Bearing fault diagnosis based on active learning and random forest

Bearing plays an important role in rotating machineries and has received increasing attention in diagnosis of its faults accurately. This paper proposes a fault diagnosis approach exploiting active learning (AL) based on random forest (RF), which can perform accurate bearing fault diagnosis with most valuable samples. First, feature vectors are obtained by empirical mode decomposition (EMD) process for original vibration signals and selected as input of the system. Second, samples with highest uncertainty are selected through AL and added to the training set to train RF classifier. Finally, trained RF is employed to perform classification for bearing faults with testing set. Experimental results demonstrate that the proposed approach can effectively and accurately identify typical bearing faults

Research on Privacy Paradox in Social Networks Based on Evolutionary Game Theory and Data Mining

In order to obtain social benefits, social networks have started taking benefits from private information of network users. While having increased concerns about the risk of privacy disclosure, users still generally disclosed under high privacy concerns, which directly formed the privacy paradox. The expansion and generalization of privacy paradox indicate that the implementation of privacy protection in social networks is still in a dilemma. Studying and solving the problem of privacy paradox is conducive to ensure the healthy development of social network industry. Based on this, this study has designed a research system that analyzes the privacy paradox of social networks from three dimensions: cause, existence and form. After studying existing research of privacy paradox in social networks, evolutionary game theory is determined to be introduced into the procedure of cause analysis, while data mining is used as a data analysis method for empirical research. Within the whole research process, the evolutionary game model of privacy paradox in social networks is built up first, while the necessary conditions for the generation of privacy paradox is addressed, which is derived from the evolutionary stable strategy. Secondly, the questionnaire survey method is used to collect private data of active users of both Weibo and WeChat. Lastly, Apriori and CHAID algorithm are used to determine the relationship of user privacy concerns, privacy behavior, and other factors, which then confirms the existence of privacy paradox on two social networks and makes a comparison between their forms of privacy paradox in specific. This research systematically makes a useful an in-depth analysis to the privacy paradox in social networks and is meaningful for establishing a hierarchical protection system of users\u27 privacy for enterprises

Knockdown of TNFAIP1 mitigates sevoflurane-induced cognitive dysfunction by activating CREB/Nrf2 pathway

Purpose: To investigate the role of tumor necrosis factor-induced protein 1 (TNFAIP1) and cAMPresponsive element binding protein (CREB)/nuclear factor-erythroid factor 2-related factor 2 (Nrf2) pathway in sevoflurane (SEV)-induced cognitive dysfunction. Methods: A SEV-induced cognitive dysfunction rat model was developed. Bcl-2, Bax, heme oxygenase-1, Nrf2, p-CREB, and CREB protein levels in rat hippocampal tissue were assessed by western blot. Learning and long-term memory were evaluated using Morris water maze test. Glutathione peroxidase, malondialdehyde, and superoxide dismutase levels in hippocampal tissue were measured by enzyme-linked immunosorbent assay (ELISA). The 2,7-dichlorodihydro-fluorescein diacetate fluorescent assay was used to measure reactive oxygen species, while TUNEL staining was used to assess neuronal cell apoptosis. Results: Knockdown of TNFAIP1 attenuated SEV-induced learning and long-term memory dysfunction (p < 0.005), oxidative stress (p < 0.005), apoptosis (p < 0.005), and inhibition of the CREB/Nrf2 signaling pathway. Conclusion: This study demonstrates that knockdown of TNFAIP1 alleviates SEV-induced cognitive dysfunction by reversing inhibition of the CREB/Nrf2 signaling pathway. Keywords: TNFAIP1; Postoperative cognitive dysfunction; Sevoflurane; cAMP-responsive element binding protein (CREB); Nuclear factor-erythroid factor 2-related factor 2 (Nrf2

An approach to fault diagnosis for rotating machinery based on feature reconstruction with LCD and t-SNE

It is crucial to effectively and accurately diagnose fault of rotating machinery. However, high dimension characteristic of features, which are extracted from vibration signals of Rotating machinery, makes it difficult to recognize accurately fault mode. To resolve this problem, t-distributed stochastic neighbor embedding (t-SNE) is introduced to reduce the dimensionality of the feature vector in this paper. Therefore, the article proposes a method for fault diagnosis of Rotating machinery based on local characteristic decomposition-sample entropy (LCD-SampEn), t-SNE and random forest (RF). Firstly, original vibration signals of rotating machinery are decomposed to a number of ISCs by the LCD. Then, feature vector is obtained through calculating SampEn of each ISC. Subsequently, the t-SNE is used to reduce the dimension of the feature vectors. Finally, the reconstructed feature vectors are applied to the RF for implementing the classification of fault patterns. Two cases are studied based on the experimental data of bearing and hydraulic pump fault diagnosis, in which the proposed method can achieve 98.22 % and 98.75 % of diagnosis rate respectively. Compared with the pear methods, the proposed approach exhibits the best performance. The results validate the effectiveness and superiority of the present method

Personality predicts words in favorite songs

Psychologists have long theorized that people actively create, select, or modify situations in order to fulfill their psychological needs. However, little is known about how people use forms of art and entertainment such as music to enhance the potential for need satisfaction in their environment. In this research, we propose that people like certain songs because the linguistic cues in their lyrics are congruent with personality and hence can satisfy personal needs. To test this hypothesis, we measured participants’ personality and the linguistic styles of their favorite songs, and identified associations between personality and word use in lyrics, while controlling for preferences for melodic attributes and uses of music. We observed significant associations between personality traits predict linguistic cues in favorite songs, such as extraverts tending to like songs expressing positive emotions and conscientious individuals tending to like songs with lyrics that display cognitive complexity. These associations between personality and lyrics were stronger for participants who generally liked a song because of its lyrics rather than melody. These results improve our understanding of how people use linguistic cues in language products to satisfy their needs, and provide a new framework for understanding the mechanisms of musical preferences. They also have important practical implications

Protein-Protein Affinity Determination by Quantitative FRET Quenching.

The molecular dissociation constant, Kd, is a well-established parameter to quantitate the affinity of protein-protein or other molecular interactions. Recently, we reported the theoretical basis and experimental procedure for Kd determination using a quantitative FRET method. Here we report a new development of Kd determination by measuring the reduction in donor fluorescence due to acceptor quenching in FRET. A new method of Kd determination was developed from the quantitative measurement of donor fluorescence quenching. The estimated Kd values of SUMO1-Ubc9 interaction based on this method are in good agreement with those determined by other technologies, including FRET acceptor emission. Thus, the acceptor-quenched approach can be used as a complement to the previously developed acceptor excitation method. The new methodology has more general applications regardless whether the acceptor is an excitable fluorophore or a quencher. Thus, these developments provide a complete methodology for protein or other molecule interaction affinity determinations in solution

Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning

The conservation of historical heritage can bring social benefits to cities by promoting community economic development and societal creativity. In the early stages of historical heritage conservation, the focus was on the museum-style concept for individual structures. At present, heritage area vitality is often adopted as a general conservation method to increase the vibrancy of such areas. However, it remains unclear whether urban morphological elements suitable for urban areas can be applied to heritage areas. This study uses ridge regression and LightGBM with multi-source big geospatial data to explore whether urban morphological elements that affect the vitality of heritage and urban areas are consistent or have different spatial distributions and daily variations. From a sample of 12 Chinese cities, our analysis shows the following results. First, factors affecting urban vitality differ from those influencing heritage areas. Second, factors influencing urban and heritage areas' vitality have diurnal variations and differ across cities. The overarching contribution of this study is to propose a quantitative and replicable framework for heritage adaptation, combining urban morphology and vitality measures derived from big geospatial data. This study also extends the understanding of forms of heritage areas and provides theoretical support for heritage conservation, urban construction, and economic development