Innovative Filter Research Articles

Neuromorphic robust estimation of nonlinear dynamical systems applied to satellite rendezvous

State estimation of nonlinear dynamical systems has long been driven by the goals of accuracy, computational efficiency, robustness, and reliability. With the rapid evolution of various industries, the demand for estimation frameworks that can simultaneously fulfill these factors has grown significantly. Leveraging recent advances in neuromorphic computing architectures, this study presents a neuromorphic approach for robust filtering of nonlinear dynamical systems called SNN-EMSIF (spiking neural network-extended modified sliding innovation filter). SNN-MSIF benefits the computational efficiency and scalability of SNNs with the robustness of EMSIF, which is an estimation framework for nonlinear systems with zero-mean Gaussian noises. Notably, the weight matrices of the networks are designed according to the system model, eliminating the need for a learning process. The efficacy of the approach is evaluated by proposing a spiking framework based on anextended Kalman filter (EKF). Through comprehensive Monte-Carlo simulations, the performance of EKF and EMSIF. Additionally, SNN-EMSIF is compared with SNN-EKF in the presence of modeling uncertainties and neuron loss by means of obtained RMSEs. Results demonstrate the validity of the proposed methods and highlight the superior performance of SNN-EMSIF in terms of accuracy and robustness. Furthermore, investigations into obtained runtimes and spiking patterns generated by the SNN-EMSIF provide compelling evidence of the achieved computational efficiency, with an impressive reduction of approximately 85% in emitted spikes compared to possible spikes. The SNN-MSIF framework presents a promising solution to address the challenges of robust estimation in nonlinear dynamical systems, opening new avenues for efficient and reliable estimation in various industries benefiting neuromorphic computing advantages.

1.X.2. Reducing inequalities in access to care in Europe: achieving universal health coverage and more

Abstract Accessibility is multi-dimensional. Universal Health Coverage emphasises the role of population coverage, the scope of services covered, and the depth of financial protection. However, there are other major barriers for patients that can inhibit universal access to health services: geographical inequalities, organisational barriers, and care denial. Health systems can provide extensive cover in some dimensions such as financial coverage or the range of services covered, but gaps in other dimensions can still act as a major barrier to care. Some groups are excluded from population coverage, other patients must navigate waiting times, medical deserts or cultural barriers in order to access care. In this session we will use an innovative filter framework outline common obstacles that patients in the European Union must overcome before they can access the care they need. We will then highlight policy responses to meet the dual goals of reducing inequalities in access to care and ensuring Universal Health Coverage. This workshop will provide a platform for sharing knowledge on reducing inequalities in access to care in EU countries. The objectives of the workshop are to i) highlight cross-cutting themes and approaches, and ii) establish opportunities for cross-country research and learning. The workshop will consist of four short presentations, followed by a broad discussion with audience interventions to bring in the experience of other Member States. Each panel member will make a short presentation in Pecha Kucha format: firstly outlining the filter framework for understanding access to health services then moving to descriptions of a policy journey to strengthening UHC in Cyprus, Estonia and Spain respectively. Bold reforms in Cyprus have expanded coverage under a new universal system; changes in Estonia have improved access to medicines; and policy-makers in Spain have sought to expand coverage of dental care. The moderated discussion will emphasise the potential for cross-country learning - identifying those policy choices of relevance for the rest of Europe. Presentations about specific policy initiatives draw on the latest Health Systems Reviews for Cyprus, Estonia and Spain. Health System Reviews underpin the Health Systems in Transition (HiT) series, a core part of country monitoring undertaken by the European Observatory on Health Systems and Policies. These Reviews are routinely updated and provide a systematic description of the functioning of health systems in countries as well as reform and policy initiatives in progress or under development. As these are in-depth descriptions which detail the intricacies of health systems and an assessment of their performance, they provide the ideal basis for understanding the true multidimensionality of accessibility. Key messages • Even health systems in high-income countries need to address inequalities in access to care to ensure universal health coverage. • The workshop will identify themes and approaches to improving access to care from a cross-country perspective through a multidimensional understanding of accessibility. Speakers/Panelists Erica Richardson European Observatory on Health Systems and Policies, London, UK Cristina Hernandez Quevedo European Observatory on Health Systems and Policies, London, UK Yulia Litvinova European Observatory on Health Systems and Policies, Berlin Hub, Berlin, Germany Gemma Williams European Observatory on Health Systems and Policies, London, UK

3D hydrophobic wood membrane with micro-nano baffle structure for multifunctional air purification filters: “Kill three eagles with one arrow”

Air pollution has presented an imminent threat to the world. Multifunctional filters with high filtration efficiency, low pressure drop, moisture resistance, antibacterial properties, and renewability are urgently needed to be developed. In this study, multifunctional wood filters are innovatively designed with a baffle structure combining macroscopic structural design and microscopic in-situ synthesis of silver nanoparticles (Ag NPs). The filter has excellent particle removal efficiency (PM0.3, 98.5 %), exceptional volatile organic compounds (VOCs) adsorption efficiency (98.4 % for formaldehyde and 91.7 % for xylene), high sterilization efficiency (99.99 %) and excellent regeneration ability (performance recovery to 98 % of the initial). Furthermore, personal protective masks were assembled by the prepared filters, whose process of replacing element is as simple as replacement of electric mosquito incense tablets. This innovative filter holds promise for applications such as daily protection, sudden accidents and prevention and control of respiratory infectious diseases. We envision that the functionally modified Ag NPs wood filters with a multilayer structure offer a comprehensive solution for more effective air purification in polluted environment.

An optimized filter design approach for enhancing imaging quality in industrial linear accelerator.

The polychromatic X-rays generated by a linear accelerator (Linac) often result in noticeable hardening artifacts in images, posing a significant challenge to accurate defect identification. To address this issue, a simple yet effective approach is to introduce filters at the radiation source outlet. However, current methods are often empirical, lacking scientifically sound metrics. This study introduces an innovative filter design method that optimizes filter performance by balancing the impact of ray intensity and energy on image quality. Firstly, different spectra under various materials and thicknesses of filters were obtained using GEometry ANd Tracking (Geant4) simulation. Subsequently, these spectra and their corresponding incident photon counts were used as input sources to generate different reconstructed images. By comprehensively comparing the intensity differences and noise in images of defective and non-defective regions, along with considering hardening indicators, the optimal filter was determined. The optimized filter was applied to a Linac-based X-ray computed tomography (CT) detection system designed for identifying defects in graphite materials within high-temperature gas-cooled reactor (HTR), with defect dimensions of 2 mm. After adding the filter, the hardening effect reduced by 22%, and the Defect Contrast Index (DCI) reached 3.226. The filter designed based on the parameters of Average Difference (AD) and Defect Contrast Index (DCI) can effectively improve the quality of defect images.

Cement based nanofiltration membrane of porous boron nitride and graphene oxide for effective oil/water separation

The development of effective filters for crude oil/water separation presents a critical environmental challenge in the face of oil spills. Porous membrane separation stands as a vital technique for removing organic solvents, oils, and dyes from water. In this study, we designed and fabricated a nanofiltration membrane composed of graphene oxide (GO) and boron nitride (BN) incorporated into a cement-based matrix to enhance the selectivity and efficiency of unidirectional flux transportation. The integration of GO, with its rich functional groups, and BN, known for its hydrophobic properties, within the cement matrix, resulted in an improved water permeation rate. Moreover, the inclusion of BN flakes in the cement matrix significantly enhances its mechanical strength, reaching up to 15.93 MPa (∼35 % greater than pristine cement), establishing it as a robust nanofiltration membrane for simultaneous flux enhancement and water purification. This innovative filter demonstrated remarkable separation efficiency, achieving up to 99 % oil removal while allowing the passage of water. To gain a deeper understanding of our experimental findings, we conducted ab initio investigations into the adsorption behavior of water and oil components, including ethanethiol, thiophene, and toluene molecules, on h-BN, GO, and their defective structures (Stone-Wales defect). Our results affirm that porous h-BN and GO nanosheets, characterized by their high specific surface areas, exhibit remarkable sorption capabilities. This nanoengineered membrane showcases substantial adsorption capacity alongside physisorption characteristics suitable for recycling, thereby holding significant potential for the development of functional porous structural materials in the realm of high-performance, cost-effective, and environmentally sustainable water treatment solutions.

Scale-Aware Edge-Preserving Full Waveform Inversion with Diffusion Filter for Crosshole Sensor Arrays.

Full waveform inversion (FWI) is recognized as a leading data-fitting methodology, leveraging the detailed information contained in physical waveform data to construct accurate, high-resolution velocity models essential for crosshole surveys. Despite its effectiveness, FWI is often challenged by its sensitivity to data quality and inherent nonlinearity, which can lead to instability and the inadvertent incorporation of noise and extraneous data into inversion models. To address these challenges, we introduce the scale-aware edge-preserving FWI (SAEP-FWI) technique, which integrates a cutting-edge nonlinear anisotropic hybrid diffusion (NAHD) filter within the gradient computation process. This innovative filter effectively reduces noise while simultaneously enhancing critical small-scale structures and edges, significantly improving the fidelity and convergence of the FWI inversion results. The application of SAEP-FWI across a variety of experimental and authentic crosshole datasets clearly demonstrates its effectiveness in suppressing noise and preserving key scale-aware and edge-delineating features, ultimately leading to clear inversion outcomes. Comparative analyses with other FWI methods highlight the performance of our technique, showcasing its ability to produce images of notably higher quality. This improvement offers a robust solution that enhances the accuracy of subsurface imaging.

Object Detection and Tracking with YOLO and the Sliding Innovation Filter.

Object detection and tracking are pivotal tasks in machine learning, particularly within the domain of computer vision technologies. Despite significant advancements in object detection frameworks, challenges persist in real-world tracking scenarios, including object interactions, occlusions, and background interference. Many algorithms have been proposed to carry out such tasks; however, most struggle to perform well in the face of disturbances and uncertain environments. This research proposes a novel approach by integrating the You Only Look Once (YOLO) architecture for object detection with a robust filter for target tracking, addressing issues of disturbances and uncertainties. The YOLO architecture, known for its real-time object detection capabilities, is employed for initial object detection and centroid location. In combination with the detection framework, the sliding innovation filter, a novel robust filter, is implemented and postulated to improve tracking reliability in the face of disturbances. Specifically, the sliding innovation filter is implemented to enhance tracking performance by estimating the optimal centroid location in each frame and updating the object's trajectory. Target tracking traditionally relies on estimation theory techniques like the Kalman filter, and the sliding innovation filter is introduced as a robust alternative particularly suitable for scenarios where a priori information about system dynamics and noise is limited. Experimental simulations in a surveillance scenario demonstrate that the sliding innovation filter-based tracking approach outperforms existing Kalman-based methods, especially in the presence of disturbances. In all, this research contributes a practical and effective approach to object detection and tracking, addressing challenges in real-world, dynamic environments. The comparative analysis with traditional filters provides practical insights, laying the groundwork for future work aimed at advancing multi-object detection and tracking capabilities in diverse applications.

MO-CCCII-Based Single-Input Multi-Output (SIMO) Current-Mode Fractional-Order Universal and Shelving Filter

This study introduces an innovative filter topology capable of providing simultaneous positive and negative gain outputs for one-fractional order LP, with high-pass, all-pass, and fractional-order shelving filter responses. The circuit, utilizing multi-output second-generation current-controlled conveyors, stands out as the first to deliver ten outputs, incorporating both integer and fractional-order filter responses, without requiring additional components. Its current-mode design simplifies the process, employing minimal active and grounded passive elements, making it appropriate for low-voltage/low-power applications. The filter utilizes fifth-order Oustaloup approximation and Foster type-I RC networks for fractional-order capacitors, providing enhanced control over the transition slope. PSpice simulations confirmed a 1 kHz cut-off, showcasing low power consumption, minimal noise, and a wide dynamic range, positioning the filter as suitable for sensors, control, and acoustic applications.

Distributed Leader Follower Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter

This paper investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode control hybrid formation control method with proof of its stability. The proposed control strategy resolves the impractical speed jump issue that exists in the conventional backstepping design. Additionally, considering the system and measurement noises, the proposed control strategy not only removes the chattering issue existing in the conventional sliding mode control but also provides smooth control input with extra robustness. After that, an adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates that are robust to modeling uncertainties. Finally, we performed multiple simulations to demonstrate the efficiency and effectiveness of the proposed formation control strategy.

Battery State of Health Estimation Using the Sliding Interacting Multiple Model Strategy

Due to their nonlinear behavior and the harsh environments to which batteries are subjected, they require a robust battery monitoring system (BMS) that accurately estimates their state of charge (SOC) and state of health (SOH) to ensure each battery’s safe operation. In this study, the interacting multiple model (IMM) algorithm is implemented in conjunction with an estimation strategy to accurately estimate the SOH and SOC of batteries under cycling conditions. The IMM allows for an adaptive mechanism to account for the decaying battery capacity while the battery is in use. The proposed strategy utilizes the sliding innovation filter (SIF) to estimate the SOC while the IMM serves as a process to update the parameter values of the battery model as the battery ages. The performance of the proposed strategy was tested using the well-known B005 battery dataset available at NASA’s Prognostic Data Repository. This strategy partitions the experimental dataset to build a database of different SOH models of the battery, allowing the IMM to select the most accurate representation of the battery’s current conditions while in operation, thus determining the current SOH of the battery. Future work in the area of battery retirement is also considered.

Bio-based ionic liquid filter with enhanced electrostatic attraction for outside filtration and inside collection of viral aerosols

Hazardous biological pathogens in the air pose a significant public environmental health concern as infected individuals emit virus-laden aerosols (VLAs) during routine respiratory activities. Mask-wearing is a key preventive measure, but conventional filtration methods face challenges, particularly in high humidity conditions, where electrostatic charge decline increases the risk of infection. This study introduces a bio-based air filter comprising glycine ionic liquids (GILs) and malleable polymer composite (GILP) with high polarity and functional group density, which are wrapped around a melamine-formaldehyde (MF) resin skeleton, forming a conductive, porous GIL functionized ionic network air filter (GILP@MF). When subjected to low voltage, the GILP@MF composite efficiently captures VLAs including nanoscale virus particles through the enhanced electrostatic attraction, especially in facing high humidity bioaerosols exhaled by human body. The filtration/collection efficiency and quality factor can reach 98.3% and 0.264 Pa−1 at 0.1 m s−1, respectively. This innovative filter provides effective VLA protection and offers potential for non-invasive respiratory virus sampling, advancing medical diagnosis efforts.

Nonlinear Transient Switching Filter for Automatic Buffer Window Adjustment in Short-term Ship Response Prediction

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution.

Interacting Multiple Model Estimators for Fault Detection in a Magnetorheological Damper.

This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is subject to a filtering process. This paper applies various model-based filters in combination with the IMM strategy. One such estimator employs the recently introduced extended sliding innovation filter (ESIF) known as the IMM-ESIF. The ESIF is an extension of the sliding innovation filter for nonlinear systems based on the sliding mode concept. In the presence of modeling uncertainties, the ESIF has been proven to be more robust compared to methods such as the extended Kalman filter (EKF). The novel IMM-ESIF strategy is also compared with the IMM strategy, which incorporates the unscented Kalman filter (UKF), referred to herein as IMM-UKF. While EKF uses Taylor series approximation to linearize the system model, the UKF uses sigma point to calculate the system's mean and covariance. The methods were applied to an experimental magnetorheological (MR) damper setup, which was designed for testing control and estimation theory. Magnetorheological dampers exhibit a diverse array of applications in the automotive and aerospace sectors, with particular relevance to attenuating vibrations through adaptive suspension systems. Applied to a magnetorheological (MR) damper with distinct operating modes determined by the damper's current, the results showcase the effectiveness of IMM-ESIF. In mixed operational conditions, IMM-ESIF demonstrates a notable 80% to 90% reduction in estimation error compared to its counterparts. Furthermore, it exhibits a 4% to 5% enhancement in correctly classifying operational modes, establishing IMM-ESIF as a promising and efficient alternative for adaptive estimation in electromechanical systems. The improved accuracy in estimating the system's behaviour, even amidst uncertainties and mixed operational scenarios, signifies the potential of IMM-ESIF to significantly enhance the overall robustness and efficiency of estimations.

Convex optimization-based structure-preserving filter for multidimensional finite element simulations

In simulation sciences, capturing the real-world problem features as accurately as possible is desirable. Methods popular for scientific simulations such as the finite element method (FEM) and finite volume method (FVM) use piecewise polynomials to approximate various characteristics of a problem, such as the concentration profile and the temperature distribution across the domain. Polynomials are prone to creating artifacts such as Gibbs oscillations while capturing a complex profile. An efficient and accurate approach must be applied to deal with such inconsistencies to obtain accurate simulations. This often entails dealing with negative values for the concentration of chemicals, exceeding a percentage value over 100, and other such problems. We consider these inconsistencies in the context of partial differential equations (PDEs). We propose an innovative filter based on convex optimization to deal with the inconsistencies observed in polynomial-based simulations. In two or three spatial dimensions, additional complexities are involved in solving the problems related to structure preservation. We present the construction and application of a structure-preserving filter with a focus on multidimensional PDEs. Methods used such as the Barycentric interpolation for polynomial evaluation at arbitrary points in the domain and an optimized root-finder to identify points of interest, improve the filter efficiency, usability, and robustness. Lastly, we present numerical experiments in 2D and 3D using discontinuous Galerkin formulation and demonstrate the filter's efficacy to preserve the desired structure. As a real-world application, implementation of the mathematical biology model involving platelet aggregation and blood coagulation has been reviewed and the issues around FEM implementation of the model are resolved by applying the proposed structure-preserving filter.

Bioinspired backstepping sliding mode control and adaptive sliding innovation filter of quadrotor unmanned aerial vehicles

Quadrotor unmanned aerial vehicles have become the most commonly used flying robots with wide applications in recent years. This paper presents a bioinspired control strategy by integrating the backstepping sliding mode control technique and a bioinspired neural dynamics model. The effects of both disturbances and system and measurement noises on the quadrotor unmanned aerial vehicle control performance have been addressed in this paper. The proposed control strategy is robust against disturbances with guaranteed stability proven by the Lyapunov stability theory. In addition, the proposed control strategy is capable of providing smooth control inputs under noises. Considering the modeling uncertainties, the adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates to improve tracking effectiveness. Finally, the simulation results demonstrate that the proposed control strategy provides satisfactory tracking performance for a quadrotor unmanned vehicle operating under disturbances and noises.

Combined Kalman and sliding innovation filtering: An adaptive estimation strategy

This paper proposes a new adaptive estimation strategy for a nonlinear system with modeling uncertainties. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) are optimal estimators which have been used extensively for state estimation in literature and industry. While the EKF uses a first order Taylor series expansion to approximate nonlinearities, the UKF uses sigma points from the projected probability distribution of states. The sliding innovation filter (SIF) is a suboptimal, yet robust estimation strategy which has recently been proposed. For nonlinear systems, the extended SIF (ESIF) is formulated by using a first order Taylor series expansion like the EKF. This work proposes a novel adaptive estimation strategy which combines and balances the optimality of the EKF and UKF with the robustness of the ESIF. These new methods are referred to as the EKF-ESIF and UKF-ESIF, respectively. A time-varying sliding boundary layer is used as a means of detecting the presence of faults or uncertainties and as a criterion for switching between the EKF or UKF and the ESIF. In normal operating conditions the algorithm computes estimates using an optimal KF-based gain, and an SIF-based gain when a fault is detected. The system examined in this study consists of a magnetorheological (MR) damper with a constant current. Faults or uncertainties are introduced as unwanted behavior in the power supply in the form of undercurrent and overcurrent. The robustness of the EKF-ESIF and UKF-ESIF was validated for force estimation exerted by the MR damper and the results were compared with the standard EKF and UKF.

Gas Transport Mechanisms through Molecular Thin Carbon Nanomembranes.

Molecular thin carbon nanomembranes (CNMs) synthesized by electron irradiation induced cross-linking of aromatic self-assembled monolayers (SAMs) are promising 2D materials for the next generation of filtration technologies. Their unique properties including ultimately low thickness of ≈1nm, sub-nanometer porosity, mechanical and chemical stability are attractive for the development of innovative filters with low energy consumption, improved selectivity, and robustness. However, the permeation mechanisms through CNMs resulting in, e.g., an ≈1000 times higher fluxes of water in comparison to helium have not been yet understood. Here, a study of the permeation of He, Ne, D2 , CO2 , Ar, O2 and D2 O using mass spectrometry in the temperature range from room temperature to ≈120°C is studied. As a model system, CNMs made from [1″,4',1',1]-terphenyl-4-thiol SAMs are investigated. It is found out that all studied gases experience an activation energy barrier upon the permeation which scales with their kinetic diameters. Moreover, their permeation rates are dependent on the adsorption on the nanomembrane surface. These findings enable to rationalize the permeation mechanisms and establish a model, which paves the way toward the rational design not only of CNMs but also of other organic and inorganic 2D materials for energy-efficient and highly selective filtration applications.

Characteristic frequency detection of steady-state visual evoked potentials based on filter bank second-order underdamped tristable stochastic resonance

Brain computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) has great potential for communication and control applications. The actual intention of the user is determined by detecting the characteristic frequency of the SSVEP signals. However, the artifacts and spontaneous electroencephalogram (EEG) signals in SSVEP signals will degrade the performance of characteristic frequency detection. How to improve detection performance is a hot research topic. In this study, an innovative filter bank second-order underdamped tristable stochastic resonance (FBSUTSR) method is proposed and evaluated to enhance the characteristic frequency detection of SSVEP signals. FBSUTSR method uses noise to enhance signal detection and effectively utilizes the harmonic components contained in the signal. At the same time, the second-order underdamping characteristic makes the system form a unique filtering effect. In this study, the performance of SSVEP signals characteristic frequency detection with FBSUTSR, second-order underdamped tristable stochastic resonance (SUTSR), underdamped second-order stochastic resonance (USSR) and canonical coefficient analysis (CCA) is compared and analyzed using the public dataset of 40 types of stimulation targets of 35 subjects. When the stimulus data length is 5 s, FBSUTSR method achieves excellent performance with an average accuracy of 97.35±3.97% and an average information transmission rate (ITR) of 60.58±4.76bit·min-1, which is significantly higher than the performance of other three methods. This study verifies that the proposed FBSUTSR method has prominent performance advantages in implementing SSVEP signals characteristic frequency detection. The BCI system based on SSVEP using FBSUTSR method has great development potential in communication and control.

Lithium-Ion Battery Health Estimation Using an Adaptive Dual Interacting Model Algorithm for Electric Vehicles

To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS’s main task is to accurately estimate a battery’s available power, referred to as the state of charge (SOC). Unfortunately, the SOC cannot be measured directly due to its structure, and so must be estimated using indirect measurements. In addition, the methods used to estimate SOC are highly dependent on the battery’s available capacity, known as the state of health (SOH), which degrades as the battery is used, resulting in a complex problem. In this paper, a novel adaptive battery health estimation method is proposed. The proposed method uses a dual-filter architecture in conjunction with the interacting multiple model (IMM) algorithm. The dual filter strategy allows for the model’s parameters to be updated while the IMM allows access to different degradation rates. The well-known Kalman filter (KF) and relatively new sliding innovation filter (SIF) are implemented to estimate the battery’s SOC. The resulting methods are referred to as the dual-KF-IMM and dual-SIF-IMM, respectively. As demonstrated in this paper, both algorithms show accurate estimation of the SOC and SOH of a lithium-ion battery under different cycling conditions. The results of the proposed strategies will be of interest for the safe and reliable operation of electrical systems, with particular focus on electric vehicles.

COMPARISON BETWEEN CENTRIFUGATION, COARSE FILTRATION AND MICROFILTRATION TECHNIQUES TO PREPARE AUTOLOGOUS BLOOD TRANSFUSION

In case of a bleeding patient, centrifugal cell salvage washing technique for autologous blood preparation is a preferred medical treatment in order to retain lost RBCs without contaminants. Although highly efficient in collection and washing shed blood, centrifugal cell salvage technology is costly and often unpractical or unavailable, especially in middle or low income countries, creating a need for more cost-effective and practical technologies. This study assessed two innovative filter devices as an alternative for the centrifugal cell salvage technology; a coarse collection filter device (Hemafuse) and a microfiltration device (HemoClear). Both devices were recently introduced in the Kenyan and European markets, respectively. Their accessible design could make these devices valuable additions to the current cell salvage technologies. We compared the effectiveness of these filtration technologies to remove plasma constituents and recover and concentrate the cellular components with a centrifugal device (autoLog®). Diluted whole blood was processed with each technology according to their manufacturer's instructions. The centrifugal technology confirmed its efficacy to remove potentially harmful solutes and capture red blood cells. The microfiltration technology (HemoClear) reached comparable levels of removal of solutes as the centrifugal technology, with a potential advantage over centrifugal technology in the ability to also recover platelets. The coarse filtration technology (Hemafuse) had no washing capacity but like the microfiltration technology, has the advantage of recovering platelets.