Local Extremes Research Articles

Hybrid Clustering Algorithm Based on Improved Density Peak Clustering

In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several drawbacks. The method primarily considers the overall structure of the data, often resulting in the oversight of many clusters. The choice of truncation distance affects the calculation of local density values, and varying dataset sizes may necessitate different computational methods, impacting the quality of clustering results. In addition, the initial assignment of labels can cause a ‘chain reaction’, i.e., if one data point is incorrectly labeled, it may lead to more subsequent data points being incorrectly labeled. In this paper, we propose an improved density peak clustering method, DPC-MS, which uses the mean-shift algorithm to find local density extremes, making the accuracy of the algorithm independent of the parameter dc. After finding the local density extreme points, the allocation strategy of the DPC algorithm is employed to assign the remaining points to appropriate local density extreme points, forming the final clusters. The robustness of this method in handling uncertain dataset sizes adds some application value, and several experiments were conducted on synthetic and real datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms some of the more recent methods in most cases.

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

AbstractWind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.

A Multi-Strategy Improved Sooty Tern Optimization Algorithm for Concrete Dam Parameter Inversion

A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved Sooty Tern Optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance. First, computational strategies to improve the traditional Sooty tern algorithm, such as chaos mapping to improve the initial position of the population, a new nonlinear convergence factor, the LIMIT threshold method, and Gaussian perturbation to update the optimal individual position, are adopted to enhance its algorithmic optimization seeking ability. Then, the measured and finite element data are combined to create the optimization inversion fitness function. Based on the MSSTOA, the intelligent optimization inversion model is constructed, the inversion efficiency is improved by parallel strategy, and the optimal parameter inversion is searched. The inversion strategy is validated through test functions, hypothetical arithmetic examples, and concrete dam engineering examples and compared with the inversion results of the traditional STOA and other optimization algorithms. The results show that the MSSTOA is feasible and practical, the test function optimization results and computational time are better than the STOA and other algorithms, the example inversion of the elastic modulus is more accurate than the traditional STOA calculation, and the results of the MSSSTOA inversion are reasonable in the engineering example. Compared with other algorithms, the local extremes are skipped, and the time consumption is reduced by at least 48%. The finite element hydrostatic components calculated from the inversion results are well-fitted to the statistical model with minor errors. The intelligent inversion strategy has good application in concrete dam inverse analysis.

Neoadjuvant Radiotherapy Is Associated with Fewer Acute Adverse Skin Reactions Without Increased Risk of Postoperative Wound Complications in High-Grade Extremity Soft Tissue Sarcoma - A Retrospective Comparative Study with A Mean Follow-Up of 7 Years

Aim: In this study, we aim to compare the adverse effects and oncological outcomes of patients with high-grade extremity soft tissue sarcoma receiving neoadjuvant or adjuvant RT. Methods: From 2008 to 2022, Sixty-four patients underwent limb-preserving surgery in our institute for localized high-grade extremity soft tissue sarcoma were included. Thirty-nine patients received adjuvant radiotherapy while twenty-five patients received neoadjuvant RT before surgery if the soft tissue sarcomas were close to the neurovascular bundles, bones or histological types of myxoid liposarcoma or rhabdomyosarcoma. Primary outcomes were acute RT-related skin adverse reactions and significant side effects comprising postoperative wound complications, pathological fractures and RT-induced sarcoma. Secondary outcomes included surgical margin quality, Local-Recurrence (LR)-free survival, metastasis-free survival and overall survivorship. Results and Discussion: Patients in the neoadjuvant RT group had significantly fewer acute RT-induced adverse skin reactions than the adjuvant RT group (p=0.044, Table 2). None in the neoadjuvant RT group suffered severe acute skin reaction (RTOG grade 3-4). Wound infection (n=2) and pathological fracture (n=1) were rare in both adjuvant and neoadjuvant RT groups (P=0.640 & 1.000 respectively). Survival curves for local-recurrence-free survival, metastasis-free survival and overall survivorship of both neoadjuvant and adjuvant groups were analyzed via Kaplan-Meier Curve. All results are statistically insignificant, with p=0.835, 0.948 and 0.540, respectively. Presence of metastasis was the only statistically significant predictor of the overall survival-adjusted hazard ratio of 8.7 (95%CI 2.7-28), p<0.001. Conclusion: Patients with neoadjuvant RT had lower cumulative radiation doses and fewer acute adverse skin reactions. There was no increased risk of postoperative wound complication compared to adjuvant RT. The LR-free survival, metastasis-free survival and overall survival were comparable in both groups. Our results suggested that postponing surgery to eight weeks after neoadjuvant RT and primary wound closure were associated with fewer wound complications, but further studies were warranted.

Quantification of the Influence of Ink Penetration and Optical Ink Density on the Print-through of Printed Straw-Based Papers

In recent years, due to environmental awareness regarding the harmfulness of polymeric materials, there has been a growing interest in using papers, especially those from alternative raw materials. The importance of using all available raw materials is imperative today. Raw materials that were once viewed as waste are now of great importance because they have the ability to replace raw materials that are used irrationally or are lacking. Cereal straw is a lignocellulosic material that could be used in the paper industry and in the production of increasingly prevalent paper packaging. The aim of this research was to analyze the relationships between the qualitative parameters of straw-based printed papers containing 30% agricultural residues (wheat, barley, or triticale). The influence of two qualitative parameters (ink penetration depth and optical ink density) on the print-through was observed using multiple regression analysis on straw-based papers produced at the laboratory level. Throughout the research, 100% recycled wood paper was used as a reference sample. The results of the regression analysis showed that none of the variables individually make a statistically significant contribution to the prediction of the dependent variable in a linear context, that is, they indicate a non-linear interaction between the variables and the specific conditions under which the dependent variable reaches local extremes and changes in the gradient. Considering the results of the regression analysis and the visualization of the relationship, the model was additionally tested with other independent variables (paper type). From the results obtained, it can be concluded that the alternative straw-based paper with 30% barley pulp has the best interaction between ink and paper, which is the most similar to the reference sample in terms of printability, while the alternative straw-based paper with 30% wheat generally differs significantly from the reference paper when all three prints are considered.

Predicting the Heat Generation Rate in Large Format Pouch-Type Lithium-Ion Battery Cells for Automotive Applications

Accurate prediction of the heat generation rate of lithium ion batteries is critical for driving towards a cost effective and efficient design of a thermal management system for various applications.[1, 2] because of the increase in battery cell dimensions associated with an increase in battery cell capacity to drive towards a reduction in range anxiety for automotive applications, as well as more aggressive operating conditions that are typically seen in the automotive industry, such as direct current fast charge and sustained driving under load, heat generation at the cell level must be considered as spatially nonuniform during operation. Proper design of the thermal management system requires prediction[3] of the heat generation of the battery cell in both a lumped fashion for system sizing, as well as a distributed mode in order to focus cooling appropriately to decrease local temperature extremes. Local temperature extremes can adversely affect the aging of the battery cell due to the non-uniformities in electrochemical utilization of the active material, increased local reaction rates, and increased mechanical stresses in active materials.In this work, we detail our efforts to characterize the heat generation[4] of a battery cell under a variety of operating conditions, quantify the impact of temperature non-uniformities[5] on the aging of the battery cell, and experimentally[6, 7] evaluate the localized aging in the battery electrodes based on local temperature conditions. Here, we will describe the modeling methods developed to link operating conditions, aging, and heat generation with the electrochemical performance of the battery cell. A discussion of the model performance for relevant automotive applications will also be included.References M. Song, Y. Hu, S.-Y. Choe and T. R. Garrick, J Electrochem Soc, 167, 120503 (2020).Y. Hu, S.-Y. Choe and T. R. Garrick, Electrochim Acta, 362, 137124 (2020).M. Song, Y. Hu, S.-Y. Choe and T. R. Garrick, J Electrochem Soc, 169, 070502 (2022).Y. Hu, S.-Y. Choe and T. R. Garrick, Applied Thermal Engineering, 189, 116709 (2021).Y. Hu, S.-Y. Choe and T. R. Garrick, J Power Sources, 532, 231350 (2022).U. Janakiraman, T. R. Garrick and M. E. Fortier, J Electrochem Soc, 167, 160552 (2020).T. R. Garrick, J. Gao, X. Yang and B. J. Koch, J Electrochem Soc, 168, 010530 (2021).

Local exposure misclassification in national models: relationships with urban infrastructure and demographics

BackgroundNational-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.ObjectiveCharacterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.MethodsWe compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed “localized difference.” We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition.ResultsWe find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations.ImpactComparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Regional temperature extremes and vulnerability under net zero CO2 emissions

Signatories to the Paris Agreement have pledged to keep global warming to well below 2 °C above pre-industrial levels and preferably below 1.5 °C above pre-industrial levels. Beyond over-shooting Paris Agreement warming levels followed by net negative emissions, achieving a state of net zero carbon dioxide emissions is required to satisfy Paris Agreement warming goals. Research on climate changes under net zero CO2 emissions is very limited to date with no comprehensive analysis of changes in extremes. In this study, we use results from Earth System Models in the zero emissions commitment model intercomparison project to understand regional mean-state climate change patterns during a 100 year period following carbon dioxide emissions cessation. We also perform an initial study of the evolution of hot and cold monthly temperature extremes after net zero CO2 emissions, including an assessment of how the change in frequency of temperature extremes affects areas of different levels of socioeconomic development based on regional Human Development Index (HDI). The results show that most land regions experience a fast and continuous cooling response following emissions cessation, with large areas of significant model agreement. In contrast, the Southern Ocean continues warming over the century after emissions cessation. The frequency of land-based local monthly high temperature extremes generally stays constant or decreases during the century after emissions cessation, however, decreases in heat extreme frequencies are generally less for locations with lower modern HDI than areas with higher HDI which suggests that inequality of climate change will remain an issue even after net zero CO2 emissions. There is an evident emergence of local monthly cold extremes following emissions cessation with most significant impact over high HDI mid- and high-latitude land regions.

Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature

Knowledge about local air temperature variations and extremes in Antarctica is of large interest to many polar disciplines such as climatology, glaciology, hydrology, and ecology and it is a key variable to understand climate change. Due to the remote and harsh conditions of Antarctica’s environment, the distribution of air temperature observations from Automatic Weather Stations is notably sparse across the region. Previous studies have shown that satellite-derived land and ice surface temperatures can be used as a suitable proxy for air temperature. Here, we developed a daily near-surface air temperature dataset, AntAir ICE for terrestrial Antarctica and the surrounding ice shelves by modelling air temperature from MODIS skin temperature for the period 2003 to 2021 using a linear model. AntAir ICE has a daily temporal resolution and a gridded spatial resolution of 1 km2. AntAir ICE has a higher accuracy in reproducing in-situ measured air temperature when compared with the well-established climate re-analysis model ERA5 and a higher spatial resolution which highlights its potential for monitoring temperature patterns in Antarctica.

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model.

It is very well known that traditional artificial neural networks (ANNs) are prone to falling into local extremes when optimizing model parameters. Herein, to enhance the prediction performance of Cu(II) adsorption capacity, a particle swarm optimized artificial neural network (PSO-ANN) model was developed. Prior to predicting the Cu(II) adsorption capacity of modified pomelo peels (MPP), experimental data collected by our research group were used to build a consistent database. Then, a PSO-ANN model was established to enhance the model performance by optimizing the ANN's weights and biases. Finally, the performances of the developed ANN and PSO-ANN models were deeply evaluated. The results of this investigation revealed that the proposed hybrid method did increase both the generalization ability and the accuracy of the predicted data of the Cu(II) adsorption capacity of MPPs when compared to the conventional ANN model. This PSO-ANN model thus offers an alternative methodology for optimizing the adsorption capacity prediction of heavy metals using agricultural waste biosorbents.

The development of the method of evaluation of complex hierarchical systems based on improved alforitm of particle swarm

The scientific task, which is solved in the research, is to increase the efficiency of the evaluation of complex hierarchical real-time systems. Finding solutions to nonlinear optimization problems and especially global optimization problems is one of the most popular problems in computational mathematics. In applied problems, the objective function, as a rule, has a large number of variables, is not given in an analytical form and is calculated as some integral characteristic of a complex dynamic process. The development of effective methods, to a certain extent adaptive to the variability of the objective function, is especially relevant in connection with the development of computer technology and the possibility of using parallel computing systems. The conducted research was aimed at developing a method of evaluating complex hierarchical systems based on an improved particle swarm. At the same time, the object of research was complex hierarchical real-time systems. The subject of research is the functioning of real-time hierarchical systems. The novelties of the proposed method consist in: ‒ creating a multi-level and interconnected description of complex systems of hierarchical real-time systems; ‒ increasing the efficiency of decision making while evaluating complex systems of hierarchical real-time systems; ‒ solving the problem of falling into global and local extremes while assessing the state of complex systems of hierarchical real-time systems; ‒ the possibilities of directed search by several individuals of the particles swarm in a given direction, taking into account the degree of uncertainty; ‒ the possibilities of re-analysis of the state of complex systems of hierarchical real-time systems; ‒ avoiding the problem of loops while visualizing the state of the national security system in real time. It is advisable to implement the specified method in specialized software, which is used to analyze the state of complex systems of hierarchical real-time systems and make management decisions.

Radiomics Model for Predicting Pulmonary Metastasis in Sarcoma Patients

The lung is the most common site of metastasis in localized extremity soft tissue sarcomas (STS). Indeterminant lung nodules leads to challenges for both patients and providers. As such, accuracy in the diagnosis of pulmonary metastasis (PM) is important for clinical decision making. The purpose of this is study is to develop and evaluate a CT-based radiomic model to predict PM in sarcoma patients with indeterminate pulmonary nodules. Fifty-seven stage I-III sarcoma patients with indeterminate lung nodules on unenhanced CT that were approved for this study by our institution's internal review board (IRB). Twenty-two developed and categorized as PM and 35 without PM. A total number of 322 indeterminate nodules were investigated, where 165 & 157 nodules were from patients with and without PM, respectively. A total of 96 radiomic features (RF) (including first order statistics, 2d shape features, gray level co-occurrence, gray level size zone, gray level run length, neighboring gray tone difference & gray level dependence matrices) were taken from unenhanced CT images of each patient. We included 2D shape features since the size and shape of indeterminate lung nodules may have some correlation with PM. Spearman correlation were used to rule out redundant features (r2>0.9). T-test statistics was used to determine feature with significant differences that can differentiate responders with and without PM. Support Vector Machine (SVM) model was fitted to combinations of groups of 2-3 radiomic features. The AUC of the ROC curve was used to evaluate the model performance. Pointwise confidence intervals (CI) for AUC were calculated using 100 bootstrap replicates. Four features were found significant after Spearman correlation and T-test statistical testing: 1. Maximum (MAX); 2. Minor Axis Length (MAL); 3. Gray Level Non-uniformity (GLNU); and 4. Interquartile Range (IR). The best performing 2-feature radiomic SVM model had an AUC of 0.69 (95% CI 0.61,0.74) for the MAX and GLNU features. The best performing 3-feature radiomic SVM model had an AUC of 0.74 (95% CI 0.70,0.80) for the MAX, MAL and IR features. CT-based radiomics model has the potential to be used to predict PM in sarcoma patients presenting with indeterminate lung nodules. Additional research will focus on investigating derived parameters for existing RF, such as the ratio of the max RF to mean RF, to see if they improve the predictive performance of the model, as well as testing the models on external patient cohort. These radiomic models could be useful tools in guiding PM screening and providing a patient specific monitoring of sarcoma patients.

How vortex dynamics affects the structural load in step cylinder flow

The vortex dynamics and the structural load in a step cylinder (consisting of a small, d, and a large, D, cylinder) flow are investigated numerically at Reynolds number ( $Re_D$ ) 150 for diameter ratios $D/d=2.0, 2.4$ and 2.8. First, the formation mechanism of a non-uniform oblique vortex shedding (the vortex shedding frequency remains unchanged as the oblique shedding angle varies) behind the small cylinder is explained: an increase in the production rate of the vortex strength and a farther downstream movement of the vortex formation position occur simultaneously as the vicinity of the step is approached along the small cylinder. Second, the structural load (the drag and lift) along the step cylinder is investigated, where four local extremes (two local minima and two local maxima) are observed. An in-depth investigation of the vortex dislocation effects on the structural load is provided, showing that the decreased circulation in the near wake and the weakened staggered Kármán vortex shedding pattern cause a major reduction (90 %) of the sectional lift amplitude and a relatively modest reduction (5.7 %) of the sectional drag amplitude, compared with the corresponding sectional force when no vortex dislocation occurs. This new knowledge combined with the three-dimensional effect of the step cylinder wake (caused by the blending of the small and larger cylinder wakes around the step) explain the formation of the four local extremes and the distribution of the structural load between them. Finally, it is found that the increasing $D/d$ amplifies the structural load variation along the step cylinder.

Source-independent elastic envelope inversion using the convolution method

Elastic full waveform inversion (EFWI) is a powerful technique. However, its strong non-linearity makes it susceptible to converging towards local extremes during the iterative process due to various factors like insufficient low-frequency information or an inadequate initial model. The existing elastic envelope inversion can offer a promising initial model for EFWI when low-frequency information is unavailable, reducing the dependence on both the initial model and low-frequency data. However, its accuracy is affected by the quality of the source wavelet, potentially causing the EFWI to run in the wrong direction if there is a discrepancy between the simulated wavelet and the field wavelet. To address these issues and enhance the reconstruction of large-scale information in the model, we propose a novel approach called source-independent elastic envelope inversion, employing the convolution method. By combining this method with source-independent multiscale EFWI, we effectively establish P- and S-wave velocity models even in situations with inaccurate wavelet information. The results of testing on a portion of the Marmousi2 model demonstrate the effectiveness of this technique for both full-band and low-frequency missing data scenarios.

Recent Increase in a Recurrent Pan-Atlantic Wave Pattern Driving Concurrent Wintertime Extremes

Abstract Wintertime extremes such as cold spells and heavy precipitation can have severe socioeconomic impacts, disrupting critical infrastructures and affecting human well-being. Here, we relate the occurrence of local and concurrent cold or wet wintertime extremes in North America and Europe to a recurrent, quasi-hemispheric wave-4 Rossby wave pattern. We identify this pattern as a fundamental mode of the Northern Hemisphere (NH) winter circulation, since wave 4 exhibits phase-locking behavior. Thus, the associated atmospheric circulation and surface anomalies reoccur over the same locations when the pattern’s wave amplitude is high. The wave pattern is most pronounced over the pan-Atlantic region, and increases the probability of extreme cold or wet events by up to 300% in certain areas of North America and Europe, as well as favoring their concurrence at different locations. High-amplitude wave-4 events have increased significantly in frequency over the past four decades (1979–2021), although no clear evidence is found relating this to modes or patterns of climate variability. The identified wave pattern may provide pathways for early prediction of local and concurrent cold or wet wintertime extremes in North America and Europe.

Predicting the Heat Generation Rate in Large Format Pouch-Type Lithium-Ion Battery Cells for Automotive Applications

Accurate prediction of the heat generation rate of lithium ion batteries is critical for driving towards a cost effective and efficient design of a thermal management system for various applications.[1, 2] because of the increase in battery cell dimensions associated with an increase in battery cell capacity to drive towards a reduction in range anxiety for automotive applications, as well as more aggressive operating conditions that are typically seen in the automotive industry, such as direct current fast charge and sustained driving under load, heat generation at the cell level must be considered as spatially nonuniform during operation. Proper design of the thermal management system requires prediction[3] of the heat generation of the battery cell in both a lumped fashion for system sizing, as well as a distributed mode in order to focus cooling appropriately to decrease local temperature extremes. Local temperature extremes can adversely affect the aging of the battery cell due to the non-uniformities in electrochemical utilization of the active material, increased local reaction rates, and increased mechanical stresses in active materials.In this work, we detail our efforts to characterize the heat generation[4] of a battery cell under a variety of operating conditions, quantify the impact of temperature non-uniformities[5] on the aging of the battery cell, and experimentally[6, 7] evaluate the localized aging in the battery electrodes based on local temperature conditions. Here, we will describe the modeling methods developed to link operating conditions, aging, and heat generation with the electrochemical performance of the battery cell. A discussion of the model performance for relevant automotive applications will also be included.References M. Song, Y. Hu, S.-Y. Choe and T. R. Garrick, J Electrochem Soc, 167, 120503 (2020).Y. Hu, S.-Y. Choe and T. R. Garrick, Electrochim Acta, 362, 137124 (2020).M. Song, Y. Hu, S.-Y. Choe and T. R. Garrick, J Electrochem Soc, 169, 070502 (2022).Y. Hu, S.-Y. Choe and T. R. Garrick, Applied Thermal Engineering, 189, 116709 (2021).Y. Hu, S.-Y. Choe and T. R. Garrick, J Power Sources, 532, 231350 (2022).U. Janakiraman, T. R. Garrick and M. E. Fortier, J Electrochem Soc, 167, 160552 (2020).T. R. Garrick, J. Gao, X. Yang and B. J. Koch, J Electrochem Soc, 168, 010530 (2021).

The development of a method for visualizing the states of the national security system

The scientific task, which is solved in the research, is the cognitive display of the state of the national security system with a complex hierarchical structure. As a rule, images are created individually taking into account a specific application field and interpreted by an expert (a group of experts) based on accumulated knowledge. Cognitive mapping is designed to support decision making by an expert (group of experts), monitoring and managing in real time. The object of research is the system of ensuring national security. The subject of the research is the functioning of the national security system. The research developed a method of visualization of the states of the national security system. An overview of the methods of visual graphic presentation of information about the state of multidimensional objects and systems was carried out. The novelties of the proposed method are: ‒ creation of a visual, multi-level and interconnected description of the national security system; ‒ increasing the efficiency of decision making while assessing the state of the national security system; ‒ solving the problem of falling into global and local extremes while assessing the state of the national security system; ‒ combination of graphic and numerical display of controlled state parameters of the national security system; ‒ avoiding the problem of loops while visualizing the state of the national security system in real time. The specified method should be implemented in specialized software, which is used to analyze the state of the national security system and make management decisions.

An Improved Sparrow Search Algorithm for Global Optimization with Customization-Based Mechanism

To solve the problems of the original sparrow search algorithm’s poor ability to jump out of local extremes and its insufficient ability to achieve global optimization, this paper simulates the different learning forms of students in each ranking segment in the class and proposes a customized learning method (CLSSA) based on multi-role thinking. Firstly, cube chaos mapping is introduced in the initialization stage to increase the inherent randomness and rationality of the distribution. Then, an improved spiral predation mechanism is proposed for acquiring better exploitation. Moreover, a customized learning strategy is designed after the follower phase to balance exploration and exploitation. A boundary processing mechanism based on the full utilization of important location information is used to improve the rationality of boundary processing. The CLSSA is tested on 21 benchmark optimization problems, and its robustness is verified on 12 high-dimensional functions. In addition, comprehensive search capability is further proven on the CEC2017 test functions, and an intuitive ranking is given by Friedman's statistical results. Finally, three benchmark engineering optimization problems are utilized to verify the effectiveness of the CLSSA in solving practical problems. The comparative analysis shows that the CLSSA can significantly improve the quality of the solution and can be considered an excellent SSA variant.

An Adaptive Cuckoo Algorithm-Based Optimization Method for Tourist Traffic Routes

This paper presents in-depth research and analysis of the optimization method of tourist traffic routes using the adaptive cuckoo algorithm. The traditional cuckoo algorithm has the disadvantages of slow convergence speed and easy falling into local extremes. This paper proposes the iterative adaptive CHSACS algorithm based on iteration. Based on the original CS algorithm, a dynamic adaptive step control amount and a segmented weighted position update formula are introduced to give a class of improved CS algorithms to coordinate the problem of local search and global search of the CS algorithm and speed up the convergence speed at the later stage. To address the problem that the out-of-bounds nests interfere with the convergence of the algorithm, a memory strategy is introduced to relocate the out-of-bounds nests in the search space to improve the stability of the algorithm. Experiments are conducted on the iterative adaptive-based conductive CHSACS algorithm with test function sets. Compared with the original CS algorithm, and ACO algorithm, the CHSACS algorithm has faster convergence, higher search accuracy and a better ability to avoid local optima when dealing with continuous function optimization problems. For the dynamic travel path problem of travel time optimization, the road travel time and sightseeing time of different periods are predicted based on historical data, and the most traveled distance of travel time between two sights is dynamically searched by using the search mechanism based on time windows. The iterative adaptive CHSACS algorithm is used to continuously find the tourist traffic route with the shortest travel time travel path from all combinations of tour sequences. This paper combines the characteristics of bus vehicle scheduling itself, takes into account the interests of both bus companies and tourists, establishes a bus vehicle scheduling model with the departure interval as the independent variable, introduces this hybrid cuckoo algorithm into bus scheduling, verifies the scientific and feasibility of the algorithm through examples, and provides a new idea for solving bus scheduling optimization problems.

An Improved Hunger Games Search Algorithm-based Multi-peak MPPT Control for PV System under Partial Shading

Background: In photovoltaic power generation systems, partial shading may cause the PV array to mismatch, thus leading to multi-peak output characteristics, which makes the conventional Maximum Power Point Tracking (MPPT) algorithm easily fall into local extremes and cause power loss. Objective: The study aimed to accurately and quickly track the maximum power point of PV arrays in partial shading through swarm intelligence algorithms. Methods: Based on the above, a MPPT control algorithm based on Chaos Adaptive Hunger Games Search with Dynamic Lévy Mutation Strategy (CAHGSL) is proposed in this paper. By introducing an improved logistics chaos map initialization population, a nonlinear adaptive convergence factor and a dynamic Lévy mutation strategy enhance their ability to jump out of local extremes during multi-peak MPPT and improve their tracking speed and efficiency. Results: Under the three working conditions, the tracking efficiency of the MPPT algorithm proposed in this paper has been achieved by more than 99.5% in an average time of 0.152s, which is higher tracking efficiency compared to the PO, PSO, and HGS algorithms. Conclusion: The results show that the MPPT algorithm proposed in this paper can balance the tracking speed and efficiency with less power oscillation during the tracking process, and can ensure stable output after convergence. The method proposed in this paper is helpful to improve the output power of PV arrays under partial shading.