Acquisition, processing of myoelectrics signals and Support-Vector Machine for movement characterization of hand-arm segment

Prediction of protein subcellular localization is an important issue in computational biology because it provides important clues for the characterization of protein functions. Currently, much research has been dedicated to developing automatic prediction tools. Most, however, focus on mono-locational proteins, i.e., they assume that proteins exist in only one location. It should be noted that many proteins bear multi-locational characteristics and carry out crucial functions in biological processes. This work aims to develop a general pattern classifier for predicting multiple subcellular locations of proteins. We use an ensemble classifier, called the min-max modular support vector machine (M3-SVM), to solve protein subcellular multi-localization problems; and, propose a module decomposition method based on gene ontology (GO) semantic information for M3-SVM. The amino acid composition with secondary structure and solvent accessibility information is adopted to represent features of protein sequences. We apply our method to two multi-locational protein data sets. The M3-SVMs show higher accuracy and efficiency than traditional SVMs using the same feature vectors. And the GO decomposition also helps to improve prediction accuracy. Moreover, our method has a much higher rate of accuracy than existing subcellular localization predictors in predicting protein multi-localization.

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Nighttime Fire/Smoke Detection System Based on a Support Vector Machine

Mathematical Problems in Engineering ◽

10.1155/2013/428545 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Chao-Ching Ho

Keyword(s):

Support Vector Machine ◽

Detection System ◽

Time Algorithm ◽

Detection Methods ◽

Support Vector ◽

Smoke Detection ◽

Fire Smoke ◽

Large Fires ◽

Monitoring Zone

Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.

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Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Chinese Physics B ◽

10.1088/1674-1056/25/6/065201 ◽

2016 ◽

Vol 25 (6) ◽

pp. 065201 ◽

Cited By ~ 4

Author(s):

Hong-Xing Yang ◽

Hong-Bo Fu ◽

Hua-Dong Wang ◽

Jun-Wei Jia ◽

Markus W Sigrist ◽

...

Keyword(s):

Principal Component Analysis ◽

Support Vector Machine ◽

Principal Component ◽

Component Analysis ◽

Laser Induced Breakdown Spectroscopy ◽

Support Vector ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown

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Auto Classification of Biomass through Characterization of their Pyrolysis Behaviors by using Thermogravimetric Analysis with Support Vector Machine Algorithm: Case Study for Tobacco

10.21203/rs.3.rs-112676/v1 ◽

2020 ◽

Author(s):

Chao Yin ◽

Xiaohua Deng ◽

Zhiqiang Yu ◽

Ruting Chen ◽

Hongxiang Zhong ◽

...

Keyword(s):

Support Vector Machine ◽

Thermogravimetric Analysis ◽

Oil Quality ◽

Support Vector ◽

Analysis Method ◽

Biomass Pyrolysis ◽

Svm Algorithm ◽

Bio Oil

Abstract Background: During the biomass-to-bio-oil conversion process, many researches focus on the study of the association between the biomass and the bio-products by using near infrared spectra (NIR) and chemical analysis method. However, the characterization of biomass pyrolysis behaviors by using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar and gases) released by tobacco pyrolysis reactions decide the sensory quality, which is similar to the use of biomass as a renewable resource through the pyrolysis process. Results: Support vector machine (SVM) has been employed to automatically classify the planting area and growing position of tobacco leaves by using thermogravimetric analysis data as the information source for the first time. 88 single-grade tobacco samples belonging to 4 grades and 8 categories were split into the training, validation and blind testing set. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower and middle positions. Error only occurs in the classification of subgrades of the middle position. Conclusions: Our results not only validated the feasibility of using thermogravimetric analysis with SVM algorithm as an objective and rapid method for automatic classification of tobacco planting area and growing position, but also showed this new analysis method would be a promising way to exploring bio-oil quality prior to biomass pyrolysis production.

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