scholarly journals Study on a Risk Model and Target Detection for Prediction and Avoidance of Unmanned Environmental Hazard

2021 ◽  
Author(s):  
Chengqun Qiu ◽  
Yuan Zhong ◽  
Jie Ji ◽  
Shuai Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Target Detection ◽  
Environmental Risk ◽  
Risk Model ◽  
Learning Algorithm ◽  
Unmanned Vehicles ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Comparison Results

Abstract Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction. In the avoidance part, the conservative driving strategy based on speed restriction is adopted according to the results of risk prediction. Additionally, the core function is achieved through the target detection technology based on deep learning algorithm and the data conclusion based on deep learning method. Moreover, the location of bounding box is further optimized to improve the accuracy of SSD target detection method based on solving the problem of unbalanced sample categories. Software such as MATLAB and Carsim are applied in the system. From the comparison results of the simulations of unmanned vehicles with or without a system, it that the system can provide effective safety guarantee for unmanned driving.

scholarly journals An Improved Deep Learning Algorithm for Risk Prediction of Corporate Internet Reporting

2020 ◽  
Vol 34 (4) ◽  
pp. 437-444
Author(s):  
Lingyan Ou ◽  
Ling Chen
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Moving Average ◽  
Arma Model ◽  
Evaluation Index System ◽  
Online Information ◽  
Deep Learning Algorithm ◽  
Research Findings

Corporate internet reporting (CIR) has such advantages as the strong timeliness, large amount, and wide coverage of financial information. However, the CIR, like any other online information, faces various risks. With the aid of the increasingly sophisticated artificial intelligence (AI) technology, this paper proposes an improved deep learning algorithm for the prediction of CIR risks, aiming to improve the accuracy of CIR risk prediction. After building a reasonable evaluation index system (EIS) for CIR risks, the data involved in risk rating and the prediction of risk transmission effect (RTE) were subject to structured feature extraction and time series construction. Next, a combinatory CIR risk prediction model was established by combining the autoregressive moving average (ARMA) model with long short-term memory (LSTM). The former is good at depicting linear series, and the latter excels in describing nonlinear series. Experimental results demonstrate the effectiveness of the ARMA-LSTM model. The research findings provide a good reference for applying AI technology in risk prediction of other areas.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽  
2021 ◽  
pp. 1-23
Author(s):  
Roholah Yazdan ◽  
Masood Varshosaz ◽  
Saied Pirasteh ◽  
Fabio Remondino
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Geometric Constraints ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Traffic Signs ◽  
Deep Learning Algorithm ◽  
Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

Progressive Image Recognition Method and Its Application in Security In-spection Machines

2020 ◽  
Vol 01 ◽  
Author(s):  
Wu Jianxing ◽  
Zeng Dexin ◽  
Ju Qiaodan ◽  
Chang Zixuan ◽  
Yu Hai
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Learning Algorithm ◽  
Local Information ◽  
Feature Mapping ◽  
Recognition Method ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Security Inspection ◽  
Security Inspection Equipment

Background:: Owing to the ability of a deep learning algorithm to identify objects and the related detection technology of security inspection equipment, in this paper, we propose a progressive object recognition method that con-siders local information of objects. Methods:: First, we construct an X-Base model by cascading multiple convolutions and pooling layers to obtain the feature mapping image. Moreover, we provide a “segmented convolution, unified recognition” strategy to detect the size of the objects. Results:: Experimental results show that this method can effectively identify the specifications of bags passing through the security inspection equipment. Compared with the traditional VGG and progressive VGG recognition methods, the pro-posed method achieves advantages in terms of efficiency and concurrency. Conclusion:: This study provides a method to gradually recognize objects and can potentially assist the operators to identify prohibited objects.

Effect of grape seed proanthocyanidins on activity of HaCaT cells in mice based on deep learning image processing

2020 ◽  
pp. 1-11
Author(s):  
Feng Xu ◽  
Jia Huang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Grape Seed ◽  
Hacat Cells ◽  
Image Processing Algorithm ◽  
Grape Seed Proanthocyanidins ◽  
Uva Irradiation ◽  
Deep Learning Algorithm ◽  
Comparison Results

BACKGROUND: Grape seed proanthocyanidin extract (GSPE) has a certain resistance to contrast light, which makes the boundary of the imaging image unclear. OBJECTIVE: Because of this, an image processing algorithm is needed to process the contrast image to study the role of GSPE in the process of anti-ultraviolet. METHODS: In this paper, the fuzzy edges of contrast images were processed by deep learning algorithm, and the changes of VEGF and PEDF expression in HaCaT cells before and after UVA irradiation and after GSPE intervention were studied. RESULTS: The experiment results show that after processing, the edge and boundary of the image become clear and separable, which can be used to compare and analyze the test process. The image comparison results show that GSPE can down regulate the expression of VEGF gene and protein, and up regulate the expression of PEDF gene and protein. The synergistic effect of GSPE and GSPE can inhibit angiogenesis. It is confirmed that GSPE has the effect of anti-ultraviolet ray induced early angiogenesis.

scholarly journals Robot visual measurement and grasping strategy for roughcastings

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199993
Author(s):  
Guoyang Wan ◽  
Guofeng Wang ◽  
Kaisheng Xing ◽  
Yunsheng Fan ◽  
Tinghao Yi
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Template Matching ◽  
Learning Algorithm ◽  
Industrial Robot ◽  
Target Localization ◽  
Visual Measurement ◽  
Deformable Template ◽  
Deep Learning Algorithm ◽  
Combined Features

To overcome the challenging problem of visual measurement and grasping of roughcasts, a visual grasping strategy for an industrial robot is designed and implemented on the basis of deep learning and a deformable template matching algorithm. The strategy helps realize the positioning recognition and grasping guidance for a metal blank cast in complex backgrounds under the interference of external light. The proposed strategy has two phases: target detection and target localization. In the target detection stage, a deep learning algorithm is used to recognize the combined features of the surface of an object for a stable recognition of the object in nonstructured environments. In the target localization stage, high-precision positioning of metal casts with an unclear contour is realized by combining the deformable template matching and LINE-MOD algorithms. The experimental results show that the system can accurately provide visual grasping guidance for robots.

scholarly journals Development and validation of a deep learning algorithm using fundus photographs to predict 10-year risk of ischemic cardiovascular diseases among Chinese population

2021 ◽  
Author(s):  
Yanjun Ma ◽  
Jianhao Xiong ◽  
Yidan Zhu ◽  
Zongyuan Ge ◽  
Rong Hua ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Risk Prediction ◽  
Chinese Population ◽  
Goodness Of Fit ◽  
Medical Information ◽  
Learning Algorithm ◽  
External Validation ◽  
Deep Learning Algorithm ◽  
Fundus Photographs

Background Ischemic cardiovascular diseases (ICVD) risk predict models are valuable but limited by its requirement for multidimensional medical information including that from blood drawing. A convenient and affordable alternative is in demand. Objectives To develop and validate a deep learning algorithm to predict 10-year ICVD risk using retinal fundus photographs in Chinese population. Methods We firstly labeled fundus photographs with natural logarithms of ICVD risk estimated by a previously validated 10-year Chinese ICVD risk prediction model for 390,947 adults randomly selected (95%) from a health checkup dataset. An algorithm using convolutional neural network was then developed to predict the estimated 10-year ICVD risk by fundus images. The algorithm was validated using both internal dataset (the other 5%) and external dataset from an independent source (sample size = 1,309). Adjusted R2 and area under the receiver operating characteristic curve (AUC) were used to evaluate the goodness of fit. Results The adjusted R2 between natural logarithms of the predicted and calculated ICVD risks was 0.876 and 0.638 in the internal and external validations, respectively. For detecting ICVD risk ≥ 5% and ≥ 7.5%, the algorithm achieved an AUC of 0.971 (95% CI: 0.967 to 0.975) and 0.976 (95% CI: 0.973 to 0.980) in internal validation, and 0.859 (95% CI: 0.822 to 0.895) and 0.876 (95% CI: 0.816 to 0.837) in external validation. Conclusions The deep learning algorithm developed in the study using fundus photographs to predict 10-year ICVD risk in Chinese population had fairly good capability in predicting the risk and may have values to be widely promoted considering its advances in easy use and lower cost. Further studies with long term follow up are warranted. Keywords Deep learning, Ischemic cardiovascular diseases, risk prediction.

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