such as variable photovoltaic system irradiance and variable reactive consumption power. Itemized simulation about the use of the algorithm Perturb and Observe (P&O) to reach maximum power point tracking in the PV system and effect install fixed capacitor bank on the hybrid grid.

关键词： Fixed capacitor bank Renewable Energy Sources reactive power compensation Maximum Power Point Tracing

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