Hybrid Filter Research Articles

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

Multi-modal recommender system for predicting project manager performance within a competency-based framework.

The evaluation of performance using competencies within a structured framework holds significant importance across various professional domains, particularly in roles like project manager. Typically, this assessment process, overseen by senior evaluators, involves scoring competencies based on data gathered from interviews, completed forms, and evaluation programs. However, this task is tedious and time-consuming, and requires the expertise of qualified professionals. Moreover, it is compounded by the inconsistent scoring biases introduced by different evaluators. In this paper, we propose a novel approach to automatically predict competency scores, thereby facilitating the assessment of project managers' performance. Initially, we performed data fusion to compile a comprehensive dataset from various sources and modalities, including demographic data, profile-related data, and historical competency assessments. Subsequently, NLP techniques were used to pre-process text data. Finally, recommender systems were explored to predict competency scores. We compared four different recommender system approaches: content-based filtering, demographic filtering, collaborative filtering, and hybrid filtering. Using assessment data collected from 38 project managers, encompassing scores across 67 different competencies, we evaluated the performance of each approach. Notably, the content-based approach yielded promising results, achieving a precision rate of 81.03%. Furthermore, we addressed the challenge of cold-starting, which in our context involves predicting scores for either a new project manager lacking competency data or a newly introduced competency without historical records. Our analysis revealed that demographic filtering achieved an average precision of 54.05% when dealing with new project managers. In contrast, content-based filtering exhibited remarkable performance, achieving a precision of 85.79% in predicting scores for new competencies. These findings underscore the potential of recommender systems in competency assessment, thereby facilitating more effective performance evaluation process.

Power quality improvement of three-phase electrical systems using active-passive hybrid harmonic filter

An overreliance on nonlinear load can result in electric current harmonics, which will ultimately result in a decrease in the quality of the electrical power. If a malfunction arises as a consequence of the internal integration of a power system in any of the internal networks, it will have an adverse effect on the overall operation of the power system. Because of this issue, there is a requirement for harmonic filters, which can minimize the levels of harmonics by referencing the IEEE-519 standards for enhancing power quality. This research offers an active-passive hybrid harmonic power filter to eliminate harmonic pollution in three-phase electrical systems. A MATLAB simulation and laboratory experiment proceeded for 240 Vac with nonlinear loads that represent by uncontrolled rectifier and series RLC load, and selected results were presented to verify the performance of the active-passive harmonic filter component. Upon completion of this research, an analysis of the conditions of a three-phase electrical system will be possible under four distinct circumstances: in the absence of a filter, with an active filter, with a passive filter, and with a hybrid filter. Furthermore, this model performs a comparative analysis of the percentage reduction in THD value achieved by the filter compared to the previous researcher. The result analysis reveals the proposed filter's good performance in eliminating harmonic pollution to 58.63 %, thereby significantly improving power quality. The findings presented in this study contribute to the ongoing efforts to ensure the reliable and efficient operation of modern power systems.

Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models.

Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient's tissue require precise sensor data. However, since the deformation of the patient's body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.

Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction

The term ‘tumor’ describes an atypical development of cells that forms a mass inside an organ; depending on the organ's invasive nature and propensity for metastasis, the growth may be benign or malignant. Improving patient outcomes requires the early detection of malignant tumors. Despite its lower resolution and noise problems, ultrasound, which is frequently used to diagnose uterine tumors, is safer and more economical than magnetic resonance imaging (MRI) scans and biopsies. Ultrasound pictures can be processed using methods including denoising, enhancement, segmentation, and feature extraction to get around these restrictions and boost their quality. Enhanced ultrasound images can reach even higher accuracy, cementing them as plausible alternatives to MRI. A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. Higher resolution and superior quality, strong segmented real-time ultrasound uterus tumour images are produced by using diffusion-based hybrid filters, Super Resolution Convolutional Neural Networks (SRCNN), and U-net segmentation technique. The Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used to extract textural features. With the help of several machine learning approaches, such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest classifiers (RFC), the extracted characteristics are immediately sent to classifiers to classify uterus tumours from ultrasound images between benign and malignant. For the categorization of uterine tumors, the RFC classifier outperformed the other classifiers. The viability of cancer detection using ultrasound pictures is significantly strengthened by the suggested machine learning methodology. Multiple hospitals provided data on ultrasound pictures of uterine tumors, which were used to develop the model and obtain the prediction findings. A radiologist with 17 years of expertise in diagnostic radiology further assessed this dataset. We could produce high-quality ultrasonic real-time images of uterine tumor datasets with the suggested machine learning model at a 97.8 % accuracy rate utilizing RFC.

High throughput clogging free microfluidic particle filter by femtosecond laser micromachining.

In recent decades, driven by the needs of industry and medicine, researchers have been investigating how to remove carefully from the main flow microscopic particles or clusters of them. Among all the approaches proposed, crossflow filtration is one of the most attractive as it provides a non-destructive, label-free and in-flow sorting method. In general, the separation performance shows capture and separation efficiencies ranging from 70% up to 100%. However, the maximum flow rate achievable (µL/min) is still orders of magnitude away from those suitable for clinical or industrial applications mainly due to the low stiffness of the materials typically used. In this work, we propose an innovative hydrodynamic-crossflow hybrid filter geometry, buried in a fused silica substrate by means of the femtosecond laser irradiation followed by chemical etching technique. The material high stiffness combined with the accuracy of our manufacturing technique allows the 3D fabrication of non-deformable channels with higher aspect ratio posts, while keeping the overall device dimensions compact. The filter performance has been validated through experiments with both Newtonian (water-based solution of microbeads) and non-Newtonian fluids (blood), achieving separation efficiencies of up to 94% and large particles recovery rates of 100%, even at very high flow rates (mL/h).

Study of Harmonic Minimization in Pulp and Paper Mill using Hybrid Active Filter -A Case Study

Increased power electronics-based nonlinear loads have harmed distribution system power quality. Variable speed drives, switched-mode power supply, and DC drives impair power quality and affect customers and utilities. Many variable-speed drives in the paper mill cause harmonic distortion, a power quality issue. This study investigates the impact of nonlinear electric drives on supply current distortion in Padmavati Pulp and Paper Mill, Ambarnath, Thane, India. Harmonic analysis is done at PCC, that is, at the secondary side of the transformer, with the help of a power quality analyzer fluke-434. Based on detailed analysis, a suitable harmonic reduction method is identified. passive filters, are the simplest solution for lowering harmonic currents but it has shortcomings like bulk size and cause resonance. In recent years active filter has been applied as an effective solution for harmonic reduction. This study suggests a hybrid filter as an economical solution to take advantage of a passive and active harmonic filter. This work present design and simulation of cascade H- bridge multilevel inverter-based hybrid filter for medium and high-power distribution system. The system for the case study is developed in MATLAB Simulink platform with suitable harmonic mitigation techniques and performance is evaluated in terms of total Harmonic Distortion Index (THDi).

Design of Filtration Approach for Image Quality Improvement in Mango Leaf Disease Detection and Pharmaceutical Treatment

The traditional method of studying and diagnosing diseases in plants relies primarily on human vision, which is ineffective in identifying diseases in the plants. The color of the leaves gets changed and develops spots such as yellow, brown and black patches as a result of the symptoms. Manually observing leaves for detection is employed to identify the disease, which takes more time, is more costly, and is less accurate. As a result, use of image processing techniques may be a better alternative than certain other old traditional approaches for speedy and exact illness identification. The symptoms may be seen on plant components such as the fruit, leaves, stems, or lesions. The target is to appropriately identify and categorize the diseases based on the leaf photographs. The processes involved in the procedure include image pre-processing, segmentation, feature extraction, and identification. Bacterial, viral, fungal, and insect-borne diseases are all considered. Mango leaves include anthracnose, bacterial canker, and black sooty mold. In this article, a hybrid filter was proposed based on image enhancement i.e., denoising, reducing blurriness and edge sharping of the images and then segmentation done by taking leaves of these three diseases and results are saved.

Superhydrophilic and superhydrophobic quartz sand hybrid filters for efficient on-demand oil/water separation

The development of an efficient and sustainable separation method for oily wastewater has become a major challenge for industrial development and environmental protection. In this work, the simple special wettability particle preparation method and the hybrid filters construction strategy was reported for enhanced oil/water separation efficiency. Two special wettability quartz sands mixed in a certain proportion could destroy the balance of dispersed droplets in the oil-water emulsion, induce the coalescence of dispersed droplets, and purify the oil-water emulsion by the tortuous channel in the filter. Based on the coupling effect of “size sieving” and “coalescence separation”, the special wettability particle hybrid filter with a mass ratio of 3:1 had good droplet coalescence ability, and the separation efficiency of various kinds of oil-water emulsions can reached more than 98%. The hybrid filters exhibited robust mechanical and chemical stability, excellent durability, which enable them to be competent for oil/water separation in multitasking and complex environment.

Analysis of youth sports physical health data based on cloud computing and gait awareness

Abstract Sub-health problems are becoming increasingly serious in today’s society, and some organizations are not paying enough attention to adolescent sports health data. For adolescent sports, health needs to be measured regularly and tested constantly so that the intake of diet and medication can be reasonably adjusted according to their biochemical indicators. The Smart Health Life Growth Cloud System can effectively manage residents’ health data digitally and informally, enabling users to manage their health data better and facilitating doctors to keep abreast of users’ health conditions, while also facilitating the government to conduct research and studies on the physical fitness of adolescents in the areas under its jurisdiction. The cloud-based management platform for student physical health management relies on the mobile internet as a practical service platform whose primary role is to provide young people with a convenient sporting life, focusing on practicality, service, and interactivity. We also collect sensor data to detect gait patterns (with or without leg contact) and filter them through an adaptive hybrid filter to differentiate between the two patterns. In turn, the Smart Health Life Growth Cloud system changes the traditional medical model and greatly improves the information and intelligence of the healthcare industry. Using the exercise individual health evaluation model in this article is controlled to be within 20%, thus concluding that the exercise individual health evaluation model proposed in this article can predict the exercise limit of an exercise individual more accurately.

Investigations into Higher-Frequency Hysteresis Current Controller for Supraharmonic Hybrid Active Filters

Hysteresis current controllers (HCCs) are extensively used in low-frequency power inverters and active power filters. Very little is known about their strengths and weaknesses when applied at supraharmonic frequencies. The major concern regarding the use of HCCs is their variable and uncontrollable switching frequencies. This results in difficulties in filter design, high switching losses, and the possibility of resonance conditions with power system elements. In this article, investigations are conducted on the application of HCCs in a hybrid filter for a 6 kHz matrix converter (MC)-coupled supraharmonic load. The effects of the MC-coupled load and VSC switches on the HCC’s switching frequency are analyzed. A novel mathematical model for obtaining a fixed-frequency HCC is presented. The model is verified in open- and closed-loop HCC control configurations for a three-phase hybrid active power filter (HAPF). Fast Fourier transform (FFT) analysis of the supply current after the implementation of the novel HCC-HAPF showed 2.31% of the 50 Hz fundamental. The analysis is verified in a MATLAB/Simulink environment.

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

<p>To address the problem of low localization accuracy in the node localization algorithms of wireless sensor networks (WSN) based on received signal strength indication (RSSI) ranging, a WSN node localization algorithm based on ranging optimization and graph optimization is proposed. In terms of RSSI ranging, the outliers are removed using the Grubbs method, and the data are processed using a moving average smoothing-Gaussian hybrid filter to establish a Bessel function ranging model to reduce the ranging error; in terms of node localization, the signal strength data are employed to construct a distance cost term, a cost function model is built based on these cost terms, and graph optimization is adopted to minimize this function. Then, the node position is estimated to minimize the overall observation error. Simulation results indicate that the proposed algorithm has higher ranging and localization accuracy than existing ranging and localization algorithms, and it can meet the requirements of node localization in large-scale WSN.</p> <p> </p>

Control strategy to improve the power factor with a hybrid filter

Nowadays an electrical system must provide adequate electrical power quality so that it meets the international standards. To do this, this paper develops a control strategy for an active compensator based on a hybrid filter topology. This topology is formed by an active power filter of series connection and a passive filter of parallel connection. The proposed strategy is based on minimizing the rms value of the voltage at the point of common coupling so that the source transfers the power demanded by the load. Thus, the determination of the reference signal is posed as a constrained optimization problem. The strategy was proposed for four-wire systems and it allowed the compensation of current harmonics, reactive power and neutral current. Finally, a simulation platform in Matlab-Simulink was developed. This allowed the verification of the new strategy for different types of loads.

Improved Structure for Three-Phase Four-Wires Hybrid Active Power Filters

This paper presents a novel three-phase four-wire low power rating flexible hybrid active filter with a power factor, harmonic and neutral currents capability for distribution systems. The passive filter is tuned to compensate the two largest harmonics components and the power factor of the load. A selective algorithm is employed in order to enable the active filter to compensate only the desirable harmonic components. Simulation results are presented in order to verify the performance of the proposed hybrid filter.

Simulation and optimization of control parameters in energy hybrid filter

Two energy hybrid filter systems were considered which reduce level of higher mains current harmonics. An advantage of such system is low demanded active filter power because in complements only passive filter operation. The first considered system has inverter switched on in series with shunt passive filter. Through optimization of control parameters it is able to eliminate specific higher mains currents harmonics left by passive filter in a point by point way. The other tested system includes two inverters which are switched on to the system in series and shunt way. Their operation is to reduce higher mains current harmonics induced by higher load currents harmonics in a continuous way. The simulations carried out proved correct operation of the system.

Multifunctional Polymer Nanocomposite Coated Antibacterial Filters for Air-Purification

Air and environmental pollution require products used and produced in industrial areas to have natural ingredients and low energy consumption. It is impossible for standard used filters to clean bacteria in homes, workplaces and areas working with biological creatures. Therefore, the production of multifunctional composite and hybrid filter papers is necessary. In the study, filter papers coated with nanocomposite solutions prepared from green synthesized nanosilver (AgNPs) with plant extracts and Polyvinyl Alcohol (PVA). Scanning Electron Microscope (SEM) analyses showed that silver nanoparticles and PVA homogeneously coat the filter paper fibers, thereby reducing the average pore diameters by only 3% compared to commercial standards of filter paper. Using nanosilver, plant extracts, and PVA, a filter with 36% higher burst pressure resistance was produced. The antibacterial effect is provided to the filters with a high effect of up to 100,000 times, by coating them with nanosilver and plant extracts. This study enabled the production of antibacterial and high mechanical strength filter paper with natural ingredients, low energy and raw material consumption, and low cost. In this way, bacteria and pollutants in the air environment will be cleaned without harming the environment and also human health.

Block-matching and 3D filtering-based denoising of acoustic images obtained through point contact excitation and detection method

A novel experimental method based on the Coulomb coupling technique is used for visualizing the interaction of ultrasonic waves in PZT (Lead Zirconate Titanate - (Pb[ZrxTi1-x]O3 (0 ≤ x ≤ 1)) ceramic materials. The method of excitation and detection through a point contact produces representations of propagating wave vectors in the time domain as a series of images. In the acoustic image, several types of noises from different types of sources corrupt the signal as well as the image. As a result, the resolution and quality of the images are reduced. Block matching and 3D filtering (BM3D) algorithm is implemented for the reduction of noises from ultrasonic signals that are in the form of 2D images. Here, we also implemented a custom-designed hybrid filter and conventional total variation (TV-L1) filter on the noisy images to compare the final denoised outputs from the BM3D filter. Comparison metrics like PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) are used for quantitative analysis of those denoised ultrasonic images with respect to their ground truth or almost noise-free counterparts. Based upon those parameter values, the BM3D filter demonstrated the best results for the denoising of the acoustic images acquired from the sample PZT material. This paper discusses and explains the results and proves the successful application of the BM3D algorithm on acoustic image data.

Compact Hybrid Bandpass Filters Using Substrate-Integrated Waveguide and Stripline Resonators

This article presents a novel class of compact hybrid bandpass filters (BPFs) using substrate-integrated waveguide (SIW) and stripline resonators. As the stripline resonators are inserted between two SIW cavities, they are able to increase the filtering order without occupying any additional area. This is beneficial for circuit miniaturization. To verify the concept, several third-order hybrid filters with various responses are designed by combining a half-wavelength stripline resonator (HWSLR) or a full-wavelength stripline resonator (FWSLR) and two SIW resonators. Their sizes are reduced by more than 33% compared with standard third-order SIW filters. In addition, a class of fourth-order box-like hybrid filters with two flexible transmission zeros (TZs) is also proposed by using the HWSLR and FWSLR simultaneously. Compared with previously reported box-like SIW filters, a size reduction of more than 65% is obtained. Three filter prototypes are fabricated and measured for validation.

Passenger Flow Prediction Method based on Hybrid Algorithm: Intelligent Transportation System

Forecasting passenger flow at metro transit stations is a useful method for optimizing the organization of passenger transportation and enhancing operational safety and transportation efficiency. Aiming at the problem that the traditional ARIMA model has poor performance in predicting passenger flow, a hybrid prediction method based on ARIMA-Kalman filtering is proposed. In this regard, ARIMA model training experimental samples are integrated with Kalman filter to create a prediction recursion equation, which is then utilized to estimate passenger flow. The simulation experiment results based on the inbound passenger flow data of Nanjing metro station show that compared with the single ARIMA model, the root mean square error of the prediction results of the proposed ARIMA-Kalman filter hybrid algorithm is reduced by 257.106, and the mean absolute error decreased by 145. 675, the mean absolute percentage error dropped by 5. 655%, proving that the proposed hybrid algorithm has higher prediction accuracy. The experiment results based on the passenger flow data of Nanjing metro station show that compared to a single ARIMA model, the proposed ARIMA Kalman filtering hybrid algorithm reduces the root mean square error of the prediction results by 257.106, the average absolute error by 145.675, and the average absolute percentage error by 5.655%. It has been proven that the proposed hybrid algorithm has higher prediction accuracy.

FG-HFS: A feature filter and group evolution hybrid feature selection algorithm for high-dimensional gene expression data

High dimensional and small samples characterize gene expression data and contain a large number of genes unrelated to disease. Feature selection improves the efficiency of disease diagnosis by selecting a small number of important genes. Unfortunately, existing algorithms do not consider the correlation between features, and search algorithms tend to fall into the local optimal solution in the feature search process. To this end, this paper proposes a feature filter and group evolution hybrid feature selection algorithm (FG-HFS) for high-dimensional gene expression data. Unlike existing algorithms, we propose using spectral clustering to group redundant features into a group. Then, we propose a redundant feature filter algorithm. According to the principle of approximate Markov blanket, grouped feature groups are filtered to delete these redundant features. Among them, filtered features are evenly divided by density according to the feature exponential strategy. Most importantly, we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets and evaluate the candidate feature subsets according to the in-group and out-group so as to select the feature subsets with the highest accuracy and the least number. Experimental results show that the average accuracy (ACC) and Matthews correlation coefficient (MCC) indexes of the selected feature subsets (FSs) by the FG-HFS algorithm on 5 gene expression datasets are 92.76% and 88.76%, respectively, which are significantly better than the existing algorithms. In addition, the FSs and ACC/FSs indexes of the FG-HFS algorithm are also better than the existing algorithms, which fully proves the superiority of the FG-HFS algorithm. More importantly, the Wilcoxon and Friedman statistical experiments results show that the feature selection effect of FG-HFS algorithm is significantly better than that of existing algorithms, no matter in pairwise comparison or multiple comparison.