关于序^＊-半格同余的最小元问题

研究关于序 -半格同余的问题,证明了N 是最小序 -半格同余,即N 是序 -半格同余的最小元.但一般来说,N 不是最小序半格同余.

The Semigroup Characterizations of Positive Implicative BCK-algebras

Adjoint semigroups of BCI algebras were introduced by Huang [1] and some of its important properties were obtained. In this paper,some characterizations of positive implicative BCK algebras are given by discussing the adjoint semigroups. Moreover,the semigroup differences between positive BCK algebras and implicative BCK algebras are discussed,and some semigroup characterizations of implicative BCK algebras are given.

Time-Ordered Collaborative Filtering for News Recommendation

Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recommendation,these news articles read by a user is typically in the form of a time sequence.However,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors.Therefore,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read.To solve this problem,this paper proposes a time-ordered collaborative filtering recommendation algorithm(TOCF),which takes the time sequence characteristic of user behaviors into account.Besides,a new method to compute the similarity among different users,named time-dependent similarity,is proposed.To demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis.

METHOD OF CONSTRUCTING BORNOLOGICAL SPACES FROM L~*-VECTOR SPACES

A new method of constructing bornological vector topologies for vector spaces is discussed.In general,the convergent sequence and bounded set are concepts only in topological spaces.However,in this paper,it is first introduced sequential convergence C and L * space which is a vector space giving some relation:x mCx between sequences and points in it,then the bounded set is defined in vector space.Let C be a sequential convergence,T(C) be a vector topology on X determined by C and B(C) be the collection of bounded sets determined by C.Then B(C)=B(T(C)).Furthermore,the bornological locally convex topological vector space is constructed by L * vector space.

Purification and characterization of iron-cofactored superoxide dismutase from Enteromorpha linza

A superoxide dismutase was purified from Enteromorpha linza using a simple and safe procedure, which comprised phosphate buffer extraction, ammonium sulphate precipitation, ion exchange chromatography on Q-sepharose column, and gel filtration chromatography on Superdex 200 10/300GL. The E. linza superoxide dismutase （E/SOD） was purified 103.6-fold, and a yield of 19.1% and a specific activity of 1 750 U/rag protein were obtained. The SDS-PAGE exhibited E/SOD a single band near 23 kDa and the gel filtration study showed E/SOD＇s molecular weight is near 46 kDa in nondenatured condition, indicating it＇s a homodimeric protein. E/SOD is an iron-cofactored superoxide dismutase （Fe-SOD） because it was inhibited by hydrogen peroxide, insensitive to potassium cyanide. The optimal temperature for its maximal enzyme activity was 35℃, and it still had 29.8% relative activity at 0℃, then E/SOD can be classified as a cold-adapted enzyme. E/SOD was stable when temperature was below 40℃ or the pH was within the range of 5 10. The first 11 N-terminal amino acids orE/SOD were ALELKAPPYEL, comparison of its N-terminal sequence with other Fe-SOD N-terminal sequences at the same position suggests it is possibly a chloroplastic Fe-SOD.

INFORMATION FUSION STEADY-STATE WHITE NOISE DECONVOLUTION ESTIMATORS WITH TIME-DELAYED MEASUREMENTS AND COLORED MEASUREMENT NOISES

White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.

Signal and Data Processing of Television Based on Multistatic Radar Systems

A television based multistatic radar system is described. The commercial television transmitter is used as the illuminator in the multistatic radar system. The reflected commercial television signals are measured by an array of sensors. A data processing scheme is developed that adapts to the poor signal processing ability. The innovation is focused on the construction of the observation space, which could reduce the non linearity error. The new method leads to better system stability than the traditional one. Monte Carlo simulation is utilized and compared with the traditional method.

Modeling Nonstationary Time Series for Gyroscopic Drift Analysing

A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.

LINEAR FILTERING FOR VASICEK TERM STRUCTURE MODEL WITH SEQUENTIALLY CORRELATED NOISE

When Kalman filter is used in the estimation of Vasicek term structure of interest rates,it is usual to assume that the measurement noise is uncorrelated.Study results are more favorable to the assumption of correlated measurement noise.An augmented state Kalman filter form for Vasicek model is proposed to optimally estimate the unobservable state variable with the assumption of correlated measurement noise.Empirical results indicate that the model with sequentially correlated measurement noise can more accurately describe the dynamics of the term structure of interest rates.

Fuzzy Ordered Filters in Implicative Semigroups

We fuzzify the concept of ordered filter in implicative negatively partially ordered semigroups and study its properties.

Application of Geodetic Receivers to Timing and Time Transfer

Two methods for smoothing pseudorange observable by Carrier and Doppler are discussed. Then the procedure based on the RINEX observation files is tested using the Ashtech Z-XII3T geodetic receivers driven by a stable external frequency at UNSO. This paper proposes to adapt this procedure for the links between geodetic receivers, in order to take advantage of the P codes available on L 1 and L 2. This new procedure uses the 30-second RINEX observations files, the standard of the International GPS Service (IGS), and processes the ionosphere-free combination of the codes P 1 and P 2; the satellite positions are deduced from the IGS rapid orbits, available after two days.

Windowed overlapped frequency domain block filtering approach for direct sequence signal acquisition

This study applies a windowed frequency domain overlapped block filtering approach to acquire direct sequence signals. As a novel viewpoint, the windows not only allow pulse shaping without front-end pulse-shaping filter, but also increase the performance of the spectrum sensing unit, which can efficiently be implemented into this frequency domain receiver and may further be used for spectrum sensing in cognitive radios or narrowband interference cancellation in military radios. The proposed receiver is applicable for the initial time synchroni- zation of different signals containing a preamble. These signals include single carrier, constant envelope single carder, multicarrier, and even generalized multicarrier signals, making the proposed receiver structure a universal unit. Furthermore, the receiver can be used to perform filtering with long codes and compute the sliding correlation of an unknown periodic preamble. The receiver can further be modified to handle large Doppler shifts. We will also demonstrate herein the computational complexity and analysis of the acquisition performance in Rayleigh and Rician fading channels.

Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences

In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.

An Efficient Adaptive Failure Detection Mechanism for Cloud Platform Based on Volterra Series

Failure detection module is one of important components in fault-tolerant distributed systems,especially cloud platform.However,to achieve fast and accurate detection of failure becomes more and more difficult especially when network and other resources' status keep changing.This study presented an efficient adaptive failure detection mechanism based on volterra series,which can use a small amount of data for predicting.The mechanism uses a volterra filter for time series prediction and a decision tree for decision making.Major contributions are applying volterra filter in cloud failure prediction,and introducing a user factor for different QoS requirements in different modules and levels of IaaS.Detailed implementation is proposed,and an evaluation is performed in Beijing and Guangzhou experiment environment.

SUBSET PARALLEL VOLTERRA FILTER FOR NARROWBAND INTERFERENCE SUPPRESSION IN DSSS COMMUNICATIONS

Subset Parallel Adaptive Volterra Filter (SPAVF) design algorithm is proposed in this letter. Contri-bution factor is introduced in SPAVF, and it can get rid of redundant elements efficiently in the extended input vector. Computational weight can be reduced largely, and BER performance of SPAVF can be improved by getting rid of the influence of redundant elements in the input vector. Simulation result proves its advantage compared to AVF and PSVF.

A method for constraining the end effect of EMD based on sequential similarity detection and adaptive filter

Aimed at the problem of the end effect when using empirical mode decomposition(EMD),a method for constraining the end effect of EMD is proposed based on sequential similarity detection and adaptive filter.The method divides the signal into many wavelets,and it changes the initial wavelet length to select the best initial wavelet that has the minimum error and maximum number of matching seed wavelets,and the wavelet slopes are used for pre-matching and secondary matching to speed up the matching speed.Then,folded self-adaptive threshold is used to select multiple seed wavelets,and finally the end waveform is predicted and expanded according to the adaptive filter method.The proposed method is used to analyze the non-stationary nonlinear simulation signal and experimental signal,and it is compared with the mirror extension and RBF extension methods.The orthogonality index and similarity index of the EMD results of the extended signal after the proposed method are better than those of the other methods.The results show that the proposed method can better constrain the end effect,and has certain validity,accuracy and stability in solving the end effect problem.

Carbon Dioxide Capture Using Gas Hydrate Technology

According to IPCC （Intergovemmental Panel on Climate Change） Fourth Report, carbon dioxide emissions from the combustion of fossil fuels have been identified as the major contributor to global warming and climate change. One of the new approaches for capturing carbon dioxide and subsequently lowering the emissions is based on gas hydrate crystallization. Gas hydrates have a large capacity for the storage of gases which also resemble an attractive method for gas filtration. The basis of the separation is the selective partition of the target component between the hydrate phase and the gaseous phase. It is expected that carbon dioxide is preferentially encaged into the hydrate crystal phase compared to the other components. In the present paper, after a comparison of gas hydrates with existing capture technologies, a novel apparatus for gas hydrate production is illustrated and results of a first set of experimental applications of the reactor for CO2 hydrate formation and separation are presented. In particular, the effects of two different promoters were investigated. Results show that the reactor allows a good level of temperature control, resulting in rapid hydrate formation and mild operating conditions. Results are a basis for setting up a procedure for CO2 separation and capture.

Regular Congruence Classes of Ordered Semigroups

The aim of this paper is to determine the subset of an ordered semigroup S that can serve as a class of some regular congruences on S. The results on semigroups without order given in [5] can be obtained as consequences.

Embedding ensemble tracking in a stochastic framework for robust object tracking

We propose an algorithm of embedding ensemble tracking in a stochastic framework to achieve robust tracking performance under partial occlusion,illumination changes,and abrupt motion.It operates on likelihood images generated by the ensemble method,and combines mean shift and particle filtering in a principled way,where a better proposal distribution is de-signed by first propagating particles via a motion model,and then running mean shift to move towards their local peaks in the likelihood image.An observation model in the particle filter incorporates global and local information within a region,and an adaptive motion model is adopted to depict the evolution of the object state.The algorithm needs fewer particles to manage the tracking task compared with the general particle filter,and recaptures the object quickly after occlusion occurs.Experiments on two image sequences demonstrate the effectiveness and robustness of the proposed algorithm.

Approximate Gaussian conjugacy： parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov-Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture （GM） prior to a Gaussian/GM posterior （termed ＇Gaussian conjugacy＇ in this paper）, form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality （including target maneuver）, intractable uncertainties （such as unknown inputs and/or non-Gaussian noises） and constraints （including circular quantities）, etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.