Bearing Fault Diagnosis via Incremental Learning Based on the Repeated Replay Using Memory Indexing (R-REMIND) Method

In recent years, deep-learning schemes have been widely and successfully used to diagnose bearing faults. However, as operating conditions change, the distribution of new data may differ from that of previously learned data. Training using only old data cannot guarantee good performance when handling new data, and vice versa. Here, we present an incremental learning scheme based on the Repeated Replay using Memory Indexing (R-REMIND) method for bearing fault diagnosis. R-REMIND can learn new information under various working conditions while retaining older information. First, we use a feature extraction network similar to the Inception-v4 neural network to collect bearing vibration data. Second, we encode the features by product quantization and store the features in indices. Finally, the parameters of the feature extraction and classification networks are updated using real and reconstructed features, and the model did not forget old information. The experiment results show that the R-REMIND model exhibits continuous learning ability with no catastrophic forgetting during sequential tasks

Bearing Fault Diagnosis via Incremental Learning Based on the Repeated Replay Using Memory Indexing (R-REMIND) Method

In recent years, deep-learning schemes have been widely and successfully used to diagnose bearing faults. However, as operating conditions change, the distribution of new data may differ from that of previously learned data. Training using only old data cannot guarantee good performance when handling new data, and vice versa. Here, we present an incremental learning scheme based on the Repeated Replay using Memory Indexing (R-REMIND) method for bearing fault diagnosis. R-REMIND can learn new information under various working conditions while retaining older information. First, we use a feature extraction network similar to the Inception-v4 neural network to collect bearing vibration data. Second, we encode the features by product quantization and store the features in indices. Finally, the parameters of the feature extraction and classification networks are updated using real and reconstructed features, and the model did not forget old information. The experiment results show that the R-REMIND model exhibits continuous learning ability with no catastrophic forgetting during sequential tasks

Does having a culture of giving support financial inclusion?

There have been studies on the benefits of financial inclusion, and culture as an alternative channel to impact financial inclusion. However, due to the multifaceted nature of culture, there have been no studies done on the impact of culture of giving or philanthropy on financial inclusion. This paper thus aims to propose that encouraging a culture of giving will positively affect financial inclusion. Using a sample of 115 countries, we adopted the Ordinary Least Squares (OLS) approach in running the regressions. Specifically, the OLS regression results indicate that a culture of giving is significantly correlated with financial inclusion, where a one standard deviation increase in the culture of giving is associated with an improvement in the financial inclusiveness of a nation by 0.557 standard deviation units. Subsequently, in order to ensure the robustness of our results, alternative measures of financial inclusion were substituted into the regression model and regressed separately. Empirical results indicate that regardless of the measures of FI used, the culture of giving remains to have a positive correlation with FI, ceteris paribus. Lastly, WGI is substituted with its 3 subcomponents and regressed separately, offering an in-depth analysis with regards to the importance and predictive effects of the different aspects of WGI on FI. Empirical results suggest that Giving Money and Giving Time could be the key areas within a culture of giving to be focused on, as they were statistically significant in predicting FI.Bachelor of Social Sciences in Economic

Adjust band gap of IATO nanoparticles to obtain desirable optical property by one-step hydrothermal oxidation

Antimony-tin-doped indium oxide (IATO) as transparent conducting oxide (TCO) exhibits significant optical property on blocking UV and Infrared(IR) for wavelengths less ∼400 nm and over ∼1400 nm as well as appropriate transmissivity on visible wavelength in our work that can be as an optional idea optical material applying in shielding film or nanocomposite to achieve desired optical application. We have successfully developed an optimal synthesis system which allows for a single hydrothermal oxidation directly synthesizing IATO nanoparticles without high-temperature calcination. These nanoparticles show superior size, crystallinity, agglomeration and are free of intermediates In(OH)3 and InOOH. We also have demonstrated they give scope to desired optical property as a result of an altered IATO band gap energy. We highlight this approach due to the shortened preparation time, the reduced energy consumption and the decreased chemical usage which dramatically save on production costs and protect environment

Study on the electronic structure and the optical performance of YB6 by the first-principles calculations

The electronic structure and the optical performance of YB6 were investigated by first-principles calculations within the framework of density functional theory. It was found that the calculated results are in agreement with the relevant experimental data. Our theoretical studies showed that YB6 is a promising solar radiation shielding material for windows