Systems Science & Control Engineering最新文献_第3页

Short-term load forecasting based on EEMD-Adaboost-BP 基于EEMD Adaboost BP的短期负荷预测

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-10-12 DOI: 10.1080/21642583.2022.2110539

Wenshuai Lin, Bin Zhang, Hongyi Li, Renquan Lu

引用次数: 1

Pedestrian detection based on YOLOv3 multimodal data fusion 基于YOLOv3多模态数据融合的行人检测

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-10-12 DOI: 10.1080/21642583.2022.2129507

Cheng Wang, Y. Liu, Fei-xiang Chang, Ming Lu

{"title":"Pedestrian detection based on YOLOv3 multimodal data fusion","authors":"Cheng Wang, Y. Liu, Fei-xiang Chang, Ming Lu","doi":"10.1080/21642583.2022.2129507","DOIUrl":"https://doi.org/10.1080/21642583.2022.2129507","url":null,"abstract":"Multi-sensor fusion has essential applications in the field of target detection. Considering the current actual demand for miniaturization of on-board computers for driverless vehicles, this paper uses the multimodal data YOLOv3 (MDY) algorithm for pedestrian detection on embedded devices. The MDY algorithm uses YOLOv3 as the basic framework to improve pedestrian detection accuracy by optimizing anchor frames and adding small target detection branches. Then the algorithm is accelerated by using TensorRT technology to improve the real-time performance in embedded devices. Finally, a hybrid fusion framework is used to fuse the LIDAR point cloud data with the improved YOLOv3 algorithm to compensate for the shortcomings of a single sensor and improve the detection accuracy while ensuring speed. The improved YOLOv3 improves AP by 6.4% and speed by 11.3 FPS over the original algorithm. The MDY algorithm achieves better performance on the KITTI dataset. To further verify the feasibility of the MDY algorithm, an actual test was conducted on an unmanned vehicle with Jetson TX2 embedded device as the on-board computer within the campus scenario, and the results showed that the MDY algorithm achieves 90.8% accuracy under real-time operation and can achieve adequate detection accuracy and real-time performance on the embedded device.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48016056","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: 4

Rotor position angle control of permanent magnet synchronous motor based on sliding mode extended state observer 基于滑模扩展状态观测器的永磁同步电机转子位置角控制

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-10-03 DOI: 10.1080/21642583.2022.2110540

Chao Wang, Bingyou Liu, Xuan Fan, Pan Yang

引用次数: 0

Dynamic modelling and optimal control of herd behaviour with time delay and media 具有时滞和媒介的群体行为动态建模与最优控制

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-09-26 DOI: 10.1080/21642583.2022.2123059

Zhenyong Li, Ting Li, Wei-jun Xu, Yan Shao

引用次数: 1

Air combat manoeuvre strategy algorithm based on two-layer game decision-making and the distributed MCTS method with double game trees 基于双层博弈决策和双博弈树的分布式MCTS方法的空战机动策略算法

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-09-26 DOI: 10.1080/21642583.2022.2123058

Qiuni Li, Fawei Wang, Zongcheng Liu, Yuqin Li

引用次数: 1

Fault detection in uncertain systems via proportional integral observer 基于比例积分观测器的不确定系统故障检测

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-09-26 DOI: 10.1080/21642583.2022.2123063

Lixiong Lin

引用次数: 0

Sentiment analysis based on Chinese BERT and fused deep neural networks for sentence-level Chinese e-commerce product reviews 基于中文BERT和融合深度神经网络的句子级中文电子商务产品评论情感分析

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-09-26 DOI: 10.1080/21642583.2022.2123060

Hong Fang, Guangjie Jiang, Desheng Li

{"title":"Sentiment analysis based on Chinese BERT and fused deep neural networks for sentence-level Chinese e-commerce product reviews","authors":"Hong Fang, Guangjie Jiang, Desheng Li","doi":"10.1080/21642583.2022.2123060","DOIUrl":"https://doi.org/10.1080/21642583.2022.2123060","url":null,"abstract":"Driven by the rapid development of Internet, more e-commerce product reviews are available on e-commerce platforms, which can help enterprises make business decisions. Currently, bidirectional encoder representations from transformers (BERT) applied in the embedding layer contributes to achieve promising results in English text sentiment analysis (SA). This paper proposes a novel model Chinese BERT with fused deep neural networks (CBERT-FDNN), extracting richer and more accurate semantic and grammatical information in Chinese text. First, Chinese BERT with whole word masking (Chinese-BERT-wwm) is used in the embedding layer to generate dynamic sentence representation vectors. It is a Chinese pre-training model based on the whole word masking (WWM) technology, which is more effective for Chinese text contextual embedding. Second, multi-channel and multi-scale convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are designed to capture further crucial features in the feature extraction layer. To obtain more comprehensive sentence attributes, these features are concatenated together. Last, the model is evaluated on 100,000 sentence-level Chinese e-commerce product reviews for sentiment binary classification. The accuracy and F1 score can achieve 94.37% and 94.34%, respectively. Compared with the baseline models, the experiments show that our proposed model has higher accuracy and better prediction performance.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41423007","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

Improved sliding mode control of mine filling slurry concentration based on preview information 基于预览信息的矿山充填浆液浓度改进滑模控制

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-09-20 DOI: 10.1080/21642583.2022.2123061

Weiqiang Tang, Haiyan Gao, Tianpeng Xu

引用次数: 0

Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones 基于高变异点区域分割的同时定位和映射中的动态重建

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-09-17 DOI: 10.1080/21642583.2022.2123062

Brayan Andru Montenegro, J. F. Flórez, Elena Muñoz

引用次数: 0

Identification technology based on geometric features of tooth print images 基于牙印图像几何特征的识别技术

IF 4.1

Systems Science & Control Engineering Pub Date : 2022-09-12 DOI: 10.1080/21642583.2022.2119440

Ning Wang, Jiafa Mao, Lixin Wang, Yahong Hu

{"title":"Identification technology based on geometric features of tooth print images","authors":"Ning Wang, Jiafa Mao, Lixin Wang, Yahong Hu","doi":"10.1080/21642583.2022.2119440","DOIUrl":"https://doi.org/10.1080/21642583.2022.2119440","url":null,"abstract":"Identity recognition technology is a type of technology that realizes identity verification based on certain biological characteristics. After entering the Internet era, this technology has become a popular research direction in the computer field. In this paper, the image of the tooth print is used as the biological feature to carry out the research on the identification algorithm. This paper adopts the target detection algorithm based on neural network to detect a single tooth imprint area of the target, build a target detection network. The experimental results show that the method has a good segmentation effect on the target area, and the accuracy rate is 91.66%. According to the contour features of the collected tooth print images, a set of tooth pore area ratio feature extraction methods are designed. To objectively evaluate the recognition and classification method, the support vector machine is used as the final classifier. The recognition accuracy rate is 94.09%, and the verification accuracy rate is 94.09%. The test accuracy rate is 91.46%, and the classification effect is excellent. This paper has made a lot of breakthroughs and obvious progress based on the previous research on the tooth impression model.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47423962","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