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Active Thermography for Residual Stresses Identification in Gears 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Corsaro, Luca Cura, Francesca Raffaella Politecn Torino Dept Mech & Aerosp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (数字)9783031704628

ISBN: (纸本)9783031704611;9783031704628

Residual stresses are a topic of great interest in mechanical field since they can impact in a positive or negative manner on both materials and components. During the years, many efforts were done to identify and to characterize residual stresses and, in many cases, for this purpose destructive techniques are required. An innovative Non-Destructive Techniques (NDT) such as Active Thermography (AT), called also stimulated thermography, is widely used in industrial environment to investigate defects inside the material. Recent studies describe the potentiality of Active Thermography also for other mechanical fields like the material characterization or the damage investigation. In this work, an alternative application of Active Thermography is proposed to identify residual stresses generated from surface treatments. In particular, thermal responses were investigated for both samples and gears subjected to case hardening and shoot peening processes. Moreover, a careful analysis on both mechanical and physical properties were performed for both samples and gears aiming to verify the Active Thermography results.

关键词： Active Thermography gears residual stress

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Physarum-Inspired Enterprise Network Redesign 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Habib, Sami J. Marimuthu, Paulvanna N. Kuwait Univ Comp Engn Dept POB 5969 Kuwait 13060 Kuwait

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

We have developed a Physarum-inspired redesign algorithm to redesign the existing enterprise network (EN) with an objective function to minimize the traffic through the backbone. In contrast to the classical Physarum model, which forms an initial network by exploring its surroundings, our redesign algorithm is developed under the constraint that the topology of EN is given. The nodes and edges of EN are analogous to the food sources and connecting tubes of the Physarum network, whereas the traffic demand on the edges of EN is reversed to match the tubular length. Our redesign algorithm exploits the foraging behavior of Physarum to generate the possible maximum-flow paths by selecting the combination of each node as a source against the remaining as a destination. A factor called affinity factor is defined to select the highly associated node(s) of each source by traversing the generated paths from each source. A clustering algorithm is added to group the associated nodes together, and the procedure is repeated for all nodes. The experimental results on a weighted undirected network revealed that the redesign algorithmmanages to reduce EN backbone traffic by amaximum of 78% from the initial single-clustered EN by optimally distributing the nodes within the clusters.

关键词： Affinity factor Backbone traffic Clustering Network redesign Physarum foraging

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A Machine Learning Approach to Predicting Open Access Support in Research Projects

A Machine Learning Approach to Predicting Open Access Suppor...

引用

Intelligent systems Conference

作者： Hoang-Son Pham Neyens, Evy Ali-Eldin, Amr UHasselt Ctr Res & Dev Monitoring ECOOM B-3500 Hasselt Belgium Hasselt Univ Data Sci Inst B-3500 Hasselt Belgium Mansoura Univ Comp Engn & Control Syst Dept Fac Engn Mansoura 35516 Egypt

ISBN: (纸本)9783031664274;9783031664281

In recent years, Open Science has emerged as a pivotal element in research and innovation policies at various levels, advocating for immediate and unrestricted access to publicly funded scientific research outcomes. While the benefits of open access to research are recognized, measuring its extent poses a challenge. In this paper, we present a pioneering approach for predicting the level of support for open access within research projects by applying machine learning algorithms. Leveraging various text-mining techniques to create relevant features, our methodology utilizes machine learning models to forecast the extent of open-access support. We incorporate diverse features, including openaccess publications by researchers and organizations, funding sources, research disciplines, and interdisciplinarity, to enhance predictive capabilities. The evaluation of our proposed approach involves implementing machine learning models on features extracted from projects available on the FRIS portal. Our findings unveil a significant correlation between the degree of open-access publications of researchers and the degree of open access within the associated projects. Furthermore, the selected models achieved an accuracy range of 76-85%, showcasing their effectiveness in precise predictions. The highest accuracy, 85%, was attained with Random Forest Regression, and classical machine learning algorithms outperformed deep learning counterparts.

关键词： Open science Open access Indicator development Regression model

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Bridging Nanomachines: Bidirectional Channel in a Bio-inspired System 1

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

作者： Jani, Athraa Juhi Jani, Jafar J. Al Nahrain Univ Baghdad Iraq Minist Labor & Social Affairs Baghdad Iraq

ISBN: (数字)9789819735594

ISBN: (纸本)9789819735587;9789819735594

In this paper, we present a bio-inspired bidirectional molecular communication model for nanomachines, specifically S-T and S-R. Our model integrates dynamic transmission and reception modes, akin to the natural systems it draws inspiration from. The communication channel is designed to mimic interconnected biological locations, mirroring the pathways of signalling molecules, inspired by calcium signalling and guided by principles from the Abelian sandpile model (ASM). Operating in discrete time slots, each nanomachine synchronizes with its local clock, alternating between transmitting and receiving modes. Minimum molecular concentrations are received in each time slot, and molecules are transmitted probabilistically, with failure considered for enhanced reliability. Channel nodes are responsive to molecular concentrations above a threshold. Validation is performed using the PRISM verification tool, confirming the model's robustness and efficiency. Numerical simulations explore various scenarios, including diverse molecular concentrations and varying nanomachine distances, illustrating the model's capacity to maintain bidirectional communication under dynamic conditions.

关键词： Nanomachines Bidirectional channel PRISM model checker Abelian sandpile model (ASM) Calcium signalling

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A Machine Learning System that Detects Abnormal Level of Inventory

A Machine Learning System that Detects Abnormal Level of Inv...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Ozcan, Anil Mert, Buse Yuceoglu, Birol Migros Ticaret AS R&D Ctr TR-34758 Istanbul Turkiye

ISBN: (纸本)9783031671913;9783031671920

Loss prevention is an important topic for retail companies to increase profitability. Losses stem from external or internal factors. In this study, we focus on an internal anomaly case about inventory movements in retail stores. Therefore, we have developed a machine learning algorithm to detect abnormal inventory levels. The machine learning algorithm developed estimates inventory levels over the last fifteen days as a time series. It then compares the actual inventory levels during this period with the predicted values to detect anomalies. This results in a store-brand level inventory error representation. A distribution of errors is obtained using errors from similar stores. The system assigns each store an anomaly label by determining whether the model-derived error is an outlier based on the distribution. Stores with labelled inventories are then sent to the business units. This allows the business units to regularly examine the stocks and predict potential fraud situations.

关键词： Anomaly Detection Machine Learning Time Series

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Generalized Krylov Subspace-Based MOR of Periodic systems 9th

Generalized Krylov Subspace-Based MOR of Periodic Systems

引用

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

作者： Hossain, Mohammad-Sahadet Oishee, Tahiya Tasneem Afroz, Atia Mumtaha, Oshin North South Univ Dept Math & Phys Dhaka Bangladesh North South Univ Dept Elect & Comp Engn Dhaka Bangladesh

ISBN: (纸本)9789819733019;9789819733026

Numerical simulation plays a vital role in representing the large-scale dynamical systems that are generated from complex physical phenomena. Naturally, simulations of large-scale models are computationally costly and time-demanding, which is not desirable in many important applications of control and optimization. Model order reduction (MOR) is the tool that reduces this computational burden by replacing the large-scale model with a much smaller one that is faster and easier to simulate. The MOR of index-2 linear discrete-time periodic system (LDTP) applying the generalized Krylov subspace approach has been suggested in this paper. The reduced-order model has been computed by applying a projection technique using the generalized Arnoldi method. In this case, it has been ensured that the periodic structure is preserved in the periodic Arnoldi approximation. This study also includes numerical results to demonstrate the efficacy and accuracy of the proposed algorithm.

关键词： Model order reduction Periodic descriptor systems Structured Krylov subspace approximation Lifted systems

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Vision-Based Smart Sprayer for Precision Farming 6th

Vision-Based Smart Sprayer for Precision Farming

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6th Iberian Robotics Conference (Robot)

作者： Deguchi, Thaidy Baltazar, Andre Rodrigues dos Santos, Filipe Neves Mendonca, Helio Univ Porto FEUP Fac Engn P-4200465 Porto Portugal INESC TEC Inst Syst & Comp Engn Technol & Sci CRIIS Ctr Robot Ind & Intelligent Syst P-4200465 Porto Portugal

ISBN: (纸本)9783031591662;9783031591679

Since the advent of agriculture, humans have considered phytopharmaceutical products to control pests and reduce losses in farming. Sometimes some of these products, such pesticides, can potentially harm the soil life. In the literature there is evidence that AI and image processing can have a positive contribution to reduce phytopharmaceutical losses, when used in variable rate sprayers. However, it is possible to improve the existing sprayer system's precision, accuracy, and mechanical aspects. This work proposes spraying solution called GraDeS solution (Grape Detection Sprayer). GraDeS solution is a sprayer with two degrees of freedom, controlled by a AI-based algorithm to precisely treat grape bunches diseases. The experiments with the designed sprayer showed two key points. First, the deep learning algorithm recognized and tracked grape bunches. Even with structure movement and bunch covering, the algorithm employs several strategies to keep track of the discovered objects. Second, the robotic sprayer can improve precision in specified areas, such as exclusively spraying grape bunches. Because of the structure's reduced size, the system can be used in medium and small robots.

关键词： precision farming machine learning computer vision robotics

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Innovative Solar Battery System Integration for Eco-Friendly Electric Vehicle Charging Stations 1

引用

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

作者： Traibiz, Omar Ben Zarouala, Rachad Oulad Abdelmalek Essaadi Univ Natl Sch Appl Sci Dept Ind & Civil Sci & Technol Tetouan Morocco

ISBN: (数字)9789819735624

ISBN: (纸本)9789819735617;9789819735624

Global concerns about climate change require an immediate shift toward sustainable transportation systems, which has led to a surge in the use of electric vehicles (EVs). The surge in electric vehicle usage poses unprecedented challenges to the grid infrastructure, necessitating a radical shift toward renewable energy-based charging solutions. To address this pressing issue, this study presents a fresh proposal for an electric vehicle charging station that integrates solar energy and battery storage system technology (BESS). Despite low solar energy generation, the use of a grid support system guarantees uninterrupted power supply and ensures its continued operation. Optimal power management is achieved using advanced control strategies like MPPT and PID techniques. The utilization of stored solar energy, fast electric vehicle charging, and efficient energy storage in BESS are made possible by these measures for future use. We utilized the powerful computational framework provided by Matlab/Simulink to extensively simulate the feasibility and performance of our solar BESS charging station, which is specifically designed for electric vehicles (EVs). Our system's simulation outcomes demonstrate its remarkable ability to significantly decrease CO2 emissions, aligning with the global demand for environmentally friendly transportation solutions that maintain the sustainability of charging operations.

关键词： Electric vehicles Solar power Charging station Battery energy storage system MPPT

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Audio Event Detection Based on Cross Correlation in Selected Frequency Bands of Spectrogram 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Hajihashemi, Vahid Gharahbagh, Abdorreza Alavi Machado, J. J. M. Tavares, Joao Manuel R. S. Univ Porto Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal Univ Porto Dept Engn Mecan Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

Audio event detection (AED) systems have various applications in modern world. Examples of applications include security systems, urban management and automatic monitoring in smart cities, and online multimedia processing. The noise and background sound vary in an urban environment, so frequency domain and normalized features usually show better efficiency in AED systems. This work proposes a Mel spectrogram-based approach that uses the spectral characteristics of audio signals and cross-correlations to build a dictionary of effective spectrogram frequency bands and their patterns in different audio events. Initially, the proposed approach extracts the Mel spectrogram of audio input. In the next step, a mathematical-statistical analysis is used to specify the effective frequency bands of the spectrogram in each audio event. The pattern of selected frequency bands varies due to the type of event, which can effectively help decrease spectrogram size as input feature, reduce errors and increase the accuracy of different AED methods. The proposed approach was implemented on the URBAN-SED database, and its efficiency was compared against deep learning base state-of-the-art researches in the field. According to the results, about 50% of the frequency bands in the spectrum are useless and can be discarded in the training process of an AED system without any loss in terms of accuracy.

关键词： Audio event detection Spectrogram Cross-correlation Feature selection Deep learning

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Retail Demand Forecasting Using Temporal Fusion Transformer

Retail Demand Forecasting Using Temporal Fusion Transformer

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Eski, Doruk Kaya, Tolga Istanbul Tech Univ Dept Management Engn TR-34367 Istanbul Turkiye

ISBN: (纸本)9783031671913;9783031671920

Demand forecasting is a vital problem that affects nearly every aspect of supply chain management operations in the retail industry. A retailer would need to precisely anticipate the upcoming demand to efficiently manage their inventory, form profitable pricing strategies, and handle logistics operations in time. Novel techniques in machine learning and deep learning literature have also been important in demand forecasting domain as they thrive at extracting complex relationships from data. In this study, a multivariate hierarchical time series forecasting problem is tackled for a leading retailer that operates with hundreds of stores in Turkey. A dataset consisting of 50 items for e-commerce channel sales starting from 2016 is used. A well-known transformer-based Deep Learning algorithm, Temporal Fusion Transformer has been compared against numerous state-of-the-art architectures such as DeepAR, N-Beats, and NHITS and a classical time series analysis technique, ARIMA. We observe that TFT significantly outperforms benchmark algorithms and can work well with a relatively smaller dataset.

关键词： Demand Forecasting Deep Learning Multivariate Time Series Analysis Supply Chain Management

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