Jinwei Sun

Multilayer sensing and aggregation approach to environmental perception with one multifunctional sensor

This paper presents a novel approach to perception of a specified environment for intelligent system or robotics applications in which high-level information must be extracted from multi-sensors data. A CdS and Fe/sub 3/O/sub 4/ material based multifunction sensor has been developed to measure temperature, humidity and brightness. The sensor focuses on the processing of the multifunctional information in a multilayer framework, which is more attractive in terms of system simplicity, performance, and compact structure. Further along, quantity creditability tactics (QCT), one multisensing data fusion method, is approached, with which quantities are sequentially aggregated to generate a general perception about the sensed environment. Different from the popular fusion strategies, the proposed algorithm also works in a step-by-step framework, and proves to be more practical and more effective when there are more variables for calculation.

Signal processing method with cold junction compensation for thermocouple

Thermocouple is a very popular and effective sensor for temperature measurement. But how to eliminate the interference of the temperature of cold junction is always a problem. A method based on recursive B-spline least-squares is proposed as a solution. The method establishes two models to convert the cold junction temperature to electromotive force (EMF) and the hot junction EMF to temperature respectively. And in the middle of them, the famous law of intermediate temperature is used to translate the measured EMF into the hot junction EMF. B-spline least-squares is adopted because of its great quality of approximation. Recursive approach is used to simplify the operation and make it suitable to be used in MPU. The simulate results show that the method is of high precision and stabilization whose average relative error is only 0.01% and has a powerful ability to reduce the nonlinearity of thermocouples.

Nonlinear Multifunctional Sensor Signal Reconstruction Based on Total Least Squares

The least squares method is often used to estimate the parameters in multi-functional sensor signal reconstruction. If the data has been contaminated, the computational result of the method turns out to be insignificant. Two methods presented in this paper are suitable for different nonlinear conditions, which are based on the combination of the total least squares algorithm with the local linearization strategy and Stone-Weierstrass theorem. The two methods evaluate both the sensor output bias and its input error. The results of emulation and theory analysis indicate that the proposed algorithms are more accurate and reliable for signal reconstruction.

Sleep Staging from the EEG Signal Using Multifractal Detrended Fluctuation Analysis

The quality of sleep is closely related to human health. Accurate monitoring of sleep quality can play an effective supervisory role in helping people improve the quality of sleep. The analysis of the electroencephalogram (EEG) can yield much useful information about sleep stage. In the present study, the MF-DFA algorithm was applied to stage the different sleep states. The two key parameters in MF-DFA algorithm the segmented length s and the order q of fluctuation function were determined by the sleep EEG data in MIT-BIH polysomnography database, and verified by the experiment. The results demonstrated that the h(q) with q=0,1,2 and s=10~100 can distinguish the wake, shallow sleep and deep sleep states in MIT-BIH database accurately, and can reflect the process of sleep state better.

Thin Plate Spline-Based Coulombic Efficiency Prediction of Lithium Battery

The performance of lithium-ion rechargeable battery depends on its coulombic efficiency, which determines the measurement accuracy of the state of charge as well. In this context, the regularity that coulombic efficiency changes with environmental temperature and charge-discharge rate was analyzed in different cases, and a TPS (Thin Plate Spline) surface fitting-based coulombic efficiency estimation method was proposed for lithium-ion rechargeable battery. The presented method has a unique advantage in 3D smooth surface interpolation, which made it possible to model the functional relationship between battery coulombic efficiency, ambient temperature and charge-discharge rate, and then adjust the reconstructed precision with the plate rigidity sequentially. The experimental results illustrate that the proposed method is able to estimate the corresponding coulombic efficiency in the presumed range of ambient temperature and charge-discharge rate without battery cycling procedure. Furthermore, the accuracy and promptness of the novel method are verified simultaneously.

B-Spline Approximation Using an EKF for Signal Reconstruction of Nonlinear Multifunctional Sensors

In this paper, a novel method based on a B-spline approximation and the extended Kalman filter (EKF) is proposed for the signal reconstruction of nonlinear multifunctional sensors. The B-spline approximation is a very effective and conventional tool for nonlinear modeling. However, the computation of the B-spline control array by the least square method is very complex for implementation on microprocessors. Therefore, the EKF, which is a suboptimal recursive filter, is proposed to compute the control array with high accuracy and a low hardware requirement. Experiments are performed to reconstruct the measurands of a two-input-two-output circuit model and a real three-input-two-output multifunctional sensor. Results show that the proposed method provides a good solution to the signal reconstruction of multifunctional sensors.

Application of an ANFIS algorithm to narrowband active noise control system

This paper proposes a new narrowband active noise control (ANC) system where an ANFIS (adaptive network-based fuzzy inference system) is utilized as an adaptive controller. The ANFIS is a combination of a neural network and a fuzzy inference system. For the purpose of computational cost reduction, the nonlinear premise parameters in the ANFIS are fixed and only its linear consequent parameters are adjusted based on a gradient descent method rather than the hybrid learning rule. The proposed system may looks somewhat more complicated than the conventional FXLMS-based ANC system, but it does not require reference signal filtering at all. Simulation results reveal that the proposed ANC system enjoys sufficient effectiveness in suppressing noise signals of sinusoidal nature.

RBF neural networks and cross validation-based signal reconstruction for nonlinear multi-functional sensor

For signal reconstruction in nonlinear multi-functional sensor, there may be some outliers caused by systematic errors or gross errors in observations. Therefore, it is worth while to get rid of the outliers from experimental data during the calculation to ensure the reliability and precision of the system. Based on the Radial Basis Function neural network and the law of cross validation, this paper presents an iterative regressing method in consideration of the existence of outliers. Cross validation is repeatedly used for random sampling the experimental data as the training data set, with which RBF neural network can complete the regressing. By repeating such procedure and updating the estimated parameters, the training data set and system function of multi-functional sensor can be optimized. Accordingly, the reconstruction of any signals can be accomplished with the selected model. The theoretic analysis and the experimental results show that the approach is effective, robust and practicable.

Sample selection based on kernel-subclustering for the signal reconstruction of multifunctional sensors

The signal reconstruction methods based on inverse modeling for the signal reconstruction of multifunctional sensors have been widely studied in recent years. To improve the accuracy, the reconstruction methods have become more and more complicated because of the increase in the model parameters and sample points. However, there is another factor that affects the reconstruction accuracy, the position of the sample points, which has not been studied. A reasonable selection of the sample points could improve the signal reconstruction quality in at least two ways: improved accuracy with the same number of sample points or the same accuracy obtained with a smaller number of sample points. Both ways are valuable for improving the accuracy and decreasing the workload, especially for large batches of multifunctional sensors. In this paper, we propose a sample selection method based on kernel-subclustering distill groupings of the sample data and produce the representation of the data set for inverse modeling. The method calculates the distance between two data points based on the kernel-induced distance instead of the conventional distance. The kernel function is a generalization of the distance metric by mapping the data that are non-separable in the original space into homogeneous groups in the high-dimensional space. The method obtained the best results compared with the other three methods in the simulation.

Application of multifunctional sensing technique with ANFIS to physical parameters estimations of ternary solution with NaCl and sucrose

On-line measurement of physical parameters of solution means a great deal in the osmotic dehydration process of food. Ternary solution with NaCl and sucrose is widely employed in this process. This study aims at reconstructing concentrations of two components and estimating density, viscosity of this ternary solution with multifunctional sensing technique in which the multifunctional sensor was previously designed by the authors. In this work, ANFIS (Adaptive Network-Based Fuzzy Inference System) is adopted to be signal reconstruction algorithm for the multifunctional sensing technique. For obtaining number of fuzzy rules and more precise initial values of premise parameters, in the process of reconstructing and estimating, subtractive clustering and fuzzy c-means clustering are used to analyze input-output space of ANFIS. Experimental results with satisfactory accuracies proved the proposed multifunctional sensing technique with ANFIS algorithm is valuable in food industry.