An Approach for Fast Fault Detection in Virtual Network

The diversity of applications in cloud computing and the dynamic nature of environment deployment makes virtual machines, containers, and distributed software systems to often have various software failures, which make it impossible to provide external services normally. Whether it is cloud management or distributed application itself, it takes a few seconds to find the fault of protocol class detection methods on the management or control surfaces of distributed applications, hundreds of milliseconds to find the fault of protocol class detection methods based on user interfaces, and the main time from the failure to recovery of distributed software systems is spent in detecting the fault. Therefore, timely discovery of faults (virtual machines, containers, software) is the key to subsequent fault diagnosis, isolation and recovery. Considering the network connection of virtual machines/containers in cloud infrastructure, more and more intelligent virtual network cards are used to connect virtual network elements (Virtual Router or Virtual Switch). This paper studies a fault detection mechanism of virtual machines, containers and distributed software based on the message driven mode of virtual network elements. Taking advantage of the VIRTIO message queue memory sharing feature between the front-end and back-end in the virtual network card of the virtualization network element and the virtual machine or container it detects in the same server in the cloud network, when the virtualization network element sends packets to the virtual machine or container, quickly check whether the message on the queue header of the previously sent VIRTIO message has been received and processed. If it has not been received and processed beyond a certain time threshold, it indicates that the virtual machine, the container and distributed software have failed. The method in this paper can significantly improve the fault detection performance of virtual machine/container/distributed application (from the second pole to the millisecond level) for a large number of business message scenarios, and provide faster fault detection for the rapid convergence of virtual network traffic, migration of computing nodes, and high availability of distributed applications

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Segmented Preprocessing Method for the Vibration Signal of an On-Load Tap Changer

The vibration signal of an on-load tap changer (OLTC) consists of a series of sharp vibration bursts, and its fault feature in certain periods is easily missed. This study considered that preprocessing the vibration signal of the OLTC in segments could effectively solve the aforementioned problem. First, the collection of the signal is discussed, the waveform characteristics of the vibration signal when the OLTC was in normal action was described, and the selection of the signal was analyzed. Second, the time domain characteristics and frequency spectrum analyses were carried out to demonstrate the necessity of segmented preprocessing. Further, the segmented preprocessing method for the vibration signal of the OLTC was presented. Finally, the main mechanical faults of the OLTC were simulated, and the vibration signals were collected to carry out the fault diagnosis experiment on the OLTC. The experimental results showed that the accuracy of the fault diagnosis increased from 94.30% of the nonsegmented preprocessing to 98.46% of the segmented preprocessing. The increase was greater, especially for contact wear faults. The method was successfully applied to the actual project

A Segmented Preprocessing Method for the Vibration Signal of an On-Load Tap Changer

The vibration signal of an on-load tap changer (OLTC) consists of a series of sharp vibration bursts, and its fault feature in certain periods is easily missed. This study considered that preprocessing the vibration signal of the OLTC in segments could effectively solve the aforementioned problem. First, the collection of the signal is discussed, the waveform characteristics of the vibration signal when the OLTC was in normal action was described, and the selection of the signal was analyzed. Second, the time domain characteristics and frequency spectrum analyses were carried out to demonstrate the necessity of segmented preprocessing. Further, the segmented preprocessing method for the vibration signal of the OLTC was presented. Finally, the main mechanical faults of the OLTC were simulated, and the vibration signals were collected to carry out the fault diagnosis experiment on the OLTC. The experimental results showed that the accuracy of the fault diagnosis increased from 94.30% of the nonsegmented preprocessing to 98.46% of the segmented preprocessing. The increase was greater, especially for contact wear faults. The method was successfully applied to the actual project

Climate Finance: Mapping Air Pollution and Finance Market in Time Series

Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM2.5, PM10, SO2, NO2, CO, and O3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer’s improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process

Climate Finance: Mapping Air Pollution and Finance Market in Time Series

Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM2.5, PM10, SO2, NO2, CO, and O3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer’s improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process

An improved Bergeron differential protection for half-wavelength AC transmission line

Half-wavelength AC transmission line has the characteristics of long transmission distance and high voltage level, and its fault characteristics are significantly different from conventional transmission line. In order to reduce the interference of distributed capacitive current on half-wavelength AC transmission line on the calculation of current differential protection, this paper proposes a new current differential protection scheme based on Bergeron model. In order to solve the problem of small differential current located at the midpoint when a short circuit fault occurs, a solution using different methods to calculate setting value in different areas is proposed. The protection can move quickly near the terminal and delay to act in the middle area. After simulation and verification on the PSCAD experimental platform, it is found that when there is a fault at both terminals of the line, the protection can quickly operate in about 10 ms; when fault occurs in the middle area, the protection can delay its operation. The experimental results show that the various actions and performance of the protection device can meet the requirements of safe operation of half-wavelength transmission line

Early warning of stator winding overheating fault of water-cooled turbogenerator based on SAE-LSTM and sliding window method

Aiming at the early warning of overheating defects in the stator winding of water-cooled turbogenerators, this paper proposes a novel method based on SAE-LSTM and sliding window method by combining the Sparse Auto-Encoder (SAE) and the Long–Short Term Memory network (LSTM) with highly time dependent time series data characteristics. Firstly, the sparse auto-encoder is used to reconstruct the operation data collected by the Distributed Control System (DCS) installed on the turbogenerator to extract the data characteristics; Secondly, the LSTM prediction model optimized by attention mechanism is used to predict the outlet temperature of each slot of the stator winding of the turbogenerator under normal working condition; Then, the sliding window method is adopted to detect the stator winding overheating defect, and the alarm threshold is defined based on both the maximum mean value and maximum standard deviation of the predicted residual within the window. Finally, the proposed method is validated by using the historical DCS data of a turbo generator with stator winding overheating defect before failure,and the results show that compared with the traditional threshold warning method, the proposed method can warn the defects 85 h in advance, which provides strong support for the stable operation of the turbogenerator