Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images.

Latent Low-Rank Representation (LatLRR) has emerged as a prominent approach for fusing visible and infrared images. In this approach, images are decomposed into three fundamental components: the base part, salient part, and sparse part. The aim is to blend the base and salient features to reconstruct images accurately. However, existing methods often focus more on combining the base and salient parts, neglecting the importance of the sparse component, whereas we advocate for the comprehensive inclusion of all three parts generated from LatLRR image decomposition into the image fusion process, a novel proposition introduced in this study. Moreover, the effective integration of Convolutional Neural Network (CNN) technology with LatLRR remains challenging, particularly after the inclusion of sparse parts. This study utilizes fusion strategies involving weighted average, summation, VGG19, and ResNet50 in various combinations to analyze the fusion performance following the introduction of sparse parts. The research findings show a significant enhancement in fusion performance achieved through the inclusion of sparse parts in the fusion process. The suggested fusion strategy involves employing deep learning techniques for fusing both base parts and sparse parts while utilizing a summation strategy for the fusion of salient parts. The findings improve the performance of LatLRR-based methods and offer valuable insights for enhancement, leading to advancements in the field of image fusion.

PM2.5 Concentration Prediction Model: A CNN-RF Ensemble Framework.

Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 were selected for model training and testing. First, CNN was implemented to extract key meteorological and pollution data. Subsequently, the RF algorithm was employed to train the model with five input factors, namely the extracted features from the CNN and spatiotemporal factors, including the day of the year, the hour of the day, latitude, and longitude. Independent observations from two stations were used to evaluate the models. The findings demonstrated that the proposed CNN-RF model had better modeling capability compared with the independent CNN and RF models: the average improvements in root mean square error (RMSE) and mean absolute error (MAE) ranged from 8.10% to 11.11%, respectively. In addition, the proposed CNN-RF hybrid model has fewer excess residuals at thresholds of 10 µg/m3, 20 µg/m3, and 30 µg/m3. The results revealed that the proposed CNN-RF ensemble framework is a stable, reliable, and accurate method that can generate superior results compared with the single CNN and RF methods. The proposed method could be a valuable reference for readers and may inspire researchers to develop even more effective methods for air pollution modeling. This research has important implications for air pollution research, data analysis, model estimation, and machine learning.

Automatic Management and Monitoring of Bridge Lifting: A Method of Changing Engineering in Real-Time.

In recent years, owing to the increase of extreme climate events due to global climate change, the foundational erosion of old bridges has become increasingly serious. When typhoons have approached, bridge foundations have been broken due to the insufficient bearing capacity of the bridge column. The bridge bottoming method involves rebuilding the lower structure while keeping the bridge surface open, and transferring the load of the bridge temporarily to the temporary support frame to remove the bridge base or damaged part with insufficient strength. This is followed by replacing the removed bridge base with a new bridge foundation that meets the requirements of flood and earthquake resistance. Meanwhile, monitoring plans should be coordinated during construction using the bottoming method to ensure the safety of the bridge. In the case of this study, the No. 3 line Wuxi Bridge had a maximum bridge age of 40 years, where the maximum exposed length of the foundation was up to 7.5 m, resulting in insufficient flood and earthquake resistance. Consequently, a reconstruction plan was carried out on this bridge. This study took the reconstruction of Wuxi Bridge as the object and established a finite element model using the SAP 2000 computer software based on the secondary reconstruction design of the Wuxi Bridge. The domestic bridge design specification was used as the basis for the static and dynamic analyses of the Wuxi Bridge model. As a result of the analysis, the management value of the monitoring instrument during construction was determined. The calculated management values were compared with the monitoring data during the construction period to determine the rationality of the management values and to explore changes in the behavior of the old bridges and temporary support bridges.

Online community collaborative map: A geospatial and data visualization tool for cancer data.

The aim of this study was to develop an online collaborative map to enable researchers to locate, explore, and share cancer data.This 2-scale (global and country-level) cancer map adopts a database-driven model, which was implemented using the Google Map Application Programming Interface (API) and asynchronous JavaScript and XML (AJAX) technology. Seven visualization techniques were used to present data. Data on worldwide cancer mortality between 1950 and 2013 were taken from the International Agency for Research on Cancer (IARC) database. Incidence data were from the IARC CI5plus database. Survival data were from the IARC SURVCAN study. Prevalence data between 1990 and 2017 were from the Institute for Health Metrics and Evaluation's (IHME) catalog while demographic data were from the World Bank Data Catalog. Cancer data for Taiwan between 1991 and 2016 were obtained from the Department of Health and Welfare. This study used visualization techniques that included: a choropleth map to display the prevalence of cancer; a tornado diagram to show the age-standardized mortality rates of all cancers among men and women in 2013; a treemap to show a ranking of cancer mortality data; a sunburst chart to show mortality rates of all cancers by gender; a line chart to show mortality trends for all cancers; a bar chart to show mortality and incidence rates and a heatmap to show variations in cancer across different countries.The world cancer map generated by this study can be accessed at http://worldmap.csmu-liawyp.tw. Country-level mortality data are presented as crude and age-standardized rates.We used visualization methodologies and constructed an easily maintainable web-based user interface with cancer data from administrative regions in 150 countries. This serves as a platform that allows researchers to manage and disseminate cancer data.

Integrating geographical information and augmented reality techniques for mobile escape guidelines on nuclear accident sites.

During nuclear accidents, when radioactive materials spread into the environment, the people in the affected areas should evacuate immediately. However, few information systems are available regarding escape guidelines for nuclear accidents. Therefore, this study constructs escape guidelines on mobile phones. This application is called Mobile Escape Guidelines (MEG) and adopts two techniques. One technique is the geographical information that offers multiple representations; the other is the augmented reality that provides semi-realistic information services. When this study tested the mobile escape guidelines, the results showed that this application was capable of identifying the correct locations of users, showing the escape routes, filtering geographical layers, and rapidly generating the relief reports. Users could evacuate from nuclear accident sites easily, even without relief personnel, since using slim devices to access the mobile escape guidelines is convenient. Overall, this study is a useful reference for a nuclear accident emergency response.