Internet False News Information Feature Extraction and Screening Based on 5G Internet of Things Combined with Passive RFID

In this paper, the authenticity of news information on the 5G Internet of Things (IoT) is studied, and a network false news information screening platform is designed and optimized by IoT combined with passive RFID. The electronic license chain based on data sovereignty is established, in which, combined with the identity identification and strong correlation ability based on the electronic license chain, a cross-industry, cross-business, and cross-field behavior record base database is formed; then, a digital library is constructed based on this base library; finally, through data sharing and management, a false news information feature extraction and screening platform is formed for the orderly management and reasonable dispatch of government resources and reducing various risks. The main functional modules implemented by the platform are the acquisition of news data and comment data, the retrieval and analysis of news data, the false detection of online news, and the visualization of false news data. However, there is still much public who are not aware or do not understand that news truth is this dynamic form. Therefore, this paper aims to inform the public that news truth in news context is a dynamic process by 5G Internet of Things combined with passive RFID. The public understands the circumstances where news truth may be dynamic truth to avoid being misled by false news.

Malaria Parasite Detection in Thick Blood Smears using Deep Learning

Malaria parasitized detection is very important to detect as there are so many deaths due to false detection of malaria in medical reports. So analysis has gained a lot of attention in recent years. Detection of malaria is important as fast as possible because detecting malaria is difficult in blood smears. Our idea is to build a transfer learning model and detect the thick blood smears whether the presence of malaria parasites in a drop of blood. The data consists of 5000 each infected and uninfected data obtained from the NIH website. In this paper, I propose to use three different types of neural networks for the performance evaluation of the malaria data by transfer learning using CNN, VGG19, and fine-tuned VGG19. Transfer learning model performed well among various other models by achieving a precision of 98 percent and an f-1 score of 96 percent.

Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN

Object detection is one of the key algorithms in automatic driving systems. Aiming at addressing the problem of false detection and the missed detection of both small and occluded objects in automatic driving scenarios, an improved Faster-RCNN object detection algorithm is proposed. First, deformable convolution and a spatial attention mechanism are used to improve the ResNet-50 backbone network to enhance the feature extraction of small objects; then, an improved feature pyramid structure is introduced to reduce the loss of features in the fusion process. Three cascade detectors are introduced to solve the problem of IOU (Intersection-Over-Union) threshold mismatch, and side-aware boundary localization is applied for frame regression. Finally, Soft-NMS (Soft Non-maximum Suppression) is used to remove bounding boxes to obtain the best results. The experimental results show that the improved Faster-RCNN can better detect small objects and occluded objects, and its accuracy is 7.7% and 4.1% respectively higher than that of the baseline in the eight categories selected from the COCO2017 and BDD100k data sets.

Automatic Georeferencing of Topographic Map Sheets Using OpenCV and Tesseract

The authors developed a pipeline for the automatic georeferencing of older 1 : 25 000 topographic map sheets of Hungary. The first step is the detection of the corners of the map content, then the recognition of the sheet identifier. These maps depict geographic quadrangles whose extent can be derived from the sheet ID. The sheet corners are used as GCPs for the georeference.The whole process is implemented in Python, using various open source libraries: OpenCV for image processing, Tesseract for OCR and GDAL for georeferencing.1147 map sheets were processed with an average speed of 4 seconds per sheet. False detection of the corners is automatically filtered by geometric analysis of the detected GCPs, while the sheet IDs are validated using regular expressions. The error of corner detection is under 1% of the sheet size for 89% of the sheets, under 2% for 99%. The sheet ID recognition success rate is 75.9%.Although the system is finetuned to a specific map series, it can be easily adapted to any other map series having approximately rectangular frame.

An improved YOLO v3 algorithm for remote Sensing image target detection

With more and more in-depth research on deep learning algorithms in recent years, how to use deep learning method to detect remote sensing images is the key to improving the utilization efficiency of remote sensing data and realizing the transformation from data to knowledge. In this paper, an improved YOLO V3 algorithm is proposed to solve the problems of missed detection and false detection of the original YOLOv3 algorithm in remote sensing image target detection with different size and wide disparity in length and width ratio. first of all, K-means algorithm is used for clustering analysis of data set to obtain the position of anchor box; Secondly, the dilated convolution with expansion rate of 2 is used to replace the general convolution in the feature extraction part; Then four scales are used for prediction; Finally, the improved algorithm is applied to the recognition of bridges, harbors and airports. The results show that the detection performance of the algorithm is improved by about 2% compared with the original algorithm.

Sensitivity of multi-PMT optical modules in Antarctic ice to supernova neutrinos of MeV energy

New optical sensors with a segmented photosensitive area are being developed for the next generation of neutrino telescopes at the South Pole. In addition to increasing sensitivity to high-energy astrophysical neutrinos, we show that this will also lead to a significant improvement in sensitivity to MeV neutrinos, such as those produced in core-collapse supernovae (CCSN). These low-energy neutrinos can provide a detailed picture of the events after stellar core collapse, testing our understanding of these violent explosions. We present studies on the event-based detection of MeV neutrinos with a segmented sensor and, for the first time, the potential of a corresponding detector in the deep ice at the South Pole for the detection of extra-galactic CCSN. We find that exploiting temporal coincidences between signals in different photocathode segments, a $$27\ \mathrm {M}_{\odot }$$ 27 M ⊙ progenitor mass CCSN can be detected up to a distance of 341 kpc with a false detection rate of $${0.01}\,\hbox {year}^{-1}$$ 0.01 year - 1 with a detector consisting of 10,000 sensors. Increasing the number of sensors to 20,000 and reducing the optical background by a factor of 70 expands the range such that a CCSN detection rate of 0.1 per year is achieved, while keeping the false detection rate at $${0.01}\,{\hbox {year}^{-1}}$$ 0.01 year - 1 .

A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

Pedestrian detection algorithm based on improved muti-scale feature fusion

Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

Optimization of Inspection Plan for Transformer Verification Line Under Uninterrupted State

At present, transformer verification line of metering centre adopts fixed cycle inspection method manually. This method requires downtime for detection, which costs a lot of time and cost. Moreover, the inspection cycle is determined based on experience and lacks rigorous basis. To solve this problem, a hybrid delivery of inspection devices is proposed to realize non-stop detection and reduce the cost of inspection time. Considering impact of cost and false detection risk on inspection cycle, a multi-objective optimization model of inspection cycle based on inspection and false detection cost is proposed. Based on NSGA-II algorithm, perturbation population is introduced to enhance the global search ability, which aims to minimize the cost of inspection and false detection. Taking the verification line’s inspection plan of the metering centre as an example. It is solved by ENSGA-II algorithm, and feasibility of hybrid delivery mode is verified, which reduced downtime by 14.58%. A more reasonable inspection cycle is obtained, inspection cost is reduced by 29.57%, and false detection cost is reduced by 6.34%. It provides a reference for the formulation of inspection plan in the actual production process.

A Multisource Situation Information Fusion Method Based on Dynamic Evidence Combination

To address the problems of fusion efficiency, detection rate (DR), and false detection rate (FDR) that are associated with existing information fusion methods, a multisource information fusion method featuring dynamic evidence combination based on layer clustering and improved evidence theory is proposed in this study. First, the original alerts are hierarchically clustered and conflicting evidence is eliminated. Then, dynamic evidence combination is applied to fuse the condensed alerts, thereby improving the efficiency and accuracy of the fusion. The experimental results show that the proposed method is superior to current fusion methods in terms of fusion efficiency, DR, and FDR.