Practical Identification Method Research Articles

Silver Nanoparticle Sensor Array-Based Meat Freshness Inspection System.

The series of biochemical reactions, metabolic pathways, and regulatory interactions that occur during the storage of meat are the main causes of meat loss and waste. The volatile compounds produced by these reactions, such as hydrogen sulfide, acids, and amines, can directly indicate changes in the freshness of meat during storage and sales. In this study, a one-pot hydrothermal method based on a surface control strategy was used to develop nanoparticles of silver with different reactivities, which were further immobilized in agar powder to develop a colorimetric sensor array. Due to the different chemical interactions with various volatile compounds, the colorimetric sensor array exhibited distinct color changes. The study demonstrates significant differences between 12 different volatile compounds and provides a quantitative and visual method to reveal rich detection indicators. The colorimetric sensor array is an economical and practical multi-analyte identification method. It has many potential applications such as food packaging, anti-counterfeiting, health monitoring, environmental monitoring, and optical filters.

System Identification of an Actuated Inclined Ball Mechanism Via Causation Entropy

Abstract Grey-box and black-box dynamic models are routinely used to model the behavior of real-world dynamic systems. When creating such models, the identification of an accurate model structure (often referred to as covariate selection, feature selection, or sparsity identification) is a critical step required to achieve suitable predictive performance by minimizing the effects of overfitting. Recently, causation entropy has been shown to be quite useful in data-driven covariate selection as it provides a mechanism to measure the causal relationships between the set of covariates and the state dynamics. This work extends previous results by applying the causation entropy covariate selection technique to data from an experimental nonlinear system consisting of a ball rolling on an actuated inclined ramp. Data collected from the system is processed by the causation entropy-based algorithm and covariate selection is performed on a black-box dynamic model. The resulting optimized model is shown to provide better predictive performance than an optimized black-box model which includes extraneous covariates. This study represents the first application of causation entropy-based covariate selection to real-world experimental data, illustrating its use as a practical system identification method.

A practical radio frequency fingerprinting scheme for mobile phones identification

Radio frequency fingerprint (RFF) has attracted a remarkable surge of attention to wireless transmitter identification due to the inimitable hardware imperfection. However, most RFF-based schemes devised in controlled environments are unable to reach the claimed performance in real-world scenarios. This paper proposes a practical RFF identification method for mobile phones in Global System for Mobile (GSM) system. Specifically, the instantaneous amplitude of near-transient part in normal burst (NB) is regarded as RFF that extracted from up-link communications. In addition, an anomaly filtering and stacking (AFS) method is introduced to obtain stable RFF in Time Division Multiple Access (TDMA) system. Lastly, the impacts of frequency point and sending power variations on RFF are fully dissected in comparison experiments. Experiments on 10 mobile phones show that the proposed RFF scheme yields 99.17% True Acceptance Rate (TAR) in real wireless environments. Experiments also show that the varying transmission power decreases the accuracy of RFF identification.

Control Optimization via A Practical Closed-loop Identification Method for A Low-pressure Heater in Power Plant

During the operation of a power plant, open-loop experiments are prohibited and this results in great difficulties for proportional-integral (PI) controller optimization. To overcome this question, a practical closed-loop identification method with feedforward control is proposed in this paper. The theoretical analyses and the implementations are carried out based on closed-loop data, and simulation results have validated the effectiveness of the proposed method. Finally, the identified system built by the proposed method is applied to the control optimization for a low-pressure heater in a 330MW power plant. Based on the identified system, the PI parameters are retuned and optimized, and the field running data validate the obvious improvement of the disturbance rejection performance. The successful application shows a promising future of the proposed closed-loop identification in practical industrial processes.

Parameter identification based on linear model for buck converters

Parameter identification is an effective method to monitor the health condition of multiple components in power electronic converters. The hybrid model-based method has been proved to be a universal approach. However, the existing hybrid model of buck converter ignores the winding resistance of filter inductor and non-ideal factors, which will cause errors in the identification results. To solve these problems, leg midpoint voltage is used to replace the input voltage and switching signals, and a linear model is proposed, which can be implemented by fewer sensors. In order to verify the superiority of the linear model, this paper proposes a simple and practical parameter identification method for passive components. Compared with the hybrid model-based method, the proposed method does not need a sensor to measure driving signals and can obtain higher accuracy under non-ideal situations. Simulation and experimental results prove the proposed method to be effective and accurate.

Machine Tool Chatter Identification Based on Dynamic Errors of Different Self-Synchronized Chaotic Systems of Various Fractional Orders

Chatter, the vibration between the workpiece and cutting tool, is a common phenomenon during the milling process. Traditionally, the Fourier transform analysis is mainly used to extract the features and determine whether chatter occurs. In this paper, an innovative and practical chatter identification method combining fractional order chaotic system and extension theory is proposed. A lathe spindle with embedded sensors is used in this study. The boundary of chattering state of the lathe spindle is decided by the center of gravity of phase plane of dynamic errors. The boundaries are then fed into extension model and relational function calculation is performed. In this way, chatter identification can be easily achieved based on the position of the chaotic center of gravity. The three chaotic systems, i.e. Lorenz, Chen-Lee, and Sprott, of different fractional orders are used and their results are compared. Compared with the traditional methods, the Fourier transform is time-consuming in terms of mathematical operations and adverse to the establishment of a real-time system. This paper uses the characteristics of the chaotic systems sensitive to input signals in order to more capably detect the boundary state from normal cutting to chattering cutting and more efficiently identify the chatter. The experiment results indicate that the Chen-Lee system (93.5%) exhibits have better chatter diagnosis rate than Lorenz (92.75%) and Sprott (69%) systems. The Chen-Lee system even reaches a diagnosis rate of 100% for orders 0.5 ~ 0.7. Therefore, the method presented in this paper has a very high diagnosis rate and is thus very effective for chatter identification of machine tools.

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and “task-free” technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

A probabilistic approach for quantitative identification of multiple delaminations in laminated composite beams using guided waves

In this study a probabilistic approach is proposed to identify multiple delaminations in laminated composite beams using guided waves. The proposed method is a model-based approach, which provides a quantitative identification of the delaminations. This study puts forward a practical damage identification method, and hence, it can identify multiple delaminations using guided wave signal measured at a single measurement point on the laminated composite beams. The proposed method first determines the number of delaminations using Bayesian model class selection method. The Bayesian statistical framework is then employed to not only identify the delamination locations, lengths and through-thickness locations, but also quantify the associated uncertainties, which provides valuable information for engineers in making decision on necessary remedial work. In addition the proposed method employs the time-domain spectral finite element method and Bayesian updating with Subset simulation to further improve the computational efficiency. The proposed probabilistic approach is verified and demonstrated using data obtained from numerical simulations, which consider both measurement noise and modeling error, and experimental data. The results show that the proposed method can accurately determine the number of delaminations, and the identified delamination locations, lengths and through-thickness locations are closed to the true values.

周波数応答関数を用いた質量・剛性・減衰特性の実用的同定手法

周波数応答関数を用いた質量・剛性・減衰特性の実用的同定手法

Boron and strontium isotopic characterization of coal combustion residuals: validation of new environmental tracers.

In the U.S., coal fired power plants produce over 136 million tons of coal combustion residuals (CCRs) annually. CCRs are enriched in toxic elements, and their leachates can have significant impacts on water quality. Here we report the boron and strontium isotopic ratios of leaching experiments on CCRs from a variety of coal sources (Appalachian, Illinois, and Powder River Basins). CCR leachates had a mostly negative δ(11)B, ranging from -17.6 to +6.3‰, and (87)Sr/(86)Sr ranging from 0.70975 to 0.71251. Additionally, we utilized these isotopic ratios for tracing CCR contaminants in different environments: (1) the 2008 Tennessee Valley Authority (TVA) coal ash spill affected waters; (2) CCR effluents from power plants in Tennessee and North Carolina; (3) lakes and rivers affected by CCR effluents in North Carolina; and (4) porewater extracted from sediments in lakes affected by CCRs. The boron isotopes measured in these environments had a distinctive negative δ(11)B signature relative to background waters. In contrast (87)Sr/(86)Sr ratios in CCRs were not always exclusively different from background, limiting their use as a CCR tracer. This investigation demonstrates the validity of the combined geochemical and isotopic approach as a unique and practical identification method for delineating and evaluating the environmental impact of CCRs.

Determining the optimum coal concentration in a general tangential-fired furnace with rich-lean burners: From a bench-scale to a pilot-scale study

The mass ratio of pulverized coal and air (coal concentration, kg/kg) in fuel-rich streams is important in the design and operation of rich-lean burners, in which the optimum coal concentration (Copt) that corresponds to the best combustion situation should be achieved. This study aims to establish a practical identification method to evaluate the Copt of the different ranks of coal in rich-lean burners. A wide range of tests were conducted in a bench-scale down-fired furnace and a pilot-scale tangential-fired furnace with rich-lean burners. Temperature distribution, unburned carbon in ash, and NOx emissions were measured, and the effects of coal quality aside from burner type and burner layout method were considered. Results show that the optimum coal concentration corresponds to the highest furnace temperature for each group of tests both in the bench-scale and pilot-scale furnaces. Copt is significantly affected by coal quality even if a change from the use of a corner-tangential to a wall-tangential furnace lowers Copt; however, the effect of a vertical rich-lean burner or a horizontal rich-lean burner on Copt is negligible. The value of Copt mainly decreases from 1.14 to 0.67 with a decrease in the volatile content from anthracite scale (<0.1) to lignite scale (>0.35). The empirical formula of Copt = 1.19–0.15VdafQnet0.7/100Mad0.1 is obtained to evaluate the optimum coal concentration of a general pulverized coal flame, and another formula, Copt = 1.18–0.17VdafQnet0.7/100Mad0.1, is especially derived for a tangential-fired furnace with a rich-lean burner. The optimum value obtained is also critical to NOx emissions because when the coal concentration surpasses the value of Copt, NOx emissions can be much more efficiently controlled through reduction of air. The findings of this study can provide practical guidance for the design and operation of rich-lean burners to achieve high combustion efficiency and low NOx emissions.

On an inverse source problem for the heat equation. Application to a pollution detection problem, II

This work considers the inverse problem of localizing and characterizing multiple stationary pollution sources in surface waters or atmospheric media. This particular problem has been studied before and a uniqueness result was shown. In this paper, we revisit this work with the objective of providing a more complete study. In particular, an alternative uniqueness proof is conducted which is valid for more general pollution source structures. Additionally, a practical numerical identification method is developed and implemented. Finally, several numerical studies are performed which illustrate practical considerations as well as the effectiveness of the approach.

Practical Damping Identification of FAST Cable Suspension

FAST focus cabin is suspended and driven by 6 parallel large span cables. Low stiffness of cables makes the cabin sensitive to disturbance and difficult to control. Structural damping then becomes a key factor that can improve control ability. Therefore, a reasonable damping estimation is important for system design. In this paper, a practical damping identification method is developed based on Ibrahim-time-domain algorithm. The method shows satisfied performance on accuracy and reliability in simulation test and is utilized in vibration experiments to identify damping ratios of both single cable model and FAST 3 m scale cable suspension model. Finally, a preliminary analysis of the damping properties is given out based on the results of identification.

Modeling and Identification of Friction Behaviors of Hydraulic Forging Press Machine

A novel modified LuGre friction model is proposed by taking pressure of the cylinders into consideration. And a practical identification method to estimate the parameters associated with the modified friction model is presented. The validity of the modified model is investigated experimentally. It is shown that the modified LuGre model can demonstrate the comprehensive friction behaviors of the forging machine with a fairly good accuracy.

Identification and Modeling of Process Damping in Milling

In this study, a practical identification method for process damping is presented for milling, and the information obtained from identification is used for modeling purposes. In the proposed approach, the process-damping coefficients in x and y directions are identified directly from the experimental stability limits. Then, they are used in identification of the indentation constant through energy balance formulation. The identified indentation constant is further used in modeling of process damping and estimation of stability limit for different cutting conditions and tool geometries. Milling tools with two different types of flank geometries, namely, planar and cylindrical, are considered in this study. The predictions are verified by time-domain simulations and experimental results. It is shown that the presented method can be used for identification and modeling of process damping in milling to determine chatter-free cutting depths at relatively low cutting speeds.

Surveillance of Adenovirus D in patients with epidemic keratoconjunctivitis from Fukui Prefecture, Japan, 1995–2010

Human adenoviruses species D (HAdV-D) are known to cause severe epidemic keratoconjunctivitis. However, the isolation rate of HAdV-D is not high, because HAdV-D is usually slow to propagate. Although new types of HAdV-D have been reported, accurate surveillance has not been performed because of difficulties in culturing the viruses and lack of a practical identification method. In this study, HAdV-Ds were detected and identified from patients with epidemic keratoconjunctivitis in the Fukui Prefecture during 1995-2010 by PCR, loop-mediated isothermal amplification (LAMP) of DNA, and conventional virus isolation and neutralization tests. All samples were subjected to culture and PCR and LAMP. A total of 124 strains of HAdV-D were detected from 157 patients with epidemic keratoconjunctivitis. The strains consisted of the following types: D8 (n = 8), D19 (n = 4), D37 (n = 40), D53 (n = 5), D54 (n = 66), and D56 (n = 1). Among these, D53, D54, and D56 are new types that have been reported recently. The results of this study demonstrated that new types of HAdV-D caused epidemic keratoconjunctivitis during 1995-2010, and included an outbreak of keratoconjunctivitis caused by HAdV-D54. The LAMP method was able to detect and identify HAdV-D53 and HAdV-D54 in 1 hr, and may therefore be applicable for use at the bedside.

Rapid identification of fungal pathogens by rolling circle amplification using Fonsecaea as a model

We aimed to describe a rapid and sensitive assay for identification of pathogenic fungi without sequencing. The method of rolling circle amplification (RCA) is presented with species of Fonsecaea, agents of human chromoblastomycosis, as a model. The internal transcribed spacer (ITS) rDNA region of 103 Fonsecaea strains was sequenced and aligned in view of designing three specific padlock probes to be used for the detection of single nucleotide polymorphisms in three Fonsecaea species. The 38 strains included for testing the specificity of RCA comprised 17 isolates of Fonsecaea pedrosoi, 13 of Fonsecaea monophora and eight of Fonsecaea nubica. The assay successfully amplified DNA of the target fungi at the level of species, while no cross reactivity was observed. The amplification product was visualised on a 1% agarose gel to verify the specificity of probe-template binding. Amounts of reagents were minimised to avoid the generation of false-positive results. The simplicity, sensitivity, robustness and low costs provide RCA a distinct position among isothermal techniques for DNA diagnostics as a very practical identification method.

非線形摩擦を含む機械モデルの同定―モータイナーシャと摩擦の実用的なオンライン同定法の研究―

It is important for a continuous path control to identify the machine parameters, such as inertia and dominant nonlinear friction. This paper presents a practical identification method for the inertia and Coulomb friction by using steepest descent method and low-pass filtered M-sequence, in which the model in the inverse transfer-function quickly approaches to the true machine parameters. It is shown in the experimentation using real machine that the steepest descent method with a dead band is very effective in order to reduce estimation error and from the practical point of view.

Dynamic Modeling and Neural Network Self‐tuning PID Control Design for a Linear Motor Driving Platform

AbstractThe purpose of this paper is to design a wavelet neural network self‐tuning proportional–integral–derivative (PID) controller for solving the problem of fast changing payload in a linear motor in the 6‐DOF platform system. The wavelet neural network PID controller demonstrates the special capabilities of fast response and self‐tuning. A practical system identification method is proposed to identify the dynamic model of a linear motor by using the balancing realization theorem and inverse discrete Fourier transform. The experimental results of the real‐time software simulation and hardware‐in‐the‐loop simulation demonstrate the novel ability of self‐tuning PID controller for adapting the quick load change in the forcer of linear motor, which activates the platform system. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Identification and modeling of process damping in turning and milling using a new approach

Process damping can be a significant source of increased stability in machining particularly at low cutting speeds. However, it is usually ignored in chatter analysis as there is no model available to estimate process damping coefficients. In this study, a practical identification and modeling method is presented where process damping coefficients are obtained from chatter tests. The method is generalized by determining the indentation force coefficient responsible for the process damping through energy analysis. This coefficient is then used for process damping and the stability limit prediction in different cases, and predictions are verified by time domain simulations and experimental results.