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关键词： Labeled data  

摘要： At present, the realization of most domain generalization learning methods relies on multi-source domains and labeled data. However, it is very difficult to collect complete monitoring data for the bearing under complex operating conditions. Aiming at the task of cross-working condition fault diagnosis with incomplete information, a multi-dimensional information cooperative neural network based bearing cross-working condition fault diagnosis method is proposed. MICNN includes cooperative normalization strategy and multi-dimensional spatiotemporal feature fusion module. CNM reduces covariate deviation and overfitting through feature unification and information fusion in two dimensions of cluster and sample individual. MSTFF enhances the extraction of domain invariant features by capturing key features and exchanging information in channel and spatial dimensions. The proposed method achieved 98.74%, 96.59% and 93.81% average accuracy in 6 different cross-working tasks of PU dataset, Jiangnan University dataset and lab-built dataset, compared with the existing mainstream methods, the proposed method has a better fault diagnosis effect. © 2025 Chinese Vibration Engineering Society. All rights reserved.

关键词： Image reconstruction  

摘要： To address the issues with complex and variable vibration signals in gearboxes, which led to low diagnostic accuracy of existing gearbox fault diagnosis methods and the risk of weak fault features being overwhelmed by noise, a new fault diagnosis method was proposed. Firstly, entropy weight fusion algorithm was used to fusion vibration sensor signals at different locations, and vector weighted mean value algorithm (INFO) was used to optimize parameters in the variational mode decomposition (VMD) algorithm. A composite evaluation index was designed as the evaluation standard for parameter optimization. The Singular Kurtosis Differential Spectrum (SKD) method was employed to reconstruct the sensitive components. What's more, the time domain and frequency domain features were extracted from the reconstructed signals and fed into the CNN model for classification. Finally, Shapley Additive Explanations (Shap) value method was used to rank the importance of input features. The impact of different feature combinations on model classification and specific fault identification was analyzed. The proposed method was validated on the Planetary Gearbox Dataset from Southeast University. It was shown that using the proposed feature combination for fault diagnosis, the CNN model achieves an accuracy of 98.24%, which is higher than other combinations. This provides an effective set of feature indicators for planetary gearbox fault diagnosis. © 2025 Chinese Vibration Engineering Society. All rights reserved.

关键词： 5G   医疗信息化建设发展   方法策略  

摘要： 随着信息技术的飞速发展,5G技术的应用正在深刻改变各行各业的运作模式,医疗领域也不例外。5G技术的超高速度、超低延迟和大连接特性,为医疗信息化建设提供了前所未有的机遇,极大地推动了远程医疗、智能医疗设备、医疗数据分析等新兴领域的发展。在5G时代,医疗信息化建设不仅是医疗行业现代化的重要标志,更是提升医疗服务质量、优化医疗资源配置、提高患者满意度的关键手段。

关键词： 创新   网络招聘   实践   信息化   创业环境   就业创业指导  

摘要： 在信息技术快速发展的背景下,信息化已成为推动社会进步和经济发展的重要力量。在这一背景下,大学生就业创业指导工作面临着前所未有的机遇与挑战。作为就业创业指导领域的专业工作者,如何充分利用信息化手段创新大学生就业创业指导工作的方式与方法,成为当前亟待解决的问题。信息化对大学生就业创业环境的影响信息化不仅改变了人们的日常生活,还深刻影响了当代大学生的就业与创业环境,为他们的未来开辟了新的路径。一方面,信息化拓宽了大学生的就业渠道。以往,大学生寻找工作往往局限于校园招聘会、报纸招聘广告或是亲朋好友的介绍。如今,随着各类招聘网站、社交媒体以及求职APP的兴起,大学生通过网络便能浏览各种招聘信息。更重要的是,信息化打破了地域限制,使得大学生不再受限于地理位置,可以轻松实现跨地域求职,极大地增加了他们的就业机会。另一方面,信息化也为大学生创业提供了前所未有的机遇和可能。在传统观念中,创业往往意味着巨大的资金投入、复杂的市场调研以及繁琐的经营管理,这些对于初出茅庐的大学生来说无疑是巨大的挑战。然而,随着电子商务、在线教育、云计算、大数据等新兴行业的蓬勃发展,大学生可以利用互联网平台,以较低的成本开展业务,迅速触达目标客户群体。新兴行业的兴起,不仅为大学生提供了广阔的创业空间,也激发了他们的创新思维和创业热情。