SVM MODELS FOR DIAGNOSING BALANCE PROBLEMS USING STATISTICAL FEATURES OF THE MTC SIGNAL

2008 ◽  
Vol 07 (03) ◽  
pp. 317-331 ◽  
Author(s):  
DANIEL T. H. LAI ◽  
REZAUL BEGG ◽  
MARIMUTHU PALANISWAMI
Keyword(s):  
Feature Selection Method ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Statistical Features ◽  
Gait Patterns ◽  
Healthy Elderly ◽  
Svm Model ◽  
Balance Problems

Trip-related falls are a major problem in the elderly population and research in the area has received much attention recently. The focus has been on devising ways of identifying individuals at risk of sustaining such falls. The main aim of this work is to explore the effectiveness of models based on Support Vector Machines (SVMs) for the automated recognition of gait patterns that exhibit falling behavior. Minimum toe clearance (MTC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with balance problems and with a history of tripping falls. Statistical features obtained from MTC histograms were used as inputs to the SVM model to classify between the healthy and balance-impaired subjects. The leave-one-out technique was utilized for training the SVM model in order to find the optimal model parameters. Tests were conducted with various kernels (linear, Gaussian and polynomial) and with a change in the regularization parameter, C, in an effort to identify the optimum model for this gait data. The receiver operating characteristic (ROC) plots of sensitivity and specificity were further used to evaluate the diagnostic performance of the model. The maximum accuracy was found to be 90% using a Gaussian kernel with σ2 = 10 and the maximum ROC area 0.98 (80% sensitivity and 100% specificity), when all statistical features were used by the SVM models to diagnose gait patterns of healthy and balance-impaired individuals. This accuracy was further improved by using a feature selection method in order to reduce the effect of redundant features. It was found that two features (standard deviation and maximum value) were adequate to give an improved accuracy of 95% (90% sensitivity and 100% specificity) using a polynomial kernel of degree 2. These preliminary results are encouraging and could be useful not only for diagnostic applications but also for evaluating improvements in gait function in the clinical/rehabilitation contexts.

scholarly journals Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Dalian Yang ◽  
Jingjing Miao ◽  
Fanyu Zhang ◽  
Jie Tao ◽  
Guangbin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Bearing Faults ◽  
Ring Fault ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Svm Model

Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.

Multivariate information fusion for identifying antifungal peptides with Hilbert-Schmidt Independence Criterion

2021 ◽  
Vol 16 ◽  
Author(s):  
Haohao Zhou ◽  
Hao Wang ◽  
Yijie Ding ◽  
Jijun Tang
Keyword(s):  
Fungal Infections ◽  
Practical Significance ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Sequence Information ◽  
Kernel Support Vector Machine ◽  
Svm Model ◽  
Antifungal Peptides ◽  
Independence Criterion ◽  
Better Than

Background: Antifungal peptides (AFP) have been found to be effective against many fungal infections. Objective: However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information). Method: In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built. Results: Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models. Conclusion: Our method will be a useful tool for identifying antifungal peptides.

scholarly journals A Forecast Model of the Number of Containers for Containership Voyage

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 193
Author(s):  
Yuchuang Wang ◽  
Guoyou Shi ◽  
Xiaotong Sun
Keyword(s):  
Container Terminal ◽  
Forecast Model ◽  
Gray Relational Analysis ◽  
Support Vector ◽  
Model Parameters ◽  
Container Ship ◽  
Kernel Support Vector Machine ◽  
Proposed Model ◽  
Svm Model ◽  
Pass Through

Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage.

Study on the Quantitative Method of Oversaturated Intersection

2014 ◽  
Vol 587-589 ◽  
pp. 2100-2104
Author(s):  
Qin Liu ◽  
Jian Min Xu ◽  
Kai Lu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Travel Speed ◽  
Urban Traffic ◽  
Support Vector ◽  
Model Parameters ◽  
Guangzhou City ◽  
Traffic System ◽  
Traffic Conditions ◽  
Svm Model

Oversaturation in the modern urban traffic often happens. In order to describe the degree of oversaturation, the indexes of intersection oversaturation degree are put forward include dissipation time, stranded queue, overflow queue and travel speed. On the basis of selected indexes, the genetic algorithm support vector machine (GA-SVM) model was proposed to quantify the degree of oversaturation. In this method the genetic algorithm is used to select the model parameters. The GA-SVM model built is used to quantify the degree of oversaturation. Combining with the volume of intersections in Guangzhou city the method is calculated and simulated through programming. The simulation results show that GA-SVM method is effective and the accuracy of GA-SVM is higher than support vector machine (SVM).This method provides a theoretical basis for the analysis of traffic system under over-saturated traffic conditions.

scholarly journals Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration

2018 ◽  
Vol 9 (1) ◽  
pp. 104 ◽  
Author(s):  
Kejun Long ◽  
Wukai Yao ◽  
Jian Gu ◽  
Wei Wu ◽  
Lee Han
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Historical Data ◽  
Multiple Source ◽  
Function Parameter ◽  
Support Vector ◽  
Model Parameters ◽  
Svm Model ◽  
Adverse Weather

Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.

SVM Parameter Optimization based on Immune Memory Clone Strategy and Application in Bus Passenger Flow Counting

2012 ◽  
Vol 4 (4) ◽  
pp. 74-80
Author(s):  
Zhu Fang ◽  
Wei Junfang
Keyword(s):  
Optimization Method ◽  
Parameters Optimization ◽  
Immune Memory ◽  
Support Vector ◽  
Model Parameters ◽  
Passenger Flow ◽  
Parameters Selection ◽  
Svm Model ◽  
Peak Model ◽  
Selection Of

The performance of support vector mchine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value. According to the above deficiency, this paper proposed a parameters optimization method of support vector machine based on immune memory clone strategy (IMC). This method can solve the multi-peak model parameters selection problem better which is introduced by n-folded cross-verification. Tests on standard datasets show that this method has higher precision and faster optimization speed compared with other four methods. Then the proposed method was applied to bus passenger flow counting. The experimental results show that the method reposed in this paper obtains higher classification accuracy.

A New Classification Method for Stored Grain Insect Infestation Using KIII and SVM Based Electronic Nose

2014 ◽  
Vol 1006-1007 ◽  
pp. 870-873
Author(s):  
Jie Li ◽  
Dong Lai Xu
Keyword(s):  
Electronic Nose ◽  
Support Vector ◽  
Model Parameters ◽  
Insect Infestation ◽  
Stored Grain ◽  
Classification Rate ◽  
Svm Model ◽  
Pattern Recognition Approach ◽  
Dimension Space ◽  
Olfactory Neural Network

Insect infestation is a common problem for stored grain. In this paper, a novel pattern recognition approach combining an olfactory neural network entitled KIII with support vector machine (SVM) is proposed and used in conjunction with an electronic nose to generate recognition models. Using this approach, feature vectors are firstly processed by KIII model which stimulates information processing function of olfactory bulb, and then classified by SVM. Through optimization of SVM model parameters, the data are mapped into high dimension space and the stored wheat samples with different degrees of insect damage are distinguished successfully. The experimental results demonstrate that the proposed method can achieve up to 100% classification rate and significantly outperforms the conventional KIII-Minimum Euclidean Distance Classifier.

A Kind of Diminishing Step Fruit Fly Optimization Algorithm

2014 ◽  
Vol 487 ◽  
pp. 687-691 ◽  
Author(s):  
Jun Yan Hou ◽  
Bing Wang
Keyword(s):  
Optimization Algorithm ◽  
Early Stage ◽  
Dynamic Balance ◽  
Step Length ◽  
Learning Performance ◽  
Support Vector ◽  
Model Parameters ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Svm Model

Parameters of Support Vector Machine are playing an important part in learning performance and generalization capability. The randomness and blindness in selecting SVM model parameters artificially could be eliminated by using group intelligent optimizing algorithm. FOA is a kind of group intelligent optimization algorithm. It has some advantages such as global convergence, connotative parallelism and fast operating speed. However, its optimum efficiency is very sensitive to the length of fixed step. In the course of optimizing, if the step is oversize, it will have preferable global optimizing performance and weak local optimizing capability. On the contrary, if the step is undersize, the local optimizing capability would be powerful and it will have the most probability to lapse into local extreme value. Therefore, a kind of algorithm named Diminishing Step FOA is proposed, the step length minishes progressively along with the process of foraging. So that it would have preferable global optimizing capability in early stage and preferable local optimizing capability in later period. And then, a dynamic balance will be achieved between global and local optimizing capability. The experimental results show that the SVM model using DS-FOA has optimal forecast precision and effect.

scholarly journals The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5219 ◽  
Author(s):  
Caner Savas ◽  
Fabio Dovis
Keyword(s):  
Support Vector Machines ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Ionospheric Scintillation ◽  
Support Vector ◽  
Running Time ◽  
Scintillation Detection ◽  
Vector Machines ◽  
The Impact

Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time.

Hyper-sphere support vector machine based on collaborative knowledge mining and SMO optimization in power load forecasting

2020 ◽  
Author(s):  
Resheng PAN ◽  
Hui Li ◽  
Zhidong WANG ◽  
Dong PENG ◽  
Lang ZHAO ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Load Forecasting ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Knowledge Mining ◽  
Power Load ◽  
Svm Model ◽  
Sample Data ◽  
Power Load Forecasting ◽  
Collaborative Knowledge

Abstract Background Due to the influence of power market reform policies, the conversion of power loads has become more and more complicated. The current load forecasting methods have long calculation times and inaccurate volatility load forecasting. The difficulty of forecasting is becoming greater and more accurate. It becomes very important to predict the electrical load. Under this background, this paper proposes the application methods of collaborative knowledge mining and SMO in solving prediction models based on hyperball support vector machine (CKM / SMO-SVM). Methods This study first analyzes the impact of historical data on samples and different parameters. The prediction of power load, sample data and various parameters have a significant impact on the prediction results. Secondly, applying weak entropy theory for collaborative knowledge mining, preprocessing sample data and historical information. Third, a short-term power load forecasting system based on the hypersphere support vector machine model is established and the problem is solved by SMO. Finally, the SVM model and BP model are selected for prediction to verify the new model. Results Our research proves that the rms relative error of the CKM / SMO-SVM model is only 2.32%, which is 0.67% and 1.56% lower than the SVM and BP models, respectively, and the optimization speed is faster. Conclusions The model proposed in this paper utilizes Hyper-sphere SVM which is suitable for Gaussian kernel function to achieve faster and more accurate load forecasting, which can provide more accurate services for energy spot transactions and energy scheduling plans.

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