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While both crude coal and clean coal increased production, in 2007, the 600,000-ton coking plant in Lu'an Huaneng Wuyang Coal Mine in Shanxi Province was put into full operation, and downstream industries such as sulfur, ammonium sulfate, crude benzene, and gas transmission and distribution were successively built. - The electricity-chemical industry chain takes shape.
Wuyang Coal Mine is a state-owned and old mine with 45 years of mining history. After several expansions and transformations, the mine production capacity has increased to 1.92 million tons and has reached the limit. In the year of 206, Wuyang Coal Mine made a decision, built a new type of industrial park, followed the road of circular economy, expanded the scale of “coal-coke-electricity-based” integrated industry, and made every effort to build a new coal chemical industry base. In that year, the coke plant with an annual production capacity of 300,000 tons in the industrial park coking plant was completed and put into production. Last year, it produced 300,000 tons of primary metallurgical coke, 6,600 tons of tar and a profit of 25 million yuan. The second phase of the project was started in March last year. The project includes the second coke oven and downstream chemical production projects such as sulfur, ammonium sulfate, crude benzene, and gas transmission and distribution, which created the shortest construction time in the same industry and the fastest record of production. .

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Yang Xuegang is the newly elected National People's Congress representative this year. He has been in the coal chemical industry for more than a decade. For more than ten years, he led a local small-scale coking enterprise and developed into a leading company in China's coke and coal chemical industry. The output of coke ranks first among independent coking enterprises in the country, and in 2007, it ranked among the top 500 Chinese manufacturing companies.
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Yang Xuegang also said that many companies now want to go to coal chemical projects, but often only stay in the planning and did not become a reality. Some companies built projects but had to lose money due to low operating rates, or had to stop production due to safety incidents. The important reason is that these enterprises have insufficient reserves in terms of technology, personnel, experience, and management and cannot achieve continuous, stable, and safe production. The launch of a project should be conducted on the basis of full investigation and preparation. For example, the new project of Xuyang Coking should carry out nearly three to four years of research and preparation work, so as to ensure the success of a successful drive. Avoid all types of accidents.
At present, Xuyang Coking is actively exploring further technological transformations and is preparing to increase the coke oven height from 5.5 meters to 6.25 meters, which is not yet a precedent in the domestic ramming coke industry. In the future, they will also implement Coal gasification technology and coking technology combined technological breakthrough. Last year, the provincial government of Hebei Province has entrusted Xuyang Group to build the Hebei Provincial Coal Chemical Engineering Technology Research Center. At present, various work has already begun.

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0 Preface

Face recognition is an important part of biometric recognition. It has a very wide range of applications in the business, security and judicial industries. The method of face recognition mainly implements an intelligent discrimination task through a computer. In general, face recognition of images can be defined and described as follows:

(1) Given a still or video image, perform a search, implement a judgment, and find a face;

(2) Determine the status of faces in a photo, such as gender, expression, age, etc.;

(3) Given an unknown face image, compare it with existing face images in the gallery to identify the classification.

These tasks are suitable for different working environment requirements, and some complex systems may contain all of the above. Face recognition methods are mainly based on geometric features and algebraic characteristics of the identification method, the former will identify the location of various organs, easy to understand, but poor robustness.

Based on algebraic features, the core idea is to treat face images as multidimensional (ie, the number of image pixels) vector set. These vectors are statistically identified or mapped to achieve the purpose of classification and identification. The method of distinguishing these multidimensional change maps is also the commonly used subspace method.

This paper uses the independent component analysis (ICA) method to recognize the face image, and the constructed training library has multiple expressions and postures. Its recognition has strong robustness and accuracy. The analysis and comparison of some of the processing results has wide application value.

1ICA basic principles

Independent component analysis is a more successful algorithm used in blind signal separation. It assumes that the signal is a mixture of several independent source signals. With proper projection, the second-order and higher-order statistical information can be extracted and high-order signals can be eliminated. Correlation. The basic approach is to use the ICA algorithm to solve the training set image to obtain a set of independent influencing bases. When the image to be tested is identified, it is projected in each base direction. In this way, distance and other discrimination methods can be used to determine the category to which the test chart belongs.

Using ICA algorithm to recognize the face, the training sample image set X of the face can be used as a linear combination of the statistically independent base image S and the reversible mixing matrix A:

X=AS

The purpose of the ICA algorithm is to find the mixing matrix A or the discrete matrix W so that it satisfies the following equation:

Among them, I is the estimation of the independent statistical base image S.

From a statistical point of view, the non-Gaussianity of random variables is closely related to statistical independence. The strongest direction of non-Gaussianity is the projection direction of ICA. Therefore, as long as the non-Gaussian property is found, the corresponding one is the direction of the independent component to be found.

2 Algorithm Overview and Basic Processing 2.1 Image Library Selection and Image Preprocessing
The image library used in this paper is the Olivetti Research Laboratory standard face database (as shown in Figure 1). There are a total of 40 face pictures in the face database, with 10 pictures per person and a total of 400 pictures. The image format is pgm. It is a mobile bitmap file and is easy to handle. Each person's picture is from different angles, at different times and influenced by a certain light and some ornaments (such as glasses). The image size is 112×92. In many literatures, certain processing can be performed on the original image, and the image size can be reduced as much as possible while maintaining the original information of the image. It can effectively reduce the number of image pixels, which is also equivalent to a dimension reduction before subsequent processing. For example, some spectral face processing methods such as wavelet processing or the use of image segmentation to extract facial pictures can improve information validity.

For the face input data (first training sample), the gray data is arranged, that is, a matrix is ​​obtained. Each line is the end of the line representing the gray value of the training graph. The number of columns is the total number of training charts. The specific image refers to the face number and can be defined by a volume label function.

The data matrix is ​​subjected to a centralization process, that is, the mean value is processed, and then the whitening process is performed so that the whitened variable covariance matrix is ​​an identity matrix, and a covariance eigenvalue decomposition is used. The main purpose of these processes is to enable the amount of computation to be reduced.

2.2 Generation of ICA independent component basis 2.3 Face recognition

The face image data to be tested is extracted, centered and whitened, and then projected on an independent component basis generated by the method described above. Each test chart uses a غير مجاز مي باشدine discriminating method to find out the most recent category. Thus categorized. The غير مجاز مي باشدine formula is expressed as follows:

This is a calculation of the correlation coefficient. The larger the correlation coefficient, the higher the similarity of the two feature vectors. In this way, the vector to be measured (such as mat2) can be judged as the face category of mat1. Comparing the judgment result with the real value, the correct rate of recognition can be calculated.

3 experimental results and analysis

In accordance with the above method, this article tests the face, and there are several issues worth paying attention to the experimental results of the algorithm. The experiment is also based on these issues to test:

(1) The effect of training sample and test sample ratio on the correct rate of test results;

(2) The total number of training samples and test samples, the impact of the correct rate of test results;

(3) The effect of the number of independent components on the test results.

3.1 Effect of Training Sample Size on Test Results

The number of training samples obviously has a great influence on the test results. If possible, as many training sets as possible are obtained, the test results can be greatly improved. Figure 2 shows the recognition accuracy rate curves obtained for 40 people with 5, 6, 7, 8 and 9 training samples.

3.2 The effect of different data set sizes on the recognition rate
The data sets of different sizes, ie the size of the training and test population, have a certain influence on the test results. Figure 3 depicts the experimental results of this effect. It can be seen that the data set size and the accuracy rate are generally negatively correlated (not very strict), and the training and test set recognition rate of only 40 people is greatly reduced. Therefore, the reliability of the recognition result must be considered for the larger and more important face recognition process. Of course, stricter parameters are used only in the number of trainings and the number of independent components to be mentioned below.

3.3 The influence of the number of independent component components on the recognition rate

The influence of the number of independent component components on the recognition rate is twofold. On the one hand, the greater the number of independent component components, the higher the recognition accuracy, but on the other hand, too many independent component components will affect the speed of calculation. Although there are few samples in the experimental process, the speed can be tolerated, but if the training set is large and the number of independent component components is large, the recognition speed will be affected. It takes more than 3 hours for a typical personal microcomputer (2.6G dual-core) to process 400 samples of 10,000-pixel images to achieve maximum accuracy. When the demand is not high, the number of independent component components reaches 30% of the training set is sufficient to achieve better results. Figure 4 shows the results of tests with different independent component numbers. There are 6 training samples in each group and 4 testing samples. As can be seen from Figure 4, for a small number of training tests, the test success rate is relatively easy to achieve a high correct recognition rate (0.975%). It can also be said that it is easy to achieve convergence.

4 Conclusion

In this paper, the face recognition experiments were performed using independent component analysis. Through the projection of the face image into the subspace for classification and identification, the purpose of judging the face is achieved. The results show that this projection method based on independent components has a high accuracy, and compared with some of the more important parameters in independent component analysis, a comparative experiment was conducted to achieve an equilibrium in computational efficiency and accuracy. The experimental results show that the number of training sets, the total number of samples and the number of independent components used have a great influence on the correctness of the calculation and present a nonlinear correlation. These results also help design face recognition software or reference when performing recognition tasks.

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"Big data" usually refers to those data sets that are large in number, hard to collect, process, and analyze, and also those that are stored in the traditional infrastructure for a long period of time. The "big" here has several layers of meaning. It can describe the size of the organization, and more importantly, it defines the scale of the IT infrastructure in the enterprise. The industry places endless expectations on big data applications – the more value the business information accumulates – the more we just need a way to tap those values.

Perhaps people’s impressions of big data come mainly from the cheapness of storage capacity, but in fact, companies are creating a lot of data every day, and more and more, and people are trying to find valuable data from vast data. Business intelligence. On the other hand, the user will also save the data that has been analyzed, because the old data can be compared with the new data collected in the future, there are still potential use possibilities.

Why big data? Why is it now?

Compared with the past, we have to face more data types in addition to the ability to store more data. Sources of these data include online transactions, social networking activities, automatic sensors, mobile devices, and scientific instruments. In addition to those fixed sources of data production, various trading activities may also speed up the accumulation of data. For example, the explosive growth of social multimedia data stems from new online transactions and records. Data is always growing, but only the ability to store large amounts of data is not enough, because it does not guarantee that we can successfully search for commercial value.

Data is an important production factor

In the information age, data has become an important production factor, just like capital, labor, raw materials and other elements, and as a general demand, it is no longer limited to the application of certain special industries. Companies from all walks of life are collecting and using a large amount of data analysis results to reduce غير مجاز مي باشدts as much as possible, improve product quality, increase production efficiency, and create new products. For example, analyzing data collected directly from the product test site can help companies improve their designs. In addition, a company can also surpass its competitors by analyzing customer behavior in depth and comparing large amounts of market data.

Storage technology must keep up
With the explosive growth of big data applications, it has spawned its own unique architecture, but also directly promote the development of storage, networking and computing technology. After all, dealing with this special demand for big data is a new challenge. The development of hardware is ultimately driven by software requirements. For this example, it is clear that the demand for big data analytics applications is affecting the development of data storage infrastructure.

On the other hand, this change is not an opportunity for storage vendors and other IT infrastructure vendors. With the continuous increase in the amount of structured data and unstructured data, and the diversification of sources of analysis data, the design of the storage system has been unable to meet the needs of big data applications. Storage vendors have realized this and they have begun to modify the architectural design of block- and file-based storage systems to accommodate these new requirements. Here, we will discuss the properties related to big data storage infrastructure and see how they meet the challenge of big data.

Capacity issues The “big capacity” mentioned here can usually reach the scale of the PB data. Therefore, the massive data storage system must also have the corresponding level of expansion capability. At the same time, the expansion of the storage system must be simple, you can increase the capacity by adding modules or disk cabinets, or even without downtime. Based on this demand, customers now increasingly favor the storage of the Scale-out architecture. The characteristic of Scale-out cluster structure is that each node not only has a certain storage capacity, but also has internal data processing capabilities and interconnected devices. Compared with the chimney architecture of traditional storage systems, the Scale-out architecture can achieve seamless smoothing. Expansion to avoid storage silos.

In addition to the large scale of data, "big data" applications also mean having a large number of files. Therefore, how to manage the metadata accumulated in the file system layer is a difficult problem. If it is handled improperly, it will affect the system's scalability and performance, and traditional NAS systems have this bottleneck. Fortunately, the object-based storage architecture does not have this problem. It can manage billions of files in one system, and it will not suffer from metadata management as traditional storage. Object-based storage systems also have wide-area scalability capabilities that can be deployed at multiple locations to form a cross-region large-scale storage infrastructure.

Delay problem "big data" applications also have real-time problems. Especially related to applications related to online transactions or finance. For example, the online advertising promotion service of the online clothing sales industry needs to analyze the browsing records of customers in real time and accurately place advertisements. This requires that the storage system must be able to support the above features while maintaining a high response speed, because the result of the response delay is that the system will push "expired" advertising content to customers. In this scenario, the scale-out architecture of the storage system can play an advantage because each node has processing and interconnection components, and the processing capacity can be increased simultaneously while increasing capacity. Object-based storage systems, on the other hand, can support concurrent data streams to further increase data throughput.

There are many "big data" application environments that require high IOPS performance, such as HPC high-performance computing. In addition, the popularity of server virtualization has also led to the demand for high IOPS, just as it has changed the traditional IT environment. In order to meet these challenges, various models of solid-state storage devices have emerged, ranging from simple internal caches in servers, to scalable storage systems with all solid-state media, and so on.

Concurrent access Once companies realize the potential value of big data analytics applications, they will incorporate more data sets into the system for comparison and allow more people to share and use the data. In order to create more business value, companies often analyze multiple data objects from different platforms. The storage infrastructure including the global file system can help users solve data access problems. The global file system allows multiple users on multiple hosts to concurrently access file data, which may be stored in multiple locations. Different types of storage devices.

Security issues Applications in certain special industries, such as financial data, medical information, and government intelligence, have their own security standards and confidentiality requirements. Although these are not different for IT managers, they all must be complied with. However, big data analysis often requires multiple types of data to refer to each other. In the past, this type of data was not mixedly accessed. Data applications have also spawned new security issues that need to be considered.

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