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Xiaoxiao X, Bin L, Ramkumar S, Saravanan S, Balaji MSP, Dhanasekaran S, Thimmiaraja J. Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model. Artif Intell Med 2020; 102:101766. [PMID: 31980103 DOI: 10.1016/j.artmed.2019.101766] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/16/2019] [Accepted: 11/18/2019] [Indexed: 12/14/2022]
Abstract
Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.
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Affiliation(s)
- Xu Xiaoxiao
- School of Entrepreneurship, Wuhan University of Technology, Wuhan Hubei Province, 430070, China.
| | - Luo Bin
- School of Foreign Languages, Wuhan Business University, Wuhan, 430056, China
| | - S Ramkumar
- School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India
| | - S Saravanan
- Department of Information Science and Engineering, CMR Institute of Technology, Bangalore, India
| | | | - S Dhanasekaran
- School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India
| | - J Thimmiaraja
- School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India
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Chen H, Tan C, Lin Z, Li H. Quantifying several adulterants of notoginseng powder by near-infrared spectroscopy and multivariate calibration. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 211:280-286. [PMID: 30557845 DOI: 10.1016/j.saa.2018.12.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 11/30/2018] [Accepted: 12/02/2018] [Indexed: 06/09/2023]
Abstract
The authentication of traditional Chinese medicine (TCM) is critically important for public-health and economic terms. Notoginseng, a classical TCM of high economic and medical value, could be easily adulterated with Sophora flavescens powder (SFP), corn flour (CF) or other analogues of low-grade (ALG) because of their similar tastes, appearances and much lower cost. The main objective of this study was to evaluate the feasibility of applying of near-infrared (NIR) spectroscopy and multivariate calibration for identifying and quantifying several common adulterants in notoginseng powder. Two datasets were prepared for experiment. The competitive adaptive reweighted sampling (CARS) was used to select informative variables. Two different schemes were used for sample set partition. Model population analysis (MPA) was made. The results showed that, the constructed partial least squares (PLS) model using a reduced set of variables from CARS can provide superior performance to the full-spectrum PLS model. Also, the sample set partition is very of great importance. It seems that the combination of NIR spectroscopy, CARS and PLS is feasible to quantify common adulterants in notoginseng powder.
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Affiliation(s)
- Hui Chen
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Hospital, Yibin University, Yibin, Sichuan 644000, China
| | - Chao Tan
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China.
| | - Zan Lin
- The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China
| | - Hongjin Li
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China
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Random subspace-based ensemble modeling for near-infrared spectral diagnosis of colorectal cancer. Anal Biochem 2019; 567:38-44. [DOI: 10.1016/j.ab.2018.12.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 12/02/2018] [Accepted: 12/10/2018] [Indexed: 02/07/2023]
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Chen H, Tan C, Lin Z. Quantitative determination of wool in textile by near-infrared spectroscopy and multivariate models. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 201:229-235. [PMID: 29753968 DOI: 10.1016/j.saa.2018.05.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 04/29/2018] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
The wool content in textiles is a key quality index and the corresponding quantitative analysis takes an important position due to common adulterations in both raw and finished textiles. Conventional methods are maybe complicated, destructive, time-consuming, environment-unfriendly. Developing a quick, easy-to-use and green alternative method is interesting. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and several partial least squares (PLS)-based algorithms and elastic component regression (ECR) algorithms for measuring wool content in textile. A total of 108 cloth samples with wool content ranging from 0% to 100% (w/w) were collected and all the compositions are really existent in the market. The dataset was divided equally into the training and test sets for developing and validating calibration models. When using local PLS, the original spectrum axis was split into 20 sub-intervals. No obvious difference of performance can be seen for the local PLS models. The ECR model is comparable or superior to the other models due its flexibility, i.e., being transition state from PCR to PLS. It seems that ECR combined with NIR technique may be a potential method for determining wool content in textile products. In addition, it might have regulatory advantages to avoid time-consuming and environmental-unfriendly chemical analysis.
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Affiliation(s)
- Hui Chen
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Hospital, Yibin University, Yibin, Sichuan 644000, China
| | - Chao Tan
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China.
| | - Zan Lin
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Department f Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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Chen H, Lin Z, Mo L, Tan C. Identification of Colorectal Cancer Using Near-Infrared Spectroscopy and Adaboost with Decision Stump. ANAL LETT 2017. [DOI: 10.1080/00032719.2017.1310880] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hui Chen
- Yibin University Hospital, Yibin University, Yibin, Sichuan, China
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan, China
| | - Zan Lin
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Mo
- The Affiliated Hospital, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Chao Tan
- Yibin University Hospital, Yibin University, Yibin, Sichuan, China
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Sun J, Zhou X, Mao H, Wu X, Zhang X, Li Q. Discrimination of pesticide residues in lettuce based on chemical molecular structure coupled with wavelet transform and near infrared hyperspectra. J FOOD PROCESS ENG 2016. [DOI: 10.1111/jfpe.12509] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang 212013 China
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education; Jiangsu University; Zhenjiang 212013 China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang 212013 China
| | - Hanping Mao
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education; Jiangsu University; Zhenjiang 212013 China
| | - Xiaohong Wu
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang 212013 China
| | - Xiaodong Zhang
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education; Jiangsu University; Zhenjiang 212013 China
| | - Qinglin Li
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education; Jiangsu University; Zhenjiang 212013 China
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Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease. Sci China Chem 2016. [DOI: 10.1007/s11426-016-0092-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Sun J, Lu X, Mao H, Wu X, Gao H. Quantitative Determination of Rice Moisture Based on Hyperspectral Imaging Technology and BCC-LS-SVR Algorithm. J FOOD PROCESS ENG 2016. [DOI: 10.1111/jfpe.12446] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang 212013 China
- Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology; Jiangsu University; Zhenjiang 212013 China
| | - Xinzi Lu
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang 212013 China
| | - Hanping Mao
- Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology; Jiangsu University; Zhenjiang 212013 China
| | - Xiaohong Wu
- School of Electrical and Information Engineering of Jiangsu University; Zhenjiang 212013 China
| | - Hongyan Gao
- Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology; Jiangsu University; Zhenjiang 212013 China
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