Hybrid Filter Research Articles

Removal of carbendazim by sorption onto a continuous fixed bed hybrid filter of calcined clay and waste derived biochar

This study focused on enhanced Carbendazim (CBZ) removal by using dual filter media of calcined clay (CC) and biochar (WB-BC) in a continuous fixed-bed column. The experiments were carried out using synthetic and real water. In the continuous system with an initial CBZ concentration of 0.2 mmol L−1, a flow rate of 4 mL min−1, a bed depth of biochar 1 cm and a pH of 6.8, the optimal values for the mass transfer zone (0.64 cm) and adsorption capacity (0.38mmolg−1) were observed. Furthermore it was observed that the dual filter media of WB-BC and CC effectively adsorbs CBZ from real water matrix, with an average adsorption capacity of 0.33mmolg−1. Significant reduction in the concentrations of chlorides, sulfates, nitrates, Ca2+, and Mg2+ were also observed in real water samples along with CBZ removal. Analysis using mass-transfer models showed that BDST model effectively described the breakthrough curve data. Density Functional Theory (DFT) calculations revealed that WB-BC exhibits a stronger interaction and higher adsorption capacity for CBZ compared to CC as evidenced by a shorter vertical distance (2.711 Å) and higher adsorption energy value (−42.95kJmol−1) in case of WB-BC-CBZ complex. Additionally, the larger band gap (ΔE) value (2.881 eV) for WB-BC-CBZ complex indicates superior structural stability and chemical hardness.

Event-Triggered Transmission of Sensor Measurements Using Twin Hybrid Filters for Renewable Energy Resource Management Systems

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms.

A Method for Removing Outlier Noise from ArrayInSAR Point Clouds based on Hybrid Filtering

A Method for Removing Outlier Noise from ArrayInSAR Point Clouds based on Hybrid Filtering

Enhancing aggregate reduction using anion exchange hybrid filter in an immunocytokine diabody fusion protein purification process

Enhancing aggregate reduction using anion exchange hybrid filter in an immunocytokine diabody fusion protein purification process

A Tourist Destination Recommender System for Nepali Tourism Industry

Tourism is one of the major sources of revenue in developing countries with natural beauty like Nepal. Understanding tourist preferences is essential to optimize this industry. Many places suffer obscurity due to not being recommended to the tourists who would’ve liked the destination. Our goal is to analyse what every tourist likes and recommend them accordingly. In case of Nepal, since there are a lot of under-explored potential tourist destinations and not many data available for the reviews and ratings, the data becomes sparse and limited. To tackle the challenge, our system utilizes a blend of content-based and collaborative filtering, which is known as hybrid filtering. The system leverages the strengths of Cosine Similarity, k-Nearest Neighbors, and Matrix Factorization to personalize recommendations based on user preferences and available destination information even in a sparse as well as limited dataset. This approach not only tackles the under-exploration of hidden gems but also presents a scalable framework applicable to any domain with limited data, potentially impacting personalized recommendations across various industries.

Maximum Bandwidth Analysis With a Universal AWLR‐Based Filter Structure

ABSTRACTThe bandwidth enhancement of the acoustic‐wave‐lumped‐element resonator (AWLR)‐based filter has been investigated for a long time. However, few researches are focused on the maximum bandwidth analysis. Based on a universal circuit structure of the AWLR‐based filter, we proposed the maximum bandwidth analysis of the hybrid filter. In the analysis process, the universal filter structure can be transformed into a novel AWLR‐based filter structure by incidentally excluding most of the other structures (including the reported hybrid filter structures) under the defined electrical characteristics. The novel hybrid filter obtains the maximum fractional bandwidth (FBW). Moreover, the proposed calculation results are better than the simulation results without artificial intervention. They are 2.12kt2 and 1.63kt2, respectively. kt2 is the electromechanical coupling coefficient of the acoustic wave resonator (AWR). Third‐order AWLR‐based bandpass filters with five‐type circuit structures have been manufactured and measured. They include the proposed novel AWLR‐based filter and the hybrid filter with the reported structure based on the universal AWLR‐based filter structure. The experimental results indicate that the proposed novel AWLR‐based filter has the maximum FBW.

Analysis on recommendation systems based on ML and DL approaches

The explosive growth of the Internet has led to an unprecedented increase in available information, creating a major challenge in identifying content that aligns with user interests. Recommendation systems (RS) address this challenge by providing personalized content recommendations based on user behavior and preferences. This article systematically reviews the evolution of recommendation systems with a focus on the methodologies of machine learning (ML) and deep learning (DL). It highlights core technologies in ML-based RS, such as content-based recommendation, collaborative filtering (CF), and hybrid filtering (HF). The article evaluates the effectiveness of various DL models, including autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN). The review also addresses key challenges faced by RS, such as cold start issues and the need for improved model transparency and interpretability. DL methods are shown to significantly enhance recommendation accuracy by leveraging complex patterns in large-scale data, to improve the user experience. Future research directions include refining data preprocessing, enhancing feature engineering, compressing and accelerating DL models, and improving interpretability through advanced mechanisms. This study provides valuable insights and references for researchers aiming to advance recommendation system design and performance optimization.

Minimum-energy switching geometric filter on lie groups for differential-drive wheeled mobile robots

Accurate state estimation plays a critical role in various applications, such as tracking, regulation, and fault detection in robotic and mechanical systems. Typically, the Kalman–Bucy filter is used as a linear state observer for this purpose. However, real-world robots often exhibit complex behavior, characterized by a combination of dynamics, making it essential to employ hybrid filters. In this context, the Switching Kalman filter stands out as a well-established solution. In this article we aim to generalize the Brownian-Markov Stochastic Model, a hybrid dynamic model for differential-drive wheeled mobile robots, to the case of a mobile robot whose center of mass is not aligned to the wheels axle middle point, and to design a geometric hybrid state estimator by exploiting the Lie groups theory. The Brownian-Markov Stochastic Model features two modes: “grip” and “slip”. These modes correspond to ideal grip and lateral slippage, with transitions governed by a state-dependent Markov chain. To validate the proposed switching filter, we conduct MATLAB® simulations of the robot’s motion in a scenario prone to lateral grip loss, comparing the state estimates produced by the switching geometric filter with those obtained using the switching Kalman filter.

Pilot scale application of a hybrid process based on ozone and BAF process: Performance evaluation for livestock wastewater treatment in a real environment

Environmental pollution was generated continuously because of the slack standard of effluent from livestock wastewater treatment even though the specific treatment process was applied. Despite the continuous ecological pollution caused by livestock wastewater effluent, a comprehensive treatment process for livestock wastewater effluent has not been investigated. This study utilized an ozone-biological aerated filter (BAF) hybrid process at a pilot scale (1st ozone–BAF–2nd ozone), with a 1 m3/day capacity for real-livestock wastewater treatment. Compared with each process configuration, the single-ozone process showed effective performance in removing chromaticity, but the removal ratio of suspended solids, organic matter (OM), and trace-organic compounds (TrOCs) was insufficient, even at high ozone concentrations. The ozone–BAF hybrid process improved the removal ratios of suspended solids and biochemical oxygen demand (BOD) by 61.61 and 17.70 %, respectively, due to aeration and filtration by the BAF; however, TrOC removal ratios remained low, at <75 %. The removal ratios of TrOCs of the 1st ozone–2nd ozone hybrid process were better than those of the ozone–BAF hybrid process (by 28.2–77.9 %) owing to enhanced oxidation from increased ozone exposure to livestock wastewater. Nevertheless, the removal ratios of suspended solids and BOD were below 40 %. Owing to the complementarity between the BAF and 2nd ozone process, the removal ratios of chromaticity, suspended solids, and TrOCs reached 98, 95, and 92 %, respectively, by the optimized 1st ozone–BAF–2nd ozone hybrid process. Our results highlight the necessity of developing a new hybrid process based on ozone and BAF for treating livestock wastewater, and provide guidelines for livestock wastewater treatment.

A methodology for enhancing bandwidth with keeping steepness of the ladder filters exploiting acoustic‐wave‐lumped‐element resonators

AbstractA methodology to acquire a wide bandwidth and steep transition of a hybrid ladder filter with the acoustic‐wave‐lumped‐element‐resonators (AWLRs) structure is proposed. Different from previous works, a unit of the new AWLR structure with two lumped elements respectively in series and shunt is proposed. It can change both the series‐resonant frequency and the parallel‐resonant frequency of the acoustic wave (AW) resonator to enhance the bandwidth of the hybrid ladder filter. For the ladder structure, all the AW resonators possess the same resonant frequency and impedance. This ladder structure is also used to keep the steep transition of the hybrid filter. For the validations, two‐stage and four‐stage AWLRs‐based hybrid filters are designed, manufactured, and tested. They include (1) a two‐stage filter with an insertion loss (IL) of 0.9 dB, a fractional bandwidth (FBW) of 1.90kt2 (kt2 is the electromechanical coupling coefficient), a center frequency of 1249.3 MHz, and (2) a four‐stage filter with the IL of 1.3 dB, the FBW of 1.86kt2, the SF30 dB/3 dB (shape factor defined as 30‐dB bandwidth divided by 3‐dB bandwidth) of 2.52, the figure of merit (defined as FBW divided by SF30 dB/3 dB) is 0.74kt2 which is the best result, a center frequency of 1249.4 MHz.

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.

Surrogate-Assisted and Filter-Based Multiobjective Evolutionary Feature Selection for Deep Learning.

Feature selection (FS) for deep learning prediction models is a difficult topic for researchers to tackle. Most of the approaches proposed in the literature consist of embedded methods through the use of hidden layers added to the neural network architecture that modify the weights of the units associated with each input attribute so that the worst attributes have less weight in the learning process. Other approaches used for deep learning are filter methods, which are independent of the learning algorithm, which can limit the precision of the prediction model. Wrapper methods are impractical with deep learning due to their high computational cost. In this article, we propose new attribute subset evaluation FS methods for deep learning of the wrapper, filter and wrapper-filter hybrid types, where multiobjective and many-objective evolutionary algorithms are used as search strategies. A novel surrogate-assisted approach is used to reduce the high computational cost of the wrapper-type objective function, while the filter-type objective functions are based on correlation and an adaptation of the reliefF algorithm. The proposed techniques have been applied in a time series forecasting problem of air quality in the Spanish south-east and an indoor temperature forecasting problem in a domotic house, with promising results compared to other FS techniques used in the literature.

КОНЦЕПЦІЯ ПОБУДОВИ ГІБРИДНОЇ СИСТЕМИ КЕРУВАННЯ МІКРОГІДРОЕЛЕКТРОСТАНЦІЄЮ

Methods of increasing the energy efficiency of micro-hydroelectric power plants are considered in the work. A new hy-brid concept of building a control system is proposed, which combines classical control laws and fuzzy logic. This makes it possible to improve the quality of the regulation of the parameters of the generated electricity regardless of changes in network operation modes and external conditions. It is shown that the use of hybrid filters will allow to in-crease the electromagnetic compatibility and, as a result, the energy efficiency of the system as a whole. References 12, figures 3.

Advanced Lung Disease Detection and Classification Using Ge-U-Net-ODLwith Gabor Filters and Entropy-Based Feature Selection

Lung X-ray images play a crucial role in the detection and diagnosis of various lung diseases,including pneumonia, tuberculosis, and lung cancer. These images provide a non-invasive method for visualizing lung structures, allowing radiologists and machine learning models to identify abnormalities such as nodules, masses, or fluid accumulation. With the advancement of deep learning techniques, lung X-ray images are now used in automated systems that can detect and classify diseases with high accuracy. By applying sophisticated algorithms like GE-U-Net-ODL, Gabor filters, and entropy-based feature extraction, these images are analyzed pixel by pixel to enhance feature representation and improve diagnostic precision. This paper presents a novel approach for lung disease detection and classification using the GE-U-Net-ODL model, which integrates advanced preprocessing techniques and deep learning architectures. The study leverages the NIH Chest X-ray dataset and employs a variety of feature extraction and selection methods, including Gabor filters, entropy-based techniques, and multi-scale inputs. A detailed comparative analysis of different model configurations demonstrates that the GE-U-Net-ODL model with Transfer Learning achieves the highest classification accuracy of 95.0%, alongside superior precision (93.5%), recall (94.0%), and F1-score (93.7%). Other configurations, such as those utilizing data augmentation and hybrid filters, also showed notable performance improvements. The research underscores the model's effectiveness in enhancing diagnostic accuracy for lung diseases while balancing training and inference times.

FPGA implementation and measurement of cusp-flattop hybrid filter shaping method for nuclear instruments

In the field of nuclear technology applications, digital multi-channel pulse amplitude analysis technology plays an indispensable role in information acquisition due to its high precision and sensitivity. However, the challenge of pulse pileup can compromise the accuracy of measurement results in this technology. While conventional filter shaping and pulse pileup processing methods can extract nuclear pulse feature information under mild pileup conditions, severe pileup can notably degrade spectral counting rate and resolution. This paper introduces a designed two-channel pulse pileup rejection method, termed the cusp-flattop hybrid filter forming algorithm, leveraging the unique features of cusp, such as high forming speed and minimal interference from pulse pileup. The two channels simultaneously process the collected signals and utilize their differences for data filtering and rejection. This approach enhances information content, improves data processing quality, and boosts the signal-to-noise ratio. Compared to three methods—three trapezoidal filter shaping algorithms, the triangle trapezoidal pulse shaping algorithm, and the pulse width discrimination algorithm—our proposed method refines the shaping threshold of pulse pileup rejection. It accurately distinguishes and extracts effective pulse amplitudes even under severe pileup conditions, generating energy spectrum results while accounting for counting rate and resolution. Building on this, we construct a digital multichannel pulse amplitude analysis system based on FPGA (Field Programmable Gate Array) and use a national standard alloy sample, YSBS41346, for testing. The system can generate energy spectrum correctly, consistent with the actual sample content. We evaluate the system's performance by comparing the peak counting rate and resolution of the energy spectrum generated by the cusp-flattop hybrid filter forming algorithm and the trapezoidal forming algorithm. A high peak counting rate represents the maximum nuclear pulse data captured by the system, while low resolution indicates more valid data after pileup rejection. Through testing, the peak counting rate reaches 12843cps, with a resolution of 6.68%, marking a significant improvement over the trapezoidal pulse forming algorithm. The system demonstrates satisfactory performance and operational effectiveness, aligning with the design objectives and presenting innovative and practical applications.

Three Phase Two-Arm Hybrid Active Filter Using Two Feedforward Loops with Self Tuning Filter for Elimination of 5th and 7th Harmonic Frequency

A novel topology for three-phase three-wire hybrid power filter with two-arm is proposed, this hybrid filter configured by a classical active filter consisting of two-arm bridge using two power electronic switches for each arm and two DC capacitors located in the DC side of the power converter, this topology uses less number of power electronic switches that allow to reduce the manufacturing cost. The active filter connected in series with a passive filter. The nonlinear load is a diode rectifier feeding a (R, C) parallel load. The proposed method is based on self-tuning filter placed in both feedback and feedforward loops dedicated to eliminate the 5th and 7th harmonics frequency. The feedforward loop consists in two branches, one to remove the 5th harmonic and the other one to remove 7th harmonic frequency. The use of self-tuning filter simplifies the control scheme by reducing the number of extraction filters, used in classical feedback and feedforward loops. More, in this case, no phase locked loop is necessary for the feedforward loop. The effectiveness of this new proposed scheme has been verified by computer simulation.

Implementation of unknown parameter estimation procedure for hybrid and discrete non‐linear systems

AbstractThe application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter.

A Solar PV-Fed Hybrid Active Power Filter Based on I-Luo Converter for Power Quality Enhancement in Distributed Generation Systems

The adoption of Distributed Generation (DG) systems, such as photovoltaic (PV) panels, is increasing due to their environmental advantages and potential to lessen reliance on fossil fuels. However, Renewable Energy Sources (RES) like PV can cause Power Quality (PQ) issues, including voltage and current harmonics, which negatively impact grid stability and efficiency. To mitigate these problems, this study introduces a Hybrid Active Power Filter (HAPF). This filter, powered by a high-gain Interleaved Luo (I-Luo) Converter fed by a PV system, effectively reduces harmonic distortions, addresses reactive power issues, and stabilizes voltage in the distribution network. To further enhance the converter's performance, a Modified Squirrel Search-based PI controller is implemented. Known for its robust and efficient convergence, this optimization algorithm adjusts the PI controller settings to improve dynamic response and ensure precise harmonic compensation. Additionally, a Recurrent Neural Network (RNN)-based harmonic extraction technique is employed to accurately identify and quantify harmonic components in voltage and current waveforms, enabling precise HAPF control. Simulations conducted in MATLAB demonstrate that the integration of RNN-based harmonic extraction, HAPF, and the I-Luo Converter with Modified Squirrel Search optimization significantly enhances Power Quality in DG systems.

Research Paper on Exploring the Landscape of Recommendation Systems: A Comparative Analysis of Techniques and Approaches

The field of recommendation systems has witnessed a profound evolution since its inception with Grundy, the first computer-based librarian, in 1979. From its humble beginnings, recommendation systems have become integral to various facets of daily life, particularly in e-commerce, thanks to breakthroughs like Amazon’s Collaborative Filtering in the late 1990s. This led to widespread adoption across diverse sectors, prompting significant research interest and investment, exemplified by Netflix’s renowned recommendation system contest in 2006. Today, recommendation systems employ various techniques such as Hybrid Filtering, Content-Based Filtering, Demographic Filtering, and Collaborative Filtering catering to personalized information needs across industries like entertainment, education, and healthcare. Moreover, emerging types of recommendation systems, including Knowledge-Based, RiskAware, Social-Networking, and Context-Aware, further broaden their applicability, addressing specific user needs and preferences. Leveraging machine learning and AI algorithms on big data, recommendation systems have become a quintessential application of big data analytics, enhancing user experience and engagement in domains like e-learning, tourism, and news dissemination. However, scaling recommendation systems present challenges due to the exponential growth of input data, necessitating strategies like Dimensionality Reduction and cluster-based methods. Integrating multiple recommendation algorithms enhances system complexity, requiring careful consideration of algorithm selection, performance monitoring, and maintenance. Transparency and explanation mechanisms become crucial in complex systems to foster user trust and understanding. Despite challenges, recommendation systems continue to drive innovation, delivering personalized recommendations and enriching user experiences across various domains.

A novel similarity measure SF-IPF for CBKNN with implicit feedback data

PurposeThe increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.Design/methodology/approachPreferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.FindingsThe proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.Originality/valueThis method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.