This study focuses on cooperative tracking of multiple high speed trains, an effective way to accommodate rising passenger demand now that train to train communication is mature. A novel distributed adaptive model predictive control algorithm built on multi-point mass model is proposed. Firstly, the cooperative tracking task is recast with individual cars as the smallest nodes, yielding a distributed model predictive control strategy in which each car acts as a control agent. Subsequently, an adaptive weighting matrix is introduced to adjust the weights of multiple control input variables during the rolling optimization of model predictive control, thereby improving control accuracy and reducing tracking error. Furthermore, to accelerate computation, an adaptive prediction horizon scheme is presented that shortens or lengthens the horizon in real time according to the current tracking error. Finally, simulations with three high speed trains demonstrate that the proposed multi-point mass distributed adaptive model predictive control approach is both feasible and stable, highlighting its potential for enhancing cooperative tracking performance of multiple high-speed trains.

With the proliferation of the Internet of Things (IoT), a wealth of multivariate time series data is being generated across various domains, creating new demands for accurate and efficient forecasting models. Despite the success of attention-based models in capturing dependencies within time series, they often fail to address two critical challenges: (1) the lag effect between output and input, which can significantly distort predictions, and (2) the limitations of classic trigonometric positional embeddings, which lack scalability and adaptability to diverse temporal patterns. To address these challenges, we propose FPPformer, a novel forecasting model that introduces two key innovations: (i) a Fractional Positional Embedding (FPE), which leverages fractional calculus to enable scalable and adaptive positional representations, and (ii) a Predictive Attention Mechanism (PAM), which explicitly models the lag effect, aligning output and input more effectively. The FPPformer architecture consists of encoder-only structure, with the core of encoder module utilizing the PAM. Experimental results demonstrate that FPPformer significantly improves forecasting perfromance, reducing the mean squared error (MSE) by 28% and the mean absolute error (MAE) by 17% across six datasets spanning four domains -electricity, weather, economy, and transportation -especially on large-scale datasets such as Traffic and Electricity. These results highlight FPPformer’s ability to address fundamental challenges in time series forecasting, providing a new perspective on leveraging positional representations and lag-aware attention mechanisms. The code for this project is available at https://github.com/jancely/FPPformer.

In response to the growing demand for high-quality steel, the detection of surface defects in steel has emerged as a prominent area of research. This paper introduces an innovative model, termed MPA-YOLO, which is based on YOLOv8 and aims to enhance the accuracy of steel surface defect detection. To improve the model's feature extraction capabilities, this study integrates and innovates upon large kernel depthwise convolution and coordinate attention mechanisms, resulting in the design of a multi-path convolution attention module (MPCA). Furthermore, MPCA is combined with C2f to create C2f-MPCA, which replaces parts of the backbone and neck network's C2f, thereby increasing the model's sensitivity to defect locations. Additionally, a partial self-attention module (PSA) is incorporated into the backbone network to capture long-range dependencies among features, thereby enhancing the representational capacity of the features. An auxiliary detection head is also introduced to gather multi-level and multi-scale feature information, which enables the model to effectively differentiate between target defects and background, thus improving its perceptual capabilities. The proposed model was assessed using the publicly available NEU-DET dataset, with experimental results indicating that the mAP of the MPA-YOLO model reached 81.5%, reflecting a 3.4% improvement over the baseline model. Concurrently, precision and recall rates increased by 3.0% and 4.7%, respectively. Furthermore, evaluations on the VOC2007 public dataset revealed a 2.5% enhancement in mAP compared to the baseline model. These findings suggest that the MPA-YOLO model is effective in the detection of steel surface defects.

Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of perceptual uncertainty is one of the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account-in a very specific way-in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing "probabilistic computation" in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception.

Robotic posture optimization and control is an important issue for industrial applications, particularly for the automobile assembly line automation workcells. The optimal posture means that a robot reaches the best configuration in order to operate on a force-related task. It can be modeled as an even joint-torque distribution problem. Through the model, four possible potential functions are developed for the posture optimization in a redundant robotic system. One of the four shows higher effectiveness than others in a simulation study. These potential functions can be more suitably applied to a dual-redundant robotic system for its multi-subtask optimization aided by a nonlinear null space control scheme for the subtask weighting factors determination.