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Image Processing Based Wood Defect Detection 2nd

Image Processing Based Wood Defect Detection

引用

2nd International Conference on information Technologies and Their Applications

作者： Ozkan, Merve Ozcan, Caner Karabuk Univ Inst Postgrad Educ Comp Engn Karabuk Turkiye Karabuk Univ Software Engn Fac Engn Karabuk Turkiye

ISBN: (纸本)9783031734199;9783031734205

Detection of defects in wooden structures in the forestry industry has become a crucial area of research. Existing studies have focused on specific categories of wood defects, failing to provide a comprehensive classification for high-quality wood. Trained human operators currently perform a variety of wood quality in wood processing facilities. However, this human-dependent process leads to time and performance losses and inaccurate type. This study aims to address all these challenges in future intelligent production systems by targeting the detection of the fungus in oak wood, one of the wood defect classes. The algorithm created based on image processing utilizes median filtering, Canny edge detection, and masking technologies using the HSV color space. The algorithm then calculates the fungal area ratio to the wooden piece's surface area on the masked image to reach the final result. While existing studies in the literature are primarily based on deep learning methods, there has been limited focus on fungus detection. The novelty of this study, conducted on oak wood, lies in its use of a specific dataset, fungal detection, and image processing. An algorithm has been developed and presented in the literature that can be used in the software of future intelligent production systems in the forestry industry.

关键词： Image Processing Wood Material Object Detection Canny Edge Detection HSV

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Reducing Intuitive-Physics Prediction Error Through Playing 5th

Reducing Intuitive-Physics Prediction Error Through Playing

引用

5th International Workshop on Active Inference

作者： Georgeon, Olivier L. de Montera, Beatrice Robertson, Paul UCLy UR CONFLUENCE Sci & Humanites EA 1598 Lyon France CNRS LIRIS SyCoSMA Villeurbanne France INRAE MetaGenoPolis Unit Jouy En Josas France DOLL Labs Lexington MA USA

ISBN: (纸本)9783031771378;9783031771385

We present a mobile robot that autonomously generates behaviors to calibrate its intuitive-physics engine, also known as the "Game Engine in the Head" (GEITH). Most POMDP and Active Inference learning techniques operate in a closed world in which the set of states is defined a priori. However, implementing an "innate" GEITH and a set of interactive behaviors allowed us to avoid these limitations and design a mechanism for information search and learning in an open world. The results show that over a few tens of interaction cycles, the robot's prediction errors decrease, which shows an improvement in the GEITH calibration. Moreover, the robot generates behaviors that human observers describe as playful.

关键词： Active inference developmental learning enaction intrinsic motivation robotics core knowledge

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Towards Better Modeling of Bicycle Lateral Motion: Model Development, Naturalistic Data Acquisition, and Model Validation 12th

Towards Better Modeling of Bicycle Lateral Motion: Model Dev...

引用

2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Alazemi, Fahd Fadhloun, Karim Rakha, Hesham Virginia Tech Virginia Tech Transportat Inst 3500 Transportat Res Plaza Blacksburg VA 24061 USA

ISBN: (纸本)9783031709654;9783031709661

This research effort starts by developing a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycles. In that regard, the research team extends the Fadhloun-Rakha bicycle-following longitudinal motion model through complementing it with a lateral motion strategy;thus allowing for overtaking maneuvers and lateral bicycle movements. For the most part, the following strategy of the FR model remains valid for modeling the longitudinal motion of bicycles except for the activation conditions of the collision avoidance strategy, which are modified in order to allow for overtaking when possible. The proposed methodology is innovative in that it makes use of the intersection of certain pre-defined regions around the bicycles to decide on the feasibility of angular motion along with its direction and magnitude. The resulting model is the first point-mass dynamics-based model for the description of the longitudinal and lateral behavior of bicycles in both constrained and unconstrained conditions. In fact, by having the FR bicycle-following model as the governing module of longitudinal behavior and a dynamic lateral module, the proposed model is found to be both robust and able to model bicyclist behavior variability. Furthermore, it is the only existing model that is sensitive to the bicyclist physical characteristics and the bicycle and roadway surface conditions given that the used longitudinal logic was previously validated against experimental cycling data. Next, this study describes a new framework for the collection of naturalistic cycling data. In that process, a new naturalistic cycling dataset is collected for the purpose of validating the developed bicycle lateral motion model. Given that the collection of naturalistic cycling data is not achievable in the traditional vehicle approach, machine learning and computer vision techniques were used to construct the naturalistic dataset from existing video feeds

关键词： Lateral bicycle motion model Naturalistic cycling behavior Data collection computer vision

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Machine Learning for Waste-to-Energy: Optimization and Predictive Analytics 4th

Machine Learning for Waste-to-Energy: Optimization and Predi...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Rana, Sanskar Thakur, Narender Singh Jyoti, Divya Mahato, Dharamender Prasad Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India

ISBN: (纸本)9783031837951;9783031837968

The burgeoning demand for sustainable energy sources has catalyzed interest in Waste-to-Energy solutions offering both environmental benefits and energy generation potential. This paper explores the integration of machine learning (ML) techniques to optimize and predict key aspects of the Waste-to-Energy process. The study leverages historical data from waste facilities incorporating variables such as waste composition, operational parameters, and environmental conditions. In this paper, machine learning models, including regression, classification, and ensemble methods, are employed to optimize combustion efficiency, predict energy output, and enhance the overall operational performance of Waste-to-Energy conversion process. Furthermore, the predictive analytics are employed to anticipate maintenance needs for mitigating the downtime and optimizing the resource allocation. The findings contribute to the growing field of the sustainable energy by showcasing the efficacy of machine learning in Waste-to-Energy systems providing a scalable and adaptive solution for the challenges inherent in this dynamic and complex process.

关键词： Combustion Efficiency Energy Output Prediction Operational Performance Waste Composition Machine Learning

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Evaluation Criteria for Causal Discovery Without Ground-Truth Graphs 6th

Evaluation Criteria for Causal Discovery Without Ground-Trut...

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6th Pacific Causal Inference Conference

作者： Wang, Lei Huang, Shanshan Jun, Liao Liu, Li Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China

ISBN: (纸本)9789819778119;9789819778126

Causal discovery in causal learning aims to derive causal graphs from observational data. However, collecting natural data is challenging and costly, so prior research has predominantly relied on synthetic datasets for validation. These synthetic or semi-real datasets, controlled artificially, may not fully reflect an algorithm's performance in real-world scenarios. Therefore, we proposed a method for evaluating causal discovery in the absence of a ground truth causal graph. First, we divided the data into training and test sets, then performed causal discovery on the training set to obtain causal graphs. We subsequently conducted Markov blanket tests on the causal graphs using the test set and determined the causal direction of each edge using multiple methods. Finally, we integrated the results of these methods through weighted voting to achieve the final accuracy. Experiments on both synthetic and real datasets demonstrated that our proposed method can reflect true accuracy to some extent.

关键词： Causal Discovery Markov Blanket Causal Graphical Model Condition Independence Testing

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Identification of Diabetic Retinopathy from Retinography Images Using a Convolutional Neural Network 10th

Identification of Diabetic Retinopathy from Retinography Ima...

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10th International Conference on Technologies and Innovation

作者： Ulloa, Francisco Sandoval-Pillajo, Lucia Landeta-Lopez, Pablo Grande-Penafiel, Natalia Pusda-Chulde, Marco Garcia-Santillian, Ivan Univ Tecn Norte Ibarra Ecuador Univ Seville Seville Spain

ISBN: (纸本)9783031757013;9783031757020

Diabetic retinopathy (DR) is a prevalent eye disease in people with diabetes worldwide and can cause vision loss or blindness. Conventional diagnostic imaging requires time, effort and specific skills of ophthalmologists. This study proposes the use of a convolutional neural network (CNN) based on the ResNet152V2 architecture to automatically analyze color images of the retina of the eye and identify DR. The Knowledge Discovery in Databases (KDD) methodology was applied for data management and analysis. Datasets of RGB images were acquired, both private from the Ecuadorian Diabetes Association (EDA) and public (EyePACS) available on the Internet. Training and validation of the model were performed with Python, the TensorFlow framework and the Keras library. The results showed that the model has an accuracy in DR identification of 80% comparable to that of ophthalmologists (specialists), showing a statistically significant association according to the chi-square test and a very high Spearman correlation (rho = 0.857). This resulted in a high concordance between both evaluations (model vs. specialists). In addition, the CNN model significantly reduced the manual DR diagnosis time from 5-10 min to 15-30 s. The implementation of this tool could potentially improve the diagnosis of DR and the prescription of appropriate clinical treatments for affected patients.

关键词： diabetic retinopathy retinography CNN ResNet Deep learning Tensorflow-Keras

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Enhancing Gamified LMS with the Integration of Games Experiences

Enhancing Gamified LMS with the Integration of Games Experie...

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Queiros, Ricardo CRACS INESC TEC Porto Portugal Polytech Porto ESMAD Porto Portugal

ISBN: (纸本)9783031834349;9783031834356

This paper presents the guidelines for game experiences integration into Learning Management Systems (LMS) through the IMS LTI (Learning Tools Interoperability) specification. At the same time, it also introduces a dynamic scoring formula, based on a given taxonomy, that goes beyond traditional metrics, allowing for the inclusion of any number of game elements, such as levels, challenges, hints used, and time taken, within the game framework. These elements are assigned weight factors, which can be configured by instructors within the LMS platform, facilitating personalized assessment criteria tailored to specific learning objectives. In order to apply these approach in a real educational scenario, an Escape Room (ER) for math skills testing was used. Using the LTI specification for integration in combination with the dynamic scoring formula provides a comprehensive assessment of student performance, giving a single score which will be vital to enhance the LMS gamified workflow. This research contributes to the field of gamification in education, offering insights into the integration of (serious or regular) games in LMS platforms.

关键词： Code generation computer Programming Gamification

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Business Intelligence for Fault Detection and Diagnosis in Electrical Meters 4th

Business Intelligence for Fault Detection and Diagnosis in E...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Borbor Villon, Melissa Cecilia Carrion Leon, Delia Isabel Banchon Ramos, Evelin Betzabeth Bustos Gaibor, Arcesio Franklin Univ Peninsula Santa Elena Avda Principal La Libertad Santa Elena La Libertad Ecuador Univ Estatal Milagro Km 1-5 Via Milagro Guayas 091050 Ecuador Univ Fuerzas Armadas ESSUNA ESPE Santa Elena Malecon Sect Chipipe Sangolqui Ecuador

ISBN: (纸本)9783031832093;9783031832109

This article explores how Business Intelligence (BI) can be applied to analyze and prevent technical failures in an electric utility's electric meters. Efficient meter management is crucial to ensure accurate metering of electricity consumption and early detection of potential problems. Using BI techniques, we can improve operational efficiency and reduce costs by identifying anomalous patterns and predicting faults before they affect electric service. The research used qualitative probabilistic non-inferential methodology for data collection and the Hephaestus methodology for the analysis of 7014 records of the year 2022 of the Santa Elena electric company. In addition, a dashboard was designed with the Power BI tool that allowed visualizing statistical data on the technical reviews performed by the meter laboratory staff showing accurate and reliable results on meter failures, providing advanced data analysis, monitoring, and predictive analysis to improve operational efficiency and decision making in response to the findings.

关键词： Business intelligence power BI electricity meters

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Traffic Forecasting with Spatio-Temporal Graph Neural Networks 4th

Traffic Forecasting with Spatio-Temporal Graph Neural Networ...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Shah, Shehal Doshi, Prince Mangle, Shlok Tawde, Prachi Sawant, Vinaya Dwarkadas J Sanghvi Coll Engn Mumbai Maharashtra India

ISBN: (纸本)9783031734762;9783031734779

This research investigates the efficacy of various Graph Neural Network (GNN) model architectures in traffic prediction tasks. We compare standard GNN models with two variants: one excluding edge features and another utilizing a k-nearest neighbors graph structure. Our comprehensive comparative analysis examines prediction accuracy and computational efficiency across these models. Experimental results demonstrate that GNN models significantly outperform the Baseline model in both prediction accuracy and training efficiency. Among the models tested, the GNN architecture incorporating edge, node, and global models exhibits superior performance, highlighting the critical role of graph structures in enhancing traffic prediction capabilities. The study underscores the potential of GNNs to improve predictive modeling in traffic systems by leveraging their ability to capture complex relationships within the data.

关键词： Traffic Estimation Graph Neural Networks Machine Learning Spatial Dependencies Transportation Planning

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Enabling Decentralized Collaboration Among Transporters Through an Optimizing Trading Network for Transport Orders 5th

Enabling Decentralized Collaboration Among Transporters Thro...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： De landtsheer, Renaud Goeminne, Aline Meurisse, Quentin Ctr Excellence Technol Informat & Commun CETIC Jean Mermoz 28 B-6041 Charleroi Belgium Univ Mons FRS FNRS & UMONS Mons Belgium

ISBN: (纸本)9783031807749;9783031807756

Nowadays, at least 15-20% of the moving trucks in the European Union are completely or nearly empty. We suggest that this is mainly due to a large quantity of transport companies being small;despite optimizing their routes, they will have to perform empty travels. Some approaches have been proposed to enable transport companies to consolidate their route through collaboration. These approaches either rely on global auction systems or require companies to share their information in a centralized optimizing system. We propose a novel approach where optimization software owned by different transport companies would trade transport orders through a standardized communication protocol to constitute a large and decentralized optimization system. We propose to build such a protocol and system based on the Very Large-Scale Neighborhood search (VLSN) optimization algorithm. It would maintain the confidentiality of private information and let the companies keep the control of the consolidation that the system will be able to explore. We conclude by listing research challenges related to this approach.

关键词： Collaborative routing Trading network Federated AI

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