2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)最新文献_第2页

Swin Transformer Based Pyramid Pooling Network for Food Segmentation 基于Swin变压器的金字塔池化网络食物分割

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832133

Qiankun Wang, Xiaoxiao Dong, Ruimin Wang, Hao Sun

{"title":"Swin Transformer Based Pyramid Pooling Network for Food Segmentation","authors":"Qiankun Wang, Xiaoxiao Dong, Ruimin Wang, Hao Sun","doi":"10.1109/seai55746.2022.9832133","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832133","url":null,"abstract":"Food segmentation is critical to human health and is one of the elements of food computing that provides the basis for nutritional assessment as well as composition testing. Food image segmentation differs from general images in that it usually does not exhibit a unique spatial layout and common semantic patterns. Current food segmentation methods mainly utilize deep visual features of convolutional neural networks(CNN) to achieve image segmentation of food, which ignore the characteristics of food images and make it difficult to achieve the best segmentation performance. In this paper, we propose a Swin Transformer-based pyramid network to capture richer background and boundary information and adaptively combine local features with global features to solve the food image segmentation task. The pyramid pooling module(PPM) aggregates contextual information from different regions of the food image, thus improving the feature representation of global information. Secondly, the multi-scale features acquired by the PPM module are constructed into a feature pyramid, and the multi-scale features are weighted, and then richer edge information is extracted. Experiments are conducted on the FoodSeg103 dataset, and the results show that the method has better results compared with the traditional method, maximizing the details of edges and veins with significant improvements.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130509361","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: 3

An Automatic Staging Method for Early Cervical Cancer on MRI Images: A Pilot Study 早期宫颈癌MRI影像自动分期方法的初步研究

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832146

L. Xiong, Chunxia Chen, Wei Mao, Chenchen Li, Yongping Lin

{"title":"An Automatic Staging Method for Early Cervical Cancer on MRI Images: A Pilot Study","authors":"L. Xiong, Chunxia Chen, Wei Mao, Chenchen Li, Yongping Lin","doi":"10.1109/seai55746.2022.9832146","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832146","url":null,"abstract":"Objective: To automatically segment sagittal mag-netic resonance imaging(MRI) images of cervical cancer (CC) patients and calculate the Intersection over Union (IoU) value of segmentation results for staging CC. Methods: This work uses 51 stage IB MRI images and 27 stage IIA MRI images as a dataset, all of which were pathologically confirmed in patients with CC. All labels used for training were manually labeled by radiologists with many years of clinical experience. MRI images and corresponding labels are put into a semantic segmentation network (based on U-net) to segment the tumor, uterus and vagina, respectively. Finally, the IoU values between the tumor and the other two tissues are calculated and the appropriate threshold is selected to classify images. Results: The segmentation of the uterus has good performance with Intersection over Union (IoU) of 0.872 and Pix Accuracy(PA) of 0.923. The classifier uses the best diagnostic threshold for tumor and vagina as the main criteria and the best diagnostic threshold for tumor and uterus as an auxiliary judgment to obtain a final classification accuracy of 0.807, the precision of 0.846, and recall of 0.863. Conclusions: In this study, the segmentation results were used to calculate the IoU values between different tissues which can distinguish the early staging of CC. It can provide some reference for radiologists to determine the tumor staging on MRI images of CC patients.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127764434","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 Deep Learning Supported Sequential Recommendation Mechanism for Ban-Pick in MOBA Games 基于深度学习的MOBA游戏Ban-Pick顺序推荐机制

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832346

Yihong Shen, Jing Zhou, Weiguo Lin, Zhebin Feng

引用次数: 0

Research for UAV Path Planning Method Based on Guided Sarsa Algorithm 基于引导Sarsa算法的无人机路径规划方法研究

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832224

He Boming, Lin Wei, Mei Fuzeng, Fan Huahao

{"title":"Research for UAV Path Planning Method Based on Guided Sarsa Algorithm","authors":"He Boming, Lin Wei, Mei Fuzeng, Fan Huahao","doi":"10.1109/seai55746.2022.9832224","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832224","url":null,"abstract":"As unmanned aerial vehicles (UA V) are widely used in unknown and complex environments, their path planning capabilities face higher requirements. In many cases, UA V cannot obtain the environmental information of the target area in advance, and the reinforcement learning (RL) algorithm to solve this problem faces the problem of slow convergence speed. To solve this problem, a UA V path planning method based on guided Sarsa algorithm is proposed, which defines the return function based on position information and improves the status update strategy. Simulation results show that the proposed method can realize fast path planning of UA V in static environment. Compared with Q learning and unimproved Sarsa algorithm, the obtained path length is shortened by 18 steps and 2 steps. At the same time, after 2000 iterations, only the results obtained by this method have obvious convergence. The convergence speed of guided Sarsa algorithm is relatively accelerated. At the same time, the trajectory tracking process of UA V is realized by MAT LAB platform, which proves the practicability and feasibility of the algorithm.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114561551","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 Skip Connection Structure in Transformer 一种新颖的变压器箕斗连接结构

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832084

Xintao Xu, Yi Liu, Zhiyuan Zhao, Gang Chen, Zhigang Li, Huaxiang Lu, Yi Bian

{"title":"A Novel Skip Connection Structure in Transformer","authors":"Xintao Xu, Yi Liu, Zhiyuan Zhao, Gang Chen, Zhigang Li, Huaxiang Lu, Yi Bian","doi":"10.1109/seai55746.2022.9832084","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832084","url":null,"abstract":"Herein, a novel skip connection structure with double bypass is employed in Transformer. The structure introduces two linear components between the input and output. Moreover, it establishes layer normalization at the beginning of the bypass computation, enhancing the model prediction efficiency. Two methods are employed to deploy the novel skip connection structure in the Transformer. In one method, the Transformer feedforward layer used for machine translation tasks is considered as a branch of the skip connection structure. In the other method, the skip connection structure is introduced into the Transformer model used for binocular parallax calculation tasks. Experimental results show that the machine translation model with the novel skip connection structure is 2% better in BLEU index. Furthermore, in the binocular parallax calculation tasks, the EPE index of the Transformer model with the skip connection structure decreases by 3.8% compared with that of a comparison model. The Transformer with the novel skip connection structure achieves a 7.6% lower 3PX ERROR than the Transformer without the skip connection structure.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126562453","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 Production Management System for Bacillus Cereus Based on Wireless Sensor Network * 基于无线传感器网络的蜡样芽孢杆菌生产管理系统*

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832176

Jiayue Song

{"title":"A Production Management System for Bacillus Cereus Based on Wireless Sensor Network *","authors":"Jiayue Song","doi":"10.1109/seai55746.2022.9832176","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832176","url":null,"abstract":"According to this paper, the design of the production control system of bacillus cereus based on wireless sensors is proposed, with the aim of solving the growing market demand of bacillus cereus by standardizing the system control process. The host computer designs the main modules, it includes the parameter configuration, interface services, fermentation tank control process, video display and so on. The modules are designed through configuration software. In practice, the design tries to solve the control of human factors, such as personnel operation lag and irregular operation for the fermentation tank. At the same time, the corresponding parameters and control processes that need to be detected are proposed in the fermentation tank control system, which can provide a certain reference for other production control system developers. In summary, the design of the bacillus cereus induction system based on wireless sensors can enhance the accuracy and speed of bacillus cereus fermentation tank operations, improve the problems of untimely and error in personnel management, and at the same time, reduce production management costs under the premise of improving production efficiency.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128399280","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 Survey of Application of Artificial Intelligence Methods in SDN 人工智能方法在SDN中的应用综述

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832340

Fanhua Song, Donghong Qin, Chen Xu

引用次数: 1

From Local to Global: A Curriculum Learning Approach for Reinforcement Learning-based Traffic Signal Control 从局部到全局:基于强化学习的交通信号控制课程学习方法

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832372

Nianzhao Zheng, Jialong Li, Zhenyu Mao, K. Tei

引用次数: 1

Q-funcT: A Reinforcement Learning Approach for Automated Black Box Functionality Testing q函数:一种用于自动化黑盒功能测试的强化学习方法

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832177

Y. López, J. Colonna, Edson De Araujo Silva, Richard Hada Degaki, Javier Martinez Silva

引用次数: 2

G-Computation Demonstration in Causal Mediation Analysis with Multiple Mediators 多重中介的因果中介分析中的g计算论证

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832193

J. Zhang, Yuanchun Chen, Xiang Hu, Jiwei Hu, Lintao Yang

{"title":"G-Computation Demonstration in Causal Mediation Analysis with Multiple Mediators","authors":"J. Zhang, Yuanchun Chen, Xiang Hu, Jiwei Hu, Lintao Yang","doi":"10.1109/seai55746.2022.9832193","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832193","url":null,"abstract":"Parametric and semiparametric estimation methods are available for the case of multiple mediators in causal mediation analysis. However, in the presence of multiple exposure-mediators and mediator-mediator interactions, it is challenging to study the direct and indirect effects of multiple mediators. In this paper, we use G-computation to derive a decomposition of the overall effect that unifies the mediating and interacting effects when multiple mediators are present, including natural direct and indirect effects, direct effects of standard and random controls, and reference and mediating interaction effects. The final regression of potential outcomes using the exposure intervention variables was used to calculate the point estimates and obtain their confidence intervals (CI). The methods presented here accommodate exposure-mediator interactions and, to some extent, mediator-mediator interactions. These methods are applicable to binary, continuous mediators and binary, continuous, or count results in the case where the exposure is discrete. This framework can also be extended to complex multivariate and longitudinal mediator settings.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"38 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132581109","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