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Holistic evaluation of gamma-irradiation effects on green teas: New linear regression based approach applied to ( plus /)ESI/MS and RPLC/UV data and comparison with PCA and CA chemometric methods

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RADIATION PHYSICS AND CHEMISTRY 2018年 149卷 126-133页

作者： Iorgulescu, Elena Voicu, Victor A. Sarbu, Costel Tache, Florentin Albu, Florin Stanculescu, Ioana Medvedovici, Andrei Univ Bucharest Dept Analyt Chem Fac Chem Panduri Ave 90 Bucharest 050663 Romania Romanian Acad Med Sci Sect Calea Victoriei 125 Bucharest 010071 Romania Univ Med & Pharm Carol Davila Dept Pharmacol Toxicol & Clin Psychopharmacol Floreasca St 8 Bucharest 014461 Romania Babes Bolyai Univ Fac Chem & Chem Engn Dept Chem Arany Janos St 11 Cluj Napoca 400028 Romania Analyt Applicat Lab Th Pallady Ave 40S Bucharest 032266 Romania Univ Bucharest Dept Phys Chem Fac Chem Regina Elisabeta Blvd 4-12 Bucharest 030018 Romania

The evaluation of the gamma-irradiation effects on green teas was holistically achieved by means of a novel algorithm based on linear regression (LRA). This algorithm was compared to the discrimination power of Principal Component Analysis (PCA) and Cluster Analysis (CA). The holistic evaluation was based on positive or negative ion monitoring Electrospray Mass Spectrometry ( + / -ESI/MS) data and Reversed Phase Liquid Chromatography with Ultraviolet Spectrometry detection (RPLC/UV) chromatograms, without involving any structural attribution and/or assay of the existing components. Five types of green teas (receiving irradiation doses of 0, 10 and 25 kGy) were considered. Extraction in ethanol and heated water was used. To ensure an increased definition of the profiles being compared, the LRA approach was applied on pairs of large experimental data series resulting from high frequency/high resolution acquisition rates, the resulting slopes, intercepts and correlation coefficients being considered as variables retaining the information contained in the raw data. The discrimination ability varied in the following order: LRA > CA > PCA. The information contained by the input data varied as following: ( + )ESI/MS spectra > (-)ESI/MS spectra > RPLC/UV chromatograms.

关键词： linear regression algorithm ( plus /-) ESI-MS RPLC-UV Multivariate methods gamma-irradiation Green teas

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Automotive radar system for multiple-vehicle detection and tracking in urban environments

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IET INTELLIGENT TRANSPORT SYSTEMS 2018年第8期12卷 783-792页

作者： Eltrass, Ahmed Khalil, Mohammed Alexandria Univ Elect Engn Dept Fac Engn Alexandria Egypt Univ Maryland College Pk MD 20742 USA

In this study, an enhanced approach for automotive radar systems is proposed to solve the detection, tracking, and track management problem in the presence of clutter with high accuracy and low computational cost. The unscented Kalman filter (UKF) with a constant turn rate and acceleration (CTRA) dynamic model is employed for target tracking, and the tracking accuracy is enhanced by incorporating the linear regression (LR) algorithm into the UKF-CTRA algorithm. We investigate, for the first time, the Joint Probabilistic Data Association (JPDA) algorithm for data association, and the composite M/N tests for track management. The capability of the proposed approach (CTRA-UKF-LR-JPDA-composite-M/N-tests) is demonstrated by comparing it with various algorithms for different single and multi-target tracking scenarios and for various sets of parameter regimes. The results show the superior performance of the proposed method over other existing techniques in automotive radar systems. This reveals the effectiveness of the proposed algorithm as a promising technique in automotive applications.

关键词： road vehicle radar radar detection target tracking radar tracking regression analysis probability sensor fusion Kalman filters nonlinear filters automobiles automotive radar system multiple-vehicle detection multiple-vehicle tracking urban environments track management problem low computational cost unscented Kalman filter constant turn rate and acceleration dynamic model single target tracking tracking accuracy linear regression algorithm UKF-CTRA algorithm joint probabilistic data association algorithm CTRA-UKF-LR-JPDA-composite-M/N-tests multitarget tracking

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linear representation of intra-class discriminant features for small-sample face recognition

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1668-1673页

作者： Shao, Changbin Gao, Shang Song, Xiaoning Yang, Xibei Xu, Gang Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang 212003 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Jiangsu Univ Sci & Technol Sch Naval Architecture & Ocean Engn Zhenjiang 212003 Peoples R China

The authors argue that the mean of discriminant features calculated across the samples of a class (intra-class samples) cannot perform well for the classification task. The main reason is that the mean feature ignores intra-class membership's different responses to their own class for a query sample. Meanwhile, they present that the discriminant features of a test sample can also be well-linear approximated by the discriminant features of intra-class memberships. The adaptive weighted intra-class features will be more suitable for the identification ability of a class than original samples via a regression algorithm. To verify this, a new linear representation-based classification method using Fisher discriminant features (LRFC) is suggested. To be more specific, they first extract Fisher discriminant features of all face samples. Second linear regression (LR) algorithm is exploited to obtain weight coefficients of intra-class feature information for the feature representation of a query sample. At last, the weighted intra-class features are re-combined as an agent of each class and the test sample is identified as the class with the maximum similarity. The method is simple but particularly effective. Experimental results on benchmark face databases verify improvements of LRFC over its original methods.

关键词： regression analysis face recognition feature extraction image classification image representation query processing linear regression algorithm intra-class feature information feature representation query sample intra-class discriminant features small-sample face classification intra-class membership Fisher discriminant features face samples linear representation

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Study on Prediction Model for Cutting Surface Roughness of Hardened GCr15 Bearing Steel

Study on Prediction Model for Cutting Surface Roughness of H...

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3rd International Conference on Advanced Engineering Materials and Technology (AEMT 2013)

作者： Yang, Ting Liu, Hongbin Wu, Haitao Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9783037857649

CBN cutting tool is used to cut hardened GCr15 bearing steel. Based on the surface roughness under different cutting parameters, people built the prediction model of surface roughness by linear regression algorithm, and carried on the significance test of it, the results show that the surface roughness forecast model set up by linear regression algorithm has a certain reliability and its linear regression equation is significant;among the cutting parameters, cutting speed and feed rate make great impact to the surface roughness and back engagement of the cutting edge has little impact to the surface roughness of test specimen.

关键词： CBN tools surface roughness linear regression algorithm cutting parameters

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A Fault Detection Method for Wireless Sensor Networks Based on Credible Sensor Nodes Set

A Fault Detection Method for Wireless Sensor Networks Based ...

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9th IEEE International Conference on e-Business Engineering (ICEBE)/8th SOAIC/6th EM2I/6th SOKMBI/4th ASOC

作者： Wang, Zhaoxing Wen, Qiaoyan Wang, Teng Zhang, Hua Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781467326018;9780769548098

In this paper, we present a fault detection scheme based on credible nodes set for wireless sensor networks. While an event occurs, the impact on the nodes around the event source can be described as a distance-related model. And the model can be transformed to a group of linear equations. The coefficients of linear equations are determined by energy value and intrinsic properties of the event source, and they can be worked out through the credible nodes' measured values and location parameters. By using improved hierarchical clustering algorithm, we can determine the credible nodes set through the similarity of nodes' measurements. Then the absolute measurement residuals of all nodes can be calculated by the linear regression algorithm and the measurement residuals will be used for concluding the final status of every node. So we can accomplish the detection. Simulation results show that the fault detection accuracy and the false alarm rate are kept high and low, respectively, even in the case where 40% of the sensor nodes are faulty.

关键词： fault detection hierarchical clustering event impact model linear regression algorithm

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linear regression method for wide-band flat-gain fiber Raman amplifier pump power automatic configuration

Linear regression method for wide-band flat-gain fiber Raman...

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Conference on Optical Transmission, Switching and Subsystems III

作者： Wen, H Wei, LH Gu, WY BUPT Opt Commun Ctr Beijing 100876 Peoples R China

ISBN: (纸本)0819460524

linear regression method for flat-gain Fiber Raman amplifier (FRA) pump power automatic configuration is proposed. it can give the optimal pump power configuration automatically according to the signal output variation with pump power changes, without any prior knowledge of fiber parameters, like attenuation, effective area, Raman gain factor, and etc. This method is based on the pump power integral model and linear regression theory and is verified by numerical simulation and experiment. The impact on pump configuration caused by power measurement uncertainty is also considered. The results show that deliberate selecting some parameters, like initial pump configuration, pump power variation amount, pump power perturbation times can reduce the impact.

关键词： optical amplifier Raman fiber amplifier pump configuration optimization linear regression algorithm WDM linear system

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Development of GMR eddy current sensors for high temperature applications and imaging of corrosion in thick multi-layer structures

Development of GMR eddy current sensors for high temperature...

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Conference on Testing, Reliability and Application of Micro- and Nano-Materials Systems II

作者： Bajjuri, S Hoffman, J Siddoju, A Meyendorf, N UDRI Ctr Mat Diagnost Dayton OH USA

ISBN: (纸本)0819453099

Detection and quantification of corrosion damage in aircraft structures is essential for condition based maintenance strategies and for the extension of the life of the aircraft. The eddy current technique was found to be one of the most favorable methods for the determination of thickness loss due to corrosion because this technique is capable of detecting corrosion in several layers of a multi-layer structure. A limitation for the eddy current technique is the eddy current penetration depth. Decreasing the analyzing frequency can increase the eddy current penetration depth. Giant Magneto Resistive sensors are highly sensitive magnetic field sensors, they have better signal to noise ratio for very low frequencies than conventional coils systems. Moreover these sensors are very efficient over a broad frequency range. Hence they allow the use of the multifrequency concept for multi-layer structures of higher thickness. Images of corrosion damage can be generated separately for different layers of a multi-layer structure by using deep penetrating GMR based eddy current probes and data acquired from the multi-frequency eddy current testing. This paper describes the design of deep penetrating GMR based eddy current probes and their application for generating images of corrosion in different layers with the help of a MAUS scanner.

关键词： deep penetrating probes temperature stable probes multi frequency eddy current conductivity measurement linear regression algorithm GMR

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GMR based Eddy Current probes for RRA treated Al-7075 T6 and other high temperature applications

GMR based Eddy Current probes for RRA treated Al-7075 T6 and...

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Conference on Testing, Reliability, and Application of Micro- and Nano-Material Systems

作者： Siddoju, A Kuhr, S Meyendorf, N Univ Dayton Res Inst Ctr Mat Diagnost Dayton OH 45469 USA

ISBN: (纸本)0819448508

Sensors for online monitoring of the heat treatment of aluminum alloys have been developed. The correlation between heat treatment parameters of Al 7075, the microstructure of the materials and NDE measurements has being studied systematically. By establishing correlations to the process parameters., it was assumed to be possible to devise a technique that provides not only insight into the aging process but also act as a quality control method for process verification. Multi-frequency eddy current allows compensation for environmental influences along with the ability to perform measurements at elevated temperatures and is therefor suitable for process monitoring.

关键词： high temperature probes multi frequency eddy current conductivity measurement linear regression algorithm process control

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