Existing single-stage target detection algorithms are insensitive to nodule detection in lung nodule detection, multiple up-samplings during feature extraction by Convolutional Neural Network (CNN) has difficult feature extraction and poor detection effect, and the existing pulmonary nodule detection algorithm models are complex and not conductive to practical application employment and implementation. To address the above problems, a real-time pulmonary nodule detection algorithm combining attention mechanism and multipath fusion was proposed, based on which the up-sampling algorithm was improved to effectively increase the detection accuracy of lung nodules and speed of model inference, the model size was small and easy to deploy. Firstly, the hybrid attention mechanism of channel and space was fused in the backbone network part of feature extraction. Secondly, the sampling algorithm was improved to enhance the quality of generated feature maps. Finally, the channels were established between different paths in the enhanced feature extraction network part to achieve the fusion of deep and shallow features, so the semantic and location information at different scales was fused. Experimental results on LUNA16 dataset show that, compared to the original YOLOv5s algorithm, the proposed algorithm achieves an improvement of 9.5, 6.9, and 8.7 percentage points in precision, recall, and average precision, respectively, with a frame rate of 131.6 frames/s, and a model weight file of only 14.2 MB, demonstrating that the proposed algorithm can detect lung nodules in real time with much higher accuracy than existing single-stage detection algorithms such as YOLOv3 and YOLOv8.
In order to solve the problem of feature information loss caused by the introduction of a large number of pooling layers in traditional convolutional neural networks, based on the feature of Capsule Network (CapsNet)——using vector neurons to save feature space information, a network model 3DSPNCapsNet (3D Small Pooling No dense Capsule Network) was proposed for recognizing 3D models. Using the new network structure, more representative features were extracted while the model complexity was reduced. And based on Dynamic Routing (DR) algorithm, Dynamic Routing-based algorithm with Length information (DRL) algorithm was proposed to optimize the iterative calculation process of capsule weights. Experimental results on ModelNet10 show that compared with 3DCapsNet (3D Capsule Network) and VoxNet, the proposed network achieves better recognition results, and has the average recognition accuracy on the original test set reached 95%. At the same time, the recognition ability of the network for the rotation 3D models was verified. After the rotation training set is appropriately extended, the average recognition rate of the proposed network for rotation models of different angles reaches 81%. The experimental results show that 3DSPNCapsNet has a good ability to recognize 3D models and their rotations.
Aiming at the problem that the data traffic rises with the increase of data visitors in large-scale Wireless Sensor Networks (WSN), a data traffic optimization WSN system framework was designed and implemented to build large-scale WSN and reduce the network data traffic. The IPv6 and IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) technology were adopted to build large-scale WSN. To integrate the WSN and traditional Internet, the Message Queuing Telemetry Transport (MQTT) and Message Queuing Telemetry Transport for Sensor Network (MQTT-SN) protocols were deployed in application layer to build system publish/subscribe model. The experimental results show that, when system has 5 sensor nodes, compared with the Constrained Application Protocol (CoAP) based WSN system, the data traffic of the proposed system is 18% of the former. It proves that the proposed system framework can effectively control the impact caused by increasing visitors to WSN data traffic.
To further reduce the great computational complexity for High Efficiency Video Coding (HEVC) intra prediction, a novel algorithm was proposed in this paper. First, in Coding Unit (CU) level, the minimum Sum of Absolute Transformed Difference (SATD) of current CU was used to decide an early termination for the split of this CU at each depth level: if the minimum SATD of this CU is smaller than the given threshold value. Meanwhile, based on statistical analysis, the probabilities of each candidate prediction modes being optimal mode were used to further reduce the number of candidate modes which have almost no chance to be selected as the best mode. The experimental results show that, the proposed algorithm can save an average of 30.5% of the encoding time with negligible loss of coding efficiency (only 0.02dB Y-PSNR(Y-Peak Signal-to-Noise Ratio) loss) compared with the reference model HM10.1. Besides, the proposed algorithm is easy to provide software and hardware implementations, and it is also easy to be combined with other methods to further reduce the great computational complexity for HEVC intra coding.
This article focused on the mobile sink scheduling problem in Wireless Sensor Networks (WSN). A mobile single-sink scheduling algorithm in wireless sensor networks was proposed based on Linear Programming (LP). Firstly, the problem was mathematically modeled and formulated in time domain, and the problem was re-formulated from time to space domain to reduce the complexity. Then a polynomial-time optimal algorithm was proposed based on linear programming. The simulations confirm the efficiency of the algorithm and the results show that the algorithm can significantly improve the network lifetime of wireless sensor networks.
For the difficulty of expressing spatial context in classification of high resolution remote sensing imagery, a new multi-scale Conditional Random Field (CRF)model was proposed here. Specifically, a given image was represented as three superpixel layers respectively being region, object and scene from fine to coarse firstly. Then features were extracted layer-by-layer, and those features from the three layers were associated with each other to form a feature vector for each node in region layer. Secondly, Support Vector Machine (SVM) was adopted to define association potential function, and Potts model weighted by feature contrast function was used to define interaction potential function of CRF model, thus a layer-by-layer feature associative and multi-scale SVM-CRF model was formed. To confirm the effectiveness of the proposed model in classification, experiments on two complex scenes from Quickbird remote sensing imagery were developed. The results show that the proposed model achieves an improved accuracy averagely 2.68%, 2.37%, 3.75% higher than that of SVM-CRF model based on either region, object or scene layer, also it consumes less time in classification.
According to characters of marine ecology domain knowledge, a marine ecology knowledge organization model was proposed. Referring to engineering field literature and the device-function knowledge representation theory that the "function" concept was used to describe marine ecology functional process; a viewpoint of device-function was fixed, a domain upper ontology for marine ecosystem was presented, and then marine ecological conceptual model and marine ecology OWL ontology were constructed. By extending OWL-DL, OWL-Process model oriented function-process was proposed, and then marine ecology function-process ontology instance was constructed. Based on constructed marine ecology ontology repository, marine ecological knowledge management system was developed. The ontology application system provides marine ecology knowledge query and crisis early warning functions; and it also verifies the validity, rationality and feasibility of constructed marine ecology ontology.
In integrated support engineering, the number of components in reliability block diagram is large, the level of mastering the principle of system is required to be high and the operational data is always incomplete. To resolve these problems, a method that identifies the reliability structure of system using the information of operational data and the reliability of the units was proposed. The system reliability was estimated by using the system performance information. In addition, all reliability structure models was traversed and the theoretical reliability was calculated with the system's units reliability information, then the deviations between the estimated value of system reliability and all the reliability theoretical values were calculated, and the identification results by the first N reliability structure models of the lowest deviation was outputted after sorting the deviations. The calculation results of a given example show that the combined system based on the voting reliability structure can be identified with the probability of around 80%, decreases to 3% of the scope out of all possible forms, it can significantly reduce the workload of the researcher to identify the system reliability structure.
To solve the problem of losing edge and texture information in the existing image denoising algorithms based on fractional-order integral, an image denoising algorithm using fractional-order integral with edge compensation was presented. The fractional-order integral operator has the performance of sharp low-pass. The Cauchy integral formula was introduced into digital image denoising, and the image numerical calculation of fractional-order integral was achieved by the method of slope approximation. In the process of iterative denoising, the algorithm built denoising mask by setting higher tiny fractional-order integral order at the rising stage of image Signal-to-Noise Ratio (SNR); and the algorithm built denoising mask by setting lower small fractional-order integral order at the declining stage of image SNR. Additionally, it could partially restore the image edge and texture information by the mechanism of edge compensation. The image denoising algorithm using fractional-order integral proposed in this paper makes use of different strategies of the fractional-order integral order and edge compensation mechanism in the process of iterative denoising. The experimental results show that compared with traditional denoising algorithm, the denoising algorithm proposed in this paper can remove the noise to obtain higher SNR and better visual effect while appropriately restoring the edge and texture information of image.