Hierarchy Topic Detection and Hot Topic Identification

2014 ◽  
Vol 701-702 ◽  
pp. 180-186
Author(s):  
Xue Mei Zhou ◽  
Shan Ying Cheng
Keyword(s):  
Public Opinion ◽  
Real Time ◽  
Hierarchical Structure ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Topic Detection ◽  
Topic Identification ◽  
Evaluation Indexes ◽  
Detection Algorithms ◽  
The Hierarchical Structure

Due to the problem that the existing topic detection algorithms can not satisfy accuracy,real time and topic hierarchical clustering at the same time, this article builds a hierarchy topic detection algorithm based on improved single pass clustering algorithm. In addition, using public opinion evaluation indexes to analyze topic temperature,the method proposed in this paper can detect hot topics accurately and timely while showing the hierarchical structure of the topic .

scholarly journals Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3646
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Silun Peng ◽  
Shixin Song ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Clustering Algorithm ◽  
Pedestrian Detection ◽  
Grid Cell ◽  
Detection Algorithm ◽  
Intelligent Vehicles ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Driving Assistance

Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

Studies on a multidimensional public opinion network model and its topic detection algorithm

2019 ◽  
Vol 56 (3) ◽  
pp. 584-608 ◽  
Author(s):  
Guanghui Wang ◽  
Yuxue Chi ◽  
Yijun Liu ◽  
Yufei Wang
Keyword(s):  
Public Opinion ◽  
Network Model ◽  
Detection Algorithm ◽  
Topic Detection

scholarly journals Accurate and Lightweight RailNet for Real-Time Rail Line Detection

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2038
Author(s):  
Zhen Tao ◽  
Shiwei Ren ◽  
Yueting Shi ◽  
Xiaohua Wang ◽  
Weijiang Wang
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Social Progress ◽  
Line Detection ◽  
Detection Accuracy ◽  
Railway Transportation ◽  
Deep Convolutional Neural Networks ◽  
Detection Algorithms ◽  
Rail Line ◽  
Binary Segmentation

Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.

scholarly journals Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas

2018 ◽  
Vol 12 (3) ◽  
pp. 599-607 ◽  
Author(s):  
Daniel P. Howsmon ◽  
Nihat Baysal ◽  
Bruce A. Buckingham ◽  
Gregory P. Forlenza ◽  
Trang T. Ly ◽  
...  
Keyword(s):  
Real Time ◽  
Artificial Pancreas ◽  
Intervention Strategy ◽  
Failure Detection ◽  
Detection Algorithm ◽  
Outpatient Setting ◽  
False Positives ◽  
Zone Model ◽  
Infusion Site ◽  
Detection Algorithms

Background: As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient’s glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures. Methods: An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed. Results: In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58). Conclusions: As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875

Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review

2020 ◽  
Vol 13 (4) ◽  
pp. 542-549
Author(s):  
Smita Agrawal ◽  
Atul Patel
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Connected Subgraph ◽  
Comprehensive Review ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Large Scale Networks

Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed.

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.

Extracting Hot Topics from Microblogging Based on Keywords Detection and Text Clustering

2013 ◽  
Vol 303-306 ◽  
pp. 2289-2293
Author(s):  
Bo Yi ◽  
Yong Wang ◽  
Xin Chen ◽  
Ying Wang
Keyword(s):  
Public Opinion ◽  
Information Entropy ◽  
Experimental Validation ◽  
Clustering Algorithm ◽  
Text Clustering ◽  
Detection Algorithm ◽  
Dynamic Clustering ◽  
Textual Information ◽  
Topic Discovery ◽  
The Third

Following with news and forums, microblogging becomes the third largest source of Internet public opinion. So it is necessary to do research of microblogging topic discovery. Firstly, we detect hot topics through the the keywords detection algorithm. Secondly, elect most popular microblogging text in the massive microblogging text by combining of keywords weigh and textual information entropy. Finally, using the dynamic clustering algorithm, the microblogging text elected, will form into different news topics by clustering polymerization. According to experimental validation of the true microblogging data, hot topic can be effectively detected from the text of a large number through the method.

scholarly journals Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4565
Author(s):  
Cedric De Cock ◽  
Wout Joseph ◽  
Luc Martens ◽  
Jens Trogh ◽  
David Plets
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Clustering Algorithm ◽  
Dead Reckoning ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Batch Mode ◽  
Access Points ◽  
Median Position ◽  
Pedestrian Dead Reckoning

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

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