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This study aims to address the challenges of detecting smoking behavior among workers in chemical plant environments. Smoking behavior is difficult to discern in images, with the cigarette occupying only a small pixel area, compounded by the complex background of chemical plants. Traditional models struggle to accurately capture smoking features, leading to feature loss, reduced recognition accuracy, and issues like false positives and missed detections. To overcome these challenges, we have developed a smoking behavior recognition method based on the YOLOv8 model, named Smoking-YOLOv8. Our approach introduces an SD attention mechanism that focuses on the smoking areas within images. By aggregating information from different positions through weighted averaging, it effectively manages long-distance dependencies and suppresses irrelevant background noise, thereby enhancing detection performance. Furthermore, we utilize Wise-IoU as the regression loss for bounding boxes, along with a rational gradient distribution strategy that prioritizes samples of average quality to improve the model’s precision in localization. Finally, the introduction of SPPCSPC and PConv modules in the neck section of the network allows for multi-faceted feature extraction from images, reducing redundant computation and memory access, and effectively extracting spatial features to balance computational load and optimize network architecture. Experimental results on a custom dataset of smoking behavior in chemical plants show that our model outperforms the standard YOLOv8 model in mean Average Precision (mAP@0.5) by 6.18%, surpassing other mainstream models in overall performance. [ABSTRACT FROM AUTHOR]

Face mask-wearing detection is of great significance for safety protection during the epidemic. Aiming at the problem of low detection accuracy due to the problems of occlusion, complex illumination, and density in mask-wearing detection, this paper proposes a neural network model based on the loss function and attention mechanism for mask-wearing detection in complex environments. Based on YOLOv5s, we first introduce an attention mechanism in the feature fusion process to improve feature utilization, study the effect of different attention mechanisms (CBAM, SE, and CA) on improving deep network models, and then explore the influence of different bounding box loss functions (GIoU, CIoU, and DIoU) on mask-wearing recognition. CIoU is used as the frame regression loss function to improve the positioning accuracy. By collecting 7,958 mask-wearing images and a large number of images of people without masks as a dataset and using YOLOv5s as the benchmark model, the mAP of the model proposed in the paper reached 90.96% on the validation set, which is significantly better than the traditional deep learning method. Mask-wearing detection is carried out in a real environment, and the experimental results of the proposed method can meet the daily detection requirements. [ABSTRACT FROM AUTHOR]

Fast and accurate smoke detection is very important for reducing fire damage. Due to the complexity and changeable nature of smoke scenes, existing smoke detection technology has the problems of a low detection rate and a high false negative rate, and the robustness and generalization ability of the algorithms are not high. Therefore, this paper proposes a smoke detection model based on the improved YOLOv5. First, a large number of real smoke and synthetic smoke images were collected to form a dataset. Different loss functions (GIoU, DIoU, CIoU) were used on three different models of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l), and YOLOv5m was used as the baseline model. Then, because of the problem of small numbers of smoke training samples, the mosaic enhancement method was used to randomly crop, scale and arrange nine images to form new images. To solve the problem of inaccurate anchor box prior information in YOLOv5, a dynamic anchor box mechanism is proposed. An anchor box was generated for the training dataset through the k-means++ clustering algorithm. The dynamic anchor box module was added to the model, and the size and position of the anchor box were dynamically updated in the network training process. Aiming at the problem of unbalanced feature maps in different scales of YOLOv5, an attention mechanism is proposed to improve the network detection performance by adding channel attention and spatial attention to the original network structure. Compared with the traditional deep learning algorithm, the detection performance of the improved algorithm in this paper was is 4.4% higher than the mAP of the baseline model, and the detection speed reached 85 FPS, which is obviously better and can meet engineering application requirements. [ABSTRACT FROM AUTHOR]

The performance of traditional imbalanced classification algorithms is degraded when dealing with highly imbalanced data. How to deal with highly imbalanced data is a difficult problem. In this paper, the authors propose an ensemble tree classifier for highly imbalanced data classification. The ensemble tree classifier is constructed with a complete binary tree structure. A mathematical model is established based on the features and classification performance of the classifier, and it is proven that the model parameters of the ensemble classifier can be solved by calculation. First, the AdaBoost method is used as the benchmark classifier to construct the tree structure model. Then, the classification cost of the model is calculated, and the quantitative mathematical description between the cost and features of the ensemble tree classifier model is obtained. Then, the cost of the classification model is transformed into an optimization problem, and the parameters of the integrated tree classifier are given through theoretical derivation. This approach is tested on several highly imbalanced datasets in different fields and takes the AUC (area under the curve) and F-measure as evaluation criteria. Compared with the traditional imbalanced classification algorithm, the ensemble tree classifier has better classification performance. [ABSTRACT FROM AUTHOR]

In order to distinguish between computers and humans, CAPTCHA is widely used in links such as website login and registration. The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. This method improves the traditional VGG network structure and introduces the focal loss function to generate a new CAPTCHA recognition model. First, we perform preprocessing such as grayscale, binarization, denoising, segmentation, and annotation and then use the Keras library to build a simple neural network model. In addition, we build a terminal end-to-end neural network model for recognition for complex CAPTCHA with high adhesion and more interference pixel. By testing the CNKI CAPTCHA, Zhengfang CAPTCHA, and randomly generated CAPTCHA, the experimental results show that the proposed method has a better recognition effect and robustness for three different datasets, and it has certain advantages compared with traditional deep learning methods. The recognition rate is 99%, 98.5%, and 97.84%, respectively. [ABSTRACT FROM AUTHOR]

The development of the digital economy is of special significance to the realization of the stable development of the macroeconomy. Based on the White Paper on Development of China's Digital Economy and the actual situation of cities in Anhui Province, this study divides the index system of digital economy into three primary indexes, namely, digital industrialization, industrial digitization, and information infrastructure, and 20 secondary indexes. The percentage weight of each index is determined by the subjective evaluation method, and the index values of 16 cities in Anhui Province are calculated according to the efficacy coefficient method. Then, the mean value, standard deviation, variance, kurtosis, skewness, and other information of each index system are statistically analyzed, and the policy suggestions for the development of digital economy are given. This study fills the blank of the index system of digital economy in Anhui Province and has a certain role in promoting the development of digital economy in Anhui Province. [ABSTRACT FROM AUTHOR]

The development of the digital economy is of special significance to the realization of the stable development of the macroeconomy. Based on the White Paper on Development of China's Digital Economy and the actual situation of cities in Anhui Province, this study divides the index system of digital economy into three primary indexes, namely, digital industrialization, industrial digitization, and information infrastructure, and 20 secondary indexes. The percentage weight of each index is determined by the subjective evaluation method, and the index values of 16 cities in Anhui Province are calculated according to the efficacy coefficient method. Then, the mean value, standard deviation, variance, kurtosis, skewness, and other information of each index system are statistically analyzed, and the policy suggestions for the development of digital economy are given. This study fills the blank of the index system of digital economy in Anhui Province and has a certain role in promoting the development of digital economy in Anhui Province. [ABSTRACT FROM AUTHOR]

In order to distinguish between computers and humans, CAPTCHA is widely used in links such as website login and registration. The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. This method improves the traditional VGG network structure and introduces the focal loss function to generate a new CAPTCHA recognition model. First, we perform preprocessing such as grayscale, binarization, denoising, segmentation, and annotation and then use the Keras library to build a simple neural network model. In addition, we build a terminal end-to-end neural network model for recognition for complex CAPTCHA with high adhesion and more interference pixel. By testing the CNKI CAPTCHA, Zhengfang CAPTCHA, and randomly generated CAPTCHA, the experimental results show that the proposed method has a better recognition effect and robustness for three different datasets, and it has certain advantages compared with traditional deep learning methods. The recognition rate is 99%, 98.5%, and 97.84%, respectively. [ABSTRACT FROM AUTHOR]

We propose time-varying coefficient model selection and estimation based on the spline approach, which is capable of capturing time-dependent covariate effects. The new penalty function utilizes local-region information for varying-coefficient estimation, in contrast to the traditional model selection approach focusing on the entire region. The proposed method is extremely useful when the signals associated with relevant predictors are time-dependent, and detecting relevant covariate effects in the local region is more scientifically relevant than those of the entire region. Our simulation studies indicate that the proposed model selection incorporating local features outperforms the global feature model selection approaches. The proposed method is also illustrated through a longitudinal growth and health study from National Heart, Lung, and Blood Institute. [ABSTRACT FROM AUTHOR]

Xie, C.; Shi, P.; Cao, F., and Qian, Y., 2019. Reliability modeling and analysis of ship communication network based on apriori algorithm. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 711–716. Coconut Creek (Florida), ISSN 0749-0208. Ship communication network is prone to distortion due to crosstalk between ships. In order to improve the accuracy of data transmission and improve communication quality, a reliability modeling method of ship communication network based on Apriori algorithm is proposed. The transmission link model of ship communication network is constructed, and the fuzzy PID neural network control model is used to control the reliability of the link forwarding node of ship communication network, so as to realize the optimal deployment of the link forwarding node of ship communication network. Taking output forwarding delay, bit error rate and network throughput as constraint parameters, the reliability modeling objective function of ship communication network is constructed. The optimal parameters are optimized by Apriori learning algorithm, and the routing detection protocol of ship communication network is designed. Implementation of network routing optimization design. The simulation results show that the reliability of ship communication network is modeled by this method, the accurate probability of data transmission is improved, the bit error rate of communication network is low, and the quality of ship communication network is improved obviously. [ABSTRACT FROM AUTHOR]

Xie, C.; Shi, P.; Cao, F., and Qian, Y., 2019. Reliability modeling and analysis of ship communication network based on apriori algorithm. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 711–716. Coconut Creek (Florida), ISSN 0749-0208. Ship communication network is prone to distortion due to crosstalk between ships. In order to improve the accuracy of data transmission and improve communication quality, a reliability modeling method of ship communication network based on Apriori algorithm is proposed. The transmission link model of ship communication network is constructed, and the fuzzy PID neural network control model is used to control the reliability of the link forwarding node of ship communication network, so as to realize the optimal deployment of the link forwarding node of ship communication network. Taking output forwarding delay, bit error rate and network throughput as constraint parameters, the reliability modeling objective function of ship communication network is constructed. The optimal parameters are optimized by Apriori learning algorithm, and the routing detection protocol of ship communication network is designed. Implementation of network routing optimization design. The simulation results show that the reliability of ship communication network is modeled by this method, the accurate probability of data transmission is improved, the bit error rate of communication network is low, and the quality of ship communication network is improved obviously. [ABSTRACT FROM AUTHOR]