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检索条件"任意字段=6th International Conference on Soft Computing Models in Industrial and Environmental Applications"

共 179 条记录，以下是61-70 订阅

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Characteristic of WiFi Network Based on Space Model with Using Turning Bands Co-simulation Method 14th

Characteristic of WiFi Network Based on Space Model with Usi...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Kaminska-Chuchmala, Anna Wroclaw Univ Sci & Technol Fac Comp Sci & Management Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783030200558;9783030200541

Recently, mostly users prefer an access to the wireless network than wired. Due to the fact of mobility of users and choosing of mobile devices such as smartphone, tablet, smartwatch etc. Extensively growth of wireless network users affect on reliable connection to the network. In this research parameters from Access Points (APs) contained in PWR-WiFi an open WiFi network belonging to Wroclaw University of Science and Technology (WUST) in Poland are investigated. A central issue in this paper is to create space models prediction of WiFi network efficiency by Turning Bands Method (TBM). Statistical analysis of considered WiFi daily data were conducted. Acquired results were discussed and conclusions with future research directions to WiFi network efficiency predictions were drawn.

关键词： WiFi Wireless network efficiency Space models Turning bands method Co-simulation method

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Growth models of Female Dairy Cattle 15th

Growth Models of Female Dairy Cattle

引用

15th international conference on soft computing models in industrial and environmental applications, SOCO 2020

作者： Marek, Jaroslav Pozdílková, Alena Kupka, Libor University of Pardubice Studentská 95 Pardubice Czech Republic

ISBN: (纸本)9783030578015

Different methods of representing animal growth are possible and are defined for different animal categories. In this paper, weight measuring of female dairy cattle will be modelled by several nonlinear models. the most commonly used methods for describing the growth of animals are: Gompertz function, logistic function, Schmalhausen function, Brody function, Weibull function, Wood function and Von Bertalanffy function. Measured weight values and estimated parameters of growth curves will be analyzed using regression analysis methods. We will work with the weight measurements of 10 calves under 25 months of age from cowsheds in village Záluží in the Czech Republic. A comparison of several growth curves will be done. the suitability of individual models will be evaluated not only by the index of determination, but also by the intrinsic curvature according to Bates and Watts. this curvature affects the size of the linearization areas in which initial solution will ensure convergence of nonlinear regression. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Animals

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Predictive Maintenance from Event Logs Using Wavelet-Based Features: An industrial Application 14th

Predictive Maintenance from Event Logs Using Wavelet-Based F...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Bonnevay, Stephane Cugliari, Jairo Granger, Victoria Univ Lyon ERIC EA3083 5 Av Pierre Mendes France F-69676 Bron France ENEDIS 124 Blvd Marius Vivier Merle Lyon France

ISBN: (纸本)9783030200558;9783030200541

In industrial context, event logging is a widely accepted concept supported by most applications, services, network devices, and other IT systems. Event logs usually provide important information about security incidents, system faults or performance issues. In this way, the analysis of data from event logs is essential to extract key information in order to highlight features and patterns to understand and identify reasons of failures or faults. Our objective is to help anticipate equipment failures to allow for advance scheduling of corrective maintenance. We propose a supervised approach to predict faults from an event log dataset using wavelets features as input of a random forest which is an ensemble learning method.

关键词： Corrective maintenance

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Leveraging Artificial Intelligence for Corrosion Detection and Prevention: A Review of Current Trends and Future Prospects

Leveraging Artificial Intelligence for Corrosion Detection a...

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6th international conference on Advanced Materials for Photonics, Sensing, and Energy applications, AMPSECA 2024

作者： Bouhouche, Ichraq Labjar, Najoua Omari, Mohamed Aarfane, Abdellatif Baraket, Abdoullatif Nasrellah, Hamid Bensemlali, Meryem Labjar, Houda Kissi, Mohamed El Hajjaji, Souad Laboratory of Spectroscopy Molecular Modeling Materials Nanomaterials Water and Environment ENSAM CERNE2D Mohammed V University in Rabat Rabat Morocco Agro-Industrial Environmental and Ecological Processes Team Faculty of Science and Techniques of Beni Mellal Sultan Moulay Slimane University The Institution Will Open in a New Tab Beni Mellal Morocco Higher School of Education and Training Chouaib Doukkali University El Jadida Morocco Higher School of Education and Training Mohammed I University Oujda Morocco Laboratory of Materials Membranes and Environment Faculty of Sciences and Technologies of Mohammedia Hassan II University of Casablanca Casablanca Morocco LIM Laboratory Faculty of Sciences and Technology Mohammedia Hassan II University of Casablanca Casablanca Morocco

ISBN: (纸本)9789819663774

this mini-review examines the intersection of corrosion science and artificial intelligence (AI), focusing on how AI technologies enhance the detection, monitoring, and mitigation of corrosion in industrial systems. It explores recent advancements in AI-driven methodologies, such as machine learning (ML), deep learning (DL), and data analytics, which improve corrosion modeling, prediction, and real-time monitoring. these advancements offer promising avenues for maintaining infrastructure integrity, reducing economic losses, and minimizing environmental impacts. By integrating AI with traditional corrosion management practices, industries can achieve more accurate, efficient, and cost-effective solutions, enhancing safety and sustainability in critical sectors. this review also discusses future prospects, including hybrid models, autonomous systems, and AI-driven materials, highlighting the potential for AI to revolutionize corrosion prevention and contribute to the broader goals of sustainable industrial development. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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MIMO Neural models for a Twin-Rotor Platform: Comparison Between Mathematical Simulations and Real Experiments 15th

MIMO Neural Models for a Twin-Rotor Platform: Comparison Bet...

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15th international conference on soft computing models in industrial and environmental applications, SOCO 2020

作者： Viana, Kerman Larrea, Mikel Irigoyen, Eloy Diez, Mikel Zubizarreta, Asier Leioa Spain

ISBN: (纸本)9783030578015

this work presents a neural model developed for a multivariable system with complex nonlinear dynamics, obtained through a tight methodology used both in simulation and in the real platform. In addition, this neural model has been studied and designed to meet the requirements of a predictive control strategy. A Twin-Rotor platform is used as an example of a Multi-Input Multi-Output (MIMO) system to study and analyse how a neural network is able to reproduce its nonlinear coupled dynamics and accurately estimate future system outputs. An in-depth study of the neural structures and their performance in the prediction of future states has been developed. Results show with comparisons, the modelization inaccuracies that appears when a proposal made just on the basis of a mathematical simulation is used to conclude the good performance of these MIMO neural models. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Simulation platform

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Trust based secure routing mechanisms for wireless sensor networks: A survey 6

Trust based secure routing mechanisms for wireless sensor ne...

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6th international conference on Advanced computing and Communication Systems (ICACCS)

作者： Prabhu, S. Anita, Mary E. A. Anna Univ Chennai Tamil Nadu India SA Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781728151977;9781728151960

Wireless Sensor Network (WSN) is a predominant technology that is widely used in many applications such as industrial sectors, defense, environment, habitat monitoring, medical fields etc., these applications are habitually delegated for observing sensitive and confidential raw data such as adversary position, movement in the battle field, location of personnel in a building, changes in environmental condition, regular medical updates from patient side to doctors or hospital control rooms etc., Security becomes inevitable in WSN and providing security is being truly intricate because of in-built nature of WSN which is assailable to attacks easily. Node involved in WSN need to route the data to the neighboring nodes wherein any attack in the node could lead to fiasco. Of late trust mechanisms have been considered to be an ideal solution that can mitigate security problems in WSN. this paper aims to investigate various existing trust-based Secure Routing (SR) protocols and mechanisms available for the wireless sensing connection. the concept of the present trust mechanism is also analyzed with respect to methodology, trust metric, pros, cons, and complexity involved. Finally, the security resiliency of various trust models against the attacks is also analyzed.

关键词： Attack Security Secure routing Trust models Wireless sensor network

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An Automated Platform for Microrobot Manipulation 15th

An Automated Platform for Microrobot Manipulation

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15th international conference on soft computing models in industrial and environmental applications, SOCO 2020

作者： Vrba, Jan Maslen, Charlie Rehor, Ivan Mares, Jan Faculty of Chemical Engineering University of Chemistry and Technology Prague Technická 5 Prague 616628 Czech Republic Institute of Organic Chemistry and Biochemistry Flamingovo Namesti 2 Prague 616000 Czech Republic

ISBN: (纸本)9783030578015

this paper presents hydrogel microrobots (100) that are directed to specific locations in their environment by an automated platform. the microrobots are actuated by focused laser light and crawl in aqueous environments. the platform consists of a stage, manipulated by stepper drivers and controlled by a Raspberry PI 4. this positions the laser light in the desired locations to move microrobots towards a goal location. the microrobot localisation is done via a microscope camera and repetitive usage of a template matching algorithm. Instead of a path planning algorithm, the optimal position for the laser is chosen before every step so that the disk reaches the goal as fast as possible. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Template matching

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Comparative Analysis of Clustering Techniques for a Hybrid Model Implementation 15th

Comparative Analysis of Clustering Techniques for a Hybrid M...

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15th international conference on soft computing models in industrial and environmental applications, SOCO 2020

作者： García-Ordás, María Teresa Alaiz-Moretón, Héctor Casteleiro-Roca, José-Luis Jove, Esteban Benítez-Andrades, José Alberto García-Rodríguez, Isaías Quintián, Héctor Calvo-Rolle, José Luis Department of Electrical and Systems Engineering University of León Escuela de Ingenierías Campus de Vegazana León24071 Spain CTC Department of Industrial Engineering CITIC University of A Coruña Avda. 19 de febrero s/n Ferrol A Coruña15405 Spain

ISBN: (纸本)9783030578015

this research is oriented to compare the performance of two clustering methods in order to know what is the best one for archiving high quality hybrid models. For testing purposes, a real dataset has been obtained of a bio-climate house located in Sotavento Experimental Wind Farm, in Xermade (Lugo) in Galicia (Spain). Between several systems installed in the house, the thermal solar generation system has been the chosen one for studying its behaviour and experimenting with the clustering techniques. Two approaches have been utilized for establishing the quality of each clustering algorithm. the first one is based on typical error measurements implied in a regression procedure such as Multi Layer Perceptron. Whereas, the second one, is oriented to the utilization of three unsupervised learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin). © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Statistical tests

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Acoustic Anomaly Detection Using Convolutional Autoencoders in industrial Processes 14th

Acoustic Anomaly Detection Using Convolutional Autoencoders ...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Duman, Taha Berkay Bayram, Baris Ince, Gokhan Istanbul Tech Univ Fac Comp & Informat Engn Istanbul Turkey

ISBN: (纸本)9783030200558;9783030200541

In the industrial plants, detection of abnormal events during the processes is a difficult task for human operators who need to monitor the production. In this work, the main aim is to detect anomalies in the industrial processes by an intelligent audio based solution for the new generation of factories. therefore, this paper presents a Convolutional Autoencoder (CAE) based end-to-end unsupervised Acoustic Anomaly Detection (AAD) system to be used in the context of industrial plants and processes. In this research, a new industrial acoustic dataset has been created by gathering the audio data obtained from a number of videos of industrial processes, recorded in factories involving industrial tools and processes. Due to the fact that the anomalous events in real life are rather rare and the creation of these events is highly costly, anomaly event sounds are superimposed to regular factory soundscape by using different Signal-to-Noise Ratio (SNR) values. To show the effectiveness of the proposed system, the performances of the feature extraction and the AAD are evaluated. the comparison has been made between CAE, One-Class Support Vector Machine (OCSVM), and a hybrid approach of them (CAE-OCSVM) under various SNRs for different anomaly and process sounds. the results showed that CAE with the end-to-end strategy outperforms OCSVM while the respective results are close to the results of hybrid approach.

关键词： Anomaly detection industrial processes Convolutional autoencoders One-Class Support Vector Machine Signal-to-Noise Ratio Audio feature extraction

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Classifying Excavator Operations with Fusion Network of Multi-modal Deep Learning models 14th

Classifying Excavator Operations with Fusion Network of Mult...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030200558;9783030200541

Prognostics and health management (PHM) aims to offer comprehensive solutions for managing equipment health. Classifying the excavator operations plays an important role in measuring the lifetime, which is one of the tasks in PHM because the effect on the lifetime depends on the operations performed by the excavator. Several researchers have struggled with classifying the operations with either sensor or video data, but most of them have difficulties with the use of single modal data only, the surrounding environment, and the exclusive feature extraction for the data in different domains. In this paper, we propose a fusion network that classifies the excavator operations with multi-modal deep learning models. Trained are multiple classifiers with specific type of data, where feature extractors are reused to place at the front of the fusion network. the proposed fusion network combines a video-based model and a sensor-based model based on deep learning. To evaluate the performance of the proposed method, experiments are conducted with the data collected from real construction workplace. the proposed method yields the accuracy of 98.48% which is higher than conventional methods, and the multi-modal deep learning models can complement each other in terms of precision, recall, and F1-score.

关键词： Excavator Classification Deep learning Multi-modal data Autoencoder Feature extraction

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