Boyong Gao

Accurately predicting the remaining useful life of bearings is of great importance for enabling intelligent operation and health management of industrial equipment. Existing methods often face challenges in jointly modeling degradation trends and transient anomalies. Furthermore, their integration of physical priors remains underexplored, limiting the reliability of predictions. To address these challenges, this paper proposes a novel bearing life prediction framework, termed the frequency-domain perception attention based dual-path network with physics-informed loss (FDPN). The model employs a dual-encoder architecture to separately extract low-frequency degradation-trend features and high-frequency transient-anomaly features, which are adaptively fused via a gated fusion module. Furthermore, a wavelet-based frequency-domain perceptual attention mechanism is designed to explicitly model multi-scale frequency characteristics in vibration signals. Moreover, a physics-informed loss function is constructed by incorporating trend-consistency constraints and an asymmetric error-penalty mechanism, ensuring that the predictions conform to degradation patterns while maintaining safety and reliability. Experimental results on the PHM2012 bearing dataset demonstrate that the proposed FDPN model outperforms mainstream methods, achieving an 8.43% reduction in root mean square error and a 10.70% improvement in safety score (SCORE) compared to the best baseline. These improvements validate FDPN’s superior performance in prediction accuracy, reliability, and engineering safety.

Extracting key features for phenotype classification from high-dimensional and complex mass spectrometry (MS) data presents a significant challenge. Conventional data representation methods, such as traditional peak lists or grid-based imaging strategies, are often hampered by information loss and compromised signal integrity, thereby limiting the performance of downstream deep learning models. To address this issue, we propose a novel data representation framework named MSIMG. Inspired by object detection in computer vision, MSIMG introduces a data-driven, “density-peak-centric” patch selection strategy. This strategy employs density map estimation and non-maximum suppression algorithms to locate the centers of signal-dense regions, which serve as anchors for dynamic, content-aware patch extraction. This process transforms raw mass spectrometry data into a multi-channel image representation with higher information fidelity. Extensive experiments conducted on two public clinical mass spectrometry datasets demonstrate that MSIMG significantly outperforms both the traditional peak list method and the grid-based MetImage approach. This study confirms that the MSIMG framework, through its content-aware patch selection, provides a more information-dense and discriminative data representation paradigm for deep learning models. Our findings highlight the decisive impact of data representation on model performance and successfully demonstrate the immense potential of applying computer vision strategies to analytical chemistry data, paving the way for the development of more robust and precise clinical diagnostic models.

With the advancement of modern technologies, the digitization of metering data has significantly improved the efficiency and accuracy of data collection, analysis, and management. However, the growing prevalence of data tampering techniques has raised serious concerns regarding the trustworthiness and integrity of such data. To address this challenge, this study proposes an improved SM2 digital signature algorithm enhanced with high-precision time information to strengthen the reliability of metering data. The proposed algorithm incorporates high-precision timestamps into the signature generation and verification processes, while optimizing the structure of the signature algorithm—particularly the modular inversion operation—to reduce computational costs. Experimental results demonstrate that the improved algorithm not only significantly enhances signature generation efficiency but also improves temporal validity and security by leveraging high-precision time information. It effectively mitigates risks associated with random number dependency and replay attacks, offering a secure and efficient solution for trustworthy metering data verification.

Although deep learning models dominate time series forecasting, they still struggle with long-sequence processing due to the challenges of extracting dynamic fluctuations and pattern features as input length increases. To address this challenge, we propose a framework -- LG-MSMixer—to enhance long-term time series forecasting through three key steps: multi-scale dual decomposition, local-global information ex-traction, and fusion prediction. Specifically, we first conduct multi-scale dual decomposition of the long input sequence to derive a seasonal-trend component combination. To capture a more comprehensive effective information within the components, we then utilize a customized patch-based triple attention local-global information extractor that models both temporal feature information and variable dependencies, alongside an MLP-based feature interaction iterator facilitating interactions among multi-scale information to guide macro-level predictions. Finally, we integrate the predictions from the multi-scale sequences to leverage their complementary advantages. In our experiments, we demonstrate the effectiveness of LG-MSMixer across various real-world long-term forecasting tasks, significantly outperforming previous baselines.

The advent of the digital era has put forward an urgent need for the digitization of metrology, and the digitization of metrology vocabularies is one of the fundamental and critical steps to achieve the digital transformation of metrology. Metrology vocabulary ontology can facilitate the exchange and sharing of data and is an important way to achieve the digitization of metrology vocabulary. Time metrology vocabulary is a special and important part of the whole metrology vocabulary, and constructing its ontology can reduce the problems caused by semantic confusion, help to smooth the progress of metrological work, and promote the digital transformation of metrology. Currently, the existing ontology for metrology vocabulary is primarily the MetrOnto ontology, but it lacks a systematic description of the vocabulary of time metrology. To address this issue, improve the metrology vocabulary ontology, and lay the groundwork for realizing the digital transformation of metrology, this paper takes time metrology vocabulary as the research object; proposes a classification principle that meets the inherent requirements of time transfer in the digital world; adopts the seven-step method of ontology construction to construct an ontology specialized in time metrology vocabulary, OTMV (Ontology of Time Metrology Vocabulary); and conducts an ontology consistency check, a machine-readable validation, a machine-understandable primary validation, and information retrieval validation on it. The validation results show that OTMV has correct syntactic and logical consistency and is capable of realizing machine-readable, machine-understandable, and information retrieval. The construction of this ontology provides a systematic description of the time measurement vocabulary that can address the problem of word expression of time metrology vocabulary in the digital world and lay the foundation for the digitization of our metrology vocabulary, as well as its readability, understandability, and sharing.

The advancement of information technology has led to the widespread application of remote measurement systems, where information in the form of images or videos, serving as measurement results, is transmitted over networks. However, this transmission is highly susceptible to attacks, tampering, and disputes, posing significant risks to the trustworthy transmission of measurement results from instruments and devices. In recent years, many encryption algorithms proposed for images have focused on encrypting the entire image, resulting in resource waste. Additionally, most encryption algorithms are designed only for single-object-type images. Addressing these issues, this paper proposes a multi-object region encryption algorithm based on an adaptive mechanism. Firstly, an adaptive mechanism is employed to determine the strategy for adjusting the sampling rate of encryption objects, achieved through an encryption resource allocation algorithm. Secondly, an improved polygon segmentation algorithm is utilized to separate single-object regions from multi-object images, dynamically adjusting the sequence of encryption objects based on the adaptive mechanism. Finally, encryption is achieved using a chaos fusion XOR encryption algorithm. Experimental validation using instrument images demonstrates that the proposed algorithm offers high efficiency and security advantages compared to other mainstream image encryption algorithms.