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.

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 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 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 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.

scholarly journals A Reliable Method for Identification of Antibiotics by Terahertz Spectroscopy and SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jin Guo ◽  
Hu Deng ◽  
Quancheng Liu ◽  
Linyu Chen ◽  
Zhonggang Xiong ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Absorption Spectra ◽  
Reliable Method ◽  
Terahertz Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Thz Spectroscopy ◽  
Model Parameters ◽  
Training Time ◽  
Svm Model

Given the extensive use of antibiotics at present, the identification of antibiotics and production quality monitoring are of high importance. However, conventional antibiotic identification methods have a low sensitivity and a long detection time. Here, we propose an identification method that combines terahertz (THz) spectroscopy and chemometric technology. THz time-domain spectroscopy (THz-TDS) was performed for sixteen types of antibiotics, including β-lactam, cephalosporins, macrolides, and tetracyclines. The absorption spectra within the frequency range of 0.2–1.5 THz were calculated. For dimensionality reduction, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were implemented, respectively. The data after dimensionality reduction were input into a support vector machine (SVM). The model parameters were optimized through grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO) methods, and the optimal identification results were obtained after comparison across these methods. Experiments indicate a differentiation of the THz absorption spectra among the sixteen types of antibiotics. After dimensionality reduction, the training time of the model significantly decreased. The use of the t-SNE-PSO-SVM model achieved the highest average accuracy on the prediction set, which was 99.91%. Thus, our study does not only confirm that the t-SNE-PSO-SVM model proves to be a reliable method for antibiotics identification, but also confirms that the combination of THz-TDS and chemometric pattern recognition has great potential for drug detection.

scholarly journals Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose

2021 ◽  
Vol 11 (5) ◽  
pp. 2337
Author(s):  
Xiaohui Weng ◽  
Cheng Kong ◽  
Hongyang Jin ◽  
Dongxue Chen ◽  
Chunguang Li ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Large Scale ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Volatile Organic ◽  
Livestock Farms

The composition of volatile organic compounds (VOCs) in large-scale livestock farms is complex, which seriously affects the health of livestock and is difficult to evaluate. In order to quickly analyze the pollution degree of VOCs in livestock farms, electronic nose technology was used in this study to detect and analyze the gases in pig and chicken houses, respectively. Firstly, the gas chromatography–mass spectrometry (GC–MS) and electronic nose were used to analyze the VOCs in the pig and chicken houses at different time and locations. The types and relative contents of VOCs were obtained from different livestock farms by GC–MS analysis. The sensor array response of the electronic nose showed similar results. In addition, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machine (SVM) analyses were performed on the electrical signal that was generated by the sensors of electronic nose, respectively. Finally, the classification rate of different odor sources in livestock farms was the highest (>85%), which indicates that SVM is a more effective method suitable for volatile gases recognition in livestock farms. The results have shown that the developed electronic nose sensor is a promising and feasible instrument for characterizing volatile odors in livestock farms.

scholarly journals Predicting Freeway Travelling Time Using Multiple-Source Data

2018 ◽  
Author(s):  
Kejun Long ◽  
Wukai Yao ◽  
Jian Gu ◽  
Wei Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Historical Data ◽  
Multiple Source ◽  
Function Parameter ◽  
Support Vector ◽  
Model Parameters ◽  
Svm Model ◽  
Source Data ◽  
Travelling Time

Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied 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 Back Propagation (BP) neural network and 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.

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