A New Processing Method for the Nonlinear Signals Produced by Electromagnetic Flowmeters in Conditions of Pipe Partial Filling

When a pipe is partially filled with a given working liquid,the relationship between the electromotive force(EMF)measured by the sensor(flowmeter)and the average velocity is nonlinear and non-monotonic.This relationship varies with the inclination of the pipe,the fluid density,the pipe wall friction coefficient,and other factors.Therefore,existing measurement methods cannot meet the accuracy requirements of many industrial applications.In this study,a new processing method is proposed by which the flow rate can be measured with an ordinary electromagnetic flowmeter even if the pipe is only partially filled.First,a B-spline curve fitting method is applied to a limited set of measurements.Second,matrix inversion required in the B-spline curve method is optimized in order to reduce the number of needed computations.Dedicated experimental tests prove that the proposed method can effectively measure the average flow velocity of the fluid.When the fluid level of the pipeline is between 50%and 100%,the relative error is less than 3.5%.

A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramat- ically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms （partial least squares （PIE）, recursive PIE （RPLS）, and kernel PIE （KPIE）） to form novel regression ones, QPLS, QRPIE and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

Multi-Dimensional Customer Data Analysis in Online Auctions

In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example, analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types： impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.

A RBF classification method of remote sensing image based on genetic algorithm

The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote imaging data of large scale and cross-time, due to the increase of remote image quantities and image resolutions. In the paper, the genetic algorithms were employed to solve the weighting of the radial basis faction networks in order to improve the precision of remote sensing image classification. The remote sensing image classification was also introduced for the GIS spatial analysis and the spatial online analytical processing (OLAP), and the resulted effectiveness was demonstrated in the analysis of land utilization variation of Daqing city.

Coalmine Safety Assurance Information System Based on GIS

The mine ventilation and safety is one of the most important factors to influence on the coal production.More attention has been paid to manage safety information in scientific, efficient, and real-time way. Therefore, it is important to develop a practical mine safety assurance information system (CSAIS). Based on analyzing the actual management mode for ventilation and safety on mine, the paper studies the structure and function of the mine safety assurance information system based on GIS in detail. Moreover, it also suggests some applications and solutions. By combining with the practical situation, the paper realizes the whole function of the present system.

Research on Online Leakage Assessment

Leakage assessment is the most common approach applied for assessing side-channel information leakage and validating the effectiveness of side-channel countermeasures.Established evaluation approaches are usually based on Test Vector Leakage Assessment(TVLA)that deployed in a divide and conquer flow with offline computations,which causes two apparent shortcomings in required memory and time.In this paper,a lightweight framework of online leakage assessment is proposed.The problems were analyzed and the evaluation approach was further validated with a Field Programmable Gate Array(FPGA).The experimental results show that it can implement online processing on newly collected data,and instantly stop to give the result when detecting credible leakage.The online leakage assessment can significantly economize on memory and time.It has good performance when there is limited memory or real-time evaluations are needed.

A study on building data warehouse of hospital information system

Background Existing hospital information systems with simple statistical functions cannot meet current management needs. It is well known that hospital resources are distributed with private property rights among hospitals, such as in the case of the regional coordination of medical services. In this study, to integrate and make full use of medical data effectively, we propose a data warehouse modeling method for the hospital information system. The method can also be employed for a distributed-hospital medical service system. Methods To ensure that hospital information supports the diverse needs of health care, the framework of the hospital information system has three layers： datacenter layer, system-function layer, and user-interface layer. This paper discusses the role of a data warehouse management system in handling hospital information from the establishment of the data theme to the design of a data model to the establishment of a data warehouse. Online analytical processing tools assist user-friendly multidimensional analysis from a number of different angles to extract the required data and information. Results Use of the data warehouse improves online analytical processing and mitigates deficiencies in the decision support system. The hospital information system based on a data warehouse effectively employs statistical analysis and data mining technology to handle massive quantities of historical data, and summarizes from clinical and hospital information for decision making. Conclusions This paper proposes the use of a data warehouse for a hospital information system, specifically a data warehouse for the theme of hospital information to determine latitude, modeling and so on. The processing of patient information is given as an example that demonstrates the usefulness of this method in the case of hospital information management. Data warehouse technology is an evolving technology, and more and more decision support information extracted by data mining and with decision-making technology is required for further research.

A digital energy spectroscopy based on FIR filter

We designed a universal digital energy spectroscopy based on online digital signal processing. A prototype system was built and tested. Signals from radiation detectors were processed via a digital filter whose coefficients could be modified without changing the hardware. The paper introduces the hardware design of the digital energy spectroscopy system as well as the full set of software consisting of the selection of the coefficients of the finite impulse response (FIR) filter and the coding in the field-programmable gate array (FPGA). The system was tested with the high purity germanium (HPGe) detector. The results showed that this prototype can achieve an energy resolution close to that of a traditional multi-channel analyzer (MCA) with a much higher counting rate.

A view-based model of data-cube to support big earth data systems interoperability

Big Earth Data-Cube infrastructures are becoming more and more popular to provide Analysis Ready Data,especially for managing satellite time series.These infrastructures build on the concept of multidimensional data model(data hypercube)and are complex systems engaging different disciplines and expertise.For this reason,their interoperability capacity has become a challenge in the Global Change and Earth System science domains.To address this challenge,there is a pressing need in the community to reach a widely agreed definition of Data-Cube infrastructures and their key features.In this respect,a discussion has started recently about the definition of the possible facets characterizing a Data-Cube in the Earth Observation domain.This manuscript contributes to such debate by introducing a view-based model of Earth Data-Cube systems to design its infrastructural architecture and content schemas,with the final goal of enabling and facilitating interoperability.It introduces six modeling views,each of them is described according to:its main concerns,principal stakeholders,and possible patterns to be used.The manuscript considers the Business Intelligence experience with Data Warehouse and multidimensional“cubes”along with the more recent and analogous development in the Earth Observation domain,and puts forward a set of interoperability recommendations based on the modeling views.

Cache-Conscious Data Cube Computation on a Modern Processor

Data cube computation is an important problem in the field of data warehousing and OLAP （online analytical processing）. Although it has been studied extensively in the past, most of its algorithms are designed without considering CPU and cache behavior. In this paper, we first propose a cache-conscious cubing approach called CC-Cubing to efficiently compute data cubes on a modern processor. This method can enhance CPU and cache performances. It adopts an integrated depth-first and breadth-first partitioning order and partitions multiple dimensions simultaneously. The partitioning scheme improves the data spatial locality and increases the utilization of cache lines. Software prefetching techniques are then applied in the sorting phase to hide the expensive cache misses associated with data scans. In addition, a cache-aware method is used in CC-Cubing to switch the sort algorithm dynamically. Our performance study shows that CC-Cubing outperforms BUC, Star-Cubing and MM-Cubing in most cases. Then, in order to fully utilize an SMT （simultaneous multithreading） processor, we present a thread-based CC-Cubing-SMT method. This parallel method provides an improvement up to 27% for the single-threaded CC-Cubing algorithm.