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deepGTT: A general trajectory tracking deep learning algorithm based on dynamic law learning

IET RADAR SONAR AND NAVIGATION 2021年第9期15卷 1125-1150页

作者： Yu, Wanting Yu, Hongyi Du, Jianping Zhang, Mengli Liu, Jianfeng PLA Strateg Support Force Informat Engn Univ Zhengzhou 450001 Henan Peoples R China State Key Lab Math Engn & Adv Comp Zhengzhou Henan Peoples R China

deep learning technology provides novel solutions for increasingly complex target tracking requirements. For traditional target tracking models, the movement of the target need to be simulated by a predefined mathematical model. However, it is extremely difficult to obtain sufficient information in advance, which makes it challenging to track changeable and noisy trajectories in a timely and precise manner. A deep learning framework is constructed for automatic trajectory tracking based on learning the dynamic laws of motion, called deepGTT. Specifically, a trajectory generator and a trajectory mapper were designed to standardise trajectory data and construct trajectory mapping, which enable the long short-term memory-based tracking network to learn general dynamic laws. Then, to discuss the interpretability of the model, the mechanism of the deep learning framework is considered and a memory factor matrix is defined. Finally, extensive experiments are conducted on various weak manoeuvring and manoeuvring scenarios to evaluate the algorithm. Experimental results demonstrate that the deepGTT algorithm remarkably improves accuracy and efficiency compared with most conventional algorithms and state-of-the-art methods. In addition, interpretability experiments qualitatively prove that the tracking network can perceive dynamic laws when estimating the target state.

关键词： Computer vision and image processing techniques trajectory data Spatial variables control long short-term memory-based tracking network recurrent neural nets deep learning (artificial intelligence) Optical, image and video signal processing matrix algebra automatic trajectory tracking Algebra position control deep learning algorithm trajectory generator dynamic law memory factor matrix object detection complex target tracking requirements deepGTT algorithm target tracking mathematical model trajectory mapping noisy trajectories

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Numerical simulation study of gas explosion in confined space based on deep learning algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 3239-3246页

作者： Li Qizhong Wang Zhongqi Wang Ye Betjing Inst Technol Beijing 100081 Peoples R China North China Inst Sci & Technol East Beijing Peoples R China

In order to determine the explosion value in the confined space, this time the simulation model of the deep learning algorithm is used to study it. The research status of deep learning algorithm is first expounded, and the numerical record of gas explosion in confined space is constructed according to computer technology. In order to ensure the optimization of numerical processing, the deep learning algorithm is used to process the simulation data to ensure the accuracy of the explosion value. In order to further test the numerical accuracy of the numerical model of gas limited space explosion, the comparison of different values in the constrained space is carried out, and the efficiency and accuracy of the deep learning algorithm are tested. The test results show the application of deep learning algorithm. The accuracy of the explosion value is further guaranteed and needs further application.

关键词： deep learning algorithm confined space gas explosion model construction

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Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm

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JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY 2019年第4期145卷 829-837页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Mepco Schlenk Engn Coll Autonomous Dept Elect & Elect Engn Sivakasi Tamil Nadu India

PurposeOral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment. In this research work, we have developed a deep learning algorithm for automated, computer-aided oral cancer detecting system by investigating patient hyperspectral *** validate the proposed regression-based partitioned deep learning algorithm, we compare the performance with other techniques by its classification accuracy, specificity, and sensitivity. For the accurate medical image classification objective, we demonstrate a new structure of partitioned deep Convolution Neural Network (CNN) with two partitioned layers for labeling and classify by labeling region of interest in multidimensional hyperspectral *** performance of the partitioned deep CNN was verified by classification accuracy. We have obtained classification accuracy of 91.4% with sensitivity 0.94 and a specificity of 0.91 for 100 image data sets training for task classification of cancerous tumor with benign and for task classification of cancerous tumor with normal tissue accuracy of 94.5% for 500 training patterns was *** compared the obtained results from another traditional medical image classification algorithm. From the obtained result, we identify that the quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis.

关键词： deep learning algorithm Medical image classification Hyperspectral image data Image labeling Oral cancer diagnosis

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Prediction of corneal back surface power - deep learning algorithm versus multivariate regression

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OPHTHALMIC AND PHYSIOLOGICAL OPTICS 2022年第1期42卷 185-194页

作者： Langenbucher, Achim Szentmary, Nora Cayless, Alan Weisensee, Johannes Wendelstein, Jascha Hoffmann, Peter Saarland Univ Dept Expt Ophthalmol Homburg Saar Germany Saarland Univ Dr Rolf M Schwiete Ctr Limbal Stem Cell & Aniridi Homburg Saar Germany Semmelweis Univ Dept Ophthalmol Budapest Hungary Open Univ Sch Phys Sci Milton Keynes Bucks England Johannes Kepler Univ Linz Dept Ophthalmol Linz Austria Augen & Laserklin Castrop Rauxel Castrop Rauxel Germany

Background The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation. This study aimed to set up and validate a deep learning algorithm to predict corneal back surface power from the corneal front surface power and biometric measures. Methods This study was based on a large dataset of IOLMaster 700 measurements from two clinical centres. N = 19,553 measurements of 19,553 eyes with valid corneal front (CFSPM) and back surface power (CBSPM) data and other biometric measures. After a vector decomposition of CFSPM and CBSPM into equivalent power and projections of astigmatism to the 0 degrees/90 degrees and 45 degrees/135 degrees axes, a multi-output feedforward neural network was derived to predict vector components of CBSPM from CFSPM and other measurements. The predictions were compared with a multivariate linear regression model based on CFSPM components only. Results After pre-conditioning, a network with two hidden layers each having 12 neurons was derived. The dataset was split into training (70%), validation (15%) and test (15%) subsets. The prediction error (predicted corneal back surface power CBSPP - CBSPM) of the network after training and crossvalidation showed no systematic offset, narrower distributions for CBSPP - CBSPM and no trend error of CBSPP - CBSPM vs. CBSPM for any of the vector components. The multivariate linear model also showed no systematic offset, but broader distributions of the prediction error components and a systematic trend of all vector components vs. CFSPM components. Conclusion The neural network approach based on CFSPM vector components and other biometric measures outperforms the multivariate linear model in predicting corneal back surface power vector components. Modern biometers can supply all parameters required for this algorithm, enabling reliable predictions for corneal back surface data where direct corneal back surface data are unavailabl

关键词： biometry corneal back surface power deep learning algorithm feedforward multi-output network neural network posterior corneal astigmatism

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Application of light sensor localization based on deep learning algorithm in tourist path recommendation in cultural scenic spots

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OPTICAL AND QUANTUM ELECTRONICS 2024年第2期56卷 238-238页

作者： He, Wei Yinchuan Univ Energy Sch Business Yinchuan 750021 Ningxia Peoples R China

In response to the personalized needs of the tourism industry, intelligent tourism planning services should be adopted. Through artificial intelligence and Big data technology, users' travel preferences, time, budget and other information are analyzed and mined in depth to provide users with more suitable travel routes and services. Therefore, according to the demand of tourist path recommendation in cultural scenic spots, this paper proposes a light sensor positioning method based on deep learning algorithm. This paper expounds the advantages of using optical sensor positioning for path recommendation, and describes the specific steps and processes of the optical sensor positioning method based on deep learning algorithm in detail. Finally, the feasibility and effect of this method are verified by experiments and data analysis. The results show that the localization of light sensor based on deep learning algorithm has high accuracy and practicability in the travel path recommendation of cultural scenic spots. Therefore, this method can provide accurate and reasonable path recommendation for tourists in cultural scenic spots and improve tourism experience and benefits. With the continuous development of the tourism market and the changing needs of users, this personalized tourism route planning and recommendation system will be increasingly widely applied and promoted.

关键词： deep learning algorithm Optical sensor positioning Cultural scenic spot Travel path Path recommendation

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Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm

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GEOMORPHOLOGY 2024年 465卷

作者： Jiang, Zongda Hu, Sheng Deng, Hao Wang, Ninglian Zhang, Fanyu Wang, Lin Wu, Songbai Wang, Xingang Cao, Zhengwen Chen, Yixian Li, Sisi Northwest Univ Shaanxi Key Lab Earth Surface Syst & Environm Carr Xian 710127 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China Northwest Univ Coll Urban & Environm Sci Xian 710127 Peoples R China Northwest Univ Inst Earth Surface Syst & Hazards Xian 710127 Peoples R China Lanzhou Univ Dept Geol Engn MOE Key Lab Mech Disaster & Environm Western China Lanzhou 730000 Peoples R China Northwest Univ Dept Geol State Key Lab Continental Dynam Xian 710069 Peoples R China

Nowadays, the detection and automatic identification of the three-dimensional structure of sinkholes is extremely lacking, which has resulted in significant gaps in sinkholes mapping, soil erosion estimation and morphological studies. In this study, we discovered 249 sinkholes on a river terrace (about 2050 m long and 100 m wide) in a small watershed of Chinese Loess Plateau. Subsequently, we used the unmanned aircraft systems (UAS) and handheld laser scanner (HLS) to investigate these loess sinkholes in detail. We introduced the PointNet ++ deep learning model to train the point cloud dataset for 50 epochs and then selected the best model. In order to evaluate the identification accuracy and transferability of the model, we input point clouds of the unknown prediction area into the trained model to predict the sinkhole point clouds. The trained model exhibits excellent transferability and can effectively identify the sinkhole point clouds in the predicted area (OA = 0.935, IoU (Sinkhole) = 0.662, mIoU = 0.794, AUC = 0.966, Recognition rate = 82.46 %), and even sinkholes with complex connected structures can be accurately identified. This study provides a new perspective for future large-area LiDAR surveys, mapping, and assessment of sinkholes.

关键词： Loess sinkholes LiDAR Detection Identification deep learning algorithm

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Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm

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INTERNATIONAL JOURNAL OF CLOTHING SCIENCE AND TECHNOLOGY 2019年第4期31卷 510-521页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India

Purpose The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm. Design/methodology/approach To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification. Findings The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate. Originality/value The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.

关键词： Computer vision deep learning algorithm Defect classifications Fabric defect detection Feature extraction

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Colorimetric microneedle sensor using deep learning algorithm for meat freshness monitoring

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CHEMICAL ENGINEERING JOURNAL 2024年 481卷

作者： Wang, Jie Xia, Linlin Liu, Han Zhao, Chong Ming, Siyu Wu, Jingyi Nanjing Agr Univ Coll Artificial Intelligence Nanjing 210031 Peoples R China Nanjing Agr Univ Coll Food Sci & Technol State Key Lab Meat Qual Control & Cultured Meat De Nanjing 210095 Peoples R China Nanjing Agr Univ Nanjing Peoples R China

Currently, the developed testing methods determining meat freshness are time-consuming, inconvenient, or have high specialty requirements. Herein, we proposed a colorimetric microneedle sensor (CMS) using a deep learning algorithm for visualized meat freshness monitoring. The CMS was obtained by molding edible hydrogels containing pH-responsive anthocyanins, which change colors because of the structure change of anthocyanins in response to pH. When attached to meat, the CMS was capable of penetrating the meat and extracting tissue fluids by capillary action. With meat spoilage, the pH of the tissue fluid gradually rose, leading to a change in CMS from pink to purple and finally to dark blue. Thus, according to variations of CMS colors, in situ and visualized detection of meat freshness was achieved. Further, a deep learning algorithm was applied to integrate with CMS to form a smartphone application (App), allowing for more convenient and accurate freshness detection. Images of CMS attached to the meat with different freshness were collected to form a training source as the input of the convolutional neural network (CNN). Through convolving CMS color features, the meat freshness classified as "fresh", "less fresh", and "spoiled" was finally outputted. With the incorporation of CNN, the App enabled users to identify the freshness of meat from stored photos or real-time images of CMS-labeled meats in a fast, accurate, portable, and universal way. This visualized detection strategy of CMS combined with an algorithm-integrated App has a promising potential for wide applications such as food safety, health monitoring, and environmental protection.

关键词： Microneedle deep learning algorithm Meat freshness Anthocyanins Visualized detection

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Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm

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PATTERN RECOGNITION LETTERS 2021年 141卷 32-36页

作者： Tan, Qulin Guo, Bin Hu, Jun Dong, Xiaofeng Hu, Jiping Beijng Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China Beijng Jiaotong Univ Inst Spatial Informat Line Engn Beijing 100044 Peoples R China Weifang Engn Vocat Coll Dept Architectural Engn Weifang 262500 Shandong Peoples R China Beijng Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijng Jiaotong Univ Acad Urban Planning & Design Beijing 100044 Peoples R China

In recent years, high spatial resolution remote sensing technology has made significant progress. High resolution remote sensing satellites provide great convenience for high-quality image acquisition. In order to adapt to changes in the appearance of the target, mainstream tracking algorithms often use pattern recognition methods to build a target appearance model with learning capabilities, and use the image frames acquired during the tracking process to update the appearance model. This paper mainly studies the object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm. In this paper, we use the splitting mechanism of the tree structure to retain the appearance model with diversity, and through the integrated learning integration strategy, the target position is collaboratively predicted. Through the comparative analysis on the OTB and VOT platforms, the algorithm works well when the requirements of the tracking standards are low (the accuracy threshold is greater than 20 pixels and the success threshold is less than 0.4 pixels). The experimental results in this paper show that compared with other advanced classification methods, the proposed method shows better generalization performance in accuracy, recall, f-measure, g-mean and AUC. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-classifier combination deep learning algorithm Object-oriented Remote sensing image Information extraction

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Sparse Representation Classification of Tobacco Leaves Using Near-Infrared Spectroscopy and a deep learning algorithm

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ANALYTICAL LETTERS 2018年第7期51卷 1029-1038页

作者： Zhang, Jianqiang Liu, Weijuan Hou, Ying Qiu, Changgui Yang, Shuangyan Li, Changyu Nie, Linru Yunnan Reascend Tobacco Technol Grp Co Ltd Kunming 650000 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming Yunnan Peoples R China Yunnan Comtestor Co Kunming Yunnan Peoples R China

A spare representation classification method for tobacco leaves based on near-infrared spectroscopy and deep learning algorithm is reported in this paper. All training samples were used to make up a data dictionary of the sparse representation and the test samples were represented by the sparsest linear combinations of the dictionary by sparse coding. The regression residual of the test sample to each class was computed and finally assigned to the class with the minimum residual. The effectiveness of spare representation classification method was compared with K-nearest neighbor and particle swarm optimization-support vector machine algorithms. The results show that the classification accuracy of the proposed method is higher and it is more efficient. The results suggest that near-infrared spectroscopy with spare representation classification algorithm may be an alternative method to traditional methods for discriminating classes of tobacco leaves.

关键词： deep learning algorithm near-infrared spectroscopy sparse representation classification

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