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2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)最新文献

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Channel Key Extraction Scheme Using Asymmetric Intelligent Reflecting Surface Transformation 基于非对称智能反射面变换的信道密钥提取方案
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00014
Yuwei Gao, Dengke Guo, Dongtang Ma, Jun Xiong
{"title":"Channel Key Extraction Scheme Using Asymmetric Intelligent Reflecting Surface Transformation","authors":"Yuwei Gao, Dengke Guo, Dongtang Ma, Jun Xiong","doi":"10.1109/AINIT54228.2021.00014","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00014","url":null,"abstract":"In physical layer secret key generation (PLSKG), the threshold-based quantization scheme is usually used to convert the channel detection value into the initial key bits. However, the data near the threshold is easily disturbed by noise fluctuation, which will lead to an increase in the key disagreement rate(KDR). We proposed an IRS-assisted key generation scheme, which uses the asymmetric change of IRS to ameliorate the downlink channel environment, to reduce the key inconsistency after quantization. Monte Carlo simulation and numerical results showed that our proposed scheme is feasible.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121331662","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 the Impact of Carbon Trading Market on Electricity Emission Reduction Based on GM-BP Model 基于GM-BP模型的碳交易市场对电力减排的影响研究
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00100
Y. Hu, Yuanjie Xu, Tiantian Ye
{"title":"Research on the Impact of Carbon Trading Market on Electricity Emission Reduction Based on GM-BP Model","authors":"Y. Hu, Yuanjie Xu, Tiantian Ye","doi":"10.1109/AINIT54228.2021.00100","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00100","url":null,"abstract":"In order to achieve energy conservation and emission reduction goals, China has included \"carbon peak\" and \"carbon neutrality\" in its national strategy. Electricity is the industry with the largest carbon emissions in China, and active efforts to reduce electricity emissions have had a significant positive impact on the achievement of the \"dual carbon\" goal. Carbon emissions trading plays an important role in promoting the large-scale optimization of energy allocation in the power industry across the country. At present, reducing carbon emissions from electricity is still focused on technological upgrading and the promotion of new energy. This article conducts an in-depth study on the counter-control of indicator analysis and forecasting methods starting from the carbon trading market. Use the grey relational model to explore the correlation between the carbon trading market and electricity carbon emission reduction. Combined with the results of the electricity carbon emission prediction model based on the BP (back propagation) neural network, it provides a reference basis and reasonable suggestions for the rapid realization of the \"dual carbon\" goal.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122580967","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 End-to-end Question Answering Model Based on Semantic-enhancing Attention Mechanism 基于语义增强注意机制的端到端问答模型
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00072
Ruocheng Li
{"title":"An End-to-end Question Answering Model Based on Semantic-enhancing Attention Mechanism","authors":"Ruocheng Li","doi":"10.1109/AINIT54228.2021.00072","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00072","url":null,"abstract":"Task-oriented question answering dialogue systems have been an important branch of conversational systems for oral language, where they first understand the query requested by users, and the models are demanded to seek for answers within the context considering the query information. Previous work models the semantic and syntactic information without taking the interaction into consideration. In this paper, we propose an end-to-end model based on semantic-enhancing attention mechanism, which enables the model to focus more on a small part of the context and enhances the model capability of extracting the interactive information. Our experiments are based on the Stanford Question Answering Dataset (SQuAD) and the experimental result verifies how the proposed model improves on the dataset.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124538776","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
Tacotron Model and CNN in Virtual Reality for Cancer Diagnosis and Communication between Doctors and Patients 虚拟现实中的Tacotron模型与CNN用于癌症诊断与医患沟通
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00093
Sha Jin, Jiayi Li
{"title":"Tacotron Model and CNN in Virtual Reality for Cancer Diagnosis and Communication between Doctors and Patients","authors":"Sha Jin, Jiayi Li","doi":"10.1109/AINIT54228.2021.00093","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00093","url":null,"abstract":"Virtual reality is widely used in various fields, such as military, industrial and medical fields. CNN is prevalent when solving problems about image classification. NLP is the most useful model to realize speech synthesis. However, image classification is seldom combined with speech synthesis to be used in a realistic scene. In order to tackle this issue, this paper proposed a system, which combines image classification with speech synthesis organically. There are three steps used to build this system in this paper. First, this paper devises a model to classify whether the patients have skin cancer. It designs a CNN model to deal with the classification of images of skin, diagnosing whether the patient suffers from Melanoma. Second, the Tacotron model is included in this paper to implement speech synthesis, telling the diagnostic results obtained from image classification to the patients. Finally, a virtual reality environment is built to display a scene when a patient entering the hospital and then being diagnosed and getting treatment.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121766525","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 Lung Cancer Detection System Based on Convolutional Neural Networks and Natural Language Processing 基于卷积神经网络和自然语言处理的肺癌检测系统
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00076
Jiahao Chen, Qianli Ma, Weixin Wang
{"title":"A Lung Cancer Detection System Based on Convolutional Neural Networks and Natural Language Processing","authors":"Jiahao Chen, Qianli Ma, Weixin Wang","doi":"10.1109/AINIT54228.2021.00076","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00076","url":null,"abstract":"Lung Cancer has long been regarded as one of the most threatened diseases to human beings, and detecting early malignant tumors is of vital importance for treatment. Contemporarily, Radiology departments in hospitals usually have to deal with multiple CT images to carry out the detection, which is a huge workload for doctors. Here, we propose a novel system to help with lung cancer detection. Specifically, deep feature based convolutional neural networks (CNN) is applied to classify lung cancer tumors, realizing an accuracy of 88%. Moreover, a chatbot based on natural language processing (NLP) technology is embedded into the system to provide immediate knowledge and information. These results shed light on how doctors’ workload might be reduced to a considerable extent.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131649494","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: 2
Static scene target detection based on VIBE algorithm 基于VIBE算法的静态场景目标检测
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00047
Mingrui Yang, Qunyi Chu
{"title":"Static scene target detection based on VIBE algorithm","authors":"Mingrui Yang, Qunyi Chu","doi":"10.1109/AINIT54228.2021.00047","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00047","url":null,"abstract":"In this paper, we analyze the foreground target extraction problem of surveillance videos and establish various mathematical models for different types of surveillance videos: two-frame difference model, Single Gaussian Model (SGM) and VIBE model, and apply these models comprehensively to achieve the extraction of foreground targets in surveillance videos with different background characteristics. On the basis of foreground target extraction, the effective features of the image sequence are extracted to determine the abnormal behavior of the crowd in the video.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131177180","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 Research on Solving Airborne Gravity Anomaly Based on FIR Low-pass Filter 基于FIR低通滤波器的机载重力异常求解研究
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00131
Wei Zheng, Xuefeng Chen, Leibing Yan, Hui Wei
{"title":"A Research on Solving Airborne Gravity Anomaly Based on FIR Low-pass Filter","authors":"Wei Zheng, Xuefeng Chen, Leibing Yan, Hui Wei","doi":"10.1109/AINIT54228.2021.00131","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00131","url":null,"abstract":"Airborne gravity measurement is a new type of dynamic measurement technology to quickly obtain information on the earth’s gravity field. In addition to equipment requirements, the solution of airborne gravity anomalies is an important part of airborne gravity measurement data processing. Based on the study of the basic mathematical model of aerial gravity measurement, this study analyzes in detail the finite impulse response (FIR) low-pass filter method used for calculating aerial gravity anomaly and uses the window function to design the FIR low-pass filter to filter the field measurement data. The test results indicate that the designed FIR low-pass filter can suppress the noise interference of the measurement data and extract the gravity anomaly signal that meets the accuracy requirements.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359893","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
Synchronization of fiber optic differential protection data in power systems based on improved sensitivity analysis 基于改进灵敏度分析的电力系统光纤差动保护数据同步
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00105
Wei Li, Lixia Wang, Dawei Wang
{"title":"Synchronization of fiber optic differential protection data in power systems based on improved sensitivity analysis","authors":"Wei Li, Lixia Wang, Dawei Wang","doi":"10.1109/AINIT54228.2021.00105","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00105","url":null,"abstract":"The traditional method of synchronizing fiber optic differential protection data is synchronized by correcting the clock of the sampling device, which has the problems of long adjustment time and large error when applied to complex systems. To this end, a method for synchronizing fiber optic differential protection data in power systems based on improved sensitivity analysis is proposed. The improved sensitivity method is used to analyze and calculate the out-of-limit values in the power system, thus facilitating the compensation of transient and steady-state capacitive currents. After constituting the power system fiber-optic differential protection criterion, the data synchronization is realized with the premise of equal communication routing. Simulation experimental results show that the error of the proposed synchronization method is reduced by about 50%, and the method has higher sensitivity and superior performance.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115749809","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
Smoke Remote Monitoring Method for Environmental Fan Linkage System in Substation 变电站环境风机联动系统烟气远程监测方法
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00096
Jianzhong Shen, Shuai An, Chenjie Wang
{"title":"Smoke Remote Monitoring Method for Environmental Fan Linkage System in Substation","authors":"Jianzhong Shen, Shuai An, Chenjie Wang","doi":"10.1109/AINIT54228.2021.00096","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00096","url":null,"abstract":"Aiming at the problem of poor accuracy of traditional substation environmental smoke remote monitoring, a remote monitoring method of substation environmental fan linkage system is proposed. This article optimizes the smoke remote monitoring equipment, thereby improving the monitoring sensitivity. The smoke concentration level is further divided, and the smoke level warning setting of the fan linkage system is realized. On this basis, the remote smoke monitoring process of the substation environmental fan linkage system is optimized, thereby improving the efficiency of remote monitoring and early warning of substation smoke, and strengthening the effect of remote monitoring of substation environmental smoke. The experimental results show that the proposed remote monitoring method for the flue gas of the substation environmental fan linkage system has high monitoring accuracy in the actual application process.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124356363","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
Convolutional Neural Network Based Diagnosis System on Skin and Breast Cancers 基于卷积神经网络的皮肤癌和乳腺癌诊断系统
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00086
Ruipu Li, Yi Lu, Haoran Zhang
{"title":"Convolutional Neural Network Based Diagnosis System on Skin and Breast Cancers","authors":"Ruipu Li, Yi Lu, Haoran Zhang","doi":"10.1109/AINIT54228.2021.00086","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00086","url":null,"abstract":"Use of artificial intelligence in medicine makes a difference in diagnosis methods. A diagnosis system based on deep neural network can efficiently make predictions for many known diseases. Our study is to construct a cancer diagnosis system using CNN models. The cancer diagnosis system is capable of giving predictions on skin cancer and breast cancer with input images. The diagnosis model for skin cancer is AlexNet, and the model for breast cancer is VGGnet. Based on the two pre-trained CNN models, we use PyQt5 to develop the user interface and construct the diagnosis system. According to the test result, the skin cancer diagnosis model achieves about 80% accuracy, and the breast cancer model achieves about 85% accuracy. As for the diagnosis system, users can upload at most three images, select cancer type, and view the analysis results on the interface. In conclusion, our diagnosis system can accurately and efficiently present skin and breast cancer diagnosis results.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117327527","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
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