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classification and recognition method of white blood cells subclasses in batches based on phase characteristics with non-orthogonal phase imaging

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JOURNAL OF MODERN OPTICS 2022年第14期69卷 791-803页

作者： Xu, Yuanyuan Han, Hao Zou, Yang Wang, Yawei Liao, Jingrong Jiangsu Univ Sch Phys & Elect Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Mech Engn Zhenjiang Jiangsu Peoples R China Jiangsu Univ Jingjiang Coll Zhenjiang Jiangsu Peoples R China

In this study, a novel identification method of white blood cells (WBCs) is proposed. Firstly, five phase characteristic parameters are introduced to characterize the detailed information used for WBCs identification. Secondly, a phase information acquisition device can simultaneously obtain two non-orthogonal phases of WBCs in batches, thereby improving the recognition accuracy. Finally, combining the threshold of the parameters and two phases in non-orthogonal directions, a label-free recognition process of WBCs subclasses based on phase features is established. Experimental results show that the method is feasible and provides new ideas and a technical basis for the analysis and classification of blood cells.

关键词： White blood cells phase characteristics classification and recognition phase imaging

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classification and recognition of the Nantong blue calico pattern based on deep learning

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2024年 19卷

作者： Sun, Ke-Ke Huang, Jing-Wan Yuan, Yu-Yang Chen, Ming-Yue Xiamen Univ Technol Sch Design Art 600 Ligong Rd Xiamen 361024 Fujian Peoples R China Xiamen Univ Technol Sch Mech & Automot Engn Xiamen Fujian Peoples R China

The Nantong blue calico pattern is a significant and indispensable part of China's intangible cultural heritage, representing an artistic form of weaving and dyeing. However, existing research on blue calico patterns is not extensive, and few studies have focused on the construction of a database categorizing them or on recognizing the Nantong blue calico pattern. Obtaining good efficiency and accuracy through manual recognition has been the primary challenge in recognizing the Nantong blue calico pattern. In light of these challenges, this study proposes the use of deep learning network model to intelligently classify and recognize blue calico ***, the patterns are classified to establish a Nantong blue calico pattern database, and the corresponding category labels are then manually assigned to each image. Second, based on the database and a backbone feature extraction network, the abilities of SSD (Single Shot Multibox Detector), Faster RCNN (Region-CNN), and You Only Look Once (to recognize the Nantong blue calico pattern were compared. The results show that the SSD model based on a VGG (Visual Geometry Group) backbone network has the best recognition accuracy of these three algorithms, with an average accuracy of 79.42%. On this basis, we selected the SSD model for parameter optimization and adjustment, and we replaced the backbone with mobilenetv2, a lighter backbone extraction network, to recognize the Nantong blue calico pattern. The results show that compared with the original SSD model, the optimized SSD model can improve the pattern recognition rate of Nantong blue calico pattern. Furthermore, this paper makes use of the characteristics of the VGG deep network, the backbone network of the SSD model, to efficiently extract the features of blue calico patterns, which provides a basis for designers to design innovative blue calico patterns.

关键词： Nantong blue calico classification and recognition feature extraction SSD YOLOv8 faster RCNN

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classification and recognition Method of Non-Cooperative Objects Based on Deep Learning

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SENSORS 2024年第2期24卷 583页

作者： Wang, Zhengjia Han, Yi Zhang, Yiwei Hao, Junhua Zhang, Yong Harbin Inst Technol Inst Precis Acousto Opt Instrument Sch Instrumentat Sci & Engn Harbin 150080 Peoples R China Tianjin Univ Sch Precis Instruments & Optoelect Engn Minist Educ Key Lab Optoelect Informat Technol Tianjin 300072 Peoples R China Tianjin Univ Key Lab MicrooptoElectroMech Syst MOEMS Technol Minist Educ Tianjin 300072 Peoples R China Tianjin Renai Coll Dept Phys Tianjin 301636 Peoples R China Harbin Inst Technol Natl Key Lab Sci & Technol Tunable Laser Harbin 150080 Peoples R China Harbin Inst Technol Sch Astronaut Dept Optoelect Informat Sci & Technol Harbin 150080 Peoples R China

Accurately classifying and identifying non-cooperative targets is paramount for modern space missions. This paper proposes an efficient method for classifying and recognizing non-cooperative targets using deep learning, based on the principles of the micro-Doppler effect and laser coherence detection. The theoretical simulations and experimental verification demonstrate that the accuracy of target classification for different targets can reach 100% after just one round of training. Furthermore, after 10 rounds of training, the accuracy of target recognition for different attitude angles can stabilize at 100%.

关键词： non-cooperation target micro-Doppler effect laser coherence detection deep learning classification and recognition

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Mushroom Image classification and recognition Based on Improved Swin Transformer 6

Mushroom Image Classification and Recognition Based on Impro...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Zhao, Kexin Huo, Yukang Xue, Lin Yao, Mingyuan Tian, Qingbin Wang, Haihua National Innovation Center for Digital Fishery Beijing100083 China Ministry of Agriculture and Rural Affairs Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Beijing100083 China China Agricultural University College of Information and Electrical Engineering Beijing100083 China Beijing Union University Smart City College Beijing100101 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China

ISBN: (纸本)9798350313444

classification of mushrooms are essential for preventing even life-threatening consequences of accidentally eating wild mushrooms. In this study, a dataset including 114 varieties of mushrooms is collected and built. Next, the Swin Transformer model with robust classification characteristics is improved to classif mushroom images. Also, the classification accuracy of the improved Swin Transformer with different parameters was compared. In addition, ResNet50 is compared with the improved Swin Transformer under the optimal parameters. The model's training speed is further enhanced by integrating the improved Swin Transformer with ResNet50, thereby proposing the Swin_ResNet classification algorithm. Experimental results show that the classification accuracy of the optimal improved Swin Transformer algorithm is 87.66%, which is 14.86% and 7.64% higher than the ResNet50 and Swin Transformer models, respectively. In addition, the classification accuracy of Swin_ResNet is 85.31%, and the training time is 86.57% shorter than that of the improved Swin Transformer. This greatly improves the training efficiency. © 2023 IEEE.

关键词： classification and recognition Convolutional Neural Network Deep Learning Mushroom Image Swin Transformer

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A classification and recognition Model of Distributed Resources Based on Feature Extraction

A Classification and Recognition Model of Distributed Resour...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Zhu, Yu Yang, Le Yang, Xizai Lu, Jiaxin Wang, Yuqing Wang, Fei State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Information and Communication Technology R & D Center Xi'an710048 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

ISBN: (纸本)9781665471640

The extensive integration of distributed resources such as distributed photovoltaic system and electric vehicles enhances the uncertainty of the load side greatly. Corresponding demand response strategies can be developed for different distributed resource types and characteristics of households' load, which is of great significance for new energy consumption and stable operation of the power system. However, only the net load power of households can be obtained through smart meters instead of the distributed resource types of households. Based on this, a distributed resource classification and identification model based on feature extraction is proposed in this paper, and effectively solves the problems of low accuracy and strong data dependence of existing recognition methods. Firstly, a generalized weather class generation algorithm based on clustering and voting is established, which is used to determine the weather class of each day to provide a basis for the following feature extraction. Then, based on the typical net load profiles under different generalized weather classes, a two-stage feature extraction and a classification identification model based on integrated learning algorithms are established to identify whether a user contains DPVS and EV, respectively. Finally, based on the category labels of each user for each stage, the final classification of the category to which the user belongs is carried out. Simulation experiments show that the recognition model built using the typical features extracted in the paper has good recognition accuracy. © 2023 IEEE.

关键词： classification and recognition Distributed photovoltaics system electric vehicles ensemble learning feature extraction

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classification and recognition of encrypted EEG data based on neural network

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2020年 54卷

作者： Liu, Yongshuang Huang, Haiping Xiao, Fu Malekian, Reza Wang, Wenming Nanjing Univ Posts & Telecommun Coll Comp Nanjing 210023 Peoples R China High Technol Res Key Lab Wireless Sensor Network Nanjing 210023 Peoples R China Malmo Univ Dept Comp Sci & Media Technol S-20506 Malmo Sweden Anqing Normal Univ Sch Comp & Informat Anqing 246011 Anhui Peoples R China

With the rapid development of Machine Learning technology applied in electroencephalography (EEG) signals, Brain-Computer Interface (BCI) has emerged as a novel and convenient human-computer interaction for smart home, intelligent medical and other Internet of Things (IoT) scenarios. However, security issues such as sensitive information disclosure and unauthorized operations have not received sufficient concerns. There are still some defects with the existing solutions to encrypted EEG data such as low accuracy, high time complexity or slow processing speed. For this reason, a classification and recognition method of encrypted EEG data based on neural network is proposed, which adopts Paillier encryption algorithm to encrypt EEG data and meanwhile resolves the problem of floating point operations. In addition, it improves traditional feed-forward neural network (FNN) by using the approximate function instead of activation function and realizes multi-classification of encrypted EEG data. Extensive experiments are conducted to explore the effect of several metrics (such as the hidden neuron size and the learning rate updated by improved simulated annealing algorithm) on the recognition results. Followed by security and time cost analysis, the proposed model and approach are validated and evaluated on public EEG datasets provided by PhysioNet, BCI Competition IV and EPILEPSIAE. The experimental results show that our proposal has the satisfactory accuracy, efficiency and feasibility compared with other solutions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： EEG Homomorphic encryption Paillier Neural network classification and recognition

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Research on feature vector construction and classification recognition of typical mine microseismic waveforms

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JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2023年 86卷

作者： Zhu, Quanjie Sui, Longkun Liu, Yan Yin, Yongming Ouyang, Zhenhua Chen, Xuexi Liu, Wenfeng North China Inst Sci & Technol Sch Emergency Technol & Management Langfang 065201 Peoples R China North China Inst Sci & Technol Sch Mine Safety Langfang 065201 Peoples R China Shiyan Co Hubei Tobacco Shiyan 442000 Peoples R China China Acad Safety Sci & Technol Beijing 100012 Peoples R China China Natl Gold Grp Liaoning Co Ltd Shenyang 110000 Peoples R China

In the realm of mining seismology, the identification of microseismic waveforms is a critical step for enhancing the precision of microseismic event localization. Addressing the complexities and diverse sources of noise in subterranean mining environments, this study has developed a novel methodology for the automated categori-zation and recognition of characteristic microseismic waveforms. Initially, a comprehensive database for mining -induced microseismic waveforms was established, catering to their non-stationary and nonlinear attributes. Based on the distinct features of different microseismic waveforms, this study introduces an approach for clas-sification and identification that relies on waveform characteristics. A feature selection model, KNN-LOO (K -Nearest Neighbor -Leave-One-Out), was employed to optimize the feature vectors, in addition to the utilization of Principal Component Analysis (PCA) for dimensionality reduction of the feature vectors. Ultimately, an enhanced BT-SVM (Binary Tree-Support Vector Machine) model was developed to accomplish the automated classification and identification of mining-induced microseismic waveforms. Experimental results indicate that, compared to conventional SVM classification methods, the proposed methodology not only exhibits theoretical and mechanistic advancements but also significantly boosts performance in practical applications. Specifically, the average misclassification rate for each category of waveforms is reduced to less than 5%, and the overall accuracy has increased by 20%. This considerably elevates both the accuracy and speed of microseismic waveform classification and recognition, thereby providing valuable insights for monitoring and analyzing microseismic activities within mining operations as well as enhancing the safety protocols for mineral extraction.

关键词： Microseismic waveform Feature extraction Vector construction BT-SVM classification and recognition

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Research on classification and recognition Method of Coal and Coal Gangue Based Spectral Imaging Micro-system 42

Research on Classification and Recognition Method of Coal an...

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Annual Conference of Chinese-Society-of-Optical-Engineering (CSOE) on Optical Spectroscopy and Imaging (AOPC)

作者： Zhao, Anna Liu, Shuyang Zhang, Chen Jia, Xiaodong Tianjin Jinhang Tech Phys Res Inst 58 Zhonghuan Xi Rd Tianjin 300381 Peoples R China

ISBN: (纸本)9781510634473

Spectral imaging technology is a information acquisition method that can obtain three-dimensional information of the object, which is widely used in remote sensing, material classification and other fields. Spectral imaging micro-system is a compact, low-cost spectral imaging system based on structure of single-chip integration. It is suitable for utilizing on unmanned airborne and has a good prospect in the field of geological exploration and mineral classification. Coal gangue is a kind of gray rock associated with coal, which is also a kind of solid waste in mining industry. Due to demand of environmental monitoring and effective mining, it is urgent to effectively monitor the distribution of coal in minerals. This study carries out the experiment on classification of coal and coal gangue samples with the spectral imaging micro-system of visible-near-infrared spectrum. And experimental results show that 80% of the pixels in the sample can be accurately classified.

关键词： Coal Coal Gangue Spectral Imaging Micro-system classification and recognition

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Two-stage Neural Network based Combined Interference classification and recognition for a GNSS receiver

Two-stage Neural Network based Combined Interference Classif...

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第十一届中国卫星导航年会

作者： Xin Zhang Guomei Zhang Hua Wang Guobing Li School of Information and Communications Engineering Xi’an Jiaotong University State Key Laboratory of Geo-information Engineering Xi’an Research Institute of Surveying and Mapping

Under the complex war environment based on Global Navigation Satellite Systems(GNSS), suppression interference and spoofing interference would be used in combination by enemy. Thus, the difficulty of

关键词： GNSS Suppression Interference Spoofing Interference classification and recognition Back Propagation Neural Network

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A classification and recognition model for the severity of road traffic accident

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ADVANCES IN MECHANICAL ENGINEERING 2019年第5期11卷 1666页

作者： Xi Jianfeng Guo Hongyu Jian, Tian Lisa Liu Liu Haizhu Jilin Univ Sch Transportat Changchun Jilin Peoples R China China Acad Transportat Sci Beijing 100029 Peoples R China AECOM Calgary AB Canada Tianjin Railway Tech & Vocat Coll Tianjin Peoples R China

A classification and recognition method for the severity of road traffic accident based on rough set theory and support vector machine was proposed in this article. Rough set theory was used to calculate the importance of attributes in human, vehicle, road, environment, and accident. On the basis of importance ranking, the factors affecting the severity of accident were extracted. Then, with the general accident and major accident as two classification labels, the classification and recognition model of the severity of road traffic accident was established by using support vector machine. The results show that the model could improve the recognition accuracy and reduce the computational workload. Moreover, it has the good ability in classification and recognition as well as generalization compared with the model using support vector machine alone.

关键词： Road traffic accident severity classification and recognition support vector machine rough set theory

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