2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)最新文献

[Title page iii] [标题页iii]

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/iccairo47923.2019.00002

引用次数: 0

A Comparative Study of Machine Learning Approaches on Learning Management System Data 学习管理系统数据中机器学习方法的比较研究

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/ICCAIRO47923.2019.00029

D. Oreški, Goran Hajdin

引用次数: 1

[Copyright notice] (版权)

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/iccairo47923.2019.00003

引用次数: 0

Multi-Object Detection in Urban Scenes Utilizing 3D Background Maps and Tracking 基于3D背景地图和跟踪的城市场景多目标检测

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/ICCAIRO47923.2019.00044

Orkény Zováthi, L. Kovács, Balázs Nagy, C. Benedek

{"title":"Multi-Object Detection in Urban Scenes Utilizing 3D Background Maps and Tracking","authors":"Orkény Zováthi, L. Kovács, Balázs Nagy, C. Benedek","doi":"10.1109/ICCAIRO47923.2019.00044","DOIUrl":"https://doi.org/10.1109/ICCAIRO47923.2019.00044","url":null,"abstract":"In this paper we propose a novel approach for upgrading real time 3D dynamic object detection methods operating on rotating multi-beam (RMB) Lidar measurements using 3D background city maps stored in new generation geographic information systems (GIS) and previously detected dynamic objects propagated by tracking. First, we apply a state-of-the-art object detection method and distinguish the predicted dynamic object candidates and the remaining static regions of the current Lidar measurement. Next we find an optimal transformation between the static part of the RMB Lidar measurements and the background city map using a multimodal point cloud registration algorithm operating in the Hough space. After the accurate alignment, we filter false-positively detected object candidates in the RMB Lidar data based on the map. To find additional objects missed by the object detector on the current measurement, we apply a Kalman-filter based object tracking. Hereby we first predict the current state of the previously detected and tracked objects. Next, we apply a Hungarian matcher based assignment between the tracked and the current objects and update the object list according to the result. For better accuracy, we keep all predictions through a couple of frames. We evaluated our method qualitatively and quantitatively in crowded urban scenes of Budapest, Hungary, and the results showed that with background map based filtering we can achieve a 26,52% improvement detecting vehicles and 9,38% for pedestrians in precision, while via tracking, a 12,84% improvement for vehicles and 14,34% for pedestrians in recall against the state-of-the-art object detection method relying purely on a single Lidar time frame.","PeriodicalId":297342,"journal":{"name":"2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126731914","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

Evaluating Particulate Matter (PM2.5 and PM10) Impact on Human Health in Oman Based on a Hybrid Artificial Neural Network and Mathematical Models 基于混合人工神经网络和数学模型评估阿曼颗粒物(PM2.5和PM10)对人类健康的影响

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/ICCAIRO47923.2019.00028

Nebras Alattar, Jabar H. Yousif

{"title":"Evaluating Particulate Matter (PM2.5 and PM10) Impact on Human Health in Oman Based on a Hybrid Artificial Neural Network and Mathematical Models","authors":"Nebras Alattar, Jabar H. Yousif","doi":"10.1109/ICCAIRO47923.2019.00028","DOIUrl":"https://doi.org/10.1109/ICCAIRO47923.2019.00028","url":null,"abstract":"The statistics of the World Health Organization (WHO) indicate that outdoor air pollution in 2016 is a significant cause of premature mortality, with an average of 4.2 million death cases. This mortality is due to exposure to PM2.5 particulate matter, which causes many diseases such as respiratory, cardiovascular, and cancers. The concentration of particulate matter (PM) is the most popular air pollutant that affects short term and long term health. The paper aims to study and investigate the concentration dispersion of particulates (PM 2.5 and PM10) and its impact on human health in Oman. The study suggested a hybrid neural and mathematical approaches for analyzing the effect rate of particulate matter (PM2.5 and PM10). The paper implements a comparative study to analyze the proposed neural and mathematical models, which predict the future levels of pollutants in a fast, cheap, and safe way. The Linear regression models achieve fewer results of R², MSE, RMSE (0.7604, 0.0673, 0.2595), respectively. However, the non-linear regression polynomial prediction model obtained excellent results based on the coefficient of determination (R²) value of 0.9394 and mean square error (MSE) rate of 0.0209, and root mean square error (RMSE) value of 0.1447. Moreover, the Neural SOM model obtained the highest results in predicting the experimental data that achieved an MSE value of 0.0064, correlation rate (R) value of 0.994, NMSE value of 0.01392, and MAE value of 0.0467. All the results were correctly verified based on suitable mathematical methods.","PeriodicalId":297342,"journal":{"name":"2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133430207","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: 3

[Title page i] [标题页i]

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/iccairo47923.2019.00001

引用次数: 0

Discrete Gradation Trajectories Computation in Electrophotography 电子摄影中的离散渐变轨迹计算

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/ICCAIRO47923.2019.00040

D. Tarasov, O. Milder

{"title":"Discrete Gradation Trajectories Computation in Electrophotography","authors":"D. Tarasov, O. Milder","doi":"10.1109/ICCAIRO47923.2019.00040","DOIUrl":"https://doi.org/10.1109/ICCAIRO47923.2019.00040","url":null,"abstract":"In printing art, color management is largely based on the adjustment and analysis of the behavior of gradation curves calculated for the initial color channels. However, this approach does not take into account the mutual influence of colors and the change in hue, for example, when inks overlap. The approach proposed by the authors earlier offers to replace the two-dimensional tone reproduction curves with three-dimensional gradation trajectories in the CIE Lab metric space. In this paper, we develop this approach. It is shown that calculations using the mathematical apparatus of the differential geometry of spatial curves describing gradation trajectories might be simplified using the discrete approach. Discretization is associated with the peculiarities of color formation in modern digital printing systems. These features are used in the approximation of gradation trajectories using polynomials. In this case, color coordinates are considered as continuous functions of filling a discrete raster cell with dye. The proposed method allows one to calculate trajectories faster and without the use of cumbersome computations. An experimental verification of this approach was carried out using the example of a digital electrophotographic printing system.","PeriodicalId":297342,"journal":{"name":"2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124462820","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

Obstacle Avoidance Navigation Using Horizontal Movement for a Drone Flying in Indoor Environment 基于水平运动的室内无人机避障导航

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/ICCAIRO47923.2019.00009

Shinya Kawabata, Jae Hoon Lee, S. Okamoto

引用次数: 5

Improving Positron Emission Tomography with Guided Filtering 引导滤波改进正电子发射断层成像技术

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/ICCAIRO47923.2019.00019

Dóra Varnyú, László Szirmay-Kalos

引用次数: 0

Experimental Evaluation of Adhesion Plate and Development of Novel Drone Capable of Adhering to Ceiling and Wall 粘接板的实验评价及新型无人机的研制

2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) Pub Date : 2019-05-01 DOI: 10.1109/ICCAIRO47923.2019.00011

K. Nohara, Jae Hoon Lee, S. Okamoto

引用次数: 1