This study shows that the intelligent recognition algorithm can identify drilling faults under interference conditions.

The widespread application of mobile crowdsourcing modes provides new ideas for generating indoor maps. By collecting and analyzing the trajectory datas of users properly, we can obtain the location information of indoor paths.  Unfortunately, currently studies usually rely heavily on a satellite location, which restricts their indoor application. In this paper, a simple and  practical method of generating indoor maps on Andriod platform is presented, and this method  is able to correct deviation duly. User's datas collected by several bulit-in sensors are preprocessed utilizing Gaussian filter, after which we adopt feature recognition to confirming one's  walking track based on multiple experiment datas. In order to integrate tracks generated by different persons, we then propose a new data structure based on a transition probability that can be updated online to store track information. In addition, we minimize possible deviations by testing the signal power launched by four Bluetooth base stations.  Discrete tracks are finally integrated into a complete indoor map using a graph_based model. We then propose a novel encryption scheme exploiting chaos in a nonlinear digital filter, where secure key generation methods are discussed in detail. The secure key scheme includes: 1)channel measurement 2)a decorrelation transform 3)multibit adaptive quantization and encoding. Experiments are conducted in   rectangle fields of 8m*8m, 44m*44m, respectively, and the results show our method can attain a maximum error of 5.94%.

Background: The entanglement of the otological drill with cotton swabs is a common milling fault in ear surgery. To improve operational safety, this paper presents a method for identifying this type of milling fault. Methods: Force and current sensors were installed on a modified otological drill. In accordance with the DC motor model and cutting force model, two features of the milling process were extracted, namely the characteristic curve and the dynamic relationship between the sensor signals. These are complementary features. An adaptive filter was designed to fuse them together and output a curve that was sensitive to milling faults and was stable during normal milling. Based on the filtering data, a rule base is presented for identifying cotton swab entanglement. Results: Five surgeons were invited to perform an experiment on calvarian bones. The average recognition rate for milling faults was 90%, whereas only 2% of normal millings were identified as milling faults. Conclusions: The presented method could adapt to the technique of different surgeons and identify milling faults exactly.

Recent years have seen significant advancement in text generation tasks with the help of neural language models. However, there exists a challenging task: generating math problem text based on mathematical equations, which has made little progress so far. In this paper, we present a novel equation-toproblem text generation model. In our model, 1) we propose a flexible scheme to effectively encode math equations, we then enhance the equation encoder by a Varitional Autoencoder (VAE) 2) given a math equation, we perform topic selection, followed by which a dynamic topic memory mechanism is introduced to restrict the topic distribution of the generator 3) to avoid commonsense violation in traditional generation model, we pretrain word embedding with background knowledge graph (KG), and we link decoded words to related words in KG, targeted at injecting background knowledge into our model. We evaluate our model through both automatic metrices and human evaluation, experiments demonstrate our model outperforms baseline and previous models in both accuracy and richness of generated problem text.

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