scholarly journals Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Dawei Li ◽  
Xiaojian Hu ◽  
Cheng-jie Jin ◽  
Jun Zhou
Keyword(s):  
Structure Learning ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Traffic Incidents ◽  
Naive Bayesian ◽  
Detection Algorithms ◽  
Naïve Bayesian ◽  
Model Training ◽  
Significant Superiority ◽  
Better Than

This study develops a tree augmented naive Bayesian (TAN) classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE) in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN) based and multilayer feed forward (MLF) neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

Highway Traffic Incident Detection and Optimal Sensor Placement Layout

2013 ◽  
Vol 650 ◽  
pp. 460-464 ◽  
Author(s):  
Lan Bai ◽  
Qi Sheng Wu ◽  
Mei Yang ◽  
Lan Xin Wei ◽  
Bo Li ◽  
...  
Keyword(s):  
Road Traffic ◽  
Detection Algorithm ◽  
Simulation Software ◽  
Incident Management ◽  
Incident Detection ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Before And After ◽  
Traffic Incident Management

Traffic incident detection is critical to the core of the traffic incident management process. In order to study the highway traffic incident detection algorithm and the layout spacing of the fixed detector, under the assumptions of the linear traffic flow, to detect traffic incidents as the goal, using TransModeler traffic simulation software to simulate the highway traffic conditions from Xian to Hanzhong, getting the changes in the macroscopic traffic parameters before and after the traffic incident, and analysis of the data, finally puts forward the optimal layout of spacing of basic road traffic incident detection.

GAID: Genetic Adaptive Incident Detection for Freeways

2003 ◽  
Vol 1856 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Prasenjit Roy ◽  
Baher Abdulhai
Keyword(s):  
Neural Networks ◽  
Feature Space ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Time Step ◽  
Detection Algorithms ◽  
Bayesian Update ◽  
Average Condition ◽  
The Difference ◽  
Input Variables

Extensive research on point-detector-based automatic traffic-impeding incident detection indicates the potential superiority of neural networks over conventional approaches. All approaches, however, including neural networks, produce detection algorithms that are location specific—that is, neither transferable nor adaptive. A recently designed and ready-to-implement freeway incident detection algorithm based on genetically optimized probabilistic neural networks (PNN) is presented. The combined use of genetic algorithms and neural networks produces GAID, a genetic adaptive incident detection logic that uses flow and occupancy values from the upstream and downstream loop detector stations to automatically detect an incident between the said stations. As input, GAID uses modified input feature space based on the difference of the present volume and occupancy condition from the average condition for time and location. On the output side, it uses a Bayesian update process and converts isolated binary outputs into a continuous probabilistic measure—that is, updated every time step. GAID implements genetically optimized separate smoothing parameters for its input variables, which in turn increase the overall generalization accuracy of the detector algorithm. The detector was subjected to off-line tests with real incident data from a number of freeways in California. Results and further comparison with the McMaster algorithm indicate that GAID with a PNN core has a better detection rate and a lower false alarm rate than the PNN alone and the well-established McMaster algorithm. Results also indicate that the algorithm is the least location specific, and the automated genetic optimization process makes it adapt to new site conditions.

Road crack detection and quantification based on segmentation network using architecture of matrix

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gang Li ◽  
Yongqiang Chen ◽  
Jian Zhou ◽  
Xuan Zheng ◽  
Xue Li
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Detection Methods ◽  
Content Type ◽  
The Road ◽  
Detection Algorithms ◽  
Model Training ◽  
Relative Errors ◽  
Detection Effect ◽  
Better Than

PurposePeriodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.Design/methodology/approachIn this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.FindingsTo improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.Originality/valueThis paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

scholarly journals A New Semantic-Based Tool Detection Method for Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Wenbai Chen ◽  
Chao He ◽  
Chen W.Z. ◽  
Chen Q.L. ◽  
Wu P.L.
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Recognition Ability ◽  
Home Helper ◽  
Point Detection ◽  
Functional Components ◽  
Instance Segmentation ◽  
Better Than

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

scholarly journals Towards Pedestrian Target Detection with Optimized Mask R-CNN

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Dong-Hao Chen ◽  
Yu-Dong Cao ◽  
Jia Yan
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Width Ratio ◽  
Detection Accuracy ◽  
Natural Scene ◽  
Original Algorithm ◽  
Detection Algorithms ◽  
Length Width ◽  
Natural Characteristics ◽  
Better Than

Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth residual network model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms.

scholarly journals Outlier Detection in High Dimensional Data

2020 ◽  
Vol 19 (01) ◽  
pp. 2040013 ◽  
Author(s):  
Firuz Kamalov ◽  
Ho Hon Leung
Keyword(s):  
Outlier Detection ◽  
High Dimensional Data ◽  
Real Life ◽  
Principal Component ◽  
Original Data ◽  
Detection Algorithm ◽  
High Dimensional ◽  
Detection Algorithms ◽  
Real Life Data ◽  
Better Than

High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on dataset of small size with a large number of features. In this paper, we propose a novel outlier detection algorithm based on principal component analysis and kernel density estimation. The proposed method is designed to address the challenges of dealing with high-dimensional data by projecting the original data onto a smaller space and using the innate structure of the data to calculate anomaly scores for each data point. Numerical experiments on synthetic and real-life data show that our method performs well on high-dimensional data. In particular, the proposed method outperforms the benchmark methods as measured by [Formula: see text]-score. Our method also produces better-than-average execution times compared with the benchmark methods.

Prediction of Heart Disease Using Different Classification Techniques

2017 ◽  
Vol 2 (2) ◽  
pp. 68-76 ◽  
Author(s):  
Sonam Nikhar ◽  
A.M. Karandikar
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Bayesian Classifier ◽  
Decision Tree Classifier ◽  
Data Mining Technique ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Tree Classifier ◽  
Better Than

Data mining is one of the essential areas of research that is more popular in health organization. Heart disease is the leading cause of death in the world over the past 10 years. The healthcare industry gathers enormous amount of heart disease data which are not “mined” to discover hidden information for effective decision making. This research intends to provide a detailed description of Naïve Bayes, decision tree classifier and Selective Bayesian classifier that are applied in our research particularly in the prediction of Heart Disease. It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian algorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4.5 would use in its decision tree when learning a small example of a training set, a combination of the two different natures of classifiers. Experiments conducted on Cleveland datasets indicate that SBC performs reliably better than NB on all domains, and SBC outperforms C4.5 on this dataset of which C4.5 outperform NB. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classifier and experiment also reveals that selective Bayesian classifier has a better accuracy as compared to other classifiers.

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

1998 ◽  
Vol 1634 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sherif S. Ishak ◽  
Haitham M. Al-Deek
Keyword(s):  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
Back Propagation ◽  
Incident Detection ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Detection Algorithms ◽  
Fuzzy Art ◽  
Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

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