Mapping Model Research Articles

Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain

This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management.

Meta-analysis Influence of Integrated Mind Mapping Inquiry Based Learning Model on Student Problem Solving Skills

A This study aims to determine the effect of inquiry-based learning model integrated mind mapping on students' problem-solving skills. This type of research is quantitative research with meta-analysis methods. Data comes from international journals or proceedings published 2015-2023. Data search through ScienceDirect, Springer, ProQuest, ERIC and Taylor of Francis databases. Research should be selected for data in meta-analyses. The inclusion criteria are that research must be experimental methods or quasi-experiments, research indexed by Scopus, Sinta, Web of Science, Microsoft Academic and EBSCO, Research has an experimental class inquiry learning model integrated mind mapping and conventional model control class, research has a number of samples (N), correlation values (r), t, and F and a sample consisting of 1021 participants. Data analysis with the help of JASP to determine the value of effect size, heterogeneity test and publication bias. The results showed a positive influence of minde mapping-based inquiry learning model on students' problem solving skills ( z = 7.019; p < 0.001; 95% CI [0.61; 1.26]. This finding shows that the influence of mind mapping-based inquiry learning models is included in the very high category (rRE = 1.31).

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms

BackgroundAberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them. MethodsThe proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains. ResultsQualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance. ConclusionsThe amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks. General significanceThe model effectively enhances the capability to extract features and knowledge from protein fluorescence images.

AI Evaluation Authorities: A Case Study Mapping Model Audits to Persistent Standards

Intelligent system audits are labor-intensive assurance activities that are typically performed once and discarded along with the opportunity to programmatically test all similar products for the market. This study illustrates how several incidents (i.e., harms) involving Named Entity Recognition (NER) can be prevented by scaling up a previously-performed audit of NER systems. The audit instrument's diagnostic capacity is maintained through a security model that protects the underlying data (i.e., addresses Goodhart's Law). An open-source evaluation infrastructure is released along with an example derived from a real-world audit that reports aggregated findings without exposing the underlying data.

Open-Vocabulary Video Relation Extraction

A comprehensive understanding of videos is inseparable from describing the action with its contextual action-object interactions. However, many current video understanding tasks prioritize general action classification and overlook the actors and relationships that shape the nature of the action, resulting in a superficial understanding of the action. Motivated by this, we introduce Open-vocabulary Video Relation Extraction (OVRE), a novel task that views action understanding through the lens of action-centric relation triplets. OVRE focuses on pairwise relations that take part in the action and describes these relation triplets with natural languages. Moreover, we curate the Moments-OVRE dataset, which comprises 180K videos with action-centric relation triplets, sourced from a multi-label action classification dataset. With Moments-OVRE, we further propose a cross-modal mapping model to generate relation triplets as a sequence. Finally, we benchmark existing cross-modal generation models on the new task of OVRE. Our code and dataset are available at https://github.com/Iriya99/OVRE.

Abstract 899: Development of a deep learning model for cell type mapping in colorectal cancer using H&E images leveraging image-based spatial transcriptomics data

Abstract Purpose The tumor microenvironment (TME) is crucial in colorectal cancer as it influences disease progression, treatment response, and patient outcomes, providing valuable insights for personalized therapies and prognostic assessments. Here, we have developed a deep learning model by integrating hematoxylin and eosin (H&E) stained images of colorectal cancer and image-based spatial transcriptomics (Xenium) to infer spatial mapping of cell types in TME only using H&E images. Methods A total of 30 H&E images of colorectal cancer obtained by tissue microarray were registered with image-based spatial transcriptomics data (Xenium). Utilizing a Variational Autoencoder (VAE) based model and leveraging reference single-cell data enables the acquisition of cell types for individual cells in image-based spatial transcriptomics. A convolutional neural network (CNN) model was developed using H&E image as inputs to predict cell types in H&E-stained tissue image patches of colorectal cancer collected from various patients. The model also estimated the cell types from H&E-stained whole slide tissue image of colorectal cancer of The Cancer Genome Atlas (TCGA-COAD). Results The accuracy of the model's predictions for cell types using H&E image patches was notably high and exhibited a significant concordance with the results obtained through the validation. The Intersection over Union (IoU) metric for image segmentation indicated a value of 0.66 for epithelial cells and 0.44 for TNK cells. The output of deep learning model for epithelial cells and T/NK cells from TCGA-COAD tissue images showed a correlation with human-labeled regions of cancer epithelium and immune cells. Conclusions Leveraging image-based spatial transcriptomics, we developed a deep learning model capable of discerning various cell types within the tumor microenvironment solely from H&E images. This clinically translatable approach is valuable for investigating tumor microenvironment to develop biomarkers associated with various cancer therapeutics particularly immuno-oncology drugs. This approach can yield objective deep learning-based models without human labels for characterizing the tumor microenvironment in single-cell resolution, particularly regarding spatial immune distribution. Citation Format: Seungho Cook, Dongjoo Lee, Myunghyun Lim, Jae Eun Lee, Daeseung Lee, Hyung-Jun Im, Jung-Soo Pyo, Kwon Joong Na, Hongyoon Choi. Development of a deep learning model for cell type mapping in colorectal cancer using H&E images leveraging image-based spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 899.

PEMFC sealing modeling relating to fractal characteristics of gasket-BPP interface based on micro-contact

The interface leaking of the gasket-BPP sealing structure affects the efficiency, safety, and reliability of PEMFCs. Aiming to reveal the basic cause of interface leaking and predict the leakage rate, the contact behavior at the micro-scale had been researched by FEM with the fractal multi-scale method, which could eliminate the negative influence of a single observation scale on the gasket-BPP rough interface modeling. Furthermore, in order to relate the contacting behavior at the micro-scale to the leakage rate at the macro-scale of PEMFCs, the mapping model had been established to analyze the sealing performance of the entire interface through the self-affine and self-similar characteristics of the fractal multi-scale contacting model of the gasket-BPP. Not only macroscopic properties of gasket rubber materials are considered in this model, but also microscopic morphologies are taken in to investigate the interface leakage rate, which had not been studied in traditional macroscopic research. By comparing the compression curves and interface leakage rates of the flange sealing device in testing and calculating approaches, the accuracy and reliability of the mapping model had been verified, leading to the analysis and the prediction of the PEMFC interface leakage rate rapidly.

Abstract 898: Deep learning-based cell types scores in tumor microenvironment estimated by H&E images associated with PD-L1 status in lung adenocarcinoma

Abstract Purpose: The spatial distribution of cell types in the tumor microenvironment (TME) is associated with functional status of tumor immunology and eventually affects the response to immuno-oncology treatment. We have developed a deep learning model that was trained by integrating H&E images of lung adenocarcinoma with spatial transcriptomic data. We applied this model to predict various cell types within the TME using only hematoxylin and eosin (H&E) images to correlate with the PD-L1 status. Methods: A deep learning model to predict five cell types enrichment score maps from H&E images was trained by spatial transcriptomics data combined with matched H&E images. The five cell types of TME included B cells, T/NK cells, myeloid cells, fibroblasts, and epithelial cells. wAnother dataset based on tissue microarray (TMA) data of H&E images of lung cancer patients (n = 94) was used to predict cell type enrichment scores associated with PD-L1 status estimated by TPS score. The cell type scores derived from the H&E-stained lung cancer images were correlated with the PD-L1 status. Results: The cell types predicted by the model using H&E image patches showed a significant correlation with those determined through spatial transcriptomic data, serving as an internal validation. All cell types (B cells, T/NK cells, myeloid cells, fibroblasts, and epithelial cells) from TMA cores showed significant differences according to the PD-L1 expression groups. The enrichment scores of B cells, T/NK cells, myeloid cells and fibroblasts were significantly higher in the PD-L1 high group. Conclusions: Our research introduces a deep learning model for precise cell type mapping within the tumor microenvironment and applied to H&E-stained TMA cores. The relationship between PD-L1 status and the cell type enrichment scores within the tumor microenvironment, as predicted by the deep learning model analyzing H&E images, demonstrates the potential for using H&E-based characterization of the tumor microenvironment as a biomarker in immuno-oncology treatments. Citation Format: Seungho Cook, Haenara Shin, Jaemoon Koh, Hongyoon Choi, Kwon Joong Na. Deep learning-based cell types scores in tumor microenvironment estimated by H&E images associated with PD-L1 status in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 898.

Integrating MBD with BOM for consistent data transformation during lifecycle synergetic decision-making of complex products

Model-based definition (MBD) can be applied to normalized define and associate the whole lifecycle data of complex products by a three-dimensional (3D) digital model, which can further provide single data source for bill of material (BOM) transforming of different lifecycle stages. Therefore, a promising application combining MBD and BOM is to ensure the accuracy and completeness of information during various BOM transformation, and to provide consistent and reliable data support for lifecycle synergetic decision-making. However, the studies in the above field are facing many challenges. For example, the research and application of MBD in the whole lifecycle is still in its infancy. MBD and BOM are studied separately, and the overall solution that integrates these two pillars for lifecycle synergetic decision-making is almost vacant. A novel approach for consistent lifecycle data transformation is proposed to address these challenges. First, an overall conceptual framework is developed to show how the approach can be implemented. Second, a unified lifecycle data representation method with the MBD main-domain dataset is proposed. Third, based on the single data source from MBD dataset, xBOM multi-view transforming are realised by designing a set of mapping models and rules. Finally, an applicable scenario for an electric multiple units (EMU) bogie is presented to illustrate the effectiveness of the proposed approach. The result shows that the proposed approach can provide significant potential for industrial practitioners to carry out synergetic and optimal decision-making in the whole lifecycle management processes.

Integration of Digital Elevation Model (DEM) for Accurate Slope and Contour Maps as Fundamental Data in Spatial Modeling

Integration of Digital Elevation Model (DEM) for Accurate Slope and Contour Maps as Fundamental Data in Spatial Modeling

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and ε feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for ε constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

Hot compression behavior and microstructure evolution of Mg-Gd-Y-Zn-Zr alloy

In this work, the hot compression behavior and microstructure evolution of Mg-8Gd-3Y-xZn-0.6Zr (x = 0, 1.5) alloys were investigated by Gleeble-1500D thermal simulation test machine, SEM, EBSD and other equipment. The results showed that, prior to hot deformation, the grain size of Mg-8Gd-3Y-1.5Zn-0.6Zr (1.5Zn) alloy was smaller than that of Mg-8Gd-3Y-0.6Zr (0Zn) alloy. Both alloys exhibited weak texture strength with predominantly large angle grain boundaries. The true stress-strain curve of the 1.5Zn alloy distinctly revealed dynamic recrystallization features. Notably, the 1.5Zn alloy exhibited higher peak stress and thermal activation energy compared to the 0Zn alloy. Furthermore, the 1.5Zn alloy demonstrated a lower critical strain for dynamic recrystallization, suggesting a heightened likelihood and increased degree of recrystallization. After hot deformation, the Mg-8Gd-3Y-xZn-0.6Zr alloy experienced a notable reduction in grain size, particularly evident in the 1.5Zn alloy, where the grains were smaller compared to the 0Zn alloy. Moreover, the texture strength of 1.5Zn alloy was obviously weaker than that of 0Zn alloy. Dynamic precipitation and dynamic recrystallization were more likely to occur at grain boundaries. Simultaneously, the constitutive model, hot processing map and recrystallization kinetics model were constructed for Mg-8Gd-3Y-xZn-0.6Zr alloy.

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics

ObjectivesTo evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM).Materials and methodsIn this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison.ResultsThe validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar.ConclusionThe NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM.Clinical relevance statementThe NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity.Key Points• The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis.• Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors.• The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Mapping invasive noxious weed species in the alpine grassland ecosystems using very high spatial resolution UAV hyperspectral imagery and a novel deep learning model

ABSTRACT The term “invasive noxious weed species” (INWS), which refers to noxious weed plants that invade native alpine grasslands, has increasingly become an ecological and economic threat in the alpine grassland ecosystem of the Qinghai-Tibetan Plateau (QTP). Both the INWS and native grass species are small in physical size and share a habitat. Using remote sensing data to distinguish INWS from native alpine grass species remains a challenge. High spatial resolution hyperspectral imagery provides an alternative for addressing this problem. Here, we explored the use of unmanned aerial vehicle (UAV) hyperspectral imagery and deep learning methods with a small sample size for mapping the INWS in mixed alpine grasslands. To assess the method, UAV hyperspectral data with a very high spatial resolution of 2 cm were collected from the study site, and a novel convolutional neural network (CNN) model called 3D&2D-INWS-CNN was developed to take full advantage of the rich information provided by the imagery. The results indicate that the proposed 3D&2D-INWS-CNN model applied to the collected imagery for mapping INWS and native species with small ground truth training samples is robust and sufficient, with an overall classification accuracy exceeding 95% and a kappa value of 98.67%. The F1 score for each native species and INWS ranged from 92% to 99%. In conclusion, our results highlight the potential of using very high spatial resolution UAV hyperspectral data combined with a state-of-the-art deep learning model for INWS mapping even with small training samples in degraded alpine grassland ecosystems. Studies such as ours can aid the development of invasive species management practices and provide more data for decision-making in controlling the spread of invasive species in similar grassland ecosystems or, more widely, in terrestrial ecosystems.

Pemetaan Persepsi Konsumen untuk Meningkatkan Daya Saing Produk: Studi Kasus T-shirt Libre Everyday

In competing, it is important to understand consumer perceptions of products. Even though consumers are satisfied with the company's brand image, the company can still lose its customers. Companies need to identify consumer characteristics in their marketing strategy, because this can help formulate the right position for the product. Understanding the concept of product or brand positioning is very important to maintain the company's continuity in competition. This research aims to determine product positioning based on consumer perceptions of product attributes. Consumer perception mapping was carried out on the Libre Everyday t-shirt product using a purposive sampling method involving 70 respondents who had been t-shirt users for more than two years and were familiar with at least three specified t-shirt brands. The data obtained was processed using IBM SPSS 23 software to produce perceptual mapping. The calculation results show that this perception map model is feasible and acceptable, as proven by the goodness of fit test with an R-square value of 0.15312 and a stress value of 0.97814. This perception map depicts brand positioning, product attributes, and closest competitors. To increase product competitiveness, it is necessary to improve product positioning by utilizing superior product attributes, improving marketing communications, and adding marketing channels.

Cloud 3D Printing Information Research

3D printing technology, as a major technological change in the global manufacturing industry, has gradually become the trend of The Times. As a service-oriented intelligent manufacturing system, cloud manufacturing has been widely studied by scholars at home and abroad in recent years. This paper takes cloud manufacturing as the carrier, studies the informatization of 3D printing service under cloud environment, elaborates the construction of 3D printing service information model in detail, and instantiates through Agent mapping model. This paper creates an implementation path for cloud 3D printing to provide on-demand precision manufacturing services.

Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution.

Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-to-susceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural networks with our approach, adaptive convolution, that learns the mapping between the additional presented information (acquisition parameters) and the changes in the phase images associated with these varying acquisition parameters. By associating a-priori information with the network parameters itself, the optimal set of convolution weights is selected based on data-specific attributes, leading to generalizability towards changes in acquisition parameters. Moreover, we demonstrate the feasibility of pre-training on synthetic data and transfer learning to clinical brain data to achieve substantial improvements in the computation of susceptibility maps. The adaptive convolution 3D U-Net demonstrated generalizability in acquisition parameters on synthetic and in-vivo data and outperformed models lacking adaptive convolution or transfer learning. Further experiments demonstrate the impact of the side information on the adaptive model and assessed susceptibility map computation on simulated pathologic data sets and measured phase data.

Forming Jeneponto Regency City Branding With Jeneponto Gammara Video

Research on Forming City Branding in Jeneponto Regency with Jeneponto Gammara Video is a mapping model and becomes a communication strategy in the study of branding strategy. This research aims to explore the tourism potential of Jeneponto Regency and then City Branding “Jeneponto Gammara” as well as becoming the iconic tagline of Jeneponto Regency. The method used is a design method oriented toward video production (audiovisual) in the form of preproduction, production, and postproduction which is then used to produce city branding-forming videos. The Jeneponto Gammara City Branding Strategy is a new strategy utilizing communication technology that synergizes with marketing tourism in building Jeneponto Regency tourism in the tourism sector. The results of this study can echo the city branding of Jeneponto Regency so that it is familiar to the eyes of the Indonesian people and the tourism potential of Jeneponto Regency is clearly mapped so that the Regency’s foreign exchange can increase from the tourism sector which will have a significant positive effect on other sectors.&#x0D; Keywords: communication strategy, city branding, jeneponto gammara, tourism

Construction of multi-features comprehensive indicator for machinery health state assessment

Health state assessment is critical for mechanical equipment’s smooth and healthy operation. This paper proposes a novel approach for health state assessment based on acoustic signals during the process of machinery running. It consists of multi-domain feature (MF) extraction and comprehensive health indicator (CHI) construction. MF is extracted from various acoustic features, including time and frequency (TF) features, mel-frequency cepstral coefficients, and gammatone frequency cepstral coefficients. The stacked long short-term memory (LSTM) is used to extract the high-level features of the MF, which are then input to the downstream PCA to obtain the LSTM-PCA health indicator (LP-HI). Parallelly, the MF is fed into the self-organizing mapping (SOM) model to calculate the minimum quantization error (MQE) as SOM-MQE health indicator (SM-HI). These two indicators are fused using weighted fusion and nonlinear mapping to calculate CHI. The experimental results on air compressor dataset show a 25.8% reduction in evaluation error compared with SOTA results in this paper. The proposed nonlinear mapping function furthermore reduces fitting error on HI by 38.9%. These demonstrate the effectiveness and superiority of the proposed method in machinery health state assessment.