Utilization Rate Of Information Research Articles

MAEE-Net: SAR ship target detection network based on multi-input attention and edge feature enhancement

Synthetic Aperture Radar (SAR) imagery has a wide range of applications in search and rescue ships lost contact and military reconnaissance. When detecting multi-scale targets, better determination of the target edge is conducive to improving the detection accuracy of the model, but most of the existing methods lack research on this aspect. To fix the problems mentioned earlier, this paper suggests using a SAR Ship target detection network called MAEE-Net. In this paper, a multi-input attention-based feature fusion module (MAFM) and an edge feature enhancement module (EFEM) are proposed. MAFM uses attention mechanism with multi-input and multiple-output to improve attention to shallow feature map target and suppress invalid information, so as to improve the information utilization rate of each layer. To make the network better at detecting the edges of ships, EFEM uses double-branched structure to carry out fine-grained information retention and edge feature extraction. PIoU v2 is introduced to enhance multi-target processing capability. Experiments were carried out on SSDD dataset and SAR-Ship-Dataset, the overall detection accuracy was as high as 98.6% and 94.7%. The detection accuracy was 93.5% and 99.3% on inshore and offshore sub-datasets of SSDD dataset. Experimental results on two datasets show that our model is impactful.

The Establishment of Chinese Culture Teaching Resources by Knowledge Graph Applied to Chinese International Education

Abstract With the development of Chinese international education, the construction of Chinese culture teaching resources has become a priority. This study uses knowledge mapping technology to optimize Chinese culture teaching resources in Chinese international education and improve the usability and efficiency of teaching resources. The study adopts a top-down approach to construct a knowledge graph of Chinese culture teaching resources, covering named entity identification, relationship extraction and knowledge meta-extraction. BERT and BiLSTM techniques are used for feature extraction, text encoding, and conditional random field algorithm for knowledge meta-extraction and relationship mining. The experimental results show that the information utilization rate of intelligent retrieval of teaching resources using this knowledge graph is 82.67% and shows good adaptability in applications between different geographical regions. In addition, through comparative experiments, the proposed method in this study performs better than the traditional method in improving the utilization efficiency of teaching resources. This study proves the effectiveness and practicality of knowledge mapping in constructing Chinese culture teaching resources in Chinese international education.

An Improved Mask R-CNN Micro-Crack Detection Model for the Surface of Metal Structural Parts.

Micro-crack detection is an essential task in critical equipment health monitoring. Accurate and timely detection of micro-cracks can ensure the healthy and stable service of equipment. Aiming at improving the low accuracy of the conventional target detection model during the task of detecting micro-cracks on the surface of metal structural parts, this paper built a micro-cracks dataset and explored a detection performance optimization method based on Mask R-CNN. Firstly, we improved the original FPN structure, adding a bottom-up feature fusion path to enhance the information utilization rate of the underlying feature layer. Secondly, we added the methods of deformable convolution kernel and attention mechanism to ResNet, which can improve the efficiency of feature extraction. Lastly, we modified the original loss function to optimize the network training effect and model convergence rate. The ablation comparison experiments shows that all the improvement schemes proposed in this paper have improved the performance of the original Mask R-CNN. The integration of all the improvement schemes can produce the most significant performance improvement effects in recognition, classification, and positioning simultaneously, thus proving the rationality and feasibility of the improved scheme in this paper.

Reorganizing heterogeneous information from host–microbe interaction reveals innate associations among samples

AbstractThe information on host–microbe interactions contained in the operational taxonomic unit (OTU) abundance table can serve as a clue to understanding the biological traits of OTUs and samples. Some studies have inferred the taxonomies or functions of OTUs by constructing co‐occurrence networks, but co‐occurrence networks can only encompass a small fraction of all OTUs due to the high sparsity of the OTU table. There is a lack of studies that intensively explore and use the information on sample‐OTU interactions. This study constructed a sample‐OTU heterogeneous information network and represented the nodes in the network through the heterogeneous graph embedding method to form the OTU space and sample space. Taking advantage of the represented OTU and sample vectors combined with the original OTU abundance information, an Integrated Model of Embedded Taxonomies and Abundance (IMETA) was proposed for predicting sample attributes, such as phenotypes and individual diet habits. Both the OTU space and sample space contain reasonable biological or medical semantic information, and the IMETA using embedded OTU and sample vectors can have stable and good performance in the sample classification tasks. This suggests that the embedding representation based on the sample‐OTU heterogeneous information network can provide more useful information for understanding microbiome samples. This study conducted quantified representations of the biological characteristics within the OTUs and samples, which is a good attempt to increase the utilization rate of information in the OTU abundance table, and it promotes a deeper understanding of the underlying knowledge of human microbiome.

Benefits Evaluation Method of an Integrated Energy System Based on a Fuzzy Comprehensive Evaluation Method

Building an integrated energy system (IES) is an excellent solution to environmental pollution and energy consumption currently. However, there are usually various IES configurations with different performances, and it is usually difficult for the decision-makers to select the most sustainable IES solution among multiple choices. In this paper, we attempt to design an evaluation method that considers the renewable energy and energy system model for evaluating the benefit of an IES. Firstly, this paper constructs an evaluation index system including four dimensions of technology, economy, environment, and society, based on the physical architecture of IES and its benefit influencing factors. It focuses on multiple energy sources or systems, and satisfies the overall evaluation of IES, resulting in accurate benefit evaluation results. Secondly, the subjective weight is determined by combining the triangular fuzzy number (TFN) and the analytic hierarchy process (AHP). The objective weight is determined by the entropy method. The combined weight is obtained by combining the subjective and objective weights, which solves the problem of the weight calculation error caused by the mutual influence between the indexes. Finally, the fuzzy comprehensive evaluation method is used to conduct the benefit evaluation of the IES, which solves the problem that the qualitative index cannot be calculated. This paper takes an IES project in a city in northern China as a case study. Through sensitivity analysis and comparative analysis, the reliability and feasibility of the evaluation results and proposed method are verified. The analysis results show that the model evaluation results are intuitive and consistent with reality, and the data information utilization rate is high.

English Article Style Recognition and Matching by Using Web Semantics

With the explosion of internet information, people feel helpless and difficult to choose in the face of massive information. However, the traditional method to organize a huge set of original documents is not only time-consuming and laborious, but also not ideal. The automatic text classification can liberate users from the tedious document processing work, recognize and distinguish different document contents more conveniently, make a large number of complicated documents institutionalized and systematized, and greatly improve the utilization rate of information. This paper adopts termed-based model to extract the features in web semantics to represent document. The extracted web semantics features are used to learn a reduced support vector machine. The experimental results show that the proposed method can correctly identify most of the writing styles.

Solar Filament Detection Based on Improved DeepLab V3+

A novel solar filament detection method based on an improved DeepLab V3+ is proposed to address the low detection accuracy of small solar filaments in Hα full-disk solar images. First, the Xception structure of the backbone network is fine-tuned, and the low-level feature information of the filaments is added to the decoder module of the network to improve the utilization of the solar filament features. Second, the receptive field of dilated convolution is expanded, and the information utilization rate is increased via cascaded dilated convolution to improve the detection accuracy of the small solar filaments. In the decoder module, two depthwise separable convolutions are used instead of ordinary convolutions to reduce incomplete detections. Finally, a dense conditional random field is added to optimize the edge of the detection results. Experiments on a public data set comprising full-disk Hα images show that compared with the original Deeplab V3+ algorithm, the proposed method improves the mean pixel accuracy, mean intersection over union, and F1-score by 1.86%, 1.95%, and 2.18%, respectively, which also demonstrates its superiority over other existing solar filament detection algorithms.

Research on the Utilization and Optimal Management of Electronic Information Resources in University Libraries

Through theoretical analysis, this paper expounds the value of strengthening the utilization of electronic information in university libraries and the reasons affecting the utilization rate of information resources in electronic libraries, so as to give better play to the role of university libraries and improve the utilization rate of electronic information in university libraries. In view of the existing problems, this paper puts forward measures to optimize the utilization rate of electronic information resources in university libraries and give full play to the role of electronic information resources, so as to promote information exchange and development.

Data switching method among heterogeneous power information system databases based on knowledge graph

AbstractIn order to solve the problems of long data switching time, low information utilization rate, and low switching accuracy in traditional data switching methods, a data switching method between heterogeneous power information system databases based on knowledge graphs is proposed. Based on the knowledge graph technology and its ternary structure, the data of the heterogeneous power information system is processed, and the N‐Gram model is used to realize the knowledge extraction, and the data error correction is established through the model. Through ODBC technology to complete the link between heterogeneous power information system databases, and then select the data access interface and determine the data extraction task, and finally realize the data exchange between the heterogeneous power information system databases. Experimental results show that the average communication channel bandwidth during the data switching process is 1.8 Gb/s, the highest value of data switching accuracy reaches 82%, and the information utilization rate after data switching is relatively high, indicating that the method has certain effectiveness.

Multi-Stage Enhanced Rain Image Restoration Network

The restoration task of rain images is beneficial to the further development of computer vision tasks. However, recent rain removal methods have difficulty coping with dense rain streaks. In this paper, we propose a Multi-stage Enhanced Rain Image Restoration Network (MERNet) for the task of rain removal. Our main idea is to design a three-stage network architecture, which makes full use of the enhanced information of the previous stage and complements the information of the next stage through transmission step by step. We give consideration to information enhancement at the same time of information transmission, fully improve the information utilization rate and the overall network effect.

Accurate investment evaluation model of power grid based on Improved Fuzzy Neural Inference

In the accurate investment evaluation of power grid, the existing model does not consider the uncertainty of the actual evaluation object, and the evaluation subject mainly relies on subjective judgment, resulting in low information utilization rate. Therefore, the accurate investment evaluation model of power grid is designed based on Improved Fuzzy Neural Inference. The evaluation index system is established from three aspects of power supply capacity, power supply quality and power grid benefit of power grid investment, so as to achieve the purpose of giving consideration to both economic and social benefits. Based on the Improved Fuzzy Neural Inference, the membership function algorithm is designed, and the parameters are adjusted to obtain the best fuzzy inference results. According to the membership function and index improvement value, the standardized data table is established. The indicators of the same level are quantified, and the weight attributes are solved to complete the construction of accurate investment evaluation model of power grid. The experimental results show that the design model has certain advantages in the utilization rate of investment information, which is 10.59%, 15.40% and 9.95% higher than the existing models. It proves that the accurate investment evaluation model of power grid constructed in this paper is closer to the actual situation and has good application effect.

Research on fusion detection of centralized mobile target based on seismic wave sensor

Aiming at the limitation of single sensor detection and the deficiency of low information utilization rate in distributed fusion mode, according to the characteristics of strong energy and long propagation distance on free surface of seismic wave and the advantages of centralized fusion detection method, a centralized fusion detection algorithm based on Neyman-Pearson criterion is proposed. The probability formula of single sensor detection and centralized fusion detection is deduced and simulated. Through the experiment of ground maneuvering target, it is proved that the algorithm can effectively improve the detection probability and performance of the target.

Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems.

Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model's perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people's requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

Analysis and Prediction of Overloaded Extra-Heavy Vehicles for Highway Safety Using Machine Learning

Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China. However, overloaded vehicles have been causing frequent traffic accidents. Thus, to alleviate or resolve the corresponding problems associated with highway engineering safety and the market economy, an improved technique for overload management is urgently required. In this study, to analyze the overload data on expressways and highways in China, we developed a machine learning model by comparing the performances of cluster analysis, backpropagation neural network (BPNN), generalized regression neural network (GRNN), and wavelet neural network (WNN) in analyzing global and local time series overload data. In a case study, our results revealed the trends of overloading on highways in Jiangsu Province. Given sufficient data, BPNN performed better than GRNN and WNN. As the amount of training data increased, GRNN performed better, but the runtime increased. WNN had the shortest runtime among the three methods and could reflect the future trends of the overload rate in the monthly data prediction of overload. Our model provides information with potential value for expressway network management departments through data mining. This information could help management departments allocate resources reasonably and optimize the information utilization rate.

Reference subsystems for Smart Manufacturing Collaborative System (SMCS) from multi-processes, multi-intersections and multi-operators

ABSTRACTIn this paper, multi-process collaboration subsystem, multi-intersection collaboration subsystem, and multi-operator collaboration subsystem are deduced through the overall architecture of Smart Manufacturing Collaborative System (SMCS). Furthermore, the detailed implementation steps of SMCS are given, and verified by a case study in an automotive enterprise, which can realize the sharing of information, data and resources between adjacent business links and improve the utilization rate of enterprise's overall information and data resources. It also can improve design efficiency, save manufacturing costs and improve supply response speed. Then, it can save communication costs, and improve the utilization rate of information service resources.