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3rd international conference on optimization, learning algorithms and applications, ol2a 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on optimization, learning algorithms and applications, ol2a 2023

ISBN: (纸本)9783031530357

The proceedings contain 26 papers. The special focus in this conference is on optimization, learning algorithms and applications. The topics include: Pest Detection in olive Groves Using YolOv7 and YolOv8 Models;Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment;an Evaluation of Image Preprocessing in Skin Lesions Detection;an Artificial Intelligence-Based Method to Identify the Stage of Maturation in olive Oil Mills;vehicle Industry Big Data Analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile Robots;movement Pattern Recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep learning and Machine learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep learning-Based Classification and Quantification of Emulsion Droplets: A YolOv7 Approach;identification of Late Blight in Potato Leaves Using Image Processing and Machine learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG Signal Filters on Machine learning Model Training: A Comparison with Clustering on Raw Signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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3rd international conference on optimization, learning algorithms and applications, ol2a 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on optimization, learning algorithms and applications, ol2a 2023

ISBN: (纸本)9783031530241

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A Pattern Mining Heuristic for the Extension of Multi-trip Vehicle Routing 3rd

A Pattern Mining Heuristic for the Extension of Multi-trip V...

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3rd international conference on optimization, learning algorithms and applications (ol2a)

作者： Karimi, Leila Little, Connor Choudhury, Salimur Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

ISBN: (纸本)9783031530241;9783031530258

Multi-trip vehicle routing problem with a variable number of wagons significantly reduces the number of vehicles and drivers needed to service customers. It is often hard to solve these problems in acceptable CPU times using exact algorithms when the problem contains very big real-world data sets. We use meta-heuristic algorithms to get a solution close to the optimal solutions for vehicle routing problems with a dynamic capacity of a vehicle. First, local search heuristics applied with genetic algorithms are proposed. Then, a pattern-mining algorithm is developed to improve the solutions found from the genetic algorithm. We perform detailed experiments on Solomon instances for vehicle routing problem with time windows (VRPTW). Our experiments establish the effectiveness of the algorithms.

关键词： Vehicle routing problem Multi-trip Various capacity Pattern mining Genetic algorithm

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Speeding up the Oscillation-Free Modified Heavy Ball Algorithm 3rd

Speeding up the Oscillation-Free Modified Heavy Ball Algorit...

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3rd international conference on optimization, learning algorithms and applications (ol2a)

作者： Gelashvili, Koba Gogishvili, Paata St Andrew First Called Georgian Univ Patriarchate 53a Chavchavadze Ave Tbilisi 0179 Georgia Ilia State Univ 3-5 Kakutsa Cholokashvili Ave Tbilisi 0162 Georgia

ISBN: (纸本)9783031530241;9783031530258

This paper presents modified, faster momentum minimization algorithms based on existing ones. The modified algorithms monotonically decrease the objective function and do not allow it to oscillate. The modification scheme aims to enhance momentum minimizers by incorporating contemporary line search procedures and restarts, akin to the state-of-the-art unconstrained minimizers. We also investigate the unique resource of oscillation-free momentum minimizers for their further acceleration. In particular, the wider range of variation in the friction-related coefficient within the model significantly impacts the performance time. Our previously developed techniques can be used to prove the convergence of modified algorithms. In this paper, we focus on the technical and experimental aspects of these algorithms. To determine the efficiency of the new algorithms, numerical experiments were conducted on standard optimization test functions and on single-layer neural networks for several datasets. Comparisons were made with the best unconstrained minimization algorithms - lcg, L-BFGS and ADAM. Oscillation-free momentum algorithms are significantly easier to design and implement than lcg and L-BFGS, while still being competitive in terms of performance. Collections of minimizers and test functions have been uploaded to GitHub.

关键词： Polyak's heavy ball method Nesterov's accelerated gradient method L-BFGS lcg

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Assessing the 3D Position of a Car with a Single 2D Camera Using Siamese Networks 3rd

Assessing the 3D Position of a Car with a Single 2D Camera U...

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3rd international conference on optimization, learning algorithms and applications (ol2a)

作者： Haj Yahia, Youssef Bel Lopes, Julio Castro Bezerra, Eduardo Rodrigues, Pedro Joao Lopes, Rui Pedro Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal CEFET RJ Rio De Janeiro Brazil Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Reg Monta Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

Using computer vision for the classification of an object's 3D position using a 2D camera is a topic that has received some attention from researchers over the years. Visual data is interpreted by the computer to recognize the objects found. In addition, it is possible to infer their orientation, evaluating their spatial arrangement, rotation, or alignment in the scene. The work presented in this paper describes the training and selection of a siamese neural network for classifying the 3D orientation of cars using 2D images. The neural network is composed of an initial phase for feature selection through convolutional neural networks followed by a dense layer for embedding generation. For feature selection, four architectures were tested: VGG16, VGG19, ResNet18 and ResNet50. The best result of 95.8% accuracy was obtained with the VGG16 and input images preprocessed for background removal.

关键词： Computer Vision Maintenance support Siamese networks Object Orientation

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Nonlinear Regression on Growth Curves for Placental Parameters in R 3rd

Nonlinear Regression on Growth Curves for Placental Paramete...

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3rd international conference on optimization, learning algorithms and applications (ol2a)

作者： Lemos, Daniela Braga, Ana Cristina Nogueira, Rosete Univ Minho Sch Engn P-4710057 Braga Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal Life & Hlth Sci Res Inst ICVS ICVS 3Bs PT Govt Associate Lab Campus Gualtar P-4710057 Braga Portugal Embryofetal Pathol Lab Unilabs CGC Genet R Sa da Bandeira 7061o P-4000431 Porto Portugal

ISBN: (纸本)9783031530241;9783031530258

Growth charts play a crucial role in the evaluation and surveillance of paediatric populations, serving as indispensable tools for paediatricians and public health researchers. The development of these growth charts for fetal parameters has been extensively used in recent decades. However, investigation of placental parameters and their relationship with obstetric outcome has been relatively neglected, resulting in a significant gap in understanding their biological significance. This study presents an alternative approach for constructing reference growth curves specific to the placental parameter, Diameter 2. Our methodology uses the generalized additive models for location, scale, and shape (GAMLSS), offering distinct advantages over traditional quantile regression methods. One of the key advantages of GAMLSS is its flexibility to accommodate any statistical distribution, allowing for the modelling of various parameters that characterize the distribution of the response variable. Through the application of our proposed methodology, we demonstrated that by using P-splines as a smoothing function and Box-Cox t (BCT) as a distribution, we can achieve a representative growth curve for the Diameter 2 of the placenta throughout gestational age (GA). The resulting models demonstrated high representativeness, with R-2 values of 0.7608 and 0.7673 and AIC scores of 7953 and 7946 for the best two models, respectively. Moreover, our approach has the ultimate goal to facilitate early diagnosis of fetal complications, thereby providing valuable assistance to healthcare professionals.

关键词： Placenta Growth curves Non-linear Semi-parametric regression model Smoothing functions Distributions GAMLSS LMS

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A Machine learning Approach for Anomaly Detection in Power Mixing Equipment Intelligent Bearing Fault Diagnosis

A Machine Learning Approach for Anomaly Detection in Power M...

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Power Electronics, Computer applications (ICPECA), IEEE international conference on

作者： Ce Fang Qian Wang Binbin Huang Zhejiang Canaan Technology Limited China International College Krirk University Bangkok Thailand Zhejiang Canaan Technology Limited Wenzhou China

Bearing and Gear are the essential and most significant pieces of pivoting machinery utilized in ventures. Normal applications incorporate planes, autos, power turbines, and steel plants. In the event of flaws happening in any bearing or gear, of these machines during working circumstances, serious outcomes are to happen. The issue determination utilizes the vibration signal at the beginning phase of shortcoming event. Vibration-based investigation is taken as a result of its simplicity of estimation. The viability of the astute shortcoming finding strategy is approved utilizing different datasets gathered from turning bearing and gear parts from an only made test rig in the research center through planned plans. For the robotized bearing shortcoming finding framework, three distinct and novel plans were figured out, and it is assessed. Also, for gear issue conclusion, another three unique and novel plans were modeled and assessed independently. In bearing issue determination, contain wavelet parcel change for highlight extraction, half and half hereditary calculation and particle swarm optimization for highlight choice and for coordinated ANFIS for navigation. In plot 2, Feature Extraction process did from the time and recurrence area of the gained signal, Hereditary Calculation and Particle Swarm optimization strategies with various wellness capabilities were applied. The outcomes from gear and bearing shortcoming analysis exhibit that the proposed smart shortcoming finding philosophy is exceptionally exact and successful in separating the issue examples of the two bearings and gears.

关键词： Vibrations Fault diagnosis Gears Signal processing algorithms Signal processing Feature extraction Turning

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