Recent Patents on Computer Science - Volume 9, Issue 1, 2016

Thickness controlling of plastic film is a very important aspect in industrial film technology. According to the thickness uniformity parameter, we should control the uniformity of the plastic film accurately on the production line. However there must be a large amount of actual sensors as the analysis basis, which increases the complexity of the operation of sensor layout. Aiming at the shortcomings of the existing detection, this paper proposes a controlling scheme with mobile sensing detection device for thickness controlling, and forecasting the thickness of plastic film by BP neural network. This paper examines eight recent patents that were awarded from January 2003 to March 2014 in the area of industrial plastic film measurements. On the basis of this, it further states the thickness detection principle and the whole control scheme. Then, the BP neural network is introduced, which can predict the thickness of plastic film by setting the appropriate parameters. The simulation results show that monitoring method based on the BP neural network can reduce production cost, combining the traditional film production line with a new idea of controlling the opening degree of wind ring.

In China note processing is a necessary work for finance and much workload is emphasized on it. The feature recognition of notes is an important work of financial management information and office automation. Feature extraction can quickly locate the notes' size and shape. It can quickly locate the position of the critical information, and improve the speed of computer recognition. In this way the financial management efficiency can be improved greatly. By SURF algorithm the image feature points are detected and Flann method is used to match feature points. In this way we extract the region with corresponding characteristics of template collection after clustering. There are also many patents on these intelligence methods. Experiments show that our algorithm has better matching rate, and it has certain robustness to the rotated images.

With a continuous liquefaction sampling device for atmospheric fine particles, we built an online system for real-time monitoring of atmospheric water-soluble inorganic ions and meteorological factors. The association rules between water-soluble inorganic ions and meteorological factors in atmospheric fine particles (PM2.5) sampled hourly between April and May 2010 in Lanzhou, China were mined out using Apriori algorithm. Also the real-time effects of meteorological factors on the mass concentrations of water-soluble inorganic ions were investigated. This paper examines seven most recent patents between October 2008 and June 2014 in the area of Business Intelligence and Atmospheric Sciences. The results show that sulfates were dominant in the composition of atmospheric water-soluble inorganic ions. The major meteorological factor affecting the mass concentrations of water-soluble inorganic ions was the joint action of temperature and relatively humidity. The ion concentrations were higher during daytime than at night, and increased at high-temperature and low-relatively humidity conditions, indicating that the photochemical effect significantly promoted the generation of water-soluble inorganic ions. The NW wind would carry the ions from the industrial zone in West Lanzhou to the downtown, since the atmospheric transport elevated the concentrations of water-soluble inorganic ions. When strong wind passed, ρ(Ca2+) increased from inward transport, but the mass concentrations of other water-soluble inorganic ions decreased. The concentrations of all ions decreased at high-relatively humidity and raining conditions.

In the coal production, gas outburst is a major threat of natural disasters; it caused huge casualties in China. There are also many patents about gas outburst. In order to predict gas outburst, we use a new intelligent algorithm (opposite degree algorithm). The opposite degree algorithm is based on priori value, posteriori value, priori matrix, posterior matrix and the relationship between calculation data. The experiment is conducted based on real coal mine data in China. By learning 18 groups of gas outburst data, forecasts the results of 7 groups of gas outburst data. The accuracy of experiment is 6/7. Meanwhile, we introduce the prediction experiments based on single index method and comprehensive index method and BP neural network for the result comparison. Results show that opposite degree method is as good as the BP neural network method or comprehensive index method. Gas outburst prediction based on opposite degree algorithm proved the algorithm is feasible and effective, and can be used in value prediction.

In order to make more accurate and scientific predictions of the industrial economy, we integrated the method of BP artificial neural network with grey relational analysis method together as an Industrial economic forecasting model in this study, and applied it to predict the gross industrial output of Huainan City in China. This paper examines two most recent patents between May 2012 and January 2013 in the area of forecasting. The efficiency and effectiveness of the new model is tested by comparing the predicted results with stepwise regression and GM (1, 1) models. The empirical results indicated that the new integrated model can improve the adaptability of the forecasting method and it also showed good forecasting stability and accuracy.

Innovation team is a kind of complex systems, for the purpose of controlling its operations and ensuring team effectiveness, based on safety management, this paper uses brittleness theory of complex system and social network analysis to study the grades of brittleness sources, which means to find out the important members who affect the team seriously. This paper examines four recent patents about safety management and social network analysis, then it collects data from innovation team W in Nanjing area as an example to construct the network model and give the analysis process. The results show that there are 11 members whose grades of brittleness sources are 1, which means if they are interfered by disturbance factors, the whole team will be affected and even collapse. This finding will be very important to the teams’ safety management.

The mechanism of coal and gas outburst is still controversial, many occurrences of accidents have been a serious threat to people's life and property safety. In order to reduce the effect of coal and gas outburst disaster, the prediction of coal and gas outburst situation has a practical significance. Patent describing intelligent algorithms have been successfully applied in the prediction of coal and gas outburst. The self organizing feature mapping neural network is a high efficiency algorithm mechanism. Based on a comparison with the self organizing feature mapping neural network and improved colony clustering method, the accuracy rate of new algorithm is higher than the old method. This research is good for safety engineering development. The experiments performed demonstrate the effectiveness of the algorithm; this method has reference significance for the prediction of coal and gas outburst.

Advance detection is the main means of geological work to predict the adverse geological bodies behind the working face. In order to realize the digitalized management of various types of advanced detection forecast information, improve the success rate of interpretation via strengthening personnel exchanges among interpretation maps. This paper examines nine latest patents awarded. Taking GOOGLE EARTH as the secondary development platform, this paper establishes the information management system for advance detection forecast of underground engineering, which achieves sort management of various types of advanced detection information, the information exchange and sharing of various types of advanced prediction among different users and 3D simulation of underground engineering. Finally, this system has been successfully applied in Ma-shang-shao tunnel at Guizhou segment of Shanghai-Kunming railway passenger line.

To ensure the safety of pit, proposed the intelligent forecasting and early warning method. Patent on one such early warning method, GRNN, is discussed. A brief review of the status about models of soil and excavation stability studies, pointed out that it is necessary that pit slope stability is conducted by the intelligent algorithm. With specific examples of projects, used HS model of PLAXIS geotechnical engineering software to analyze finite element including seepage calculation and get the training data and test data required by generalized regression neural network. With the date made inversion calculation of the soil parameters. Then the strength reduction was combined with the warning grading thought to build intelligent early warning system to predict excavation stability. The study pointed out that the pit overall stability intelligent forecasting and early warning method can effectively control error and avoid ambiguity forecast.

Conventional Matlab implementation of software ranging code generator requires code shifts to produce the ranging codes assigned to different satellites. Unfortunately, it is unable to implement software BeiDou B1I code generator using this method, because the B1I code shifts are unknown. Based on the generator revealed in BeiDou Interface Control Document (BICD), a software generator is implemented by using Simulink blocks in conjunction with Matlab programs in this paper. The Simulink blocks enable the generator to compute the BeiDou B1I code shifts, as well as reducing the complexity of the implementation. Simulation results show that the implementation is feasible and reliable. All B1I code shifts are calculated, which can be used for both software and hardware implementation in the future. Patents describing a wide range of applications based on the BeiDou system have also been proposed and implemented.

Background: Many large software systems composed of lots of files are expected to offer customers 24/7 services. On the other hand, periodic and frequent updates are forced to fix any bugs and generate several revisions. Therefore, how to manage the revisions without human intervention is challenging. The aims of this paper are to introduce readers to one versioning-based acceleration method for software online upgrade, to discuss the details of versioning-based upgrade and download acceleration, and review some recent patents on software online upgrade. Methods: This paper describes Patent Publication Number CN102868731A, titled "Method and appliance for software online upgrade and downloading acceleration", issued by State Intellectual Property Office of the P.R.C. on January 9, 2013. The patent is based on versioning-based acceleration method for software online upgrade. First, client computer system requests distributed hash table (DHT) nodes to obtain update resources. Second, DHT node discovers resource seeds (version control server) or peers (client systems). Finally, resource seeds send the client a list of applicable updates. Results: Versioning-based upgrade and download acceleration are proposed to address the issue of software online upgrade. DHT algorithm is used to accelerate the download process and manage the update contents using version control tools. As a result of P2P network, the update process runs smoothly under single node failure. Although there is room for improvement of this method, the current applicability has been illustrated by efficiently solving software online upgrade problems. Conclusion: The idea and a disclosed embodiment of a patent (Patent CN102868731A, issued by State Intellectual Property Office of the P.R.C.) are presented, which is based on versioning-based acceleration method for software online upgrade. It includes receiving software updates at groups of distributed hash table (DHT) nodes, finding resource seeds (version control server) or peers (client systems), sending the client a list of applicable updates from resource seeds or peers.

Background: Power consumption has become one of the most serious obstacles ahead of performance increase. Due to increased power consumption, increasing number of transistors does not seem to be viable anymore. We are being forced to consider other ways of increasing performance without increasing power consumption. To fınd solution to the problem, the power consumption issue is being looked at more and more carefully at architectural level. Methods: Recent publications and patent databases are reviewed to find extraordinary and innovative architectural techniques developed for reducing power consumption and increasing performance. Results: In most of the cases the focus was specifically on memory area because of the studies that indicated that memory is the biggest power consumer in a typical processor system. The focus of the most methods was also on the cache memory part of the processor system since cache memory is a part that is used with every instruction execution. Any reduction in power cost of the cache memory results in sizable power reduction. Among the methods suggested were techniques of linking cache memory cells to reduce number of operations, reducing size of the cache memory by using multiple small cache memories activated one at a time. Conclusion: Based on the study it can be concluded that future innovative developments to reduce power consumption should concentrate on: a) Main memory part: to reduce the power consumption of main memory by turning on/off unused blocks of memory without effecting performance, b) Cache memory part: use of cache memory efficiently without decreasing performance, c) Efficient multiprocessing architectures. By integrating above mentioned methods to multiprocessors it may be possible to reduce the power consumption further.