To overcome the detrimental influence of α impulse noise in power line communication and the trap of scarce prior information in traditional noise suppression schemes , a power iteration based fast independent component analysis (PowerICA) based noise suppression scheme is designed in this paper. Firstly, the pseudo-observation signal is constructed by weighted processing so that single-channel blind separation model is transformed into the multi-channel observed model. Then the proposed blind separation algorithm is used to separate noise and source signals. Finally, the effectiveness of the proposed algorithm is verified by experiment simulation. Experiment results show that the proposed algorithm has better separation effect, more stable separation and less implementation time than that of FastICA algorithm, which also improves the real-time performance of communication signal processing.
This paper deals with the design of an interval state estimator for linear time-varying (LTV) discrete-time systems subject to component faults and uncertainties. These component faults and uncertainties are assumed to be unknown but bounded without giving any other information, whose effect can be approximated using these bounds. In the first part of this work, an interval state estimator for such systems is designed to deal with these component faults and uncertainties. The result is then extended to find an interval state estimator for a non-cooperative LTVdiscrete-time system subject to component faults and uncertainties by similarity transformation of coordinates. The proposed interval state estimator guaranteed bounds on the observed states that are consistent with the system states. The observer convergence is also ensured. The designed method is simple and easy to be implemented. Two numerical examples are given to show the effectiveness of the proposed method.
Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.
Brushless DC motors are often used as the power sources for modern ship electric propulsion systems. Due to the electromagnetic torque ripple of the motor, the traditional control method reduces the drive performance of the motor under load changes. Aiming at the problem of the torque ripple of the DC brushless motor during a non- commutation period, this paper analysis the reasons for the torque ripple caused by pulse- width modulation (PWM), and proposes a PWM_ON_PWM method to suppress the torque ripple of the DC brushless motor. Based on the mathematical model of a DC brushless motor, this method adopts a double closed-loop control method based on fuzzy control to suppress the torque ripple of the DC brushless motor. The fuzzy control technology is integrated into the parameter tuning process of the proportional–integral–derivative (PID) controller to effectively improve the stability of the motor control system. Under the Matlab/Simulink platform, the response performance of different PID control methods and the torque characteristics of different PWM modulation methods are simulated and compared. The results show that the fuzzy adaptive PID control method has good dynamic response performance. It is verified that the PWM_ON_PWM modulation method can effectively suppress the torque ripple of the motor during non-commutation period, improve the stability of the double closed-loop control system and meet the driving performance of the motor under different load conditions.
A new soft-fault diagnosis approach for analog circuits with parameter tolerance is proposed in this paper. The approach uses the fuzzy nonlinear programming (FNLP) concept to diagnose an analog circuit under test quantitatively. Node-voltage incremental equations, as constraints of FNLP equation, are built based on the sensitivity analysis. Through evaluating the parameters deviations from the solution of the FNLP equation, it enables us to state whether the actual parameters are within tolerance ranges or some components are faulty. Examples illustrate the proposed approach and show its effectiveness.
While the Slope Fault Model method can solve the soft-fault diagnosis problem in linear analog circuit effectively, the challenging tolerance problem is still unsolved. In this paper, a proposed Normal Quotient Distribution approach was combined with the Slope Fault Model to handle the tolerances problem in soft-fault diagnosis for analog circuit. Firstly, the principle of the Slope Fault Model is presented, and the huge computation of traditional Slope Fault Characteristic set was reduced greatly by the elimination of superfluous features. Several typical tolerance handling methods on the ground of the Slope Fault Model were compared. Then, the approximating distribution function of the Slope Fault Characteristic was deduced and sufficient conditions were given to improve the approximation accuracy. The monotonous and continuous mapping between Normal Quotient Distribution and standard normal distribution was proved. Thus the estimation formulas about the ranges of the Slope Fault Characteristic were deduced. After that, a new test-nodes selection algorithm based on the reduced Slope Fault Characteristic ranges set was designed. Finally, two numerical experiments were done to illustrate the proposed approach and demonstrate its effectiveness.
Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.
The objective of this paper is to evaluate the self- healing properties of a commercially-available geosynthetic clay liner (GCL) using flexible-wall permeameter. The GCLs are produced by the same factory, but the contents of bentonite are different. Also the hydraulic conductivities (HC) of GCLs with no defect are different. In this study, specimens were completely saturated under the backpressure of 20 kPa before the test. Permeability tests were performed on GCL specimens with penetrating flaw and also on specimens permeated with distilled water and CaCl2 solutions. The test results were presented and discussed. Experimental results showed that the GCL with penetrating flaw did not exhibit complete self-healing in the case of flaw. After 120 days, the hydraulic conductivity increased by approximately an order of magnitude. In addition, CaCl2 solutions had a significant influence on the hydraulic conductivity. The research findings might be of interest to researchers and engineers who design liners for landfills and other liquid containment facilities
In this paper, crushability of foundry sand particles was studied. Three kinds of in-service silica sands in foundry enterprises selected as the study object, and foundry sand particles were subjected to mechanical load and thermal load during service were analyzed. A set of methods for simulating mechanical load and thermal load by milling and thermal-cold cycling were designed and researched, which were used to characterize the crushability for silica sand particles, the microstructure was observed by SEM. According to the user’s experience in actual application, the crushability of Sand C was the best and then Sand B, the last Sand A. The results indicated that mechanical load, thermal load and thermal-mechanical load can all be used to characterize the crushability of foundry sand particles. Microscopic appearances can qualitatively characterize the crushability of foundry sand particles to a certain extent, combining with the additions and cracks which are observed on the surface.
Real-time monitoring of deformation of large structure parts is of great significance and the deformation of such structure parts is often accompanied with the change of curvature. The curvature can be obtained by measuring changes of strain, surface curve and modal displacement of the structure. However, many factors are faced with difficulty in measurement and low sensitivity at a small deformation level. In order to measure curvature in an effective way, a novel fibre Bragg grating (FBG) curvature sensor is proposed, which aims at removing the deficiencies of traditional methods in low precision and narrow adjusting. The sensor combines two FBGs with a specific structure of stainless steel elastomer. The elastomer can transfer the strain of the structure part to the FBG and then the FBG measures the strain to obtain the curvature. The performed simulation and experiment show that the sensor can effectively amplify the strain to the FBG through the unique structure of the elastomer, and the accuracy of the sensor used in the experiment is increased by 14% compared with that of the FBG used for direct measurement.
To investigate the adsorptive properties of a local laterite deposited in Chenzhou, Hunan province, China, the adsorptive properties of the natural laterite were investigated by batch technique in this study. The effects of contact time, pH, ionic strength, temperature, and the concentration on adsorption properties were also analyzed. The obtained experimental results show that the main mineral composition of laterite is kaolinite and montmorillonite. The adsorption process achieved equilibrium within 60 minutes and 90 minutes for Sr(II) and Cr(VI), respectively. The adsorption capacities for Cr(VI) and Sr(II) by the laterite were about 7.25 mg·g-1 and 8.35 mg·g-1 under the given experimental conditions, respectively. The equilibrium adsorption data were ﬁtted to the second-order kinetic equation. The adsorption capacity for Sr(II) onto the laterite increased with increasing pH from 3–11 but decreased with increasing ionic strength from 0.001 to 1.0 M NaCl. The Sr(II) adsorption reaction on laterite was endothermic and the process of adsorption was favored at high temperature. Similarly, the adsorption capacity for Cr(VI) onto the laterite increased with increasing pH from 3–11, however, the ionic strength and temperature had an insigniﬁcant effect on Cr(VI) adsorption. The adsorption of Cr(VI) and Sr(II) was dominated by ion exchange and surface complexation in this work. Furthermore, the Langmuir and Freundlich adsorption isotherm model was used for the description of the adsorption process. The results suggest that the studied laterite samples can be effectively used for the treatment of contaminated wastewaters.
Abstract Sucrose phosphate synthase (SPS) is a key enzyme catalyzing sucrose metabolism in plants. In this study, we isolated the SPS cDNA from Saccharum spontaneum and designated as SsSPS (GenBank accession no. MF398541). The full-length of SsSPS cDNA was 4153-bp with an opening reading frame (ORF) of 3132 nucleotides, which encoded a 1043-amino acid protein. The nucleotide sequences alignment showed that it had 98%, 97% and 87% homology with S. officinarum, Setaria italica and Lolium perenne, respectively. Moreover, the SsSPS was detected to express in leaf and stem tissues of S. spontaneum and exhibited a predominant expression in the stem tissue. However, there was no significant difference in the expression level of SsSPS between young leaves and mature ones. Additionally, we generated transgenic S. spontaneum using Agrobacterium-mediated transformation. Our data will provide a valuable foundation for further study of the potential role of SPS in plants.