2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)最新文献_第10页

Load-Aware Computation Offloading with Latency Limitation in Mobile Edge Computing 移动边缘计算中具有延迟限制的负载感知计算卸载

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105679

Kang Xiong, Guanglun Huang, Jianming Liu, Minghe Zhang

{"title":"Load-Aware Computation Offloading with Latency Limitation in Mobile Edge Computing","authors":"Kang Xiong, Guanglun Huang, Jianming Liu, Minghe Zhang","doi":"10.1109/NNICE58320.2023.10105679","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105679","url":null,"abstract":"Mobile edge computing (MEC) has emerged as a promising computing paradigm that can provide computing resources to mobile devices. However, when there are massive tasks are not processed, the edge node will have excessive load, which will significantly impact the task execution and degrade the user experience of applications. Due to the dynamic nature of the load on the edge nodes, it is complex to make the best performance decision for each mobile device. In this paper, we investigate the optimization of resource allocation and task offloading decisions in MEC systems for non-divisible and latency-sensitive tasks, considering edge load dynamics and latency constraints. To determine offloading decisions and minimize the average system energy cost in a dynamic environment, we design a distributed offloading algorithm considering deep reinforcement learning. Specifically, we use long short-term memory to implement load prediction and combine reinforcement learning techniques (including priority replay buffers, double-DQN techniques, and dueling deep Q networks) to improve algorithm performance. Simulation results show that our proposed algorithm can better allocate resources to achieve lower average system energy consumption with guaranteed low latency compared to the three benchmark methods.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"46 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":"134166626","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

Monintoring Student Depression by Mobile Devices with Autoencoder Networks 基于自编码器网络的移动设备监测学生抑郁

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105715

Xiong Ge, Zhong Chen, Lin Li, Q. Long, Zhaohui Yuan

{"title":"Monintoring Student Depression by Mobile Devices with Autoencoder Networks","authors":"Xiong Ge, Zhong Chen, Lin Li, Q. Long, Zhaohui Yuan","doi":"10.1109/NNICE58320.2023.10105715","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105715","url":null,"abstract":"The timely assessment of mental health is difficult since we lack the objective measurements of symptoms, especially for the Covid-19 pandemic quarantined students. Fortunately, smart phones can capture the real-world data such as the GPS traces and the phone active time et.al that link the behavioral patterns to the mental health. However, recent studies are based on a very small size of participants and only collect fewer phone features, which means that the effective predicting models which require various features are hardly adopted. In this paper, we develop an android application to record multidimensional data of users as well as a PHQ-9 and a SAS questionary, and we distribute it to 176 college students to collect larger scale data when in quarantine period. To address the unprecise problem of handcrafted feature extraction, we design an autoencoder machine learning model to monitor the student mental health. Extensive experiments indicate that the performance of the proposed method improves its F-1 score for PHQ-9 and SAS by 5% and 6% to the state of the current studies, respectively.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"55 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132497059","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

SAR and Multispectral Image Fusion Based on Dual-Channel Hybrid Attention Block and Dilated Convolution 基于双通道混合注意块和扩展卷积的SAR与多光谱图像融合

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105680

FaTing Chong, Zhangyu Dong, Xuezhi Yang, QingWang Zeng

{"title":"SAR and Multispectral Image Fusion Based on Dual-Channel Hybrid Attention Block and Dilated Convolution","authors":"FaTing Chong, Zhangyu Dong, Xuezhi Yang, QingWang Zeng","doi":"10.1109/NNICE58320.2023.10105680","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105680","url":null,"abstract":"The fundamental task of image fusion is to extract image features. Due to the difference in channel dimensions between SAR (Synthetic Aperture Radar, SAR) images and Multi spectral (MS) images, it is difficult to fully extract and utilize the high frequency detail information of SAR images and the low frequency spectral information of MS images for existing algorithms to alleviate the detail loss and color distortion problems of fused images. This image fusion algorithm is based on two-channel hybrid attention and dilated convolution. Firstly, a two-channel network is used to extract high-frequency detail features and low-frequency spectral features of SAR and MS images, and then a dilated convolution is introduced to capture the image's context and channel context information, followed by mapping to the hybrid attention module for feature enhancement, and finally, the image is reconstructed by decoding network to fuse the images. And the loss function based on Spectral Angle Distance (SAD) is constructed, which can further alleviate the problems of spectral distortion and detail loss. Experiments show that the fusion network proposed in this paper has a more powerful feature extraction ability for images than other methods and is better than existing algorithms in subjective and objective evaluations.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"20 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":"132104854","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

Application of adaptive beamforming technology in underwater acoustic 自适应波束形成技术在水声中的应用

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105792

B. Yin, Jianqing Chen

引用次数: 0

An Integration method for calibration and positioning of space-based external radiation sources 天基外辐射源标定与定位的集成方法

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105688

Liang Yin, Hu Xie, Ying Luo, Zhe Su

引用次数: 0

BERTGACN: Text Classification by Combining BERT and GCN and GAT BERTGACN:结合BERT与GCN和GAT的文本分类

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105710

Yang Xue

{"title":"BERTGACN: Text Classification by Combining BERT and GCN and GAT","authors":"Yang Xue","doi":"10.1109/NNICE58320.2023.10105710","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105710","url":null,"abstract":"With the explosive growth of electronic data, text has become one of the most important information carriers, and it has become the most common form of data display. Text classification involves many fields such as artificial intelligence and pattern recognition, so it has important academic research significance and commercial value. How to make the computer accurately locate the effective information in the text and realize automatic classification has become one of the research hotspots. In this study, we propose the BERTGACN model, which combines the large-scale pre-training model and the double-tower structure Graph Neural Networks (GNNs) model for text classification tasks. The BERTGACN model creates a heterogeneous graph for representing documents and words as nodes, respectively. By jointly training the Bidirectional Encoder Representations from Transformers (BERT) module, Graph Convolutional Network (GCN) module and Graph Attention Network (GAT) module, the BERTGACN model effectively learns the structural information of the graph and the association information between nodes, which enhances its text classification ability. Experiments show that the BERTGACN model achieves better results than previous models in a wide range of text classification tasks.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"987 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120870230","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 power prediction of clean energy and operation optimization of clean energy supply system for typical regions 典型地区清洁能源电力预测与清洁能源供应系统运行优化研究

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105787

Qiang Mu, Haini Zhou, Shuai Zhang, Xiangchen Fan, Qingqing Chi, Anqi Liang, Yifeng Ding, Shuang Zeng

{"title":"Research on power prediction of clean energy and operation optimization of clean energy supply system for typical regions","authors":"Qiang Mu, Haini Zhou, Shuai Zhang, Xiangchen Fan, Qingqing Chi, Anqi Liang, Yifeng Ding, Shuang Zeng","doi":"10.1109/NNICE58320.2023.10105787","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105787","url":null,"abstract":"With the world energy shortage and the trend of global warming, the focus of energy is gradually shifting to clean energy. The promotion of clean energy represented by solar energy and wind energy and the development of efficient energy supply system and operation have gradually become the focus of attention. However, when large-scale photovoltaic power generation is integrated into the grid, it is easy to cause the paralysis of the power system. At the same time, due to the lack of effective energy system operation strategy, it is easy to cause the problem of resource waste and low energy utilization in typical regional clean energy supply system. Based on this, this paper first proposed a short-term photovoltaic power prediction method based on LSTM according to the characteristics of strong timing of photovoltaic power, predicted the irradiance data, and evaluated and compared the predicted effect. Then, a high-efficiency CCHP microgrid structure for typical areas is proposed, and the operation strategy of this system is optimized based on particle swarm algorithm. The experimental results show that the proposed short-term photovoltaic power prediction method based on LSTM has higher prediction accuracy than other prediction methods. At the same time, the micro-grid structure and the operation strategy of the design proposed in this paper have high economy, thus verifying the advanced nature of the work in this paper.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"114 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":"116536161","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

Fault Location of Cabled Seafloor Observation Network Based on Fault Feature and Fault Distance Relation Mining Using Deep Learning 基于故障特征和深度学习故障距离关系挖掘的海底电缆观测网故障定位

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105701

Shaoze Luan, Guang-si Li, Weiming Gan, Weiguang Xing

{"title":"Fault Location of Cabled Seafloor Observation Network Based on Fault Feature and Fault Distance Relation Mining Using Deep Learning","authors":"Shaoze Luan, Guang-si Li, Weiming Gan, Weiguang Xing","doi":"10.1109/NNICE58320.2023.10105701","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105701","url":null,"abstract":"Cabled seafloor observation networks (CSONs) have evolved over the last decade to provide abundant electric energy and data transmission to enable marine science. To ensure reliable operation of the CSONs, the technology for fault location with high speed and accuracy is essential. However, the existing methods for CSON fault location fail to deeply mine the relation between fault feature and fault distance, hence the location performance is relatively unsatisfactory. In this paper, we prove that there is relation between fault distance and transient current signal created by fault. Based on this, we propose a fault location method based on Fault feature and Fault distance Relation Mining, named FaRM. Firstly, we simulate CSON and acquire transient current signal by PSCAD. Then we employ variational mode decomposition (VMD) to mine the fault feature in the transient current signal. Finally, FaRM is proposed to exploit the mined fault feature to predict the fault distance. Experimental results demonstrate that the fault location performance of FaRM is better than the existing CSON fault location methods.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"22 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":"121927502","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

Machine Translation Based on Syntactic Structure 基于句法结构的机器翻译

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105743

Xiangjun Zhou, Siling Zhang

{"title":"Machine Translation Based on Syntactic Structure","authors":"Xiangjun Zhou, Siling Zhang","doi":"10.1109/NNICE58320.2023.10105743","DOIUrl":"https://doi.org/10.1109/NNICE58320.2023.10105743","url":null,"abstract":"Machine translation refers to the process of translating source language sentences into semantically equivalent target language sentences through computers, and is an important research direction in the field of natural language processing. Neural machine translation only needs to use neural networks to achieve end-to-end translation from source language to target language, which has become the mainstream direction of machine translation research. There are also some problems such as fluent but not faithful translation, difficulty in processing rare words, poor performance of low-resource languages, poor cross-domain adaptability, and low utilization of prior knowledge. Inspired by the research of statistical machine translation, it has become a hot topic in the field of neural machine translation to integrate linguistic information into the neural machine translation model and utilize the existing linguistic knowledge to alleviate the inherent difficulties faced by neural machine translation and improve the translation quality. According to the classification system of grammatical units, researches in this field can be divided into three categories: neural machine translation (NMT) integrating word structure information, neural machine translation integrating phrase structure information and neural machine translation integrating syntactic structure information. Current researches also focus on these three aspects. Firstly, the challenges and causes of neural machine translation are described, and then the research status and main results of neural machine translation based on language knowledge are introduced. At last, the questions in existing research are summarized, and the future research direction is prospected.","PeriodicalId":263114,"journal":{"name":"2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)","volume":"34 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":"127312838","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 adaptive rate algorithm in bee colony ad hoc network 蜂群自组网中自适应速率算法的研究

2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE) Pub Date : 2023-02-24 DOI: 10.1109/NNICE58320.2023.10105796

He Huang, Jiawei Su, Hongmin Zhang, Youping Mao

引用次数: 0