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In order to find the variations of mechanical properties of millet stalk during dynamic cutting, a three-dimensional model of cutting parts and a double-layer flexible model of millet stalk were established in this study. The mechanical cutting properties of millet stalk at different cutting speeds and blade oblique angles were investigated based on ANSYS/LS-DYNA, while the verification tests were carried out based on the self-made cutting test bench. Simulation results showed that the maximum Von Mises stress was concentrated on the contact point of the stalk and the moving blade. The maximum Von Mises stress of stalk during extrusion, cutting, and after cutting were 60.03 MPa, 60.72 MPa, and 39.87 MPa, respectively, and the cutting energy of stalk epidermal tissue was greater than that of inner tissue. The cutting stress and the unit area cutting energy decreased first and changed steadily as cutting speed was increased when the cutting speed was 0.5-1.5 m/s. When the blade oblique angle was 0°-48°, the cutting stress decreased as the blade oblique angle was increased, while the unit area cutting energy decreased first and then increased. Verification tests showed that the cutting speed and the blade oblique angle had significant effects on the cutting mechanical properties (P < 0.05), which was consistent with the simulation test results. Research results can be used to optimize the cutting parameters of millet stalk

With the rapid development of information technology, the production and management of all industries are inseparable from the use of Internet technology, and it is even more so in the construction...

Robotic automated assembly in large-scale space is a challenging task both in aerospace engineering and automobile manufacturing industries, which involves several technical metrics, such as accuracy, efficiency as well as stability of the assembly system. In this work, we propose a high-accuracy pose measurement system to tackle this problem. Compared to existing measurement technologies, our system can adequately cover the assembly space and reach a high pose accuracy ( 0.2° on rotation and 0.2 m m on translation), which meets the requirements of most robotic automated assembly tasks in large-scale space (usually in sub-millimeter). To achieve that, we design a multi-degree-of-freedom measurement platform, which allows the camera to move in the motion space so as to find the best viewpoints to respectively measure the robot end-effector and the assembly target. Another key issue is the localization of the robot end-effector in the assembly space. Traditional calibration methods cannot be applied directly, since the robot and the camera are movable. Hence, we design a practical mechanical tooling which is attached to the sixth axis of the robot. Cubes are uniformly distributed on the tooling, serving for the precise localization of the end-effector. We also propose a novel calibration method to solve the kinematics solution. Moreover, we design a camera-pointcloud collaborative method to effectively compute the accurate 3D coordinates in camera space for sub-pixel marker centers, which can make a significant promotion in measurement accuracy.

Real time speech recognition technology, as a key cross technology in the field of artificial intelligence in recent years, has been widely used in the fields of intelligent voice toys, industrial control and intelligent rehabilitation. Because the real-time speech recognition technology based on embedded technology has obvious advantages in the volume, power consumption and research and development cost of the system, it has become a hot carrier to achieve efficient speech recognition technology. In order to realize a simple and practical real-time speech recognition system based on embedded system, this paper designs a basic framework of machine learning based on Markov random field theory combined with machine learning theory, and studies the algorithm of real-time speech vocabulary matching recognition based on this framework. In detail, the algorithm proposed in this paper will process the speech signal from the aspects of preprocessing, signal detection, feature extraction and quantization. Finally, this paper will build a real-time speech recognition system based on DSP processor. The experimental results show that the real-time speech recognition algorithm proposed in this paper can improve the real-time recognition speed of the system. The corresponding speed changes from about 12 s to about 200 ms, and the corresponding real-time accuracy rate increases to about 95%.

Android is an open source mobile operating system represented by the Open Handset Alliance (OHA), developed by Google and other organizations since 2007, which has taken up most of the market share of smart devices. However, the applications on the platform are facing the increasingly serious security threat. Although the Android system itself provides a set of security mechanism to protect the safety of the system and applications, there are still many security risks. In order to hide the vulnerability of the applications and prevent the malicious users from tampering the apps, multiple anti-analysis methods have been applied by many Android packers to consolidate the apps. Bytecode-hiding is one of the most effective anti-analysis method, which can extract some bytecode from the Dex files and hide them from the vision of malicious analysts. Mostly, the hidden bytecode was encrypted, which can be recovered in runtime. But the conventional bytecode-hiding methods are always low-efficient and unsafe on some occasions, where the hidden bytecode can be recovered by the malicious analysts in some way. In this paper, we propose a bytecode-hiding scheme based on Ahead-Of-Time (AOT) compilation, called DroidPro, which can compile some chosen bytecode of Dex files of apps to native code in ahead-of-time that will be much harder to reverse. In our experiments, the apps packed by the packer associated with our bytecode-hiding scheme are more efficient and safer than other packers that use other bytecode-hiding schemes.

Thin-walled components have characteristics of high intensity and light mass and are used widely in aeronautical industry. NC milling is one of the most popular machining methods for the thin-walled components. Because of their poor rigidity, the deformation of the thin-walled workpieces caused by cutting force is a main factor that decreases the machining precision and increases aeronautical product cost. Based on the analyses of the machining deformation mechanism and basic machining deformation characteristics of the thin-walled workpieces, a NC milling deformation forecasting approach is presented based on CAD/CAM/FEA integration to forecast the machining deformation of the aeronautical thin-walled workpieces. Taking a typical workpiece for example, the proposed deformation forecast framework is validated by the analytic and experimental results. The straight side walls of aluminum LY12CZ workpieces are processed on a vertical machining center THA5656 and their errors are analyzed. Based on orthogonal tests, the deformation characters of the thin-walled workpieces are discussed under different cutting conditions. By the comparison of experimental results and FEA results, although there exist definite difference, their variation tendencies are similar on the whole. Thus, the FEA model is validated. The research can provide support for machining deformation control and machining simulation model verification.

It has already been the key problem for development of current higher education that how to allocate educational resource in the higher education park to realize resource sharing and how to make full use of the educational resource in the park to improve scientific and sustainable development of it. It is necessary to put forward new models and techniques to avoid various problems met before and to solve the problem of educational resource sharing fundamentally. This paper mainly analyses how to realize integration and interoperation of heterogeneous resources by means of grid technology from the perspective of technology. Moreover, it emphasizes on discussion of methods and prospects of applying grid technology to educational field for the purpose of realizing sharing and construction of resources in Higher Education Parks.

Empirical mode decomposition (EMD) is designed to decompose non-stationary, nonlinear data into a series of intrinsic mode functions (IMFs) adaptively. This procedure is automatic, data-driven and time-variant. And then a Hilbert transform is applied to these IMFs. The combination of EMD with a Hilbert transform is known as Hilbert–Huang transform (HHT). HHT can be used to calculate meaningful multi-resolution instantaneous frequency (HHT based instantaneous frequency). Currently, the application of EMD and HHT to seismic data is performed mainly for noise attenuation. In this paper, we demonstrate new insights of EMD and HHT to seismic data analysis. We first extend the research of Flandrin et al. and analyze how EMD behaves on a Gaussian band-pass signal; we then employ HHT based instantaneous frequency on wedge model and real seismic data to delineate thickness variations. Numerical examples of Gaussian band-pass noise indicate that EMD acts as an adaptive, multi-band overlapping filter bank. The analysis of a wedge model and 2D real seismic data illustrates that HHT based instantaneous frequency is more effective than conventional Hilbert transform based instantaneous frequency in delineating the thickness variation of seismic thin bed.

As for the various risks for internet transaction, the paper puts forward a CA system model based on PKI and introduces the concrete methods under the environment of Linux so as to realize the network ID authentication and handle the network security problem. Different from commercial CA, this system structure is based on open source code, especially fits for small and medium self-established campus network CA authentication system.

This paper reviews the programming landscape for parallel and network computing systems, focusing on four styles of concurrent programming models, and example languages/libraries. The four styles correspond to four scales of the targeted systems. At the smallest coprocessor scale, Single Instruction Multiple Thread (SIMT) and Compute Unified Device Architecture (CUDA) are considered. Transactional memory is discussed at the multicore or process scale. The MapReduce style is examined at the datacenter scale. At the Internet scale, Grid Service Markup Language (GSML) is reviewed, which intends to integrate resources distributed across multiple datacenters.

The infrared thermal imager is widely used in infrared radiation measurement, but the accuracy of radiation measurement is greatly affected by the changing environment and self-heating effect. In this article, the influence of self-heating effect is analyzed in a stable environment and the phenomenon of temperature drift caused by the self-heating effect is found. Then the drift rules are summarized by experiment, and a weighted-based compensation method is presented, the result shows that the temperature measurement error is less than 0.7 centigrade after compensation.

A multitarget tracking system based on an infrared (IR) fish-eye lens is built to satisfy urgent requirements of large-field IR, multitarget, real-time reconnaissance and tracking. Utilizing an IR fish-eye lens and a 512×512 pixel PtSi detector, the system can detect threatening targets at the whole space domain and the whole time domain. The hardware of the system based on a dual-digital signal processor is designed to implement data processing for multitarget tracking algorithms, which include a track initiation algorithm and a modified generalized probability data association algorithm. We also carried out a tracking experiment for two aerial maneuvering targets. Comparing the theoretical and experimental tracks, the availability of the system and the real-time capability of multitarget tracking are validated.

MapReduce has become an effective approach to big data analytics in large cluster systems, where SQL-like queries play important roles to interface between users and systems. However, based on our Face book daily operation results, certain types of queries are executed at an unacceptable low speed by Hive (a production SQL-to-MapReduce translator). In this paper, we demonstrate that existing SQL-to-MapReduce translators that operate in a one-operation-to-one-job mode and do not consider query correlations cannot generate high-performance MapReduce programs for certain queries, due to the mismatch between complex SQL structures and simple MapReduce framework. We propose and develop a system called Y Smart, a correlation aware SQL-to-MapReduce translator. Y Smart applies a set of rules to use the minimal number of MapReduce jobs to execute multiple correlated operations in a complex query. Y Smart can significantly reduce redundant computations, I/O operations and network transfers compared to existing translators. We have implemented Y Smart with intensive evaluation for complex queries on two Amazon EC2 clusters and one Face book production cluster. The results show that Y Smart can outperform Hive and Pig, two widely used SQL-to-MapReduce translators, by more than four times for query execution.

University students'management,especially the decision-making part,is very heavy and complicated,so it may have subjectivity without scientific measures. As key index in students' integrated development evaluation,students' quality evaluation achievements play an important role in their development. This thesis puts forward decision support system for students'comprehensive evaluation based on data mining.On the basis of brief introduction to definition of data mining technique,mining process and routine method,this thesis emphasizes study on application of association rules analytic method in students'comprehensive evaluation. By means of data mining,it analyzes data of students'comprehensive evaluation achievements and gets some useful results,which will aid decision making in university students' objective evaluation and play as guidance for scientific management and evaluation of students.

MapReduce-based data warehouse systems are playing important roles of supporting big data analytics to understand quickly the dynamics of user behavior trends and their needs in typical Web service providers and social network sites (e.g., Facebook). In such a system, the data placement structure is a critical factor that can affect the warehouse performance in a fundamental way. Based on our observations and analysis of Facebook production systems, we have characterized four requirements for the data placement structure: (1) fast data loading, (2) fast query processing, (3) highly efficient storage space utilization, and (4) strong adaptivity to highly dynamic workload patterns. We have examined three commonly accepted data placement structures in conventional databases, namely row-stores, column-stores, and hybrid-stores in the context of large data analysis using MapReduce. We show that they are not very suitable for big data processing in distributed systems. In this paper, we present a big data placement structure called RCFile (Record Columnar File) and its implementation in the Hadoop system. With intensive experiments, we show the effectiveness of RCFile in satisfying the four requirements. RCFile has been chosen in Facebook data warehouse system as the default option. It has also been adopted by Hive and Pig, the two most widely used data analysis systems developed in Facebook and Yahoo!

E-commerce is highlighted in the present Internet development, among which payment is its core issue. This paper, after analyzing the characteristics of online payment, gives a common online payment system model with emphasis on the discussion of the electronic credit card, electronic cash, and electronic check of three online payment modes and finally points out the respective application environment.

Because of their poor rigidity, the deformation of thin-walled workpieces caused by cutting force in NC milling is the main factor that decreases machining precision and increases aeronautical product cost. The straight side walls of aluminum LY12CZ workpieces are machined on a vertical machining center THA5656 and their errors are analyzed. Based on orthogonal tests, the deformation characters of the thin-walled workpieces are discussed under different cutting conditions. The research can provide support for machining deformation control and machining simulation model verification.

For the purpose of evaluating the performance of university researchers with objective, just and scientific, on the basis of surveying the achievements appraisal and rewarding method of many universities, it shows a quantization index system of performance evaluation for university researchers, takes a transition from qualitative analysis to quantitative analysis for each index system, and builds quantitative model of performance evaluation. It realizes the large quantity and complex mathematical calculation and disposal of whole process by computer software design; different universities would fulfill the actual requirements by modifying qualitative description base of indexes in all levels and its corresponding index value base, as well as the conversion base of actual and standard number, the system is commonality and flexibility.

It analyzes the necessity of scientific research collaboration from its development and demand of market economy; it selects scientific research collaboration system as the communication media of scientific research collaboration by analyzing the richness of communication media and combining with situation, and also shows system’s design and implementation.

A tracking device of multiple target is proposed, whose field range reaches hemisphere airspace(sr) because of the using of IR Fish-eye system. The infrared ray is collected by Fish-eye lens, by this way can get the thermal image about the whole space, whose range depends on the lens used. Thanks to the Fish-eye system and Ptsi staring focal plane array, the tracking device can get a big range, that means can get multiple target at one time. Depends on the algorithm adopted to process the image has been gotten, the tracking device specify some points with intensive ray which means elevated temperature, and that will be the targets. At the next step after targets detection, the algorithm will compute out the speed and orientation of every target, then figure out a quotient-for-danger about every target. With this quotient, the algorithm can judge which one is the most dangerous from those flying to us, and it should be treated firstly, then the less dangerous ones, etc. To ensure the whole system run at real-time, all of the processing has to be finished at a moment. A TM320 DSP is adopted to run the algorithm. The result will be displayed at a LCD with every target labeled by their quotient-for-danger. The reaction speed and precision of this tracking device has been proved on our multiple target experiment platform.

The paper presents the algorithms of data process on multi-target tracking and threat level sequence in large visual area IR (infrared ray) fish-eye system. The targeting information is associated and filtered with the polar coordinates. Firstly,The characteristic point of every target is picked up and tracked with special restriction for each of them. Secondly,the parameters of fairway, which are gained by aberration revising and researching the characters of temperature area, are picked up from serial images. The threat level of attacking objects is sorted by the speed, acceleration and direction of all targets. Finding out the most prior target which is in the certain distance in order to make sure that effective warning information is emitted in time. The algorithms are proved to be quick and effective through simulation and the muiti-target experiment system developed by the writers.

Used infrared fisheye lens and PbSi staring focal plane array, we design a novel Infrared observing system. This observing system can observe and recognize the infrared dim and small target in hemispher airspace(2 µ sr.).

MapReduce is an important programming model for building data centers containing ten of thousands of nodes. In a practical data center of that scale, it is a common case that I/O-bound jobs and CPU-bound jobs, which demand different resources, run simultaneously in the same cluster. In the MapReduce framework, parallelization of these two kinds of job has not been concerned. In this paper, we give a new view of the MapReduce model, and classify the MapReduce workloads into three categories based on their CPU and I/O utilization. With workload classification, we design a new dynamic MapReduce workload predict mechanism, MR-Predict, which detects the workload type on the fly. We propose a Triple-Queue Scheduler based on the MR-Predict mechanism. The Triple-Queue scheduler could improve the usage of both CPU and disk I/O resources under heterogeneous workloads. And it could improve the Hadoop throughput by about 30% under heterogeneous workloads.

This paper presents OverFly, a large-scale publish-subscribe system developed to support our grid middleware, Vega Grid Operating System, short as GOS. OverFly differs from most existing publish-subscribe systems in three ways. First, it introduces a message store at the client-side which enables clients to be more resilient to intermittent system faults such as intermittent network partitions and intermittent event broker errors. The client message store also enables further optimizations, such as messages packing and compression. Second, it proposes a transparent topic location and relocation design scheme, with which OverFly can tolerate permanent event broker crashes. Third, it provides a delegation model for sending and receiving messages. Besides flexibility, the delegation model can optimize system usages in single machine environments as well as interconnected environments.

Computer controlled optical surfacing (CCOS) technique is widely used in machining process of large and medium aspheric surfaces, because of its high accuracy, simple proce ss conditions, low cost and other merits. The characteristic of the removal function of a polishing tool is a key factor in determining convergence rate in CCOS process. The shape parameters, which influence the characteristic of the removal function of a dual-rotation polishing tool, are the rotational speed ratio and the eccentricity ratio. A method is presented to optimize these two shape parameters, in which the error correcting capability of the removal function is considered. The optimized parameters are compared with those from other references. The results show that a removal function with a certain size has the highest error correcting capability when the eccentricity ratio is 0.8 and the rotational speed ratio -3. With the optimized parameters, the final figuring of a 500 mm f3 paraboloid mirror is accomplished. With 95 hours of polishing time, the mirror is taken from an rms deviation of 0.241 waves to an rms deviation of 0.015 waves, where a wave is 0.6328 m. The results show that higher precision and efficiency will be achieved with the optimized parameters in CCOS. Keywords: aspheric surfaces, computer controlled optical surfacing (CCOS), removal function

With the rapid development of e-commerce in recent years, it brings unlimited opportunities for people, but it also gives people a very serious subject, namely, how to ensure security of network. applications. The PKI is a technology platform that can be used to address the problem of e-commerce security effectively. This paper presents a self-built CA system model for enterprise based on PKI, We describe the overall structure and design model in detail, and give specific implementation methods. Compared with other CA system, some features of this system include: open source code based, low cost to achieve, implementation of database technology, excellent compatibility with other enterprise application systems, realization of dual key pairs, separating the signature / authentication key pairs from encryption / decryption key pairs. This enterprise's CA system can preferably solve the authentication, sensitive data transmission and other issues of network security, and promote the process of e-commerce. the application has broad prospects.

The detection for motive small target in infrared image sequence has become a hot topic nowadays. Background suppress algorithm based on minim gradient median filter and temporal recursion target detection algorithm are introduced. On the basis of contents previously mentioned, a four stages pipeline structure infrared small target detection process system, which aims at characters of algorithm complexity, large amounts of data to process, high frame frequency and exigent real-time character in this kind of application, is designed and implemented. The logical structure of the system was introduced and the function and signals flows are programmed. The system is composed of two FPGA chips and two DSP chips of TI. According to the function of each part, the system is divided into image preprocess stage, target detection stage, track relation stage and image output stage. The experiment of running algorithms on the system presented in this paper proved that the system could meet acquisition and process of 50Hz 240×320 digital image and the system could real time detect small target with a signal-noise ratio more than 3 reliably. The system achieves the characters of large amount of memory, high real-time processing, excellent extension and favorable interactive interface.

In this paper, we proposed a new method to segment a given image, based on curve evolution and edgeflow techniques. The approach automatically detect boundaries, and change of topology in terms of the edgeflow fields. We present the numerical implementation and the experimental results based on the semi-implicit method. Experimental results show that one can obtains a high quality edge contour.