Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experience of the appraisers or the various measuring instruments, and the methods are either easily influenced by appraisers’ experience or require too much work. To solve the above problems, there are studies using image recognition and intelligent algorithms to identify minerals. However, current studies cannot identify many minerals, and the accuracy is low. To increase the number of identified minerals and accuracy, we propose a method that uses both mineral photo images and the Mohs hardness in deep neural networks to identify the minerals. The experimental results showed that the method can reach 90.6% top-1 accuracy and 99.6% top-5 accuracy for 36 common minerals. An app based on the model was implemented on smartphones with no need for accessing the internet and communication signals. Tested on 73 real mineral samples, the app achieved top-1 accuracy of 89% when the mineral image and hardness are both used and 71.2% when only the mineral image is used.
The traditional stator single-phase-to-ground fault protective schemes are hard to completely satisfy the requirement of selectivity for bus-connected Powerformers. In order to overcome the aforementioned problem, a new stator single-phase-toground fault protective scheme based on the hierarchical clustering algorithm is proposed in this paper. The direction and magnitude of zero-sequence current and leakage current are discussed and selected as feature vectors. Then, the data set are divided into two groups by hierarchical clustering algorithm, and cluster center of each group is calculated. Finally, the space relative distance among detected pattern and two cluster centers is calculated to discriminate the faulty Powerformer. The fundamental component and the third harmonic component of the data set are combined to realize 100% coverage of fault detection for the stator windings of Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish internal faults from external faults reliably, and can detect in which machine a stator single-phase-to-ground fault is occurring even if the fault resistance is at 5 kΩ.
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