Spiral Complete Coverage Path Planning Based on Conformal Slit Mapping in Multi-connected Domains

Generating a smooth and shorter spiral complete coverage path in a multi-connected domain is an important research area in robotic cavity machining. Traditional spiral path planning methods in multi-connected domains involve a subregion division procedure; a deformed spiral path is incorporated within each subregion, and these paths within the subregions are interconnected with bridges. In intricate domains with abundant voids and irregular boundaries, the added subregion boundaries increase the path avoidance requirements. This results in excessive bridging and necessitates longer uneven-density spirals to achieve complete subregion coverage. Considering that conformal slit mapping can transform multi-connected regions into regular disks or annuluses without subregion division, this paper presents a novel spiral complete coverage path planning method by conformal slit mapping. Firstly, a slit mapping calculation technique is proposed for segmented cubic spline boundaries with corners. Then, a spiral path spacing control method is developed based on the maximum inscribed circle radius between adjacent conformal slit mapping iso-parameters. Lastly, the spiral path is derived by offsetting iso-parameters. The complexity and applicability of the proposed method are comprehensively analyzed across various boundary scenarios. Meanwhile, two cavities milling experiments are conducted to compare the new method with conventional spiral complete coverage path methods. The comparation indicate that the new path meets the requirement for complete coverage in cavity machining while reducing path length and machining time by 12.70% and 12.34%, respectively.

Cross-corpus Readability Compatibility Assessment for English Texts

Text readability assessment has gained significant attention from researchers in various domains. However, the lack of exploration into corpus compatibility poses a challenge as different research groups utilize different corpora. In this study, we propose a novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA), to address this issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, and RACE. Linguistic features, GloVe word vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) and deep learning methods (BiLSTM, Attention-BiLSTM) were employed. (3) Compatibility metrics: RJSD, RRNSS, and NDCG metrics. Our findings revealed: (1) Validated corpus compatibility, with OSP standing out as significantly different from other datasets. (2) An adaptation effect among corpora, feature representations, and classification methods. (3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework. The outcomes of this study offer valuable insights into corpus selection, feature representation, and classification methods, and it can also serve as a beginning effort for cross-corpus transfer learning.

Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay

Self-supervised learning has become a popular approach in recent years for its ability to learn meaningful representations without the need for data annotation. This paper proposes a novel image augmentation technique, overlaying images, which has not been widely applied in self-supervised learning. This method is designed to provide better guidance for the model to understand underlying information, resulting in more useful representations. The proposed method is evaluated using contrastive learning, a widely used self-supervised learning method that has shown solid performance in downstream tasks. The results demonstrate the effectiveness of the proposed augmentation technique in improving the performance of self-supervised models.

Bio-inspired Obstacle Avoidance for Flying Robots with Active Sensing

This paper presents a novel vision-based obstacle avoidance system for flying robots working in dynamic environments. Instead of fusing multiple sensors to enlarge the view field, we introduce a bio-inspired solution that utilizes a stereo camera with independent rotational DOF to sense the obstacles actively. In particular, the rotation is planned heuristically by multiple objectives that can benefit flight safety, including tracking dynamic obstacles, observing the heading direction, and exploring the previously unseen area. With this sensing result, a flight path is planned based on real-time sampling and collision checking in state space, which constitutes an active sense and avoid (ASAA) system. Experiments demonstrate that this system is capable of handling environments with dynamic obstacles and abrupt changes in goal direction. Since only one stereo camera is utilized, this system provides a low-cost but effective approach to overcome the view field limitation in visual navigation.

Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding

While training an end-to-end navigation network in the real world is usually of high cost, simulation provides a safe and cheap environment in this training stage. However, training neural network models in simulation brings up the problem of how to effectively transfer the model from simulation to the real world (sim-to-real). In this work, we regard the environment representation as a crucial element in this transfer process and propose a visual information pyramid (VIP) model to systematically investigate a practical environment representation. A novel representation composed of spatial and semantic information synthesis is then established accordingly, where noise model embedding is particularly considered. To explore the effectiveness of this representation, we compared the performance with representations popularly used in the literature in both simulated and real-world scenarios. Results suggest that our environment representation stands out. Furthermore, an analysis on the feature map is implemented to investigate the effectiveness through inner reaction, which could be irradiative for future researches on end-to-end navigation.

Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis

While training an end-to-end navigation network in the real world is usually of high cost, simulations provide a safe and cheap environment in this training stage. However, training neural network models in simulations brings up the problem of how to effectively transfer the model from simulations to the real world (sim-to-real). In this work, we regard the environment representation as a crucial element in this transfer process and propose a visual information pyramid (VIP) model to systematically investigate a practical environment representation. A novel representation composed of spatial and semantic information synthesis is then established accordingly. To explore the effectiveness of this representation, we compared the performance with representations popularly used in the literature in both simulated and real-world scenarios. Results suggest that our proposed environment representation behaves best. Furthermore, an analysis on the feature map is implemented to investigate the effectiveness through inner reaction, which could be irradiative for future researches on end-to-end navigation.

Machine Discovery of Partial Differential Equations from Spatiotemporal Data

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery ($\text{S}^3\text{d}$), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning; which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics.

Artificial Intelligent Diagnosis and Monitoring in Manufacturing

The manufacturing sector is heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. It has been reported that 35% of US manufacturers are currently collecting data from sensors for manufacturing processes enhancement. Nevertheless, many are still struggling to achieve the 'Industry 4.0', which aims to achieve nearly 50% reduction in maintenance cost and total machine downtime by proper health management. For increasing productivity and reducing operating costs, a central challenge lies in the detection of faults or wearing parts in machining operations. Here we propose a data-driven, end-to-end framework for monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to extract hidden degradation features from noisy data. We demonstrate the proposed framework on several representative experimental manufacturing datasets drawn from a wide variety of applications, ranging from mechanical to electrical systems. Results reveal that the framework performs well in all benchmark applications examined and can be applied in diverse contexts, indicating its potential for use as a critical corner stone in smart manufacturing.

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions.