Search Results (7)

Autonomous navigation in subterranean mines is hindered by deformable terrain, dust-laden visibility, and densely packed, safety-critical machinery. We propose a systems-oriented navigation framework that embeds semantic priorities into reactive planning for energy-aware autonomy in Robot Operating System (ROS). A lightweight Convolutional Neural Network (CNN) detector fuses RGB-D and LiDAR data to classify obstacles like humans, haul trucks, and debris, writing risk-weighted virtual LaserScans to the local planner so obstacles are evaluated by relevance rather than geometry. By integrating class-specific inflation layers in costmaps within a cyber–physical systems architecture, the system ensures ISO-compliant separation without sacrificing throughput. In Gazebo experiments with three obstacle classes and 60 runs, high-risk clearance increased by 34%, collisions dropped to zero, mission time remained statistically unchanged, and estimated kinematic effort increased by 6% relative to a geometry-only baseline. These results demonstrate effective systems integration and a favorable safety–efficiency trade-off in industrial cyber–physical environments, providing a reproducible reference for scalable deployment in real-world unstructured mining environments. Full article

Economics is based on data and indicators. Although their interpretation can be complicated, their effects can be calculated in advance. In other words, economic crises are not as complicated and unpredictable as natural disasters. If economic news, news that reflects the thoughts of society, and especially the experiences and predictions of economic experts, is semantically processed from the news texts written by economic experts, economic crises can be predicted long in advance. In addition, the frequency of news about crises in society is also effective. Events that affect society are often mentioned. This can be an indication of some economic crises. In this research, we attempted to detect the economic crises and inflation increases in Turkey in December 2021 and in Germany in September 2022 several months in advance with natural language processing (NLP) models. In the study, the daily news retrieved via RSS from the leading news channels and newspapers was first preprocessed and then the similarities were checked with NLP models. Finally, similarities and changes were analyzed in comparison with inflation data. It was found that similar changes a few months ago had a high correlation with inflation data. Full article

In the information age, semantic parsing technology drives efficiency improvement and accelerates the process of intelligence. However, it faces complex understanding, data inflation, inappropriate evaluation, and difficult application of advanced large models. This study analyses the current challenges and looks forward to the development trend of the technology. Specific approaches include: this study adopts a systematic review method and strictly follows the PRISMA framework, deeply analyzes the key ideas, methods, problems, and solutions of traditional and neural network methods, and explores the model performance, API application, dataset, and evaluation mechanism. Through literature analysis, the technology is classified according to its application scenarios. Then, the practical application contributions are summarized, current limitations such as data size, model performance, and resource requirements are analyzed, and future directions such as dataset expansion, real-time performance enhancement, and industrial applications are envisioned. The results of the study show significant advances in semantic parsing technology with far-reaching impacts. Traditional and neural network methods complement each other to promote theoretical and practical innovation. In the future, with the continuous progress and in-depth application of machine learning technology, semantic parsing technology needs to further deepen the research on logical reasoning and evaluation, to better cope with technical challenges and lead the new development of natural language processing and AI. Full article

Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U²-Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U²-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U²-Net network feature extraction and enhanced the model accuracy and generalization ability. Full article

The concept of green conservation has become a popular expression in parallel to the inflated development of innovative green practices and products for cultural heritage. However, the absence of a consistent definition of emerging green concepts does not prevent the dilution of the terms in scientific research and commercial propaganda. On this basis, this article discusses the several meanings of the concept of green conservation and its related terms toward a viable and comprehensive definition. The semantic analysis relies on the identification of different sources to evaluate the coherence of the meanings in usage in scientific and non-scientific domains. Search terms—such as “bio”, “eco”, and “green”—were collected and classified into lemmas of emerging constructs. Lastly, two glossaries resulting from the two main sources provide a comparative analysis to evaluate the degree of intersection and divergence among equal terms. The research drew from over 100 studies and five international databases to generate a hierarchical classification among 220 constructs and to identify six definitions of green conservation. This paper contributes to greater clarity and encourages a semantic discussion toward a common vision for a green conservation perspective for future research and informed preservation practice. Full article

This article begins with disputing the teleologically charged notion of unstoppable information growth, pointing at the alternation of informational contraction and expansion in open dynamic systems. Narrowing the focus, it turns to the 20th century totalitarian systems as particularly paradoxical informational environments: Being less capable of processing information than their democratic counterparts and therefore more vulnerable to overloads, they are particularly prone to suppressing informational transmission in some areas, codes and media. Dilution and conflation are singled out as the most common ways of lessening the informational value of communication in totalitarian societies. Whereas the first greatly increases the ratios of signs to messages and messages to interactions, causing redundancy and semantic inflation, the second rolls back preexisting functional differentiations (person vs. social role, sender vs. message, message vs. information etc.) within societies and their communicative system. It is argued that both attempts at semantic impoverishment of public communication in totalitarianism lead to the pathological states, failing to reduce the overall amount of information within the systems in question and precipitating the very informational explosions they were designed to prevent. Full article

This presentation sketches out three scenarios of information suppression in social environments ravaged by pervasive feelings of insecurity and looming breakup. While containment (common in totalitarian regimes) strives to decrease the amount of information in the system by encouraging redundancy and semantic inflation, escape (typical for populist milieus) results in informational nihilism (information = noise). Inversely, tolerance (common for conspiracy adepts) interprets all signs—and even non-signs—as meaningful cues reinforcing pre-existing beliefs (noise = information). It is argued that these attempts at uncertainty reduction typically lead to pathological states, failing to reduce the overall amount of information within the systems in question. Full article