Analysis on the Operation Safety Management Mode of Urban Rail Transit

With the continuous progress and renewal of science and technology in China, great changes have taken place in all aspects of people’s life[1]. The city is growing faster and faster. Economics, human culture and urban connotation have been greatly improved. It can be said that the progress of urban rail transit accelerates the development and prosperity of the city. The progress of rail transit provides people with great convenience. However, the phenomenon of traffic congestion has not been improved. The frequency of traffic accidents has not decreased. Therefore, researchers began to focus on the operation of urban rail transit operation safety management. This paper analyzes and discusses the safety management mode of rail transit, and finally draws a conclusion

One More Time, I Love You —— 我有所念人，隔在远远乡

One More Time, I Love You ——我有所念人，隔在远远乡 is a thesis project that delves into the profound nature of obsession, which surpasses the boundaries of life and death, as well as the mortal world and the underworld. The interpretation of this type of obsession varies among individuals, and my understanding of it originates from the traditional Chinese myth concerning the afterlife journey. According to this myth, upon departing from the mortal realm, the deceased traverse the Bridge of Helplessness, cross the Forgotten River, peruse their past, present, and future lives on a Three Lives Stone, and then partake in the Soup of Oblivion. This soup cleanses all memories of their current life, leaving behind only an unblemished and pure consciousness ready for the next life. Although the tangible and discernible elements have completely vanished, the intense nature of obsession can permeate the flesh and bones, disregarding the coexistence of yin and yang within this world. My fascination lies in this delicate, yet profoundly overpowering emotion that transcends the boundaries of life and death. This thesis serves as an introspective exploration of my emotions at a specific juncture in time. Through my artistic works, I attempt to express the ineffable obsessions that elude verbalization, encapsulating both my personal experiences and the resonant emotions they evoke. These works bear the invisible burdens I may not even be conscious of, eventually converging into a nuanced spectrum of blue, symbolizing my own version of the Soup of Oblivion and Three Lives Stone, embodying tangible connections within my uncertain future

MINING CONCEPT IN BIG DATA

To fruitful using big data, data mining is necessary. There are two well-known methods, one is based on apriori principle, and the other one is based on FP-tree. In this project we explore a new approach that is based on simplicial complex, which is a combinatorial form of polyhedron used in algebraic topology. Our approach, similar to FP-tree, is top down, at the same time, it is based on apriori principle in geometric form, called closed condition in simplicial complex. Our method is almost 300 times faster than FP-growth on a real world database using a SJSU laptop. The database is provided by hospital of National Taiwan University. It has 65536 transactions and 1257 columns in bit form. Our major work is mining concepts from big text data; this project is the core engine of the concept based semantic search engine

Smoothing and mean-covariance estimation of functional data with a Bayesian hierarchical model

Functional data, with basic observational units being functions (e.g., curves, surfaces) varying over a continuum, are frequently encountered in various applications. While many statistical tools have been developed for functional data analysis, the issue of smoothing all functional observations simultaneously is less studied. Existing methods often focus on smoothing each individual function separately, at the risk of removing important systematic patterns common across functions. We propose a nonparametric Bayesian approach to smooth all functional observations simultaneously and nonparametrically. In the proposed approach, we assume that the functional observations are independent Gaussian processes subject to a common level of measurement errors, enabling the borrowing of strength across all observations. Unlike most Gaussian process regression models that rely on pre-specified structures for the covariance kernel, we adopt a hierarchical framework by assuming a Gaussian process prior for the mean function and an Inverse-Wishart process prior for the covariance function. These prior assumptions induce an automatic mean-covariance estimation in the posterior inference in addition to the simultaneous smoothing of all observations. Such a hierarchical framework is flexible enough to incorporate functional data with different characteristics, including data measured on either common or uncommon grids, and data with either stationary or nonstationary covariance structures. Simulations and real data analysis demonstrate that, in comparison with alternative methods, the proposed Bayesian approach achieves better smoothing accuracy and comparable mean-covariance estimation results. Furthermore, it can successfully retain the systematic patterns in the functional observations that are usually neglected by the existing functional data analyses based on individual-curve smoothing.Comment: Submitted to Bayesian Analysi

BFDA: A MATLAB Toolbox for Bayesian Functional Data Analysis

We provide a MATLAB toolbox, BFDA, that implements a Bayesian hierarchical model to smooth multiple functional data samples with the assumptions of the same underlying Gaussian process distribution, a Gaussian process prior for the mean function, and an Inverse-Wishart process prior for the covariance function. This model-based approach can borrow strength from all functional data samples to increase the smoothing accuracy, as well as simultaneously estimate the mean-covariance functions. An option of approximating the Bayesian inference process using cubic B-spline basis functions is integrated in BFDA, which allows for efficiently dealing with high-dimensional functional data. Examples of using BFDA in various scenarios and conducting follow-up functional regression are provided. The advantages of BFDA include: (1) simultaneously smooths multiple functional data samples and estimates the mean-covariance functions in a nonparametric way; (2) flexibly deals with sparse and high-dimensional functional data with stationary and nonstationary covariance functions, and without the requirement of common observation grids; (3) provides accurately smoothed functional data for follow-up analysis