2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)最新文献_第6页

Aircraft engine remaining life prediction method with deep learning 基于深度学习的飞机发动机剩余寿命预测方法

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930216

Ye Zhu, Zhiqiang Liu, Zhenjie Luo, Chenglie Du, Hao Wang

{"title":"Aircraft engine remaining life prediction method with deep learning","authors":"Ye Zhu, Zhiqiang Liu, Zhenjie Luo, Chenglie Du, Hao Wang","doi":"10.1109/AICIT55386.2022.9930216","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930216","url":null,"abstract":"The prediction of the remaining life of aircraft engines plays an indispensable role in engine health management, and is of great significance to ensuring flight safety and improving maintenance efficiency. This paper proposes a life prediction model combining convolutional neural network and long short-term memory network in order to solve the problems of difficult model establishment and low calculation accuracy in aircraft engine RUL prediction. Different from the conventionally used single neural network, the proposed ensemble model can combine the advantages of both networks, using convolutional neural network to extract high-level spatial features in the data and long short-term memory network to extract temporal features. Validated on the N-CMAPSS public data set provided by NASA, and compared with a single convolutional neural network and long short-term memory network algorithm, the experimental results show that the accuracy of the prediction results of this method is better than that of a single model, which proves the proposed model. It can fully mine the information contained in the data.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132222134","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

Brain Tumor Type Recognition Algorithm Fused with Double Residual Structure and Attention Mechanism 融合双残差结构和注意机制的脑肿瘤类型识别算法

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930300

Shubin Yang, Feng-ge Wang, Chunlin Dong

{"title":"Brain Tumor Type Recognition Algorithm Fused with Double Residual Structure and Attention Mechanism","authors":"Shubin Yang, Feng-ge Wang, Chunlin Dong","doi":"10.1109/AICIT55386.2022.9930300","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930300","url":null,"abstract":"Brain tumor type recognition plays an important role in brain tumor diagnosis, In order to solve the problems of low accuracy and poor real-time performance of existing recognition algorithms, this paper combines double residual structure and attention mechanism to accurately and real-time brain tumor type recognition based on ResNet34. The first step is to reduce the number of parameters while enhancing the algorithm’s ability to extract multi-scale features by replacing the original convolution in the algorithm with a multi-scale convolution to avoid the loss of too large or too small features; Secondly, the loss of features due to deep layers is mitigated by changing the original structure to a double residual structure to prevent degradation of the algorithm. Finally, in order to enhance the weight of important features, an attention mechanism module is embedded in the sidechain residual structure to avoid the impact of redundant features on recognition accuracy. The extracted features are then passed to a classifier for accurate identification of the type of brain tumour. The improved algorithm was validated under the kaggle public dataset, and its accuracy reached 97.4% and the number of parameters was 7. 59M, which is 2% more accurate than the original model and 33% of the number of parameters, and outperformed some existing classical and mainstream algorithms. The experimental results show that the algorithm can accurately and quickly identify the type of brain tumour, which can help doctors in the subsequent treatment.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133168995","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 Neural Network in rod pumping’s Fault Diagnosis 神经网络在有杆泵故障诊断中的应用

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930160

Minghao Cao, Liu Jian

引用次数: 0

Research on Rapid Gas Classification and Incremental Learning Based on Sensor Array 基于传感器阵列的气体快速分类与增量学习研究

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930266

Jianyi Zhong, Lei Cheng, Qing-Xue Zeng

{"title":"Research on Rapid Gas Classification and Incremental Learning Based on Sensor Array","authors":"Jianyi Zhong, Lei Cheng, Qing-Xue Zeng","doi":"10.1109/AICIT55386.2022.9930266","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930266","url":null,"abstract":"In the field of machine olfaction, odor recognition whose focus is to accurately classify odor information and improve efficiency, is one of the important research directions. In recent years, deep learning for gas identification has been used in some researches, which significantly improves the identification accuracy. As a variant of deep learning, incremental learning is widely used in the field of image classification and can effectively solve the problem of catastrophic forgetting in image classification. Incremental learning has not been practically used in gas identification research, this paper will try to use an incremental learning method to identify and classify multiple types of gases. The main objectives of the research are as follows: 1) The experimental data set was divided into two equal parts, a data preprocessing method was used to convert the sensor array data into gas images which would be the input of incremental learning. And the pre-part was trained and classified by the incremental learning network. 2) The latter part of the dataset was taked as a newly emerging gas dataset, which would be incrementally learned and classified by incremental learning model. This study used an open source gas dataset, applied machine learning and deep learning architecture to analyze and compare with the algorithm. The proposed supervised contrastive replay (SCR) was used for data training and classification in this study, achieving a 94.63% classification accuracy, and the training time is only 69.7s.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125637003","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

Self-attentive Semantic Segmentation Model Based On Generative Adversarial Network 基于生成对抗网络的自关注语义分割模型

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930225

Hongchang Yang, Jun Zhang

{"title":"Self-attentive Semantic Segmentation Model Based On Generative Adversarial Network","authors":"Hongchang Yang, Jun Zhang","doi":"10.1109/AICIT55386.2022.9930225","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930225","url":null,"abstract":"To address the problems that existing recognition networks rely on a large amount of labeled data and the perceptual field is limited to the local area of convolution, and the lack of understanding of contextual information, this paper proposes a self-attentive semantic segmentation method based on generative adversarial networks. The method is based on generating adversarial networks, constructing semantic segmentation networks and discriminators, where the semantic segmentation network uses Resnet101 as the backbone to connect the spatial pyramid pooling module of PSPNet and adopts the cross-attention method in order to overcome the problem of too many parameters of classical attention models. The model was simulated in the publicly available PASCAL VOC 2012 dataset, and the results showed that the MIoU of the model reached 73.1%, 74.4%, and 75.1% for 1/8, 1/4, and 1/2 with labels, respectively, in the first semi-supervised experiments without improving the segmentation network compared to the control group, which were 3.6%, 2.3%, and 1.3% higher, respectively. The MIoU value reached 75.4% after improving the segmentation network, which proved the superiority and effectiveness of this model.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121469394","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

Lightweight Semantic Segmentation Network Based on DeepLabV3+ 基于DeepLabV3+的轻量级语义分割网络

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930215

Yanfei Chen, Chao Zhou, Zhangchen Yan, Tiange Huang, G. Wang, Jinhu Hu

{"title":"Lightweight Semantic Segmentation Network Based on DeepLabV3+","authors":"Yanfei Chen, Chao Zhou, Zhangchen Yan, Tiange Huang, G. Wang, Jinhu Hu","doi":"10.1109/AICIT55386.2022.9930215","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930215","url":null,"abstract":"Embedded mobile devices have limited computing power and insufficient running memory, and it is difficult to deploy high-precision, high-complexity and time-consuming semantic segmentation models. We propose a lightweight semantic segmentation model based on DeepLabV3+. This model optimizes the original DeepLabV3+ model from the perspective of reducing the amount of parameters and ensuring segmentation accuracy. The original backbone network is replaced by the MobileNetV2 network with lower parameters and computational complexity to speed up model inference. We design a 3-branch parallel structure and introduce a Semantic Embedding Module (SEB) to enhance low-level feature map semantic information and pixel point feature representation. The model adds a recurrent cross-attention mechanism module (RCCA) to capture the global correlation of all pixels and obtain dense contextual information. The model achieves 74.81% Mean IoU on the mixed dataset consisting of PASCAL VOC 2012 and Semantic Boundaries Dataset, with a parameter size of 8.27MB. The comprehensive performance of the model is better than that of networks such as SegNet, BiSeNetV2 and ENet, and a good balance is achieved between segmentation accuracy and model complexity.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122679560","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

SOC Estimation Of Energy Storage Power Station Based On SSA-BP Neural Network 基于SSA-BP神经网络的储能电站荷电状态估计

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930242

Yuchen Lu, Liu Jian

引用次数: 0

Research on the Obstacle Avoidance System of Restaurant Delivery Robot 餐厅送餐机器人避障系统研究

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930201

Jingcheng Fang, Shihong Qin, Hang Zhou

引用次数: 2

Design and Implementation of Fall Detection Equipment for the Elderly Based on NB-IoT 基于NB-IoT的老年人跌倒检测设备的设计与实现

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930275

Xiuwei Fan, Zicheng Li, Lei Zhang

{"title":"Design and Implementation of Fall Detection Equipment for the Elderly Based on NB-IoT","authors":"Xiuwei Fan, Zicheng Li, Lei Zhang","doi":"10.1109/AICIT55386.2022.9930275","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930275","url":null,"abstract":"In today’s society, the phenomenon of elderly people getting lost and accidentally falling often occurs, and the death caused by falling without timely assistance has become a public concern. In order to solve this problem, a low-power fall detection wearable device for the elderly based on Narrowband Internet of Things (NB-IoT) technology is designed and developed. The device uses STM32 MCU as the control core, and collects position information and motion status through ATK1218-BD positioning module and MPU6050 acceleration sensor respectively. When an elderly person falls, the device will immediately upload the fall alarm signal and location information to the OneNET cloud platform through the M5310A 4G wireless transmission module. The cloud platform can notify the elderly’s family by SMS through the mobile phone APP, so that the family can provide timely assistance to the elderly. Compared with traditional fall detection devices, the device is not only much smaller in size and power consumption, but also can achieve richer functions such as remote positioning and tracking, fall detection, and one-touch call for help. The experimental results prove that this device provides more reliable security for the health monitoring of the elderly.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122114143","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

A Novel Method for Online Detection of FeO Content in Sinter 烧结矿中FeO含量在线检测的新方法

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930289

C. Shao, Zicheng Li, Chenxing Guo

引用次数: 0