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Peace Science: Orientation and Reorientation

100%

Cheng L. , Spiegel E.

Polish Political Science Yearbook

2016

tom 45

245–256

Peace is non–violence and there is only one way to achieve it: peace as structural and interpersonal non–violence. The daily non–violence is as instructive as the spectacular actions of Mahatma Gandhi and Martin Luther King. Peace education is better based on demonstration what we “can” than to postulate of what we should do. The Peace Studies prefer a resource–oriented approach to education instead of a deficit–oriented. Our central thesis is that the youth is living in a kind of transculturality, the best conditions for peacebuilding. Considering the increasing sensitivity we expected that latest in 2075 we will make the war a taboo. The central key to solve conflicts nonviolently is conflict transformation in trusting a spiritual third power in between the opponents, even secularized people. The peace education has to help us to discover the third in nonviolent activities. There is a lot of difficult issues that the non–violence has to reflect in future, including elimination of the extreme violence, reconciliation, an impact of economy, the peacebuilding’s relevance of structural measures.

Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism

70%

Zhang J. , Kong X. , Cheng L. , Qi H. , Yu M.

Eksploatacja i Niezawodność

2023

tom Vol. 25, no. 1

art. no. 16

Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.