Earticle

The integration of informatization and industrialization (IOII) is a policy guidelines proposed by the Chinese Government to promote the production upgrade and the transformation of economic development mode, which is given increasingly attention by academic circles. In this paper, a brief introduction to IOII is presented. The research collections of IOII required to study the concept, development and process are surveyed as well as the application evaluation. By summarizing the development, relationship and the existing concept of IOII, a new concept of IOII is put forward. Then by studying comparatively the process and the application evaluation of IOII, suggestions for future empirical investigation is offered.

The density functional theory (DFT) and self-consistent periodic calculation were used to investigate methanol adsorption on the PtCrCu (111) surface. The adsorption energies, equilibrium geometries and vibration frequencies of thirteen type’s methanol adsorption models on the PtCrCu (111) surface were predicted and the favorite adsorption model for methanol is the fcc-Pt2Cr. The strong anti-poisoning ability of the optimal adsorption model fcc-Pt2Cr has been found when the catalysts resistance to carbon monoxide (CO) was explored. Furthermore, after doping of Cr and Cu atoms in Pt-based catalysts energy band has been broadened and the original peak of the d-orbital for atom Pt which was close to the Fermi level disappears, these phenomena can explain why this new ternary alloy catalyst for the enhancement of electrocatalytic activity to some extent. The rate-determining step of possible decomposition pathway was investigated with transition state searching and the calculation results show that the activation barrier of O-H bond breaking with PtCrCu (111) ternary alloy catalyst is lower than that of O-H bond breaking without catalyzer in gas phase.

It usually contains a large amount of redundant information to use the hyperspectral information to create a model, which will increase the difficulty of the model analysis. Therefore, it’s so important to select the characteristic wavelength in an effective and quick way. This study is proposed by using the competitive adaptive reweighed sampling (CARS) to select the characteristic wavelength for detecting the reducing sugar content in the potatoes. In that experiment a total of 238 samples are prepared. Among them, 190 samples are selected as the calibration set and 48 samples as the validation set. The performance of CARS is compared with full spectrum and classical variable extraction methods such as Monte Carlo uninformative variable elimination (MC-UVE), genetic algorithm (GA) and moving window partial least squares (MWPLS). Experimental results show that the band screened by algorithm CARS has the best effect, compared to full spectrum modeling, the wavelength of the model reduces from 203 to 33, the model validation set coefficient R2 increases from 0.8464 to 0.8965, and the root mean square error prediction (RMSEP) decreases from 0.0758 to 0.0416. The results demonstrate that it is feasible to detect the reducing sugar content of potatoes by using CARS combined with hyperspectral imaging.

This paper investigates an issue of the modified Bayer pattern color filter array (CFA). A demosaicking algorithm is considered as a color super-resolution approach, which is widely used in digital camera industry and employed to reconstruct a full color (three-channel) images from existing one color component with missing two color components. In a pair of Bayer pattern CFA, there are two green pixels and one red pixel and one blue pixel exist. We consider this format as RGGB. However, there could be alternatives such as RRGB or RGBB formats. In this paper, we investigate the effect of three CFA scenarios. Experimental results show that the obtained filters provide satisfactory performance.

Due to the rapid development of information technology, especially mobile network development, its information and increase the data exponentially, so in such a huge data through effective retrieval, fast, accurate and convenient access to useful data and information mobile Internet has become a serious problem; for this problem, this paper the characteristics of mobile Internet and data services, using Java to achieve rapid retrieval capabilities, and in accordance with the search function, complete the mobile Internet a variety of online services. According to the data analysis can be obtained to achieve the proposed method can effectively solve the problem of mobile Internet retrieval.

Software reliability testing plays a vital role to identify many flaws in software design as well as functional aspects. Reliability testing encompasses the analysis of the software’s capacity to carry out intended tasks, particular environmental conditions within the given time instance. So, reliability estimation should be carried out within the initial stage of the software development. Through this paper we have shown the importance of the testing time and testing coverage in the analysis of software reliability. Testing time is the time interim required to carry out testing mechanism. Testing coverage includes the amount of tests exercised by the test set or batch. In order to enhance the efficiency of the proposed system we have applied the non-homogeneous Poisson process.

A method had been constructed for mobile cloud computation offloading switch based on decision value model. In this method, two important ratios had been built: one is the calculation ratio which are offloaded on a proxy server completed tasks time and at the mobile node completed tasks time. Another is the calculation ratio which is offloaded on proxy server completed tasks energy and at the mobile node completed tasks energy. And then, decision value function was produced by combining with these two ratios. Finally, consider the optimization switch problem of computing tasks between different proxy servers. Experimental results show that the proposed method has a smaller time consumption and energy consumption by comparing with random offloading switch and minimum time-based offloading switch, and battery remaining of mobile node can be used rationally.

Adder circuits play an important role in reversible computation, which is helpful in diverse areas such as Low power CMOS design, quantum computing and nano technology. A reversible gate has equal number of inputs and outputs; so that, there should be one to one mapping between input and output vectors. Therefore, the input vector states can be always uniquely recovered from the output vector states. This paper presents a reversible residue circuit that requires only two reversible gates i.e. Modified TSG gate and Modified Fredkin Gate and consumes Low power. The design provides a significant reduction in the quantum cost of the circuit compared to the existing Residue Adder reversible logic implementation. For coding of design, VHDL language has been used. Xilinx design tool 14.4 and Xilinx project navigator tools are used for synthesis and simulation purpose.

The wave of voice signal is narrow and unstable. Besides, it has a non-continuity in time domain and an inconsistent decline of the radio channel, especially the short wave channel, which makes estimating the time delay of the radio’s voice signal very difficult. To solve such problems, the authors creatively combine the voice activity detection with the short-segment processing technology. Meanwhile, by using Weighted Least Squares Phase Fitting method to work out the time delay, we conduct a research in the time delay estimation of radio’s voice signal. The laboratory finding finally shows that this method is far better than the typical Generalized Correlation Method for estimation of time delay.

The reconstruction of a volatility based on a Black-Scholes option pricing model is ill-posed. In order to overcome the ill-posedness, a homotopy perturbation inversion method is designed to solve the inverse problem. The proposed method is a modified version of the Landweber method. The reconstruction of a volatility is a nonlinear problem which is needed to be linearized. Hence, numerical experiments consist of the reconstruction of a policy parameter based on a Todaro model which is a linear inverse problem and the reconstruction of a volatility based on a Black-Scholes option pricing model in order to test the performance of the proposed method. Numerical examples show that the proposed method is more accurate and faster than the Landweber method.

In modern storage system, most file system uses journaling operation to preserve data in-safe and recover fast from power loss. However, journaling operations generate excessive IO requests and degrades IO throughput. In addition to that, it also makes a lot of possible-discard regions that could be invalidated physically through discard command. However, due to it short period of time between journal transactions, there occur a lot of tiny lurked discard regions which give much overheads. In this paper, we identify that there exist many tiny lurked discard regions between successive journaling commit operations, and investigate a dynamic interval controlling scheme of journal transactions commit to reduce tiny lurked discard regions, as well as enlarge IO size of upcoming commit transaction. With the help or dynamic controlling of transaction interval, overall IO bandwidth can be enhanced.

There are two effective ways to maintain the system to perform a high or required level of reliability and availability: one is spare units for key units; the other is several maintainers for the system. To solve reliability indexes of complex mechanical system under these assumptions, a system model was established, which consists of two dissimilar subsystems in series and each subsystem is warm standby system, which consists two same units. We divided maintain time into replace time and repair time. In this paper, we arrange two maintainers for the system, and take ‘the system in normal states’ as maintenance strategy. Taking high efficiency of cooperative maintainers into account, the formula of system availability, system reliability, rate of occurrence of failure (ROCOF) and mean time to first failure (MTTFF) can be derived by operating the state transition probability matrix. This research provides evidence for further studies of complex mechanical system reliability.

The data quality in information systems affects system robustness significantly. Although a large body of research focuses on the issue of data quality, seldom has literature been done to develop effective control policies to manage system robustness associated with data errors. A process-oriented methodology is proposed to manage system robustness measured by performance, control cost and control time, which is achieved through establishing formal model of information systems and mathematical optimization models of system robustness. The proposed methodology captures how structural and functional characteristics of tasks affect system robustness and finds the optimal control policies, which facilitates the robustness-based design and management of information systems respectively. The methodology is illustrated in the case study.

Approximate computing is the theme that a system can let applications trade off accuracy for efficiency. It involves any technique where the system intentionally let on incorrectness to the application layer in order for conserving some resource. Floating point numbers i.e. approximate real number arithmetic to save space and time over arbitrary precession numerical representation. Approximate (APP) Computing is a technique of computation which ripostes less accuracy in results instead of an accurate output, which is enough for the desired application. The proposed work gives the basic allies on approximate computing based on arithmetic units using Verilog HDL. In dual mode operation, full adders and its types of adders resemble minimizing the power consumption and overall delay. Results show the great power saving efficiency and the 37mV and overall delay of 15.519ns.

The half-precision fertilization seeder of rice and wheat is designed against the problems of large straw mulching quantity, high soil water content, the traditional rice and wheat seeder easily blocked and poor fertilization seeding uniformity in Jianghuai region. The machine uses the rotary tillage stubble cleaning way to actively block and improves the traditional furrowing fertilization seeding method easy to cause winding grass. Electric control mode is used to drive the seed fertilizer shaft for sowing and fertilizing, the machine can carry on the straw returning to field, applying fertilizer, sowing, covering earth, rolling and other operations at one time, and can achieve the goal of half-precision fertilization seeding rice and wheat. The test results show that the machine has good anti-blocking performance and high operation efficiency. It works stably and reliably, and the various technical indexes meet the requirements of precision and half-precision mechanization planting, suitable for rice and wheat.

It is easy for the internet learners to generate learning fatigue because of the long-term lack of emotional interaction in the learning process, and learning fatigue often manifests through the eye condition, in order to do effective monitoring for remote intelligent tutoring system, the learning fatigue eye state recognition algorithm is put forward based on Gabor wavelet and HMM. The algorithm has certain distinguishing characteristics aiming at the degree of eye openness of network learner under 3 learning states: normal learning, fatigue and confusion, first, it does gray difference disposal for eye image by Laplace operator in YCbCr color space, then, it selects two-dimension Gabor kernel function to build 48 optimal filters, obtain 48 characteristic values, these 48 characteristic values generate 48 eigenvectors, at last, it use a set of observation sequence O formed by eigenvector of HMM for eye state image to do eye state recognition. Experimental results show that the recognition rate of this algorithm for network learning reaches 95.68%, and this algorithm has a good robustness.

Influenced by light, houses, street lamps and other factors, tree images often contain noise and complex background information. The existing Otsu segmentation algorithms have deficiencies such as poor anti-noise capability, ignoring the class cohesion and so on. Based on the traditional gray value-neighborhood average gradient Otsu segmentation method, we propose an improved two-dimensional Otsu algorithm for tree image segmentation. The algorithm takes into account the between-class distance and within-class distance, which combined the average variance concept of two categories and proposed new threshold selection method, and reduce the interference of noise effectively. To achieve the best segmentation and reduce over-segmentation of background information, a method of removing small areas and morphological processing are used to optimize segmentation results. Experimental results show that the proposed algorithm has a good inhibition effect on noise and the effect of tree image segmentation is better than that of the traditional one.

The upsurge in the use of social media in public discourses has made it possible for social scientists to engage in emerging and interesting areas of research. Normally, public debates tend to assume polar positions along political, social or ideological lines. Generally, polarity in the language used is more of blaming the opposing group in such debates. In this paper, we investigated the detection of polarizing language in tweets in the event of a disaster. Our approach entails combining topic modeling and Machine Learning (ML) algorithms to generate topics that we consider to be polarized thereby classifying a given tweet as polar or not. Our latent Dirichlet allocation (LDA)-based model incorporates external resources in the form of a lexicon of blame-oriented words to induce the generation of polar topics. The Collapsed Gibbs sampling is used to infer new documents and to estimate the values of parameters employed in our model. We computed the log likelihood (LL) ratios using our model and two other state-of-the-art LDA-based models for evaluation. Furthermore, we compared polarized detection classification accuracy using the features extracted from polarized topics, bag of words (BOW) and part of speech (POS)-based features. Preliminary experiments returned higher overall accuracy results of 87.67% using topic-based features compared to BOW and POS-based features.

For the current test of concentrator relay routing learning function in the laboratory, based on the hardware cost and the deployment environment, unable to realize large-scale electric energy meter group network, and real scene environment cannot be simulated. In order to solve these problems, the design of test platform for concentrator based on virtual electric energy meter is proposed in this paper. The design aims to simulate the communication routing network algorithm of electric energy meter and help the communication instruction to establish an optimal topology network which through using the specified data source, target source and the route address. So the concentrator can get the specific data through this preset network. This paper have also observed and analyzed the hierarchy order topology and compared with the autonomous learning topology of the concentrator relay routing algorithm. Then verify the deviation between communication routing topology and preset value, and eliminate the deviation. At the same time, verify whether the relay routing algorithm can get the optimal communication topology network.

By dynamic analysis, the dynamic modeling of designed magnetorheological(MR) shock absorber with metering pin is established, and using the experimental data. The relation of kinematic viscosity and current, and shear stress and current are respectively identified, and the parameters of dynamic model are identified by the least square identification method. The experiment results show that the identified model has high accuracy, and can be used for the performance analysis and control system design of the shock absorber.

In order to solve such problems as background noise amplification, excessive image edge enhancement and ring effect in the output image caused by the difficulty of the existing self-adaption image enhancement algorithm in identifying the smooth and fine areas of an image, a self-adaption image enhancement algorithm based on rule fuzzy inference system is proposed in this paper. Firstly, the pixel field of the image to be enhanced is locally and statically analyzed to obtain the low-frequency component of the image; secondly, five logic rules are defined and meanwhile the local statistical information is combined with the local standard deviation to establish the rule fuzzy inference system so as to calculate the contrast gain factor and accordingly complete the self-adaption enhancement of the image and optimize the pixel field dimension. The simulation result shows: compared with existing contrast enhancement technology, the proposed technology has better visual enhancement effect and can obviously eliminate ring effect.

Flame edge detection from color images is a challenging research area recently. In this paper, an extension of active contour model is proposed by adding the two information types to both internal and external energy terms. Therefore, the combination of these two forces allows for flexible initialization of the contours. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. Edge extraction of different flame images using C-V model and the classical edge detection operators are compared and analyzed. Experimental results show that the existing methods do not emphasize the continuity and clarity of the flame and fire edges while the proposed method identifies the continuous and clear edges of the flame fire.

Various projects to digitize the document and book inventories around the world are proceeding recently. At the same time, utilizing the IT/ICT technologies for the paleographic works is becoming an interesting issue among the historical and academic circles especially in the Republic of Korea where both Chinese characters and native Korean alphabets have been used in their ancient articles. There are some subtle differences in the typefaces and Great Seals used in each dynasty or year depending on the writers or the popular writing style of the time. The typeface recognition technique proposed in this article includes an alphabet analysis DTW algorithm and an alphabet position analysis method to determine the periods and possible authors of old documents. The results of analysis showed that the former was more reliable compared to the latter.

MIMO system performance mainly by correlation of spatial interference presence and space, In order to overcome these effects, it is necessary by pre-coding technology to achieve, Here we mainly use block diagonalization algorithm both linear pre-coding for analysis and further integration of the average power allocation algorithm and power allocation algorithm MATLAB simulation. Through research and data analysis, system performance block diagonalization algorithm is better than under zero forcing algorithms. When the same number of transmit antennas, the number of receive antennas are the same, the average power and power calculation algorithm with SNR increases, can effectively channel capacity to achieve the desired optimum value.

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.