Application of Fisher's Discriminant Method and Bayes' Discriminant Method Based on “R Language Analysis” as an Example

Discriminant analysis is a statistical discriminant and grouping technique, which uses the sample data of the research object to find a discriminant rule, which is called a discriminant function, which is used to explain the difference between two or more groups of groups and can classify a new product. The data determines which of the known types the new product belongs to. This paper discusses the discriminant analysis of Fisher's discriminant method and Bayes' discriminant method, and shows the application of the two methods in real life and the difference between them on the basis of R language

The Test of Univariate Normality and Multivariate Normality Based on R Language

The normal distribution is a widely used distribution and has a very important probability distribution in the fields of mathematics, physics and engineering. In natural and social phenomena, a large number of random variables obey or approximately obey the normal distribution, so it is always customary to assume that they are in line with normality when doing data analysis, but whether the assumption is true or not depends on the normality of test. Therefore, the judgment method of normal distribution is also particularly important. This paper proposes corresponding test methods for univariate normality and multivariate normality under the premise of R language

Knowledge graph embedding by dynamic translation

Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the performance of knowledge acquisition, fusion and inference. Among various embedding models appeared in recent years, the translation-based models such as TransE, TransH, TransR and TranSparse achieve state-of-the-art performance. However, the translation principle applied in these models is too strict and can not deal with complex entities and relations very well. In this paper, by introducing parameter vectors into the translation principle which treats each relation as a translation from the head entity to the tail entity, we propose a novel dynamic translation principle which supports flexible translation between the embeddings of entities and relations. We use this principle to improve the TransE, TransR and TranSparse models respectively and build new models named TransE-DT, TransR-DT and TranSparse-DT correspondingly. Experimental results show that our dynamic translation principle achieves great improvement in both the link prediction task and the triple classification task

Deep neural network-based image enhancement algorithm for low-illumination images underground coal mines

Due to the complexity of the spatial environment and poor lighting conditions in underground coal mines, the images obtained by vision devices are prone to problems such as insufficient contrast and poor texture details, which seriously affect the reliability of the work of vision devices and limit further image-based intelligent applications. To improve the contrast of low-illumination images in underground mines while enhancing their texture details, a deep neural network-based low-illumination image enhancement model is proposed, which contains three sub-networks, namely, decomposition network, illumination adjustment network and reflection reconstruction network. The decomposition network decomposes the underground coal mine image into light and reflection components; the light adjustment network effectively reduces the parameters of the model using depth-separable convolutional structure and strengthens the feature extraction ability of the network; in addition, the MobileNet network structure is introduced to further lighten the light adjustment network while maintaining its feature extraction accuracy and effectively realizing the contrast adjustment of light components; the reflection reconstruction network introduces a residual network structure to improve the contrast adjustment of light components. Finally, the processed illumination and reflection components are fused based on Retinex theory to obtain enhanced images, which achieve contrast enhancement and detail enhancement of underground mine images, overcoming the problems of detail loss, blurred edges, and lack of contrast and clarity of the enhanced image that exist in existing enhancement algorithms. Numerical experiments show that the proposed model can effectively enhance the texture details of the image while improving the contrast of underground mine images, and has good stability and robustness, which can well meet the needs of low-light image enhancement in coal mines

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images

"LIVING HIGH-TRAINING LOW" ALTITUDE TRAINING ON IMPROVEMENT OF SEA LEVEL HEMOGLOBIN/HEMATOCRIC IN MALE AND FEMALE ELITE SWIMMERS

This study investigated the effect of "living high-training low" altitude on improvement of sea level hemoglobin and hematocric in male and female elite swimmers. A total of ten elite swimmers at the international and national level was recruited and randomly divided into two groups, altitude training group and control group. The athletes in altitude training group lived high condition while training at sea level for three weeks. The altitude was set at 2,800m. The all subjects in both groups accepted same training in the intensity, frequency and duration. Hemoglobin and hematocric were measured at sea level on seven occasions, the day before starting the experiment, during the period of experiment and the day completing the experiment. The results showed that the hemoglobin and hematocric in altitude training group increased 8.6% and had significant difference compared with those before the experiment. While the level of hemoglobin and hematocric in control group did not show any obvious change. "Living high-training low" altitude training can significantly improve the level of hemoglobin and hematocric