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 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.

HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.

An Improved K -Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods.

On the testing and validation of stray light attenuation for microsatellite star tracker baffles.

This thesis describes strategies to perform stray light testing in an earthbound laboratory while accounting for atmospheric and surface scattering phenomena that make these measurements difficult. We present a method to analyze and predict the stray light performance for a baffled star tracker optical system. This method involves a hybrid stray light analysis procedure that combines experimental measurements of a star tracker lens optics and uses ray-tracing to obtain attenuation curves. We demonstrate these analytical techniques using an engineering model ST-16 star tracker from Sinclair Interplanetary along with a baffle prototype. The system attenuation curve's accuracy is validated by comparing independently measured baffle attenuation curves with equivalent ray-tracing models. Additionally, exclusion angles are defined for the ST-16 sensor by calculating the false detection rate that varies with system attenuation levels. These techniques provide a versatile alternative to conventional testing for preliminary design stages for a star tracker baffle that emphasizes the use of modest infrastructure.

On the testing and validation of stray light attenuation for microsatellite star tracker baffles.

This thesis describes strategies to perform stray light testing in an earthbound laboratory while accounting for atmospheric and surface scattering phenomena that make these measurements difficult. We present a method to analyze and predict the stray light performance for a baffled star tracker optical system. This method involves a hybrid stray light analysis procedure that combines experimental measurements of a star tracker lens optics and uses ray-tracing to obtain attenuation curves. We demonstrate these analytical techniques using an engineering model ST-16 star tracker from Sinclair Interplanetary along with a baffle prototype. The system attenuation curve's accuracy is validated by comparing independently measured baffle attenuation curves with equivalent ray-tracing models. Additionally, exclusion angles are defined for the ST-16 sensor by calculating the false detection rate that varies with system attenuation levels. These techniques provide a versatile alternative to conventional testing for preliminary design stages for a star tracker baffle that emphasizes the use of modest infrastructure.

Offline Signature Authentication Algorithm Based on the Fuzzy Set

There exists a problem that it is difficult to identify the authenticity of offline signatures. Firstly, a segmentation model is established based on the theory of fuzzy sets to extract signatures completely. Secondly, statistical shape model (SSM) and variance distance discretization of intraclass signatures are introduced for stability analysis and quantification. Finally, multilayer classifiers are constructed to realize signature authentication. The algorithm has low false detection rate and short authentication time.

Size Detection Method of Embedded Ball Based on Aviation Bearing Image

In the aerospace industry, bearing is widely used in various rotating machinery. The performance of bearing affects the operation of the whole machinery and even aviation equipment. The wrongly assembled ball due to size is an important reason for unqualified bearing. To solve this problem, an accurate ball detection method based on the bearing image is proposed. Firstly, according to the imaging characteristics of bearing and light propagation characteristics, an image collection system based on the coaxial light source is designed. Then, aiming at the problem that the embedded ball is occluded by the bearing ring and the cage, only partial ball in the narrow gap can be used to predict the full ball and the high-precision requirement of ball detection, a ball segmentation model based on DeepLab v3+ network is used to segment the local ball, and CBAM is added in the Xception network of the original network. According to the characteristics of the segmentation result, a circle detection algorithm based on circle fitting evaluation designed for incomplete short arc is proposed. Finally, according to the detection results, judge whether the bearing is qualified or not and evaluate the feasibility of this method. Experimental results show that the ball detection accuracy is about 27 microns, and the wrongly assembled ball with a size difference of only 198 microns can be distinguished. The false detection rate of unqualified bearing is 1%. As the last line of defense of bearing quality inspection, this method can achieve zero false detection rate of unqualified bearing in the industry.