External force sensing is essential for enabling robot manipulators to perform force control tasks in industrial applications. However, the use of force sensors increases costs and requires additional calibration tasks, such as gravity compensation. This study proposes a sensor-less force estimation strategy to develop a virtual force estimator for n-link industrial robot manipulators. A model-based identification scheme is used to determine dynamic parameters and construct an inverse dynamics model for external force estimation. Neural learning-based semi-parametric friction compensation model further enhances joint friction estimation, mitigating uncertainties during low-speed motion. The proposed method is validated through experiments on the industrial robot manipulator HIWIN RT605–710. A surface interaction experiment demonstrated a mean absolute error of 4.7388 N of contact force estimation in the Z-direction during a 12-s contact process with an applied force of approximately 40 N using the proposed force estimator.
This article presents the development of a database and an initial analysis aimed at detecting potential correlations between mobile phone usage and frailty levels among elderly individuals. The primary objective is to conduct exhaustive monitoring of all information provided by smartphones, focusing on both the use of common applications and sensor data related to mobility during activities of daily living (ADLs), and to contrast these findings with assessed frailty levels. This work forms part of a broader study involving elderly participants aged between 73 and 96 years, based in the Barcelona area. The database constructed for this study is based on two main strategies. First, to gather comprehensive data on smartphone usage, a group of 10 elderly individuals was observed under normal daily living conditions over a period of up to four weeks. For this initial group, key geriatric indices—Barthel Index, Lawton–Brody Instrumental Activities of Daily Living Scale, and Frail-VIG Index—were previously determined through ambulatory medical evaluations. Second, the database was expanded by generating synthetic data using Generative Adversarial Networks (GANs) and Gaussian Copula models, incorporating an additional group of real patients for whom frailty indices were already known. For those patients, we do not have any data from their own smartphones. The combined real and synthetic dataset was used to identify the most significant features and to support feature selection for classification. Generalized Linear Models (GLMs) were subsequently applied to develop decision trees, with the goal of establishing correlations between smartphone usage patterns and different frailty indices. Finally, the model outputs were analysed to extract meaningful insights that could inform future clinical applications.
In recent years, the development of blockchain has been flourishing.Blockchain provides decentralization, improved security, and increased transparency compared to conventional centralized transaction systems, which lowers costs and increases transaction efficiency. One frequently used tool in blockchain is the Rank-1 Constraint System (R1CS).By providing a more condensed method of achieving zeroknowledge proofs, R1CS improves the blockchain's scalability and security even further. The goal of this work is the standardization of R1CS so that any combination of constraints can be transformed into one and uniform format. Additionally, it makes it possible to combine seemingly different but fundamentally similar sets of R1CS constraints into a single new R1CS set, which eliminates duplicate R1CS sets in the process and increases the efficiency of later steps of the creation of zero-knowledge proofs. This study proposes a concise method for standardizing R1CS constraint sets. Given any R1CS constraint set (comprising three matrices A, B, C, the solution vector and their corresponding parameter vector), regardless of whether the original problem is represented by a single variable or multiple variables, it can be converted into a unique standard representation. The study utilized symbolic and equation operations to determine the actual problem to be solved and merges various potentially equivalent R1CS constraint sets into one. Additionally, we use a flow chart to illustrate our process of standardizing R1CS constraint sets. In the experiment section, each experimental group engaged in nine different equivalent transformations. The results showed that all could be successfully converted into a unique R1CS constraint set representation. Compared to previous related research, the time required for R1CS standardization was significantly reduced.
The use of autonomous robots is rapidly increasing in industries, requiring advanced navigation strategies to ensure safe operations. SWARM robotics, inspired by natural swarm intelligence, faces critical challenges in collision detection and avoidance. Traditional rule-based methods often struggle in dynamic environments, whereas neural network-based approaches provide adaptive solutions. This paper presents a CNN-based collision detection and avoidance system utilizing LiDAR data for SWARM multi-robot systems. The proposed model was trained and tested using simulated data in the Gazebo and ROS 2 environments. A comprehensive description of the data set, a detailed model architecture, training parameters, and evaluation metrics are provided in this paper. The results show a precision of 85.65
Artificial Intelligence (AI) is evolving rapidly, and Knowledge Graphs (KGs) are widely used in tasks such as smart question answering, recommendation, and search engine optimization due to their semantic search, reasoning, and knowledge aggregation capabilities. However, in industrial applications with complex and heterogeneous data, the quality of KGs suffers, leading to data inconsistency and decision-making errors, which poses a challenge to intelligent manufacturing. We propose an adversarial learning-based error detection method for industrial knowledge graphs (GAN-MKED). This method extracts features from multimodal industrial data including documents, images, and sensor data using a fine-tuned BERT model for document encoding, SENet for image feature extraction, and a combined BiLSTM and FFT approach for sensor signals. A multimodal attention mechanism fuses these features, while a relation-aware graph attention network (R-GAT) aggregates neighborhood information. Spectral Normalization GAN (SN-GAN) introduces random noise to generate pseudo-neighborhood features, improving robustness and stability. The difference between real and generated features is computed and used within an adaptive thresholding mechanism to detect errors effectively. Experimental results on a self-constructed industrial KGs dataset demonstrate that GAN-MKED achieves high accuracy and robustness in error detection for industrial knowledge graphs.
