On-line Procedure Research Articles

E2GK-pro: An Evidential Evolving Multimodeling Approach for Systems Behavior Prediction

Nonlinear dynamic systems identification and nonlinear dynamic behavior prediction are important tasks in several areas of industrial applications. Multiple works proposed multimodel-based approaches to model nonlinear systems. Multimodeling permits to blend different model types together to form hybrid models. It advocates the use of existing, well known model types within the same model structure. Recently, a multi modeling strategy based on belief functions theory was developed based on a fuzzy rule based system. We propose a different approach of this latter taking advantage of new efficient evidential clustering algorithms for the determination of the local models and the assessment of the global model. In particular, we propose an online procedure based on the Evidential Evolv- ing Gustafsson-Kessel (E2GK) algorithm that ensures an evolving partitioning of the data into clusters that correspond to operating regions of the global system. Thus the estimation of the local models is dynamically performed by upgrading and modifying their parameters while the data arrive. Each local model is weighted by a belief mass provided by E2GK, and the global model (multimodel) is a combination of all the local models.

Active noise control with on-line estimation of non-Gaussian noise characteristics

Active noise control (ANC) is a methodology for attenuating noise based on adaptive signal processing algorithms. ANC is well assessed for the attenuation of Gaussian noise, but the rejection of non-Gaussian impulsive noise signals represents a much more critical task that may even impair algorithm convergence. To overcome this problem the adaptive filter weight update process must be modified by discarding or discounting samples associated with impulsive noise. This can be done either by modeling the impulsive noise with a non-Gaussian distribution such as the Symmetric α -stable ( S α S ) distribution or by applying an outlier detection method. With both approaches the accuracy in the noise description appears to be crucial for effective noise reduction. This paper proposes two novel approaches for the attenuation of impulsive noise both for invariant and time-varying noise distributions. The first one is based on the on-line estimation of an S α S model of the noise probabilistic description. The second relies on a simple on-line recursive procedure that reliably estimates amplitude thresholds for outlier detection. Both methods compare favorably with competitor approaches, while maintaining a sufficiently low algorithm complexity. Several examples are shown to demonstrate the algorithms' effectiveness.

Introducing an on-line adaptive procedure for prostate image guided intensity modulate proton therapy

With on-line image guidance (IG), prostate shifts relative to the bony anatomy can be corrected by realigning the patient with respect to the treatment fields. In image guided intensity modulated proton therapy (IG-IMPT), because the proton range is more sensitive to the material it travels through, the realignment may introduce large dose variations. This effect is studied in this work and an on-line adaptive procedure is proposed to restore the planned dose to the target. A 2D anthropomorphic phantom was constructed from a real prostate patient's CT image. Two-field laterally opposing spot 3D-modulation and 24-field full arc distal edge tracking (DET) plans were generated with a prescription of 70 Gy to the planning target volume. For the simulated delivery, we considered two types of procedures: the non-adaptive procedure and the on-line adaptive procedure. In the non-adaptive procedure, only patient realignment to match the prostate location in the planning CT was performed. In the on-line adaptive procedure, on top of the patient realignment, the kinetic energy for each individual proton pencil beam was re-determined from the on-line CT image acquired after the realignment and subsequently used for delivery. Dose distributions were re-calculated for individual fractions for different plans and different delivery procedures. The results show, without adaptive, that both the 3D-modulation and the DET plans experienced delivered dose degradation by having large cold or hot spots in the prostate. The DET plan had worse dose degradation than the 3D-modulation plan. The adaptive procedure effectively restored the planned dose distribution in the DET plan, with delivered prostate D98%, D50% and D2% values less than 1% from the prescription. In the 3D-modulation plan, in certain cases the adaptive procedure was not effective to reduce the delivered dose degradation and yield similar results as the non-adaptive procedure. In conclusion, based on this 2D phantom study, by updating the proton pencil beam energy from the on-line image after realignment, this on-line adaptive procedure is necessary and effective for the DET-based IG-IMPT. Without dose re-calculation and re-optimization, it could be easily incorporated into the clinical workflow.

Consumer-to-Consumer Internet Auction Models

Internet auctions have become an increasingly common method for exchanging goods and services across the world both among consumers themselves, as well as between businesses and consumers. These Internet auction mechanisms have the scope of incorporating procedures of much greater complexity and variety, and they exhibit characteristics and properties that are quite distinct from conventional auctions. In this paper, the authors provide an experimental study of the performance characteristics and operational behaviour of a number of online auction models, including the fixed time forward auctions, the Vickrey auctions, and models with soft close variable auction times. These online auction models are studied through systematic simulation experiments, based on a series of operational assumptions, which characterize the arrival rate of bids, as well as the distribution from which the private values of buyers are sampled. Suggestions for efficient online auction design and procedures for improving auction performance are given, and the behaviour of the average auction income and average auction duration are quantified and compared.

MO-D-BRB-10: Optical Surface Imaging for Online Rotation Correction and Real-Time Motion Monitoring with Threshold Gating for Frameless Cranial Stereotactic Radiosurgery

Purpose: To develop an image‐guided procedure for online head‐rotation correction and real‐time motion monitoring with threshold gating for clinical frameless stereotactic radiosurgery(SRS) using video‐based optical 3D surfaceimaging (AlignRT) to ensure accurate tumor localization and clinically‐acceptable head motion at all treatment couch angles during treatment. Methods: Eight patients, immobilized with a deep‐head mold and a mouth‐piece fixed to the couch, were treated with single‐fraction frameless SRS. The planning target volume was defined by a 3.0mm‐margin around the gross tumor volume for these patients with brainmetastasis. A ceiling‐mounted AlignRT system with three camera pods was used to correct residual head rotation before cone‐beam computed tomography(CBCT) setup, verify setup at all treatment couch angles, and monitor the head motion with 1.0mm gating threshold for beam hold. The external contour of the planing CT and on‐site surface reference images were used for setup verification and motion monitoring, respectively, using the maximum visible skin surface within the entire mid‐to‐upper face as the region of interest. An anthropomorphous head phantom and an adjustable platform (accuracy of 0.1 mm) were used to determine the geometric accuracy of frameless setup procedure and AlignRT motion detection. Results: The phantom experiment shows that the motion detection accuracy was less than 0.1 mm. For these patients, <1 degree head rotation was achieved at the setup by using surface‐image‐guided head repositioning. The patient head motion was found to be <1.0mm for 98% of the time after CBCT setup, while the beam‐hold gating was used for <1% of beam‐on time. The required workload and machine time of this procedure are similar to those of conventional frame‐based SRS. Conclusions: This non‐invasive frameless SRS procedure provides accurate and reliable means to perform clinical SRS. The image‐guided setup and motion control provides adequate accuracy, yet more convenient to the patients, comparing with frame‐based SRS. This research is in part supported by VisionRT through a clinical research agreement.

Automated on-line solid-phase extraction coupled to liquid chromatography–tandem mass spectrometry for determination of lipophilic marine toxins in shellfish

Automated on-line solid-phase extraction (SPE) coupled to liquid chromatography–tandem mass spectrometry (LC–MS/MS) has been developed for fast determination of lipophilic marine toxins in shellfish samples. The direct coupling of an on-line SPE column to LC–MS/MS was accomplished using column switching techniques. Suitable chromatographic separation was performed on a reversed-phase C18 column under alkaline conditions (pH 11).The selected reversed-phase C18 SPE column allowed rapid and efficient on-line desalting of hydrolysed shellfish samples, avoiding signal suppression during mass spectrometry detection. Furthermore, the on-line SPE procedure allowed reducing matrix effects observed in raw and hydrolysed shellfish extracts.The proposed method was evaluated in terms of linearity, precision, accuracy and limits of detection (LODs). Quantitative recovery (97–102%) and satisfactory inter-day precision (RSD<8%) were achieved for all target compounds. LODs in the sub-μgkg−1 level (0.37–0.68μgkg−1) were obtained for all toxins except for okadaic acid, which showed a value of 2.75μgkg−1.Several mussel samples from North-western Spain were finally analysed in order to demonstrate the applicability of the method. Okadaic acid was the predominant toxin in all samples, although other lipophilic toxins were also detected.

On-line distillation and solid phase extraction preconcentration of volatile phenolic compounds in water samples with detection by spectrophotometry

A novel sequential injection on-line distillation procedure for volatile phenolic compounds is developed. The derivative between phenol and 4-aminoantipyrine (4-APP) is adsorbed onto an Amberlite XAD-7 resin packed mini-column. The phenol-4-APP derivative retained on the mini-column is effectively recovered by using a small amount of ethanol followed by spectromphotometric detection. The present procedure is suitable for the direct determination of volatile phenolic compounds at mg/L levels. It can also be used for the quantification of much lower concentration of volatile phenolic compounds at μg/L level after preconcentration with a larger sample volume. With the sample loading volume of 4 mL, a linear range of 0.004–0.3 mg/L is obtained along with a detection limit is 0.002 mg/L. A relative standard deviation of 1.4% (n = 9) is achieved at 0.1 mg/L. The procedure is applied to the determination of low level of volatile phenolic compounds in water samples and spiking recoveries within 96%–102% are achieved.

Impact of Concentration of Ellagitannins in Oak Wood on Their Levels and Organoleptic Influence in Red Wine

Some wood substances such as ellagitannins can be extracted during wine aging in oak barrels. The level of these hydrolyzable tannins in wine depends of some parameters of oak wood. Their impact on the organoleptic perception of red wine is poorly known. In our research, oak staves were classified in three different groups according to their level of ellagitannins estimated by NIRS (near infrared spectroscopy) online procedure (Oakscan). First, the ellagitannin level and composition were determine for each classified stave and an excellent correlation between the NIRS classification (low, medium and high potential level of ellagitannin) and the ellagitannin content estimated by HPLC-UV was found. Each different group of NIRS classified staves was then added to red wine during its aging in a stainless tank, and the extraction and evolution of the ellagitannins were monitored. A good correlation between the NIRS classification and the concentration of ellagitannins in red wine aging in contact with the classified staves was observed. The influence of levels of ellagitannins on the resulting wine perception was estimated by a trained judge's panel, and it reveals that the level of ellagitannins in wine has an impact on the roundness and amplitude of the red wine.

The neutronic design of a critical lead reflected zero-power reference core for on-line subcriticality measurements in Accelerator Driven Systems

In the framework of the GUINEVERE project (Generation of Uninterrupted Intense NEutrons at the lead VEnus REactor) carried out at SCK⋅CEN in Mol, the continuous deuteron accelerator GENEPI-3C is coupled to the VENUS-F fast simulated lead-cooled reactor. With this installation the neutronic behaviour of an Accelerator Driven System (ADS) during different phases of operation is studied. Because of the subcritical character of ADS, an on-line reactivity monitoring system is required. The main goal of the GUINEVERE project is to design such a system, to evaluate its performance and accuracy, and to evaluate an extrapolation towards full-size ADS. The on-line subcriticality monitoring procedure within the GUINEVERE project requires a well-known critical reference state in order to determine the absolute subcriticality level of ADS. Direct absolute subcriticality measurement techniques can then be evaluated. The neutronic design of different types of critical configurations within the VENUS vessel is motivated, given the core fuel and lead reflector as basic elements. The neutronic analysis comprises operational aspects such as kinetic parameters, the weight of control and safety rods, the weight of a peripheral assembly, which are investigated by means of both probabilistic and deterministic neutronic codes. The outcome of this analysis determines the most suitable VENUS-F critical reference core. Critical reference configurations inside the VENUS vessel can be set-up with the available fuel (96 fuel assemblies) taking into account the uncertainty on the nuclear data. The flexible neutronic design allows an easy operation of the core. A sensitivity analysis on the nuclear data shows that an uncertainty corresponding to the weight of several peripheral fuel assemblies should be taken into account while designing this type of small cores.

Dithizone immobilized silica gel on-line preconcentration of trace copper with detection by flame atomic absorption spectrometry

A novel adsorbent-silica gel bound dithizone (H2Dz-SG) was prepared and used as solid-phase extraction of copper from complex matrix. The H2Dz-SG is investigated by means of FT-IR spectra and the SEM images, demonstrating the bonding of dithizone. The H2Dz-SG quantitatively adsorb copper ions, and the retained copper is afterwards collected by elution of 10% (v/v) nitric acid. An on-line flow injection solid-phase extraction procedure was developed for trace copper separation and preconcentration with detection by flame atomic spectrometry. By loading 5.4mL of sample solution, a liner range of 0.5–120μgL−1, an enrichment factor of 42.6, a detection limit of 0.2μgL−1 and a precision of 1.7% RSD at the 40μgL−1 level (n=11) were obtained, along with a sampling frequency of 47h−1. The dynamic sorption capacity of H2Dz-SG to Cu2+ was 0.76mgg−1. The accuracy of the proposed procedure was evaluated by determination of copper in reference water sample. The potential applications of the procedure for extraction of trace copper were successfully accomplished in water samples (tap, rain, snow, sea and river). The spiking recoveries within 91–107% are achieved.

Statistical Surveillance of Volatility Forecasting Models

This paper elaborates sequential procedures for monitoring the validity of a volatility model. A state space representation describes dynamics of daily integrated volatility. The observation equation relates the integrated volatility to its measures such as the realized volatility or bipower variation. On-line control procedures, based on volatility forecasting errors, allow us to decide whether the chosen representation remains correctly specified. A signal indicates that the assumed volatility model may no longer be valid. The performance of our approach is analyzed within a Monte Carlo simulation study and illustrated in an empirical application for selected US stocks.

On-line identification of cascade control systems based on half limit cycle data

An on-line identification procedure is presented for cascade control systems in which both inner and outer loop process dynamics are modelled simultaneously by performing a single experiment. Departing from the conventional relay autotuning method where the controller is replaced by a relay, the proposed method is carried out on-line without breaking the closed-loop control. Exact analytical expressions are derived for process model parameters in terms of a few critical parameters of half period data of limit cycle output. Simulation examples are included to demonstrate the effectiveness of the proposed method.

Gas-Lift Optimization with Choke Control using a Mixed-Integer Nonlinear Formulation

This paper presents an extension to the iterative offline—online procedure proposed by Rashid 4 to efficiently optimize gas-lifted production networks. In particular, this work extends the original scheme to additionally include a single subsurface choke in each of the production wells. Hence, a revised offline formulation is presented that encompasses dual control in each well, comprising both lift-gas injection and a choke setting. As the latter can be discrete (on/off), have fixed positions or be treated as continuous, a mixed-integer nonlinear program (MINLP) results, necessitating the use of a suitable MINLP solver. The open source Bonmin solver is utilized for this purpose. Moreover, as chokes enable better field pressure and constraint management, in addition to well activation and closure, a broader spectrum of production optimization scenarios can be tackled. Significantly, the paper demonstrates that not only does the offline—online methodology enable the application of a MINLP solver, but the results are obtained in an extremely efficient manner, as per the initial development. Results from a 26-well production network are presented, with and without the imposition of additional operating constraints.

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

We describe a model to estimate motion from monocular visual and inertial measurements. We analyze the model and characterize the conditions under which its state is observable, and its parameters are identifiable. These include the unknown gravity vector, and the unknown transformation between the camera coordinate frame and the inertial unit. We show that it is possible to estimate both state and parameters as part of an on-line procedure, but only provided that the motion sequence is ‘rich enough’, a condition that we characterize explicitly. We then describe an efficient implementation of a filter to estimate the state and parameters of this model, including gravity and camera-to-inertial calibration. It runs in real-time on an embedded platform. We report experiments of continuous operation, without failures, re-initialization, or re-calibration, on paths of length up to 30 km. We also describe an integrated approach to ‘loop-closure’, that is the recognition of previously seen locations and the topological re-adjustment of the traveled path. It represents visual features relative to the global orientation reference provided by the gravity vector estimated by the filter, and relative to the scale provided by their known position within the map; these features are organized into ‘locations’ defined by visibility constraints, represented in a topological graph, where loop-closure can be performed without the need to re-compute past trajectories or perform bundle adjustment. The software infrastructure as well as the embedded platform is described in detail in a previous technical report.

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis–patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis–patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle–foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern.

Active control of impulsive noise with on-line outlier detection

AbstractConventional active noise control algorithms may fail to achieve convergence in the presence of non-Gaussian or impulsive noise. Typical workarounds for this problem are based on the concept of perturbing as little as possible the filter weight update process by discarding or discounting samples associated with outliers. The effectiveness of these methods depends on how accurately outliers can be detected while maintaining a sufficiently low algorithm complexity. A simple on-line recursive procedure is here suggested that reliably estimates amplitude thresholds for outlier detection. The resulting adaptive scheme for noise control can be shown to outperform existing methods, both for invariant and time-varying noise distributions.

Selective forgetting extreme learning machine and its application to time series prediction

To solve the problem of extreme learning machine (ELM) on-line training with sequential training samples, a new algorithm called selective forgetting extreme learning machine (SF-ELM) is proposed and applied to chaotic time series prediction. The SF-ELM adopts the latest training sample and weights the old training samples iteratively to insure that the influence of the old training samples is weakened. The output weight of the SF-ELM is determined recursively during on-line training procedure according to its generalization performance. Numerical experiments on chaotic time series on-line prediction indicate that the SF-ELM is an effective on-line training version of ELM. In comparison with on-line sequential extreme learning machine, the SF-ELM has better performance in the sense of computational cost and prediction accuracy.

A Wavelet-Based Approach for Online External Leakage Diagnosis and Isolation from Internal Leakage in Hydraulic Actuators

This paper presents experimental evaluation of applying wavelet transform for on-line external leakage fault detection and isolation from internal leakage in hydraulic actuators. In this work, the more realistic case of an actuator that is driven to track pseudorandom position reference inputs against a load is considered. The wavelet-based method developed in this paper, decomposes a limited-duration pressure signal at either chamber of a hydraulic actuator into effective approximate and detail wavelet coefficients. The limited-duration pressure signal is collected using a sliding window technique. It is shown that the root mean square (RMS) value of the level four approximate wavelet coefficient collectively establishes a feature index that can effectively be used for on-line detection of external leakage. Once the external leakage occurs, this index value decreases proportionally. Therefore, one can report the faulty operating condition by monitoring this index. Additionally, built upon the previous work in which the level two detail coefficient was found to be sensitive to internal leakage fault, we further investigate the isolation of external leakage from internal leakage in an actuator. Extensive validation tests demonstrate the effectiveness of the proposed technique, given any position reference input, loading condition, and controller type or effectiveness. Experimental tests show promising results for detecting external leakage as low as 0.3 L/min and isolating it from internal leakage as low as 0.48 L/min, during an on-line testing procedure.

Online MTPA Control Strategy for DTC Synchronous-Reluctance-Motor Drives

This paper presents an online procedure for the automatic search of the maximum-torque-per-ampere operating region for a synchronous reluctance motor. The algorithm is based on a signal-injection method with a random-based perturbation pattern applied to a common direct-torque-controlled drive. Among motor parameters, only the stator resistance is required to perform the automatic procedure. Simulations and experimental results are presented in the paper, demonstrating the benefits of the proposed algorithm. The solution is easily extended to any ac drive.

On-line parameter estimation and optimal control strategy of a double-skin system

To reduce the potential problems of window systems such as undesired heat gain (loss), glare, and thermal discomfort due to asymmetric radiation, double-skin systems have been introduced. The current problem with double-skin systems is that their operation requires an adequate simulation model to realize optimal control of the system. The estimation of the parameters in the lumped model developed in a previous study [1] was based on ‘laborious’ off-line calibration procedure. This effort has to be repeated for every different size, different type, or differently oriented façade system. Different façade components are characterized by different thermal and optical properties of glazing and blind slats, system configurations [height, width, depth], other simulation variables, etc. For each type the parameter set in the lumped model has to be established through a calibration procedure. In view of micro climate variations even same type systems within one façade but on different heights may have to be calibrated separately. In order to avoid the laborious off-line calibration of every single façade component, an on-line self-calibrating procedure is developed in this paper. The true advantage of the technique is that every component can be pre-wired and ready to be hooked to the calibration set-up when it is brought to the site. The paper will explain the simulation model, selection of calibration parameters, and the process of on-line self-calibration, model validation and application of optimal control. It is shown that the on-line self-calibrating simulation model far outperforms the off-line calibrated model. Consequently, the plug and play self-calibration technique will render the current in-situ ‘laborious’ off-line calibration process obsolete.