Hongbo Zheng, Suyuan Wang, Neeraj Gangwar, Nickvash Kani. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

In the multirate sampling processes, the process data are usually collected from various operating conditions and display multimodal characteristics. To monitor these multirate multimode processes, a multirate mixture probability principal component analysis model is proposed for process modeling and fault detection. In this model, the local multirate models are built first for each mode and all of them are subsequently fused with the mixture modeling approach. Such model is able to deal with multirate data with various amount of sampling rates, contributing to a remarkable fault detection and mode identification performance by utilizing all the available measurements even if some variables are unobserved. Then the expectation$-$maximum algorithm is utilized to estimate all the model parameters in the probabilistic framework and the corresponding monitoring method is also developed based on the constructed models. Finally, the effectiveness of the proposed method is demonstrated through a PRONTO benchmark and a real multimode ammonia synthesis process.Note to Practitioners—Motivated by the practical problem of ununiform sampling intervals in multimode processes, this paper proposes a novel multirate mixture probability principle component analysis model for processes modeling and monitoring. In this model, all the available observations with different sampling rates can be incorporated, which contributes greatly to capturing the multimodal characteristics within the industrial processes. Such ability is the key to realize multimode process monitoring, evaluation, fault diagnosis, and process optimization. In addition, although this paper only focuses on the continuous multirate data in industry, it is equally applicable to other forms of multirate data, such as images and videos.

Abstract To solve the problem that the deep learning‐based image matting algorithm cannot balance accuracy and model size, a lightweight image matting algorithm based on deep learning is proposed. Considering the limitation of memory and computing resources, and aiming at lightweight. We construct a network and gradually improved it. Firstly, apply deep detachable convolution to deep image matting networks to form faster and stronger encoder and decoder networks. The simultaneous use of depth‐separable convolution can also reduce the number of corresponding model parameters and computation. And then attention mechanism is integrated into the model and SE Block was used to assign different weights to feature channels to improve the accuracy of the model. Finally, knowledge distillation scheme is designed in part of the encoder‐decoder structure, the corresponding loss function is proposed, and the method of knowledge distillation is used to improve the feature learning ability of the lightweight neural network. Compared with the original deep image matting model, the number of parameters in the new model is greatly reduced without too much loss of accuracy.

Abstract The development of deep learning provides a new way for solving the colorization problem on the grayscale image. Excellent coding‐based methods appear in the automatic image colorization task, avoiding the unsaturated colour effect problem of previous methods based on the L2 loss function. Traditional neural networks come with high computational costs and a large number of parameters. Considering the limitation of memory and computing resources and aiming at lightweight, a novel grey image automatic colorization network is proposed. The basic idea of coding‐based methods is used, regarding the colorization task as a pixel‐level classification problem, meanwhile redesign and improve the colour encoding and decoding process. This network architecture leverages a lightweight convolution to reduce the computation and combines an efficient attention model to form a residual block as the kernel of the backbone network. Furthermore, an efficient image self‐attention mechanism placed at the end of the network is applied to enhance the ultimate colouring results. The method proposed in this paper can maintain the natural colouring effect and significantly reduce the computational amount and network model parameters.

An adaptive algorithm is proposed on the basis of the filtered-x least mean square (FxLMS) algorithm for active control of parameter-varying vibration systems. The adaptive algorithm utilizes an auxiliary filter to improve online estimation of the secondary path. The secondary path is represented by the transfer functions of the auxiliary filter and the controller, and its finite impulse response (FIR) model is estimated with the LMS algorithm. Convergence analysis of the adaptive algorithm is presented as well. Both simulation and experiment were conducted to evaluate the effectiveness of the adaptive algorithm. The results have demonstrated that the adaptive algorithm is effective even in case of an abrupt change in the parameters of the secondary path.

An improved single image fast deblurring algorithm based on hyper‐Laplacian constraint is proposed. The algorithm is improved in three aspects: image blur kernel estimation sub‐region selection, blur kernel precise estimation, and fast non‐blind deconvolution. First, image amplitude and gradient are used as the basis of blur kernel estimation. On the basis of analysing the edge amplitude and gradient of the image, the image sub‐region for blur kernel estimation is selected. Then the sparsity of the blur kernel is restricted by hyper‐Laplacian, and the fast solving mode of alternately solving different variables is designed. The blur kernel information is accurately estimated. In the fast non‐blind deconvolution restoration phase of the image, the regularised constraint term of the hyper‐Laplacian model is improved and the image gradient distribution is constrained. The blind growth trend of the regional gradient near the strong edge can be suppressed well, and the deblurred image with clear edge structure is generated. Experimental results show that the proposed algorithm can achieve better image deblurring effect and high efficiency.

This paper describes the design and techniques of HAIBAO intelligent robot which is developed for Shanghai World Expo 2010. Compared with previous exhibition service robots, the HAIBAO robot has a more flexible motion ability, a more anthropomorphic interactive ability and a more intelligent cognitive and decision ability. Some key techniques including a four-wheeled omni-directional mechanism and its motion control and compensatory algorithm, the anthropomorphic interaction design, and the architecture of multi-tasks scheduling are introduced. During the Expo, totally 184 days, 37 HAIBAO robots have successfully serviced for the tourists by information providing, photography service, hall guiding, chat and various entertainments. Their robust, stability, flexibility and friendliness have been greatly commended.