Search

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, human sleep staging methods based on electroencephalogram (EEG) signals show a trend towards single-channel and deep network models, however, single-channel information acquisition makes EEG lose the positional information of brain regions, and the features characterizing sleep stages in EEG tend to be sparse and thus difficult to extract, at the same time, the common problems of deep networks-the artificial setting of the model and its training hyperparameters make the training process blind and inefficient, and these problems lead to the low accuracy of automatic sleep staging methods. Therefore, this paper proposed to use the inter-layer feature reuse function of DenseNet to explore the sleep state information hidden in EEG signals, and improved the DenseNet model for the low-frequency characteristics of single-channel EEG signals in the frequency domain and the long-range dependence of single-channel EEG signals in the time domain, so as to achieve the fast and accurate sleep staging of the human body. In order to further improve the performance of DenseNet, it used a deep deterministic policy gradient (DDPG) algorithm to optimize and automatically adjust the key hyperparameters of DenseNet using the reinforcement learning idea during the network learning and training process. The experimental results show that the staging accuracy of the algorithm model on the Sleep-EDFx dataset reaches 89.23%, and the overall performance is better than other advanced staging algorithms in recent years, demonstrating good application prospects. [ABSTRACT FROM AUTHOR]

Forestry and fruit industry are two of the most significant components in the agricultural sector, high ecological and economic benefits in China. The forest fruits have been ever-increasingly produced annually. However, it is a high demand for the high-quality forest products, as living standards rise. Accurate assessment of fruit quality can dominate food safety to optimize the transportation and storage for the subsequent grading. Therefore, it is necessary to develop rapid, accurate, and non-destructive quality detection for forest fruits. Particularly, deep learning has been widely applied in the non-destructive quality detection of forest fruits in recent years, due to the automatic features extraction, wide applicability, and end-to-end learning. This review was focused on the research progress of deep learning in forest fruit quality from three aspects: safety, external and internal quality. It was found that Convolutional Neural Networks (CNN) were the most widely used deep learning models in the field of non-destructive quality detection of forest fruits. However, several challenges still remained, including data quality, generalization, and practical implementation. Therefore, some recommendations were then proposed for further application of deep learning. The publicly available datasets should be established to validate the model performance. Future endeavors should explore the application of deep learning-based data augmentation to generate spectral data, beyond only image datasets. Additionally, interdisciplinary collaboration should be fostered to combine microbiology, insect ecology, and plant pathology. Furthermore, standardized evaluation metrics should be developed to validate the scientific soundness of deep learning models, in order to generate various pest and disease data. In terms of models, meta-learning can be expected to design models that can adapt and improve learning strategies. Some challenges are to overcome the influences of origin, batch, environment, and variety on the accuracy of the model. Since most existing studies have been focused mainly on the high model accuracy in recent years, only a little attention can be given to model runtime and detection efficiency. Consequently, a tradeoff between accuracy and efficiency should be explored to develop lightweight models. Moreover, the deep fusion of multiple models is crucial to leverage their respective strengths. In terms of applications, quantitative studies can be concentrated on the severity of fruit pests, diseases, mechanical damage, and appearance defects using semantic segmentation. The time detection of pest infection and mechanical damage occurrence can be improved to increase the collection time and frequency, in order to improve the database for model training. Future research on forest fruit flavors should also be emphasized to facilitate consumer choice. Since deep learning is mostly confined to the laboratory stage, adequate attention can be received in practical production. Therefore, there is a high demand to integrate industry, academia, and research for the intelligent assessment equipment of fruit quality. Some insights can be provided for the further development of deep learning in non-destructive fruit quality assessment, particularly in the digitalization of the fruit industry. [ABSTRACT FROM AUTHOR]

Illegal cultivation and reclamation activities are expressly prohibited in the area of reclamation of steep slopes. Large areas of sloping farmland have posed a high risk of soil erosion to reduce land productivity, even seriously threatening food security. However, manual ground surveys have been confined to investigating the illegal cultivation on the steep slopes and the prohibition of reclamation, due mainly to time-consuming and laborious. Meanwhile, the steep slopes are often distributed in dangerous areas, such as mountains and hills, leading to delays in the manual supervision of regional soil and water conservation. Fortunately, remote sensing interpretation and detection can be applied to monitor soil and water conservation in steep slopes, particularly with the rapid development of remote sensing and unmanned aerial vehicle (UAV) technology in recent years. Deep learning has been used to extract sloping farmland in the field of cultivated land identification. However, it is still lacking to consider the terrain slope, which has a great impact on soil erosion. At the same time, the large-scale remote sensing images often contain both concentrated contiguous fields and scattered small and micro fields; There are quite different directions and shapes of the fields, due to the differences in topography and landform, leading to the missed, false detection and low confidence in the detection of convolutional neural networks. The low detection efficiency and high time cost can also be caused by manual visual inspection in the supervision of illegal cultivation areas. In this study, a new model was designed to detect the remote sensing images from the illegal cultivation areas on steep slopes using the improved YOLOv9. The datasets were used from the GF-1 satellite and ALOS PALSAR. Firstly, 12.5 m resolution DEM was calculated to extract the potential slope farmland areas using ALOS data. Then the GF-1 satellite remote sensing images in the potential slope farmland area were utilized as experimental data. The specific procedures and main contribution were as follows :1) The lightweight self-attention mechanism (SimAM) was introduced into the backbone network of YOLOv9 for the richer background and the better discrimination of features; 2) The ultra-lightweight upsampling operator (DySample) was selected to replace the kernel-based dynamic one, in order to reduce the number of network parameters for the high recognition speed and accuracy; 3) The recognition accuracy of YOLO was verified for the illegal tillage areas after the introduction of SimAM and DySample; Finally, the comparison was made on the Faster RCNN, YOLOv5, YOLOv7, and Yolov8 models. Field investigation and remote-sensing image migration experiments were also carried out to verify the effectiveness of the improved model. The results show that the average recognition accuracy of the improved YOLOv9 model was 82.27%, which was better than that of mainstream object detection, such as Faster RCNN, YOLOv7, and YOLOv8. The high reliability and effectiveness were achieved in the improved model. The ablation test showed that the accuracy, recall, mean average accuracy, and F1 score of the improved model increased by 3.62, 3.78, 1.86, and 3.7 percentage points, respectively, compared with the original model. The findings can provide high-quality data support for soil and water conservation, particularly for the decision-making on the regional control of soil erosion, ecological environmental protection, as well as green and low-carbon sustainable production. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The hole segmentation of core images is important for oil exploration. In order to solve the problems of discontinuous hole edge segmentation, low segmentation accuracy and large number of parameters, an improved model based on UNet network MSHAM-UNet was proposed. Firstly, aiming at the problem of semantic information loss caused by the hopping connection of UNet models to feature maps of different scales, a multi-scale hybrid fusion attention module (MSHAM) combined with a hybrid attention mechanism (HAM) was designed, which performed attention feature fusion on shallow feature maps with spatial information and deep feature maps with semantic information, so as to enhanced the ability of the network to aggregate information at different scales. Secondly, the GPbneck module was used to replace part of the ordinary convolution, which can reduce the number of model parameters and deepen the network, and enhance the network feature extraction ability. The experimental results show that the F1-score, intersection over union ( IoU) and mean intersection over union (MIoU) of the MSHAM-UNet network on the core hole dataset reach 87.35%, 77.27% and 90.21%, respectively, which are 5.29%, 4.02% and 4. 84% higher than the original model, and are also improved compared with the mainstream semantic segmentation model, which provides a new idea for core pore research. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor question-answering performance caused by complex Chinese characters and semantic information, a method called Chinesebert-KBQA was proposed. Chinese pre-trained language model ChineseBERT was used as the semantic embedding layer of the text and the font and pinyin information were integrated. It improves the performance of traditional semantic parsing methods in the sub-tasks of entity reference recognition and relationship prediction. Specifically, the two models proposed were ChineseBERT-CRF based entity reference recognition model and ChineseBERT-TextCNN-Softmax based relationship prediction model, which comprehensively improved the semantic understanding ability of Chinese text. Finally, combined with the relevant information among the sub-tasks, the final answer prediction was carried out. The MOOC Q&A and NLPCC2018, both educational question-answering data sets, were used for experiments. The experimental results show the effectiveness and accuracy of the proposed method in Chinese knowledge graph question-answering. [ABSTRACT FROM AUTHOR]

In response to the limitations observed in existing communication modulation recognition technologies, including a restricted number of recognition types and a suboptimal overall recognition rate, an automatic modulation recognition method for communication signals based on feature fusion (ACCLNN) was proposed. Initially, the method leveraged ASPP to capture diverse input information across different scales. Spatial features were then extracted through a parallel convolutional neural network (CNN), incorporating the CBAM attention module to enhance feature representation in critical data areas and extract low-level features. Subsequently, a long short-term memory network (LSTM) was employed for temporal feature extraction, accompanied by the introduction of a self-attention module for profound feature extraction. The final step involved fusing low-level features and deep features to achieve comprehensive feature extraction and mapping. Experimental results demonstrate the efficacy of the proposed method in identifying communication signal categories in low signal-to-noise ratio (SNR) environments, achieving an overall recognition rate exceeding 93% . [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract:Techniques based on deep learning neural network models are widely used in computer vision, natural language processing, and other fields. However, researchers have found that neural network models have significant security risks, such as vulnerability to adversarial sample attacks. Studying the techniques related to adversarial samples for image classification can help people recognize the vulnerability of neural network models, which in turn can promote the research of security hardening mechanisms for related models. To overcome the challenges of high time overhead and perturbation redundancy of the JSMA method, a fast JSMA adversarial example generation method with low disturbance redundancy called S-JSMA is proposed. The S-JSMA method replaces the iterative operation with a single-step one to simplify the work flow of the JSMA algorithm. Moreover, the proposed method adopts a simple perturbation rule rather than the salient graph based perturbation used in JSMA. Consequently, S-JSMA significantly reduces the time overhead and the disturbance redundancy of generating adversarial examples. The experimental results on the MNIST dataset demonstrate that, compared with the JSMA and the FGSM methods, the proposed S-JSMA achieves considerable attacking effects with a significantly shorter time period. [ABSTRACT FROM AUTHOR]

Objective To investigate the diagnostic performance and application value of an artificial intelligence (AI) bone disease diagnosis system in the diagnosis of thoracic fractures. Methods A retrospective analysis was performed on 726 cases of thoracic fractures confirmed by chest CT re-examination 3-6 weeks after trauma emergency admission at Shenzhen University General Hospital. The recall rate, precision rate, and F1 score of AI, two radiologists, and the radiologists assisted by AI in diagnosing thoracic fractures were calculated.Results The recall rate and F1 score of AI in detecting rib fractures were 0.91 and 0.92, respectively, both higher than those of Radiologist 1 (0.77, 0.85) and Radiologist 2 (0.84, 0.90). The precision rate of AI (0.92) was lower than that of Radiologist 1 (0.95) and Radiologist 2 (0.96). With AI assistance, the recall rate, precision rate, and F1 score of Radiologist 1 and Radiologist 2 in detecting rib fractures were 0.94, 0.95, 0.94 and 0.97, 0.98, 0.97, respectively. For detecting other thoracic fractures, the recall rate and F1 score of AI (0.90, 0.90) were higher than those of Radiologist 1 (0.62, 0.74) and Radiologist 2 (0.73, 0.81). With AI assistance, the recall rate, precision rate, and F1 score of Radiologist 1 and Radiologist 2 in detecting other thoracic fractures were 0.94, 0.95, 0.94 and 0.97, 0.97, 0.97, respectively. Conclusion AI can efficiently and sensitively detect thoracic fractures in chest CT scans of emergency trauma patients, potentially optimizing the diagnosis and treatment process for emergency trauma patients. [ABSTRACT FROM AUTHOR]

With the extension of lead time for extrapolation, the attenuation of radar echoes becomes increasingly pronounced, and the predictive performance for intense echoes rapidly deteriorates. These constitute two quintessential characteristics of the current radar extrapolation results inaccuracy. To address the issues mentioned above, a novel methodology called DiffREE (diffusion radar echo extrapolation algorithm) was introduced. This algorithm skillfully fuses the spatial and temporal information from past radar echo frames using a conditional encoding module. It employs a Transformer encoder to automatically extract spatiotemporal features from the echoes, which are then used as conditions to drive the diffusion model in reconstructing the current radar echo frames. Experimental results demonstrate that this method can produce high-precision, high-quality radar forecast frames, achieving significant improvements of 42. 2%, 51. 1%, 49. 8%, and 39. 5% in CSI, ETS, HSS, and POD, respectively, compared to the best-performing baseline algorithm [ABSTRACT FROM AUTHOR]

Addressing the issues of slow speed and poor performance in multi-object grasping detection in unstructured environments, a method of performing object detection before grasping detection is pro- posed. In object detection, to accelerate the network's running speed, this paper improved the YOLOv5 network by employing depth wise separable convolutions and coordinate attention mechanisms. For the grasping task.a single-stage grasping pose detection algorithm was designed. Firstly, considering the in- terference present in unstructured environments, RGB-D images were selected as the input data for the grasping network, and GG-CNN was chosen as the backbone network. Secondly, to enhance the feature extraction capabilities of the grasping network, the parallel use of different size convolutional kernels in the Inception-ResNet module was utilized to broaden the network's receptive field. Additionally, the inte gration of a parameter-free three-dimensional attention mechanism enabled the network to focus more on grasping information features and suppress background noise. Finally, a grasping quality evaluation was employed to refine the grasping boxes, and the grasping box with the highest confidence score was out- put. The experimental results indicate that the improved object detection network has a parameter count of 2 776 708 and achieves 102 frames per second (FPS). On the public Cornell dataset, the improved grasping detection network achieves an accuracy of 96. 57% with a FPS of 54, 17. The combination of the two improved networks can be deployed on robotic arms and effectively accomplish grasping tasks in multi-object scenarios, making them suitable for practical industrial applications. [ABSTRACT FROM AUTHOR]

Inorganic Chemistry, typically taught to freshmen, is a foundational yet theoretically intense course that often challenges students’ ability to synchronize theory with practical experiments. To address these issues and particularly to enhance the learning capabilities of top-performing students, reforms have been made thoroughly. Utilizing a blended learning model supported by an Information resource platform and artificial intelligence knowledge graph, we have managed to integrate learning processes effectively, enhancing individualized learning and self-study capabilities. Additionally, the introduction of virtual simulation experiment has bridged the gap between theoretical instruction and experimental application, enriching students’ deep learning experiences. Immersive learning environment, facilitated by collaborations with local geological museums and our school’s laboratory platforms, has been established to integrate theory with practical application effectively, boosting students’ practical and innovative skills. Through these innovations, the course has been enriched resource availability, increased recognition from peers, and significant enhancement in students’ practical and innovative capabilities. [ABSTRACT FROM AUTHOR]

Objectives：In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal. Methods: Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified. Results:After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides. Conclusions: This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification. [ABSTRACT FROM AUTHOR]

Objectives: Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning. Methods: The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset. Results: The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively. Conclusions: The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters. [ABSTRACT FROM AUTHOR]

Re-identification technology for pedestrians and vehicles has been successfully applied in the field of intelligence analysis. However, there is a lack of research on re-identification technology for ship targets. In this paper, we propose a double-feature fusion-based maritime defogging re-identification network for intelligence analysis and supervision of ship targets. To reduce the impact of negative samples on features, we adopt a perspective-assisted adaptive query expansion method and a similarity-based feature fusion method. Furthermore, a defogging branch is embedded in the shallow layer of the re-identification branch. This branch utilizes weight sharing technology to extract fog-free features. The defogged image is then reconstructed using upsampling technology and the pyramid model, enhancing the recognition ability of the re-identification network in low-visibility scenarios. Finally, a pseudo-IOU based non-maximum suppression method is proposed to enhance the detection accuracy of ship targets. This method modifies the confidence of the detection frame. Experimental results demonstrate that the proposed method outperforms existing methods, and each module contributes to the network's performance. [ABSTRACT FROM AUTHOR]

To solve the problem of low recognition accuracy of radar active jamming in strong noise environment, an algorithm for ER-C-L(Extended ResNet-CNN-LSTM) network model based on one-dimensional composite features is proposed. Firstly, the amplitude, instantaneous frequency, instantaneous envelope of power spectrum and their composite features are taken as network input to compare their recognition accuracy in ResNet-CNN model. The composite features of amplitude and instantaneous envelope of power spectrum with high detection probability and small data volume are selected as the optimal features. Then, the complex features are injammed into the ER-C-L network to identify six new active jamming models. Simulation experiments show that the recognition accuracy of jamming is 98.5% in strong noise environment within the JNR of -10 dB. Compared with other deep learning algorithms such as CNN, ResNet-CNN, extended ResNet-CNN and LSTM, it has higher interference recognition accuracy. [ABSTRACT FROM AUTHOR]

Mountain flash floods are one of the most dangerous natural disasters globally, characterized by strong suddenness, rapid development, great destructive power, and difficult short-term prediction. Traditional mountain flood prediction and early warning methods mainly rely on hydro-hydraulic mountain flood process simulation based on physical mechanisms. However, this method is computationally complex, time-consuming, and difficult to meet the short-term prediction needs of mountain floods. Taking Renli Village, Lin'an, Zhejiang as an example, based on 8 378 rainfall time series generated from hydro-hydraulic physical simulation and the corresponding mountain flood inundation spatiotemporal sequence dataset, a deep neural network model was constructed using convolutional gated recurrent units (convGRU) as the core to build a spatiotemporal sequence prediction agent model for mountain flash floods. This model can rapidly predict the spatiotemporal evolution process of mountain flood inundation in the next 6 hours by inputting the past 24 hours of rainfall observation time series and the next 6 hours of rainfall forecast time series. The agent model can reliably predict the hourly mountain flood inundation range, the maximum inundation depth, and inundation position in the next 6 hours in the test set, with a mean coefficient of determination of 0.96, and the prediction speed is on average 15 625 times faster than physical simulation. This indicates that the agent model can capture the complex mapping relationship from rainfall to mountain flash floods in physical simulation, achieve rapid prediction of mountain flash floods in the target area, and provide a strong model foundation for mountain flash flood early warning and emergency response decision-making. [ABSTRACT FROM AUTHOR]

Sericulture has a pivotal position in the agricultural production of China. The number of silkworms is one of the key indicators in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms. Accurate detection of the number of silkworms can ensure the quality of sericulture products. However, manual counting has not fully met the needs of large-scale production in recent years. There is a high demand to combine AI technology for silkworm counting. In this study, silkworm detection and counting were studied from the perspective of deep learning. Several challenges were solved in the detection of silkworms. Since the silkworms belonged to the small targets, it was difficult to detect the fewer features; When the number of silkworms was high, serious masking occurred to cause interference in the detection of densely distributed silkworms; It was difficult to ensure that the clear images of silkworms were captured from the actual production. The blur images were easy to deteriorate the accuracy of detection. Two tasks were covered as follows. 1) The image dataset of silkworms was constructed; 2) MAM-YOLO, a silkworm detection and counting model was proposed using multi-dimensional perception. The pictures of silkworms were first taken from the Guangxi Sericulture Technology Promotion Station. These pictures of silkworms were preliminarily processed. The polygon annotation was then selected, according to the body and distribution of silkworms. The pictures of silkworms were annotated with the open-source tool (Labelme). Finally, the dataset was expanded using image transformation and noise addition. The silkworm picture dataset was obtained for the subsequent training, verification, and testing. An improved YOLOv8 (MAM-YOLO) was proposed to detect the silkworm, in order to realize the accurate detection and counting. A silkworm detection and counting model was proposed using space-to-depth transformation and image deblurring. The fine-grained detection performance of blurred images was improved in the MAMYOLO model. The space-to-depth transformed convolution was utilized instead of stride convolution for the pooling layer in the feature extraction. The fine-grained features were extracted from the images, in order to further improve the detection of the low-resolution and small targets. In addition, the impact of blurred images on the detection performance was reduced by introducing a deblurring module into the model. RCS-OSA module was added to the YOLOv8 model. The network feature extraction was improved to integrate the information of different sizes in the receptive fields. The detection head of the original network was improved into the multi-dimensional sensing head. The three-dimensional features of size, space, and channel were aggregated to enhance the detection of silkworm targets. The loss function was replaced with the EIOU loss function. The regression accuracy of the detection frame was improved to reduce the object occlusion. Experimental results show that the MAM-YOLO more accurately detected the silkworm targets. The detection speed of mAP@0.5:0.95 reached 74.8% and 22.2 frames per second, indicating the better functions of silkworm detection and counting with high precision and high rate. The detection requirements were fully met in the actual work of the Guangxi sericulture station. The results show that the silkworm detection and counting system of the technical indicators met the application requirements. The finding can provide the detection services for the actual production work of the silkworm breeding station. In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 dataset. It was found that the MAM-YOLO was also achieved in the better detection performance of wheat ears, particularly on the small targets similar to dense occlusion.Sericulture has a pivotal position in China's agricultural production. The number of silkworms is a key indicator in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms, and accurate detection of the number of silkworms can ensure the quality of sericulture products. The traditional way of counting silkworms manually is inefficient, and it needs to be combined with technology to intelligently complete this kind of heavy work. In this thesis, from the perspective of deep learning, a silkworm detection and counting method is studied, and several problems in silkworm detection are solved: silkworms belong to small targets, which have fewer features so that they are not easy to detect; silkworms are densely distributed, and when the number of silkworms is high, serious masking phenomenon occurs which causes interference in the detection; it is difficult to ensure that the images of silkworms obtained in the actual production are clear, and blur images will make the detection affected. The main research content of this thesis is as follows: This chapter completed two main tasks: 1) Completed the production of silkworm image data set; 2) MAM-YOLO, a silkworm detection and counting model based on multi-dimensional perception, was proposed.In this paper, pictures of silkworms were taken by workers from Guangxi Sericulture Technology Promotion Station. First, these pictures of silkworms were preliminically processed. Then, according to the characteristics of the body and distribution of silkworms, the polygon annotation method was selected, and the pictures of silkworms were annotated with the open-source tool Labelme. Finally, the data set is expanded by means of image transformation and noise addition, and finally the silkworm picture data set is obtained for subsequent training, verification and testing.This chapter proposes an improved silkworm detection method based on YOLOv8, MAM-YOLO, which realizes accurate detection and counting of silkworm. Secondly, aiming at the problems of loss of fine-grained information and poor detection performance of blurred images in the MAM-YOLO model, a silkworm detection and counting model based on spaceto-depth transformation and image deblurring is proposed. The model utilizes space-to-depth transformed convolution instead of stride convolution and pooling layer in the feature extraction process, so that fine-grained features in the image can be extracted, which further improves the detection capability for low-resolution and small targets. In addition, the impact of blurred images on the detection performance is reduced by introducing a deblurring module into the model. By adding RCSOSA module to YOLOv8 model, the capability of network feature extraction is improved, and the information of different size receptive fields is integrated. The detection head of the original network is improved into multi-dimensional sensing head, and the three-dimensional characteristics of size, space and channel are aggregated to enhance the detection ability of silkworm targets. By replacing the loss function with EIOU loss function, the regression accuracy of detection frame is improved and the problem caused by object occlusion is reduced. Experiments show that MAM-YOLO can more accurately detect silkworm targets, and the detection speed of mAP@0.5:0.95 reaches 74.8% and 22.2 frames per second, which realizes the functions of silkworm detection and counting with high precision and high rate, and can meet the detection requirements in the actual work of Guangxi sericulture station. results show that the silkworm detection and counting system of the technical indicators have met the application requirements, and can provide detection services for the actual production work of silkworm breeding station.In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 data set, and it was found that MAM-YOLO also showed a high improvement in the detection performance of wheat ears, proving that the proposed method can generalize to other small targets similar to dense occlusion. [ABSTRACT FROM AUTHOR]

Aortic dissection (AD) is a critical emergency, where rapid and accurate diagnosis and treatment significantly influence the prognosis of AD patients. However, achieving such precise and swift diagnosis and treatment poses substantial challenges. In recent years, the application of deep learning based on CT images in assisting the diagnosis and treatment of AD has increasingly gained prominence, particularly in the identification, segmentation, and prediction of patient outcomes, thereby offering significant value for clinical decision-making in AD management. To better understand the application of deep learning based on CT images in AD, this review will summarize and discuss the current applications, existing challenges, and potential optimization strategies of deep learning in the diagnosis and treatment of AD. [ABSTRACT FROM AUTHOR]

The federated learning framework that uses autoencoder model to detect malicious model updates is an excellent defense framework for poisoning attacks. However, the existing autoencoder-based models face several challenges such as training difficulties and limited anomaly detection capability. In view of the above problems, this paper proposed an algorithm for detecting malicious model updates of federated learning based on β-VAE: the central server stabilized the training dataset by suppressing random attributes of the training samples, and used the dataset to train β-VAE anomaly detection model in real time. The model computed the anomaly score of task model updates uploaded by the clients, and then to detect and exclude malicious model updates based on the dynamic threshold of anomaly score. This paper evaluated the performance of the proposed algorithm on three federated learning tasks. Specifically, these tasks include classification on the MNIST dataset using the logistic regression (LR) model, classification on the FEMNIST dataset using the convolutional neural network (CNN) model, and character prediction on the Shakespeare dataset using the recurrent neural network (RNN) model. The experimental results show that, the task model under this algorithm achieves higher accuracy compared to other defense algorithms. This indicates that in Non-IID scenarios, the algorithm exhibits strong robustness against poisoning attacks in federated learning. [ABSTRACT FROM AUTHOR]

In order to improve the robustness of visual SLAM in dynamic scenes, this proposed a new visual SLAM algorithm called SAD-SLAM. This algorithm actively extracted features using the GCNv2 network to obtain a set of evenly distributed feature points and accelerate the extraction speed. Additionally, it detected objects within the scene using the YOLOv8-seg semantic segmentation network and classified them based on their ability to move autonomously. Furthermore, it used a semantic association method to filter potential dynamic objects at both the 2D and depth levels, determining their likelihood of movement. Finally, it constructed a dense 3D point cloud map containing semantic information, avoiding interference from dynamic objects. The effectiveness of this algorithm is demonstrated through experiments using the TUM dataset and real-world scenes. The results show that compared to ORB-SLAM3 and other related dynamic SLAM algorithms, SAD-SLAM achieves better positioning accuracy in dynamic scene. [ABSTRACT FROM AUTHOR]

NeRF is a deep learning model aimed at modeling three-dimensional implicit spaces, and it holds significant value in the representation and rendering of 3D scenes. However, due to the complex training process, substantial computational resources, and time requirements, the usability and practicality of the NeRF algorithm are somewhat limited. Addressing the pain points of NeRF optimization has become a hot topic in the field of computer vision. This paper aimed to provide a comprehensive review of the optimization and application of NeRF. Firstly, it delved into the basic principles of NeRF and outlined the current optimization status from the perspectives of rendering quality, computational complexity, and pose. Secondly, it enumerated the application scenarios of NeRF to provide references for future, more efficient and practical algorithmic optimizations. Finally, it summarized the strengths and limitations of NeRF and proposed potential future directions tailored to harness the tremendous potential of NeRF in 3D rendering, scene synthesis, and beyond. [ABSTRACT FROM AUTHOR]

Camera pose estimation plays a crucial role in tasks such as autonomous driving and robotics, elucidating the direction and position of the camera in relation to a given scene through the estimation of its positional coordinates and angular deviations around the three coordinate axes. To facilitate the understanding of researchers in the realm of camera pose estimation, this paper comprehensively reviewed the current research status and latest progress in this field will. Firstly, it introduced the fundamental principles, evaluation indicators, and pertinent datasets associated with camera pose estimation. Subsequently, the review elaborated and summarized the two-stage model structure method and single-channel model structure method from the two key technologies of scene relationship construction and camera pose calculation. It conducted classification and analysis based on the diverse core algorithms and scene information employed, with performance comparisons drawn from indoor and outdoor public datasets. Lastly, it expounded the current challenges in the field and future development trends. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)