Search Method Research Articles

Recognizing objects in complex scenes: A Recent Systematic Review

This systematic review provides a comprehensive examination of recent advancements in object recognition within complex scenes, focusing on addressing challenges such as clutter, occlusion, and diverse environmental conditions. Leveraging the PRISMA framework, the study meticulously analysed a diverse range of literature from esteemed sources, employing advanced search methods on Scopus and WoS databases to identify and analyse primary research studies (n = 25). The review encompasses three key themes: Theme 1 concentrates on Image Noise Identification and Removal Techniques, while Theme 2 delves into Image Classification and Recognition under Noise. Additionally, Theme 3 explores Innovative Models and Approaches for Noise-Robust Image Analysis. Despite the progress achieved, contemporary recognition systems struggle with real-world complexities such as varied scales, lighting variations, and different viewpoints. The synthesis of findings emphasizes the necessity for innovative strategies that capitalize on contextual cues and harness the potential of deep learning to enhance precision in object recognition within intricate visual environments. The insights gleaned from this synthesis are poised to guide future research directions, informing the development of more resilient algorithms capable of navigating challenges and catalysing advancements in the field of object recognition.

COMPARATOR REGRESSION ANALYSIS IN CONDITIONS OF FUZZY INITIAL DATA

Abstract. The canonical task of regression analysis consists in studying the problem of estimation and mathematical description of the influence caused by one or more independent variables (predictors, explanatory variables) on the dependent (explained) variable. The subject of comparator identification problem is a specific modification of the standard regression analysis problem in the case when, for some reason, it is not possible to measure values of the dependent, explained variable. However, the problem of reconstructing a regression polynomial can be solved if it is possible to qualitatively compare the results of experiments and rank them, for example, in the order of increasing. Taking into account the results of ranking the values of the explained variable in different experiments, a corresponding system of inequalities is formed. The purpose of the research consists in developing a method for mathematical processing of the resulting system of inequalities for analytical assessment of the level of influence of explanatory variables on the explained variable. The method for solving the problem is based on transforming a system of inequalities into a system of linear algebraic equations. A significant drawback of the known approach to solving this problem consists in the fact that the resulting system of linear algebraic equations is an underdetermined one (the number of unknowns in this system exceeds the number of equations). At the same time, the system has an infinite number of solutions, among which there may be an uncontrollable number of unacceptable ones. The exhaustive search method for finding acceptable solutions is futile. In this regard, the problem of developing a correct method of comparator identification remains relevant. Another difficulty that accompanies the actual procedure of comparator identification when solving practical problems consists in the fact that measurements of values of the controlled parameters are not accurate. The uncertainty that arises in the conditions of a small sample of initial data can be removed using the tools of fuzzy mathematics. It is assumed that, based on preliminary research, a mathematical description of the corresponding membership function can be obtained for each of the controlled parameters. As a result, effective approaches to solving emerging problems have been proposed, based on the use of developed optimization procedures in the conditions of a small sample of fuzzy initial data.

Average volume-assured pressure support versus fixed pressure support in chronic hypercapnic respiratory failure: a systematic review and meta-analysis

Rationale: Chronic hypercapnic respiratory failure occurs due to alveolar hypoventilation resulting in carbon dioxide retention. This is commonly managed with noninvasive ventilation (NIV) with modalities including fixed pressure support and average volume-assured pressure support (AVAPS). However, there is limited information comparing outcomes with these two modes of ventilator support in the management of chronic hypercapnic respiratory failure. Objective: This review and meta-analysis analyze the outcomes with fixed pressure NIV versus average volume-assured pressure support NIV in managing chronic obstructive pulmonary disease (COPD) with chronic hypercapnic respiratory failure, focusing on patients’ perception of symptom burden and gas exchange based on arterial blood gases. Search methods: PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science databases were searched; the latest search date was December 1, 2023. Inclusion criteria: randomized control trials and crossover studies in English in adults over the age of 19 with the diagnosis of COPD and chronic hypercapnic respiratory failure. Exclusion criteria: patients less than 19 years old, patients with acute exacerbations, and patients with central respiratory failure or neuromuscular disease. Outcomes included blood gas analysis after use of NIV measured in mmHg and patient perception of mental health, symptom burden, and comfort. Results for each outcome were analyzed in RevMan using an inverse variance statistical method with a fixed effect analysis. The final analysis included 7 studies with 252 participants. Results: The patients were 64 ± 9 years old. Baseline pulmonary function testing showed a forced expiratory volume in the first second (FEV1) of 34.6 ± 14.2 % predicted, consistent with severe COPD per GOLD criteria, and a baseline PaCO2 of 55.2 ± 9.2 mmHg. Primary outcomes for ventilation showed no statistical difference between AVAPS and fixed pressure support groups in PaCO2 (Odds Ratio [OR] -1.51; 95% Confidence Interval [CI]: -3.18, 0.16; p=0.08). Patient perceived outcomes were evaluated using several questionnaires, including St. George Respiratory Questionnaire (SGRQ), Short Form 36 Health Survey Questionnaire (SF-36), and Visual Analogue Scale (VAS). Comparable results were not available for all studies, but no statistically significant differences were found when comparing study results. Conclusions: There was little or no clinically significant difference between fixed pressure support and AVAPS in gas exchange. There are inadequate data to draw conclusions about the effect of fixed pressure support compared to AVAPS on patient perceived outcomes, such as comfort and symptom burden. No studies evaluated mortality benefit, cost effectiveness, or hospitalizations. Key words: COPD, chronic hypercapnic respiratory failure, non-invasive ventilation, average volume-assured pressure support ventilation

Protocol for a systematic search and critical discourse analysis of research on national HIV pre-exposure prophylaxis programmes among gay, bisexual, and other men who have sex with men.

HIV pre-exposure prophylaxis (PrEP) is a medication that prevents the acquisition of HIV, most commonly taken in the form of a pill. PrEP is an efficacious tool for HIV prevention, including among gay, bisexual, and other men who have sex with men (gbMSM). PrEP is often provided through formal PrEP programmes. Research on these programmes may employ discourses shaped by heteronormativity and homophobia. Given that expert language influences how HIV prevention is understood and delivered, problematic discourses in research likely extend into PrEP implementation. This study will use critical discourse analysis (CDA) to explore research on PrEP programme implementation for gbMSM. Within this literature, we will identify interpretive repertoires used to discuss gbMSM; the subject positions afforded to gbMSM; and the implications of these interpretive repertoires and subject positions for gbMSM engaging with HIV PrEP programmes. Systematic search methods identified relevant articles through timebound searching (2012-2023) in ProQuest ASSIA, EBSCOhost PsycInfo, OVID Medline, OVID Embase, and EBSCOhost CINAHL, with forward and backward citation searching of included studies. Grey literature will be identified through ProQuest and Google Scholar. Screening will be conducted by two independent reviewers, who will conduct random partial double screening for titles and abstracts and for all full text articles. Data will be analysed and synthesised using CDA informed by critical realism (CR). CDA focuses on relationships between language and power, including how language enables inequality. The analytic process will explore the background of included studies, identify themes, and analyse external and internal relations in included studies. Highlighting issues with discourses in PrEP implementation may enhance reflective engagement with assumptions underlying this research, preventing further stigmatisation of gbMSM's sexual and protective practices. As PrEP programmes become more common, diverse and inclusive perspectives in PrEP programme research may inform interventions that enhance their acceptability and implementation.

List-Based Threshold Accepting Algorithm with Improved Neighbor Operator for 0–1 Knapsack Problem

The list-based threshold accepting (LBTA) algorithm is a sophisticated local search method that utilizes a threshold list to streamline the parameter tuning process in the traditional threshold accepting (TA) algorithm. This paper proposes an enhanced local search version of the LBTA algorithm specifically tailored for solving the 0–1 knapsack problem (0–1 KP). To maintain a dynamic threshold list, a feasible threshold updating strategy is designed to accept adaptive modifications during the search process. In addition, the algorithm incorporates an improved bit-flip operator designed to generate a neighboring solution with a controlled level of disturbance, thereby fostering exploration within the solution space. Each trial solution produced by this operator undergoes a repair phase using a hybrid greedy repair operator that incorporates both density-based and value-based add operator to facilitate optimization. The LBTA algorithm’s performance was evaluated against several state-of-the-art metaheuristic approaches on a series of large-scale instances. The simulation results demonstrate that the LBTA algorithm outperforms or is competitive with other leading metaheuristics in the field.

A Task-driven Adversarial Channel Selection Method for Binary Classification Based on Magnetocardiography.

As the number of sensors in magnetocardiography (MCG) arrays increases to capture detailed cardiac activity, some channels contribute minimally to task performance, resulting in data redundancy and resource consumption. Although existing methods can reduce the number of channels required to meet task demands, they often struggle to balance computational time and the accuracy of the selected channels and overlook the scalability of the selected channels. This limitation means that when environmental conditions change, or when sensors malfunction, redesigning channel configurations becomes necessary, which increases experimental uncertainties. This study introduces a task-driven adversarial channel selection method tailored for binary classification of MCG signals. The optimal channel combination is determined through a group-wise search using a heuristic algorithm, whose objective function is designed to maximize the difference between the classification accuracy and cosine similarity of the selected channel. In evaluations using an MCG dataset from Qilu Hospital of Shandong University, the proposed method successfully reduced the number of channels from 36 to 5 without compromising classification performance. Furthermore, it outperforms existing hybrid sequential forward search method by achieving comparable accuracy with fewer channels, while also demonstrating superior scalability compared to both hybrid sequential forward search and pearson-rank methods. This approach strikes a balance between computational consumption and accuracy, while improving the scalability of the selected channel combinations, enhancing the efficiency and practicality of the MCG system.

Research on Non-Destructive Testing of Log Knot Resistance Based on Improved Inverse-Distance-Weighted Interpolation Algorithm

The objective of this paper is to propose a non-destructive resistance detection imaging algorithm for log knots based on improved inverse-distance-weighted interpolation algorithm, i.e., the eccentric circle-based inverse-distance-weighted (ECIDW) method, to predict the size, shape, and position of internal knots of logs; evaluate its precision and accuracy; and both lay a theoretical foundation and provide a scientific basis for predicting and assessing knots in standing trees. Six sample logs with natural knots were selected for this study. Resistance measurements were performed on the log cross-sections using a digital bridge, and resistance tomography was conducted using the improved ECIDW algorithm, which combines the azimuth search method with the eccentric circle search method. The results indicated that both the conventional inverse-distance-weighted (IDW) algorithm and the ECIDW algorithm accurately predicted the positions of the knots. However, neither algorithm was able to predict the shape of the knots with high precision, leading to some discrepancies between the predicted and actual knot shapes. The relative error (Dt1) between the knot areas measured by the IDW algorithm and the actual knot areas ranged from 18.97% to 88.34%. The relative error (Dt2) for the knot areas predicted by the ECIDW algorithm ranged from 1.82% to 74.16%. The average prediction accuracy for the knot areas using the IDW algorithm was 51.58%, compared to 72.90% using the ECIDW algorithm. This indicates that the ECIDW algorithm has higher accuracy in predicting knot areas compared to the conventional IDW algorithm. The ECIDW algorithm proposed in this paper provides a more reasonable and accurate prediction and evaluation of knots inside logs. Compared to the conventional IDW algorithm, the ECIDW algorithm demonstrates greater precision and accuracy in predicting the shape and size of knots. While the resistance method shows significant potential for predicting internal knots in logs and standing trees, further improvements to the algorithm were needed to enhance the imaging effects and the precision and accuracy of knot area and shape predictions.

An event tree-based distance transform algorithm for simultaneously determining mountain railway alignments and station locations

The concurrent optimization of railway alignments and station locations (RA&SL) is a complex task, especially for the main railway lines in mountainous regions. The intricate coupling constraints between RA and SL are very challenging for the conventional RA&SL search methods, which consider limited solution types to generate an optimized RA&SL solution. To solve this problem, an event tree-based distance transform (ET-DT) algorithm is proposed. The study area is first divided into grid cells, followed by a preprocessing step to identify infeasible areas for station locations. Next, an event tree (ET) is constructed to represent all the possible solution types during the RA&SL design process. Afterward, in the ET-DT method, a total of eight types of RA&SL search operators are integrated to generate diverse RA&SL alternatives. Ultimately, the proposed method is applied to a realistic case with significantly-undulating terrain. Results show that ET-DT generates 2.8 times more solutions than a contemporary DT-based method (PreDT) and further improves solution quality through an 8.7% reduction in construction cost compared to the best manually-designed alignment, while PreDT only achieves a 5.7% reduction.

Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions

Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates.

Nucleation and phase transition of decagonal quasicrystals.

In this work, we study the nucleation of quasicrystals from liquid or periodic crystals by developing an efficient order-order phase transition algorithm, namely, the nullspace-preserving saddle search method. In particular, we focus on nucleation and phase transitions of the decagonal quasicrystal (DQC) based on the Lifshitz-Petrich model. We present the nucleation path of DQC from the liquid and demonstrate one- and two-stage transition paths between DQC and periodic crystals. We provide a perspective of the group-subgroup phase transition and nucleation rates to understand the nucleation and phase transition mechanisms involving DQC. These results reveal the one-step and multi-step modes of symmetry breaking or recovery in the phase transition from DQC, where the multi-step modes are more probable.

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties, the quantification of prediction uncertainty has seldom been investigated, thus compromising the confidence in using these predicted properties in practical applications. Here, we integrated graph convolutional neural networks (GCNN) with three uncertainty prediction techniques, including deep ensemble, Monte Carlo (MC)-dropout, and evidential learning, to provide insights into the uncertainty quantification and utility. The deep ensemble model outperforms others in accuracy and shows the highest reliability in estimating prediction uncertainty across all elementary reaction property data sets. We also verified that the deep ensemble model showed a satisfactory capability in recognizing epistemic and aleatoric uncertainties. Additionally, we adopted a Monte Carlo Tree Search method for extracting the explainable reaction substructures, providing a chemical explanation for DL predicted properties and corresponding uncertainties. Finally, to demonstrate the utility of uncertainty qualification in practical applications, we performed an uncertainty-guided calibration of the DL-constructed kinetic model, which achieved a 25% higher hit ratio in identifying dominant reaction pathways compared to that of the calibration without uncertainty guidance.

Combining Genetic Algorithm with Local Search Method in Solving Optimization Problems

This research is focused on evolutionary algorithms, with genetic and memetic algorithms discussed in more detail. A graph theory problem related to finding a minimal Hamiltonian cycle in a complete undirected graph (Travelling Salesman Problem—TSP) is considered. The implementations of two approximate algorithms for solving this problem, genetic and memetic, are presented. The main objective of this study is to determine the influence of the local search method versus the influence of the genetic crossover operator on the quality of the solutions generated by the memetic algorithm for the same input data. The results show that when the number of possible Hamiltonian cycles in a graph is increased, the memetic algorithm finds better solutions. The execution time of both algorithms is comparable. Also, the number of solutions that mutated during the execution of the genetic algorithm exceeds 50% of the total number of all solutions generated by the crossover operator. In the memetic algorithm, the number of solutions that mutate does not exceed 10% of the total number of all solutions generated by the crossover operator, summed with those of the local search method.

Implementing Swarm Robotics for Coordinated Multi-Agent Systems in Search and Rescue Operations to Improve Efficiency and Success Rates in Disaster Response

When there is a crisis, search and rescue efforts that are quick and successful can mean the difference between life and death. But standard search and rescue methods often have a hard time in emergency areas because they are so complicated and hard to plan for. This study suggests using swarm robots for organized multi-agent systems in search and rescue operations to deal with these problems. The objective is to form these operations more effective and effective. Swarm robots employments the thoughts of self-organization and decentralized control that came from studying social bug colonies to form frameworks with numerous free specialists that can act as a entirety. These bots, which are as a rule exceptionally basic and as it were have fundamental sense and talking capacities, work together without any issues to reach their objectives. These mechanical frameworks are solid, adaptable, and adaptable since they utilize the control of swarm insights. These are all imperative qualities for dealing with changing and perilous fiasco settings. One of the finest things almost utilizing swarm robots for look and protect is that it lets you rapidly cover huge zones. Conventional strategies that depend on a little bunch of organized robots or human labourers may not be able to look expansive calamity scenes rapidly. Swarm robots, on the other hand, lets you send out numerous units that can investigate the area at the same time, making it simpler to reach and quicker to reply. Swarm mechanical technology is additionally more strong when things go off-base or the world changes since it isn't controlled . The system can change and reform without centralized control if individual workers run into problems or run into problems. This keeps the search and recovery efforts going.

Open shop scheduling with group and transportation operations by learning-driven hyper-heuristic algorithms

Open shop scheduling problems (OSSPs) are complex scheduling problems, which have been extensively studied in the literature. Group and transportation activities are two important aspects of OSSPs that still need attention. This work considers an OSSP with group and transportation operations to minimize maximum completion time by solving three key sub-problems: job assignment among groups, job sequence in groups and group sequence on machines. Firstly, an integer programming model is formulized to define the problem. Secondly, a learning-driven hyper-heuristic algorithm is developed by incorporating a Q-learning method and four meta-heuristics, i.e., genetic algorithm, artificial bee colony optimization, variable neighborhood search method and Jaya algorithm. The Q-learning method is devised to select the most promising meta-heuristic for performing at each iteration. Three neighborhood structures are designed by integrating critical machines and critical paths. Finally, the developed model is verified by an exact solver CPLEX, and the comparison results exhibit that CPLEX is effective for instances with ten jobs. For the instances with more than ten jobs, the developed algorithm wins CPLEX in terms of computation accuracy and efficiency, signifying its excellent performance in finding better solutions. Furthermore, four meta-heuristics mentioned above and three state-of-the-art meta-heuristics are employed for comparisons in solving a set of benchmark test instances. The results confirm that the formulated model and algorithm have stronger competitiveness in handling the considered problems.

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

The complexities of permafrost changes, driven by climate warming and engineering activities, coupled with challenges in data acquisition, make it crucial and challenging to accurately predict the artificial permafrost table, particularly for subgrades in high-temperature unstable permafrost regions. To address this, this study developed a hybrid machine learning model (RF-LSTM-XGBoost) for permafrost table prediction. By analyzing climate change and ground temperature data from various positions and depths along the subgrade in the Tuotuo River section of the Qinghai-Xizang Highway, the Spearman correlation coefficient method was initially used to determine the important influencing factors. Random Forest (RF), Long Short-Term Memory Neural Network (LSTM), and Extreme Gradient Boosting (XGBoost) models were used to predict the artificial permafrost table, and grid search and cross-validation methods were employed to optimize the hyperparameters of each model. A linear weighted combination method based on the minimum cumulative absolute error was utilized to merge the models, and its performance was compared with the individual RF, LSTM, and XGBoost models. Subsequently, the feature importance of the variables in the machine learning model was analyzed. The results indicated a strong correlation between artificial permafrost table changes and factors such as daily average atmospheric temperature, subgrade surface ground temperature, and subgrade surface ground heat flux during the freezing-thawing cycle. The combined model highlighted daily atmospheric temperature as the most influential predictor, followed by ground heat flux, with the surface ground temperature being less impactful. The combined model demonstrated improved predictive accuracy, with MSE, MAPE, RMSE, MAE, and R2 values of 0.003, 0.052, 0.0085, 0.029, and 0.989, respectively, surpassing those of individual models. This model offers a rapid, accurate, and reliable approach for permafrost table prediction, advancing subgrade stability research in challenging permafrost environments.

A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.

Urban freight distribution with electric vehicles: comparing some solution procedures

The Vehicle Routing Problem (VRP) is a well-known discrete optimization problem that has an impact on theoretical and practical applications. In this paper, a freight distribution model that includes a charging system located at the depot, making it feasible for real world-implementation, is proposed. Two different solution methods are proposed and compared: a genetic algorithm (GA) and a population-based simulated annealing (PBSA) with the number of moves increasing during the iterations. Among the variety of algorithm used to solve the VRP, population-based search methods are the most useful, due to the ability to update the memory at each iteration. To demonstrate the practical aspects of the proposed solution a case study is solved using travel time on a real network to evaluate the potentiality for a real-world application.

New Media Advertising Information Search Method Based on XML Technology

The sheer volume and complexity of new media advertising information pose significant challenges for users in searching and filtering advertisements. To enhance the match between search content and keywords, this paper proposes a multifunctional search method for new media advertising information based on eXtensible Markup Language (XML) technology. The collected user attribute data are organized into a set, and a membership function for user attributes is established. An information certainty index and a query certainty index are constructed to obtain the integrated attributes of new media advertisements and classify their attributes. The average value of the constant threshold element is calculated, and an attraction model of new media advertisements for random Internet users is constructed. The similarity between text information and query methods is determined, along with the calculation of browsing duration and frequency in any given area. According to the search model for new media advertising information, the sorted new media advertising information documents are merged to obtain search results. Experimental results demonstrate that the introduction of interactive elements enhances attraction, and dynamic new media advertisements exhibit stronger attraction compared to static advertisements. The proposed method shows significant advantages in search efficiency, achieving over 90% search efficiency, with the match between search content and keywords consistently remaining above 98%, the user satisfaction is higher, the system response time is shorter, the results can be obtained faster, and the search efficiency is improved.

Machine learning framework for predicting water quality classification

ABSTRACT Groundwater serves as the source for nearly half of the world's drinking water, yet understanding of global groundwater resources remains incomplete, and management of aquifers falls short, particularly concerning groundwater quality. This research offers insights into the groundwater quality in 242 stations of Maharashtra and Union Territory of Dadra and Nagar Haveli and nine parameters (pH, TDS, TH, Calcium (Ca2+), Magnesium (Mg2+), Chloride (Cl−), Sulphate (SO42−), Nitrate (NO3−), Fluoride (F−)) were considered for computing the Water Quality Index (WQI) and hence Water Quality Classification (WQC) based on Water Quality Index (WQI). This research introduces the utilisation of Machine Learning (ML) models, specifically, Random Forest, Adaptive Boosting (AdaBoost), Gradient Boosting, XGBoost, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) model for predicting WQC and models are tested. Grid search method as a hyperparameter tuning of parameters is utilized to achieve the best possible performance of ML models. The performance metrics that are used for evaluating and reporting the performance of classification models are Accuracy, Precision, Recall or Sensitivity, F1 Score. SVM achieved the highest performance in predicting WQC. With accurate predictions of WQC, these findings have the potential to enhance NEP concerning water resources by facilitating ongoing improvements in water quality.

A Method for Single-Phase Ground Fault Section Location in Distribution Networks Based on Improved Empirical Wavelet Transform and Graph Isomorphic Networks

When single-phase ground faults occur in distribution systems, the fault characteristics of zero-sequence current signals are not prominent. They are quickly submerged in noise, leading to difficulties in fault section location. This paper proposes a method for fault section location in distribution networks based on improved empirical wavelet transform (IEWT) and GINs to address this issue. Firstly, based on kurtosis, EWT is optimized using the N-point search method to decompose the zero-sequence current signal into modal components. Noise is filtered out through weighted permutation entropy (WPE), and signal reconstruction is performed to obtain the denoised zero-sequence current signal. Subsequently, GINs are employed for graph classification tasks. According to the topology of the distribution network, the corresponding graph is constructed as the input to the GIN. The denoised zero-sequence current signal is the node input for the GIN. The GIN autonomously explores the features of each graph structure to achieve fault section location. The experimental results demonstrate that this method has strong noise resistance, with a fault section location accuracy of up to 99.95%, effectively completing fault section location in distribution networks.