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Considering the intricate structure, challenging maintenance requirements, and the interdependent nature of the detection parameters within a wind turbine gearbox, this study employs a stacked auto-encoder model for the offline analysis and modeling of standard operational data from the gearbox. The deviation in health factors post-model reconstruction serves as a metric for monitoring the gearbox’s operational status, with the unit’s health score being derived from an enhanced encoder architecture.

The paper proposes a residual dual encoder network using distance metric learning for intelligent fault recognition with unknown classes. The network is made up of two encoders and one decoder. In both the encoders and the decoder, residual blocks are used as the main structure for deep feature extraction. Besides, distance metric learning with triplet loss is used to train the residual dual encoder network to obtain features which could represent different health conditions. Benefiting from the metric learning principle, the proposed model could recognize the potential faults in mechanical systems even with a few additional unknown fault classes. The superiority of the residual dual encoder network is demonstrated by comparing with several intelligent detection methods on three different experimental datasets. Results indicate that the proposed residual dual encoder network could effectively recognize the unknown faults with an average classification accuracy of 98.3%, 99.9% and 94.4% and a recognition rate of 93.8%, 94.1% and 94.8% in three cases.

In this paper, by combining TMS320F28335DSP and the grating encoder, the 4× frequency count of the grating encoder displacement measurement is realized, which improves the accuracy of the displacement measurement. Moreover, the human-computer interaction with the developed host computer is carried out through RS232 serial communication to display the displacement. At the same time, the system realizes the positive and negative rotation of displacement measurement by judging the increase and decrease of the pulse signal. Specifically, firstly, two pulse signals output by the grating encoder are preprocessed, and the analog signal is converted to the digital signal. The DSP microprocessor is used for pulse counting, direction recognition, and displacement correlation calculation, and the human-computer interaction is carried out through serial communication. The experimental results show that the method has stable data transmission, accurate measurement value, and improved accuracy of encoder displacement measurement.

Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition follows from certain premises. While most learning in artificial intelligence (AI) still relies on the assumption that a single (gold) interpretation exists for each item, a growing body of research aims to develop learning methods that do not rely on this assumption. In this survey, we review the evidence for disagreements on NLP and CV tasks, focusing on tasks for which substantial datasets containing this information have been created. We discuss the most popular approaches to training models from datasets containing multiple judgments potentially in disagreement. We systematically compare these different approaches by training them with each of the available datasets, considering several ways to evaluate the resulting models. Finally, we discuss the results in depth, focusing on four key research questions, and assess how the type of evaluation and the characteristics of a dataset determine the answers to these questions. Our results suggest, first of all, that even if we abandon the assumption of a gold standard, it is still essential to reach a consensus on how to evaluate models. This is because the relative performance of the various training methods is critically affected by the chosen form of evaluation. Secondly, we observed a strong dataset effect. With substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from aggregated or even gold labels. This result holds for both hard and soft evaluation. But when the above conditions do not hold, leveraging both gold and soft labels generally achieved the best results in the hard evaluation. All datasets and models employed in this paper are freely available as supplementary materials.

The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years. Statistical MT, which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural machine translation (NMT), which tackles translation with a single neural network. In this work we will trace back the origins of modern NMT architectures to word and sentence embeddings and earlier examples of the encoder-decoder network family. We will conclude with a short survey of more recent trends in the field.

The load release decision speed is a critical parameter of the pneumatic catapult system. Speed testing is of great significance for calculating load kinetic energy and evaluating the overall performance of pneumatic catapult systems. To obtain the load release decision speed during the catapult process of pneumatic catapult systems, three devices were used to conduct actual measurements: a high-speed camera, a high-precision angle encoder, and a Hall-type positioning speed sensor. The test results indicate that all three testing devices can effectively obtain the load release decision speed, and the testing method is feasible. Among the three testing schemes, the high-speed camera provides the highest speed measurement results, the angle encoder provides the lowest results, and the positioning speed sensor’s results fall between the two. The maximum relative error among the three measurements is only 0.3%, and the measurement results show good consistency.

We describe our system for SemEval-2026 Task 6 (CLARITY: Unmasking Political Question Evasions), which classifies English political interview responses by coarse-grained clarity (3-way) and fine-grained evasion strategy (9-way). Since responses frequently exceed the 512-token limit of standard Transformer encoders, we apply an overlapping sliding-window chunking strategy with element-wise Max-Pooling aggregation over chunk representations. A shared RoBERTa-large encoder supplies two task-specific heads trained jointly via a multi-task objective, with inference-time ensembling over 7-fold stratified cross-validation. Our system achieves a Macro-F1 of 0.80 on Subtask 1 and 0.51 on Subtask 2, ranking 11th in both subtasks.

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validate STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively. Breakthrough technologies for spatially resolved transcriptomics have enabled genome-wide profiling of gene expressions in captured locations. Here the authors integrate gene expressions and spatial locations to identify spatial domains using an adaptive graph attention auto-encoder.

Vector Quantization (VQ) underpins many modern generative frameworks such as VQ-VAE, VQ-GAN, and latent diffusion models. Yet, it suffers from the persistent problem of codebook collapse, where a large fraction of code vectors remains unused during training. This work provides a new theoretical explanation by identifying the nonstationary nature of encoder updates as the fundamental cause of this phenomenon. We show that as the encoder drifts, unselected code vectors fail to receive updates and gradually become inactive. To address this, we propose two new methods: Non-Stationary Vector Quantization (NSVQ), which propagates encoder drift to non-selected codes through a kernel-based rule, and Transformer-based Vector Quantization (TransVQ), which employs a lightweight mapping to adaptively transform the entire codebook while preserving convergence to the k-means solution. Experiments on the CelebA-HQ dataset demonstrate that both methods achieve near-complete codebook utilization and superior reconstruction quality compared to baseline VQ variants, providing a principled and scalable foundation for future VQ-based generative models. The code is available at: https://github.com/CAIR- LAB- WFUSM/NSVQ-TransVQ.git

Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object’s position or contour, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm relies on optical systems that—instead of performing imaging—act as encoders that optically compress images into low-dimensional spaces by extracting salient features; however, the performance of these encoders is typically limited by their linearity. Here we report a nonlinear, multilayer optical neural network (ONN) encoder for image sensing based on a commercial image intensifier as an optical-to-optical nonlinear activation function. This nonlinear ONN outperforms similarly sized linear optical encoders across several representative tasks, including machine-vision benchmarks, flow-cytometry image classification and identification of objects in a three-dimensionally printed real scene. For machine-vision tasks, especially those featuring incoherent broadband illumination, our concept allows for a considerable reduction in the requirement of camera resolution and electronic post-processing complexity. In general, image pre-processing with ONNs should enable image-sensing applications that operate accurately with fewer pixels, fewer photons, higher throughput and lower latency.A nonlinear optical neural network image sensor based on an image intensifier enables efficient all-optical image encoding for a variety of machine-vision tasks.