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Research on spectral turbidity compensation for water quality monitoring based on UV-Vis spectroscopy 基于紫外可见光谱的水质监测浊度补偿研究
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2639226
Zhengguo Huang, Caixia Wang
{"title":"Research on spectral turbidity compensation for water quality monitoring based on UV-Vis spectroscopy","authors":"Zhengguo Huang, Caixia Wang","doi":"10.1117/12.2639226","DOIUrl":"https://doi.org/10.1117/12.2639226","url":null,"abstract":"When using UV-Vis spectroscopy to detect water quality parameters, the scattering of suspended matter in water will cause the overall spectral curve to rise nonlinearly, which will affect the accuracy of the experimental results. Aiming at the problem that the spectrum is easily interfered by the light scattering of suspended matter, a total light scattering compensation method based on Mie scattering theory is studied to compensate for the interference of spectral turbidity. The extinction spectrum of , and then differentiated from the original spectrum, to achieve accurate compensation for turbidity interference, and this method does not require prior data support, which can improve the detection accuracy of organic matter content by UV-Vis spectroscopy.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131464254","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
MOOC recommendation algorithm based on learning process sequence modeling and quantitative analysis 基于学习过程序列建模和定量分析的MOOC推荐算法
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2639275
Fen He, Huili Xue, Rongxia Wang
{"title":"MOOC recommendation algorithm based on learning process sequence modeling and quantitative analysis","authors":"Fen He, Huili Xue, Rongxia Wang","doi":"10.1117/12.2639275","DOIUrl":"https://doi.org/10.1117/12.2639275","url":null,"abstract":"MOOC platform is one of the most important data sources of educational big data, so the correlation analysis of MOOC learning behavior data has become a research hotspot in educational data mining and learning analysis. The purpose of this paper is to study the MOOC recommendation algorithm based on the learning process sequence modeling and quantitative analysis. Aiming at the problem of frustration caused by dropping classes in MOOC, this study improves the recommendation feature model, and proposes an adaptive process recommendation method. Based on the data modeling of MOOC learning process and quantifying the learning status, it realizes multi-feature adaptive trade-off recommendation, and achieves Reduce the purpose of dropping out. First, the traditional recommendation characterized by interest is improved, and a new feature model is adopted to reflect the learner's satisfaction needs and reduce frustration. Secondly, the influence of various similarity distances such as time distance and knowledge distance on learning features is considered to improve the recommendation accuracy. Finally, the recommendation results are evaluated. The experimental results show that when k1 is 10, the recall of MRSS reaches 0.42, and the accuracy rate is the best.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126276776","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 deep learning-based system for IoT intrusion detection 基于深度学习的物联网入侵检测系统
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2639322
Jianbin Ye, Bofu Liu
{"title":"A deep learning-based system for IoT intrusion detection","authors":"Jianbin Ye, Bofu Liu","doi":"10.1117/12.2639322","DOIUrl":"https://doi.org/10.1117/12.2639322","url":null,"abstract":"The Internet of Things devices has rapidly increased and been widely used in recent years. The era of the Internet of Everything is quietly coming, which puts forward higher requirements for the research on network traffic classification in the Internet of Things environment. However, traffic in the network layer and link layer is often ignored. This paper proposes a network traffic classification and feature extraction tool that covers multiple layers of network protocols to convert the original network traffic into digital features. With the features, two deep neural network models constructed were trained, and evaluation of their multiple indicators proved the effectiveness and superiority of our proposed intrusion detection system for IoT. It can achieve a classification accuracy of 98% and 97% of detection rate.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122273909","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
Research on deep neural network model construction and overfitting 深度神经网络模型构建与过拟合研究
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2639137
Tong Li, Hui Zhang, Linchang Fan, Hao Wang, Qian Liu
{"title":"Research on deep neural network model construction and overfitting","authors":"Tong Li, Hui Zhang, Linchang Fan, Hao Wang, Qian Liu","doi":"10.1117/12.2639137","DOIUrl":"https://doi.org/10.1117/12.2639137","url":null,"abstract":"In recent years, deep neural network has been widely used in image recognition, natural language processing, computer vision and other fields, but it is prone to overfitting during network training. To solve this problem, this paper uses TensorFlow2.0 framework to construct multilayer perceptron deep network for Fashion-MNIST dataset, and uses dropout algorithm to solve the overfitting problem in the process of network training. The research results show that the dropout algorithm is applied to the deep neural network, which can make the deep neural network model have strong generalization ability and can effectively solve the overfitting problem of the training network. The research on overfitting problem has important practical significance for reducing the identification error of deep network.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134040797","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
Tone error detection of continuous Mandarin speech for L2 learners based on TAM-BLSTM 基于TAM-BLSTM的二语学习者普通话连续语音声调错误检测
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2639121
Yizhi Wu, Tong Guan
{"title":"Tone error detection of continuous Mandarin speech for L2 learners based on TAM-BLSTM","authors":"Yizhi Wu, Tong Guan","doi":"10.1117/12.2639121","DOIUrl":"https://doi.org/10.1117/12.2639121","url":null,"abstract":"To effectively help second language (L2) Chinese learners to produce tones correctly in computer assisted language learning (CALL), tone recognition of continuous speech is necessary. Because of the complex tone variation in continuous speech, this paper proposed TAM-BLSTM tone recognition model. Firstly, the generation model, target approximation model (TAM) is used to simulate fundamental frequency (f0) from original f0 contour in the unit of prosodic words, and the TAM parameters for each Chinese character are derived. Then BLSTM model with attention mechanism is set up with input feature of the TAM parameters and basic acoustic features, such as statistical f0 parameters, vowel duration, to solve the problem of tone detection of Mandarin continuous speech. Finally, the trained tone detection model is applied to the tone error detection of the L2 learners. The experimental results with Biaobei corpus show that the accuracy of the feature set combined with TAM parameters is 2.3% higher than that of using basic acoustic features alone, and the overall accuracy of ATT-BLSTM network model is higher than that based on ATT-LSTM.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133361667","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 and challenges of deep neural network in fault diagnosis of aviation equipment 深度神经网络在航空设备故障诊断中的应用与挑战
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2640466
Sen Wang, Peng Li, Wei-hua Niu
{"title":"Application and challenges of deep neural network in fault diagnosis of aviation equipment","authors":"Sen Wang, Peng Li, Wei-hua Niu","doi":"10.1117/12.2640466","DOIUrl":"https://doi.org/10.1117/12.2640466","url":null,"abstract":"With the development of big data, artificial intelligence and other technologies, data-driven aviation equipment fault diagnosis and prediction technology has gradually become a research hotspot in the aviation field. Many typical intelligent algorithm models have been applied to this field. However, limited by the airborne embedded computing environment, there are still some problems in the deployment of intelligent prediction models represented by deep neural networks on aircraft. This paper summarizes and analyzes the research and application of typical deep neural networks such as convolutional neural networks in the field of aircraft fault diagnosis and prediction. Facing the airborne embedded environment, the current difficulties in deploying the deep neural network algorithm model in the airborne environment are analyzed. The development direction of the application of fault prediction and diagnosis algorithms represented by neural networks in the future is discussed.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132135629","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 ECDSA timing attack based on hidden Markov model 基于隐马尔可夫模型的ECDSA定时攻击研究
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2639130
Huihui Jia, Yuanyuan Yang, Haohao Song
{"title":"Research on ECDSA timing attack based on hidden Markov model","authors":"Huihui Jia, Yuanyuan Yang, Haohao Song","doi":"10.1117/12.2639130","DOIUrl":"https://doi.org/10.1117/12.2639130","url":null,"abstract":"Timing attack is a side channel attack method. Elliptic curve cryptography (ECC) is one of the most important publickey cryptography. In this paper, a new timing attack on the Elliptic Curve Digital Signature Algorithm (ECDSA) based on Hidden Markov Model (HMM) was presented. Precisely speaking, the Grover algorithm was used to retrieve the parts of the ephemeral key, and the Koblitz Curve K-409 which was recommended by NIST was attacked successfully. The experiment results showed that the attack could recover almost all the key bits in a few minutes by collecting only once timing dates, and was easy to experiment at a high success rate.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525131","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 novel CNN+LSTM classification model based on fashion-MNIST 基于fashion-MNIST的CNN+LSTM分类模型
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2639667
Yaran Ji
{"title":"A novel CNN+LSTM classification model based on fashion-MNIST","authors":"Yaran Ji","doi":"10.1117/12.2639667","DOIUrl":"https://doi.org/10.1117/12.2639667","url":null,"abstract":"Nowadays, Convolutional Neural Network (CNN) based image recognition is a popular research direction. This study uses the Fashion-Mnist dataset, which is more challenging than the Mnist dataset. aims to add Long short-term memory (LSTM) to the structure of CNN to create a hybrid model of CNN and LSTM, called CNN+LSTM model. This model is used to complete and optimize the image classification problem on Fashion-Mnist dataset. The final image classification accuracy of the obtained model is 91.36%, which still needs to be improved, but the accuracy results are better compared to the accuracy of other models.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134096278","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
Research on the actuality of GPS technology in deformation monitoring technology GPS技术在变形监测技术中的应用现状研究
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2640356
Ziqiong Ren, Haoze Yu
{"title":"Research on the actuality of GPS technology in deformation monitoring technology","authors":"Ziqiong Ren, Haoze Yu","doi":"10.1117/12.2640356","DOIUrl":"https://doi.org/10.1117/12.2640356","url":null,"abstract":"GPS technology has the characteristics of high precision, high sampling, real-time, and simultaneous determination of three-dimensional coordinates of points, which can not be compared with other monitoring technologies. It plays a very important role in deformation monitoring. Starting from the composition of GPS positioning system, this paper expounds the three components of GPS positioning system, as well as the association and coordination between each component of the work; then the GPS deformation monitoring mode and several error sources in the monitoring process are introduced. The advantages and disadvantages of GPS technique in deformation monitoring are analyzed and its application trend is predicted. GPS positioning technology is applied in all aspects of our life, creating a lot of social and economic value for us.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134000047","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
Optical temperature field reconstruction based on joint algorithm of ART and neural network 基于ART和神经网络联合算法的光学温度场重建
Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI: 10.1117/12.2640346
Jie Chen, Siyi Liu
{"title":"Optical temperature field reconstruction based on joint algorithm of ART and neural network","authors":"Jie Chen, Siyi Liu","doi":"10.1117/12.2640346","DOIUrl":"https://doi.org/10.1117/12.2640346","url":null,"abstract":"In order to obtain more accurate online information of boiler temperature field and achieve the purpose of real-time measurement and monitoring of flame temperature field distribution in the furnace, an algebraic reconstruction-neural network algorithm (ART-NN) based on optical tomography measurement was proposed. The algorithm combines the advantages of Algebraic Reconstruction Algorithm (ART) and BP neural network. Using this algorithm in the case of adding random errors, a variety of classical temperature fields are numerically simulated. The results show that the stability and reconstruction results of the ART-NN algorithm are better than those of traditional algorithms such as ART and TSVD under the same error level. Optical tomography temperature field measurement provides an efficient method.","PeriodicalId":336892,"journal":{"name":"Neural Networks, Information and Communication Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133171321","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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