Using a Parallel Helical Sensing Cable for the Distributed Measurement of Ground Deformation.

Surface and underground stretched deformation is one of the most important physical measurement quantities for geological-disaster monitoring. In this study, a parallel helical sensing cable (PHSC) based on the time⁻domain reflectometry (TDR) technique is proposed and used to monitor large ground stretched deformation. First, the PHSC structure and manufacturing process are introduced, and then, distributed capacitance, distributed inductance, and characteristic impedance were derived based on the proposed stretched-structure model. Next, the relationship between characteristic impedance and stretched deformation was found, and the principle of distributed deformation measurement based on the TDR technique and PHSC characteristic impedance was analyzed in detail. The function of the stretched deformation and characteristic impedance was obtained by curve fitting based on the theoretically calculated results. A laboratory calibration test was carried out by the designed tensile test platform. The results of multi-point positioning and the amount of stretched deformation are presented by the tensile test platform, multi-point positioning measurement absolute errors were less than 0.01 m, and the amount of stretched deformation measurement absolute errors were less than 3 mm, respectively. The measured results are in good agreement with the theoretically calculated results, which verify the correctness of theoretical derivation and show that a PHSC is very suitable for the distributed measurement of the ground stretched deformation.

Study on an Online Detection Method for Ground Water Quality and Instrument Design.

The online measurement of ground water quality, as one important area of water resource protection, can provide real-time measured water quality parameters and send out warning information in a timely manner when the water resource is polluted. Based on ultraviolet (UV) spectrophotometry, a remote online measurement method is proposed and used to measure the ground water quality parameters chemical oxygen demand (COD), total organic carbon (TOC), nitrate nitrogen (NO3-N), and turbidity (TURB). The principle of UV spectrophotometry and the data processing method are discussed in detail, the correlated mathematical modeling of COD and TOC is given, and a confirmatory experiment is carried out. Turbidity-compensated mathematical modeling is proposed to improve the COD measurement accuracy and a confirmatory experiment is finished with turbidity that ranges from 0 to 100 NTU (Nephelometric Turbidity Unit). The development of a measurement instrument to detect the ground water COD, TOC, NO3-N, and TURB is accomplished; the test experiments are completed according to the standard specification of China's technical requirement for water quality online automatic monitoring of UV, and the absolute measuring errors of COD, TOC, and NO3-N are smaller than 5.0%, while that of TURB is smaller than 5.4%, which meets the requirements for the online measurement of ground water quality.

Recent advances in electronic nose techniques for monitoring of fermentation process.

Microbial fermentation process is often sensitive to even slight changes of conditions that may result in unacceptable end-product quality. Thus, the monitoring of the process is critical for discovering unfavorable deviations as early as possible and taking the appropriate measures. However, the use of traditional analytical techniques is often time-consuming and labor-intensive. In this sense, the most effective way of developing rapid, accurate and relatively economical method for quality assurance in microbial fermentation process is the use of novel chemical sensor systems. Electronic nose techniques have particular advantages in non-invasive monitoring of microbial fermentation process. Therefore, in this review, we present an overview of the most important contributions dealing with the quality control in microbial fermentation process using the electronic nose techniques. After a brief description of the fundamentals of the sensor techniques, some examples of potential applications of electronic nose techniques monitoring are provided, including the implementation of control strategies and the combination with other monitoring tools (i.e. sensor fusion). Finally, on the basis of the review, the electronic nose techniques are critically commented, and its strengths and weaknesses being highlighted. In addition, on the basis of the observed trends, we also propose the technical challenges and future outlook for the electronic nose techniques.

[A wavelength selection approach of near infrared spectra Based on SCARS strategy and its application].

According to the characteristics of near infrared spectral(NIR)data, a new tactic called stability competitive adaptive reweighted sampling (SCARS) is employed to select characteristic wavelength variables of NIR data to build PLS model. This method is based on the stability of variables in PLS model. SCARS algorithm consists of a number of loops. In each loop, the stability of each corresponding variable is computed at first. Then enforced wavelength selection and adaptive reweighted sampling (ARS) is used to select important variables according to the stability of variables. The selected variables are kept as a variable subset and further used in the next loop. After the running of all loops, a number of subsets of variables are obtained and root mean squared error of cross validation (RMSECV) of PLS models is computed. The subset of variables with the lowest RMSECV is considered as the optimal variable subset. Validated by NIR data set of protein fodder solid-state fermentation process, the SCARS-PLS prediction model is better than PLS models based on wavelengths selected by competitive adaptive reweighted sampling (CARS) and Monte Carlo uninformative variable elimination (MC-UVE) methods. As a result, twenty one wavelength variables are selected by SCARS method to build the PLS prediction model with the predicted root mean square error (RMSEP) valued at 0.0543 and correlation coefficient (Rp) 0.9908. The results show that SCARS tactic can efficiently improve the accuracy and stability of NIR wavelength variables selection and optimize the precision of prediction model in solid-state fermentation process. The SCARS method has a certain application value.

Rapid determination of pH in solid-state fermentation of wheat straw by FT-NIR spectroscopy and efficient wavelengths selection.

In the work discussed in this paper we investigated the feasibility of determination of the pH of a fermented substrate in solid-state fermentation (SSF) of wheat straw. Fourier-transform near-infrared (FT-NIR) spectroscopy was combined with an appropriate multivariate method of analysis. A genetic algorithm and synergy interval partial least-squares (GA-siPLS) were used to select the efficient spectral subintervals and wavelengths by k-fold cross-validation during development of the model. The performance of the final model was evaluated by use of the root mean square error of cross-validation (RMSECV) and correlation coefficient (R (c)) for the calibration set, and verified by use of the root mean square error of prediction (RMSEP) and correlation coefficient (R (p)) for the validation set. The experimental results showed that the optimum GA-siPLS model was achieved by use of seven PLS factors, when four spectral subintervals were selected by siPLS and then 45 wavelength variables were chosen by use of the GA. The predicted precision of the best model obtained was: RMSECV = 0.0583, R (c) = 0.9878, RMSEP = 0.0779, and R (p) = 0.9779. Finally, the superior performance of the GA-siPLS model was demonstrated by comparison with four other PLS models. The overall results indicated that FT-NIR spectroscopy can be successfully used for measurement of pH in solid-state fermentation, and use of the GA-siPLS algorithm is the best means of calibration of the model.

[Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000-4 000 cm(-1), and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 1040-1 topology construction as follows: R(p) = 0.961 8 and RMSEP = 0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.

Semi-global adaptive backstepping control for parametric strict-feedback systems with non-triangular structural uncertainties.

In this paper, two semi-global adaptive control schemes are proposed for a class of parametric strict-feedback systems with non-triangular structural uncertainties. Unknown nonlinear functions containing non-triangular structural uncertainties and unknown parameters are allowed to exist in every state equation. Because the linear relation between the state vector and its transformed vector cannot be ensured, the improved backstepping method used to deal with non-triangular structural uncertainties cannot be used directly in controller design. To solve this problem, semi-global stability analysis is introduced in the controller design. As a result, the linear relation mentioned above is no longer needed and thus the stability of the closed-loop system is guaranteed. The effectiveness of the proposed schemes is verified by simulation studies.

Sub-band target alignment common spatial pattern in brain-computer interface.

BACKGROUND AND OBJECTIVE: In the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a specific frequency band. However, in the cross-subject classification, due to the individual differences between different subjects, the performance is limited. METHODS: This paper introduces the idea of transfer learning and presents the sub-band target alignment common spatial pattern (SBTACSP) method and applies it to the cross-subject classification of motor imagery (MI) EEG signals. First, the EEG signals are bandpass-filtered into multiple frequency bands (sub-band filtering). Subsequently, the source domain trails are aligned into the target domain space in each frequency band. The CSP algorithm is then employed to extract features among which more representative features are selected by the minimum redundancy maximum relevance (mRMR) approach from each sub-band. Then the features of all sub-bands are fused. Finally, conventional linear discriminant analysis (LDA) algorithm is used for MI classification. RESULTS: Our method is evaluated on Datasets Ⅱa and Ⅱb of the BCI Competition Ⅳ. Compared with six state-of-the-art algorithms, the proposed SBTACSP method performed relatively the best and achieved a mean classification accuracy of 75.15% and 66.85% in cross-subject classification of Datasets Ⅱa and Ⅱb respectively. CONCLUSION: Therefore, the combination of sub-band filtering and transfer learning achieves superior classification performance compared to either one. The proposed algorithms will greatly promote the practical application of MI based BCIs.

Non-fragile dissipative filtering of cyber-physical systems with random sensor delays.

This paper considers the problem of non-fragile state estimation under dissipative constraint for a class of nonlinear cyber-physical systems (CPSs) with sensor delays. The dynamics of the considered CPSs is characterized by the well-known T-S fuzzy model and system measurements are valued by wireless sensors. The communication link between the filter and the plant is described by a relatively practical model and sensor delays occurred in signal transmissions are taken into consideration. A stochastic variable which yields the standard Bernoulli distribution is exploited to model sensor delays encountered by the sensor measurements. With the help of a basis-dependent Lyapunov function and predefined performance constraint, sufficient conditions are then developed to establish the stochastic stability as well as strict dissipativity for the resultant filtering error system. The existence of the corresponding filter is guaranteed and the expression of desired filter parameters are shown explicitly. In the end, the established theoretical results are validated by a tunnel diode circuit example and corresponding simulations are also provided.

Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR spectroscopy and L1-PLS regression.

This study aimed to investigate the potential of FT-NIR spectroscopy technique combined with chemometrics method, which employed to monitor time-related changes of alcohol concentration and residual glucose during solid state fermentation (SSF) of ethanol. Characteristic wavelength variables were firstly selected by use of L1-norm regularization approach. Then, the partial least squares (PLS) regression model was finally developed using the variables selected by L1-norm regularization method to quantitative determine alcohol concentration and residual glucose in SSF of ethanol. Compared with the best results of full-spectrum PLS, the L1-PLS model obtained better results as follows: RMSECV = 1.0392 g/L, Rc = 0.9911, RMSEP = 1.0910 g/L, Rp = 0.9917 for alcohol concentration; RMSECV = 1.7002 g/L, Rc = 0.9880, RMSEP = 2.1859 g/L, Rp = 0.9896 for residual glucose. The overall results sufficiently demonstrate that FT-NIR spectroscopy technique coupled with appropriate chemometrics method is a promising tool for monitoring the process of SSF of ethanol.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm.

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV=0.0776, R(c)=0.9777, RMSEP=0.0963, and R(p)=0.9686 for pH model; RMSECV=1.3544% w/w, R(c)=0.8871, RMSEP=1.4946% w/w, and R(p)=0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.