2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)最新文献_第4页

Research on Sentence Embeddings for Text Matching through Multiview Interactive Features 基于多视图交互特征的句子嵌入文本匹配研究

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105673

Xiaohui Liu, Fumin Chen, Yong Hu, Xinjian Li

{"title":"Research on Sentence Embeddings for Text Matching through Multiview Interactive Features","authors":"Xiaohui Liu, Fumin Chen, Yong Hu, Xinjian Li","doi":"10.1109/NNICE58320.2023.10105673","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105673","url":null,"abstract":"In this paper, we propose a sentence embedding for text matching method through multi-view interactive features: SBERT-Inter. This method is improved from the Sentence-BERT(SBERT) model based on semantic matching. Existing text matching models including representation-based methods get the vector representation of two sentences and calculate the similarity, but the interaction between sentences is lost. Or based on the interactive method to splice two sentences and directly output the similarity, but not through the sentence representation. Aiming at the problems of the two methods, a text matching method based on representation and interaction is proposed, which combines more fine-grained interaction modules after obtaining the representation features, through the multi-view and multi-perspective fusion of sentence and token features such as embedding difference, embeddings similarity, and token's self-attention, we can better learn the fine-grained interactive features between embeddings and we can get better semantic representation. It improves the matching performance between sentence pairs. On the public dataset semantic textual similarity benchmark(STS-B), the Spearman correlation of this method is enhanced by 6% compared with other sentence embedding methods.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"51 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113937632","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

Soil Nutrient Evaluation System Based on Improved Recurrent Neural Network 基于改进递归神经网络的土壤养分评价系统

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105705

Yingbo Bu, Xinjie Yu, Yao-Tai Yang

{"title":"Soil Nutrient Evaluation System Based on Improved Recurrent Neural Network","authors":"Yingbo Bu, Xinjie Yu, Yao-Tai Yang","doi":"10.1109/NNICE58320.2023.10105705","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105705","url":null,"abstract":"Soil nutrients are a comprehensive reflection of soil characteristics, and this paper proposes a recurrent neural network method with a fusion mechanism to construct a soil nutrient evaluation model in this area. Soil nutrient indicators are used as the input of the neural network, and soil nutrient levels are used as the output. The results obtained by the recurrent neural network are better through experimental simulation training and prediction, and the experimental comparison with related neural network models shows that the grade results obtained by the recurrent neural network are more accurate, and the establishment and prediction of this model provides a new way for soil nutrient fertility grade evaluation, and the MSE 0.031 of the fused recurrent neural network proposed in this paper is smaller than the mean square error value of the traditional model. The decision coefficient R2 of 0.978 is closer to 1 compared with other models, indicating that the initial weights and thresholds of the recurrent neural network optimized using the algorithm of this paper can effectively improve the prediction accuracy and fitting performance of the model.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121488282","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

BeiDou Short Message Transmission Method Based on High-quality Image Compression and Reconstruction 基于高质量图像压缩与重构的北斗短报文传输方法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105730

Nanxu Gong, Zijun Li, Jie Tan, Hu Chen

{"title":"BeiDou Short Message Transmission Method Based on High-quality Image Compression and Reconstruction","authors":"Nanxu Gong, Zijun Li, Jie Tan, Hu Chen","doi":"10.1109/NNICE58320.2023.10105730","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105730","url":null,"abstract":"Limited by bandwidth, Beidou short message system has certain limitations in the application of image transmission. To improve the problem, this paper proposes a Beidou short message transmission method based on high-quality image compression and reconstruction. We introduce the current mainstream deep learning framework based on variational autoencoders as our baseline. In order to achieve high-quality image compression and reconstruction, firstly, the attention mechanism is added to make the network automatically pay attention to beneficial information. Secondly, the image quality discriminator is designed that can provide the image definition score explicitly and is differentiable. Furthermore, based on the discriminator, a two-stage training method is proposed: first, the discriminator is trained with the data set synthesized by the Gaussian Blur, then the weights of the discriminator are fixed and the output of the discriminator is used to guide the network to generate images with high quality. The effectiveness of the method is verified by a series of experiments.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123026252","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

Short-term PV Power Prediction Based on Bi-directional Gated Recurrent Unit Network and Adaptive Chirp Mode Decomposition 基于双向门控循环单元网络和自适应啁啾模式分解的光伏短期功率预测

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105683

Shunjiang Wang, Dianyang Li, Gang Liu, Zhaowei Ling, Duo Wang

{"title":"Short-term PV Power Prediction Based on Bi-directional Gated Recurrent Unit Network and Adaptive Chirp Mode Decomposition","authors":"Shunjiang Wang, Dianyang Li, Gang Liu, Zhaowei Ling, Duo Wang","doi":"10.1109/NNICE58320.2023.10105683","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105683","url":null,"abstract":"With the increasing proportion of installed PV capacity and access to the system, accurate short-term PV power forecasting is critical to maintain safe and stable operation of grids containing a high proportion of PV power sources. In addition, a single prediction model has limited ability to improve the accuracy. To address the above issues, this paper proposes a short-term PV power prediction method based on bi-directional gated recursive unit (Bi-GRU) network and adaptive chirp modal decomposition (ACMD). First, to reduce the complexity of the historical PV power time series data, the ACMD algorithm is used for signal decomposition. Then a Bi-GRU network is built for the prediction of the sequences after noise reduction processing. The model is validated using actual power output data from a PV plant in Xinjiang, China. By comparing with SVM, ELM and BiGRU prediction models, it is verified that the ACMD-BiGRU model proposed in this paper has better results and can effectively improve the short-term PV power prediction accuracy.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133449769","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 pavement marking recognition method based on MATLAB 基于MATLAB的路面标线识别方法研究

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105794

Ruchun Li, Xinxiu Zhang

引用次数: 0

Complex-Valued Relative Positional Encodings for Transformer 变压器的复值相对位置编码

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105716

Gang Yang, Hongzhe Xu

引用次数: 0

Cycling image encryption algorithm based on the new composite chaotic mapping 基于新型复合混沌映射的循环图像加密算法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105707

Chengshuai Li, Wanbo Yu

引用次数: 0

An Urban Road Semantic Segmentation Method Based on Bilateral Segmentation Network 基于双边分割网络的城市道路语义分割方法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105801

Linshan Du, Ye Zhang, B. Liu, He Yan

引用次数: 1

Subspace-based Optimization Method with Adaptive Total Variation for Microwave Imaging 基于子空间的微波成像自适应全变分优化方法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105668

Wenjin Zhu, Wanyu Li, Xing Jiang, F. Sun

引用次数: 0

Polynomial Improved Convolution Kernel Graph Network For Fault Diagnosis 基于多项式改进卷积核图网络的故障诊断

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105666

Huan Zhang, Zili Wang, Le-miao Qiu, Shuyou Zhang, Gaopeng Yang, Feifan Xiang

引用次数: 0