2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)最新文献_第2页

Permanent Magnet Wind Generators: Neodymium vs. Ferrite Magnets 永磁风力发电机:钕与铁氧体磁铁

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461291

M. Özdemir, C. Ocak, A. Dalcalı

引用次数: 2

Robust Model Predictive Control for Autonomous Lane-Changing 自主变道鲁棒模型预测控制

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461391

S. Coskun

{"title":"Robust Model Predictive Control for Autonomous Lane-Changing","authors":"S. Coskun","doi":"10.1109/HORA52670.2021.9461391","DOIUrl":"https://doi.org/10.1109/HORA52670.2021.9461391","url":null,"abstract":"Autonomous vehicles need to plan trajectories to a specific goal while avoiding collisions with surrounding vehicles. To this aim, it is essential to take into account the inherited uncertainties due to unmodeled dynamics, uncertain localization, and disturbances. This paper deals with the problem of robust trajectory planning for autonomous lane-changing in the presence of uncertainties. Considering trajectory planning as an online decision-making problem, we propose a robust model predictive control (rMPC), which minimizes deviations from a reference speed and a lateral target position while keeping a subject vehicle within road limits and avoiding collisions with an in-lane vehicle. Uncertainties are explicitly modeled as an additive disturbance in the formulation, wherein the optimal control decisions are obtained by solving a quadratic program (QP). The resulting rMPC guarantees robust state-input satisfaction under the additive disturbance even when the QP solver iterations are stopped prematurely. A set of simulation experiments is studied under different initial scenarios to validate the design, demonstrating the potential utility of the proposed control algorithm for reliable lane-changing.","PeriodicalId":270469,"journal":{"name":"2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114961048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Metaheuristic algorithm based PI controller design for Linearized Quadruple-Tank Process 基于元启发式算法的线性化四缸过程PI控制器设计

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461399

A. K. Sahin, Tuba Taş, Emre Bertuğ, M. S. Ayas

引用次数: 1

Implementation of pick-and-place algorithms for the purposes of wafer handling robotics simulations 晶圆处理机器人模拟的取放算法的实现

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461187

Nikolay Bratovanov

引用次数: 1

Lightweigth Convolutional Neural Networks for Person Re-Identification 用于人物再识别的轻量级卷积神经网络

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461269

Fatih Aksu, C. Direkoğlu

引用次数: 0

High layer thickness influence on melt pool sizes and defects appearing of Ti6Al4V at high scan speeds 高层厚对高扫描速度下Ti6Al4V熔池尺寸和缺陷出现的影响

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461334

Nada Hassine, S. Chatti, M. B. Slama

引用次数: 0

An Enhanced Slime Mould Algorithm for Function optimization 函数优化的改进黏菌算法

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461325

Davut Izci

{"title":"An Enhanced Slime Mould Algorithm for Function optimization","authors":"Davut Izci","doi":"10.1109/HORA52670.2021.9461325","DOIUrl":"https://doi.org/10.1109/HORA52670.2021.9461325","url":null,"abstract":"This study focuses on enhancement of one of the recently published metaheuristic algorithms known as slime mould algorithm (SMA). The slime mould algorithm has been shown to be a good competitive approach in the field of metaheuristics, however, it still suffers from poor exploitative behaviour, thus, suffers from slow convergence and lacks providing potentially better solutions which eventually requires an improvement. Considering the latter fact, this study attempts to further enhance the capability of the original version of slime mould algorithm so that it can be utilized for optimization problems as an even better approach. Therefore, Nelder-Mead (NM) simplex search method was utilized as an aiding structure to enhance the slime mould algorithm in terms of local search, as well. The constructed hybrid approach (SMA-NM) utilizes the slime mould algorithm for diversification and Nelder-Mead method for intensification which consequently enhances the algorithm due to better refinement of balance between exploration and exploitation stages. To assess the capability of the proposed approach, unimodal and multimodal benchmark functions were used. The performance of the proposed hybrid algorithm was tested against those test functions, in terms of exploitation, exploration, statistical significance and ranking. by comparing it with the grey wolf optimization, arithmetic optimization, and the original version of slime mould algorithms. The performed analyses have shown the proposed approach to be a greater competitive approach to deal with optimization problems.","PeriodicalId":270469,"journal":{"name":"2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130918269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Educational Data Mining Using Semi-Supervised Ordinal Classification 基于半监督有序分类的教育数据挖掘

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461278

Ferda Ünal, Derya Birant

{"title":"Educational Data Mining Using Semi-Supervised Ordinal Classification","authors":"Ferda Ünal, Derya Birant","doi":"10.1109/HORA52670.2021.9461278","DOIUrl":"https://doi.org/10.1109/HORA52670.2021.9461278","url":null,"abstract":"Until now, many different data mining techniques have been used for classification in the field of education. The main problems associated with this issue are insufficient labeled data and classification using nominal class labels, rather than ordinal ones. To overcome these problems, in this study, the Semi-Supervised Ordinal Classification (SSOC) method was tested on education data and satisfactory results were obtained. The SSOC method combines both semi-supervised learning and ordinal classification approach. Thanks to this method, a significant increase in accuracy achieved on three different education data. In the experiments, the SSOC method was tested by using different base learners, including Decision Tree, Random Forest, and Neural Network. For semi-supervised learning, the classification results were obtained with different quantities of labeled instances varying from 15% to 50% with 5% increments and finally as 75% labeled data. The datasets used in this study have an ordinal categorical structure such as $(c1 lt c2 lt c3)$. The experimental results show that the SSOC method can achieve satisfactory performance in case of using data that has ordinal structure but has small amount of labeled instances and large amount of unlabeled instances. In the field of education, it is highly likely that datasets related to student achievement have ordinal structure. Therefore, SSOC can be successfully used in classification studies in education.","PeriodicalId":270469,"journal":{"name":"2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130919722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of Dataset Size on Deep Learning in Voice Recognition 数据集大小对语音识别中深度学习的影响

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461395

A. Çayır, T. S. Navruz

引用次数: 10

Customer Churn Prediction For Business Intelligence Using Machine Learning 使用机器学习的商业智能客户流失预测

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI: 10.1109/HORA52670.2021.9461303

Victor Chimankpam Nwaogu, Kamil Dimililer

引用次数: 3