Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

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.

Optical Ciphering Scheme for Cancellable Speaker Identification System

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.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

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.

Non-coherent sequence detection scheme for satellite-based automatic identification system

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.

Combined iterative cross-correlation demodulation scheme for mixing space borne automatic identification system signals

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.

Constitution Identification System for Traditional Chinese Medicine (TCM) Based on Correlation Between TCM Constitution and Physical Examination Indices

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.

A Study on Automatic Latent Fingerprint Identification System

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.

Establishment and application of an SNP molecular identification system for grape cultivars

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.

A secure identification system using coherent states

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.

Real-time Monitoring of Subsea Gas Pipelines,Offshore Platforms,and Ship Inspection Scores Using an Automatic Identification System

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.

Development and Testing of an Automatic Turning Movement Identification System at Signalized Intersections

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.

Increase of Indicator Values of Identification Systems Quality on the Recognition of Human Face on the Basis of Photo Portraits

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.

Remote Intelligent Identification System of Structural Damage

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

Diabetic diagnose test based on PPG signal and identification system

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.

Comparison between Classical and Intelligent Identification Systems for Classification of Gait Events

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.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

System error iterative identification for underwater positioning based on spectral clustering

The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Identification of the Asymmetric Transmission Error and Gear Mesh Dynamic Parameters using Full-Spectrum Responses in a Geared-Rotor System

A dominant source of vibration in geared-rotor systems is the gear mesh fault parameters.They include the asymmetric transmission error(TE),phases of TE,the gear mesh stiffness,the gear mesh damping,and the gear runouts.The present work deals with the experimental identification of the aforementioned parameters.A mathematical model of a geared-rotor system has been developed using Lagrangian dynamics.Equations of motion are transformed into the frequency domain using the full-spectrum response analysis.These transformed equations are used to develop an identification algorithm(IA)based on least-squares fit to estimate the TE and gear mesh dynamic parameters.The system IA is initially verified using numerical simulations.The robustness of the algorithm is checked by introducing white Gaussian noise in the simulated responses.A geared-rotor experimental rig was developed and used to measure responses at gear locations in two orthogonal directions.Measured responses are transformed in the frequency domain using the full-spectrum analysis and used in the present novel IA to identify the gear parameters.The identified parameters are validated by comparing the numerically generated full-spectrum response using experimentally estimated parameters and that from the experimental rig.

A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.