Earticle

Extended-range electric vehicle is considered to be the ideal transition type for electric vehicle. The optimal operation curve control strategy was proposed for a 12 meter-long range extended electric bus. With exponential function inertia weight adjustment and local chaos substitution, an improved chaotic particle swarm optimization algorithm was applied to optimize the key parameters of energy management strategy. Based on MATLAB/Simulink, full vehicle model and corresponding control strategy were built. The simulation results with typical city driving cycles illustrate that, comparing with standard particle swarm optimization, the new algorithm can greatly improve the convergence speed and optimizing precision, and the optimal parameters can be obtained.

This paper discusses the Cloudanalyst tool. Cloudanalyst tool is used to determine the better load balancing algorithm from various scheduling and load balancing techniques e.g. round robin algorithm. This learning will help valued understanding to design infrastructure services of the Cloud. Different areas like coordination between one data center and other data center, algorithms of load balancing as well as other value-added services are also kept in mind, that are possible like service broker policies, which synchronize efficient

Resource allocation for multi-tenancy application is one key issue for cloud service providers. To deal with the competitive problem of limited resources among tenants, this paper proposes a resource allocation strategy based on game model, which combines tenants’ SLA and their resource utilization, in order to deal with the shortcomings of the traditional allocation strategies that consider few about differentiated resource requirements. A framework for multi-tenant resources allocation based on game is presented, and the corresponding game model is established. The related processes auction and resource allocation are designed. The experimental results indicate that it can meet the personalized and differentiated demand of multi-tenant better, and help to improve the system’s overall resource utilization and decrease the operating costs of cloud service providers.

More and more electric vehicles have been used in China, and many charging station has been built in China. Due to the reactive power loss caused by charging devices, the method to measure the power consumed by charging device is critical for power company. Based on impact loads generated by electric vehicles to the grid in fast charging mode, this paper studies the energy measurement method of electric vehicle charging mode. Since each phase current of charging post imbalance, to prevent the sum of each phase current vector of charging posts over the protection current threshold and the charging post breaker misusing in power circuit, preventing leakage circuit breaker current imbalance system is designed. Finally, this paper studies the power consumption detection methods of electric vehicle charging posts. By comparing the fast Fourier transform and wavelet analysis algorithms, an electric energy measurement device of electric vehicle charging machine based on FFT and wavelet analysis are proposed. Combined with fast data processing functions of DSP, the device can quickly and accurately measures electric vehicles charging amounts under different charging modes.

Doubly fed Induction generators are used commonly in variable speed wind energy conversion systems due to their high output energy. This paper analyzes the steady state of wind power systems based on doubly-fed induction generator when subjected to different voltage sags at grid and the various parameters undergoing variations due to this. PWM converters are used as excitation power supply source to the DFIG wind energy conversion system of VSCF, which join rotor to the used grid. To control the convertor rotor side, vector control scheme is used which is based on stator voltage strategy. The model based on this scheme is simulated with the help of MATLAB software.

This article put forward an optimization algorithm of triangle grid model based on Laplacian coordinate constraint and a fast reconstruction algorithm of model topology structure based on STL file, such methods may enhance the quality of triangular patch as well as splendidly remain local geometric features of original grid model. The character of it is there’s unnecessary to distinguish constraint vertex and free vertex no longer and to carry on a double restraints to location and Laplacian coordinate for all vertex, then solve a linear system, which contains double restraint, with least-squares sense to carry on an relocation for all vertex. The experimental results showed that proposed algorithm is provided with a certain advantage in terms of detail features of grid compared with precedent Laplacian optimization algorithm.

To improve the performance of the Magnetotelluric Occam inversion, by in-depth analysis of the sequential algorithm, we develop a multi-level hybrid parallel computing scheme for MT Occam inversion based on MPI+OpenMP+CUDA and implement it on a small heterogeneous cluster. We implement the parallel algorithm for solving linear equations with Gauss elimination, jacobian matrix, cross-product matrix calculations and Cholesky decomposition. Through reasonable decomposition, combination and mapping of computing tasks, the scheme reduces the data traffic and realizes the purpose of load balancing. By changing the matrix storage order，the memory access speed is significantly increased. The scheme is tested with multiple synthesis data from 2-D theoretical models and the execution efficiency of sequential code and parallel code on a 4 nodes PC cluster is comparatively analyzed. The test results show that the realization of this hybrid parallel algorithm is feasible and efficient. Compared with the sequential code and pure message passing algorithm, the inversion speed is obviously increased.

The segmentation technique of 3D mesh models plays a key role in comprehending and processing digital geometric models. A new segmentation algorithm for 3D mesh models based on conformal factor and k-means clustering is proposed to deal with the existing problems in segmentation methods, which includes a lack of semantic information and low performance in segmentation results. It is easy to obtain an effective segmentation results with semantic information since the conformal factors carry global feature information of the model. Firstly, the discrete Gaussian curvatures of each vertex are figured out. Secondly, the conformal factors are calculated by employing Laplace-Beltrami operators. Finally, the clustering of meshes is realized by modified k-means clustering algorithm. Experimental results show that the proposed algorithm not only can achieve meaningful segmentation of 3D models, but also has good anti-jamming performance caused by pose variation of the model.

The current investment and management mode of power grid construction project is inadequate to the whole optimization, the overall process of the project has obvious time-phased, therefore, the paper strongly advocates to carry out life-cycle management in power grid construction project, and divides the construction project into five stages as preliminary planning, design and planning, construction and operation, scrap and disposal, and evaluate and control the cost of operating stage, investment benefit of decision stage, design stage management and risk of construction stage, and sets up performance evaluation model of power grid project, in order to provide certain practical significance for the establishment of life-cycle management mode of power grid enterprises.

The multiprocessor computing is composed of more than one central processing units (CPU) that simultaneously execute the task of a parallel application for obtain quick results, to process a massive amount of data, and to solve a problem in expected time. If Scheduling is done properly in task allocation then they are increase the performance of the system. Task scheduling in a parallel environment is one of the NP-problems, which deals with the optimal assignment of a task. In this paper, various algorithms are surveyed that apportion a parallel program impersonate by an edge-weighted Directed Acyclic Graph (DAG). These include Bounded no. of Processors (BNP), Unbounded no. of Clusters (UNC), Task Duplication Based scheduling (TDB) and Arbitrary Processor Network scheduling algorithm (APN). The objective of this paper is to study and explore several DAG based task scheduling algorithm and the performance of all of the algorithms is evaluated and compared against each other on a unified basis by using various scheduling parameters.

The core of the comprehensive evaluation problem is how to distribute the weight of each attribute. The attribute coordinate evaluation model (ACEM) has realized dynamic distribution for weight according to the evaluator preference curve, but there still is a problem that maybe a few evaluation results are unreasonable after transforming from locally satisfactory degree to globally satisfactory degree. In order to solve this problem, this paper proposes the improved attribute coordinate evaluation model (IACEM) which directly calculates the global satisfaction, simultaneously, gives the principle to determine whether the final evaluation result is reasonable or not, namely, the principle of weight priority is adopted for the evaluated objects with the same total score level, and the principle of total score priority is adopted for the evaluated objects with different total score levels. To eliminate the errors caused by the change of total score planes, this paper also proposes a parameter adjustment method. The final simulation verifies the correctness and the rationality of the improved model.

A multiple smooth model is proposed by smoothing technique and piecewise technique for large scale data. Mapping the training data to the hidden space with a hidden function, the proposed model divides the original data into several subclasses by Fuzzy C Means (FCM), whose initial cluster centers are selected by samples with large density indexes; derives the smooth differentiable model by utilizing the entropy function to replace the plus function of the slack vector, and introduces linking rules to combine results of subclasses. Simulations demonstrate that the obtained algorithm maintains good classification accuracies, reduces the training time and hardly varies with kernel parameters.

Despite much research and progress in the areas of cloud computing project, many cloud computing projects have a very high failure rate when it comes to the banking organizations. The aim of this study is to propose a new conceptual framework modelling for cloud computing risk management in banking organizations. There are the main five stages for a successful cloud computing framework in a banking organization as described in Figure 1: Cloud mobility and cloud banking applications, cloud service models, cloud deployment models, cloud risk management models, and cloud security models. As a future work, we will apply the framework in the real banking world to mitigate and control the security issues. A successful framework modelling for cloud computing risk management will greatly improve the probability of cloud computing success in banking organizations.

In view of the existing sequence planning problem, this paper presents a process scheduling model based on polychromatic sets theory. To attain the similarity and individuality of constraints of parts, the process route decision was divided into stages. A feature machining chain and process feature ranking model were built beginning with the factors that influence the processing stage and technology feature. The adoption of polychromatic sets theory simplifies the evaluation of process constraints; thus, knowledge can be expressed easily. This method not only ensures the versatility of the system, but also satisfies the customer-oriented process resource.

The computer networks have rapidly developed, and its field of application is also vigorously rising for the past many years, especially electronic commerce. However, due to each link of the supply chain will return a large number of products, so e-commerce model in reverse logistics becomes the first difficult problem. In order to solve the problem of the return of enterprise products, we apply the principles of efficiency savings, and allow more businesses to benefit. Meanwhile Reverse Logistics Model concept came into being. This paper focuses on the background of reverse logistics and the current problems, in the same time, conduct a detailed analysis of reverse logistics model. We adopt QS_R strategies to improve and provide case which let people understand easily. Through this research we hope that government agencies and businesses can make more highly valued the importance of reverse logistics, and have a preliminary understanding reverse logistics models. Our aim is to better make outstanding contributions to our social-economic.

The project deployment diagram, which is used to uniformly describe the process and resource of virtual enterprise is established aiming at how to choose virtual enterprise partner of time-cost tradeoff and set task-resource assignment graph as scheduling model, the iterative heuristic algorithm is adopted based on relative cost-effectiveness to solve. The production of virtual partner and transportation time and cost are taken into account in algorithm, which may ensure deadline constraint as well as effectively cut down overall cost. Such algorithm applying to resource distribution and a vast amount of simulative tests proved that this method is able to effectively cut down the operation time of obtaining prioritization scheme.

Recently, service of quality (QoS) of cloud platform has attracted more and more attentions as plenty of commercial clouds are deployed in real world. However, providing desirable QoS for end-users still remains a challenging problem because of the unpredictable workload in cloud platforms. In this paper, we present a novel QoS framework called QVRP, which enables us to enhance user’s QoS satisfactory through a flexible virtual machine reservation mechanism. The implementation of QVRP is implemented as an integrated middleware that makes it easy to be incorporated in many cloud platforms. Extensive experiments are conducted to examine the effectiveness and performance of QVRP, and the results indicate that it can significantly improve many important QoS metrics, which in turn improves the cloud user’s QoS satisfactory.

Cloud Computing is a new technology that allows access to applications as utilities over the internet. Cloud computing environment provides a great flexibility and availability of computing resources at a lower cost. However, it brings new security concerns mainly when users understand exactly how a process is running. One of the main important challenges in cloud computing is data security, as users need to access data they share securely. So the main problem is how to employ an effective authentication procedure for ensuring data security and preventing unauthorized users to access the authorized user’s data. This paper identifies the security issues of single level authentication and the problem of single password. This study proposed a new security mechanism for cloud computing based on multilevel authentication. The proposed scheme aimed to enhance the security and authentication process in cloud computing. The proposed scheme consists of three level of authentication, and the data will be splitting on this level depending on the sensitivity to confidential (C), secret (S), and top secret (TS). Data at level (C) have the lowest sensitivity. The user at this level has single textual password to access this level data. The user at level (S) has two passwords, textual and biometrics password to access this level and the lower level. User at level (TS) has three password textual, biometrics password and image sequencing password. The data at this level is the more sensitive data so it is encrypted using RSA algorithm before storing in cloud database. The results of the proposed multilevel authentication for cloud computing were promising.

In recent years, the superconducting technology is popular in our production and life. An increasing number of researchers and institutions participate in study of the super-conducting magnetic energy storage (SMES) system. SMES is widely used in power sup-ply system, new energy system and pulsed power supply system. However, the leakage magnetic field of traditional SMES is more serious, which limited the application in engi-neering. This paper proposes an improved scheme for the 1 MJ single-solenoid structure magnet by using the actively shielding theory. Moreover, by using particle swarm optimi-zation, the structural parameters of the superconducting magnets are optimized respec-tively. Finally, the ANSYS modeling simulation is built by these structural parameters, all simulation data are analyzed and compared.

As the increasing of IT-infrastructure in cloud platforms, rapidly growth of energy consumption becomes a critical problem in many cloud datacenters. Conventionally, most of studies on energy-efficiency optimization concentrate on CPU related energy costs instead of memory subsystem, since CPU often dominates the total energy consumption in modern servers. However, such a situation is gradually changing as more and more cloud datacenters are equipped with larger and larger memory systems for dealing with data-intensive applications. In this paper, we present a novel mechanism, namely frequency scaling on virtualized memory (FSVM), which applies DVFS technology on memory subsystem based on the characteristics of active VM instances. Comparing with previous studies, our approach provides a fine-grained memory energy consumption conservation mechanism for virtualized servers. Extensive experiments are conducted to investigate the effectiveness and performance of our FSVM, and the results indicate that it can significantly improve the energy-efficiency of memory subsystem in virtualized servers.

In recent years, with the development of cloud computing, more and more service providers appear, making the cloud computing market more open and competitive. However, it is difficult for service consumers to choose the most suitable service provider for their own, especially for the service providers to provide the best service in the negotiated price, while the service providers and the service consumers themselves can't automatically negotiate, thus in this paper, we propose a cloud service broker framework based on dynamic game theory for bilateral SLA negotiation in cloud environment, aiming for successful negotiation among participators. This paper also proposes a Nash equilibrium point and a satisfaction degree difference algorithm, aiming to find the optimal value of SLA attributes (price and bandwidth) at the minimum satisfaction degree difference among the participators. Experimental results show that the service consumers and the service providers are able to achieve the same satisfaction degree on price and bandwidth.

Hadoop uses a reliable, efficient and scalable way to process data. It provides a good solution for dealing with big data. The task scheduler is the core component of Hadoop, and it is responsible for the managing and allocating the cluster resources. Therefore, scheduling algorithm directly affects the overall performance of Hadoop platform and utilization of cluster resource. Based on this, the improved accelerate particle swarm algorithm (IAPSO) is introduced to the cloud environment, and to solve the cloud task scheduling problem in this article. When we use particle swarm algorithm for task scheduling, the tasks are considered as particles, the resource pool is seen as the search space, and the process of finding the optimal solution is considered as a process of task scheduling. If all the sub tasks find the appropriate resources, then stop the iteration and allocate sub asks to the resource nodes. Finally, we simulate the experiment by using CloudSim software. When a single type of task is committed, our algorithm and the other three algorithms can also be used to complete the task scheduling process, and our algorithm is more efficient. But in practice, the cloud computing environment is facing multiuser, and the types of tasks are also varied. With the increase in the number of tasks, the advantage of the other three algorithms decreases gradually, but algorithm in this paper has been exhibited higher efficiency. In addition, with the increase of the number of nodes, task completed time of the algorithm in this paper is significantly less than the other three algorithms, and it has a steady downward trend. Therefore, IAPSO algorithm which is proposed in this paper is applied to solve task scheduling problem in the cloud environment, and it can effectively improve the efficiency of task scheduling.

Packed bed thermal energy storage system is an important component of solar energy conversion system. Conventionally packed bed systems have been designed on the basis of the thermal energy considerations only. Charging and then discharging of bed complete one cycle. It has now been realized that in cycling process special attention needs to be paid to the design to take into account the energy losses as well as the maximum utilization of available energy. In this work, an investigation has been carried out on the effect of cycling (charging and discharging cycle) on the performance of packed bed solar energy storage system. Temperature profiles during charging and discharging have been determined. It has been found that during finite time discharging, the bed cannot be fully discharged and certain amount of unused energy still remains entrapped in the bed. Stabilized unused energy has been found which has been seen to be strongly affected by the bed parameters, namely, the element size and shape, void fraction and aspect ratio of the bed. The values of these parameters to result in minimum value of this stabilized unused energy have been found within the range of investigation. It has been found that unused energy can be minimized by keeping element size to be small and by using highly non spherical elements combined by high void fraction and large aspect ratio of the bed.

Optimal coverage of wireless sensor networks is one of the most fundamental problems for constructing efficient perception layer of the Internet of Things. On the basis of research on spatial neighborhood, the node coverage and area coverage models are analyzed, then an optimal coverage algorithm of wireless sensor networks is proposed based on particle swarm optimization with coherent velocity. Experimental results show that the algorithm can significantly improve the network coverage; in addition, the coherent velocity can effectively avoid network prematurely into a local optimal solution, so as to enhance the network coverage.

Since the idea of the supply chain management is proposed, many enterprises have attached great importance to the supply chain management and pay a lot of manpower and resources to study. It is also the focus to study the inventory in the field of the supply chain. Quantity of the inventory is not only related to the profit of the enterprises, but also related to the survival of the entire supply chain. Predicting the inventory can improve the ability of enterprises to prevent risk, increase the profits and reduce the losses. In order to predict better on inventory, we propose an improved BP neural network algorithm. In the algorithm, we use the improved GSA algorithm to optimize the parameters of BP neural network algorithm and improve the BP neural network algorithm aiming at its deficiency. The experimental results show that this method has good prediction effect.

The potential of cloud computing for overriding the needs for deploying various infrastructures for running a server based services brought up a revolutionary change in the way the traditional demands of the people use to be handled. Cloud computing provides the software’s on pay per use option which make it easier for the people who are not able to meet the economic requirements. Since the whole scenario is beneficial to big industries like facebook, google, orkut etc , various order fields are also getting dependent on cloud computing. Since tons of data is uploading every second to the cloud server does needs to be mined properly for efficient data storage.in this paper we try to integrate the data preprocessing technique with data classification technique to mine big data’s of asthma based patients.

The uncertainty and fluctuation brought by distributed type of intermittent power generation in micro grid bring huge challenges to power quality of micro grid in isolating operation and power control in parallel operation. Nevertheless energy storage is able to well solve this problem. Hybrid energy storage integrates the respective advantages of storage battery and supercapacitor to have improved the performance of energy storage system. Service life of storage battery is tightly related to its charging/discharging times and problem of frequent charging/discharging storage battery still exists in current hybrid energy storage technology. In this paper, it respectively puts forward control strategies for storage battery and supercapacitor. As to control strategy for storage battery, it uses double-layered control strategy, namely, single loop control and power hysteresis control, to feedback low-frequency fluctuation of power and reduce charging/discharging frequency of storage battery. As to control strategy for supercapacitor, it uses inner loop current with power to control voltage in outer loop so as to feedback high-frequency fluctuation of power and it is able to well maintain stability of DC bus voltage. Simulation results suggest the validity of aforesaid control strategies.

At present, the technology of the distributed power grid gradually become mature. The focus of the study is from controlling power quality to reducing the loss of power grid, improving the network efficiency and reducing the cost. In the link of distributed generation grid connected power generation, the loss of the switching device is an important factor affecting the efficiency of grid connection. This paper puts forward a systematic solution to this problem. Firstly, as for topology structure of grid connected controller, aiming at the research on NPC, a traditional topology which is energy-saving and low loss, we present an improved NPC topology with a more continuous flow state. At the same time for this topology, the corresponding control strategies are designed in this paper. It makes the switching device of the grid connected controller have more freedom of switch and make the system can achieve the precise control of the switch tube ,which can achieve the balance of system device lose heat so as to solve the problem of local high temperature from inverter. The system is verified by grid connected test, the power adjustable PWM strategy can be used to achieve different working states without affecting the output current of the system. Through the comparison between light and heat sensors finds that the topology and control strategy proposed in this paper are successfully completed by the new hardware topology and control strategy to achieve the loss balance of the system switching devices.

Improving voltage stability, reducing real power loss (PL) and voltage deviation (VD) are the most important tasks in the operation of electrical power systems. Voltage instability and voltage collapse are the severe problems which may take place because of deficit reactive power at load buses due to increased loading or contingencies. In this paper, the problem of obtaining optimal location and size of SVC is formulated as true multi-objective optimization problem for simultaneous minimization of the two objectives namely real power losses and load bus voltage deviation. The two algorithms real coded genetic algorithm (RCGA) and non-dominated sorting genetic algorithm-II (NSGA-II) with a feature of adoptive crowding distance have been used for solving nonlinear constrained multi-objective optimization problem. Both the algorithms have been used for obtaining optimal location and sizing of SVC. Voltage security of the power system has also been analyzed separately for all placement of SVC to ensure secure operation of the system. The proposed approaches have been implemented on IEEE 30-bus test system. The simulation results of the two algorithms have been compared for solution quality, computational complexity and computational time. It has been found that NSGA-II presents better performance in solving multi-objective optimization problem and also in obtaining a diverse set of solutions which converge near the true Pareto-optimal front. The simulation results of NSGA-II have also been presented to exhibit the capabilities of the algorithm to generate well-distributed Pareto-optimal front.