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Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Online knowledge distillation trains two or more peer models simultaneously to improve the model performance via learning the extracted features from each other collaboratively. Existing methods focus on aligning the features directly but neglect to learn distinctive and robust features around the decision boundary. This paper utilized consistency regularization to guide the model to learn the discriminant features effectively. Specifically, it equipped each model with a feature extractor and a pair of task-specific classifiers. It measured intra-model consistency for each model by the distribution distance between the two classifiers of the model, and evaluated an inter-model consistency based on the classifier distributions across models. The two types of consistency guided to update the shared feature extractors and regularized the feature learning around the decision boundary. In addition, the intra-model consistency generated adaptive weights as the mean prediction of each model in the final model ensemble, and the weighted ensemble guided all classifiers to learn for joint alignment of peer models. The proposed method achieves superior classification performance consistently on multiple public datasets. [ABSTRACT FROM AUTHOR]

Voice-driven adaptive cooperative systems have been widely used. However, the ambiguity of the voice-based task goals remains a challenge. How to integrate fuzzy task goals into adaptive cooperative systems to make decisions is a problem that should be resolved imperatively. We propose a fuzzy voice-driven self-adaptive cooperative framework and a corresponding decision method. Firstly, the system publishes tasks, submits tenders and makes decisions based on the market-based approach. Secondly, multi-attributes evaluation information is transformed into 2-tuple linguistic and the VIKOR method is used to rank the candidates. Finally, we take the heterogeneous robot self-adaptive cooperative system as an example to illustrate the decision-making steps of the proposed method, and the experimental results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

科学的课程整合，可以通过其在体系建构、系统评估、综合调整方面的重要功能，捋顺和优化其 与法学专业总体人才培养计划以及其内部各实践教学课程之间的辩证关系，打通由于传统教学及其管 理定式形成的多重"壁垒"，在提高教学资源利用的集约化的同时，创造和释放出法学专业实践教学改 革与建设的多种可能性,从而在法律人才的培养上形成合力. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)