Search results

To address the issue of large visual measurement errors caused by insufficient information collected by monocular vision when performing six-degree-of-freedom (6DOF) position measurements on metal castings, which hinders the robot’s ability to visually guide grasping, this paper aims to propose a 6DOF position measurement method that integrates monocular vision with deep neural networks.

This method enhances the robot’s ability to visually grasp small-sample industrial objects with high accuracy. By establishing a mapping relationship between the two-dimensional (2D) position of the object’s image and its three-dimensional (3D) position in space, the proposed approach achieves 6DOF position measurement of the target workpiece using monocular vision. An image enhancement algorithm based on a generative adversarial network (GAN) is introduced to improve robustness in industrial environments by addressing the challenge of acquiring image data for small-sample objects. Additionally, the method combines single-phase object detection using deep neural networks with 2D-3D affine transformation to achieve accurate 3D position measurements.

The introduction of the GAN-based image enhancement algorithm significantly mitigates the robustness issues posed by the difficulties in obtaining image data for small-sample objects in industrial settings. The integration of single-phase object detection and 2D–3D affine transformation allows for precise 3D position measurement of the workpiece. Experimental results demonstrate that the proposed method provides high accuracy in 6DOF position measurements for industrial objects.

This approach overcomes the limitations of traditional vision algorithms for 3D position measurement of industrial objects, such as high cost and poor robustness. The experimental validation confirms that the proposed method achieves excellent 6DOF position measurement accuracy for industrial objects.

The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.