Fifth International Conference on Computer Information Science and Artificial Intelligence最新文献_第4页

An image analysis tool for cell migration assay with a large sample size 用于大样本量细胞迁移试验的图像分析工具

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667479

Di Yin, Hongbo Zhang, Shih-Mo Yang, R. Yin, Wenjun Zhang

{"title":"An image analysis tool for cell migration assay with a large sample size","authors":"Di Yin, Hongbo Zhang, Shih-Mo Yang, R. Yin, Wenjun Zhang","doi":"10.1117/12.2667479","DOIUrl":"https://doi.org/10.1117/12.2667479","url":null,"abstract":"Cell migration assay is the most common research approach for cell migration. Quantitative research and analysis are carried out by measuring the migration of cells into the region that is artificially created among confluent monolayer cells. To improve the efficiency and accuracy of the analysis, the software/tools were developed to assist the image analysis process. However, these software and tools are still at the stage of measuring a single sample, which cannot satisfy the requirement of large sample size for cell migration assay device. In this paper, an image analysis tool based on Fiji is developed, which can segment multiple samples from a scanned image and then analyze a single sample in batch. In addition, the screening function should be added for the application scenario of large sample size. The samples can be filtered according to different conditions to improve the consistency of experimental conditions. The results show that the developed analysis tool ATCA has high accuracy in identifying cell-free zones, with a difference of 2.3% from the tool WHST and 2.9% from manual operation. The analysis efficiency of this tool is 15 times that of manual operation.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121570785","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

Automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization 基于粒子群优化的支持向量机微震首点自动拾取方法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667714

Tieniu Li, Binxin Hu, Zengrong Sun, Feng Zhu, Hua Zhang, Quancheng Yang

{"title":"Automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization","authors":"Tieniu Li, Binxin Hu, Zengrong Sun, Feng Zhu, Hua Zhang, Quancheng Yang","doi":"10.1117/12.2667714","DOIUrl":"https://doi.org/10.1117/12.2667714","url":null,"abstract":"Automatic and accurate arrival time pickup of microseismic first-arrival waves is an important prerequisite for high precision microseismic source location. Aiming at the low efficiency of the traditional manual pickup method and the low accuracy of the long, short window energy ratio (STA/LTA) method commonly used in automatic pickup for low signal-to-noise ratio signals, an automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization is proposed. Firstly, according to the amplitude and energy of microseismic signal and the energy ratio of adjacent time, the signals are marked with different categories. Then the parameters are optimized by particle swarm optimization algorithm to construct the support vector machine model of microseismic first-arrival. Finally, the data is substituted to extract the microseismic first-arrival. The experiment is carried out with the microseismic monitoring data of underground roadway in a gold mine. The experimental results show that, under the condition of low SIGNal-to-noise ratio, the picking accuracy of the proposed method is 96.4%, the average pickup error is 3.9ms, and the picking accuracy and accuracy are better than STA/LTA method.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125140378","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

Continuous sign language recognition based on 3DCNN and BLSTM 基于3DCNN和BLSTM的连续手语识别

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667649

Hengbo Zhang, Daming Liu, Nana Fu

{"title":"Continuous sign language recognition based on 3DCNN and BLSTM","authors":"Hengbo Zhang, Daming Liu, Nana Fu","doi":"10.1117/12.2667649","DOIUrl":"https://doi.org/10.1117/12.2667649","url":null,"abstract":"Sign language recognition can make the communication between deaf mutes and healthy people more convenient and fast. In recent years, with the continuous development of deep learning, the research method of deep learning has also been introduced into the field of sign language recognition. Compared with the recognition of isolated words, the recognition of continuous sign language is more time-dependent. The current research still has shortcomings in recognition accuracy. Therefore, we proposed a continuous sign language recognition method based on 3DCNN and BLSTM. Based on the spatial feature information extracted by 3DCNN and the short-term temporal relationship established, the global temporal modeling of the video information of continuous sign language is carried out by using the bidirectional semantic mining ability of BLSTM. The CTC loss function is constructed to solve the problem of time series label misalignment. At the same time, we add the calculation of auxiliary loss function and auxiliary classifier. Experiments show that the auxiliary loss function and classifier can effectively reduce the error rate of the network. The word error rate of the continuous sign language recognition algorithm proposed in this paper on the large continuous sign language dataset RWTH-PHONEIX-Weather 2014 is as low as 23.5%, which is lower than the previous algorithm.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125644394","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

TransCGan-based human motion generator 基于transgan的人体运动发生器

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2668277

Wenya Yu

引用次数: 0

A method for wireless communication interference signal identification based on extreme learning machine 一种基于极限学习机的无线通信干扰信号识别方法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667713

Xiaozheng Liu, Yue Wang, Xiaofei Wang, Jian Geng

引用次数: 0

Ranking influential nodes by combining normalized degree centrality and fine-grained K-Shell 结合归一化度中心性和细粒度K-Shell对影响节点进行排序

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667305

Xiuyong Mao, Fan Yang, Kaiyu Fan, Weizhou Hu, Chungui Li

引用次数: 0

Weld defect recognition method based on improved DenseNet 基于改进DenseNet的焊缝缺陷识别方法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667731

Li Huadu, Luo Renze, Tang Xiang, Wu Yong, Li Yalong

引用次数: 0

Detection of violent crowd behavior based on mean kinetic streak flow 基于平均运动条纹流的暴力人群行为检测

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667803

Yin-Chang Zhou

引用次数: 0

Multi-scale context-aware segmentation network for medical images 医学图像的多尺度上下文感知分割网络

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667684

Qing Li, Yuqing Zhu

引用次数: 0

Embedding diverse features in latent space for face attribute editing 在潜在空间中嵌入多种特征进行人脸属性编辑

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667748

Rui Yuan, Xiping He, Dan He, Yue Li

{"title":"Embedding diverse features in latent space for face attribute editing","authors":"Rui Yuan, Xiping He, Dan He, Yue Li","doi":"10.1117/12.2667748","DOIUrl":"https://doi.org/10.1117/12.2667748","url":null,"abstract":"Face attribute editing, one of the important research directions in face image synthesis and processing techniques, aims to photorealistic editing single or multiple attributes of face images on demand using editing and generation models. Most existing methods are based on generative adversarial networks, using target attribute vectors to control the editing region or Gaussian noise as conditional input to capture texture details. However, these cannot better control the consistency of attributes in irrelevant regions, while the generation of fidelity is also limited. In this paper, we propose a method that uses an optimized latent space to fuse the attribute feature maps into the latent space. At the same time, make full use of the conditional information for additional constraints. Then, in the image generation phase, we use a progressive architecture for controlled editing of face attributes at different granularities. At last, we also conducted an ablation study on the selected training scheme further to demonstrate the stability and accuracy of our chosen method. The experiments show that our proposed approach, using an end-to-end progressive image translation network architecture, obtained qualitative (FID) as well as quantitative (LPIPS) face image editing results.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125549720","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