Earticle

In this paper, we described a genetic algorithm that allows finding the optimal configuration for a stochastic discrete events simulator when multiple performance measures have to be considered simultaneously. This type of algorithmic approach provide particularly interesting when the decision making authority is shared by multiple decision makers with conflicting priorities. The optimal solutions found with this algorithm typically represent a middle ground solution that may be acceptable to all the involved parties. The multi-criteria approach relies on an interval based variant of the Promethee method, which is combined with a feasibility score to obtain the ranking of the chromosomes within a certain generation of the genetic algorithm.

The food security relates to the people’s health and is the top priority in the daily lives. In this paper, in order to solve the food security problem, we propose the food safety supply chain traceability management system which is based on the Internet of things. Through the relevant application of the RFID technology, we establish the effective food safety supply chain traceability management system. It not only can supervise the food from the source, but also makes the internal rights of the food supply chain clear. Once appearing the food security accident, using the system can help enterprises to supervise the production process of the food effectively, identify the cause of the problem and find out the source of the problem. The application of the system can protect the food security better and enhance the security sense of the consumers.

With the development of economy and the progress of science and technology, E- commerce is rapidly penetrating into all fields of people's life. On the one hand, e-commerce plays a more and more prominent role in the consumption of the circulation. On the other hand, E-commerce distribution has become an important force pulling the express service increasing. But, express logistics service level in China is poor and it constrains the development problem. Therefore, establishing a scientific evaluation system is very necessary. In this paper, we propose Regression-AHP (RAHP) method. This new method can reduce the influence of subjectivity on the assessment result. The instance analysis shows that this method can evaluate the target equitable, objective and effective.

The color Doppler echocardiography allows the clinician and researchers to indentify the blood flow patterns, direction, and velocity in case any dis-functioning like valvular and regurgitation stenosis. Jet area is the most used method by the clinicians for MR severity because it is directly propositional to severity of MR. Methods A method is developed for automatic segmentation of jet area and finding the jet area value in numerical terms. Intensity based region growing has been used for segmentation. Before this some preprocessing steps have been applied to make the image suitable for intensity based region growing. Total nine images have been examined taken from the different patients suffering from mitral regurgitation. Results Proposed method finds the two cases are under the moderate MR category and other seven detected as the case of severe MR. in case of clinical point of view three cases are to be found under the moderate and six under the category of sever MR. Conclusions This work is expected to enhance the clinician’s capability in fast determining the extent of severity of mitral regurgitation. The algorithm developed is capable of removing the spurious areas during the process of segmentation.

Multi Degree of Freedom Joints (MDJ) are typically thought to be used for industrial purposes however they are beginning to gain the attention of the medical field. A current application pertains to using the precision and stability of a robot arm with MDJ to assist in minimally invasive surgery. In this research, adaptive sliding mode controls are presented as robust controls for multi degree of freedom joints. The objective of the study is to design controls for actuators without the knowledge of boundary of uncertainties/disturbances by using an adaptive sliding mode control while elucidating the robustness of the adaptive sliding mode control. A sliding mode control provides for ultimate accuracy in the presence of the bounded disturbance/uncertainties, although the sliding mode control also causes chattering. Chattering is undesirable for use with robot medical tools, since it might causes damage to them with a subsequent loss of accuracy and oscillation on end-effectors tools. Such chatter is caused by overestimation of the controller gain. An adaptive sliding mode is proposed as a solution to the problems created by chattering.

With the unceasing development of space activity, the total number of space debris is ever increasing, which greatly threatens orbiting space vehicles. To research the characteristics of debris clouds which are produced by conical projectiles hypervelocity impact on thin plates, SPH (smoothed particle hydrodynamics) methods in AUTODYN is used. Characteristics of debris cloud produced by conical projectiles with different ratio between length and diameter hypervelocity impact on thin plates are investigated with the same mass and velocity by numerical simulation. Effects of the ratio between length and diameter of the projectiles affecting on the form of debris cloud, axial location of debris clouds, radial extension of debris cloud and tip particle velocity of debris cloud are obtained. It is shown that the degree of fragmentation of the projectiles and debris cloud diffusion is changed with the velocity of the projectiles, the ratio between length and diameter and the impacting direction. The increasing ratio between length and diameter of projectiles can reduce the damage of thin plates but rise the damage of the wall of spacecraft by the debris clouds impact on with the same mass and velocity.

To determine the effect of molar ratios on compressive strength of magnesium oxysulfate cement (MOS), the hardened MOS cement pastes mixed with different molar ratios of MgO/MgSO4 and H2O/MgSO4 were tested on the compressive strength. The examinations of strength development, phase compositions and microstructure of MOS cement are discussed in detail. The results show that these starting materials can significantly affect the compressive strength of MOS cement. The mixture with higher molar ratio of MgO/MgSO4 and lower molar ratio of H2O/MgSO4 can produce more 5Mg(OH)2 ·MgSO4·7H2O(517 phase) and less Mg(OH)2, which benefits the compressive strength of MOS cement.

A lot of time of the users is consumed in searching appropriate papers related to the desired topic. It takes time to look through the paper also. In this paper, a hybrid method is introduced to classify research papers. This algorithm is designed to classify all research papers at the time of uploading in the repository. Hence it becomes easy to explore appropriate paper on a specific topic in minimum time. A data set has generated with research papers on different topics like natural language processing, machine learning, etc. The proposed algorithm passes the most frequent items fetched from the training data set to k-nearest neighbor method instead of the whole data set, to make clusters. The performance of the proposed method is compared with traditional KNN method which results the accuracy, improved by the factor of 7.46%.

In this article we have discussed the relative humidity sensor based on fiber grating Fabry-Perot (FBG-FP) coated with polyimide (PI) as a moisture sensitive film in theory. The moisture expansion coefficient of PI film as moisture strain on the FBG-FP sensor, which will result the change of the fiber core effective refractive index, the length of the F-P cavity and the period and effective refractive index of FBG. For that reason, the interference fringes of the reflection spectrum will be changed also. The shift of peak wavelength of the spectrum is traced by the simulation of Matlab. We discuss three situations: the coating on the FBG, FP cavity and the both, and regardless of the temperature changes, the simulation results show that the RH sensitivity can reach to 3.77pm/%RH, 2.12 pm/%RH, 4.05 pm/%RH, respectively.

Everyday internet users are collecting huge data, and the challenge they now facing is how to convert the raw data inside the relational databases to meaningful information for them to take effective decisions based on what is going on now and to predict what will happen in the near future. Data Mining and Web Data Mining deal with the extraction of interesting knowledge from the World Wide Web, the main usage of Data Mining is to discover the trend of business development and make the right decisions. This paper innovate an intelligence platform by presenting a case study to improve future Feasibility Studies. In our case we combined a lot of Data Mining techniques and Web Mining methods to product an interactive platform that can help users to get there goals.

Aiming at solving the NP-hard workshop production scheduling problems, proposed one kind based on mind evolutionary algorithm. The algorithm in the traditional ant colony algorithm is established, and the combination of evolutionary thought and local optimization idea overcomes the basic ant colony algorithm is easy to fall into local optimal defects, the improved state transition rules, defining a pheromone range, improve the pheromone update strategy, and the increase of neighborhood search. Experimental results show that, for a typical production scheduling problems, based on mind evolutionary ant colony algorithm can obtain the optimal solution in theory, optimal solution, the solution and average three indicators are better than the basic ant colony algorithm, showed good performance.

Data mining technology is a powerful tool to solve the problem. It is widely used to identify potentially useful information. Fuzzy principle based fuzzy C- means is a modification of commonly used C- means clustering technique. In the paper, a modified fuzzy C-means algorithm is used in the gene sequence. The modification is through taking a pseudo F statistics into the method. In the simulation, we use nodes instead of gene to verify the validity. According to the simulation, we get the optimal cluster number, the structure of the classification of the nodes. In order to test the performance of the algorithm, it has been used to process large amount of data, and results show that it has higher processing speed and stable performance. The algorithm can be used in the gene description.

Cloud computing is a computing paradigm where applications, resources and services are provided over the internet. Software and hardware can be used to pay as service basis, without buying them. The key role of scheduling is to manage different tasks in different cloud environment. Cloud computing service providers use the available resources efficiently to achieve maximum profit. This makes task scheduling as a challenging issue for cloud service providers. This paper gives an introduction about cloud computing, various existing scheduling algorithms in different task scheduling environments, existing problem and the future suggestions in existing algorithms.

The emerging of social networks opens opportunities for viral marketing. It is a fundamental issue to find a subset of diffusion nodes such that targeting them initially will maximize the range of the information spreading. The problem of finding the most influential spreaders is unfortunately NP-hard. The best known approximation algorithm has been proven to be with an approximation ratio of(1−1/e), however, the performance and the time complexity of the approximation algorithm are not suitable for large-scale social networks. In this paper, we propose a community-based diffuse efficiency algorithm, which is differing from approximation algorithm for mining diffusion nodes through the whole networks, our algorithm identity nodes from the view of the community. The algorithm encompass two steps: Firstly, detect the number of communities in the networks by taking into account information diffusion; and then a dynamic programming algorithm for selecting communities to find diffusion nodes. The performance of the proposed algorithms is evaluated by experiments on a data set of 4000 people call logs, the results show that the community based diffuse efficiency algorithm performs better than the other two algorithms.

The classification and measurement of rock fragments is very important in mining and construction engineering. The monitoring system acquires and analyses the fragment images from a gravitational falling stream at the end of a moving conveyor belt, and the key function of the system is to construct an image segmentation algorithm. To achieve this goal, an adaptive thresholding algorithm with fuzzy comprehensive evaluation is proposed. Firstly, a grabbed image is roughly segmented by using a global auto-thresholding algorithm. Then each of the objects is measured and analyzed if it includes the multiple fragments touching each other, based on the fuzzy comprehensive evaluation method in which the salient fragment features of area, perimeter, shape, gradient magnitude and gray-level flatness are extracted, and for each of the features, the membership function is constructed experimentally. Finally, each of the touching fragment regions, as one image, is auto-thresholded again, and this procedure is repeated until no region can be further separated. The experimental results show that compared to cluster analysis, graph based, and FCM image segmentation algorithms the new algorithm can make image segmentation well for the falling fragments on-line.

This paper presents for isolated handwritten Roman numerals recognition a research interested for carrying out both comparisons between the performances in terms of precision and rapidity, the first comparison is realized between four hybrid methods used to extract the features from numerals that are the zoning combined with Radon transform in first time, then combined with Hough transform in second time, then combined with Gabor filter in third time, finally combined in the same time with all these three descriptors, while the other comparison is performed between three classifiers which the first one is neuronal that is the multi-layer perceptron whereas the second is probabilistic that is hidden Markov model, the third classifier is realized via a combination between these both classifiers. For this purpose we have employed for pre-processing each numeral image the median filter, the thresholding, the centering and the edge detection techniques.

Task scheduling in cloud environment is a key technical problem on how to allocate available cloud resources to cloud users. Usually, a task in cloud environment can be cut into a serials of subtasks, which have precedence and dependency relationships among themselves. We describe the problem by means of a DAG (Directed Acyclic Graph) model. Then, we propose a priority algorithm for DAG task scheduling, and a priority-driven ACO (Ant Colony Optimization) algorithm for DAG task scheduling on the basis of the DAG model. Finally, we compare these two algorithms with the greedy algorithm through simulation in the CloudSim platform. The simulation results show that the priority-driven ACO algorithm is effective to solve DAG task scheduling problem in cloud environment.

ASIC and FPGA ASIC and FPGA are considered to be the ideal platform for special fast calculations because of the hardware structure, and how to achieve computational algorithm by is the hotpot of research. The CORDIC (Coordinate Rotational Digital Computer) can break the basis functions down to operations of shift and addition or subtraction, which can be used to lay the foundation for the realization of complex logic. But the functions selected by traditional CODIC for angle encoding are too complex, which will lead to some problems, such as too much of area consumption and large delay. In this paper, an optimization of CORDIC algorithm are proposed, which reduce the consumption of Adders and comparators, decrease the complexity and delay of the algorithm implement in hardware. The proposed algorithms are modeled in Verilog Hardware Description Language and implemented with FPGA. The simulation results show that the functions of sine and cosine are realized successfully, and the proposed algorithm not only improves the computation speed but also reduces the system hardware resources.

With an increasing focus on mobility and an innate desire to access data and information efficiently and as quickly as possible, users are turning to technologies that can service their needs with the minimum of fuss and a high degree of reliability. In this paper, we have designed a proposed model that seeks to combine Billboard Manager with an open source cloud operating system known as eyeOS that, together, can help achieve the goals desired in a private cloud environment. The eyeOS is essentially a web-based operating system that allows file read/write/update operations in cloud. The architecture that we have proposed in this paper, involves a mix of the Internet, a private cloud zone, and a Billboard Manager managing an eyeOS powered layer. In this model, any user would be able to create, update, access, and delete almost any file from anywhere where there is Internet connectivity options available. The Billboard Manager would enable users to store or retrieve files across distributed storage nodes in a private cloud zone. The architecture that we have proposed is aimed at providing a thin-client architecture to any user with a PC, laptop, or a suitably-enabled mobile computing device, such that the user is able to collaborate and communicate his/her work within a private cloud zone from almost any location.

An improved algorithm of quantum particle swarm with chaos is presented to solve the problem that the traditional particle swarm algorithm is easy to fall into local optimum and converges very slowly. Through analysis the current state of particles in the iteration, to determine and deal with the particle of poor performance, and keep the normal state continue to complete the search optimal solution, which effectively inhibit premature phenomenon of the particles, and improve the overall search ability of particle swarm. At the same time, in order to improve the performance of the algorithm, introducing chaos mechanism, further enhance the search ability of particle. The experiments of benchmark function show that the improved algorithm has obvious advantages compared with the other two algorithms, it has higher stability and accuracy and faster convergence speed at the same time.

There are many things to consider in teaching activities of colleges and universities. Teaching quality guarantee involves complex system engineering. In order to address multi-attribute evaluation of teaching quality, this paper proposes a teaching quality guarantee model of talents training based on grey correlation analysis. A new type of teaching quality guarantee system of talent training is constructed and factor sets for guarantee analysis are confirmed. Then, the state sets of the teaching quality guarantee analysis and classical domains are discussed. A teaching quality guarantee model of talents training is established based on grey correlation analysis in order to obtain the grey correlation coefficient and the grey correlation degree. The teaching quality level of talent training is judged according to the grey correlation degree. Finally, a case is studied to verify the model.

It is discussed that a new method for active power measurement under the conditions of distortion signals. Accurate measurement of the power consumed by the distorted signals has been focused on in recent years due to its practical utilities. In this work, a mathematical description of powers is deducted in the conditions of distorted signals according to a simplified model of the electrical network. Then, a rational measuring method of the electric energy for distorted signals is proposed on the basis of the analysis of the power flow. Also, a measuring method based on multiple wavelets was employed for measuring the active power in the conditions of unsteady distorted signals. Experimental results demonstrate that, by using the simulated typical distorted signals, the proposed measurement method can produce extremely accurate results compared to theoretical results.

In the resource allocation process of multi-user OFDM system, in order to realize the purpose of maximizing the total transmission rate under constant power, a new subcarrier allocation algorithm has been proposed in this paper, which is to introduce cultural evolution method into the original artificial fish swarm algorithm. Because the population space in cultural algorithm framework has the advantage of guiding search process, the new algorithm can effectively overcome the defect of falling into local extreme which generally exists in the fish swarm algorithm in the resource allocation. At the same time, the proposed new algorithm also can make it easy to quantify the optimized goals and variable values. Simulation results show that the proposed artificial fish swarm algorithm with cultural evolution (CE-AFS) has been greatly improved the global search ability and convergence speed compared with the AFS algorithm and Shen algorithm.

In the validation process of power supply subsystem of aviation electro-mechanical system, connecting the power supply equipment directly can lead to accidents caused by current overload or voltage overload. In order to prevent such accidents, this paper designed a kind of emulational power supply subsystem exciter based on virtual instrument technology. The design scheme uses AC and DC programmable power supplies and PXI series analog output devices which produced by National Instrument as power sources and analog signal sources representing lubricating oil temperature, oil surplus and so on. It uses PXI data acquisition devices as signal collectors. It accomplishes the data transmission between the hardware system and the principal computer software programmed by LabVIEW through PXI-8375 optical fiber communication interface devices. The software achieves functions of the real-time acquisition and the display of signals such as the power supply, the oil temperature, the oil surplus, the alarm and so on. The test shows that the system has good stability, high reliability in data transmission and strong versatility. This system has realized functions of data collection, storage and playback of the power supply, alarm signals and so on.

Properly allocation of virtual machines is important for computing infrastructures scheduling. This paper presents systemic method on virtual machine array optimization control based on artificial intelligence and matrix control theory. According to request service data from users to provide proper VMs roughly via intelligent pattern recognition based on RBFNN, the data is sent to a multiple-targets optimization process to produce VMs allocation matrix precisely, thus enable to minimize the cast and enhance efficiency of the whole array to achieve low consumption optimization and ensure the stability of the system. Simulation experiments confirmed the effectiveness of this model and adaption ability in online dynamics.

S-P Chart Analysis is an effective method to diagnose the learning status of students and items quality. This method is very suitable for use in adaptive learning systems. However, it only handles dichotomous data without considering the test data of partial credit scoring. The main purpose of this study is to develop a tool for analyzing the test data of partial credit scoring according to S-P Chart Analysis method. First, the study proposed the Partial Credit S-P Chart Analysis. Then, the study developed the software to analyze partial credit data according to the method proposed. PCSP 1.0 software developed by this study can help the user to reduce the complex mathematical calculations and provide results accurately and quickly. The interaction between users and the software is very easy through a graphical user interface. PCSP 1.0 software handles not only dichotomous data but also partial credit data. This software is an actually useful tool for evaluating and diagnosing the achievement of students.