Filtering Method Research Articles

A Comparative Study of Mean Square Error, Dimensions, Signal to Noise Ratio of Colored and Non-Colored Clustered Original Images Along with Compressed Version After the Image Segmentation and Filtering Method

Primarily author has already done one fundamental paper work on image clustering and segmentation but here in this paper author has continued that same type of work on clustered and segmented images as a mode of comparative study for author has chosen three different parameters like mean square error, peak SNR and dimensions of images (length, width, height). The author has all three parametric methods on one particular to justify the comparison. So this paper is a cumulative case of a comparative study for which author has chosen the above mentioned parameters to justify the best results of the clustered and segmented images.

Design of a high-power harmonic waveguide filter for radar transmitter

In this article, we present the research results on the designing and manufacturing method of a high-power harmonic filter applied in the X-band transmitter system. The harmonic filter utilizes Waffle-iron filters and quarter-wavelength converters on the waveguide. As a result, the loss and ripple within the passband are improved, while meeting the requirements of cutting off high-order harmonics. Based on the application of microwave theory and simulation software tools, the research team have calculated, designed and manufactured a harmonic filter that meets all the requested specifications for application in X-band transmitter systems: Loss ≤ 0,12dB@8,9-9,6GHz; Reflection coefficient ≤ -20dB@8,9-9,6GHz; Frequency selectivity ≤ -40dB@2nd harmonic, 3rd harmonic, 4th harmonic.

LLpowershap: logistic loss-based automated Shapley values feature selection method

BackgroundShapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap.MethodsWe present a novel feature selection method, LLpowershap, that takes forward these recent advances by employing loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. We also enhance the calculation of p-values and power to identify informative features and to estimate number of iterations of model development and testing.ResultsOur simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or comparable predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods. LLpowershap is also ranked the best in mean ranking among the seven feature selection methods tested on the benchmark datasets.ConclusionOur results demonstrate that LLpowershap is a viable wrapper feature selection method that can be used for feature selection in large biomedical datasets and other settings.

Multi-innovation adaptive Kalman filter algorithm for estimating the SOC of lithium-ion batteries based on singular value decomposition and Schmidt orthogonal transformation

The state of charge (SOC) of lithium battery is a key parameter for effective management of battery management systems (BMS). To address the problems of low precision, complex computation and poor robustness of traditional charging state estimation methods, an enhanced algorithm based on Unscented Kalman filter (UKF) is proposed. The singular value decomposition (SVD) method is adopted to ensure the normal operation of the Unscented Kalman Filter (UKF) algorithm even when the matrix P lacks positive semi-definiteness from a mathematical perspective. This enhancement significantly improves the theoretical robustness of UKF. Schmidt orthogonal transformation is concurrently used to reduce the computational complexity in the sampling point selection process, and the multi-innovation theory is combined with adaptive noise control to further improve the accuracy of SOC estimation. The algorithm is validated using the Urban Dynamometer Driving Schedule (UDDS) condition. The simulation results are excited using Singular value decomposition-multi-innovation adaptive Schmidt orthogonal unscented Kalman filter method (SVD-MIASOUKF). 0.95 % and 1.29 % of maximum errors are obtained at 25 °C and −10 °C, while the maximum errors are 2.46 %–2.99 % using SVD-UKF and UKF. The proposed approach shows faster convergence speed and higher estimation accuracy in comparison to traditional algorithms.

Using an ensemble Kalman filter method for a soil nitrogen transport model in the real rice field

The overuse of nitrogen fertilizer in rice field of China leads to nitrogen loss and serious water pollution, so it is vital to accurately predict soil nitrogen transport in rice field. But the prediction errors of soil nitrogen transport are great due to complex chemical and reactive conditions and uncertain parameters in real rice fields. In this study, a prediction model of soil nitrogen transport in a rice field was established via modifying the HYDRUS-1D source code, and a data assimilation method called the ensemble Kalman filtering (EnKF) was coupled, based on the observed NH4+-N and NO3–-N concentrations at different depths in a real rice field. Study results for two different protocols of assimilating observed NH4+-N and NO3–-N concentrations simultaneously and separately were compared. It indicated the predictions accuracy of NH4+-N and NO3–-N concentrations was improved significantly via the EnKF method, and the former protocol is better than the latter. Moreover, for the latter protocol, observations of NO3–-N concentrations were more efficient than NH4+-N to improve the predictions accuracy of NH4+-N and NO3–-N concentrations at different depths. Inversed parameters of urea hydrolysis, NH4+-N volatilization, soil adsorption of NH4+-N, nitrification and denitrification increased over time. On the whole, the inversed model parameters were more stable at deep soil than shallow soil, which were different at different depths. With soil depths increase, parameters of the NH4+-N adsorption and NO3–-N denitrification increased, while parameters of urea hydrolysis, NH4+-N volatilization and nitrification decreased. This study improved the model predictions accuracy and inversed the model parameters, revealing the mechanism of nitrogen loss in real rice fields, which can provide scientific basis to reduce serious environmental problems caused by the overuse of nitrogen fertilizer.

A Novel Demodulator Design for Signal Processing of Inductive Oil Debris Sensors

Abstract Metal debris from bearing wear occurred in the lubrication system of aircraft engines represents valuable information, which can be utilized to assess the current engine condition and further enable the life-cycle prediction. The demodulation of debris signals consists of two parts: mixing and filtering. In traditional methods, the mixing process is complex to adjust and prone to failure, while the filtering employs the fixed low-pass filtering, which significantly affects the demodulation performance when the debris flow rate changes. To address these issues, this paper proposes a novel demodulation model to enhance the demodulation performance and increase the signal-to-noise ratio of chip signals. Firstly, the circuit innovatively adopts a self-frequency mixing method to avoid the complex phase adjustment required by traditional reference-frequency mixing methods for the improved stability of the circuit. Following that, a digital potentiometer is employed to implement the flow rate filtering, enabling precise control over the filter type and range, thus mitigating signal distortion caused by changes in flow rate and increasing the signal-to-noise ratio. Throughout extensive experiments and data analysis, the self-frequency mixing method improves the signal-to-noise ratio by 0.72 dB and reduces the signal standard deviation by 59.12% as compared to the reference-frequency mixing method. Considering the debris flow rate in the range of 0.2 to 0.5 m/s, the flow rate filtering method improves the signal-to-noise ratio by 19.71 dB and reduces the amplitude standard deviation by 66.18%, as compared to results by the traditional fixed low-pass filtering method. Results demonstrate that the aforementioned circuit design effectively enhances the signal amplitude and stability of inductive oil debris sensors in the wear assessment of mechanical systems and provides useful insights into the development of advanced sensors used in the field of structural health monitoring.

DroneSilient (drone + resilient): an anti-drone system

AbstractIt is imperative to take a holistic strategy to thwarting drone threats, including the identification of drones and drone-like gadgets like ornithopters that visually imitate birds. In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. A modified bloom filter method is used to further identify the recognized object after a drone-like object has been found, allowing for the differentiation between regular drones, ornithopters, and genuine birds. The CNN (Convolutional Neural Network) method, created using the Google Cloud Platform and AutoML widget, is used in our model for object identification and categorization. DroneSilient has an RF sensor that can identify and imitate the threat presented by recognized drones. Convolutional Network, Modified Blooms Algorithm, RFID, and RF Sensor systems are all integrated into the DroneSilient system as part of this methodology combination, which provides a thorough method for identifying and eliminating drone threats. The bloom filter proposed takes 27.6 to 12.4 microseconds. Overall time for handling the unauthorized drone will be less than 180 s. By tackling every facet of the problem, our strategy outperforms many current anti-drone solutions in terms of functionality.

Bivariate Correlation-Based Attack: A Challenge for Privacy Preservation of Location Sequence Data

Abstract. Data correlation is a critical issue for location sequence data privacy protection. Series-indistinguishability provides a theoretical basis for the differential privacy protection of temporally correlated location data and is implemented by the correlated Laplace mechanism (CLM), which has become a novel privacy protection method. However, location sequence data is essentially a multivariate time series with data correlation not only within each dimension but also between different dimensions. The CLM-based location privacy scheme only adapts to the auto-correlation in each dimension and ignores cross-correlation between different dimensions, which compromises its privacy performance. To evaluate its actual privacy protection performance, we propose a bivariate correlation-based attack (BCA) utilizing a filtering method and theoretically derive the optimal filter parameters. Based on simulations and real data experiments, the results show that the privacy performance of the CLM-based privacy scheme is significantly reduced under BCA, confirming that this scheme cannot achieve complete series-indistinguishability for bivariate sequences. Furthermore, the results suggest that BCA is an effective tool for privacy performance evaluation.

Bearing fault signal detection and prediction based on an improved particle filter method

Awareness and predicting bearing health are challenging because of uncertainties in the operating environment and noise in fault-signal measurements. Considering the sudden occurrence of bearing faults and their relatively brief duration over the bearing lifespan, this paper presents a fusion framework that combines an eighth-order polynomial model with a cuckoo search thought particle filter (CST-PF) method to estimate the failures occurrences. The fault characteristic signals are extracted from the amplitude spectrum within a specific frequency range and modeled by an eighth-order polynomial model. Cuckoo search (CS) is introduced to address the particle dilution in particle filter (PF). Furthermore, the proposed CST-PF refines the step size update equations and discovery probability for balancing the search speed and accuracy, thus enhancing the adaptability to bearing fault diagnosis and prediction. It ensures the parameter convergences in the prediction model by learning the fault characteristic signal during regular bearing operations, facilitating accurate predictions of subsequent failures. Validation results show that the eighth-order model accurately captures changes in fault characteristic signals throughout the bearing lifespan, with MAE and RMSE metrics surpassing those of traditional models. The CST-PF method demonstrates superior predictive ability compared to the Cuckoo search particle filter (CS-PF), traditional PF, and other PF variants.

A Kalman Filtering Method on Time–Frequency Discrimination Analysis

A Kalman Filtering Method on Time–Frequency Discrimination Analysis

Target detection and obstacle avoidance for UAV based on dynamic vision sensor

Aiming at the problem of UAV sensing dynamic targets and avoiding high-speed dynamic obstacles in dynamic environments, a target detection and obstacle avoidance algorithm based on dynamic vision sensor is designed. A filtering method and motion compensation algorithm are designed to filter out background noise and hot spot noise in the event stream as well as redundant events caused by the camera's own motion. A dynamic target fusion detection algorithm that fuses event images and RGB images is designed to ensure the reliability of detection. The target motion trajectory is estimated based on the detection results, and the speed obstacle method is improved to avoid dynamic obstacles by combining the obstacle motion characteristics and UAV dynamic constraints. A large number of simulation tests, handheld tests and flight tests verify the feasibility of the proposed algorithm.

An Optimal SVD Filtering Method for Measurement Accuracy Improvement against Harmonic Disturbance in Grid-Connected Inverters

An Optimal SVD Filtering Method for Measurement Accuracy Improvement against Harmonic Disturbance in Grid-Connected Inverters

Omics exploration of Tetraselmis chuii adaptations to diverse light regimes.

Microalgae are significantly influenced by light quality and quantity, whether in their natural habitats or under laboratory and industrial culture conditions. The present study examines the adaptive responses of the marine microalga Tetraselmis chuii to different light regimes, using a cost-effective filtering method and a multi-omics approach. Microalgal growth rates were negatively affected by all filtered light regimes. After six days of cultivation, growth rate for cultures exposed to blue and green filtered light was 67%, while for red filter was 83%, compared to control cultures. Transcriptomic analysis revealed that the usage of green filters resulted in upregulation of transcripts involved in ribosome biogenesis or coding for elongation factors, exemplified by a 2.3-fold increase of TEF3. On the other hand, a 2.7-fold downregulation was observed in photosynthesis-related petJ. Exposure to blue filtered light led to the upregulation of transcripts associated with pyruvate metabolism, while photosynthesis was negatively impacted. In contrast, application of red filter induced minor transcriptomic alterations. Regarding metabolomic analysis, sugars, amino acids, and organic acids exhibited significant changes under different light regimes. For instance, under blue filtered light sucrose accumulated over 6-fold, while aspartic acid content decreased by 4.3-fold. Lipidomics analysis showed significant accumulation of heptadecanoic and linoleic acids under green and red light filters. Together, our findings indicate that filter light can be used for targeted metabolic manipulation.

Improving reverse time migration of ground-penetrating radar under zero-time imaging conditions using cross-correlation superposition projection

ABSTRACT Aiming to address the issue of significant artefact interference in conventional reverse time migration (RTM) imaging under zero-time imaging conditions of ground-penetrating radar (GPR), this study proposes an improvement of RTM using cross-correlation superposition projection (CSP) to enhance imaging quality. The method is based on the original RTM imaging framework, where all single-trace signals are individually processed using RTM. The individual imaging results are then multiplied pairwise and added together to create a CSP. After normalizing the CSP and properly setting the adaptive threshold, the CSP can focus RTM imaging on the GPR signal reflection interface area, effectively removing artefact interference. Synthetic data test confirms that the CSP-improved RTM, when compared to conventional RTM and Laplace filtering methods, not only preserves all effective information in imaging but also eliminates artefact interference. This improvement significantly enhances the quality and resolution of the imaging results. Furthermore, the practicality and effectiveness of the CSP-improved RTM method in engineering applications have been validated using a set of measured GPR data.

An Enhanced Particle Filtering Method Leveraging Particle Swarm Optimization for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments

An Enhanced Particle Filtering Method Leveraging Particle Swarm Optimization for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments

Understanding Heat Dissipation Factors for Fixed‐Tilt and Single‐Axis Tracked Open‐Rack Photovoltaic Modules: Experimental Insights

ABSTRACTThis paper presents the results of long‐term experiments conducted on fixed‐tilt (FT) and single‐axis tracked (SAT) open‐rack photovoltaic (PV) modules in South Africa. Utilising Faiman's heat dissipation model and data filtering method, the study demonstrates favourable comparisons of FT experimental results with literature while yielding novel heat dissipation factors for SAT modules. Enhanced heat dissipation is observed in no/low wind conditions for SAT modules compared to FT modules. Analyses reveal the influence of plane‐of‐array (POA) irradiance, wind speed and direction on module temperature, with SAT modules exhibiting greater heat dissipation stability. An investigation into data filtering methods suggests minor sensitivity for both configurations, with a slightly more pronounced impact on SAT modules. Assessments comparing module temperature predictions using diverse heat dissipation factors for FT modules reveal negligible sensitivity. This suggests that exact heat dissipation factor values may not be crucial for accurate predictions of module temperature in FT open‐rack systems. Annual power output simulations using PVsyst software demonstrate a 2.9% and 3.3% enhancement for FT and SAT configurations, respectively, when employing experimentally determined heat dissipation factors. These findings highlight the importance of realistic, configuration‐specific heat dissipation factors in optimising PV system performance, particularly in the competitive context of modern PV power plant construction and techno‐economic calculations.

Study on the Robust Filter Method of SINS/DVL Integrated Navigation Systems in a Complex Underwater Environment

This paper proposes an improved adaptive filtering algorithm based on the Sage–Husa adaptive Kalman filtering algorithm to address the issue of measurement noise characteristics impacting the navigation accuracy in strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integrated navigation systems. Addressing the non-positive definite matrix problem prevalent in traditional adaptive filtering algorithms and aiming to enhance measurement noise estimation accuracy, this method incorporates upper and lower thresholds determined by a discrimination factor. In the presence of abnormal measurement data, these thresholds are utilized to adjust the covariance of the innovation, subsequently re-estimating the system’s measurement noise through a decision factor based on the innovation. Simulation and experiment results demonstrate that the proposed improved adaptive filtering algorithm outperforms the classical Kalman filter (KF) in terms of navigation accuracy and stability. Furthermore, the filtering performance surpasses that of the Sage–Husa algorithm. The simulation results in this paper show that the relative position positioning error of the improved method is reduced by 49.44% compared with the Sage–Husa filtering method.

Adaptive interacting multiple model particle filter based on a dual-pattern bat algorithm for maneuvering target tracking

The interacting multiple model particle filter (IMM-PF) is a filtering method commonly used for nonlinear non-Gaussian radar systems. Its transfer probabilities are usually fixed and dependent on prior information. When tracking maneuvering targets, the IMM-PF may cause excessive tracking errors due to the model switching lag. In addition, the particle impoverishment caused by the resampling of the particle filter seriously affects the filtering accuracy. Therefore, the IMM-PF has difficulty meeting the accuracy and speed requirements of modern high-performance radar target tracking systems. To address these problems, an adaptive interacting multiple model particle filter based on the dual-pattern bat algorithm (ADIMM-DPBA-PF) is proposed for tracking maneuvering targets under nonlinear and non-Gaussian conditions. First, the adaptive model switching mechanism is established to adjust the transition probabilities using the model probability posterior information at consecutive times, improving the model switching efficiency. Second, a particle filter based on the dual-pattern bat algorithm (DPBA-PF) is proposed. The filter exploits global search and local search strategies to optimize the particles and intelligently move the particles to the high likelihood region. Finally, a particle filter based on the dual-pattern bat algorithm is used to form the adaptive interacting multiple model method. The experimental results show that the proposed algorithm has better comprehensive performance than the IMM-PF.

Investigation on the Seismic Damage and Identification of Lateral Stiffness for Ancient Timber Structures

ABSTRACTThis paper investigates the seismic damage and identification of lateral stiffness for ancient timber structures. Based on the shaking table test of a palace‐style timber structure, the lateral displacement responses, interstory equivalent lateral stiffness (IELS), and sensitivity of lateral displacement to degradation of IELS were analyzed to investigate the degradation and identification of IELS. The state and observation equations of a simplified mechanical model of the timber frame considering the friction slipping of the column base were predicted. Considering the noise disturbance in the test, the IELS of the model was identified by the partial least squares–singular value decomposition (PLS‐SVD) and extended Kalman filter (EKF) method. Results shown that the ratio of the IELS of the column frame layer to Ru‐Fu layer decreased as the seismic damage increased, and the peak lateral displacement was more sensitive to the damage of the column frame layer than the Ru‐Fu layer. Under nondamage conditions, the identification error of the IELS was about 10%, while the error ranged from 15% to 20% under damage conditions. Through the identification of the equivalent lateral stiffness of the Xi'an bell tower, it was validated that the hybrid method is effective in monitoring the IELS of ancient timber structures.

Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments

Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.