4th International Conference on Information Science, Electrical and Automation Engineering最新文献_第3页

Design of intelligent wheelchair control system based on speed planning 基于速度规划的智能轮椅控制系统设计

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2690059

S. Zeng, Xiaochong Tian

引用次数: 0

Research on the adjustable potential of flexible loads based on storage electric heating 基于蓄电加热的柔性负载可调电势研究

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689643

Yu Long, Yongli Wang, Wenjun Ruan, Mingyang Zhu, Z. Yan, Yunfei Zhang, Meimei Duan

引用次数: 0

Data generation strategy of power flow calculation for AC/DC hybrid system 交直流混合系统潮流计算的数据生成策略

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689819

Yaohui Huang, Zhiqiang Song, Jianzhong Xu, Xiufang Jia

引用次数: 0

Research on DEMATEL method based on three-parameter interval grey number and its application 基于三参数区间灰数的DEMATEL方法及其应用研究

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689338

Huabin Cheng, Ping Xiong

{"title":"Research on DEMATEL method based on three-parameter interval grey number and its application","authors":"Huabin Cheng, Ping Xiong","doi":"10.1117/12.2689338","DOIUrl":"https://doi.org/10.1117/12.2689338","url":null,"abstract":"The decision-making trial and evaluation of laboratory method (DEMATEL) can effectively analyze the relationship between the intrinsic factors of complex systems, so it is widely used in many fields. In this paper, based on a new ordered weighted average operator of three-parameter interval grey number (TPIGN-OWA), a novel DEMATEL model is given, which has three advantages: The first one is that the three-parameter interval grey number (TPIGN) can reflect the real intention of the decision maker more than the interval grey number. The second one is that the new TPIGN-OWA takes into account the weight of the two intervals assembly, thus, the intention of experts can be more accurately expressed. The last one is that in the decision-making process, it is not necessary for the decision-maker to have strong mathematical knowledge background, but only to give the corresponding TPIGN according to the scale of the importance of factors, which is easier for the decision-maker to operate. Finally, this method is applied to the evaluation of fire safety management, and its the effectiveness and practicability is verified.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125734564","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

Deep neural network for MIMO-SCMA detection MIMO-SCMA检测的深度神经网络

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689814

Shiwei Zhang, Wenping Ge

{"title":"Deep neural network for MIMO-SCMA detection","authors":"Shiwei Zhang, Wenping Ge","doi":"10.1117/12.2689814","DOIUrl":"https://doi.org/10.1117/12.2689814","url":null,"abstract":"This article introduces deep learning into the multiple-input multiple-output (MIMO) sparse code multiple access (SCMA) system and proposes a MIMO-SCMA detection scheme based on deep neural networks (DNN) to improve bit error rate (BER) performance. The DNN learns the codebook of each user through channel feature learning on different transmission antennas. The fully connected DNN is designed as the decoder at the receiving end, which does not require traditional multi-antenna detection and multi-user detection, and can obtain user data with one decoding operation. The encoder and decoder are trained using an end-to-end training method. All learning models of the DNN are generated offline and the learned models are used for online testing. In this model, the received signal and channel coefficients are set as input data, and the label corresponding to the transmitted symbol is set as output data for offline learning. After offline learning is completed, the model can be deployed online with fixed weights and biases. Through simulation experiments, the proposed DNN encoder-decoder method can reduce the BER and computational complexity of the receiver in the MIMO-SCMA system.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126169981","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 sea cucumber recognition network based on improved YOLOv5 基于改进YOLOv5的海参识别网络

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689413

Qian Xiao, Lide Zhao, Hao Chen, Qian Li

{"title":"A sea cucumber recognition network based on improved YOLOv5","authors":"Qian Xiao, Lide Zhao, Hao Chen, Qian Li","doi":"10.1117/12.2689413","DOIUrl":"https://doi.org/10.1117/12.2689413","url":null,"abstract":"A lightweight network based on YOLOv5 is proposed in this paper to improve the real-time detection ability of underwater targets for fishing gear and to solve the difficulty of deploying model algorithms on embedded devices. First, the Shuffle_Block module replaces the leading feature extraction network in YOLOv5, reducing parameters and improving the algorithm's inference speed. Second, this module is combined with depthwise separable convolution to construct the feature fusion Shuffle-PANet, significantly reducing network parameters and improving detection speed while ensuring accuracy. The proposed method in this paper has been verified to reduce the parameter count by 89% compared to the YOLOv5 source code while doubling the detection speed of the source code. Additionally, the weight file size is reduced by 83%. The mAP50 reaches 96.3%, which is only a 2% decrease compared to YOLOv5. The lightweight network proposed in this paper can recognize sea cucumbers well and has fast recognition speed and lightweight design characteristics. Shuffle-YOLOv5 has significant advantages compared to the original model and can complete real-time target detection on low-power embedded devices.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121009828","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

Behavior recognition algorithm based on motion capture and enhancement 基于动作捕捉和增强的行为识别算法

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689663

Yuqi Yang, Jianping Luo

{"title":"Behavior recognition algorithm based on motion capture and enhancement","authors":"Yuqi Yang, Jianping Luo","doi":"10.1117/12.2689663","DOIUrl":"https://doi.org/10.1117/12.2689663","url":null,"abstract":"Motion modeling and temporal modeling are crucial issues for video behavior recognition. When extracting motion information in two-stream network, the optical flow diagram needs to be calculated in advance and the end-to-end training cannot be realized. 3D CNNs can extract spatiotemporal information, but it requires huge computational resources. To solve these problems, we propose a plug-and-play motion capture and enhancement network (MCE) in this paper, which consists of a temporal motion capture module (TMC) and a multi-scale spatiotemporal enhancement module (MSTE). The TMC module calculates the temporal difference of the feature-level and captures the key motion information in the short temporal range. The MSTE module simulates long-range temporal information by equivalent enlarging the temporal sensitive field through multi-scale hierarchical sub-convolution architecture, and then further enhances the significant motion features by referring to the maxpooling branch. Finally, several experiments are carried out on the behavior recognition standard datasets of Something-Something-V1 and Jester, and the recognition accuracy rates are 49.6% and 96.9%, respectively. Experimental results show that the proposed method is effective and efficient.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122661132","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

Based on reverse mirror research on lossless transmission technology of modulated signal in wireless spread spectrum communication 基于反向镜像的无线扩频通信中调制信号无损传输技术研究

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689561

X. He, Shengbo He

引用次数: 0

Multi-objective quintuple polynomial trajectory optimization for unmanned underwater vehicles based on waypoint navigation 基于航路点导航的无人潜航器多目标五元多项式轨迹优化

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689506

Tianyu Zhang, Yongliang Li

引用次数: 0

GOGCN: using deep learning to support insertion of new concepts into gene ontology GOGCN:利用深度学习支持将新概念插入基因本体

4th International Conference on Information Science, Electrical and Automation Engineering Pub Date : 2023-08-10 DOI: 10.1117/12.2689526

Cheng Chen, Lingyun Luo

{"title":"GOGCN: using deep learning to support insertion of new concepts into gene ontology","authors":"Cheng Chen, Lingyun Luo","doi":"10.1117/12.2689526","DOIUrl":"https://doi.org/10.1117/12.2689526","url":null,"abstract":"Many biomedical ontologies develop regularly and change over time. An ontology new release will update its data, containing that fix some errors in the previous version and add many new concepts to adapt to the development in the domain. Insertion of new concepts into their proper positions on a terminology is a challenging problem in the automatic enrichment of ontologies. In the past, the new concepts are always created by domain experts. Then the experts will run a traditional classifier or manual operation to insert the new concepts in proper place. With the development of technology, the methods based on Machine learning (ML) have been proposed to help terminology researchers to develop and maintain the ontologies. We propose an new approach that is based on providing only the concept name and using a Graph Convolutional Network (GCN) aggregated the sub-string neighbor information learning method. We chose a Bidirectional Long Short-term Memory Networks (Bi-LSTM) model as our classifier for the predicted task. We first tested this method within Gene Ontology (GO) 2020 January release and achieved an average of 89.68% precision and an F1 score of 0.9081 in task of predicting direct IS-A links. In comparing the January 2020 release with the March 2022 release, we predicted the links related to new concepts, our average Accuracy score was 0.6996.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131991187","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