2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)最新文献_第3页

Electronic Nose System implemented on ZYNQ Platform for Fruits Freshness Classification ZYNQ 平台上的电子鼻系统用于水果新鲜度分类

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082522

Yuan Huang, Xudong Ren, Yudong Wang, Dongbo Sun, Lei Xu, Feng Wu

{"title":"Electronic Nose System implemented on ZYNQ Platform for Fruits Freshness Classification","authors":"Yuan Huang, Xudong Ren, Yudong Wang, Dongbo Sun, Lei Xu, Feng Wu","doi":"10.1109/ITNEC56291.2023.10082522","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082522","url":null,"abstract":"The electronic nose (e-nose) is a novel bionic detection system that is widely used in the food safety industry. Currently, most e-nose systems deploy the recognition algorithm on a PC, which limits the portability of the e-nose. This work implements an electronic nose based on the ZYNQ7000 hardware platform for fruit freshness classification. Software simulations were performed before the hardware implementation. According to the response characteristics of the sensor array, a transient feature extraction method is proposed to reduce the time required for recognition. Meanwhile, the principal component analysis-kernel fisher discriminant analysis (PCA-KFDA) model is proposed to reduce the dimensionality of the extracted features. Then, A combination of three-point descent and the Mann-Kendall trend test was designed to enable the hardware circuit to automatically detect the response onset point. The results show that based on the support vector machine classification algorithm, the PCA-KFDA reduction model has higher classification accuracy than the traditional principal component analysis-linear discriminant analysis model (PCA-LDA). Finally, we achieved 92.9% accuracy in fruit freshness on the ZYNQ7000 platform.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249145","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

The early warning model of HFMD which is implemented by the multivariable deep learning neural network 采用多变量深度学习神经网络实现手足口病预警模型

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082278

Yiwan Lai, Peishun Liu, Fucheng Yang, H. Duan, Feifei Li, Wenqiang Ge, Zuohao Li

{"title":"The early warning model of HFMD which is implemented by the multivariable deep learning neural network","authors":"Yiwan Lai, Peishun Liu, Fucheng Yang, H. Duan, Feifei Li, Wenqiang Ge, Zuohao Li","doi":"10.1109/ITNEC56291.2023.10082278","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082278","url":null,"abstract":"At present, many neural networks have achieved good results in disease prediction. For hand foot mouth disease(HFMD), mastering its epidemic law can provide scientific basis for effective formulation of prevention and control measures. However, most existing models use SIER infectious disease dynamics model, seasonal difference moving ARIMA or RNN, LSTM and other traditional networks to predict, and do not take climate and other factors into account. In this paper, firstly, a new data set is established, and BiLSTM is used to predict hand, mouth and foot disease. In addition, climate factors are taken into account in the prediction process. Thirdly, we conducted the Controlled experiment with the traditional models to calculate their MAE, MSE, RMSE, MAPE and other evaluation values. And the experiment shows that robustness of BiLSTM model in HFMD is better than these models. Finally, we analyzes the model from the perspective of time step, and sets the time step to 7 days, 14 days, and 21 days. It is found that when the time step is 14 days, the prediction performance is the best. Finally, we also made a comparative analysis through ablation experiments, and found that the HFMD dataset with meteorological factors was better than the HFMD dataset without meteorological factors in prediction accuracy.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130225610","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 and Design on Architecture for Big Data Platform in Power Grid Dispatching and Control System 电网调度控制系统大数据平台体系结构研究与设计

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082357

Qiong Feng, Fangchun Di, Ruili Ye, Lin Xie, Yan Wang, Lei Tao, Yunhao Huang, Dapeng Li, Chaohan Feng

引用次数: 1

Research on APF Based on Adaptive Analysis Instantaneous Reactive Power Harmonic Detection Method 基于自适应分析的有源滤波器瞬时无功谐波检测方法研究

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082334

Jun Yi, Yazhou Gao, Mengmeng Wang, Jiangyu Zhu, Jin Ma, Wenchao Shi, Haoliang Wang

引用次数: 1

A Contrast-Enhanced Graph Neural Network Recommendation Algorithm 一种对比度增强图神经网络推荐算法

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082290

Jialiang Liu, Xiao-Sheng Cai, Qingsong Zhou

引用次数: 0

Application of Fuzzy Neural Networks for Intelligent Irrigation of Wheat 模糊神经网络在小麦智能灌溉中的应用

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082703

Fangchao Ming, Jun Mou, Yuanhui Cui

引用次数: 0

A Maximum Power Point Criterion of MPPT and its Application MPPT最大功率点判据及其应用

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082306

Linlin Liu, Shulin Liu

引用次数: 0

Deep Retinex image enhancement algorithm under weak Light Conditions 弱光条件下深度视网膜图像增强算法

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082369

Jiu-long Zhao, Zi-Yuan Chen, Hong-yue Jiang, Qian Zhang

引用次数: 0

Small Photoresist Defect Samples Augmentation Based on Generative Adversarial Network 基于生成对抗网络的小光刻胶缺陷样本增强

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082214

Guang Yang, Zhihang Li, Zhijia Yang, Shuping Cui

引用次数: 1

Dual channel Chinese sentiment analysis of characters and words based on deep learning 基于深度学习的双通道汉语字词情感分析

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI: 10.1109/ITNEC56291.2023.10082296

Shuyi Zhao, Fuping Yang

{"title":"Dual channel Chinese sentiment analysis of characters and words based on deep learning","authors":"Shuyi Zhao, Fuping Yang","doi":"10.1109/ITNEC56291.2023.10082296","DOIUrl":"https://doi.org/10.1109/ITNEC56291.2023.10082296","url":null,"abstract":"In recent years, sentiment analysis has been a hot research topic in the field of natural language processing. In this field, in view of the complex Chinese semantics in Chinese sentiment analysis tasks, the traditional sentiment analysis methods have insufficient effective information extraction and low classification accuracy, and there is room for further improvement in disambiguation. This paper proposes a sentiment analysis model (CWBCNN-Att) based on the fusion of Chinese character vectors and word vectors and an attention mechanism. Among them, the dimension of characters is added to the traditional word-based analysis method, which can extract richer local information, alleviate the problem of unregistered words in the vocabulary, and reduce the problem of ambiguity in Chinese texts to a certain extent; Secondly, this paper uses two parallel and independent bidirectional LSTM and GRU layers to extract the feature information of words respectively to speed up the processing; Furthermore, an attention mechanism is applied to the output of the bidirectional layers of our model to emphasize different weights for different words. To reduce the dimension of features and extract location-invariant local features, we utilize convolution and pooling mechanisms. The experimental results show that the method has excellent performance in terms of accuracy and recall.","PeriodicalId":218770,"journal":{"name":"2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115823531","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