scholarly journals Gazing as actual parameter for drowsiness assessment in driving simulators

2019 ◽  
Vol 13 (1) ◽  
pp. 170
Author(s):  
Arthur Mourits Rumagit ◽  
Izzat Aulia Akbar ◽  
Mitaku Utsunomiya ◽  
Takamasa Morie ◽  
Tomohiko Igasaki
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Eye Gaze ◽  
Support Vector ◽  
Driving Simulators ◽  
Drowsy Driving ◽  
Expression Evaluation ◽  
The Relationship

Many traffic accidents are due to drowsy driving. However, to date, only a few studies have been conducted on the gazing properties related to drowsiness. This study was conducted with the objective of estimating the relationship between gazing properties and drowsiness in three facial expression evaluation (FEE) categories: alert (FEE = 0), lightly drowsy (FEE = 1−2), heavily drowsy (FEE = 3−4). Drowsiness was investigated based on these eye-gazing properties by analyzing the gazing signal utilizing an eye gaze tracker and FEE in a driving simulator environment. The results obtained indicate that gazing properties have significant differences among the three drowsiness conditions, with p < 0.001 in a Kruskal–Wallis test. Furthermore, the overall classification accuracy of the three drowsiness conditions based on gazing properties using a support vector machine was 76.3%. This indicates that our proposed gazing properties can be used to quantitatively assess drowsiness.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

2016 ◽  
Vol 25 (3) ◽  
pp. 417-429
Author(s):  
Chong Wu ◽  
Lu Wang ◽  
Zhe Shi
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Financial Distress ◽  
Classification Accuracy ◽  
Feature Space ◽  
Support Vector ◽  
Input Space ◽  
Financial Distress Prediction ◽  
Support Vectors ◽  
Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

6-DOF Motion System of Amphibious Vehicle Driving Simulator Motion Parameter Design Research

2011 ◽  
Vol 460-461 ◽  
pp. 704-709
Author(s):  
Shu Tao Zheng ◽  
Zheng Mao Ye ◽  
Jun Jin ◽  
Jun Wei Han
Keyword(s):  
Design Research ◽  
Driving Simulator ◽  
Motion Parameter ◽  
Driving Simulators ◽  
Motion System ◽  
Vehicle Acceleration ◽  
Vehicle Driving Simulator ◽  
Simulator Motion ◽  
Result Analysis ◽  
The Relationship

Vehicle driving simulators are widely employed in training and entertainment utilities because of its safe, economic and efficient. Amphibious vehicle driving simulator was used to simulate amphibious vehicle on land and in water. Because of the motion difference between aircraft and amphibious vehicle, it is necessary to design a reasonable 6-DOF motion system according to the flight simulator motion system standard and vehicle motion parameter. FFT of DSP and PSD were used to analysis the relationship between them. Finally according to the result analysis, a set of reasonable 6-DOF motion system motion parameter was given to realize the driving simulator motion cueing used to reproduce vehicle acceleration.

Feature Selection Method Based on Mutual Information and Support Vector Machine

2021 ◽  
pp. 2150021
Author(s):  
Gang Liu ◽  
Chunlei Yang ◽  
Sen Liu ◽  
Chunbao Xiao ◽  
Bin Song
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Standard Data ◽  
Feature Dimension

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

scholarly journals Prediction of preload attenuation of ball screw based on support vector machine

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879916 ◽  
Author(s):  
Kai Chen ◽  
Li Zu ◽  
Li Wang
Keyword(s):  
Support Vector Machine ◽  
Working Condition ◽  
Friction Torque ◽  
Ball Screw ◽  
Support Vector ◽  
Theoretical Derivation ◽  
Mechanical Field ◽  
Changing Trend ◽  
Engineering Significance ◽  
The Relationship

Ball screw is a mechanical device widely used in mechanical field. The reverse clearance of ball screw will reduce its precision. In order to eliminate the reverse clearance, it is necessary to apply preload to the ball screw. It is very difficult to measure the preload in real time, and the data are large and time-consuming. By using machine learning method to predict and supervise preload, the changing trend of working condition of ball screw can be evaluated in advance, and the working precision of screw is controlled, which has important engineering significance. In this article, the relationship between the preload and the friction torque is obtained through theoretical derivation and experimental verification. Then, the support vector machine is used as a tool to model the friction torque of ball screw with the parameters of material, lubrication, and revolution, and predict the value and trend of preload to complete the supervision and prediction of the preload of the ball screw. By comparing the experimental results, it is proved that the support vector machine is feasible in predicting and supervising the attenuation of the preload of ball screw.

Damage Identification for Simply-Supported Bridge Based on SVM Optimized by PSO (PSO-SVM)

2011 ◽  
Vol 80-81 ◽  
pp. 490-494 ◽  
Author(s):  
Han Bing Liu ◽  
Yu Bo Jiao ◽  
Ya Feng Gong ◽  
Hai Peng Bi ◽  
Yan Yi Sun
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Damage Identification ◽  
Fitness Function ◽  
Support Vector ◽  
Detection Accuracy ◽  
Damage Localization ◽  
Swarm Optimization ◽  
Simply Supported ◽  
Beam Bridge

A support vector machine (SVM) optimized by particle swarm optimization (PSO)-based damage identification method is proposed in this paper. The classification accuracy of the damage localization and the detection accuracy of severity are used as the fitness function, respectively. The best and can be obtained through velocity and position updating of PSO. A simply supported beam bridge with five girders is provided as numerical example, damage cases with single and multiple suspicious damage elements are established to verify the feasibility of the proposed method. Numerical results indicate that the SVM optimized by PSO method can effectively identify the damage locations and severity.

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

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