Performance of microbiological control by a point-of-use filter system for drinking water purification

Purification capacity of a faucet mounted type water filter for home use was evaluated, particularly with regard to microbiological performance under different running conditions. Biofilms were formed inside the filter, affecting the bacterial quality of the effluent water. Low flow rate, long stagnation period and high filter temperature were found favorable for bacterial growth inside. By commercial analytical profile index （API） kits, ten different bacterial species were identified in drinking water, four of which were probably contributed to the biofilm formation since they were also present in the biofilm. Fluorescence in situ hybridization （FISH） was used to confirm the API identification results, and direct viable count （DVC） method was employed to improve the sensitivity of FISH for the isolated Acinetobacter spp. and Pseudomonas putida as models. Relationship between the filter operating condition and the bacterial community alteration was partly revealed, which could provide the basic knowledge for the filter design and its practical use.

FLOW CHARACTERISTICS OF WALLFLOW DIESEL PARTICULATE FILTER SYSTEM WITH REVERSE PULSE AIR REGENERATION

To simulate steady airflows inside of wall-flow diesel particulate filters （DPF） with different reverse blowing pipes collocation, a mathematical model of the flow in a DPF is established by an equivalent continuum approach. The experimental results agree well with the theoretical values calculated from the model. Simulation shows that the velocity and the pressure distribution of the filters in the regenerative process are key factors to the filter＇s regeneration. How to decrease the mal-distribution of the flow in the filter and how to achieve the better regenerative performance at the least cost of air consumption in the regenerative process are the ultimate goals of the study. Calculation and experiments show that the goals can be realized through adjusting the angle of two reverse blowing pipes and their relative location suitably.

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

Recursive Filtering for Stochastic Systems With Filter-and-Forward Successive Relays

In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems:Tackling Multiplicative Noises and Missing Measurements

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented system.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.

A Double Sensitive Fault Detection Filter for Positive Markovian Jump Systems With A Hybrid Event-Triggered Mechanism

This paper is concerned with the double sensitive fault detection filter for positive Markovian jump systems. A new hybrid adaptive event-triggered mechanism is proposed by introducing a non-monotonic adaptive law. A linear adaptive event-triggered threshold is established by virtue of 1-norm inequality.Under such a triggering strategy, the original system can be transformed into an interval uncertain system. By using a stochastic copositive Lyapunov function, an asynchronous fault detection filter is designed for positive Markovian jump systems(PMJSs) in terms of linear programming. The presented filter satisfies both L_-gain(?_-gain) fault sensitivity and L_1(?_1)internal differential privacy sensitivity. The proposed approach is also extended to the discrete-time case. Finally, two examples are provided to illustrate the effectiveness of the proposed design.

A novel DPSS filter optimization scheme to reduce the intrinsic interference of FBMC-QAM systems

In order to reduce the intrinsic interference of the filter bank multicarrier-quadrature amplitude modulation(FBMC-QAM)system,a novel filter optimization scheme based on discrete prolate spheroidal sequences(DPSS)is proposed.Firstly,a prototype filter function based on DPSS is designed,since the eigenvalue can be used as an indicator of the energy concentration of DPSS,so a threshold is set,and the sequence with the most concentrated energy is selected under the threshold,that is,the sequence with the eigenvalue higher than the threshold,and the prototype filter function is rewritten as a weighted sum function of multiple eigenvectors.Under the energy constraints of the filter,the relationship between the eigenvectors and the intrinsic interference function is established,and the function problem is transformed into an optimization problem for the weighted coefficients.Through the interior point method,the most suitable weight is found to obtain the minimum intrinsic interference result.Theoretical analysis and simulation results show that compared with the prototype filters such as Type1 and CaseC,the DPSS filter applying the proposed optimization algorithm can effectively suppress the intrinsic interference of the system and obtain a better bit error rate(BER)performance.

Improving the Position Accuracy and Computational Efficiency of UAV Terrain Aided Navigation Using a Two-Stage Hybrid Fuzzy Particle Filtering Method

Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.

APPLICATION OF INTERVAL KALMAN FILTER TO AN INTEGRATED GPS/INS SYSTEM

An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval Kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.

Application of Morphological Filter in Rosette Scanning Sub-Imaging System

To restore the sub image in a rosette scanning system and provide target recognition system with a low distorted image, the sub image is processed with morphological filters. Morphological filter can process rosette scanning sub images more effectively. It can restore the original area and shape of an object effectively, and keep the energy information of the object. To process sub images got by a rosette scanning system, morphological filter is more effective than traditional low pass filter.

Application of unscented Kalman filter to novel terrain passive integrated navigation system

To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.

Distributed H_∞ fusion filter design for INS/WSN integrated positioning system

In order to keep stable navigation accuracy when the blind node （BN） moves between two adjacent clusters, a distributed fusion method for the integration of the inertial navigation system （INS） and the wireless sensor network （WSN） based on H∞ filtering is proposed. Since the process and measurement noise in the integration system are bounded and their statistical characteristics are unknown, the H∞ filter is used to fuse the information measured from local estimators in the proposed method. Meanwhile, the filter can yield the optimal state estimate according to certain information fusion criteria. Simulation results show that compared with the federal Kalman solution, the proposed method can reduce the mean error of position by about 45% and the mean error of velocity by about 85 %.

COMPARISON AND EVALUATION OF CURRENT REFERENCE GENERATION STRATEGIES FOR ACTIVE POWER FILTERS IN THREE-PHASE FOUR-WIRE SYSTEMS

For three phase four-wire active power filters （APFs）, several typical power theories and corresponding current reference generation strategies are induced, p-q, d-q, unify power factor （UPF） and instantaneous active current （IAC） methods are analyzed and compared with each other. The interpretation of active and reactive currents in non-sinusoidal and unbalanced three-phase four-wire systems is given based on the generalized instantaneous reactive power theory. The performance and the characteristic are evaluated, and the application conditions of current reference generation strategies are concluded. Simulation results under different source voltages and loads verify the evaluation result.

FREQUENCY-DOMAIN IMPLEMENTATION OF FILTERED-X ALGORITHMS WITH ON-LINE SYSTEM IDENTIFICATION FOR VIBRATION CONTROL

This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain controller. When the frequency-domain LMS step size is allowed to vary as a function of frequency,the frequency-domain algorithm exhibits a better vibration reduction than the time-domain algorithm for the weaker frequencies in the energy spectrum.

Application of Adaptive Kalman Filtering Algorithm in IMU/GPS Integrated Navigation System

The IMU（inertial measurement unit） error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering is applied in IMU/GPS integrated navigation system, in which the adaptive factor is replaced by the fading factor. A practical example is given. The resuits prove that the adaptive filter combined with the fading factor is valid and reliable when applied in IMU/GPS integrated navigation system.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

Attitude sensor fault diagnosis based on Kalman filter of discrete-time descriptor system

To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.

Quaternion-Based Kalman Filter for Micro-machined Strapdown Attitude Heading Reference System

A Kalman filter used in strapdown AHRS (Attitude Heading Reference System) based on micro machined inertial sensors is introduced. The composition and principle of the system are described. The attitude algorithm and error model of the system are derived based on the quaternion formulation. The real time quaternion based Kalman filter is designed. Simulation results show that accuracy of the system is better than 0.04 degree without disturbance of lateral acceleration and reduced to 0.44 degree with l...

Simultaneous removal of hydrogen sulfide and volatile organic sulfur compounds in off-gas mixture from a wastewater treatment plant using a two-stage bio-trickling filter system

Simultaneous removal of hydrogen sulfide (H2S) and volatile organic sulfur compounds (VOSCs) in off-gas mixture from a wastewater treatment plant (WWTP) is difficult due to the occasional inhibitory effects of H2S on VOSC degradation. In this study, a two-stage bio-trickling filter (BTF) system was developed to treat off-gas mixture from a real WWTP facility. At an empty bed retention time of 40 s, removal efficiencies of H2S, methanethiol, dimethyl sulfide, and dimethyl disulfide were 90.1, 88.4, 85.8 and 61.8%, respectively. Furthermore, the effect of lifting load shock on system performance was investigated and results indicated that removal of both H2S and VOSCs was slightly affected. Illumina Miseq sequencing revealed that the microbial community of first-stage BTF contained high abundance of H2S-affinity genera including Acidithiobacillus (51.43%), Metallibacterium (25.35%), and Thionomas (8.08%). Analysis of mechanism demonstrated that first stage of BTF removed 86.1% of H2S, mitigating the suppression on VOSC degradation in second stage of BTF. Overall, the twostage BTF system, an innovative bioprocess, can simultaneously remove H2S and VOSC.