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Neural Network for Image Classification of Laryngeal Cancer 基于神经网络的喉癌图像分类
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00051
Fanfan Wu, Peizhe Wu, Yuanyuan Hou, Huiliang Shang
{"title":"Neural Network for Image Classification of Laryngeal Cancer","authors":"Fanfan Wu, Peizhe Wu, Yuanyuan Hou, Huiliang Shang","doi":"10.1109/INSAI54028.2021.00051","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00051","url":null,"abstract":"At present, convolution neural network has been applied to the classification of laryngeal cancer. An improved network model, RICN model, is constructed by combining Resnet model with Inception model. By using Resnet model, Inception model and RICN model to classify laryngeal cancer images, the performance of these three network models is evaluated and compared. The experimental data show that the improved RICN network model is superior to the traditional two network models in terms of accuracy, specificity and sensitivity. And the validity and robustness of the RICN network model are also verified.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134115076","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
An Intelligent Plant Landscape Design Method Driven by Knowledge-graph 基于知识图谱的智能植物景观设计方法
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00046
Jingwei Zhang, Zhenping Xie
{"title":"An Intelligent Plant Landscape Design Method Driven by Knowledge-graph","authors":"Jingwei Zhang, Zhenping Xie","doi":"10.1109/INSAI54028.2021.00046","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00046","url":null,"abstract":"With the general development of social economy, the quality of people's lives is constantly improving. Plant landscape is indispensable in urban gathering areas such as public parks and community squares. However, unreasonable design problems will cause a negative impact on the urban environment. Also, different scenes cause different requirements in the design concept and ecological setting technology for landscape designers. The intelligent landscape design method driven by the knowledge base proposed in this paper attempts to intelligently generate the layout of landscape plants and plant communities in a given scene. We build the knowledge base using the plant database, and take the data provided in the knowledge base as the basis and judgment condition for the plants' requirements for the environment. A rational inference and rule construction, in combination with the design principles of landscape architecture, is created to facilitate the arrangement of landscape plants.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134084591","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
Robust Channel Assignment for Hybrid NOMA Systems with Condition Number Constrainted DRL 条件数约束DRL的混合NOMA系统鲁棒信道分配
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00025
Jianzhang Zheng, Xuan Tang, Xian Wei, Liang Song, H. Muhsen, Adib Habbal
{"title":"Robust Channel Assignment for Hybrid NOMA Systems with Condition Number Constrainted DRL","authors":"Jianzhang Zheng, Xuan Tang, Xian Wei, Liang Song, H. Muhsen, Adib Habbal","doi":"10.1109/INSAI54028.2021.00025","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00025","url":null,"abstract":"The Hybrid Non-Orthogonal Multiple Access (NOMA) is an alternative solution for future multiple access techniques, and the performance of hybrid NOMA systems relies on the quality of channel assignment. Conventional optimization approaches rely on the perfect Channel State Information (CSI), which hinders the deployment of the Hybrid systems. Deep Reinforcement Learning (DRL) approaches are robust to uncertain environments, and have been applied to deal with the dynamic channel assignment in hybrid NOMA systems. In this paper, a novel DRL approach based on condition number constraint is proposed to further enhance the robustness of the model. The simulation results show that the proposed approach achieves higher average spectral efficiency under imperfect CSI, compared to unconstrained DRL approaches and conventional approaches. This is useful for critical infrastructure systems such as base stations that require a high degree of robustness.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123441057","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
NSGA-II Based Multi-objective Optimization Method for Power IoT Terminal Sensors 基于NSGA-II的电力物联网终端传感器多目标优化方法
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00056
Jun Zhang, Yang Hu, Chizhi Huang, Dongliang Wang, Lintao Deng, Lingzhe Meng, Zhibao Wang, Chengxin Pang
{"title":"NSGA-II Based Multi-objective Optimization Method for Power IoT Terminal Sensors","authors":"Jun Zhang, Yang Hu, Chizhi Huang, Dongliang Wang, Lintao Deng, Lingzhe Meng, Zhibao Wang, Chengxin Pang","doi":"10.1109/INSAI54028.2021.00056","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00056","url":null,"abstract":"In the IoT system of transmission, substation and distribution power with complex sensing nodes, the problem of taking into account the dynamic configuration of terminal sensor parameters and battery life in the sensing layer needs to be addressed. To address this issue, a multi-objective optimization method using NSGA-II genetic algorithm is presented for solving a multi-objective optimization model with communication distance and lifetime as optimization objectives to obtain multiple sets of parameter configurations for the transmit power, communication channel and sampling period of the terminal sensor, while using a fuzzy affiliation function to choose the optimal solution to achieve the optimal configuration of the parameters. The model and method are applied to the optimal configuration of parameters of temperature and inclination sensors, and the feasibility of the proposed method is confirmed.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995532","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
Millimeter Scale Localization of Auditory Event-Related Human Cerebral Cortex Based on Invasive EEG Analysis 基于有创脑电图分析的听觉事件相关人脑皮层毫米尺度定位
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00038
Kaiqiang Feng, Jingyi Liu, Shize Jiang, Liang Chen, Xinhua Zeng, Huiliang Shang
{"title":"Millimeter Scale Localization of Auditory Event-Related Human Cerebral Cortex Based on Invasive EEG Analysis","authors":"Kaiqiang Feng, Jingyi Liu, Shize Jiang, Liang Chen, Xinhua Zeng, Huiliang Shang","doi":"10.1109/INSAI54028.2021.00038","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00038","url":null,"abstract":"Human cerebral cortex has been divided into several regions, which are responsible for different neural activities. However, rapid and precise localization of event-related cortex areas has always been a basic theoretical problem in neuroscience research and clinical practice. In this study, 27 subjects were implanted with invasive electrodes in the brain for epilepsy treatment, and each position was unique. We induced and collected EEG signals based on the experimental paradigm composed of two kinds of name audio stimuli. After data preprocessing, we used the algorithm composed of RBF-SVM and EEGNet to classify and discriminate the two kinds of situations namely \"task & rest\" and \"myself & other\". After analysis, we found the cortex areas that judge whether to receive sound stimulation are different from those that recognize sound content, and the accuracy of the former is higher. Furthermore, the invasive method and algorithms can locate millimeter-scale event-related cortical points on the basis of locating the cerebral cortex region. We believe our results can make an important contribution to exploring the mysteries of the human brain and providing clinical suggestions.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124184084","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
Design of Transformer Fault Intelligent Diagnosis System 变压器故障智能诊断系统的设计
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00062
Ruliang Wu, Cuicui Li
{"title":"Design of Transformer Fault Intelligent Diagnosis System","authors":"Ruliang Wu, Cuicui Li","doi":"10.1109/INSAI54028.2021.00062","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00062","url":null,"abstract":"Transformer is an important equipment of power system, and its working condition can affect the safety of the power system. Therefore, the adoption of advanced technology to monitor the working condition of transformers is of great significance to the safe operation of the power system. The traditional manual empirical methods have low accuracy. This paper proposes an intelligent diagnosis method for transformer faults, which effectively combines the advantages of sparrow search algorithm (SSA) and support vector machine (SVM). The gas composition ratio in transformer oil is used as the system input of the diagnostic system, and the parameters of SVM are optimized by SSA. The experiments show that the intelligent diagnosis model proposed in this paper, with 100% accuracy and 35% improvement in accuracy, is an effective method that can be used for intelligent diagnosis of transformer faults with good application results.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116934113","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 Comparitive Study of 3D Digital Reconstruction for Indoor Static Scenes Based on RGB D Data 基于RGB D数据的室内静态场景三维数字重建比较研究
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00036
Wei Zhu, Di Li, Wei Ni, Dongfang Xie, Liang Song
{"title":"A Comparitive Study of 3D Digital Reconstruction for Indoor Static Scenes Based on RGB D Data","authors":"Wei Zhu, Di Li, Wei Ni, Dongfang Xie, Liang Song","doi":"10.1109/INSAI54028.2021.00036","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00036","url":null,"abstract":"3D digital reconstruction of indoor static scenes is a key requirement in many areasˈsuch as indoor navigation, augmented reality, heritage restoration and computer vision, etc. The popularity of current low-cost RGB-D sensors has opened new lines of research in indoor 3D reconstruction. We focus on an experimental review of state-of-the-art 3D digital reconstruction methods using RGB-D images. Specifically, we propose the research goals and core issues in this field, summarize the acquisition methods of RGB-D data and some popular RGB-D datasets, and divide the 3D reconstruction techniques based on RGB-D into geometry-based and primitive-based categories, in addition, to better compare current researches in the area, some existing methods are experimentally evaluated on the same test datasets. Finally, respect to the open research problems in this field are discussed.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116763384","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 Video-based Action Quality Assessment 基于视频的动作质量评价综述
2021 International Conference on Networking Systems of AI (INSAI) Pub Date : 2021-11-01 DOI: 10.1109/INSAI54028.2021.00029
Shunli Wang, Dingkang Yang, Peng Zhai, Qing Yu, Tao Suo, Zhan Sun, Ka Li, Lihua Zhang
{"title":"A Survey of Video-based Action Quality Assessment","authors":"Shunli Wang, Dingkang Yang, Peng Zhai, Qing Yu, Tao Suo, Zhan Sun, Ka Li, Lihua Zhang","doi":"10.1109/INSAI54028.2021.00029","DOIUrl":"https://doi.org/10.1109/INSAI54028.2021.00029","url":null,"abstract":"Human action recognition and analysis have great demand and important application significance in video surveillance, video retrieval, and human-computer interaction. The task of human action quality evaluation requires the intelligent system to automatically and objectively evaluate the action completed by the human. The action quality assessment model can reduce the human and material resources spent in action evaluation and reduce subjectivity. In this paper, we provide a comprehensive survey of existing papers on video-based action quality assessment. Different from human action recognition, the application scenario of action quality assessment is relatively narrow. Most of the existing work focuses on sports and medical care. We first introduce the definition and challenges of human action quality assessment. Then we present the existing datasets and evaluation metrics. In addition, we summarized the methods of sports and medical care according to the model categories and publishing institutions according to the characteristics of the two fields. At the end, combined with recent work, the promising development direction in action quality assessment is discussed.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116801757","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: 9
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