scholarly journals An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 296 ◽  
Author(s):  
Yingying Wang ◽  
Chengsong Yang ◽  
Changqing Zhu ◽  
Kaimeng Ding
Keyword(s):  
Detection Efficiency ◽  
Detection Threshold ◽  
Detection Algorithm ◽  
Multiple Watermarking ◽  
Detection Model ◽  
Detection Algorithms ◽  
Geographic Data ◽  
Multiple Watermarks ◽  
Watermark Algorithm ◽  
Made In

Vector geographic data play an important role in location information services. Digital watermarking has been widely used in protecting vector geographic data from being easily duplicated by digital forensics. Because the production and application of vector geographic data refer to many units and departments, the demand for multiple watermarking technology is increasing. However, multiple watermarking algorithm for vector geographic data draw less attention, and there are many urgent problems to be solved. Therefore, an efficient robust multiple watermark algorithm for vector geographic data is proposed in this paper. The coordinates in vector geographic data are first randomly divided into non-repetitive sets. The multiple watermarks are then embedded into the different sets. In watermark detection correlation, the Lindeberg theory is used to build a detection model and to confirm the detection threshold. Finally, experiments are made in order to demonstrate the detection algorithm, and to test its robustness against common attacks, especially against cropping attacks. The experimental results show that the proposed algorithm is robust against the deletion of vertices, addition of vertices, compression, and cropping attacks. Moreover, the proposed detection algorithm is compatible with single watermarking detection algorithms, and it has good performance in terms of detection efficiency.

Collision Detection Algorithm of Belt Grinding of the Blisk Based on Improved Octree Segmentation Method

2021 ◽  
Author(s):  
Zhi Huang ◽  
Xing Yang ◽  
Jie Min ◽  
Hongyan Wang ◽  
Pengxuan Wei
Keyword(s):  
Collision Detection ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Segmentation Method ◽  
Belt Grinding ◽  
Detection Model ◽  
Detection Algorithms ◽  
Bladed Disk ◽  
Abrasive Belt ◽  
Algorithm Verification

Abstract In the process of belt grinding aero-engine Blisk(Bladed Disk), the abrasive belt can easily interfere with the Blisk, which will damage the valuable Blisk. Therefore, it is indispensable and significant to study the collision detection of belt grinding the Blisk. However, the application of traditional collision detection algorithms in this complicated realistic scene is difficult to obtain satisfactory results. In order to improve the accuracy and efficiency of the collision detection of grinding the Blisk, a collision detection algorithm based on the improved octree segmentation method is proposed in this paper. Firstly, the Oriented Bounding Box (OBB) is applied to establish the collision detection model for the abrasive belt. Secondly, the traditional octree segmentation method is optimized based on the k-means clustering algorithm, and an improved octree segmentation method is presented, in addition, the flow chart of the collision detection algorithm for belt grinding of the Bliskis given. Finally, algorithm verification and experimental verification are carried out based on a certain type of the Blisk. The results suggest that compared with the traditional method, the method in this paper not only promotes the accuracy of collision detection, but also promotes the efficiency of collision detection, and meets the requirements of object collision detection in this tanglesome scene with both accuracy and speed.

Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing

2018 ◽  
Vol 22 (7) ◽  
pp. 1114-1119
Author(s):  
Hongbing Meng ◽  
Keyword(s):  
Fault Detection ◽  
Data Streams ◽  
Data Stream ◽  
Error Probability ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Traditional Methods ◽  
Detection Algorithms ◽  
Parallel Data ◽  
Soft Fault

In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.

DESIGN AND APPLICATION OF BOUNDING VOLUME HIERARCHY COLLISION DETECTION ALGORITHM BASED ON VIRTUAL SPHERE

2019 ◽  
Vol 19 (07) ◽  
pp. 1940044
Author(s):  
MONAN WANG ◽  
SHAOYONG CHEN ◽  
QIYOU YANG
Keyword(s):  
Collision Detection ◽  
Detection Efficiency ◽  
Vastus Lateralis ◽  
Detection Algorithm ◽  
Virtual Surgery ◽  
Vastus Lateralis Muscle ◽  
Detection Algorithms ◽  
Bounding Volume ◽  
High Detection Efficiency ◽  
Bounding Volume Hierarchy

The result of collision detection is closely related to the further deformation or cutting action of soft tissue. In order to further improve the efficiency and stability of collision detection, in this paper, a collision detection algorithm of bounding volume hierarchy based on virtual sphere was proposed. The proposed algorithm was validated and the results show that the detection efficiency of the bounding volume hierarchy algorithm based on virtual sphere is higher than that of the serial hybrid bounding volume hierarchy algorithm and the parallel hybrid bounding volume hierarchy algorithm. Different collision detection algorithms were tested and the results show that the collision detection algorithm based on virtual sphere has high detection efficiency and good stability. As the number of triangular patches increased, the advantage was more and more obvious. Finally, the proposed algorithm was applied to two large and medium-sized virtual scenes to implement the collision detection between the vastus lateralis muscle, thigh and surgical instrument. Based on the virtual sphere, the collision detection algorithm of bounding volume hierarchy can implement efficient and stable collision detection in a virtual surgery system. Meanwhile, the algorithm can be combined with other acceleration algorithms (such as the multithread acceleration algorithm) to further improve detection efficiency.

scholarly journals Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

2021 ◽  
Vol 13 (22) ◽  
pp. 4610
Author(s):  
Li Zhu ◽  
Zihao Xie ◽  
Jing Luo ◽  
Yuhang Qi ◽  
Liman Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Dynamic Object ◽  
Shared Feature ◽  
Detection Model ◽  
Detection Algorithms ◽  
Adaptive Inference ◽  
Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

scholarly journals Ablation studies on YOLOFruit detection algorithm for fruit harvesting robot using deep learning

2021 ◽  
Vol 922 (1) ◽  
pp. 012001
Author(s):  
O M Lawal ◽  
Z Huamin ◽  
Z Fan
Keyword(s):  
Detection Efficiency ◽  
Activation Function ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Detection Time ◽  
Detection Algorithms ◽  
Spatial Pyramid Pooling ◽  
Feature Pyramid ◽  
Harvesting Robot ◽  
Spatial Pyramid

Abstract Fruit detection algorithm as an integral part of harvesting robot is expected to be robust, accurate, and fast against environmental factors such as occlusion by stem and leaves, uneven illumination, overlapping fruit and many more. For this reason, this paper explored and compared ablation studies on proposed YOLOFruit, YOLOv4, and YOLOv5 detection algorithms. The final selected YOLOFruit algorithm used ResNet43 backbone with Combined activation function for feature extraction, Spatial Pyramid Pooling Network (SPPNet) for detection accuracies, Feature Pyramid Network (FPN) for feature pyramids, Distance Intersection Over Union-Non Maximum Suppression (DIoU-NMS) for detection efficiency and accuracy, and Complete Intersection Over Union (CIoU) loss for faster and better performance. The obtained results showed that the average detection accuracy of YOLOFruit at 86.2% is 1% greater than YOLOv4 at 85.2% and 4.3% higher than YOLOv5 at 81.9%, while the detection time of YOLOFruit at 11.9ms is faster than YOLOv4 at 16.6ms, but not with YOLOv5 at 2.7ms. Hence, the YOLOFruit detection algorithm is highly prospective for better generalization and real-time fruit detection.

scholarly journals Edge Detection Model Based on Involuntary Tremors and Drifts of the Eye

2007 ◽  
Vol 11 (6) ◽  
pp. 648-654 ◽  
Author(s):  
András Róka ◽  
◽  
Ádám Csapó ◽  
Barna Reskó ◽  
Péter Baranyi
Keyword(s):  
Eye Movements ◽  
Edge Detection ◽  
Ganglion Cells ◽  
Dynamic Properties ◽  
Receptive Fields ◽  
Detection Algorithm ◽  
Point Of View ◽  
Detection Model ◽  
Detection Algorithms

Recent results in retinal research have shown that ganglion cell receptive fields cover the mammalian retina in a mosaic arrangement, with insignificant amounts of overlap in the central fovea. This means that the biological relevance of traditional and widely adapted edge-detection algorithms with convolution-based overlapping operator architectures has been disproved. However, using traditional filters with non-overlapping operator architectures leads to considerable losses in contour information. This paper introduces a novel, tremor- and drift-based edge-detection algorithm that reconciles these differences between the physiology of the retina and the overlapping architectures used by today’s widely adapted algorithms. The algorithm takes into consideration data convergence, as well as the dynamic properties of the retina, by incorporating a model of involuntary eye tremors and drifts and the impulse responses of ganglion cells. Based on the evaluation of the model, two hypotheses are formulated on the highly debated role of involuntary eye tremors: 1) The role of involuntary eye movements has information theoretical implications 2) From an information processing point of view, the functional role of involuntary eye movements extends to more than just the maintenance of action potentials. Involuntary eye-movements may be responsible for the compensation of information losses caused by a non-overlapping receptive field architecture.

V-Detector-Based Immune Negative Intrusion Detection Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2966-2971
Author(s):  
Lin Wan
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Efficiency ◽  
Detection System ◽  
Detection Threshold ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Training Stage ◽  
Testing Stage

Research the problem of intrusion detection in network security. For the defects that existing intrusion detection system can't recognize unknown attacks, to improve the detection efficiency and reduce false alarm rate, this paper basing on V-detector immunity negative intrusion detection algorithm, bring up an adaptive variable radius detector. In the training stage, randomly generate candidate detector with different detection threshold. In the testing stage, according to the size of hole and detection accuracy automatically adjust the radius of the detector. The experiment and the data of KDDCUP1999 show: under the circumstance of the same number of detector, compared with V-detector, this detector has higher coverage, less hole.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaheen Syed ◽  
Bente Morseth ◽  
Laila A. Hopstock ◽  
Alexander Horsch
Keyword(s):  
Threshold Value ◽  
Detection Algorithm ◽  
Classification Model ◽  
Type I ◽  
Accelerometer Data ◽  
Deep Convolutional Neural Networks ◽  
Major Drawback ◽  
Wear Time ◽  
Detection Algorithms ◽  
Type Ii Errors

AbstractTo date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

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