College of Electronics and Electrical

The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and load curve drawing. The attractor is obtained using an improved deterministic algorithm based on the fractal interpolation function, a day's load is predicted by three days' historical loads, the maximum relative error is within 3.7%, and the average relative error is within 1.6%. The experimental result shows the accuracy of this prediction method, which has a certain application reference value in the field of short-term load prediction.

Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on cutting tool wear, resulting in uncertainty, so it is difficult for experimental data and result to have good stability. However, Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims to study the methods of tool vibration signal processing, pattern recognition and trend prediction. Collected on tool vibration signal at different times, wavelet noise reduction is used to pretreat the vibration signals. Then, for the self-similar vibration signals, we propose the fractional Brownian motion (FBM) theory with long-range dependence (LRD). Combined with Wigner-Ville spectrum, characteristic parameter can be extracted, so the cutting tool wear state can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum. Finally, we use FBM model to predict the trend of tool vibration signals. Experiments show that the methods have a good effect on tool wear state recognition and trend prediction.

Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractio...

In this paper, the alternating direction method of multipliers (ADMM) algorithm is applied to the compressed sensing theory to realize the sparse optimization of vibration signal. Solving the basis pursuit problem for minimizing theL1norm minimization under the equality constraints, the sparse matrix obtained by the ADMM algorithm can be reconstructed by inverse sparse orthogonal matrix inversion. This paper analyzes common sparse orthogonal basis on the reconstruction results, that is, discrete Fourier orthogonal basis, discrete cosine orthogonal basis, and discrete wavelet orthogonal basis. In particular, we will show that, from the point of view of central tendency, the discrete cosine orthogonal basis is more suitable, for instance, at the vibration signal data because its error is close to zero. Moreover, using the discrete wavelet transform in signal reconstruction there still are some outliers but the error is unstable. We also use the time complex degree and validity, for th...

The paper is aimed to analyze and get a further understanding of the dynamic characteristics of the tire by means of computing modal analysis. A tire modal testing system is built on the basis of the previous studies. We also attempt to study how the amplitude of exciting force and loading changes affect the tire modal test, which lays a foundation for applying the tire modal test into practical use. Besides, the relations of experimental modal and computing modal will also be analyzed. The analysis and application of the tire modal lay a solid foundation for medium-high frequency tire mechanical model which is being established by our research group. And it takes advantage of HYPERMESH and ANSYS to set up the finite element model of the tire and to analyze the tire structural dynamics, laying a foundation for setting, recognizing the correctness of experimental modal parameter as well as studying the relationship between calculating modal and experimental modal. And it also studies the law of tire's dynamic characteristics and its reason. The third part makes a summary of the full text.

In quantum key distribution experiments, ground motion is usually used to simulate satellite-based motion. The posture fluctuation of the platform affects the normal operation of the acquisition, tracking, and pointing (abbreviated as ATP) system seriously. To achieve the verification of the ground motion platform, the ATP parameters of the ground simulation motion system cannot be designed only according to the satellite-based ATP parameters. To solve this problem, a set of initial pointing system and inertial stabilization system is added to the simulation ATP system. This provides a technical solution for the ground simulation ATP system similar to the satellite-based motion platform. In the meanwhile, a tracking control strategy based on the identification method is proposed by establishing identification symbols. Compared with traditional proportion, integral, and differential (abbreviated as PID) control, this method overcomes the shortcoming of tentative modification of the c...

Forecasting non-stationary stochastic time series represents a rather complex problem. The reason is that such temporal series are not only self-similar but also exhibit a Long-Range Dependence (LRD). As it is known, the Fractional Brown Motion (FBM) can generate a non-stationary stochastic time series with self-similarity and LRD. In this study we investigate the properties of the LRD for identification of selfsimilarity and the LRD of non-stationary stochastic series by Hurst exponent. Parameter estimation is proposed for Stochastic differential Equation (SDE) of FBM based on Maximum Likelihood Estimation (MLE), and proves the convergence of MLE. The SDE is discretized.The difference equation constructed is the prediction model of the iterative format based on FBM. Monte Carlo simulation is applied to check the validity and accuracy of parameter estimation. We also give a practical example to demonstrate the appropriateness of the predictive model.

With the rapid development of Internet and information technology, the network has been deeply integrated into our lives. However, the rich services and applications of the Internet bring more security problems. Technology of network security assessment is a strategy to deal with the problems of network security at present. The basic concepts and research significance are shed light on. This paper described the architecture of network security assessment, and analyzed the research status mainly focusing on the method based on mathematical model, the method based on knowledge reasoning and the method based on pattern recognition. Then the advantages and disadvantages were pointed out respectively. Finally, some future research directions were given at the end.

Tool wear can cause dimensional accuracy and poor surface quality in milling process. During the operation of tool wear, it can also cause breakage and damage of the workpieces. To prevent these conditions, it's important that the tool wear is monitored and the remaining useful life (RUL) is predicted in real time. In this paper, time domain and frequency domain statistical features are firstly extracted using multi-sensory fusion method, including the cutting force, vibration and acoustic emission sensor. Seven eigenvectors are selected as the input of the prediction model based on the distance correlation coefficient between 140 feature vectors and the wear value, which provide the most sensitive features to wear faults. The paper establishes a nonlinear relationship between high-dimensional feature vectors and tools wear based on the evolving connectionist system (ECoS), which uses the incremental learning algorithm to realize real-time prediction of the tools wear. Finally, using the wear value predicted by ECoS as hidden state sequence of Hidden Semi-Markov Model (HSMM), the RUL prediction of the tool based on HMM is established. The 2010 PHM challenge data were used to train the model. The experimental result shows that in comparison with artificial neural network, the ECoS model has higher prediction accuracy, and its mean RMSE error for three tools is 14.8. In comparison with the RUL prediction of HMM model, Probability-based RUL prediction of HSMM is more stable. INDEX TERMS Multi-sensor fusion, evolving connectionist system, incremental learning algorithm, HSMM, remaining useful life prediction. ABBREVIATIONS RUL Remaining useful life. HMM Hidden markov model. ECoS Evolving connectionist system. PHM Prognostic and health management. HSMM Hidden semi-markov model. RMSE Root mean square error. The associate editor coordinating the review of this manuscript and approving it for publication was Zhaojun Steven Li .

Anti-scalants, which are widely used for scale control in desalination processes, are essentially impurities capable of inhibiting scale precipitation and weakening scale adhesion on a flow surface. Many basic studies indicate that metallic ion impurities, notably Zn ions, can hinder significantly CaCO 3 precipitation from hard waters and alter crystal morphology. Recent studies demonstrate a substantial potential for suppressing CaCO 3 scale formation in hard water heating systems by trace concentrations of Zn ions. The objective of the present work was to explore the possibility of controlling CaCO 3 scale formation in RO membrane systems by a similar technique. Results obtained with a tubular RO system are most promising. A Zn þþ concentration of the order of 2 PPM was able to exert a marked suppression effect on both bulk precipitation of CaCO 3 and on membrane scaling on waters of moderate hardness.

Anti-scalants, which are widely used for scale control in desalination processes, are essentially impurities capable of inhibiting scale precipitation and weakening scale adhesion on a flow surface. Many basic studies indicate that metallic ion impurities, notably Zn ions, can hinder significantly CaCO 3 precipitation from hard waters and alter crystal morphology. Recent studies demonstrate a substantial potential for suppressing CaCO 3 scale formation in hard water heating systems by trace concentrations of Zn ions. The objective of the present work was to explore the possibility of controlling CaCO 3 scale formation in RO membrane systems by a similar technique. Results obtained with a tubular RO system are most promising. A Zn þþ concentration of the order of 2 PPM was able to exert a marked suppression effect on both bulk precipitation of CaCO 3 and on membrane scaling on waters of moderate hardness.

Reverse osmosis (RO) concentrates containing high amounts of dissolved salts and anti-scalants are commonly discharged directly. Considerable economic and environmental benefits could be achieved by extending current water recovery limits. One way for reuse of concentrates and thus increasing the water recovery is to recycle part of the brine concentrate to the feed, after precipitating the scaling salts such as CaSO 4 /CaCO 3 held in solution by anti-scalants. In the present study, scaling salt removal was carried out from RO CaSO 4 concentrate in the presence of four anti-scalants via addition of six kinds of inorganic particles. Analyses of the kinetics of gypsum removal and the morphologies of CaSO 4 crystals were performed. The results show that the method was effective for the removal of scaling salt. After the precipitation removal reactions, the test solutions reached thermodynamic equilibrium. Furthermore, the precipitated gypsum appeared shorter and broader crystal forms. The economic analysis shows the method is feasible.

One way for the reuse of reverse osmosis (RO) concentrates and thus increasing the water recovery is to recycle part of the brine concentrate to the feed, after precipitating the scaling salts such as CaSO 4 / CaCO 3 held in solution by antiscalants. However, antiscalants impart considerable stability to the supersaturated solution. In the present study, desupersaturation of RO CaSO 4 concentrate in the presence of antiscalants was studied by addition of coagulant polyaluminum chloride (PACl) and surfactant sodium dodecyl sulfate (SDS). Simulated experiments for testing the changes in antiscalant concentration were also conducted. The results indicated that under certain conditions, the above methods were effective for desupersaturation of RO concentrate. After the removal precipitation reactions, the calculated supersaturation ratio of the test solution deceased to about 1.0, which demonstrated that the test solution reached thermodynamic equilibrium. With respect to the removal of scaling salt, it was found to follow the second order kinetics. The removal of scaling salt was always accompanied by the adsorption precipitation of antiscalants. The ecomonic analyses show the proposed methods were feasible.

The reuse of reverse osmosis (RO) concentrates generally involves the removal of scaling salts such as CaSO 4 /CaCO 3 , and then the recycle part of the treated concentrate to the feed. By this method, the water recovery can be remarkably increased. However, the time necessary for precipitating the scaling components may extend to a few days, especially in the presence of antiscalants. In the present study, CaCO 3 scaling salt removal was carried out from RO concentrates in the presence of three antiscalants by accelerating precipitation approaches. Three methods were adopted, including the air-blow method, inorganic inducer addition, and the combinative method of air-blow and inorganic inducer addition. Calcium ion concentration, pH and antiscalant concentration of the test solutions were monitored. Morphologies of the precipitated CaCO 3 were observed by scanning electron microscopy (SEM). These methods were found to be effective, reaching a calcium removal ratio as high as 50-80%. The removal of scaling salt was always accompanied by the adsorption precipitation of antiscalants. In the presence of antiscalants, saw-toothed growth edges and holes on the precipitated CaCO 3 surfaces were found. Moreover, fragmented small particles were also observed. The economic analysis shows that the methods are feasible.

Residence time distribution (RTD) techniques can be used to diagnose the flow characteristics in spiral wound reverse osmosis (RO) modules. However, the methods of processing tracer response conductivity signals and mathematically modeling of RTD curves often involve complicated steps including conductivity-concentration transformation, baseline selection and the use of exit age distribution function of E t , or dimensionless exit age distribution function of E θ . In this paper, a simple and direct method for processing RTD signals from conductivity data was developed for spiral wound membrane RO system. Two models were tested: axial dispersion (AD) model and exponentially modified Gaussian (EMG) model. The results show that the present method provides a simple, fast and accurate RTD data reduction. The levels of the axial mixing intensities, characterized by the dispersion number D/uL, indicated significant deviations from ideal plug flow in both the laboratory and the industrial size modules. In both the modules, the dispersion coefficient D increased roughly linearly with the Reynolds number. Membrane fouling and worn-out led to an increase in D. Moreover, the values of mean residence timet and D/uL obtained from the EMG model were more stable against the change of the curve tail length, especially for the parameter D/uL. Furthermore, RTD analysis also indicated that the membrane wearing-out could lead to dead zones.

A simple and reliable technique for on line detection and diagnosis of scaling and fouling deposits could improve significantly operational control of RO plants. This paper presents results of an ongoing research project aiming to develop a novel method for online detection of membrane fouling by a residence time distribution (RTD) technique. The method is based on determination of flow dispersion intensities from online RTD signals that can be simply measured. The systems investigated in this paper are spiral wound membranes fouled with either Mg(OH) 2 or CaCO 3 . Analyses of RTD data of fouled membranes show that an increasing membrane permeability loss is accompanied by a systematic increase in the dispersion coefficient, thus providing an indicator for detecting a fouling event. It is also found that the magnitude of the dispersion coefficient is affected by deposit morphology, thus indicating the possibility for diagnosing the nature of the fouling deposit. Another fouling indicator may be derived from the observation that the extent of the dead volume monitored by the RTD signal increases with the fouling level while dead volumes are absent in clean modules.

The prerequisite condition for maintaining dropwise condensation on a surface is that the condensing surface should have a low surface energy. In the present paper, two different techniques were employed to achieve steam dropwise condensation over a vertical copper tube at atmospheric pressure: one is selfassembled monolayers (SAMs), and the other is electroless composite plating. The use of SAMs of docosanoic acid promotes dropwise condensation. The electroless Ni-P-PTFE coatings also achieve excellent dropwise condensation. Compared with film condensation, the condensation heat transfer coefficient can be enhanced by more than one order of the magnitude on these coated surfaces. The electroless composite coating surface of high PTFE content has a higher condensation heat transfer coefficient than that of low PTFE content. The SAM surface of docosanoic acid has the lowest surface energy which leads to the lowest thermal resistance of liquid condensate, and a negligible thickness of 30 Å with a negligible additional heat transfer resistance, thus it has better heat transfer characteristics than those of the Ni-P-PTFE surfaces.