High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,wh...High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.展开更多
The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detecti...The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.展开更多
Aiming at the potential presence of mixing automatic identification system(AIS) signals,a new demodulation scheme was proposed for separating other interfering signals in satellite systems.The combined iterative cross...Aiming at the potential presence of mixing automatic identification system(AIS) signals,a new demodulation scheme was proposed for separating other interfering signals in satellite systems.The combined iterative cross-correlation demodulation scheme,referred to as CICCD,yielded a set of single short signals based on the prior information of AIS,after the frequency,code rate and modulation index were estimated.It demodulates the corresponding short codes according to the maximum peak of cross-correlation,which is simple and easy to implement.Numerical simulations show that the bit error rate of proposed algorithm improves by about 40% compared with existing ones,and about 3 dB beyond the standard AIS receiver.In addition,the proposed demodulation scheme shows the satisfying performance and engineering value in mixing AIS environment and can also perform well in low signal-to-noise conditions.展开更多
Objective Identification of one’s constitution based on a combination of features and back propagation neural network theory is needed in modern medicine and traditional Chinese medicine(TCM).We describe a method to ...Objective Identification of one’s constitution based on a combination of features and back propagation neural network theory is needed in modern medicine and traditional Chinese medicine(TCM).We describe a method to identify one’s constitution based on TCM constitution classification and a physical index model.Methods We created a constitution identification system based on neural network using Visio Studio development tool.We report the initial implementation of the system,the accuracy of which was verified using actual data.Results We found a relatively strong correlation between TCM constitution and physical indicators.Conclusion Finally,our report describes a possible application of the proposed system.展开更多
Here we present a simple yet effective gas chromatography-mass spectrometry(GC-MS)identification approach for the detection of heteroatom-containing compounds(HACCs)in petroleum fractions.The MS/AMDIS(Automated Mass S...Here we present a simple yet effective gas chromatography-mass spectrometry(GC-MS)identification approach for the detection of heteroatom-containing compounds(HACCs)in petroleum fractions.The MS/AMDIS(Automated Mass Spectral Deconvolution and Identification System)program was used to identify parts per million(ppm)HACC concentrations in petroleum fractions in place of traditional techniques(extraction and standard injection).Polycyclic aromatic sulfur heterocycles(S-PAHs)were used as model compounds to confirm the validity of the AMDIS identifiers,which were compared with extracted results using the off-line X-calibur software.AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate.There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate.AMDIS was also used to detect nitrogen-containing compounds(NCCs)and alkylphenols in oil condensate.Our results confirmed the presence of 2-methylbenzothiazole,carbazole,and 2,4-ditertbutyl phenol.In a crude oil sample,AMDIS identification of m/z=191 biomarkers wa s consistent with empirical results.Therefore,AMDIS can help to reduce the number of experimental steps in identification protocols.展开更多
Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints ...Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.展开更多
We aimed to develop a set of single nucleotide polymorphism(SNP) markers that can be used to distinguish the main cultivated grape(Vitis L.) cultivars in China and provide technical support for domestic grape cultivar...We aimed to develop a set of single nucleotide polymorphism(SNP) markers that can be used to distinguish the main cultivated grape(Vitis L.) cultivars in China and provide technical support for domestic grape cultivar protection, cultivar registration, and market rights protection. A total of 517 high-quality loci were screened from 4 241 729 SNPs obtained by sequencing 304 grape accessions using specific locus amplified fragment sequencing, of which 442 were successfully designed as Kompetitive Allele Specific PCR(KASP) markers. A set of 27 markers that completely distinguishes 304 sequenced grape accessions was determined by using the program, and 26 effective markers were screened based on 23 representative grape cultivars. Finally, a total of 46 out of 48 KASP markers, including 22 markers selected by the research group in the early stage, were re-screened based on 348 grape accessions. Population structure, principal component, and cluster analyses all showed that the 348 grape accessions were best divided into two populations. In addition, cluster analysis subdivided them into six subpopulations. According to genetic distance, V. labrusca, V. davidii, V. heyneana, and V. amurensis were far from V. vinifera, while V. vinifera×V. labrusca and V. amurensis×V. vinifera were somewhere in between these two groups. Furthermore, a core set of 25 KASP markers could distinguish 95.69% of the 348 grape accessions, and the other 21 markers were used as extended markers. Therefore, SNP molecular markers based on KASP typing technology provide a new way for mapping DNA fingerprints in grape cultivars. With high efficiency and accuracy and low cost, this technology is more competitive than other current identification methods. It also has excellent application prospects in the grape distinctness, uniformity, and stability(DUS) test, as well as in promoting market rights protection in the near future.展开更多
A quantum identification system based on the transformation of polarization of a mesoscopic coherent state is proposed. Physically, an initial polarization state which carries the identity information is transformed i...A quantum identification system based on the transformation of polarization of a mesoscopic coherent state is proposed. Physically, an initial polarization state which carries the identity information is transformed into an arbitrary elliptical polarization state, To verify the identity of a communicator, a reverse procedure is performed by the receiver, For simply describing the transformation procedure, the analytical methods of Poincaré sphere and quaternion are adopted. Since quantum noise provides such a measurement uncertainty for the eavesdropping that the identity information cannot be retrieved from the elliptical polarization state, the proposed scheme is secure.展开更多
The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship i...The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.展开更多
A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of ...A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.展开更多
Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment...Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment, and traffic demand estimation. However, it is very time consuming and costly to obtain vehicle turning movement information manually. Previous efforts to simplify this process were focused on solving the problem using an O-D matrix, but this method proved to be inaccurate and unreliable with the existing data acquisition system. Another study involved the identification of vehicle turning movements from the detector information, but the presence of shared lanes led to uncertainties in vehicle matching, thus limiting application of the method only to intersections without shared lanes. In light of those unsuccessful attempts, this paper develops and tests a system called the Automatic Turning Movement Identification System (ATMIS), which estimates vehicle turning movements at a signalized intersection in real time, regardless of its geometry. The results from lab experiments as well as a field test show that the algorithm is very promising and may potentially be expanded for field applications.展开更多
In this paper, algorithms of automatic identification of persons on the basis of their photographs are considered. For identification of persons, the comparative analysis of control systems by bases of images created ...In this paper, algorithms of automatic identification of persons on the basis of their photographs are considered. For identification of persons, the comparative analysis of control systems by bases of images created in the different periods is carried out and their applied possibilities are shown.展开更多
The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be charact...The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies. Key words RBFNN - inteligent identification - structural damage - Brower/Server (B/S) model CLC number TP 183 Foundation item: Supported by the Natural Science Foundation of Hubei Province in China (2001ABB0778), The Science and Technology Foundation for Wuhan Young Scholar (20015005039)Biography: RAO Wen-bi (1967-), female, Ph. D, associate professor, research direction: artificial intelligence展开更多
Most current security and authentication systems are based on personal biometrics.The security problem is a major issue in the field of biometric systems.This is due to the use in databases of the original biometrics....Most current security and authentication systems are based on personal biometrics.The security problem is a major issue in the field of biometric systems.This is due to the use in databases of the original biometrics.Then biometrics will forever be lost if these databases are attacked.Protecting privacy is the most important goal of cancelable biometrics.In order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases.One methodology to achieve non-invertibility is the employment of non-invertible transforms.This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system.This system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption process.The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and feasible.The encryption and cancelability effects are good and reveal good performance.Also,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.展开更多
In this paper, photoplethysmogram (PPG) signals from two classes consisting of healthy and diabetic subjects have been used to estimate the parameters of Auto-Regressive Moving Average (ARMA) models. The healthy class...In this paper, photoplethysmogram (PPG) signals from two classes consisting of healthy and diabetic subjects have been used to estimate the parameters of Auto-Regressive Moving Average (ARMA) models. The healthy class consists of 70 healthy and the diabetic classes of 70 diabetic patients. The estimated ARMA parameters have then been averaged for each class, leading to a unique representative model per class. The order of the ARMA model has been selected as to achieve the best classification. The resulting model produces a specificity of %91.4 and a sensitivity of, %100. The proposed technique may find applications in determining the diabetic state of a subject based on a non-invasive signal.展开更多
Gait event detection is important for diagnosis and evaluation. This is a challenging endeavor due to subjectivity, high amount of data, among other problems. ANFIS （Artificial Neural Fuzzy Inference Systems）, ARX ...Gait event detection is important for diagnosis and evaluation. This is a challenging endeavor due to subjectivity, high amount of data, among other problems. ANFIS （Artificial Neural Fuzzy Inference Systems）, ARX （Autoregressive Models with Exogenous Variables）, OE （Output Error models）, NARX （Nonlinear Autoregressive Models with Exogenous Variables） and models based on NN （neural networks） were developed in order to detect gait events without the problems mentioned. The objective was to compare developed models＇ performance and determinate the most suitable model for gait events detection. Knee joint angle, heel foot switch and toe foot switch during normal walking in a treadmill were collected from a healthy volunteer. Gait events were classified by three experts in human motion. Experts＇ mean classification was obtained and all models were trained and tested with the collected data and experts＇ mean classification. Fit percentage was obtained to evaluate models performance. Fit percentages were： ANFIS： 79.49%, ARX： 68.8%, OE： 71.39%, NARX： 88.59%, NNARX： 67.66%, NNRARX： 68.25% and NNARMAX： 54.71%. NARX had the best performance for gait events classification. For ARX and OE, previous filtering is needed. NN＇s models showed the best performance for high frequency components, ANFIS and NARX were able to integrate criteria from three experts for gait analysis. NARX and ANFIS are suitable for gait event identification. Test with additional subjects is needed.展开更多
BACKGROUND The Asia-Pacific Colorectal Screening(APCS)score was designed with the purpose of distinguishing individuals at high risk(HR)for colorectal advanced neoplasia(AN).Traditional Chinese medicine(TCM)constituti...BACKGROUND The Asia-Pacific Colorectal Screening(APCS)score was designed with the purpose of distinguishing individuals at high risk(HR)for colorectal advanced neoplasia(AN).Traditional Chinese medicine(TCM)constitution was also linked with colorectal cancer(CRC).AIM To integrate the APCS score with TCM constitution identification as a new algorithm to screen for CRC.METHODS A cross-sectional multicenter study was carried out in three hospitals,enrolling 1430 patients who were asymptomatic and undergoing screening colonoscopy from 2022 to 2023.Patients were considered to have average risk,moderate risk,or HR with their APCS score.Odd ratios assessed the relationship between TCM constitution and disease progression.A TCM constitution risk score was created.The sensitivity and specificity of the new algorithm were calculated to evaluate diagnostic performance in detecting advanced adenoma(AA),CRC,and AN.RESULTS Of the 1430 patients,370(25.9%)were categorized as average risk,755(52.8%)as moderate risk,and 305(21.3%)as HR.Using the combined APCS score and the TCM constitution(damp-heat,qi-deficiency,yang-deficiency,phlegm-dampness,and inherited special constitution as positive)algorithm,72.2%of patients with AA and 73.7%of patients with AN were detected.Compared with the APCS score alone,the new algorithm significantly improved the sensitivity for screening AA[72.2%,95%confidence interval(CI):64.4%-80.0%vs 49.2%,95%CI:40.5%-57.9%]and AN(73.7%,95%CI:66.4%-81.1%vs 51.1%,95%CI:42.7%-59.5%).CONCLUSION The combination of APCS and TCM constitution identification questionnaires was valuable in identifying Chinese individuals who were asymptomatic for colorectal screening prioritization.展开更多
A state-of-the-art review is presented of mathematical manoeuvring models for surface ships and parameter estimation methods that have been used to build mathematical manoeuvring models for surface ships. In the first...A state-of-the-art review is presented of mathematical manoeuvring models for surface ships and parameter estimation methods that have been used to build mathematical manoeuvring models for surface ships. In the first part, the classical manoeuvring models, such as the Abkowitz model, MMG, Nomoto and their revised versions, are revisited and the model structure with the hydrodynamic coefficients is also presented.Then, manoeuvring tests, including both the scaled model tests and sea trials, are introduced with the fact that the test data is critically important to obtain reliable results using parameter estimation methods. In the last part, selected papers published in journals and international conferences are reviewed and the statistical analysis of the manoeuvring models, test data, system identification methods and environmental disturbances used in the paper is presented.展开更多
A novel parameter identification method for magnetic levitation bearing rotor systems is proposed,based on the modulation function method.The fundamental principle of the modulation function method for parameter ident...A novel parameter identification method for magnetic levitation bearing rotor systems is proposed,based on the modulation function method.The fundamental principle of the modulation function method for parameter identification is derived on the basis of the characteristics of the modulation function.The transformation of the differential equation model of a continuous system into a general algebraic equation model is effectively achieved,thereby avoiding the influence of errors introduced by the initial value and differential derivation of the system.Modulation function method parameter identification models have been established for single-degree-of-freedom and multi-degree-of-freedom magnetic levitation bearing rotor systems.The influence of different parameters of Hartley modulation function on the accuracy of system parameter identification has been investigated,thus providing a basis for the design of Hartley modulation function parameters.Simulation and experimental results demonstrate that the modulation function method can effectively identify system parameters despite the presence of system noise.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
基金The National Natural Science Foundation of China under contract No.61362002the Marine Scientific Research Special Funds for Public Welfare of China under contract No.201505002
文摘High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.
文摘The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.
基金Project(9140C860304) supported by the National Defense Key Laboratory Foundation of China
文摘Aiming at the potential presence of mixing automatic identification system(AIS) signals,a new demodulation scheme was proposed for separating other interfering signals in satellite systems.The combined iterative cross-correlation demodulation scheme,referred to as CICCD,yielded a set of single short signals based on the prior information of AIS,after the frequency,code rate and modulation index were estimated.It demodulates the corresponding short codes according to the maximum peak of cross-correlation,which is simple and easy to implement.Numerical simulations show that the bit error rate of proposed algorithm improves by about 40% compared with existing ones,and about 3 dB beyond the standard AIS receiver.In addition,the proposed demodulation scheme shows the satisfying performance and engineering value in mixing AIS environment and can also perform well in low signal-to-noise conditions.
基金funding support from the Traditional Chinese Medicine of Sichuan Province Youth Science and Technology Research Special Fund (No.2016Q065)Chengdu University of TCM Fund for Development of Science and Technology (No.ZRQN1790)
文摘Objective Identification of one’s constitution based on a combination of features and back propagation neural network theory is needed in modern medicine and traditional Chinese medicine(TCM).We describe a method to identify one’s constitution based on TCM constitution classification and a physical index model.Methods We created a constitution identification system based on neural network using Visio Studio development tool.We report the initial implementation of the system,the accuracy of which was verified using actual data.Results We found a relatively strong correlation between TCM constitution and physical indicators.Conclusion Finally,our report describes a possible application of the proposed system.
文摘Here we present a simple yet effective gas chromatography-mass spectrometry(GC-MS)identification approach for the detection of heteroatom-containing compounds(HACCs)in petroleum fractions.The MS/AMDIS(Automated Mass Spectral Deconvolution and Identification System)program was used to identify parts per million(ppm)HACC concentrations in petroleum fractions in place of traditional techniques(extraction and standard injection).Polycyclic aromatic sulfur heterocycles(S-PAHs)were used as model compounds to confirm the validity of the AMDIS identifiers,which were compared with extracted results using the off-line X-calibur software.AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate.There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate.AMDIS was also used to detect nitrogen-containing compounds(NCCs)and alkylphenols in oil condensate.Our results confirmed the presence of 2-methylbenzothiazole,carbazole,and 2,4-ditertbutyl phenol.In a crude oil sample,AMDIS identification of m/z=191 biomarkers wa s consistent with empirical results.Therefore,AMDIS can help to reduce the number of experimental steps in identification protocols.
文摘Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.
基金provided by the National Key R&D Program of China(2019YFD1001401)the China Agriculture Research System of MOF and MARA(CARS-29-yc-1)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2017-ZFRI)。
文摘We aimed to develop a set of single nucleotide polymorphism(SNP) markers that can be used to distinguish the main cultivated grape(Vitis L.) cultivars in China and provide technical support for domestic grape cultivar protection, cultivar registration, and market rights protection. A total of 517 high-quality loci were screened from 4 241 729 SNPs obtained by sequencing 304 grape accessions using specific locus amplified fragment sequencing, of which 442 were successfully designed as Kompetitive Allele Specific PCR(KASP) markers. A set of 27 markers that completely distinguishes 304 sequenced grape accessions was determined by using the program, and 26 effective markers were screened based on 23 representative grape cultivars. Finally, a total of 46 out of 48 KASP markers, including 22 markers selected by the research group in the early stage, were re-screened based on 348 grape accessions. Population structure, principal component, and cluster analyses all showed that the 348 grape accessions were best divided into two populations. In addition, cluster analysis subdivided them into six subpopulations. According to genetic distance, V. labrusca, V. davidii, V. heyneana, and V. amurensis were far from V. vinifera, while V. vinifera×V. labrusca and V. amurensis×V. vinifera were somewhere in between these two groups. Furthermore, a core set of 25 KASP markers could distinguish 95.69% of the 348 grape accessions, and the other 21 markers were used as extended markers. Therefore, SNP molecular markers based on KASP typing technology provide a new way for mapping DNA fingerprints in grape cultivars. With high efficiency and accuracy and low cost, this technology is more competitive than other current identification methods. It also has excellent application prospects in the grape distinctness, uniformity, and stability(DUS) test, as well as in promoting market rights protection in the near future.
基金Project supported by the National Natural Science Foundation of China （Grant No 60472018）.
文摘A quantum identification system based on the transformation of polarization of a mesoscopic coherent state is proposed. Physically, an initial polarization state which carries the identity information is transformed into an arbitrary elliptical polarization state, To verify the identity of a communicator, a reverse procedure is performed by the receiver, For simply describing the transformation procedure, the analytical methods of Poincaré sphere and quaternion are adopted. Since quantum noise provides such a measurement uncertainty for the eavesdropping that the identity information cannot be retrieved from the elliptical polarization state, the proposed scheme is secure.
文摘The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.
基金supported by the Elite Scholar Program of Northwest A&F University (Grant No.Z111022001)the Research Fund of Department of Transport of Shannxi Province (Grant No.22-23K)the Student Innovation and Entrepreneurship Training Program of China (Project Nos.S202110712555 and S202110712534).
文摘A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.
文摘Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment, and traffic demand estimation. However, it is very time consuming and costly to obtain vehicle turning movement information manually. Previous efforts to simplify this process were focused on solving the problem using an O-D matrix, but this method proved to be inaccurate and unreliable with the existing data acquisition system. Another study involved the identification of vehicle turning movements from the detector information, but the presence of shared lanes led to uncertainties in vehicle matching, thus limiting application of the method only to intersections without shared lanes. In light of those unsuccessful attempts, this paper develops and tests a system called the Automatic Turning Movement Identification System (ATMIS), which estimates vehicle turning movements at a signalized intersection in real time, regardless of its geometry. The results from lab experiments as well as a field test show that the algorithm is very promising and may potentially be expanded for field applications.
文摘In this paper, algorithms of automatic identification of persons on the basis of their photographs are considered. For identification of persons, the comparative analysis of control systems by bases of images created in the different periods is carried out and their applied possibilities are shown.
文摘The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies. Key words RBFNN - inteligent identification - structural damage - Brower/Server (B/S) model CLC number TP 183 Foundation item: Supported by the Natural Science Foundation of Hubei Province in China (2001ABB0778), The Science and Technology Foundation for Wuhan Young Scholar (20015005039)Biography: RAO Wen-bi (1967-), female, Ph. D, associate professor, research direction: artificial intelligence
文摘Most current security and authentication systems are based on personal biometrics.The security problem is a major issue in the field of biometric systems.This is due to the use in databases of the original biometrics.Then biometrics will forever be lost if these databases are attacked.Protecting privacy is the most important goal of cancelable biometrics.In order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases.One methodology to achieve non-invertibility is the employment of non-invertible transforms.This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system.This system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption process.The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and feasible.The encryption and cancelability effects are good and reveal good performance.Also,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.
文摘In this paper, photoplethysmogram (PPG) signals from two classes consisting of healthy and diabetic subjects have been used to estimate the parameters of Auto-Regressive Moving Average (ARMA) models. The healthy class consists of 70 healthy and the diabetic classes of 70 diabetic patients. The estimated ARMA parameters have then been averaged for each class, leading to a unique representative model per class. The order of the ARMA model has been selected as to achieve the best classification. The resulting model produces a specificity of %91.4 and a sensitivity of, %100. The proposed technique may find applications in determining the diabetic state of a subject based on a non-invasive signal.
文摘Gait event detection is important for diagnosis and evaluation. This is a challenging endeavor due to subjectivity, high amount of data, among other problems. ANFIS （Artificial Neural Fuzzy Inference Systems）, ARX （Autoregressive Models with Exogenous Variables）, OE （Output Error models）, NARX （Nonlinear Autoregressive Models with Exogenous Variables） and models based on NN （neural networks） were developed in order to detect gait events without the problems mentioned. The objective was to compare developed models＇ performance and determinate the most suitable model for gait events detection. Knee joint angle, heel foot switch and toe foot switch during normal walking in a treadmill were collected from a healthy volunteer. Gait events were classified by three experts in human motion. Experts＇ mean classification was obtained and all models were trained and tested with the collected data and experts＇ mean classification. Fit percentage was obtained to evaluate models performance. Fit percentages were： ANFIS： 79.49%, ARX： 68.8%, OE： 71.39%, NARX： 88.59%, NNARX： 67.66%, NNRARX： 68.25% and NNARMAX： 54.71%. NARX had the best performance for gait events classification. For ARX and OE, previous filtering is needed. NN＇s models showed the best performance for high frequency components, ANFIS and NARX were able to integrate criteria from three experts for gait analysis. NARX and ANFIS are suitable for gait event identification. Test with additional subjects is needed.
文摘BACKGROUND The Asia-Pacific Colorectal Screening(APCS)score was designed with the purpose of distinguishing individuals at high risk(HR)for colorectal advanced neoplasia(AN).Traditional Chinese medicine(TCM)constitution was also linked with colorectal cancer(CRC).AIM To integrate the APCS score with TCM constitution identification as a new algorithm to screen for CRC.METHODS A cross-sectional multicenter study was carried out in three hospitals,enrolling 1430 patients who were asymptomatic and undergoing screening colonoscopy from 2022 to 2023.Patients were considered to have average risk,moderate risk,or HR with their APCS score.Odd ratios assessed the relationship between TCM constitution and disease progression.A TCM constitution risk score was created.The sensitivity and specificity of the new algorithm were calculated to evaluate diagnostic performance in detecting advanced adenoma(AA),CRC,and AN.RESULTS Of the 1430 patients,370(25.9%)were categorized as average risk,755(52.8%)as moderate risk,and 305(21.3%)as HR.Using the combined APCS score and the TCM constitution(damp-heat,qi-deficiency,yang-deficiency,phlegm-dampness,and inherited special constitution as positive)algorithm,72.2%of patients with AA and 73.7%of patients with AN were detected.Compared with the APCS score alone,the new algorithm significantly improved the sensitivity for screening AA[72.2%,95%confidence interval(CI):64.4%-80.0%vs 49.2%,95%CI:40.5%-57.9%]and AN(73.7%,95%CI:66.4%-81.1%vs 51.1%,95%CI:42.7%-59.5%).CONCLUSION The combination of APCS and TCM constitution identification questionnaires was valuable in identifying Chinese individuals who were asymptomatic for colorectal screening prioritization.
基金the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineeringfinanced by the Portuguese Foundation for Science and Technology (Fundacao para a Ciência e Tecnologia-FCT) under contract UIDB/UIDP/00134/2020。
文摘A state-of-the-art review is presented of mathematical manoeuvring models for surface ships and parameter estimation methods that have been used to build mathematical manoeuvring models for surface ships. In the first part, the classical manoeuvring models, such as the Abkowitz model, MMG, Nomoto and their revised versions, are revisited and the model structure with the hydrodynamic coefficients is also presented.Then, manoeuvring tests, including both the scaled model tests and sea trials, are introduced with the fact that the test data is critically important to obtain reliable results using parameter estimation methods. In the last part, selected papers published in journals and international conferences are reviewed and the statistical analysis of the manoeuvring models, test data, system identification methods and environmental disturbances used in the paper is presented.
基金supported by the National Science and Technology Major Project(Grant No.J2019-Ⅳ-0003-0070).
文摘A novel parameter identification method for magnetic levitation bearing rotor systems is proposed,based on the modulation function method.The fundamental principle of the modulation function method for parameter identification is derived on the basis of the characteristics of the modulation function.The transformation of the differential equation model of a continuous system into a general algebraic equation model is effectively achieved,thereby avoiding the influence of errors introduced by the initial value and differential derivation of the system.Modulation function method parameter identification models have been established for single-degree-of-freedom and multi-degree-of-freedom magnetic levitation bearing rotor systems.The influence of different parameters of Hartley modulation function on the accuracy of system parameter identification has been investigated,thus providing a basis for the design of Hartley modulation function parameters.Simulation and experimental results demonstrate that the modulation function method can effectively identify system parameters despite the presence of system noise.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
