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Proceedings of the 6th International Conference on Robotics and Artificial Intelligence最新文献

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A Simple Basic Waveform of Chirp Spread Spectrum Communication System 啁啾扩频通信系统的简单基本波形
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449339
Sihai Qiu, Dongyan Zhao, Yubo Wang, Xiaoke Tang, Xu Zhao, Yubing Zhang
{"title":"A Simple Basic Waveform of Chirp Spread Spectrum Communication System","authors":"Sihai Qiu, Dongyan Zhao, Yubo Wang, Xiaoke Tang, Xu Zhao, Yubing Zhang","doi":"10.1145/3449301.3449339","DOIUrl":"https://doi.org/10.1145/3449301.3449339","url":null,"abstract":"The Chirp Spread Spectrum is a widely used technology in the Internet of Things today. Frequency shift Binary Orthogonal Keying is a basic method which is used in the Chirp Spread Spectrum communication system. Its modulation waveform includes the Down-Chirp and Up-Chirp. The initial phase of the chirp signal can also be used to construct the modulation waveform. In addition to these, a new chirp based modulation waveform is reported in this paper. The parabola phase of this waveform is reshaped by utilizing the correlation detection characteristics.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100471","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
A Research and Design of Lightweight Convolutional Neural Networks Accelerator Based on Systolic Array Structure 基于收缩阵列结构的轻量级卷积神经网络加速器的研究与设计
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449342
Yunping Zhao, Xiaowen Chen, Rui Xu, Jinhui Wei, Jianzhuang Lu, Bo Yuan
{"title":"A Research and Design of Lightweight Convolutional Neural Networks Accelerator Based on Systolic Array Structure","authors":"Yunping Zhao, Xiaowen Chen, Rui Xu, Jinhui Wei, Jianzhuang Lu, Bo Yuan","doi":"10.1145/3449301.3449342","DOIUrl":"https://doi.org/10.1145/3449301.3449342","url":null,"abstract":"With the wide application of convolutional neural networks(CNNs) in the field of artificial intelligence, more attention has been paid to the architecture design of CNNs accelerator. But even so, there is little research on hardware acceleration of light-weight CNNs, and there is a lack of systematic and in-depth exploration of lightweight CNNs accelerator design space. In this paper, we propose a design scheme for the lightweight CNNs accelerator based on the systolic array structure. Taking the MobileNet series, the typical representative of lightweight CNNs, as the test benchmark, we carry out detailed experiments and research analysis on the accelerator performance under different data-flow modes and different core computing array scales. Based on the systematic and comprehensive experiments, we provide powerful experimental data supporting and scientific guidance for the design space of the systolic array based lightweight CNNs accelerator and the trade-off of various indicators including operational efficiency, acceleration ratio, cycle time and so on, which makes up for the blank of current research in this field, and makes great convenience for subsequent designers to develop lightweight CNNs accelerators. Through our research, MobileNet V1 is speeded up nearly 1.2 times under certain conditions.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129622360","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
Research on VPN in Experimental Simulation Environment Based on GRE and IPSec 基于GRE和IPSec的VPN实验仿真环境研究
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449338
Jiaqi Li, Min Zhou
{"title":"Research on VPN in Experimental Simulation Environment Based on GRE and IPSec","authors":"Jiaqi Li, Min Zhou","doi":"10.1145/3449301.3449338","DOIUrl":"https://doi.org/10.1145/3449301.3449338","url":null,"abstract":"With the popularization of Internet network technology, its potential network information security is also pervasive in various fields. In this paper, VPN technology is based on GRE and IPSec, and GNS3 simulator is used to simulate and test four kinds of experiment programs to realize VPN combined with routing and message analysis to testify the experiment. Finally, the comparative results are presented and the conclusions are drawn that the security of data transmission is more reliable.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125362724","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
Research on Evaluation Algorithm of Teacher's Teaching Enthusiasm Based on Video 基于视频的教师教学热情评价算法研究
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449333
Yujia Chen, Chongwen Wang, Zefeng Jian
{"title":"Research on Evaluation Algorithm of Teacher's Teaching Enthusiasm Based on Video","authors":"Yujia Chen, Chongwen Wang, Zefeng Jian","doi":"10.1145/3449301.3449333","DOIUrl":"https://doi.org/10.1145/3449301.3449333","url":null,"abstract":"Most of the current research on teachers' teaching enthusiasm is to define teachers' emotions in a qualitative way, but lacks intuitive digital or image representations. Based on the online teaching platform, this research collects various types of teaching videos, and establishes a teacher's teaching emotion data set based on the PAD emotion model, then extracts teacher's teaching features through various algorithms such as sound feature extraction, facial expression recognition, target detection, and pose estimation. Analyze the features of different modalities, and cascade the features based on the contribution of each feature to the result. Finally, use BP neural network to predict teachers' enthusiasm. The study found that the model prediction performance is better after adopting the cascading feature fusion method based on feature contribution. The loss of the P prediction model is 0.0586, the loss of the A prediction model is 0.0517, the loss of the D prediction model is 0.0428, and the average loss is 0.0510.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117099470","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: 2
Book Rating Model Based on Self-Attention and LSTM 基于自注意和LSTM的图书评价模型
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449327
Xiaotong Zhao
{"title":"Book Rating Model Based on Self-Attention and LSTM","authors":"Xiaotong Zhao","doi":"10.1145/3449301.3449327","DOIUrl":"https://doi.org/10.1145/3449301.3449327","url":null,"abstract":"With the rapid development of Internet technology and online communication communities, text data has exploded. Emotion analysis of network information and research on the emotional tendency of users in various industries for products have become important research topics. Neural networks have good performance in the field of natural language processing. The traditional recurrent neural network has the problem of gradient disappearance. Therefore, this paper combines Self-Attention mechanism and LSTM model to realize the multi-classification of text emotional attributes and effectively obtain complete long sequence information. The experiment uses the Goodreads book review dataset for sentiment analysis. The experimental results show that the Self-Attention + LSTM model has a higher prediction accuracy than RNN. This proves that the model proposed in this paper can be used to improve the accuracy of text sentiment classification and has certain research value.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"489 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116179203","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: 2
Seven Degrees of Freedom Simulation Comparison for Industrial Process in San Pedro Sula, Honduras 洪都拉斯圣佩德罗苏拉工业过程七自由度仿真比较
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449330
A. Vásquez, Jose Luis Ordoñez Avila
{"title":"Seven Degrees of Freedom Simulation Comparison for Industrial Process in San Pedro Sula, Honduras","authors":"A. Vásquez, Jose Luis Ordoñez Avila","doi":"10.1145/3449301.3449330","DOIUrl":"https://doi.org/10.1145/3449301.3449330","url":null,"abstract":"Honduras is a country full of need and while some companies provide massive jobs for the textile, food, and beverage manufacturing, these industries are lacking industrial robots due to the lack of financial resources and trained personnel. Therefore, this study's main objective is to implement a degree of freedom to an articulated robot with 7 degrees of freedom which is traditional in Honduras's industry. Comparing a 6-degree-of-freedom robot against a 7-degree robot employing simulations software ACE where the 7-degree robot showed greater consistency in the different tests with a rate of 15.24 pieces per minute. Finally, it is concluded that the industrial processes in Honduras where robots are applied are mostly the harness industry. In this industry robots are used for Pick & Place and welding. The food industry uses robots for palletizing on conveyor belts based on the concepts of Pick and Place.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115814160","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
Radio Frequency-based Techniques of Drone Detection and Classification using Machine Learning 基于射频的无人机检测和机器学习分类技术
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449348
Mariam M. Alaboudi, M. A. Talib, Q. Nasir
{"title":"Radio Frequency-based Techniques of Drone Detection and Classification using Machine Learning","authors":"Mariam M. Alaboudi, M. A. Talib, Q. Nasir","doi":"10.1145/3449301.3449348","DOIUrl":"https://doi.org/10.1145/3449301.3449348","url":null,"abstract":"This research paper provides a comprehensive survey review on drone detection using Radio Frequency (RF)-based techniques along with machine learning and localization algorithms. RF signals proved its effectiveness in detecting drones, however, due to the lack of a published survey, this research paper reviews the newly emerged RF-based techniques by addressing the implemented methods and discussing the results obtained in terms of the testing environment, range of detection and accuracy of the system. In this survey review, thirty conference and journal papers have been collected, however only selected papers have been discussed depending on the contribution and limited space of the paper. Finally, this survey also discusses the challenges encountered in drone detection using RF due to its great impact on the efficiency of the system.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125210911","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: 2
A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm 匈牙利算法下基于空间约束的成本矩阵优化方法
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449324
Yu Ye, Xiao Ke, Zhiyong Yu
{"title":"A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm","authors":"Yu Ye, Xiao Ke, Zhiyong Yu","doi":"10.1145/3449301.3449324","DOIUrl":"https://doi.org/10.1145/3449301.3449324","url":null,"abstract":"At present, with the continuous development of single object tracking(SOT) tracker, more and more SOT tracker are applied to multi-object tracking(MOT) tasks. However, in the traditional method of affinity computation, affinity model is used as the metric of Hungarian algorithm, which has the low discrimination rate of similar objects and leads to ID switch easily. In order to solve this problem, we propose a cost matrix optimization method based on spatial constraints under Hungarian algorithm. In the data association stage, Kalman filter is used to estimate the motion vector of the object, so that the current position of the object can be linearly predicted. The weight of the cost matrix is modified according to the spatial relationship between the estimated position and the detection results, which is used for the subsequent re-identification task. It is worth noting that the above methods do not need extra training and can be directly used in other multi-object tracking models. Our method has been evaluated on MOT16(46.7%), MOT17(49.7%), and achieved the effect of SOTA. The entire results can be found on MOTChallenge website1 .","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122242146","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: 2
An Overview of Crowd Counting on Traditional and CNN-based Approaches 传统和基于cnn的人群计数方法综述
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449779
Xinyu Chen, Mingzhe Liu, Jun Ren, Chuan Zhao
{"title":"An Overview of Crowd Counting on Traditional and CNN-based Approaches","authors":"Xinyu Chen, Mingzhe Liu, Jun Ren, Chuan Zhao","doi":"10.1145/3449301.3449779","DOIUrl":"https://doi.org/10.1145/3449301.3449779","url":null,"abstract":"Crowd counting, which is one of the primary research lines of computer vision, has achieved significant advancement due to the affordable computational cost, and it is a particularly useful means of ensuring public security in public places, especially in crowded places. In recent years, crowd counting methods based on public places have emerged one after another given crowd congestion. Firstly, this paper introduces in detail from traditional approaches to deep learning approaches, focusing on crowd counting approaches based on the convolution neural network (CNN), and compares and analyzes the advantages and disadvantages of each approach. Next, this paper summarizes the commonly used datasets and the main indicators of evaluating crowd counting algorithms. Finally, this paper expounds on the challenges existing in the field of crowd counting and the possible research directions in the future.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"69 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126980266","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
Efficient Mini-batch Training for Echo State Networks 回声状态网络的高效小批量训练
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449341
Chunyuan Zhang, Chao Liu, Jie Zhao
{"title":"Efficient Mini-batch Training for Echo State Networks","authors":"Chunyuan Zhang, Chao Liu, Jie Zhao","doi":"10.1145/3449301.3449341","DOIUrl":"https://doi.org/10.1145/3449301.3449341","url":null,"abstract":"Echo state networks (ESNs) are generally optimized by the ordinary recursive least squares (ORLS) algorithm. Although ORLS has fast convergence, it can process only one sample per iteration, which makes ESNs difficult to scale to large datasets. To tackle this problem, a novel mini-batch RLS (MRLS) algorithm is proposed in this paper. On this basis, to prevent overfitting in the ESN training, an L1regularization method is suggested for MRLS. In addition, to make ESNs more suitable for time-varying tasks, an adaptive method of the forgetting factor is also introduced for MRLS. Experimental results of two time-series problems show that ESNs have faster processing speed and better convergence performance with MRLS than with ORLS. CCS CONCEPTS • Computing methodologies → Machine learning; Machine learning approaches; Neural networks; Machine learning; Learning settings; Batch learning.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123846305","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
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