Recognition of Design Fixation via Body Language Using Computer Vision

The main objective of this study is to recognize design fixation accurately and effectively. First, we conducted an experiment to record the videos of design process and design sketches from 12 designers for 15 minutes. Then, we executed a video analysis of body language in designers, correlating body language to the presence of design fixation, as judged by a panel of six experts. We found that three body language types were significantly correlated to fixation. A two-step hybrid recognition model of design fixation based on body language was proposed. The first-step recognition model of body language using transfer learning based on a pretrained VGG-16 convolutional neural network was constructed. The average recognition rate achieved by the VGG-16 model was 92.03%. Then, the frames of recognized body language were used as input vectors to the second-step fixation classification model based on support vector machine (SVM). The average recognition rate for the fixation state achieved by the SVM model was 79.11%. The impact of the work could be that the fixation can be detected not only by the sketch outcomes but also by monitoring the movements, expressions, and gestures of designers, as it is happening by monitoring the movements, expressions, and gestures of designers.

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Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.188.629 ◽

2011 ◽

Vol 188 ◽

pp. 629-635

Author(s):

Xia Yue ◽

Chun Liang Zhang ◽

Jian Li ◽

H.Y. Zhu

Keyword(s):

Fault Diagnosis ◽

Hybrid Model ◽

Large Scale ◽

Recognition Accuracy ◽

Recognition Rate ◽

Double Layers ◽

Support Vector ◽

Practical Applications ◽

Average Recognition Rate ◽

Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

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A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽

10.3390/sym13050757 ◽

2021 ◽

Vol 13 (5) ◽

pp. 757

Author(s):

Yongke Pan ◽

Kewen Xia ◽

Li Wang ◽

Ziping He

Keyword(s):

Optimization Algorithm ◽

Oil And Gas ◽

The Other ◽

Whale Optimization Algorithm ◽

Classification Model ◽

Support Vector ◽

Recognition Model ◽

Novel Approach ◽

Whale Optimization ◽

Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

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Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

Journal of Chemistry ◽

10.1155/2015/619685 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Hui Chen ◽

Zan Lin ◽

Chao Tan

Keyword(s):

Cancer Diagnosis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Model ◽

Support Vector ◽

Biomedical Analysis ◽

First Derivative ◽

Svm Model ◽

High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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A Fast Detection Method for Wheat Mould Based on Biophotons

10.1101/2020.10.13.337246 ◽

2020 ◽

Author(s):

Gong Yue-hong ◽

Yang Tie-jun ◽

Liang Yi-tao ◽

Ge Hong-yi ◽

Chen Liang

Keyword(s):

Recognition Rate ◽

Implementation Process ◽

Approximate Entropy ◽

Classification Model ◽

Support Vector ◽

Fast Detection ◽

Materials Used ◽

Specific Implementation ◽

Wheat Kernels

AbstractMould is a common phenomenon in stored wheat. First, mould will decrease the quality of wheat kernels. Second, the mycotoxins metabolized by mycetes are very harmful for humans. Therefore, the fast and accurate examination of wheat mould is vitally important to evaluating its storage quality and subsequent processing safety. Existing methods for examining wheat mould mainly rely on chemical methods, which always involve complex and long pretreatment processes, and the auxiliary chemical materials used in these methods may pollute our environment. To improve the determination of wheat mould, this paper proposed a type of green and nondestructive determination method based on biophotons. The specific implementation process is as follows: first, the ultra-weak luminescence between healthy and mouldy wheat samples are measured repeatedly by a biophotonic analyser, and then, the approximate entropy and multiscale approximate entropy are separately introduced as the main classification features. Finally, the classification performances have been tested using the support vector machine(SVM). The ROC curve of the newly established classification model shows that the highest recognition rate can reach 93.6%, which shows that our proposed classification model is feasible and promising for detecting wheat mould.

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Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Molecules ◽

10.3390/molecules25061442 ◽

2020 ◽

Vol 25 (6) ◽

pp. 1442 ◽

Cited By ~ 2

Author(s):

Tao Shen ◽

Hong Yu ◽

Yuan-Zhong Wang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Related Species ◽

Predictive Accuracy ◽

Classification Model ◽

Venn Diagram ◽

Support Vector ◽

Stacked Generalization ◽

Svm Model

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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Research on Indicator Diagram of Shock Absorber Fault Recognition Based on Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2195 ◽

2013 ◽

Vol 765-767 ◽

pp. 2195-2198

Author(s):

Wei Dong Xie ◽

Kan Gao ◽

Ji Sheng Shen

Keyword(s):

Support Vector Machine ◽

Shock Absorber ◽

Recognition Rate ◽

Support Vector ◽

Indicator Diagram ◽

Fault Recognition ◽

Average Recognition Rate ◽

Rbf Kernel ◽

On Line ◽

Rbf Kernel Function

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.

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Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Sensors ◽

10.3390/s19010097 ◽

2018 ◽

Vol 19 (1) ◽

pp. 97 ◽

Cited By ~ 4

Author(s):

Siddharth Chaudhary ◽

Sarawut Ninsawat ◽

Tai Nakamura

Keyword(s):

Hyperspectral Imaging ◽

Hyperspectral Image ◽

Imaging System ◽

Classification Model ◽

Support Vector ◽

Trace Detection ◽

Svm Model ◽

Detection Of Explosives ◽

Non Destructive ◽

Hyperspectral Sensor

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.

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Real-time flood forecast using the coupling support vector machine and data assimilation method

Journal of Hydroinformatics ◽

10.2166/hydro.2013.075 ◽

2013 ◽

Vol 16 (5) ◽

pp. 973-988 ◽

Cited By ~ 8

Author(s):

Xiao-Li Li ◽

Haishen Lü ◽

Robert Horton ◽

Tianqing An ◽

Zhongbo Yu

Keyword(s):

Support Vector Machine ◽

Real Time ◽

Time Scale ◽

Support Vector ◽

Flood Forecast ◽

Risk Minimization ◽

Svm Model ◽

The Impact ◽

Assimilation Time ◽

Better Than

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

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Forecasting Dry Bulk Freight Index with Improved SVM

Mathematical Problems in Engineering ◽

10.1155/2014/460684 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Qianqian Han ◽

Bo Yan ◽

Guobao Ning ◽

B. Yu

Keyword(s):

Wavelet Transform ◽

Support Vector ◽

Data Series ◽

Shipping Industry ◽

Combined Model ◽

Final Model ◽

Proposed Model ◽

Svm Model ◽

The Impact ◽

Short Term Trend

An improved SVM model is presented to forecast dry bulk freight index (BDI) in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

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Study about Recognition of Digital Meter Dial Reading Based on SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.615.194 ◽

2014 ◽

Vol 615 ◽

pp. 194-197

Author(s):

Zhen Yuan Tu ◽

Fang Hua Ning ◽

Wu Jia Yu

Keyword(s):

Support Vector Machine ◽

Cross Validation ◽

Recognition Rate ◽

Classification Model ◽

Support Vector ◽

Feature Points ◽

Svm Classification ◽

Performance Factors ◽

Model Combining ◽

Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

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