Earticle

In this paper we focused on practical method to perform tolerance analysis of analog circuits. Any circuit is the combination of passive, controlled and independent sources. The characteristics of analog circuit is totally depends upon the values of components used. So this paper gives detailed analysis of circuit parameter variation. To execute this analysis we use sensitivity method. Using this method we can analyze any analog circuit component tolerance. Tolerance is one of the most important parameter which is considered during the design of analog VLSI circuit. So with the help of sensitivity analysis we can also calculate the component tolerance. We perform sensitivity analysis of various circuit in this paper and show how the value of any component parameter change the circuit behavior. All the equation and model for sensitivity analysis of analog VLSI circuit and simulation are done with the help of MATLAB tool.

Prostate segmentation from MRI is a necessary first step and plays an important role in clinical decision making process. A MR T1 Image segmentation method of the prostate based on distance regularized level set evolution (DRLSE) is proposed. To smooth the prostate image to reduce the noise, a preprocessed prostate image with Gaussian kernel and get an edge indicator is convolved. We construct an energy function with a distance regularization term and an external energy term containing the edge indicator and minimize it by solving the gradient flow which can be implemented with a finite difference scheme. In segmentation experiments, the impact of parameters λ , μ , α and ε in DRLSE model on the image segmentation is analyzed, and an optimal value of these parameters is given. The experiment results confirm the effectiveness of the MRI segmentation method of prostate for different situation of different patients.

In cloud manufacturing environment, for a scheduling Job, there may be a lot of servicizing manufacturing resources and manufacturing capability that can be used to support for its realizing. Therefore, how to efficiently solve FJSS problem becomes more complex and significant. First, this paper uses disjunctive graph model to analyze the characteristic of FJSS problems, and then, focusing on machine selection sub problem, this paper designs multi-rules to solve machine selection conflicts in different scenarios. Finally, on this basis, an improved multi-rules-based ACO algorithm is proposed. The algorithm is applied to the typical examples of the flexible job-shop scheduling problem. Compared with other algorithms, final experimental results indicate that this algorithm is effective.

The perception that call center operators’ interactions with customers make effects on service quality or customer satisfaction is getting proliferated. Accordingly, emotional labor of call center operators attracts attentions. Call Center Information System plays very important role in interactions between call center operators and customers, and call center operators can reduce their stress when they can use information system effectively. It would decrease emotional labor of call center operators and increase performances of individuals and organization. Accordingly, in this study a hypothesis is made that call center information systems quality and emotional labors make effects on the organizational performance. In test of hypothesis, it is verified that the information systems quality makes effects on emotional labor of call center operators, and emotional labor makes effects on organizational performance, in other words it makes effects on customer-orientedness and organizational satisfaction.

In this paper, there are three scheme of PLC+ PC control program have been put forward for l0T/h hot water boiler. The multi variable, nonlinear, uncertain complex process automatic control is realized, and the thermal efficiency of the hot water boiler is improved. Each boiler in the system is controlled by the respective PLC, the public part is controlled by a set of PLC, an industrial control computer as an engineer station, an industrial control computer as an operator station, field bus Profibus-DP network was comprised by Industrial control computer, PLC. Fameview software was designed to achieve the redundant control of the upper monitor of the two boiler.

Mobility load balancing (MLB) is a significant technology in Long Term Evolution Advanced heterogeneous cellular networks (HCN). To solve high handover performance loss issue and the potential Ping-Pong problem, a novel MLB scheme based on cell priority is proposed. Considering the impact of different user mobile speeds and cell coverage radius of HCN, we define a handover tolerance as the priority of load balancing to reduce the system performance loss caused by user handover. Furthermore, we use the load balancing index to evaluate the load balancing of HCN, then the load allocation mechanism of overlapping cells is deployed to overcome potential Ping-Pong problem. Simulation results show that the proposed MLB scheme has better performance than the original MLB scheme in terms of the system throughput and the number of unsatisfied users. Meanwhile, the handover times are also improved.

3D maps may be treated as a new data source for urban navigation and used to improve positioning accuracy in urban canyons However, 3D digital maps are not available in most cities. This paper proposed a new approach to build a 3D model by using a GNSS VT receiver. The system architecture was described first, and then the method of 3D modeling by analyzing received data was presented. The premise for building the 3D model is signal classification. Analysis of data collected by the VT receiver was described in detail. Using a GNSS signal recorded in a dense urban environment showed the effectiveness of the proposed approach.

Aiming at the incomplete information systems on the condition of no prior domain knowledge, several known model extension based on the rough set theory are introduced at first, such as the tolerance relation, non-symmetric similarity relation, limited tolerance relation and valued tolerance relation. Then the merits and drawbacks of several existing valued tolerance relations are compared in this article. Next, the experiments on some UCI data sets have been done ,based on the experimental result, the author discuss the relationship between the threshold selection and classification accuracy of statistical valued tolerance relation(SVT) . Directing at the difficulty of selecting a suitable threshold, the author presents a new improved valued tolerance relation (NVT) which can choose proper threshold automatically on the basis of each data set’s feature. Experiment results indicate that the new relation can get better classification accuracy than the other extension models in dealing with the incomplete system which has small incomplete degree

Selangor is an important river basin in adjacent to the city of Kuala Lumpur, the federal capital of Malaysia and it supplies about 70% of the water required for domestic and industrial use for the city. Selangor river basin is presently regulated by two water supply dams, namely the Tinggi dam and the Selangor dam. Water is abstracted at an intake located 21 and 42 km downstream of the Tinggi and Selangor dam respectively. In the wet season, when unregulated flows downstream of the dams are sufficient for abstraction, no releases from the dams are required. However, releases are required in the dry season when flows downstream fall below the normal level. The present practice in dam operation is to use recession analysis in low flow forecasting during prolonged dry periods. Recession constants were derived using stream flow data and future flows were forecasted using the current flow and the recession constants assuming that there is no rain for the coming period where forecasts were made. Decisions were then made for releases from the dams. The disadvantage of recession analysis in forecasting low flow is that the forecast is not accurate if rain falls during the period and over release will occur. This study reports the use of Artificial Neural Network (ANN) models to forecast one and two time steps ahead river flows at the Rantau Panjang gauging station near the water supply intake for different travel times from the dams to the intake point to help in determining the regulating releases from the dams for more efficient reservoir operation. Two different ANN models, the Multi -Layer Perceptron (MLP) and the General Regression Neural Network (GRNN), were developed and their performances were compared. Endogenous and exogenous input variables such as stream flow and rainfall with various lags were used and compared for their ability to make future flow predictions. The input variables required are decided considering statistical properties of the recorded rainfall and flow such as cross-correlation between flow and rainfall, auto and partial autocorrelation of the flows which are best in representing the catchment response. Results show that both methods perform well in terms of R² but GRNN models generally give lower RME and MAE values indicating their superiority compared to MLP models.

It is an important foundation for the in-depth application of the propositional logic to compute all reductions, and this is a hot subject at present. This paper defines the identity element concept and the zero element concept in concept lattice. It proves that the minimal transversal of waned values hypergraph of zero element concept is a reduction in inducible context, and it also proves that the minimal transversal set is a full reduction set. Furthermore, the paper presents a new method based on intent waned values and hypergraph minimal transversal of concept lattice. By this method, all reductions can be quickly computed all at once. It develops the reducing algorithm. Compared with the original algorithm, which compute a reduction every time in first and gradually aggregate all reductions, the operating number of the improved algorithm is remarkably reduced. The approach has the remarkable advantages and good results.

For the improvement of voltage stability, it is suggested that Flexible AC Transmission System (FACTS) devices are to be incorporated properly in the transmission lines. In this paper the voltage stability enhancements with two FACTS devices namely unified power flow controller (UPFC) and interline power flow controller (IPFC) have been studied using GA-GSA hybrid algorithm. A comparative analysis is made between the two devices based on different factors such as voltage profile improvement, loss minimization, cost of investment and voltage stability improvement. Newton-Raphson method is used for power flow analysis before and after insertion of each FACTS device. Depending on the voltage collapse scenario the best locations for inserting the FACT device are determined by using GA algorithm. At each location the rating of FACTS device is determined by using GSA algorithm. After that final optimal location and the exact capacity of FACTS device is calculated by considering the losses and cost of device .That optimal rated FACT device is inserted at the selected location and the performance of the system is observed. The complete evaluation is done on IEEE-30 bus test system by using MATLAB. From the simulation results, an elaborate comparison has been made between UPFC and IPFC based on the performance of the system with each of these two FACTS devices for voltage stability improvement.

The krill herd (KH) algorithm is a novel swarm intelligent algorithm which is inspired the herding behavior of the krill swarms. The various test results in the relevant literature show that the KH algorithm has better performance than the other swarm intelligent algorithm for optimization problem. In order to further improve the performance of the KH algorithm, an improved KH is proposed in this paper. The algorithm is performed on ten test functions and the results are compared with the basic KH algorithm, PSO, DE and GA algorithm. The experimental results indicate that the improved algorithm is a good method for numerical optimization problem.

This paper discusses bit plane allocation performance. The bit plane images are generated by bit plane slicing. The bit plane slicing is the process of conversion from original image to multilevel image. Normally, the number of multilevel is two. By bit plane slicing, a given image is sliced into n-level planes, where importance of planes is different. For example, the 0th bit plane is the least significant one and the 7th bit plane is the most significant one. Five metrics were used for evaluating bit plane, CMSE, CPSNR, S-CIELAB, FSIM and file size. Simulation results show the performance in each metrics.

Currently, TM images has a very high practical value and widely used in all aspects of agricultural. Unmanned Aerial Vehicles (UAV) remote sensing platform mounted ADC-Lite multi-spectral sensor has consistent channels response functions with TM sensor in TM2, TM3 and TM4, demonstrated to compete with TM sensor, due to low operational cost, high operational flexibility, high spatial resolution of imagery (0.018m with flight altitude 50m) and heterogeneity both at time and spatial-scale. In order to make sure whether it has widely used as TM sensor, moreover, the aim of this work is to assess ADC-Lite performance such as its adaptability and practicability. In this paper, ADC-Lite multi-spectral data, ground truth ASD hyperspectral and Leaf area index (LAI) data were acquired in soybean planting area, Jiaxiang County, Shandong Province on September18th, 2015. Since the ADC-Lite has different spatial scales with TM, this paper used TM simulation data transformed by ground truth ASD data, constructed LAI inversion model by empirical model based on two sensors and ground measured data, using 5 vegetation indices: ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil adjust vegetation index (SAVI), difference vegetation index (DVI) and triangular vegetation index (TVI). Determination coefficient R2, root mean square error (RMSE) and the estimation accuracy (EA) 3 indicators were acquired to assess the model. This work suggests that the established model of ADC-Lite sensor with TM simulation sensor has high consistency in accuracy. NDVI linear regression model derived from both of them presented a strong correlation with ground-measured LAI. It’s preliminarily shown that ADC-Lite images assess soybean LAI is feasible. This is anticipated to have tremendous implications that ADC-Lite can be made supplement for existing satellites, aerial and ground sensing, provide important information for Crop condition monitoring and critical data to support crop maturity, nutrition monitoring and fertilization management.

Patent pool partner selection is a typical multi-attribute decision making (MADM) problem, and that based on intuitionistic fuzzy (IF) theory is widely applied in this research field. In this paper, a dynamic MADM method based on time degrees and intuitionistic fuzzy entropy (IFE) is proposed. We determine the attribute weights under different time sequence based on IFE; and establish a non-linear programming model combining the principle of time degrees with grey entropy theory to calculate time weights vector; the dynamic intuitionistic fuzzy weighted geometric-operator(DIFWG) is used to aggregate different periods of weighted intuitionistic fuzzy decision-making matrix; TOPSIS was integrated into dynamic intuitionistic fuzzy multiple attribute decision making (DIF-MADM), by calculating the relative closeness of alternatives with ideal solution, we obtain the optimum alternative; finally, an example is given to illustrate the applicability and rationality of the proposed approach.

The main objective in this research is comparative study between three types of control methodology: proportional-integral-derivative (PID) controller, computed torque controller (CTC), and sliding mode controller (SMC) with application to active multi degrees of freedom actuators. PID controller is a linear model-free controller; to control of nonlinear system based on PID controller system linearization is the main challenge. Computed torque controller and sliding mode controller are model-base and nonlinear. In order to design computed torque controller, an accurate dynamic model of nonlinear system plays an important role. To modelling an accurate dynamic system, modelling of complex parameters is needed to form the structure of system’s dynamic model. It may be very difficult to include all the complexities in the system dynamic model. Computed torque controller is work very good in certain condition but in uncertainty, PID and CTC have some challenges. Conventional switching sliding mode controller is an apparent nominates to design a controller using the bounds of the uncertainties and external disturbance. In partly uncertainties, sliding mode controller is more robust than CTC and PID. In this paper these three types controller are test in MATLAB/SIMULINK.

Differential evolution (DE) algorithms have been extensively and frequently applied to solve optimizationproblems. Theoretical analyses of their properties are important to understand the underlying mechanismsand to develop more efficient algorithms. In this paper, firstly, we introduce an absorbing Markovsequence to model a DE algorithm. Secondly, we propose and prove two theorems that provide sufficientconditions for DE algorithm to guarantee converging to the global optimality region. Finally, we design two DE algorithms that satisfy the preconditions of the two theorems, respectively. The two proposed algorithmsare tested on the CEC2013 benchmark functions, and compared with other existing algorithms.Numerical simulations illustrate the converge, effectiveness and usefulness of the proposed algorithms.

Due to the high density of the deployment of wireless sensor network, makes a certain range of redundancy data acquisition, inefficient communication.And the data fusion technology to reduce data redundancy, improve the accuracy of information, etc.Therefore, this paper puts forward a kind of wireless sensor network based on data fusion, through simulation experiment, the method to handle well the data redundancy and reduce the energy consumption data transmission, prolong the life cycle of the network.

There are several types of transport on the road; vehicles, bikes, motorcycles (two and four wheels), kids’ bikes, construction vehicles, etc. In this paper, we present an enhanced algorithm for the detection rate to classify the two-wheelers with riding people using the normalized cross correlation (NCC) and its application of histogram of oriented gradients (HOG). HOG descriptors which are one of the well known methods for object detection are the feature descriptor using edge intensity in a local region. Using the NCC algorithm which is used to make a match from template images, local weighting values are calculated from template cell features. And the combined cell features are classified by using a boosting algorithm (Adaboost). The improvement detection rates are confirmed through experiments using bicycles and motorcycles data set for the front direction.

Job scheduling is an important component of Hadoop. Hadoop default FIFO scheduling algorithm is simple and easy to achieve and widely used; but it lacks consideration in the characteristic of data locality, which will lead to heavy traffic during network transmission and task execution, then computing resources cannot be fully utilized and a series of other drawbacks. Meanwhile the static function of resource slots in Map and Reduce stages make such defects worse. This paper proposes a job scheduling algorithm (Interactive Scheduler Algorithm) IS based on interacting the master node and slave nodes from the data locality and tasks allocation. The algorithm improves FIFO and the usage of computing resource, realizes the dynamic conversion of map slots and reduces slots. In the end through the comparison of experiment it is proved that the IS has a great improvement in job scheduling for Hadoop.

Relations between medical concepts convey meaningful medical knowledge and patients’ health information. Relation extraction on Clinical texts is an important task of information extraction in clinical domain, and is the key step of building medical knowledge graph. In this research, the task of relation extraction is based on the task of concept recognition and is implemented as relation classification by the adoption of a CRF model. The proposed CRF-powered classification model depends on features of context of concepts. To remedy the problem of word sparsity, a deep learning model is applied for features optimization by the employment of auto encoder and sparsity limitation. The proposed model is validated on the data set of I2B2 2010. The experiments give the evidence that the proposed model is effective and the method of features optimization with the deep learning model shows the great potential.

The past two decades have seen the incorporation of robotics into medical applications. From a manufacturing perspective, robots have been used in pharmaceuticals, preparing medications. But on more novel levels, robots have been used in service roles, surgery, and prosthetics. The capability of high-precision operation in manufacturing settings gave the medical industry high hopes that robots could be used to assist in surgery. Not only are robots capable of much higher precision than a human, they are not susceptible to human factors, such as trembling and sneezing, that are undesirable in the surgery room. Another advantage to robots in medicine is the ability to perform surgery with very small incisions, which results in minimal scar tissue, and dramatically reduced recovery times. The popularity of these minimally invasive surgical (MIS) procedures has enabled the incorporation of robots in surgeries. In this paper, model reference computed torque controller is recommended for four degrees of freedom serial links robot manipulator, which modeled in SIMSCAPE. To design stable controller conventional computed torque controller is recommended. It is a nonlinear, stable, and reliable controller. The proposed approach effectively combines of design methods from nonlinear controller, and linear Proportional-Derivative (PD) control to improve the performance and stability. This paper has two important objectives: a) study on modeling of 4 degrees of freedom (DOF) based on Simscape software and b) design PD model reference computed torque controller to improve the sensitivity of surgical robot manipulator.

Service providers and service consumers in the cloud environment are two important subjects to regulate the cloud computing market and the interests of the game between these two have a certain dynamic. Therefore, in this paper, a framework of SLA negotiation based on evolutionary game theory is proposed in the cloud environment, and the purposes are not only that the parties can negotiate successfully, but also that it clearly demonstrates dynamic evolutionary game process of the participant groups and the internal of the groups. The experimental results show that there are two long-term stable strategies of service providers and service consumers: Firstly, if the service providers take high-quality strategy, then service consumers adopt the high-price strategy; secondly, if service providers take low-quality strategy, then service consumers adopt the low-price strategy. And the analysis by the evolutionary game model and the evolutionary game path shows that the cooperation of the service providers and the service consumers in the consultation are mainly affected by the direct incomes, the distribution percentage, the preferential subsidies, the violation penalties, the violation revenues, the payment costs and other factors .

Random number generation is an art and science of deterministically generating a Sequence of numbers that is difficult to distinguish from a true random sequence. It is become important tool in cryptography to generate secrete key. There are so many approaches to generate a pseudo random number. But they have some limitations too. In this paper we represent a new random number generation algorithm with the help of existing conventional methods & some new tricks to enhance the programs randomness. We already know some methods like mid-square or congruent. But, in our random numbers, we just use a congruential method which is pretty much different with conventional methods. That’s why we can consider it as a hybrid method of pseudo random number generator.

Time series prediction is a challenging research area with broad application prospects in machine learning. Accurate prediction on a time series’ value can provide important information for the decision-makers. In the literature, many works were reported to extend different architecture of artificial neural networks to work with time series prediction. However, most of the work only considered the target time series itself, while neglecting the impact of the relevant time series. In this paper we proposed a novel method MANNP that makes use of multiple artificial neural networks to conduct the time series prediction. The proposed method creates time series model and forecast time series. To verify the effectiveness of the proposed method, we apply MANNP to a shipping price index time series prediction. The experimental results show that this method can improve accuracy of prediction when compared with traditional methods.

In recent years, Multimedia technology has been popularized in higher education. In order to construct the multimedia system design the author study the multi-layer C/S technology and the Joomla framework based on MVC model. By using PHP and MySQL database, Joomla framework can realize the commonly used function in Web system. On the other hand, as English teaching mainly to impart language knowledge, however, the teaching method that cultivates learners' ability has not been paid enough attention. Based on the empirical analysis, the author analyzes the college English teaching mode based on autonomous inquiry; the result shows that 60% teacher use multimedia aided instruction, 70% teacher willing to organize English dialogue, 30% teacher will held group discussion in English. While only 10% teacher select case analysis and specify the students to introduce knowledge. Through the contrast experiment, the results show that the inquiry teaching can significantly improve the students' English learning ability; the students' score has been significantly improved.