Mapping Model Research Articles

A Segment Anything Model based weakly supervised learning method for crop mapping using Sentinel-2 time series images

Automatic crop mapping is essential for various agricultural applications. Although fully convolutional networks (FCNs) have shown effectiveness in crop mapping, they rely on labor-intensive pixel-level annotations. Weakly supervised semantic segmentation (WSSS) methods offer a solution by enabling pixel-level segmentation with less costly annotations, such as point, bounding box (bbox), and image-level annotations. However, these weak annotations often lack complete object information, leading to reduced accuracy. Moreover, there is limited exploration of WSSS methods in medium-resolution satellite imagery, such as Sentinel-2. Therefore, this study proposes SAMWS, a WSSS method based on the Segment Anything Model (SAM) for crop mapping using Sentinel-2 time series images. Leveraging SAM, our method can generate high-quality pseudo labels and accommodate various weak annotations without extensive adjustments. SAMWS comprises three stages: 1) finetuning SAM with adapters; 2) generating pseudo labels using weak annotations and finetuned SAM; and 3) training a segmentation network using pseudo labels for crop mapping. Experiments conducted on PASTIS and Munich datasets demonstrate the superiority of our approach. After SAM was finetuned with adapters, the F1-scores for parcel segmentation using point and bbox prompts increased by 75.0 % and 14.4 %, respectively, reaching 0.880 and 0.931. Additionally, our method achieves classification results closest to fully supervised learning when using point, bbox, and image-level annotations, with F1-scores of 0.810, 0.817, and 0.779, respectively. Our approach offers valuable insights into leveraging foundation models in the remote sensing domain and contains significant potential for crop monitoring. The relevant code will be made publicly available at https://github.com/Nick0317Sun/SAMWS.

Auditory perception based milling posture detection and depth control enhancement for orthopedic robots

The use of robots for bone milling is thought to improve surgical efficiency and reduce surgeon fatigue. Surgeons can use their hearing to perceive the milling state and adjust the position of surgical tools. Inspired by this, this paper proposes an automatic tool position control method based on acoustic signal feedback. Firstly, the mapping model between milling depth and sound energy was calibrated through experiments, and the influence of different angles on depth estimation accuracy was tested. Then, a network of CNN-GRU with an attention mechanism is designed to realize milling angle recognition using acoustic features as inputs. Finally, a milling depth automatic control framework with angle optimization was designed and validated through relevant experiments. The results of the experiment showed that the depth estimation error was larger when the milling angle of ball end milling tools (BMT) was within 70-90°. After adding the angle adjustment function, the milling depth accuracy in angle interval 1 (80-90°) and angle interval 2 (70°-80°) increased by about 30% and 10%, respectively.

Creep-fatigue damage level evaluation based on the relationship between microstructural evolution and mechanical property degradation

Creep-fatigue interaction is identified as a primary failure mode for components operating under high temperatures. As operational durations extend, this interaction not only alters the material's microstructures but also initiates a gradual degradation in mechanical properties, significantly impacting its deformation and damage behaviors. In this work, the dynamic microstructural evolution of GH4169 superalloy during creep-fatigue was elucidated via qualitative characterization, and damage level evaluation method was subsequently developed by bridging microstructure degradation to mechanical property degradation. Creep-fatigue tests were performed at 650 °C with various tensile holding times and were interrupted at lifetime fractions of 10 %, 50 % and 80 % for further analysis and tensile evaluations. Results revealed that the prolonged exposure to holding times induced the coarsening of γ″ precipitates alongside an increase in low-angle grain boundaries, culminating a reduction in creep-fatigue strength. The development of voids and cracks exacerbated the degradation of elongation, leading to a hybrid fracture mode encompassing both intergranular and transgranular cracking paths. Synthesizing microstructural evolutions to qualitatively categorize diverse degradation levels imparted a robust physical basis for damage evaluation. A mapping model was established to correlate the average kernel average misorientation (micro-degradation indicator) with the tensile plastic strain energy density (macro-degradation indicator). The damage level evaluation method was endowed with quantitative metrics utilizing this model, and its generality was additionally validated in P92 steel. This work offers an insight into the quantitative damage evaluations of creep-fatigue-induced degradations in materials, thereby providing a theoretical basis for the development of operation management and inspection plans of components.

Landslide susceptibility mapping using modified frequancy ratio method in Correb area, South Wollo, North-Western Ethiopia

Correb area in Mekdela Woreda, South-Wollo Zone, North-Western Ethiopia is one of the most landslide affected region. The primary aim of this study was to assess the landslide susceptibility of the area and create a landslide susceptibility map. A bivariate statistical frequency ratio model was used to accomplish this goal. Using fieldwork and Google Earth picture interpretation, a map of landslides was made. A total of 524 landslides were identified and then classified in to training landslide (75%) for model development and validation landslide (25%) for model validation. Eight landslide causative factors slope, aspect, elevation, distance to drainage, distance to spring, slope material, distance to lineament, and vegetation cover were integrated with training landslides in order to determine frequency ratio value(weight) for each class of landslide causative factors. Furthermore, each causative factor’s prediction rate or weight was established. Relative frequency values were allocated to the appropriate factor classes and prediction rate values were allocated to the appropriate causative factors and summed using a raster calculator algorithm in order to produce the landslide susceptibility map. The final landslide susceptibility map revealed that 27.54% (31.52 km2) of the study area falls under very low susceptible, 30.35% (34.74 km2) low susceptible, 21.36% (24.44 km2) moderate susceptible, 14.74% (16.88 km2) high susceptible, and the rest 6.01% (6.88 km2) as very high susceptible. This shows that more than 20% of the area has probability and risk of landslide while about 80% has relatively low probability and risk of this natural hazard. The performance of modified frequency ratio model for landslide susceptibility mapping was validated using simply overlay, relative landslide density index, and receiver operating characteristics curve. Both training and validation landslides were rare in the very low and low susceptibility classes and strongly concentrated in the high and very high susceptibility classes, according to validation using simple overlay and the relative landslide density index. The implemented model performed exceptionally well, as seen by the receiver operating characteristics curve, which showed 82.6% success rate and an 83.1% prediction rate. In general, the model produced reasonable accuracy. The resultant map would be useful for general land use planning, site selection, and landslide prevention and mitigation programs.

Developing mapping algorithms to predict EQ-5D health utility values from Bath Ankylosing Spondylitis Disease Activity Index and Bath Ankylosing Spondylitis Functional Index among patients with Ankylosing Spondylitis

BackgroundPreference-based measures of health-related quality of life (HRQoL), such as the EQ-5D or the SF-6D, are essential for health economic evaluation. However, they are rarely included in clinical trials of ankylosing spondylitis (AS). This study aims to develop mapping algorithms to predict EQ-5D-3L and EQ-5D-5L health utility scores from the Bath Ankylosing Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI).MethodsPatients with AS were recruited from the largest tertiary hospital in Shandong province, China, between December 2019 and October 2020. Patients were selected by convenience sampling method according to the following criteria: (1) diagnosed with AS according to the New York criteria; (2) aged 18 years and above; and (3) without mental disorders; (4) able to understand the questionnaires; (5) without serious complications. There were 243 patients who completed the face-to-face questionnaire survey, and 5 cases with missing values in key variables were excluded. Ordinary least squares, censored least absolute deviations, Tobit, adjusted limited dependent variable mixture model and beta-mixture model (BM) in the direct approach and ordered logit and multinomial logit (Mlogit) model in the response approach were used to develop mapping algorithms. Mean absolute error, root mean square error, Spearman’s correlation coefficient and concordance correlation coefficient were used to access predictive performance.ResultsThe 238 patients with AS had a mean age of 35.19 (SD = 9.59) years, and the majority (74.47%) were male. The observed EQ-5D-3L and EQ-5D-5L health utility values were 0.88 (SD = 0.12) and 0.74 (SD = 0.27), respectively. The EQ-5D-5L had higher conceptual overlap with the BASDAI and BASFI than the EQ-5D-3L did. The Mlogit was the best-performing model for the EQ-5D-3L, and the BM showed better performance in predicting EQ-5D-5L than other direct and indirect mapping models did.ConclusionThis study demonstrates that the EQ-5D-5L, rather than EQ-5D-3L, should be selected as the target outcome measure of HRQoL in patients with AS in China, and the BM mapping algorithm could be used to predict EQ-5D-5L values from BASDAI and BASFI for health economic evaluation.

Simulink Modeling and Analysis of a Three-Dimensional Discrete Memristor Map

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos.

Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms

Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms

GIS-Based Digital Twin Model for Solar Radiation Mapping to Support Sustainable Urban Agriculture Design

GIS-Based Digital Twin Model for Solar Radiation Mapping to Support Sustainable Urban Agriculture Design

Message steganography using separate locations and blocks

A novel method of message steganography is introduced to solve the disadvantages of traditional least significant bit (LSB) based methods by dividing the covering-stego image into a secret number of blocks. A chaotic logistic map model was performed using the chaotic parameters and the number of image blocks for generating a chaotic key. This key was then sorted, and the locations of blocks 1 to 8 were used to select the required blocks to be used as covering-stego blocks. The introduced method simplifies the process of message bits hiding and extracting by adopting a batch method of bits hiding and extracting. A comparative analysis was conducted between the outcomes of proposed method and those of prevalent approaches to outline the enhancements in both speed and quality of message steganography.

Research on the construction and mapping model of knowledge organization system driven by standards

With the rapid development of artificial intelligence and enterprise digital transformation, the standardization organization, storage and management of semantic knowledge in computers have become the current research focus. As the core theory of knowledge system construction, knowledge organization (KO) provides theoretical support for the study of semantic knowledge organization and representation, among which knowledge organization system (KOS) is the important tool of semantic organization. At present, many scholars have carried out research from different perspectives of KOS based on theory, which provides the direction for the sustainable development of KOS. However, most of these studies focus on some aspects of KOS, which are in a "scattered" state, lacking systematic analysis of the basic principles of KOS construction and semantic organization based on theories and international standards. Therefore, this paper firstly constructs KOS theoretical models in the conceptual world and computer world respectively through a comprehensive study of multi-disciplinary basic theories such as semantics, logic, system theory, and international standards such as ISO 1087:2019, ISO 25964:2013, and ISO 11179:2023, and traces the iterative construction, organization and mapping process from "concept" in the conceptual world to "metadata" knowledge and semantics in the computer world. The semantic organization based on metadata is realized in computer. Secondly, on this basis, in order to realize ontology representation of domain knowledge, the ontology construction method based on MDR metadata is proposed. Finally, taking the semantic organization and ontology construction of Epicentre model in petroleum field as an example, the feasibility of the ideas and methods proposed in this paper is verified. The model and method proposed in this paper is independent of the specific type of KOS, so it is innovative and universal. The methodology is also applicable to other fields of conceptual system modeling, metadata standard construction, and data model modeling.

Real-time digital twin of autonomous ships based on virtual-physical mapping model

The advancement of intelligent technology has propelled the development of smart unmanned vessels into a new phase. To address the urgent demands of current smart ship development, this paper develops a comprehensive ship digital twin system based on a virtual-real mapping algorithm, focusing on the fundamental elements of digital twin model construction. Using the smart unmanned experimental ship Dolphin 1 as a prototype, a digital twin virtual model is proposed. This system leverages real-time internal and external data from the entire vessel to track its navigational status, performance indicators, sailing trends, and surrounding flow field information, offering coordinated “human-machine” navigation assistance. Based on historical data collected from the vessel's long-term navigation, a real-time precise prediction of the vessel's navigational state and hydrodynamic performance is conducted using physics-informed neural network algorithm. This establishes a self-learning iterative virtual-physical mapping model that enables autonomous updates and evolution. As the real navigation data of the vessel continuously update, the virtual model can more accurately simulate the vessel's state in real time. The proposed digital twin model has been tested through sea trials under real sea conditions, demonstrating its high accuracy, robustness, and potential for enhancing navigational safety and efficiency. This system marks a significant step forward in the integration of digital twin technology with maritime navigation, providing a valuable tool for the future development of smart shipping.

Design of TCP-actuator-driven, soft-tendon-integrated anthropomorphic dexterous hand: SoroAgilHand-1

This paper presents the design, analysis, and development of a dexterous hand driven by TCP actuators, termed the SoroAgilHand-1. Using the elastic stress of the TCP array with the measured Young's modulus, a mapping model of the tendon-contracted length to the joint angle of the dexterous hand was developed and validated by the recorded trajectory of the thumb-index finger. We introduced a self-sensing model for the TCP array in the SoroAgilHand-1 system and established an integrated closed-loop control system using the tensile sensors and strain-measuring devices. Considering the Kapandji test and the Feix taxonomy, the grasping performance of SoroAgilHand-1 was evaluated using postures from the collected data. The experimental results show that the soft dexterous hand can achieve 33 grasping postures and pass the Kapandji test, implying that it fulfils the anthropomorphic design goal using TCP actuators. Moreover, based on the grabbing stability test from the Feix taxonomy, the finger-tip trajectory of the closed-loop controller for the driven TCP was significantly improved and exhibited greater stability than that in open-loop driving.

Uncertainty Cost Functions for Wave Energy

Wave energy is considered as an important energy source for people living near coastal areas to meet their basic energy needs. Innovative and new technologies can be used for this energy utilization to run the loads from sea waves. Due to its uncertain power availability, we can use uncertainty cost functions based on probability distributions for the availability of the operation of the wave energy microgrid. These probability distributions are based on several mapping models designed for the wave energy, wave height, and wave speed mapping to make the energy more stable and reliable. In contrast to other renewable energy sources, wave energy is inherently unpredictable, making it impossible to model with a single, globally applicable probability distribution function (PDF). The prediction of the behavior of primary wave energy for this purpose is completely based on several probability distribution functions (PDFs) which can be considered as the best for all conditions. In this paper, we have used the Weibull-Rayleigh probability distribution model to develop the uncertainty cost function for wave energy. The Monte-Carlo process is carried out to get the results supported by the Rayleigh probability distribution model consisting of wave height, and uncertain cost histograms. Cost is minimized by using the Weibull Rayleigh model for both overestimation and underestimation costs of wave energy. Monte-Carlo simulation results are further compared with analytical calculation and error between them.

Mapping the Influence of Light Intensity on the Transgenerational Genetic Architecture of Arabidopsis thaliana.

Light is a crucial environmental factor that influences the phenotypic development of plants. Despite extensive studies on the physiological, biochemical, and molecular mechanisms of the impact of light on phenotypes, genetic investigations regarding light-induced transgenerational plasticity in Arabidopsis thaliana remain incomplete. In this study, we used thaliana as the material, then gathered phenotypic data regarding leaf number and plant height under high- and low-light conditions from two generations. In addition to the developed genotype data, a functional mapping model was used to locate a series of significant single-nucleotide polymorphisms (SNPs). Under low-light conditions, a noticeable adaptive change in the phenotype of leaf number in the second generation suggests the presence of transgenerational genetic effects in thaliana under environmental stress. Under different lighting treatments, 33 and 13 significant genes associated with transgenerational inheritance were identified, respectively. These genes are largely involved in signal transduction, technical hormone pathways, light responses, and the regulation of organ development. Notably, genes identified under high-light conditions more significantly influence plant development, whereas those identified under low-light conditions focus more on responding to external environmental stimuli.

Digital twins and deep maps

AbstractMapping is now thoroughly digital at all stages of production and maps are widely used in digital form. This digital turn has transformed the nature of mapping and maps. Maps need no longer be static representations, but rather constitute spatial media, providing an interactive, dynamic means for creating, discussing, and sharing spatial information and mediating spatial practices. This has included the development of 3D mapping, including nascent digital twins and digital deep maps. In this short paper, we reflect on our attempts to produce a 3D city information model for Dublin that acts as a basic digital twin, which we have also used to explore deep mapping, as well as map projecting data onto a printed 3D map model of the city. We consider what digital twins and deep maps mean for how we understand the nature of mapping, arguing that they produce a dyadic intertwining of map and territory; a literal, material expression of post‐representational, ontogenetic conceptions of mapping.

A feasibility study of dosimetry for breast cancer radiotherapy based on body surface changes.

The requirement for precise and effective delivery of the actual dose to the patient grows along with the complexity of breast cancer radiotherapy. Dosimetry during treatment has become a crucial component of guaranteeing the efficacy and security. To propose a dosimetry method during breast cancer radiotherapy based on body surface changes. A total of 29 left breast cancer radiotherapy cases were retroactively retrieved from an earlier database for analysis. Non-rigid image registration and dose recalculation of the planning computed tomography (CT) referring to the Cone-beam computed tomography were performed to obtain dose changes. The study used 3D point cloud feature extraction to characterize body surface changes. Based on the correlation proof, a mapping model is developed between body surface changes and dose changes using neural network framework. The MSE metrics, the Euclidean distances of feature points and the 3D gamma pass rate metric were used to assess the prediction accuracy. A strong correlation exist between body surface changes and dose changes (first canonical correlation coefficient=0.950). For the dose deformation field and dose amplitude difference in the test set, the MSE of the predicted and actual values were 0.136 pixels and 0.229 cGy, respectively. After deforming the planning dose into a deformed one, the feature points' Euclidean distance between it and the recalculated dose changes from 9.267±1.879mm to 0.456±0.374mm. The 3D gamma pass rate of 90% or higher for the 2mm/2% criteria were achieved by 80.8% of all cases, with a minimum pass rate of 75.9% and a maximum pass rate of 99.6%. Pass rate for the 3mm/2% criteria ranged from 87.8% to 99.8%, with 92.3% of the cases having a pass rate of 90% or higher. This study provides a dosimetry method that is non-invasive, real-time, and requires no additional dose for breast cancer radiotherapy.

A Unique Conditions Model for Landslide Susceptibility Mapping

Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic regression. We discuss the assumptions and interpretations of these methods, the relationships between them, and their strengths and weaknesses in case of categorical factors. Of particular interest is the conditional independence of the controlling factors and its effect on model bias. To avoid lack of conditional independence of factors and model bias, we present a unique conditions model that is always unbiased. To illustrate the theoretical developments, a practical application is given using observed landslides and geo-environmental factors from a previous study. The unique conditions model appears superior to the other models.

How artificial intelligence can enable data classification for market sizing - Insights from applications in practice

Determining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging across competitive, market, financial, and technology sources, and typically necessitating significant manual work to analyze, reconcile, and integrate. The authors present an approach for classification of data from one of these sources, facilitating aggregation and analysis of intelligence information. We describe a concept proof using machine learning that extends a model for automatic mapping of publicly available budget data to segments and subsegments of a market segmentation taxonomy. This approach automates the tagging of market and market segment for each program and cost element by training classification models on the manually labeled historical data. We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. This work's contribution is demonstrating how NLP and machine learning techniques can provide useful data classification and automatic classification even when source data diverges from its specified taxonomic description.

Spatial syntax analysis on the renewal of the external space of mining heritage buildings —— A case study of Zollverein Coal Mine Industrial Complex

ABSTRACT The renovation of external spaces of mining heritage buildings should respect their historical and cultural value and be adapted to the specific characteristics of the mining area for spatial restructuring and functional replacement. Current research often relies on subjective experience and focuses on qualitative analysis, lacking in-depth quantitative analysis. Building upon the summary of research on mining heritage exterior spaces and spatial syntax studies, this paper proposes a research methodology that quantitatively analyzes the accessibility and integration of mining heritage exterior spaces using spatial syntax convex space and axis map models. Taking the exterior connecting spaces and characteristic spaces before and after the renovation of the German Zollverein Coal Mine Industrial Complex as the research objects, the impact of spatial location on different circulation organizations and functions of spatial places is visually expressed, providing a more intuitive explanation of the complex relationships in mining heritage exterior spaces. The research findings reveal that the spatial syntax model is adaptable in evaluating the renovation of mining heritage exterior spaces, with spaces exhibiting high integration being essentially consistent with key and highly utilized exterior spaces within the renovated mining area. In summary, it can be concluded that the integration of mining heritage exterior spaces and consequently the utilization rate and value can be improved through spatial “Connection”, “Growth”, and “Darning”, thereby offering direct reference for subsequent research and practical organization of mining heritage architectural exterior space renewal.

Explainable artificial intelligence models for mineral prospectivity mapping

Explainable artificial intelligence models for mineral prospectivity mapping