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The CNN-based Coronary Occlusion Site Localization with Effective preprocessing method

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2020年第10期15卷 1549-1551页

作者： Park, YeongHyeon Yun, Il Dong Kang, Si-Hyuck Hankuk Univ Foreign Studies Dept Comp & Elect Syst Engn Yongin 17035 South Korea Seoul Natl Univ Bundang Hosp Ctr Cardiovasc Internal Med Seongnam 13620 South Korea

The coronary artery occlusion (CAO) acutely comes to human, and it highly threats the human's life. When CAO detected, percutaneous coronary intervention (PCI) should be conducted timely. Before PCI, localizing the CAO is needed firstly, because the heart is covered with various arteries. We handle the three kinds of CAO in this paper and our purpose is not only localization of CAO but also improving the localizing performance via preprocessing method. We improve the area under the receiver operating characteristics curve at CAO site localization from a minimum of 0.150 to a maximum of 0.372 via our noise reduction and pulse extraction-based method. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： coronary artery occlusion localization preprocessing method

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preprocessing method of night vision image application in apple harvesting robot

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 158-163页

作者： Weikuan Jia Yuanjie Zheng De’an Zhao Xiang Yin Xiaoyang Liu Ruicheng Du School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu UniversityZhenjiang 212013China School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000China Institute of Life Sciences and Key Lab of Intelligent Information Processing Shandong Normal UniversityJinan 250014China

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for *** machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation *** to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting *** different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be *** addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise *** results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper *** preprocessing method can provide a theoretical and technical reference for subsequent image processing.

关键词： apple harvesting robot night vision image preprocessing method color analysis noise analysis

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A preprocessing method of AdaBoost for Mislabeled Data Classification 29

A Preprocessing Method of AdaBoost for Mislabeled Data Class...

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第29届中国控制与决策会议

作者： Xiangyang Liu Yaping Dai Yan Zhang Qiao Yuan Linhui Zhao School Of Automation Beijing Institute Of Technology

ISBN: (纸本)9781509046584

AdaBoost is one of the most popular algorithm for classification and has been successfully used for text classification,face detection and *** noise sensitivity is regarded as a major disadvantage and previous works show that AdaBoost will be overfitting when dealing with the data sets with noisy *** improve the noise tolerance of conventional AdaBoost,this paper proposed a preprocessing method of AdaBoost for mislabeled data to find the noisy data and correct *** decision stump is selected as the weak learner of the AdaBoost algorithm for *** comparison of simulation results between conventional AdaBoost and the method proposed in this paper shows that the proposed algorithm has improved testing accuracy of the data sets with the noisy data.

关键词： AdaBoost Classification Mislabeled data preprocessing method decision stump

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The preprocessing method of Control Points in Geometric Correction for UAV Remote Sensing Image

The Preprocessing Method of Control Points in Geometric Corr...

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2016 5th International Conference on Advanced Materials and Computer Science(ICAMCS 2016)

作者： Lirong Diao Riuan Liu Tingting Chen School of electronic and communication engineering Tianjin Normal University

This paper focuses on the preprocessing method of control points in geometric correction for UAV(Unmanned Aerial Vehicle) remote sensing *** points preprocessing refers to find out the calibration points from the selected control points,so as to use the calibration points to fit the geometric correction *** in traditional K-means algorithm,clustering results have a strong dependence on initial clustering centers,so selecting initial clustering centers randomly will lead to the clustering results instability when preprocessing control points with traditional K-means algorithm,and it will influence the effect of the UAV remote sensing image geometric ***,the paper imports the thought of Huffman tree to traditional K-means algorithm,aiming at optimizing the selection of initial clustering centers and improving the effect of UAV remote sensing image geometric correction ultimately.

关键词： Control points preprocessing method K-means algorithm Huffman tree

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Composite leading search index: a preprocessing method of internet search data for stock trends prediction

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ANNALS OF OPERATIONS RESEARCH 2015年第1期234卷 77-94页

作者： Liu, Ying Chen, Yibing Wu, Sheng Peng, Geng Lv, Benfu Univ Chinese Acad Sci Sch Management Beijing 100190 Peoples R China

Previous studies have revealed that Internet search data is a new source of data that can be used to predict the stock market. In this new, data-driven research field, choosing a method for preprocessing data is crucial to achieving accurate prediction performance. This paper proposes a preprocessing method of Internet search data: composite leading search index (CLSI), which is composed of three steps: (a) keyword selection, (b) time difference measurement, and (c) leading index composition. We demonstrate the validity of CLSI by comparing this method's results with the results from search volume index (SVI), which is most commonly used in previous literatures. We build a time series model (TS) with error correction and support vector regression (SVR) for stock trend prediction, and combine into four models for comparison: SVI-TS, CLSI-TS, SVI-SVR, and CLSI-SVR. We test these four models in the context of the Chinese stock market, which interests more and more investors nowadays, and analyzed results in nine datasets: stable periods, peak periods and trough periods of Shanghai Composite Index, Shenzhen Composite Index, and Hushen 300 index respectively. The results show that using TS and SVR as forecasting models, CLSI performs better than SVI on majority of the test dataset while has almost the same performance with that of SVI on the remaining test dataset. It is to some extent convincing that CLSI is a more efficient preprocessing method of Internet search data for stock trend prediction.

关键词： Internet search data preprocessing method Stock trend predication Investor attention Composite leading search index Search volume index Support vector regression

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Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine-acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2024年第1-2期32卷 46-54页

作者： Chao, Hui Qian, Shichuan Wang, Zhi Sheng, Xin Zhao, Xinping Lu, Zhiyan Li, Xiaoxia Xu, Yinguang Jin, Shaohua Li, Lijie Chen, Kun Beijing Inst Technol Sch Mat Sci & Engn Dept Polymer Mat & Sci Zhongguan Cun South 5 Beijing 100081 Peoples R China Gansu Yinguang Chem Ind Grp Co Ltd Baiyin Peoples R China

Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA-acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction (MSC), first derivative (1stDer), second derivative (2ndDer), and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x-y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.

关键词： preprocessing method near infrared spectroscopy multi-verse optimizer algorithm random forest

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Interpreting deep convolutional neural network classification results indirectly through the preprocessing feature fusion method in ship image classification

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JOURNAL OF APPLIED REMOTE SENSING 2020年第1期14卷

作者： Wang, Bo Huang, Chengeng Tao, Jiahui Luo, Jiancheng Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing Peoples R China Univ Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing Peoples R China

In spite of widespread adoption, deep convolutional neural networks (DCNNs) have a reputation for remaining "black boxes." Due to the feature extraction ability of DCNN itself, good predicted results can be achieved without any processing of the raw input images, but it is hard to explain what features of the raw images the results are based on. We propose a preprocessing feature fusion method, based on the Sobel and Canny edge operators, which we treat as the interpretation of the raw images. This method obtains the interpretable edge feature by fusing features in raw ship images, which helps to explain that the DCNN ship classification result is based on the ship edge feature. That is to say, through the interpretation of the raw ship images, we indirectly interpret the classification results of the DCNN to some degree. In subsequent experiments, the highest average accuracy of 85.31% was achieved from the fusion-1 dataset, whereas the highest average accuracy of 74.4% was achieved from the balanced fusion-1 dataset, and the best mean average accuracy of 74.3% is achieved from the cross-validated experiments, which helps to verify the validity of our preprocessing method. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： deep convolutional neural network preprocessing method feature fusion Sobel Canny

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Screening of preprocessing method of Biolog for Soil Microbial Community Functional Diversity

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Agricultural Science & Technology 2015年第10期16卷 2247-2251,2255页

作者：党雯郜春花张强李建华卢朝东靳东升卢晋晶山西大学生物工程学院山西太原030006 山西省农业科学院农业环境与资源研究所山西太原030006

As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the preprocessing meth- ods reported in the literatures were not the same. In order to screen the best pre- processing method, this paper took three typical treatments to explore the effect of different preprocessing methods on soil microbial community functional diversity. The results showed that, method B＇s overall trend of AWCD values was better than A and C＇s. method B＇s microbial utilization of six carbon sources was higher, and the result was relatively stable. The Simpson index, Shannon richness index and Car- bon source utilization richness index of the two treatments were B〉C〉A, while the Mclntosh index and Shannon evenness were not very stable, but the difference of variance analysis was not significant, and the method B was always with a smallest variance. method B＇s principal component analysis was better than A and C＇s. In a word, the method using 250 r/min shaking for 30 minutes and cultivating at 28 ℃ was the best one, because it was simple, convenient, and with good repeatability.

关键词： Biolog method preprocessing method Soil microbial community Func- tional diversity AWCD values

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Innovative strategies for protein content determination in dried laver ( Porphyra spp.): Evaluation of preprocessing methods and machine learning algorithms through short-wave infrared imaging

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FOOD CHEMISTRY-X 2024年 23卷 101763页

作者： Kim, Eunghee Park, Jong-Jin Lee, Gyuseok Cho, Jeong-Seok Park, Seul-Ki Yun, Dae-Yong Park, Kee-Jai Lim, Jeong-Ho Korea Food Res Inst Smart Food Mfg Project Grp Wonju 55365 South Korea Korea Food Res Inst Food Safety & Distribut Res Grp Wonju 55365 South Korea

In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900-1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScalerSupport vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp2) 2 ) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth.

关键词： Dried laver Short-wave infrared imaging Protein content prediction preprocessing method Machine learning model

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Identification of adulteration in GTL synthetic lubricant via DD-SIMCA and C-H stretching Raman spectra

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MICROCHEMICAL JOURNAL 2025年 208卷

作者： Yu, Yingtao Li, Jinlin Wang, Yuxuan Wang, Zhongqi Fu, Mengyu Zhou, Ziru Han, Haoxuan Yu, Yingxia Yang, Jiawei Dalian Maritime Univ Coll Environm Sci & Engn Dalian 116026 Peoples R China Tsinghua Univ Future Lab Beijing 100084 Peoples R China Fushun Vocat Tech Inst Dept Finance & Econ Fushun 113122 Liaoning Peoples R China

DD-SIMCA was combined with Raman spectra of C-H stretching vibrations to accurately distinguish between authentic gas-to-liquid (GTL) synthetic lubricating oil and samples adulterated with mineral oil at various levels. The effects of principal components, normalization and preprocessing methods on the model's performance were investigated, by which the model optimization was comprehensively considered for authentic samples in both the training and test sets, as well as for the adulterated samples in test set. Heatmap analysis on a parameter epsilon, defined as "1 - (sensitivity of training set x sensitivity of test set x type II error)", was conducted to optimize the model for authentic samples in the training and test sets. The model's performance for adulterated samples was evaluated using a parameter delta, which measured the distance in the ODlog dimension between the centroid of samples in the test set and the acceptance boundary for authentic samples. Generally, increasing the number of principal components had a significant effect on enlarging the ODlog of the authentic sample centroid in the test set while it slightly affected the training set. When the training and test sets both achieved 100 % sensitivity, area normalization was observed to be more effective than maximum normalization in optimizing the model for authentic GTL samples. In addition to accurately classifying authentic samples, multiplicative scatter correction (MSC) resulted in a model with excellent identification ability for adulterated samples when employing either maximum intensity normalization with six principal components or area normalization with four principal components.

关键词： DD-SIMCA Model optimization C-H stretching vibration Principal component preprocessing method Normalization

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