Fuzzy Logic Filter Research Articles

A Hypersensitive Intelligent Filter for Detecting Explicit Content in Learning Environments

In today’s digital age, educational institutions aim to ensure safe learning environments in the light of pervasive explicit and inappropriate content. This study proposes an innovative approach to enhance safety by integrating convolutional neural networks (CNNs) for visual analysis with an intuitionistic fuzzy logic (IFL) filter for explicit content identification. Additionally, it utilizes GPT-3 to generate contextual warnings for users. A large-scale dataset comprising explicit and educational materials is used to evaluate the system. The results show that this hypersensitive filter has high accuracy performance, particularly in handling ambiguous or borderline content. The proposed approach provides an advanced solution to tackle the challenges of detecting explicit content and promotes safer learning environments by showcasing the potential of combining generative AI techniques across various domains.

Simplified Control Structure of Fuzzy Logic and Kalman Filter for Induction Motor Drive

The paper deals with the utilization of Kalman filter and fuzzy logic control in induction motor drive with direct torque control (DTC). In order to lower ripple of stator current vector in DTC drive, pulse width modulation technique with high switching frequency is applied. However, the performance of the DTC also depends on the accuracy of both stator resistance and stator current vector. In the paper, the stator resistance and stator current components are assumed to be distorted by Gaussian noises. In order to reduce the effect of noises especially at low speed and very low speed regions, a simple Kalman filter is applied for filtering current components, and fuzzy logic theory is used to increase the flexibility of proportional-integral (PI) compensator in the speed controller of the drive structure. Simulations are implemented in conditions of high-level noises of stator current and stator resistance, and a wide range of load torque. An ITAE-based criterion is utilized to evaluate the performance of drive structures. Results confirmed the expected dynamic properties of the proposed drive structure. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Personal Protection Equipment detection system for embedded devices based on DNN and Fuzzy Logic

The large extension and complex structure of most industrial and construction areas very often make it unfeasible or inconvenient for human operators to constantly survey all the workers to detect those who do not properly wear their Personal Protection Equipment (PPE) devices. However, such a detection is of utmost importance to reduce the number of worker injuries. Consequently, the adoption of a computer vision system based on Deep Neural Networks (DNNs) that performs PPE detection by analysing the video streams from surveillance cameras is an appealing option. For instance, smart video cameras placed in the workplace might process the video frames at run-time and trigger alarms whenever they detect workers not correctly wearing PPE devices. However, in order to be sufficiently accurate, DNN-based object detection requires a high computational power that is difficult to embed in cameras. Moreover, DNN training has to be done on a large dataset with thousands of labeled image samples, and therefore the creation of a customized DNN to detect special PPE devices requires a huge effort in finding and labeling images to train the network. This paper proposes a PPE detection framework that combines DNN-based object detection with human judgement through fuzzy logic filtering. The proposed framework runs in near real-time on embedded devices and can be trained with a low number of images (i.e., few hundreds), still providing good accuracy results.

Impact of Polyphase Induction Motor on Photovoltaic Water Pumping System

An improved dynamic behavior of water pumping system is presented in this paper. The system consists of a seven-phase induction motor powered by a photovoltaic generator (PVG) and a battery bank, via two static converters controlled independently. A boost converter based on fuzzy logic controller is utilized to adapt the voltage of the photovoltaic generator in order to extract maximum power. A three levels neutral point clamped inverter (NPC) is introduced to attain a low voltage harmonic distortion in the system. In addition, an improved direct field oriented control with minimizing non-sequential currents for the seven-phase induction motor using fuzzy logic filter is implemented. To verify and validate the proposed system, an optimal operating point is chosen to locate the dynamic and static operating limits of the system. Moreover, the proposed system is simulated and analyzed. The minimization of the non-sequential currents component induces a quality energy improvement with minimizing of the Joule losses, which increase the system efficiency. The use of the seven-phase machine brings a degree of additional freedom to the system, its tolerance to faults is a capital issue for the isolated places.

Framework and implementation of a fuzzy logic filter-an optimization strategy for the BWMS based on stakeholders' perspectives.

The International Convention for the Control and Management of Ships' Ballast Water and Sediments (IMO 2018 Edition) was adopted in 2004 and came into force on 8 September 2017, aiming to introduce global regulations to control the transfer of potentially invasive species. Large efforts have been made by the maritime industry in creating reliable strategies for the installation of systems on board (Register 2014). Environmental considerations (INTERTANKO 2018) and optimization for the management systems (IMO 2017 Report) are factors broadly considered to tackle this matter. A consistent implementation strategy must be stated before the ballast water management system (BWMS) installation project starts-the management of stakeholders (e.g. ship owners, classification societies, administration, shipyards) is an important aspect of this process. This relies on their expertise, which in turn results in a high level of engagement and supports the implementation plan into the organizations. The creation of a framework for the optimization process, considering the implementation project of a BWMS on board of oil tankers, is the first part of this research. The use of fuzzy logic principles in the second part-as an evaluation instrument from a ranking obtained by multicriteria principles-sums up the aim of this paper, where the peculiarities about oil tankers' modelling will be discussed throughout the analysis of 2 optimization cases (Suezmax and Aframax).

Prediction of Harvested Energy for Wireless Sensor Node

Energy harvesting wireless sensor nodes are interesting for the Internet of Things, since they can provide continuous operation by adapting workload not only to the current energy reserves, but to the amount of energy that can be harvested in the future also. We present a multistage day ahead hourly solar energy prediction algorithm. The predictor uses cloud cover and precipitation probability predictions from weather forecast obtained once per day for 24 hours in advance. To compensate for short-term weather changes until the next weather forecast data is obtained, forecast errors of humidity and atmospheric pressure are fed to the fuzzy logic filter. The filter adjusts predictions of cloud cover and precipitation probability, which are applied to the clear-sky radiation model in order to obtain prediction of solar energy. The prediction of solar energy is additionally corrected based on the energy prediction error in the preceding time slot. The results show that the proposed predictor outperforms the state-of-the-art predictors in terms of prediction error. Proposed predictor and state-of-the-art predictors were also evaluated using a simulated wireless sensor node with the simple energy management algorithm, where the proposed predictor was the most efficient at maintaining energy neutrality.

Fault detection in a grid-connected photovoltaic system using adaptive thresholding method

In this paper, an adaptive monitoring scheme with Fuzzy Logic Filter (FLF) is developed and applied to monitor a Grid-Connected Photovoltaic System (GCPVS). This method is based on Principal Component Analysis (PCA) and Moving Window Principal Component Analysis (MWPCA). It is designed to generate adaptive thresholds for its monitoring indices. The FLF filters the monitoring indices to reduce the number of False Alarms (FA) and increase the Fault Detection Rate (FDR). The application is carried out on the GCPVS of the Power Electronics and Renewable Energy Research Laboratory (PEARL) of Malaya University. The proposed technique is compared against PCA method in terms of FAR reduction. The detection ability of the adaptive thresholding with FLF monitoring scheme is tested first on simulated faults then it is applied to detect a real abnormal behaviour. The results show that the proposed method is effective in reducing the number of false alarms and in detecting different types of faults with high accuracy.

A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection

Principal Component Analysis (PCA) is the most common Multivariate Statistical Process Control (MSPC) method that is widely used for Fault Detection and Diagnosis (FDD). Since early abnormality detection with high accuracy is required for safe and reliable process operation, False Alarms Rate (FAR), Missed Detection Rate (MDR) and the detection time delay are the major factors that must be taken into consideration when developing any process monitoring scheme. Unfortunately, the PCA performance, with fixed limits, is weak in terms of the stated factors. In contrast, conventional Moving Window PCA (MWPCA) is an adaptive technique which updates both the PCA model and the thresholds once a new normal observation is available. Yet, MWPCA methodology still does not reduce the MDR and the detection delay. In this paper, a Modified MWPCA (MMWPCA) with Fuzzy Logic Filter (FLF) is proposed to enhance the monitoring performance of PCA. It is an adaptive approach with a fixed model that combines both aforementioned techniques. The aim of using FLF is to ensure robustness to false alarms without affecting the Fault Detection (FD) performance. The application of the proposed method has been carried out on the Tennessee Eastman Process (TEP). Hold-one and hold-five MMWPCA with FLF are applied and compared to recent FDD work in the literature. The obtained results demonstrate the superiority of the proposed technique in detecting different types of faults with high accuracy and with shorter time delay.

A Modified Moving Window dynamic PCA with Fuzzy Logic Filter and application to fault detection

Principal Component Analysis (PCA) model is constructed from measured data and used to monitor new testing samples. In fact, the statistical independency assumption between observations is true only for long sampling intervals. Nowadays, industrial systems are sophisticated and fast for which this assumption becomes no longer valid and the current observation becomes highly dependent on the past observations. In another hand, Dynamic PCA (DPCA) is a PCA extension to deal with the aforementioned problem, but monitoring process using this method with fixed control limits showed a high False Alarms Rate (FAR), high Missed Detection Rate (MDR) and long Detection Time Delay (DTD). In this paper, a Modified Moving Window DPCA (MMW-DPCA) with Fuzzy Logic Filter (FLF) is proposed to address the above issue. The developed monitoring scheme continually updates control limits throughout an obtained DPCA-based model. The adaptive thresholds are established by moving a fixed size window over the data. The dynamic behavior of the data is handled by DPCA, whereas the sensitivity enhancement and the FAR reduction are handled by the developed adaptive thresholds for which the FLF is employed to ensure robustness to outliers and noise without affecting the fault detection performances. The proposed technique has been tested on Tennessee Eastman Process (TEP). It has been compared to other well-known fault detection methods. The obtained results demonstrate that the MMW-DPCA with FLF detects different types of faults with high accuracy and in a short time delay. The experimental application of the MMW-DPCA with FLF has been carried out on cement rotary kiln. The obtained results illustrate that the proposed method has successfully detected a real fault.

Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments

Abstract. The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25 % of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coefficient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium-duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echo features, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than 1 min, allowing its use on future field campaigns.

Stability Conditions of Fuzzy Filter Type III

A digital fuzzy logic filter of type III interacts with a real model signal reference to obtain the best answer in the sense of minimum mean square error of the output. The key part of the filter is a fuzzy mechanism that adaptively selects and emits answer according to the changes of the external reference signal. Based on input signal level, this fuzzy filter selects the best parameter values from a set of membership in the knowledge base (KB), and the filter weights are updated according to the reference signal in a natural form. With this fuzzy structure the filter reduces error. The simulation result shows the stability of the filter. The states of the filtering process require that all of its answers are bounded by the error criteria probabilistically.

Designing of a type-2 fuzzy logic filter for improving edge-preserving restoration of interlaced-to-progressive conversion

This paper focuses on recently advanced fuzzy models and the application of type-2 fuzzy sets in video deinterlacing. The final goal of the proposed deinterlacing algorithm is to exactly determine an unknown pixel value while preserving the edges and details of the image. To begin, we will discuss some artefacts of spatial, temporal, and spatio-temporal domain deinterlacing methods. In order to address the aforementioned issues, we adopted type-2 fuzzy sets concepts to design a weight evaluating approach. In the proposed method, the upper and lower fuzzy membership functions of the type-2 fuzzy logic filters are derived from the type-1 (or primary) fuzzy membership function. The weights from upper and lower membership functions are considered to be multiplied with the candidate deinterlaced pixels. Experimental results proved that the performance of the proposed method was superior, both objectively and subjectively to other different conventional deinterlacing methods. Moreover, the proposed method preserved the smoothness of the original image edges and produced a high-quality progressive image.

Real-time fuzzy digital filters (RTFDF) properties for siso systems

This paper describes real-time fuzzy digital filter (RTFDF) concepts considering a description of fuzzy logic and adaptive digital filter concepts with real-time properties: the basic characteristic operation of this kind of filters is known as operation regions, knowing it as linguistic natural descriptions.

A new fuzzy logic filter for image enhancement

This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.

Design of stability-guaranteed fuzzy logic controller for nuclear steam generators

A fuzzy logic controller (FLC) and a fuzzy logic filter (FLF), which have a special type of fuzzifier, inference engine, and defuzzifier, are applied to the water level control of a nuclear steam generator (S/G). It is shown that arbitrary two-input, single-output linear controllers can be adequately expressed by this FLC. A procedure to construct stability-guaranteed FLC rules is proposed. It contains the following steps: 1) the stable sector of linear feedback gains is obtained from the suboptimal concept based on LQR theory and the Lyapunov's stability criteria: 2) the stable sector of linear gains is mapped into two linear rule tables that are used as limits for the FLC rules; and 3) the construction of an FLC rule table is done by choosing certain rules that lie between these limits. This type of FLC guarantees asymptotic stability of the control system. The FLF generates a feedforward signal of S/G feedwater from the steam flow measurement using a fuzzy concept. Through computer simulation, it is found that the FLC with the FLF works better than a well-tuned PID controller with variable gains to reduce swell/shrink phenomena, especially for the water level deviation and abrupt steam flow disturbances that are typical in the existing power plants.