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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 177 条记录，以下是51-60 订阅

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Generating 2.5D Photorealistic Synthetic Datasets for Training Machine Vision Algorithms 15th

Generating 2.5D Photorealistic Synthetic Datasets for Traini...

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

作者： Peleka, Georgia Mariolis, Ioannis Tzovaras, Dimitrios Centre for Research and Technology Hellas - CERTH Information Technologies Institute 6th km Charilaou-Thermi Rd. Thessaloniki Greece

ISBN: (纸本)9783030578015

the continued success of deep convolution neural networks (CNN) in computer vision can be directly linked to vast amounts of data and tremendous processing resources for training such non-linear models. However, depending on the task, the available amount of data varies significantly. Particularly robotic systems usually rely on small amounts of data, as producing and annotating them is extremely robot and task specific (e.g. grasping) and therefore prohibitive. Recently, in order to address the aforementioned problem of small datasets in robotic vision, a common practice is to reuse features that are already learned by a CNN within a large-scale task and apply them to different small scale ones. this transfer of learning shows some promising results as an alternative, but nevertheless it can not be compared with the performance of a CNN that is specifically trained from the beginning for that specific task. thus, many researchers turned to synthetic datasets for training, since they can be produced easily and cost effectively. the main issue of such datasets that already exist, is the lack of photorealism both in terms of background and lighting. Herein, we are proposing a framework for the generation of completely synthetic datasets that includes all types of data that state-of-the-art algorithms in object recognition, and tracking need for their training. thus, we can improve robotic perception without deploying the robot in time-consuming real-world scenarios. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Control of industrial AGV Based on Reinforcement Learning 15th

Control of Industrial AGV Based on Reinforcement Learning

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

作者： Sierra-García, Jesus Enrique Santos, Matilde University of Burgos Burgos Spain Institute of Knowledge Technology Complutense University of Madrid Madrid Spain

ISBN: (纸本)9783030578015

Automatic Guided Vehicles (AGV) suffer degradation in their electro-mechanical components which affect the navigation performance over time. the use of intelligent control techniques can help to alleviate this issue. In this work a new approach to control an AGV based on reinforcement learning (RL) is proposed. the space of states is defined using the guiding error, and the set of control actions provides the reference for the velocities of each wheel. Two different reward strategies are implemented, and different updating policies are tested. Simulation results show how the RL controller is able to successfully track a complex trajectory. the controller has been compared with a PID obtaining better results. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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An Introduction to Some Methods for soft computing in Fluid Dynamics 14th

An Introduction to Some Methods for Soft Computing in Fluid ...

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

作者： Le Clainche, Soledad Univ Politecn Madrid Sch Aerosp Engn E-28040 Madrid Spain

ISBN: (纸本)9783030200558;9783030200541

In recent years the terms big data, soft computing and machine learning have been widely extended in several research fields, where the analysis of data (mainly time series) provides useful information to solve different types of engineering problems. Turbulent flows and reduced order models (ROMs) are two terms related to the field of fluid dynamics that can be compared to the previous expressions, which are generally used in data science. this paper presents a short introduction to some of the methodologies generally used in fluid dynamics for the analysis of complex flows to extract spatio-temporal features. these methods are also used to construct ROMs to predict spatio-temporal events using a small number of variables. the paper intends to connect the field of computer and data science with the classical terms and methodologies used in fluid dynamics for soft computing and machine learning, known as ROMs.

关键词： soft computing Machine learning Reduced order model Fluid dynamics Big data CFD Data science POD DMD

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Supported Decision-Making by Explainable Predictions of Ship Trajectories 15th

Supported Decision-Making by Explainable Predictions of Ship...

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

作者： Burkart, Nadia Huber, Marco F. Anneken, Mathias Fraunhofer IOSB Karlsruhe Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Stuttgart Germany Fraunhofer IPA Stuttgart Germany Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9783030578015

Machine Learning and Deep Learning models make accurate predictions based on a specifically trained task. For instance, models that classify ship vessel types based on their trajectory and other features. this can support human experts while they try to obtain information on the ships, e.g., to control illegal fishing. Besides the support in predicting a certain ship type, there is a need to explain the decision-making behind the classification. For example, which features contributed the most to the classification of the ship type. this paper introduces existing explanation approaches to the task of ship classification. the underlying model is based on a Residual Neural Network. the model was trained on an AIS data set. Further, we illustrate the explainability approaches by means of an explanatory case study and conduct a first experiment with a human expert. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ships

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Bio-Inspired System for MRP Production and Delivery Planning in Automotive Industry 15th

Bio-Inspired System for MRP Production and Delivery Planning...

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

作者： Simić, Dragan Svirčević, Vasa Calvo-Rolle, José Luis Ilin, Vladimir Simić, Svetislav D. Simić, Svetlana Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad21000 Serbia Lames d.o.o Sremska Mitrovica22000 Serbia Department of Industrial Engineering University of A Coruña Avda. 19 de febrero s/n Ferrol A Coruña15405 Spain Faculty of Medicine University of Novi Sad Hajduk Veljkova 1–9 Novi Sad21000 Serbia

ISBN: (纸本)9783030578015

Material requirement planning (MRP) can be characterized as an organized flow of material in the production process with the required sequence determined by the technological procedure. this paper presents biological swarm intelligence in general, as well as firefly optimization for modelling MRP and delivery planning in the production system of automotive industry. the aim of this research is to create a model which will satisfy production, deliver processes and optimize raw material inventory with real production constraints. the proposed system is tested in the data set collected in Lames Italian automotive company in the factory located in Serbia. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automotive industry

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Machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution 15th

Machine Learning in Classification of the Wax Structure of B...

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

作者： Procházka, Aleš Mudrová, Martina Cejnar, Pavel Mareš, Jan University of Chemistry and Technology Prague 6166 28 Czech Republic Czech Technical University Prague 6160 00 Czech Republic

ISBN: (纸本)9783030578015

Texture analysis and classification of image components belong to common problems of the interdisciplinary area of digital signal and image processing. the paper is devoted to the pattern matrix construction using features evaluated by the discrete Fourier transform (DFT) or the discrete wavelet transform (DWT) using the relative power in selected frequency bands or scale levels, respectively. Image features are then used to recognize groups of similar pattern vectors by self-organizing neural networks forming a mathematical tool for cluster analysis. Further classification methods including the decision tree, support vector machine, nearest neighbour method and neural networks are then applied for construction of specific models and evaluation of their accuracy and cross validation errors. the proposed algorithm is applied for analysis of given microscopic images representing wax structures covering breathing openings on leaves affected by environmental pollution in different locations. the classification accuracy depends upon the method used and it is higher than 92% for all experiments. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Generating three-Dimensional Fields from Two-Dimensional soft computing Strategies 14th

Generating Three-Dimensional Fields from Two-Dimensional Sof...

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

作者： Miguel Perez, Jose Le Clainche, Soledad Manuel Vega, Jose Univ Politecn Madrid Sch Aerosp Engn E-28040 Madrid Spain

ISBN: (纸本)9783030200558;9783030200541

the main goal of this work is to provide a new tool to reconstruct three-dimensional data from a few two-dimensional planes. To this purpose, spatio-temporal higher order dynamic mode decomposition (HODMD) is applied to a group of vertical and horizontal planes, located at different positions in space. Firstly, the method extracts the relevant frequencies and wavenumbers related to the field analyzed, providing two-dimensional HODMD expansions. Secondly, a system of equations is constructed in the vertical planes using the previous information. Solving this equation system, it is possible to expand the two-dimensional HODMD solutions to recover a three-dimensional HODMD expansion, representing the complete three-dimensional flow field. this method has been successfully tested to reconstruct a three-dimensional toy problem with great precision (error smaller than 10(-10)). the method could be potentially used in the analysis of turbulent flows (numerical and experimental data), extracting relevant three-dimensional information from the two-dimensional data with reduced computational cost (soft computing).

关键词： three-dimensional reconstruction HODMD Reduced order models

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Building Robust Prediction models for Defective Sensor Data Using Artificial Neural Networks 14th

Building Robust Prediction Models for Defective Sensor Data ...

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

作者： de Sa, Claudio Rebelo Shekar, Arvind Kumar Ferreira, Hugo Soares, Carlos Twente Univ Enschede Netherlands Robert Bosch GmbH Stuttgart Germany INESC TEC Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783030200558;9783030200541

Sensors are susceptible to failure when exposed to extreme conditions over long periods of time. Besides they can be affected by noise or electrical interference. models (Machine Learning or others) obtained from these faulty and noisy sensors may be less reliable. In this paper, we propose a data augmentation approach for making neural networks more robust to missing and faulty sensor data. this approach is shown to be effective in a real life industrial application that uses data of various sensors to predict the wear of an automotive fuel-system component. Empirical results show that the proposed approach leads to more robust neural network in this particular application than existing methods.

关键词： Neural networks

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An industrial Application of soft computing for the Design of Personalized Call Centers 14th

An Industrial Application of Soft Computing for the Design o...

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

作者： Griol, David Manuel Molina, Jose Sanchis, Araceli Univ Carlos III Madrid Dept Comp Sci Leganes Spain

ISBN: (纸本)9783030200558;9783030200541

In service industries such as telecommunications, hotels, insurance, banking, retail, or medical services, companies are increasingly paying more attention to human-computer communication systems that are in direct contact with customers, and focused on achieving the desired profit and market share goals. For this reason, chatbots are increasingly used in service industries starting with simple chat conversation up to more complex functionalities based on soft computing methodologies. Evaluation methodologies for chatbots try to provide an efficient means of assessing the quality of the system and/or predicting the user satisfaction. In this paper we present a clustering approach to provide insight on whether user profiles can be automatically detected from the interaction parameters and overall quality predictions, providing a way of corroborating the most representative features for defining user profiles. We have carried out different experiments for a practical dialog system, from which the clustering approach provided an efficient way of easily distinguishing between different user groups and complete a more significant evaluation of the system.

关键词： Chatbots Spoken interaction soft computing Clustering User modeling Evaluation

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soft computing Techniques to Analyze the Turbulent Wake of a Wall-Mounted Square Cylinder 14th

Soft Computing Techniques to Analyze the Turbulent Wake of a...

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

作者： Amor, Christian Perez, Jose M. Schlatter, Philipp Vinuesa, Ricardo Le Clainche, Soledad Univ Politecn Madrid Sch Aerosp Engn E-28040 Madrid Spain KTH Mech Linne FLOW Ctr Stockholm Sweden

ISBN: (纸本)9783030200558;9783030200541

this paper introduces several methods, generally used in fluid dynamics, to provide low-rank approximations. the algorithm describing these methods are mainly based on singular value decomposition (SVD) and dynamic mode decomposition (DMD) techniques, and are suitable to analyze turbulent flows. the application of these methods will be illustrated in the analysis of the turbulent wake of a wall-mounted cylinder, a geometry modeling a skyscraper. A brief discussion about the large and small size structures of the flow will provide the key ideas to represent the general dynamics of the flow using low-rank approximations. If the flow physics is understood, then it is possible to adapt these techniques, or some other strategies, to solve general complex problems with reduced computational cost. the main goal is to introduce these methods as machine learning strategies that could be potentially used in the field of fluid dynamics, and that can be extended to any other research field.

关键词： soft computing Fluid dynamics Turbulence flow CFD Data science POD DMD

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