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2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)最新文献

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Research on the application of Internet of things technology in building intelligent teaching 物联网技术在智能教学建设中的应用研究
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778378
Dong Wang, Yong Ji, Honghe Wei
{"title":"Research on the application of Internet of things technology in building intelligent teaching","authors":"Dong Wang, Yong Ji, Honghe Wei","doi":"10.1109/ICSP54964.2022.9778378","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778378","url":null,"abstract":"In order to meet the needs of smart city construction, this paper will take the building intelligent engineering technology specialty as an example to carry out the teaching reform of the course \"Development and application of Internet of things system\" in higher vocational colleges. The course content has been adjusted and determined. The smart home system and other systems are designed. Taking a specific ultrasonic ranging project as an example, this paper explains the design and application process of the Internet of things system. The study of this course can enable students to master the basic knowledge and skills of the Internet of things and improve their professional ability.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121720478","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 Residual Learning of Deep CNN for Image Denoising 深度CNN残差学习图像去噪研究
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778434
Feida Gu
{"title":"Research on Residual Learning of Deep CNN for Image Denoising","authors":"Feida Gu","doi":"10.1109/ICSP54964.2022.9778434","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778434","url":null,"abstract":"Image denoising is a classical but still popular research topic. Removing noise from corrupted images is an indispensable step for many practical applications. Deep Learning for image denoising has shown favorable performance. Residual Learning of Deep CNN (DnCNN) is proposed for image denoising, and shows desired performance. In the paper, based on DnCNN, some hyperparameters are adjusted for better performance. In addition, a validation step is added during the training process, which allows us to observe the training process to avoid overfitting. With the validation step during the training process, a novel method of learning rate adjustment is introduced to help train the best model for the network. The results show the adjusted network has a better performance compared to the baseline of DnCNN.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130137765","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: 33
Magnetic Type Classification in Sunspot Group Based on Semi-supervised Learning and Knowledge Distillation 基于半监督学习和知识蒸馏的太阳黑子群磁型分类
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778594
Junhong Liu, Baoping Li, Zihui Luo
{"title":"Magnetic Type Classification in Sunspot Group Based on Semi-supervised Learning and Knowledge Distillation","authors":"Junhong Liu, Baoping Li, Zihui Luo","doi":"10.1109/ICSP54964.2022.9778594","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778594","url":null,"abstract":"Sunspot group, known as active solar regions, is the main sources of solar storms. The morphological and magnetic characteristics of solar active regions play a very important role in solar storm forecasting and is well described by Mount Wilson Sunspot Classification Scheme. The development of convolutional neural network methods in the field of image processing makes efficient magnetic type classification possible. In this paper, we propose a method based on semi-supervised learning and knowledge distillation for magnetic type classification in sunspot group. On the sunspot magnetic type classification dataset, our method achieves 95.14% total classification accuracy, 97.4%, 94.43% and 85.71% F1-scores of Alpha, Beta, and Beta-x types respectively.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133982814","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
Personal Image Classifier Based Handy Pipe Defect Recognizer (HPD): Design and Test 基于个人图像分类器的手持管道缺陷识别器(HPD):设计与测试
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778676
A. Moshayedi, Amir Khan, Shuxin Yang, S. M. Zanjani
{"title":"Personal Image Classifier Based Handy Pipe Defect Recognizer (HPD): Design and Test","authors":"A. Moshayedi, Amir Khan, Shuxin Yang, S. M. Zanjani","doi":"10.1109/ICSP54964.2022.9778676","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778676","url":null,"abstract":"Pipelines are known as a traditional solution for transporting various media such as gas, oil, and water. But pipes are always combined with the defects. Some of these defects are caused by the manufactory process, while some defects occur after installation due to the type of medium and environmental condition. Currently, various methods are used to detect these defects from industry to the site, but vision-based systems due to huge amount of data that can capture machine learning development algorithms have more demand. In this research paper Personal Image Classifier (PIC) is used as the machine learning method combine with the MIT APP inventor to make the handy system called Handy Pipe Defect assistant recognizer (HPD) to address the defect name and help the user to investigate and somehow correct their process of product in industry (as the production stage) or the possible change in usage place (as the maintenance stage). The HPD designed based on the hypothesis of having the handy, portable and available tool for the operator in industries as the quality control and site engineers. The HPD can classify the pipe defects, especially the welding type by taking the picture of the defect and give the user feedback. The HPD focuses on the welding defect with the manufactory source over 150 image number trained and tested with the real 28 sample image. The design process and training model for HPD described and result based on sample images from manufactory and real situation are shown the 100% defect name detection of the defect along with image quality affected by environment light and similarity between the defects.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131499346","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: 5
Design of Intelligent Zebra Crossing System Based on Cloud-network Convergence 基于云-网络融合的智能斑马线系统设计
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778393
L. Gao
{"title":"Design of Intelligent Zebra Crossing System Based on Cloud-network Convergence","authors":"L. Gao","doi":"10.1109/ICSP54964.2022.9778393","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778393","url":null,"abstract":"With the Internet of things (IoT) applied in various fields, instantaneous information perception and collection has become possible, significantly improving the perception capability of the traffic system more than ever. This paper proposed a construction scheme of intelligent zebra crossings based on cloud-network convergence. When the pedestrians are detected crossing the zebra crossing, the system activates the zebra crossing warning lights according to the pre-programmed operation strategy. The light, combined with audible warning, can warn both drivers and pedestrians, so as to reduce car accidents. The real-time monitoring is connected to the government cloud platform via the operator network, allowing for detection and maintenance of any unusual conditions that may arise, ensuring the continued operation of the facilities.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131620774","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 Method for Maritime Target Tracking Based on Kernelized Spectral Filter 一种基于核谱滤波的海上目标跟踪方法
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778466
Lu Bai, Yulong Qiao
{"title":"A Method for Maritime Target Tracking Based on Kernelized Spectral Filter","authors":"Lu Bai, Yulong Qiao","doi":"10.1109/ICSP54964.2022.9778466","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778466","url":null,"abstract":"Maritime target tracking can be applied in the fields of intelligent marine transportation and resource protection. The spectral filter tracking method focuses on the adaptability of the local appearance changes of targets, with the graph representation. Considering the complexity and diversity of the marine environment, we propose a maritime target tracking algorithm based on kernelized spectral filter. The spectral filtering is modeled as a tracking framework based on kernel regression. According to graph signal processing and spectral graph theory, based on the description of kernel regression on graph signals, we deduce and discuss the construction of the filter, the calculation of kernel matrix, the solution of kernel regression model, and the prediction of tracking position. With the help of a nonlinear model, it can effectively improve the precision under complex tracking conditions. The experimental results on the benchmark dataset verify the effectiveness of the proposed method, especially when the target tracking is affected by waves or wakes.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117326889","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
Ship Target Detection Method in SAR Imagery Based on Generalized Pareto Manifold 基于广义帕累托流形的SAR图像舰船目标检测方法
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778473
Zhaozhe Xie, Yongqiang Cheng, Hao Wu
{"title":"Ship Target Detection Method in SAR Imagery Based on Generalized Pareto Manifold","authors":"Zhaozhe Xie, Yongqiang Cheng, Hao Wu","doi":"10.1109/ICSP54964.2022.9778473","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778473","url":null,"abstract":"Ship target detection based on Synthetic aperture radar (SAR) imagery is a challenging task. With the continuous improvement of SAR resolution, the false alarm rate set by manual experience in traditional CFAR detection tends to lead to missed detection and weaken the detection performance. To solve this problem, this paper proposes a ship target detection method based on the generalized Pareto manifold in SAR imagery. The generalized Pareto distribution family is used to construct the SAR images’ statistical manifold, and the tangent vector length is applied to represent the local neighborhood of each pixel of SAR images, which indicates the difference between targets and the background clutter significantly, implementing the precise positioning of ship target and the effective suppression of background clutter in SAR images. The test results of Gaofen-3 satellite data show that compared with the traditional CFAR algorithm, this method achieves significantly better performance.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117304011","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
Carbon emission prediction study in Hebei Province based on system dynamics 基于系统动力学的河北省碳排放预测研究
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778573
Yunpeng Ling, Jing Nie, Nan Xu, Hongshan Zhang, Yongli Wang, Yiwen Li, Chengcong Cai
{"title":"Carbon emission prediction study in Hebei Province based on system dynamics","authors":"Yunpeng Ling, Jing Nie, Nan Xu, Hongshan Zhang, Yongli Wang, Yiwen Li, Chengcong Cai","doi":"10.1109/ICSP54964.2022.9778573","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778573","url":null,"abstract":"Based on the system dynamics theory, this paper constructs the causal relationship diagram of population subsystem, economic subsystem, energy subsystem and environmental subsystem in the carbon emission system of Hebei Province, establishes a dynamic model of carbon emission system in Hebei Province, and uses the model to predict and analyse the carbon dioxide emissions in Hebei Province from 2020 to 2030. By promoting or inhibiting carbon emissions in Hebei Province, different scenarios are set for the carbon emission system of Hebei Province, and the carbon emissions of Hebei Province are simulated and predicted based on different scenarios, combined with the above scenario prediction and analysis of the carbon system in Hebei Province and the economic and social status of Hebei Province, the optimal scenario is selected to provide reference for the future carbon emission reduction road of Hebei Province.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997864","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 Convey on Text Classification Method based on Deep Learning 基于深度学习的文本分类方法研究
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778518
Zhizhi Wang, Zhonglin Zhang
{"title":"Research Convey on Text Classification Method based on Deep Learning","authors":"Zhizhi Wang, Zhonglin Zhang","doi":"10.1109/ICSP54964.2022.9778518","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778518","url":null,"abstract":"Deep learning technology has been rapidly developed in recent years and has been increasingly applied in the field of text classification, and many effective and novel classification methods have emerged. The development history of text classification is introduced, the text classification problem based on deep neural networks is analyzed, the characteristics and performance of various classical classification methods are compared and summarized, and it is shown that deep neural networks are more advantageous than traditional machine learning methods in the field of text classification on the whole. The shortcomings of current deep text classification models are pointed out and future research directions prospect.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132881461","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 Contrastive Learning Framework for ECG Anomaly Detection 心电异常检测的对比学习框架
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2022-04-15 DOI: 10.1109/ICSP54964.2022.9778634
Fang Li, Hui Chang, Min-lan Jiang, Yihuan Su
{"title":"A Contrastive Learning Framework for ECG Anomaly Detection","authors":"Fang Li, Hui Chang, Min-lan Jiang, Yihuan Su","doi":"10.1109/ICSP54964.2022.9778634","DOIUrl":"https://doi.org/10.1109/ICSP54964.2022.9778634","url":null,"abstract":"ECG is important for the recognition and diagnosis of cardiac arrhythmias as a physiological signal characterizing the condition of the heart. A lot of studies have started to experiment with statistical and traditional machine learning methods to analyze and detect ECG data, thus to the heart and other organs of intelligent auxiliary treatment. Although a lot of work has been done in ECG signal processing, the existing work still suffers from the following deficiencies:i) Since the number of various types of signals is unbalanced when classifying ECG signals, and end-to-end deep learning models are very sensitive to unbalanced data, which can affect the automatic detection and classification tasks. ii) The models lack robustness due to inconsistent ECG data representation. For this reason, in this paper, we first design a data augmentation-based contrast learning module to alleviate the data imbalance and robustness problems of the model. Thus, a new contrast learning ECG abnormality detection framework is designed by capturing the underlying patterns of ECG signals. Many experiments show that our abnormality detection framework outperforms the baseline methods, which provides a new view for cardiovascular disease prevention and automatic diagnosis.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128914831","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
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