2019 International Conference on Computational Science and Computational Intelligence (CSCI)最新文献_第7页

Traffic Sign Identification Using Deep Learning 使用深度学习识别交通标志

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00063

Ratheesh Ravindran, M. Santora, M. Faied, Mohammad Fanaei

{"title":"Traffic Sign Identification Using Deep Learning","authors":"Ratheesh Ravindran, M. Santora, M. Faied, Mohammad Fanaei","doi":"10.1109/CSCI49370.2019.00063","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00063","url":null,"abstract":"One of the most crucial enabling technologies for automated driving systems is the ability to reliably detect and classify a wide range of traffic signs in various driving conditions at different distances. Due to the complexity and dynamic nature of driving environments, it is difficult to reliably detect traffic signs with conventional image processing methods. Artificial intelligence in combination with image processing has proven to be a great success to address this problem in recent studies. This paper focuses on the selection of Deep Neural Networks (DNN) based on the application-oriented performance by taking into consideration the mean Average Precision (mAP) and Frames Per Second (FPS) as the major evaluation criteria. Faster Region-based Convolutional Neural Network (Faster R-CNN) is a newly proposed DNN in the literature that has proven to exhibit a balanced tradeoff between mAP and FPS performance measures. This paper starts with a DNN transfer learning and then implements the Faster R-CNN algorithm for the real-time detection and classification of traffic signs using the Robot Operating System (ROS). To reduce the errors due to DNN inaccurate detection, Tesseract\" is added to detect the text in the identified traffic signs. The German Traffic Sign Detection Benchmark (GTSDB) dataset is used in this paper, and additional dataset are created to solve the lack of certain traffic signs in the GTSDB dataset. Simulation with ROS-Gazebo and real-time trials using the Polaris Gem e2 equipped with NVIDIA Drive PX2 demonstrate the efficiency of the proposed integration of DNN with Tesseract in detecting and classifying a wide range of traffic signs.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574183","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

Economic Dispatch for Power System with Short-Term Solar Power Forecast 太阳能发电短期预测下的电力系统经济调度

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00096

E. Espinosa-Juárez, Jorge Luis Solano-Gallegos, F. Ornelas‐Tellez

引用次数: 3

University Online Courses: Correlation between Students' Participation Rate and Academic Performance 大学网络课程:学生参与率与学习成绩的关系

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00147

Sahar Voghoei, Navid Hashemi Tonekaboni, D. Yazdansepas, H. Arabnia

{"title":"University Online Courses: Correlation between Students' Participation Rate and Academic Performance","authors":"Sahar Voghoei, Navid Hashemi Tonekaboni, D. Yazdansepas, H. Arabnia","doi":"10.1109/CSCI49370.2019.00147","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00147","url":null,"abstract":"It has been generally believed that higher participation in discussion forums in online classes would result in better student performance. To better understand this correlation on a large scale, we have studied 291 distinct online courses offered during Summer 2019 at Georgia Gwinnett College. Several studies in the literature have focused on analyzing the data from the Massive Open Online Courses (MOOCs). However, in this research, we have focused on University-based Online Courses (UOCs) for undergraduate students, where the curriculum enforces students to take these courses. Although a higher participation rate in online forums has a direct correlation with a higher grade in MOOCs, in OUCs, students with top grades are not necessarily the most active students. Our analysis shows a consistent pattern in UOCs where during the first two-thirds of the semester, students who belong to the GPA range of ~70 to ~80 percentile of the class have the highest rate of participation, while during the last one-third of the semester, the ones who belong to the GPA range of ~87 to ~93 percentile, contribute the most. On the other hand, we found out that the common characteristic of top students in all classes, is their consistency in participation throughout the semester, regardless of the number of their posts.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117260599","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

Who Is the Father of Deep Learning? 谁是深度学习之父?

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00067

C. Tappert

引用次数: 12

Performance Study of PID and Voltage Mode Controllers in Voltage Regulator for Smart DC Wall-Plug 智能直流插座稳压器中PID和电压型控制器的性能研究

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00139

R. Hasanah, Rakhmat Ramadhan, H. Suyono, T. Taufik

引用次数: 0

[Title page iii] [标题页iii]

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/csci49370.2019.00002

引用次数: 0

Trade-Offs between Early Software Defect Prediction versus Prediction Accuracy 早期软件缺陷预测与预测准确性之间的权衡

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00216

L. Alhazzaa, Anneliese Amschler Andrews

引用次数: 0

New Algorithms to Solve Integral Equations Automatically 自动求解积分方程的新算法

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00094

Jun Zhang, Weiwei Zhu, Fangyang Shen

引用次数: 2

A Real-Time Based Intelligent System for Predicting Equipment Status 基于实时的智能设备状态预测系统

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/csci49370.2019.00084

Seungchul Lee, Daeyoung Kim

引用次数: 1

Detection of Phishing Attacks with Machine Learning Techniques in Cognitive Security Architecture 基于认知安全架构的机器学习技术检测网络钓鱼攻击

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00071

Ivan Ortiz Garcés, María Cazares, R. Andrade

引用次数: 16