Mapping Model Research Articles

High-traversability and efficient path optimization for deep-sea mining vehicles considering complex seabed environmental factors

In cobalt-rich crust regions, rapid and safe pathfinding is critical for the operation of Deep-Sea Mining Vehicles (DSMVs). However, existing pathfinding solutions rarely account for traversability predictions in complex mining environments and extensive map data often results in computational inefficiencies. This paper proposes a High-Traversability and Efficient Path Optimization (HTEPO) strategy designed to ensure safe navigation and rapid decision-making accounting for complex seabed environmental factors. Initially, we develop a traversability assessment model combining the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation (FCE) method. This model assigns traversability scores to map out high-risk and impassable areas effectively. We then introduce a novel map model that integrates these scores with Voronoi diagrams to reduce search space and improve computing efficiency, ensuring safer route selections. Further, based on traversability index and map model, an enhanced A* algorithm equipped with adaptive heuristic weights is utilized to swiftly identify high-traversability paths for the DSMVs. Finally, three case studies with different seabed mining environments validate the excellent performance of the proposed method over other major existing methods in terms of path safety and computational efficiency.

Extended Beta models for poverty mapping. An application integrating survey and remote sensing data in Bangladesh

Extended Beta models for poverty mapping. An application integrating survey and remote sensing data in Bangladesh

Digital-driven in-situ monitoring for thermally-induced volumetric errors of CNC machine tools

In order to realize the in-situ monitoring of thermally-induced volumetric errors of CNC machine tools, a digital twin system is designed and developed through combined programming of C#, ANSYS, and MATLAB. The thermal error mapping models are constructed through superposing the axis motion-induced thermal errors and the spindle rotation-induced thermal deformations. The models of axis motion-induced thermal errors are established based on the orthogonal polynomial tabular modeling method, and real-timely corrected through correction models which are constructed based on the predicted and measured errors. The spindle rotation-induced thermal deformations are obtained via digital twin for thermal characteristics of the spindle system. The error distributions are generated by MATLAB through pre-creating the DLL file. Experimental results show that the prediction accuracy of digital twin-driven in-situ monitoring for thermally-induced volumetric errors is greater than 95 %, which effectively improvs the prediction accuracy of thermally-induced volumetric errors of CNC machine tools and provides a basis for thermal error compensation.

Spatial Characteristics Visualization of Transfer Point Infrastructure

The government's efforts to increase public transport users in Palembang by providing public transport such as LRT, Teman Bus and angkot feeder LRT Musi Emas. The previous research shows that there are still deficiencies in public transport services. The condition of physical integration between public transport, such as no bus stop, inadequate sidewalk facilities and even in some locations they are not available, no crossing bridges, no lighting, no information on routes, schedules and several other things. Based on these conditions, this research will study physical integration between public transport modes by visualizing the spatial characteristics of transit point facilities in Palembang City. The method used for visualization is a mapping model using the GIS 10.8 application. The results of this research show that the districts with the most transfer points in Palembang are Ilir Timur I and Alang-Alang Lebar which have LRT stations, bus stops and river mode infrastructures. The results of this research can be used as a reference for improving public transport physical integration in Palembang in areas served by public transport. The visualization results of transfer point users in Palembang have shown that Ilir Barat I and Sukarame Districts have the highest number of users of transfer points infrastructure LRT station. Based on this research, it is known that the availability of transfer points infrastructure is not necessarily accompanied by a high number of public transport users in one sub-district when compared to other sub-districts. This is partly due to inadequate condition of physical integration between public transport.

Mapping Crop Types for Beekeepers Using Sentinel-2 Satellite Image Time Series: Five Essential Crops in the Pollination Services

Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, this study aimed to develop DL-based classification models for mapping five essential crops in pollination services in Quebec province, Canada, by using Sentinel-2 SITS. Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1DTempCNNs), one-dimensional spectral CNNs (1DSpecCNNs), and long short-term memory (LSTM). Accordingly, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1DTempCNN and LSTM models, and spectral features using the 1DSpecCNN model. Our findings indicated that the LSTM model (macro-averaged recall of 0.80, precision of 0.80, F1-score of 0.80, and ROC of 0.89) outperformed both 1DTempCNNs (macro-averaged recall of 0.73, precision of 0.74, F1-score of 0.73, and ROC of 0.85) and 1DSpecCNNs (macro-averaged recall of 0.78, precision of 0.77, F1-score of 0.77, and ROC of 0.88) models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using Sentinel-2 SITS. Furthermore, applying one-dimensional convolution (Conv1D) across the spectral domain demonstrated greater potential in distinguishing land covers and crop types than applying it across the temporal domain. This study contributes to providing insights into the capabilities and limitations of various DL-based classification models for crop mapping using Sentinel-2 SITS.

Pseudorandom high‐frequency injection synchronous reluctance motor sensorless control with parameter variation consideration

AbstractThe sensorless control of synchronous reluctance motor (SynRM) in the zero and low‐speed domain using the traditional high‐frequency injection method has the problems of audible noise caused by the high‐frequency injection and high‐frequency loss caused by the change of working conditions, which limits the practical application of SynRM in the industrial field. To solve the problem of high‐frequency noise and loss, a pseudo‐random high‐frequency injection method considering parameter variation is proposed in this paper. Firstly, the signal injection form was adjusted to expand the power spectral density of the high‐frequency current, and the current energy spike was suppressed to reduce high‐frequency noise. Secondly, the current demodulation was combined with the flux map model to complete the injection voltage amplitude adjustment, so that the response current was kept constant under multiple operating conditions to reduce the high‐frequency loss. At the same time, the flux map model is applied to the observer to reduce the rotor position estimation error caused by the cross‐coupling effect. Under the condition of satisfying the dynamic and steady performance requirements of sensorless control, the high‐frequency loss and sharp noise caused by the high‐frequency injection method are effectively suppressed. Finally, experiments were carried out on a 1.5 kW SynRM drive platform to verify the feasibility and effectiveness of the sensorless control scheme in this paper.

West Iranian in historical perspective

Abstract The development of the Persian definitive object marker -rā from Old Persian rādī through Middle Persian benefactive rāy has been established as a model grammaticalization process by Bossong (1985). His explanations on other modern Iranian languages, and their interplay in the historical development, have received far less research attention. This article offers a novel and comprehensive account of the historical development of -rā in West Iranian, from Old Persian up to approximately 20 New Iranian languages. Based on the semantic map model, it demonstrates that concerning -rā, most West Iranian languages can be subdivided into three typological groups, two of which represent more or less clearly defined genetic subgroups within the continuum of West Iranian. The interplay of genetic and typological/functional factors is complex, and open questions remain. The present study aims to provide empirical data, and to raise relevant questions, for future in-depth studies.

RETRACTION: Supply Chain Operation Evaluation and Management Decision by Fuzzy Cognitive Map Model

<scp>RETRACTION</scp>: Supply Chain Operation Evaluation and Management Decision by Fuzzy Cognitive Map Model

Estimation of direction and zero errors of satellite laser terminals in low-light conditions based on machine learning

In the assembly, launch, and on-orbit operation of satellite optical communication terminals, small deviations are difficult to avoid, which can lead to pointing errors and challenges to the establishment of optical communication links. To estimate the pointing errors of on-orbit satellite terminals, a calibration algorithm is developed based on lunar surface imagery. First, a feature extraction algorithm for low-light images is employed to process consecutive frames of low-light images to obtain a lunar surface feature map. Then, by combining the feature map and error estimation model, predictions of direction errors and zero errors were achieved. The ground validation results demonstrate the effectiveness and feasibility of the proposed on-orbit error estimation algorithm under low-signal-to-noise-ratio conditions.

Bibliometric Analysis of Spline Regression Model for Trend Mapping and Strategy Development Research Using Vosviewer

Bibliometric analysis is a quantitative method used to measure and analyze scientific literature. This technique involves the collection and analysis of publication data, such as journal articles, books, and conferences, to evaluate and understand patterns of scientific communication, research productivity, author collaboration, journal impact, and trends within a scientific field. Bibliometric analysis has a limitation in that it is challenging to visualize effectively, necessitating the use of software for proper visualization. Therefore, this article discusses bibliometric analysis on spline regression for trend mapping and strategy development using VOSviewer software. The research results show that the visualization of spline regression keywords with VOSviewer can help in understanding patterns of relationships between variables, research trends, and network structures in scientific literature. Based on the analysis results, bibliometric analysis on spline regression can be visualized in trend mapping, which can aid in planning further research strategies, including identifying collaboration opportunities and underexplored research areas.

Mining and Visualization of Tourism Cultural Image Based on the Information Transmission Model of Tourism Cultural Map—Taking Nanjing Xuanwu Lake Tourist Attraction as an Example

Mining and Visualization of Tourism Cultural Image Based on the Information Transmission Model of Tourism Cultural Map—Taking Nanjing Xuanwu Lake Tourist Attraction as an Example

Can we use viral receptor mapping and particle deposition models to predict the clinical severity of novel airborne pathogens?

Can we use viral receptor mapping and particle deposition models to predict the clinical severity of novel airborne pathogens?

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

A deep learning model optimized by Bayesian Optimization with Hyperband for fast prediction of the elastic properties of 3D tubular braided composites at different temperatures

AbstractThree dimensional (3D) tubular braided composites are widely used in various industries due to their excellent mechanical properties and lightweight characteristics. However, traditional numerical and experimental methods face challenges in predicting mechanical properties quickly and accurately due to factors such as ambient temperature, component materials, and geometric parameters. To address this issue, this paper combines deep neural networks (DNN) and two‐scale finite element analysis to accelerate the solution speed. The dataset is first obtained through a two‐scale finite element model with temperature based on micro‐CT. Then, the mapping model of macroscopic compression elastic properties and the influencing factors of material properties is established by DNN and Bayesian Optimization with Hyperband (BOHB) hyperparameter optimization algorithm. The rapid prediction of axial compression elastic properties of 3D tubular braided composites under different ambient temperatures, component materials, porosities, braiding angles and fiber volume contents is achieved. Finally, the accuracy of the predicted results of the constructed model is verified by experiments.Highlights A BOHB optimized deep learning model coupled with a finite element framework is proposed Fast prediction of elastic properties of 3D tubular braided composites at different temperatures The accuracy of the prediction results of the constructed model is verified by experiments

Correlation between Different Factors of Non-point Source Pollution in Yangtze River Basin

This research used the improved export coefficient model to estimate non-point source load in sub-watersheds and a self-organizing map model to identify the correlation relationship of factors, that can be affected by non-point source load in Yangtze River Basin. Research results indicate that total nitrogen load is 4.87 - 15.53 kg/ha; total phosphorus load is 0.18 - 0.51 kg/ha; Dong Ting Lake sub-basin has the lowest contribution value of total nitrogen and phosphorus; Tai Lake sub-basin has the highest contribution load of total nitrogen and total phosphorus; Jinsha River sub-basin reveals the highest value of total nitrogen and phosphorus contribution on grassy land and desert land; Tai Lake sub-basin has the highest total nitrogen and total phosphorus load on forest, agricultural land, and urban construction land. The correlation relationship of factors reveals a very close correlation with each other as precipitation, total nitrogen load, and total phosphorus load factors have correlation in the highest value, in which total nitrogen load and total phosphorus load factors have a close correlation with each other from the lowest value to highest value; wetland and barren land factors correlate with the lowest value and the highest value; agricultural land, forestland, and grassy land factors correlate with one another in the high value and the highest value. The forest cover rate factor, as well as the population factor, does not correlate with other factors.

Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting

Urban planning and management increasingly depend on accurate building and population data. However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. This research contributes to bridging the gap between data needs and availability in resource-constrained urban environments, offering a scalable solution for global application in urban planning and population mapping.

Combining style generative adversarial networks with particle swarm optimisation-support vector regression to design affective social robot for public health intervention

In order to improve the attractiveness of social robot serving health interventions in public workspace, we propose a product design method with the combination of Style-generative adversarial network (StyleGAN) model and particle swarm optimisation-support vector regression (PSO-SVR). This paper aims to explore the modelling generation method of social robots for health intervention based on artificial intelligence generated content (AIGC) and a mapping model between the shape characteristics and users’ visual perception based on Kansei Engineering (KE). Firstly, to address the defects of typical KE over-reliance on existing samples, we introduce the StyleGAN model in AIGC to learn and train existing robots’ shape samples and generate new robots’ shape sample images. Secondly, the morphological deconstruction method is used to deconstruct the shape features of the new robot sample. Factor analysis (FA) is also used to reduce dimension and cluster emotional words and establish a Likert scale between the shape features of robots and users’ emotional vocabulary. Finally, particle swarm optimisation-support vector regression (PSO-SVR) is used to establish a mapping model between the shape features of social robots and the emotional images of users, thus obtaining the most attractive shape combination scheme. The research results showed that the StyleGAN model in AIGC can be used to assist industrial designers in creative expression and provide rich sample sources for KE; and the PSO-SVR of machine learning can be used to build a mapping model among users’ visual feelings, emotional images and shape features. In the end, we designed an attractive social robot for public health intervention.

An Indoor-Outdoor Layered HD Map Construction for Unmanned Ground Vehicles

Abstract. High-Definition Maps (HD Maps) are lane-level 3-dimensional road network models that ensure the safe operation of autonomous vehicles and drive the continuous advancement of autonomous driving technology to higher levels. However, HD Maps currently face challenges regarding limited coverage and description of indoor environments (e.g. underground parking), as well as the lack of uniformity between indoor and outdoor map formats. Therefore, this paper proposes a Layered HD Map model for both indoor and outdoor environments. The map model consists of 6 layers, allowing effective representation of indoor and outdoor environments, as well as transitional areas. The paper also establishes an Unmanned Ground Vehicle (UGV) to collect and update map data using LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) and autonomous exploration technology, aiming to enhance the efficiency and automation of HD Map acquisition. In order to verify the validity of the proposed map model, this paper conducted a navigation experiment in a double decker parking lot containing entrances and exits. The results of the experiments demonstrate that the Layered HD Map generated through autonomous LiDAR SLAM can effectively describe the indoor and outdoor environments, and enable successful navigation of UGVs.

Research and application of intelligent diagnosis method for impeller fault based on centrifugal pump digital twin flow field cloud map

With the development of industrial technology, the demand for health diagnosis and maintenance of centrifugal pumps is becoming increasingly urgent. Combining digital twin and machine vision technology, this paper proposes an intelligent diagnosis method for centrifugal pump impeller machinery fault based on digital twin flow field cloud map. Firstly, the centrifugal pump digital twin model is used to simulate the evolution of random fracture fault of impeller blades, and the impeller flow field pressure and velocity cloud maps with different fault characteristics are generated; secondly, based on the learning and training of Yolov5 algorithm, two types of machine vision models of pressure and velocity cloud maps are obtained, and the preliminary diagnosis of impeller faults is realized by combining statistical analysis; then, considering the complementary advantages of the two types of detection models, the two are integrated based on the idea of stacking integration to improve the accuracy of impeller fault diagnosis. Experimental verification shows that for random fracture faults of impeller blades, the centrifugal spring intelligent fault diagnosis method proposed in this paper can achieve a diagnostic accuracy of more than 0.99, and the developed intelligent diagnosis system for centrifugal pump impeller machinery faults enables the method in this paper to be put into practice.

Influence of Carbon-Nitride Dot-Emitting Species and Evolution on Fluorescence-Based Sensing and Differentiation.

Carbon dots have attracted widespread interest for sensing applications based on their low cost, ease of synthesis, and robust optical properties. We investigate structure-function evolution on multiemitter fluorescence patterns for model carbon-nitride dots (CNDs) and their implications on trace-level sensing. Hydrothermally synthesized CNDs with different reaction times were used to determine how specific functionalities and their corresponding fluorescence signatures respond upon the addition of trace-level analytes. Archetype explosives molecules were chosen as a testbed due to similarities in substituent groups or inductive properties (i.e., electron withdrawing), and solution-based assays were performed using ratiometric fluorescence excitation-emission mapping (EEM). Analyte-specific quenching and enhancement responses were observed in EEM landscapes that varied with the CND reaction time. We then used self-organizing map models to examine EEM feature clustering with specific analytes. The results reveal that interactions between carbon-nitride frameworks and molecular-like species dictate response characteristics that may be harnessed to tailor sensor development for specific applications.