Real-time Identification Method Research Articles

Retrograde Ureteral Indocyanine Green Injection at the Time of Pelvic Lymph Node Debulking for Metastatic Cervical Cancer

Study Objective Demonstrate a safe and easy method for ureteral identification during laparoscopy. Design Case report. Setting Patient was in dorsal lithotomy with steep Trendelenburg. Patients or Participants 1 patient. Interventions Retrograde ureteral indocyanine green injection. Measurements and Main Results N/A Conclusion Retrograde ureteral indocyanine green injection is a simple and feasible method for real time identification of the ureter during laparoscopy and helps to prevent ureteral injury. Demonstrate a safe and easy method for ureteral identification during laparoscopy. Case report. Patient was in dorsal lithotomy with steep Trendelenburg. 1 patient. Retrograde ureteral indocyanine green injection. N/A Retrograde ureteral indocyanine green injection is a simple and feasible method for real time identification of the ureter during laparoscopy and helps to prevent ureteral injury.

Convolutional neural network human gesture recognition algorithm based on phase portrait of surface electromyography energy kernel

Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.

Determining the nominal body contour image using wideband millimeter-wave radar for characterizing person-worn threats

AbstractPrecise characterization of concealed person-worn objects will speed up the passenger screening process by reducing the rate of nuisance alarms, while also enhancing the airport security imaging systems. This paper presents an automatic, real-time method for wideband millimeter-wave radar identification of the nominal surface contours of the human body – even with affixed foreign objects or when a segment of the body cross-section is not captured by the radar – without relying on the body's bilateral symmetry. The developed method is verified experimentally when applied to the actual images generated by a laboratory airport scanning prototype developed recently by the US Department of Homeland Security (DHS). Our method uses the noisy collection of radar cross-section reflectivity data to extract the main contours and estimates the nominal body surface cross-sections through fitting a small-term Fourier series of circumferential variation. This is a necessary step for accurate characterizing of concealed terrorist threat objects affixed to the body.

Association between SP-A rs1965708 gene polymorphism and allergic rhinitis risk in Chinese population.

BackgroundPulmonary surfactant protein A (SP‐A) in the respiratory tract plays an important role in host. In the present, we assessed the association between SP‐A gene polymorphism and allergic rhinitis.MethodsUsing a case–control design, we compared the genotype frequencies of SP‐A rs1965708 between allergic rhinitis patients and healthy control group. Genotyping was performed using real‐time quantitative PCR‐based molecular identification methods. Univariate and multivariate logistic regression were performed to quantitatively assess the association between rs1965708 polymorphism and allergic rhinitis, and the odds ratio (OR) and 95% confidence interval (CI) were also calculated.Results500 patients with allergic rhinitis and 500 healthy controls were included in the study. Compared with the CC genotype, we found that AA genotype of rs1965708 could increase the allergic rhinitis risk in the univariate analysis (OR = 2.63, 95% CI: 1.56–4.54, p = 0.000). For dominant model, we found no significant difference in the dominant model (OR = 1.14, 95% CI: 0.86–1.52, p = 0.367). In the recessive model, the CC genotype could elevate the risk of allergic rhinitis compared with CC + AA genotype (OR = 2.70, 95% CI: 1.61–4.54, p = 0.000). Similar results were also found in the allele model (OR = 1.28, 95% CI: 1.07–1.54, p = 0.008). Interactions between rs1965708 AA or AC and smoking increased the allergic rhinitis risk.ConclusionsThe rs1965708 variants of SP‐A gene polymorphism are associated with allergic rhinitis, and the A allele could increase the allergic rhinitis risk. The AA SNP variants that interact with smoking may alter the susceptibility to allergic rhinitis.

Design, Sensing, and Control of a Novel UAV Platform for Aerial Drilling and Screwing

Hole drilling and bolt screwing are frequently performed tasks in construction, decoration, and maintenance. Traditionally, sending human workers to perform these tasks in hard-to-reach locations is both dangerous and costly. In this letter, we present an aerial manipulation platform that allows a human user to remotely conduct omnidirectional drilling and screwing. The design of the platform features a quadrotor UAV with each pair of rotors independently tilted by a servo, forming an “H” configuration, on which a 1-DOF manipulator carrying a motorized drill or screw driver is mounted. With such a design, the end-effector can face any direction on the longitudinal plane and exert a big enough contact force for drilling and screwing without the need of changing the vehicle body's orientation. Compared to previous UAVs that can only drill holes vertically into the ground, the proposed design also allows horizonal drilling/screwing into a wall or a cliff, making it suitable for a vast range of real applications. Based on the dynamic equations of the system, a dual-level control law is proposed. The low-level attitude controller uses an adaptive robust control (ARC) to accurately regulate the attitude angles in the presence of force/torque uncertainties that may occur during the drilling and screwing process, while a selective impedance controller is implemented at high level to indirectly control the contact force commanded by the user. In addition, a vision-based real-time target identification and tracking method integrating a YOLO v3 real-time object detector with feature tracking, and morphological operations is developed to identify and track the target point for drilling and screwing specified by the user. Various in-lab experiments on a self-made prototype demonstrate the feasibility and effectiveness of the proposed approach for aerial drilling and screwing.

Clump interpolation error for the identification of damage using decentralized sensor networks

Recent developments in the field of smart sensing systems enable performing simple onboard operations which are increasingly used for the decentralization of complex procedures in the context of vibration-based structural health monitoring (SHM). Vibration data collected by multiple sensors are traditionally used to identify damage-sensitive features (DSFs) in a centralized topology. However, dealing with large infrastructures and wireless systems may be challenging due to their limited transmission range and to the energy consumption that increases with the complexity of the sensing network. Local DSFs based on data collected in the vicinity of inspection locations are the key to overcome geometric limits and easily design scalable wireless sensing systems. Furthermore, the onboard pre-processing of the raw data is necessary to reduce the transmission rate and improve the overall efficiency of the network. In this study, an effective method for real-time modal identification is used together with a local approximation of a damage feature, the interpolation error, to detect and localize damage due to a loss of stiffness. The DSF is evaluated using the responses recorded at small groups of sensors organized in a decentralized topology. This enables the onboard damage identification in real time thereby reducing computational effort and memory allocation requirements. Experimental tests conducted using real data confirm the robustness of the proposed method and the potential of its implementation onboard decentralized sensor networks.

Sequential Quadriplex Real-Time PCR for Identifying 20 CommonemmTypes of Group AStreptococcus

We developed a sequential quadriplex real-time PCR-based method for rapid identification of 20emmtypes commonly found in invasive group AStreptococcus(iGAS) strains recovered through the Centers for Disease Control and Prevention’s Active Bacterial Core surveillance. Eachemmreal-time PCR assay showed high specificity and accurately identified the respective targetemmtype, includingemmsubtypes in the United States.

Research on dynamic characteristics and identification method of local defect on the roll surface

Local defects are generally produced on the surfaces of roll during long-term work, which not only causes abnormal vibration of the roll mill but also affects the quality of the produced steel strips. In particular, identifying the defects on a well-lubricated roll surface is a challenge. Therefore, a time-varying oil film stiffness model is proposed based on the elastohydrodynamic lubrication theory. A Sendzimir twenty-high roll mill model was developed and combined with the time-varying oil film stiffness model to analyse the vibration characteristics of the roll mill. Simultaneously, a new method for real-time identification of the defect sizes during the rolling process was proposed. Agreement between the simulated and experimental results was used to validate the effectiveness of the proposed model. The changes to the oil film stiffness and roll mill vibration characteristics for different defect sizes on the roll surface are thus analyzed to provide theoretical support for the identification of the local defects.

Real-time hysteresis identification in structures based on restoring force reconstruction and Kalman filter

Identifying the local nonlinear hysteretic behaviors in structures arises as an important issue in structural health monitoring. This paper develops a new hysteresis identification framework where rather than identifying the hysteretic parameters, the restoring forces are reconstructed so as to identify the hysteretic loops. Under this new framework, there is no need to get a priori knowledge on the hysteretic models, enabling a wider range of applications, and the involved system equation is linear with unknown restoring forces, tending to be more efficient. Then, the Kalman filter is adopted for real-time restoring force reconstruction because the Kalman filter is celebrated for its efficiency, effectiveness and reliability in linear system estimation with noisy measured data. In doing so, the restoring forces are additionally augmented as state variables and such augmentation is shown to act as the role of Tikhonov regularization, by which the proper covariance of the augmentation as the regularization parameter is selected via the L-curve method. Numerical examples are presented and an experimental test is conducted to verify the effectiveness and robustness of the proposed real-time hysteresis identification method.

Real-Time Identification of LCL Filters Employed With Grid Converters

This article presents a real-time identification method for LCL filters used with three-phase grid converters. The method can be applied to identify both the inductance and capacitance values of the filter and the series resistance seen by the converter. As a side-product, an estimate of the grid inductance seen from the point of connection is also obtained. A wideband excitation signal is added to the converter voltage reference. During the excitation, converter current and converter voltage reference samples are used for identification. The samples are preprocessed in real time by removing dc biases and significant grid-frequency harmonics. Parameters of two discrete-time models are estimated at each sampling instant with a recursive estimation algorithm. Depending on the estimated model, the model parameter estimates are translated to either the resistance or the inductance and capacitance values of the system. The method can be embedded to a control system of pulsewidth-modulation-based converters in a plug-in manner. Only the dc-link voltage and converter currents need to be measured. Simulation and experimental results are presented for a 12.5-kVA grid converter system to evaluate the proposed method.

Identification of the status of individual drug-administration behavior in heroin-withdrawal rats based on FrA EEG

Objective: To identify the compulsive drug-seeking behavior of the individual in the heroin-addicted rat, a novel analysis method of telemetering electroencephalogram (EEG) in the frontal association cortex (FrA) induced by heroin-dependent position preference in rats. Methods: Thirty clean-grade Wistar rats after implantation of prefrontal cortex electrodes, were randomly divided into the surgical control group (n=10) and heroin-inducing group (n=20). The heroin-induced group was subcutaneously injected with heroin 0.5 mg/(kg.d), and then increased daily by 0.25 mg/kg for seven days. The control group was injected with the same amount of normal saline at the same time. Using the CPP video system combined with electroencephalogram (EEG) wireless telemetry technology, EEG signals in FrA areas of the addicted rats were recorded simultaneously in four behaviors: white-black shuttle, black-white shuttle, black-chamber stay and white-chamber stay. The areas with EMG and other noisy signals in the original EEG were identified, and wavelet decomposition and amplitude threshold denoising pre-processing were used. The sample entropy values of EEG data and wavelet coefficients corresponding to 4 rhythm frequencies under different behavioral states standard deviation were extracted, and support vector machine algorithm (SVM) was used to achieve real-time identification of different behavioral states of heroin-addicted rats. Results: SVM real-time classification recognition rate of 20 heroin abstinence rats, which are staying in black or white chamber of video box, shuttling between black-white chambers or between white - black chambers, was about 80%. Among them, the real-time recognition rate of black-white shuttle, which is closely related to drug-seeking behavior, reached 83.88%. Conclusion: In this paper, the real-time identification method of heroin-induced obsessive-compulsive drug-seeking behavior in rats can be used as an effective method to detect the initiation and occurrence of heroin-seeking drug-seeking behavior in rats. It can be used for the clinical observation of heroin-dependent patients and the prevention of drug-seeking behavior.

Frequency Comparison Function Method for Real-Time Identification of Breathing Crack at Welding Joint

In this research, the frequency comparison function (FCF) method is proposed and studied to realize high-sensitive real-time crack identification at the welding joint area for a beam-type structure. This method is derived from the frequency response function (FRF). During FCF, we use the response signal collected from the designated point of the structure instead of the excitation. The standard deviation of the FCF amplitude curve is calculated to detect and evaluate the possible crack and its induced vibration perturbations afterward. Vibration responses are simulated in ANSYS by the use of the finite element analysis of a welded beam structure, and these signals are then analyzed with the FCF algorithm. It is concluded that FCF is an efficient method for breathing crack identification and can be easily applied under different excitation conditions, including harmonic and random ones. Meanwhile, FCF can be applied without any pre-processing algorithms such as filtering and smoothing. So, it can be used for real-time crack identification. By combining the FCF with the smart coating sensor composed of piezoelectric layers, the crack identification with high sensitivity is realized. The crack is detectable at its very early stage (starting from 3% of the beam thickness). Experimental studies under harmonic and random excitations are processed, and the results prove high sensitivity and feasibility of the proposed crack detection method.

On-line real-time mistuning identification and model calibration method for rotating blisks based on blade tip timing (BTT)

Identifying the mistuning parameters and calibrating the finite element model (FEM) of the mistuned blisk in real-time is a meaningful way to acquire the blisk’s dynamic characteristics and predict its stress response. Because of the wearing and other reasons, the blisk’s mistuning is changing during the operation. Hence a BTT based rotating-blisk mistuning identification and model calibration (BR-IDC) method in real-time has become a necessity. The rotating effects (stiffening, softening and Coriolis force) are considered in the BR-IDC. The synergistic effect of the mass and stiffness mistuning is analyzed, and the effect is presented by a standard mistuning coefficient. Based on the BR-IDC, the on-line real-time mistuning identification for the rotating blisk is realized. Furthermore, the FEM was calibrated by the identification results. The BR-IDC approach is verified by FEM simulation and experimental work. The precision of the FEM is improved through the calibration.

Study on Real-time Lithology Identification Method of Logging-while-drilling

In geophysical exploration and development, lithology identification is the basis for understanding the strata and solving the reservoir parameters. Through the comparative study of three popular lithology identification methods including SVM, GRNN and Elman, each method in the experiments takes the same logging data as a sample to predict and identify, and the most suitable identification method is selected and applied to the automatic identification of lithologic interpretation. It is found that the prediction accuracy of SVM is highest in automatic lithology identification, and the method is suitable for computer automatic analysis in terrestrial interpretation system.

Automatic gear-shifting strategy for fuel saving by tractors based on real-time identification of draught force characteristics

Adopting an automatic gear-shifting strategy for agricultural tractors can be an important factor affecting fuel consumption, and a reasonable gear-shifting strategy can effectively improve fuel economy. However, as an important part of the resistance exerted on the tractor–implement combination during tillage, the draught force of the implement strongly influences the driving state of the tractor. The changing characteristics of draught force with work speed largely determine the optimal timing of gear shifting. To realise automatic gear shifting to ensure fuel economy, it is necessary to obtain the varying characteristics of draught force in real time. However, draught force fluctuates is affected by many on-site factors, such as the physical properties of soil, operating speed and the design of the implements. A method is proposed using a recursive least-squares algorithm for the real-time identification of the changing characteristics of draught force. By analysing the forces exerted on the tractor–implement combination and the characteristics of the tractor powertrain, a mathematical model of the tractor–implement combination during field work was established. On this basis, four-parameter gear-shifting schedules were developed for tractor with different types of agricultural implements. The operating conditions were designed based on tractor operating characteristics. The validity and accuracy of the real-time identification method for the changing characteristic of draught force was verified. The designed gear-shifting schedules were proven to realise the automatic gear-shifting function according to the real-time working conditions on the premise of ensuring fuel economy and power performance.

PLL Position and Speed Observer With Integrated Current Observer for Sensorless PMSM Drives

In this article, a phase-locked loop (PLL) rotor position and speed observer, including an integrated decoupled stator current observer, is proposed for wide-speed-range sensorless control of permanent magnet synchronous motor (PMSM) drives. This new PLL scheme consists of two interconnected observers: 1) a second-order PLL position observer using the direct current error as correction term and 2) a third-order extended Luenberger speed observer, based on the mechanical model of the drive, using the quadrature current error as the correction term. The estimated speed is just an unrefined reference for the position integrator, and it is further corrected by the PLL. The current observer is a subsystem integrated as a PLL phase detector. Key relations between the current errors and the position and speed errors are proven, and a practical two-step observer design method is presented. A method for real-time identification of the stator resistance at low speeds and of the permanent magnet flux at high speeds is proposed, using the current errors in the current reference frame. Experimental results for a 0.5-kW surface-mounted PMSM drive confirm the effectiveness, excellent dynamic performance, and robustness of the new PLL observer. Stable motor and generator operation at 15 r/min (0.01 p.u.) with ramp and step speed reversals, high-speed operation, and stator resistance adaptation is shown.

An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

Abstract WP93: Cross-Institution Adaptive CT Perfusion Protocol for Stroke Workup Can Save Time, Radiation Dose and Improve Diagnostic Quality

Introduction: CT perfusion (CTP) is an integral part of the diagnosis and treatment pathway for acute ischemic stroke patients, however CTP protocols vary in scan duration and radiation dose across institutions. An ideal protocol would customize scan acquisition for each individual patient in real-time to avoid truncation-related postprocessing errors and minimize time and radiation dose. We explore the potential time and radiation dosage savings if such an ideal adaptive CTP protocol were implemented across institutions. Methods: We retrospectively assessed 148 CTP scans across two institutions, postprocessed by an expert reader with the CT perfusion 4D Neuro software package (GE Healthcare). Bolus arrival, peak and exit time for the arterial input (AIF) and venous output (VOF) functions were determined via inflection point analysis of time-attenuation curves. An empirically determined customized adaptive scan protocol (Figure) was generated based on each patient’s AIF/VOF curves. Resultant reduction in radiation dose, scan acquisition time and total protocol time were calculated. Results: With the adaptive protocol, all scans would have optimal diagnostic image quality, which addresses the 3% and 18% exams of sub diagnostic quality at the two institutions respectively. Average total protocol time decreased by 153 s and 12 s (59% and 9%) and average radiation dose decreased by 61% and 12% respectively, in comparison to current protocols at the two institutions. Scan acquisition time increased for 7% (11 of 148) of exams. Conclusions: An adaptive CTP protocol would realize significant time and radiation savings across institutions. Methods for real-time identification of VOF end times are currently under development.

Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning

Accurate on-line weld defect detection in robotic arc welding manufacturing is still challenging, due to the complexity and diversity of weld defects. In this study, a new real-time defect identification method is proposed for Al alloys in robotic arc welding, using arc optical spectroscopy and an integrated learning method. Spectrum feature was extracted, based on the absolute coefficients of the principal components. Feature importance was quantitatively evaluated using the mean decrease accuracy of Principal Component Analysis-Random Forest (PCA-RF). A new indicator, e.g., Importance Factor, was proposed, based on the variance of the out-of-bag test error of RF to select the optimal feature subset. The proposed PCA-RF proved to effectively identify five classes of weld defects with better performance than support vector machine and back propagation neural network. Finally, the selection pattern of spectrum feature subset was investigated, before revealing the correlation mechanism of the selected lines spectrum and weld process.

A method for real-time identification and tracking of traveling ionospheric disturbances using ionosonde data: first results

Traveling Ionospheric Disturbances (TIDs) are wave-like propagating irregularities that alter the electron density environment and play an important role spreading radio signals propagating through the ionosphere. A method combining spectral analysis and cross-correlation is applied to time series of ionospheric characteristics (i.e., MUF(3000)F2 or foF2) using data of the networks of ionosondes in Europe and South Africa to estimate the period, amplitude, velocity and direction of propagation of TIDs. The method is verified using synthetic data and is validated through comparison of TID detection results made with independent observational techniques. The method provides near real time capability of detection and tracking of Large-Scale TIDs (LSTIDs), usually associated with auroral activity.