Earticle

Distribution network is an important part of the power system which delivers the electric power to consumers. With the increasing attention to the new energy power generation, more and more distributed photovoltaic power systems are connected to the distribution network. In this paper, it researches on new energy power grid-integration on grid power quality. First, the technologies of wind and solar power are introduced. Second, the concepts of power quality and evaluation index are introduced. Finally, voltage deviations and harmonics are mainly elaborated.

This paper presents the application of a One Rank Cuckoo Search Algorithm (ORCSA) to bi-objective load dispatch (BOLD) problem where two objectives including fuel and emission are taken into consideration. ORCSA is an improvement of basic Cuckoo search algorithm (BCSA) where several modifications are carried out so as to improve the performance of the BCSA. The performance of the proposed ORCSA is validated by using two systems including a three-unit system with one load case and a six-unit system with three load cases and comparing the obtained results with other methods available in the article. The analysis on the result comparison indicates that the ORCSA is very efficient for the problem.

With the rapid development of the cloud computing and big data, many data center network are growing more large scale, and the number of servers is increasing at an exponential rate. In this paper, VCG (Vickrey-Clarke-Groves) mechanism was introduced into replica placement. Mapping from replica placement model to VCG mechanism was established and suitable payment function was designed. Data storage strategy is built on the distributed file system. In order to ensure the reliability of data storage GFS and HDFS are further both referred to the concept of replica of data block. This paper generally introduced replica strategy, and mentioned that the storage location of data replica is a key point of the research on replica technology currently. This paper mainly applied one cloud platform simulation tool---the CloudSim to simulate the algorithm. The performance of the algorithm was analyzed in the CloudSim platform and compared it with other related algorithms. The simulation experiments were done on the replica placement strategy of the data block, which shows that replica placement strategy can also achieve the established goal.

Scheduling algorithm is always a hot topic in cloud computing environment. In order to eliminate system bottleneck and balance load dynamically. A load balancing task scheduling algorithm based on weighted random and feedback mechanisms was proposed in this paper. At first the chosen cloud scheduling host chose resources by needs and made static quantification, and then sorted them; secondly the algorithm chose resources from which sorted by weight randomly; then it acquired corresponding dynamic information to make load filter and sort the left. At last it achieved the self-adaptively to system load through feedback mechanisms. The experiment shows that the algorithm has avoided the system bottleneck effectively and has achieved balanced load as well as self-adaptability to it.

Finite automata-based algorithms are becoming increasingly important for a wide range of application domains to process large volumes of unstructured data. Current processor technologies are not well suited to accelerate massively parallel operations related to the search and analysis of complex and unstructured data streams pattern identification problems. Hardware designers are exploring new processing technologies to accelerate pattern identification problems. One such technology is the recently introduced Micron Automata Processor (AP), which is a novel and powerful reconfigurable non-von Neumann processor that can be used for direct implementation of multiple Non-deterministic Finite Automata (NFAs) running concurrently on the same input stream. AP has a linear-scalable, two-dimensional MISD (Multiple Instruction Single Data) fabric comprised of thousands of interconnected small processing elements called State Transition Elements (STEs) to analyze complex data streams simultaneously to accelerate solving massively complex problems. The AP is promising future technology which provides new operations and new avenues for exploiting parallelism to accelerate the growing and important class of automata-based algorithms. In this paper, we present a survey of the state-of-the-art in automata processor based hardware accelerators. We describe AP hardware architecture, its programming environments, summarize its current successful applications in a wide range of diverse fields and explore future research trends and opportunities in this increasingly important area of automata computing paradigm.

This paper presents an hardware design solution for integrated protection of distribution systems (Network Protection Unit) by combining transient polarity comparison technique, which is based on the detection and processing of fault generated transient current signals. The integrated protection relays installed at each substation of a distribution network are communicated with the Network Protection Unit through specially designed Packet Transport Network (PTN) for fast and reliable transmission of transient polarity current signals. The relay detects the faulted generated super-imposed current signals. The transient polarity identification algorithm is then applied to the super-imposed signals to identify the polarity of the signal detected. The Network Protection Unit can collect all the transient polarity current signals under its protection area. Then The direction of a fault is determined by comparison of the polarity of the signals derived from all the line sections connected to the substation. The actual faulted section is identified by the Network Protection Unit through comparing the directional information from various stations. Simulation results presented in the paper demonstrate the feasibility of the scheme.

Suitable location and optimal sizing are impact on voltage stability margin of the distributed system. It is important to accurately simulate the random output active power of Distributed Generation (DG). In order to model uncertainties of intermittent distributed generation and load, this paper proposes a multi-scenario tree model of wind-photovoltaic-load using multiple scenarios technique based on the Wasserstein distance metrics, which generates asymptotically optimal scenario. And in this paper, a multi-objective optimizes control model with scenario tree is presented, which including objectives that are the total active power losses and the voltage deviations of the bus. Moreover, a new hybrid Honey Bee Mating Optimization and Particle Swarm Optimization (HBMO-PSO) algorithm is proposed to solved the problems. In the HBMO-PSO algorithm, the mating process is corrected, which the PSO algorithm is combined with the HBMO algorithm to improve the performance of HBMO. Finally, a typical IEEE 33-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method. Simulation results illustrate the correctness and adaptability of the proposed model and the improved algorithm.

Cloud computing contains a huge amount of data, which are featured as being widely distributed, heterogeneous, and dynamic. Thus, aiming at how to mine useful parts in these information, this paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k frequency set and uses the method of generating association rules to improve effectiveness of data mining. Simulation experiments show that mining algorithm in this paper is highly effective and suitable for data mining in the context of cloud computing.

Text understanding is a hot research in Natural Language Processing and Information Retrieval. In recent years, it has received wide attention and research. In the era of big data, Understanding text in large-scale datasets is a challenge. Although the earliest systems designed for these workloads, such as MapReduce, gave users a powerful, but low-level, procedural programming interface. So, MapReduce doesn’t compose well for lager text applications. Recently, Spark, an in-memory cluster-computing platform, has been proposed. It has emerged as a popular framework for large-scale data processing and analytics. It provides a general-purpose efficient cluster computing engine and simpler for the end users. In this work, we consider using Vector Space Model (VSM) and TF-IDF weighting schema and feature hashing feature extraction techniques in order to solve the problem of large-scale text data similarity computing by Spark. As a result, Experimental results that using Spark in order to solve document similarity computation problems as soon as quickly by 20Newsgroups. In additions, It is more benefit from document classification and clustering of machine learning tasks.

In this paper, network reconfiguration has been conducted to improve the power quality and reliability of distribution systems in presence of distributed generations. In this case, network reconfiguration is analyzed for different indices. The most important indices are the number of propagated voltage sags (N sag), sustained average interruption frequency index, average system interruption frequency index and momentary average interruption frequency index. The Imperialist competitive algorithm (ICA) is hired to find the optimal configuration of the distribution system in order to minimize the mentioned indices. Simulation result is discussed with and without distributed generation.

Synesthesia effect between music and color is a researching direction which is concerned much. The problem of mismatching between music playing and color had always existed in applications. On the basis of researching music and color, colors which are corresponding to various chords respectively are determined, and the frequency spectrum analysis on chords is performed. The real-time frequency domain transformation to time domain audio signal being played is carried out. The closest chord to audio signal being played can be identified via comparison between a chord spectrum and the spectrum of audio signal being played, and the color corresponding to the audio signal is determined. To improve the accuracy of recognition the method is investigated with which wavelet transform is adopted to de-noise processing of audio signal. Through tests on the audio experiment platform, color mixed effect test, optimization of the audio signal processing parameters, and processing time sequence scheming have been performed. A novel audio player is developed in application to realize the optimal RGB-LED mixed light which automatically presents matching color effect for arbitrary music being played. The research can reveal relationships between music and color from the perspective of frequency spectrum, promote the development of the color music, which has broad application prospects in urban landscape, stage lighting, and psychotherapy.

An extensional method for existing business processes is proposed to adapt new environments based on process variants in this paper. The core processes describing maximal commonalities of process variants are introduced according to inheritance relation. Then, extensional business processes are defined based on the core processes. The algorithms of the core processes and extensional business are given and proved. And the soundness of two kinds of processes is justified. An example shows that extensional business processes can adapt to the new environment better than the original process.

Prediction of links or relations between the objects in any network is no longer a new task these days; in fact it has become a high rated area of research and has attracted many researchers seeking their contribution to the mentioned area. Research has seen an exponential growth over the passing years, and the active researchers do not hesitate in linking with fellow researchers working in same domain irrespective of their geographic location. However this in turn has generated a very complex network of objects and links which are needed to be analyzed and dealt with. Prediction of co-authorship is the sub domain of link prediction and with the increasing complexity of co-authorship network the authors are treated as heterogeneous entity not as homogeneous ones. The rule is simple analyze the data preprocess it, train the classifier according to desired classification rules and then get the classified form of data. But irrelevant features always reflect various impacts and issues on generation of a classifier and consequently the impact is sustained to further classification results. Therefore, this paper proposes streaming classification algorithm combined with Correlation based Feature selection as a solution to the stated problem. The consistent and relevant features are selected with the help of feature selection algorithm and then these features are classified with the help of streaming classification algorithm- Very Fast Decision Tree (VFDT). VFDT is a streaming classification algorithm and it takes the dataset in the form of continuous stream as an input. Finally the effectiveness of the proposed algorithm can be seen in the experimental results.

With the development of P2P technology, more and more distributed systems are applying it for deploying large-scale storage infrastructure. However, maintaining desirable data availability in P2P environments is still an opening issue because of the vulnerability of participated peers. In this paper, a novel replication model is proposed to improving the data availability of P2P storage platforms. In the proposed replication model, the probability of peer’s failure is estimated by semi-Markov chain, which enables us to significantly improving the prediction accuracy of peer’s failure in a given period. Massive experiments are conducted in a real-world P2P platform to examining the performance of the proposed replication model, and the results indicate that it can achieve better tradeoffs between performance and data availability comparing with other replication schemes.

As a commercial distributed computing mode, cloud computing needs to meet the quality of service (QoS) requirement of users, which is its top priority. However, cloud computing service providers also need to consider how to reduce the overhead of data center, and keep load balancing is one of the key points to maximize the use of the resource in the data center. In this paper, we propose an improved multi-objective niched Pareto genetic algorithm (NPGA) to take load balancing into consideration without affecting performance of time consumption and financial cost of handling the user’s cloud computing tasks by presenting the load balancing shift mutation operator. The simulation results and analysis show that the proposed algorithm performs better than NPGA in maintaining the diversity and the distribution of the Pareto-optimal solutions in the cloud tasks scheduling under the same population size and evolution generation.

Under the environment of cloud manufacturing, in view of the self-organization of manufacturing resources based on the ideas of the hybrid P2P and Chord agreement, three layer network models of manufacturing resources have been constructed. According to the structure of geographical area, the bottom centralized resources ring was built in each area; logic ring was structured by the gathering nodes, namely regional resources managers; logic main ring was formed by super nodes. And logic rings are DHT overlay, which are two layers of network. This paper expounds the dynamic organization way for manufacturing resources of two layers of DHT and the layered trust mechanism of the gathering network. In the end, the prototype instance of the resources cloud was presented.

Due to the complexity of modern manufacturing and mechanical equipments, it is difficult for equipment users or maintainers to accomplish fault diagnosis independently. In this paper, the development history of fault diagnosis technology is surveyed and investigated, especially remote fault diagnosis system based on Internet in detail. Then, a remote fault diagnosis system based on grid computing technology is proposed to enable collaborative resource sharing and problem solving among multiple equipment suppliers and equipment users by integrating all kinds of diagnostic resources. The architecture of this fault diagnosis system is presented, as well as the Client-Master-Worker computation model and the diagnostic workflow. Finally, a prototype system is implemented using the data-driven middleware-BitDew, in which multiple fault diagnosis grid services are integrated in a unified Web portal. The case study of data-driven fault data analysis is conducted in our prototype which has proved the effectiveness of the system.

Available bandwidth is one the key challenges that is present in every new developed standard in the computer networks. There are many proposed methodologies in the existing research literature for measuring the available bandwidth. In this paper we will present summary of the main contributions in this field, research problems and solutions and we will present possible ways for improving the suggested solutions in the existing literature. Furthermore, survey of the most important tools for measuring the available bandwidth in wired and wireless networks is presented in details.

Recently, cloud computing has becoming a promising networking infrastructure paradigm which enable us to deploy large-scale applications in a cost-effective manner. However, many existing cloud platforms are designed for supporting commercial applications instead of large-scale scientific computing workloads. As a result, the effectiveness of running such kind of applications on cloud platforms is still an opening issue, especially when the performance penalties introduced by virtualization technology is taken into consideration. In this work, we take effects on analyzing the scheduling performance of cloud platforms for Parameter Sweep Applications (PSA), which is one of most used program models in large-scale scientific computing applications. All the experiments are conducted on our integrated performance evaluation middleware. The experimental results indicate that many conventional scheduling algorithms which are effective in classic distributed systems (i.e. grid, cluster) need to take into account the negative effects introduced by virtualization technology. In addition, the experimental results also indicate some useful hints for improving the scheduling performance of PSA workloads in cloud environments.

For the large scale services with high-dimensional QoS attributes and distributed environment, traditional service selection approaches are faced with unprecedented challenges in terms of efficiency and performance of QoS. To address these challenges, we propose a three-phase large scale Skyline service selection framework for service composition in clouds. This framework adopts distributed parallel Skyline computation with MapReduce to prune redundant candidate services, and employs parallel multi-objective optimization algorithm based on MapReduce to select Skyline services from the tremendous amount of Skyline services warehouse for composing single service into a set of more powerful Skyline composite services, then applies Top-k query processing technology or multiple attribute decision making support method to select k Skyline composite services from the set of Skyline composite services. Through theoretical analysis, the framework can efficiently solve the service selection problem with large scale services, high-dimensional QoS in cloud computing environment, and quickly generate better composite services with the global optimal QoS.

Now-a-days Mobile-Cloud Computing is a global computing infrastructure. Mobile-Cloud computing is the combination of mobile computing and cloud computing. Today’s smart phones are facing problem of small size and low battery life. One of the solutions to the problem of size of the mobile devices is by using the mobile device via extra touchpad device either by wireless fidelity network or Bluetooth. Low battery can be replaced by a powerful battery. The problem in cloud network is the higher response time of nodes while performing data communication through co-operative caching. This problem can be overcome by placing a Central Controller device as a hub in star topology network. The Central Controller device keeps track of the status of all other devices during the communication by running passive scan over the devices.

In the service composition life cycle, the target between service requester and service provider is quite different, which will cause the Web service SLA negotiation problem. The implementation of Web service relies on the interaction between the service provider and the service requester. In this article, the game model of Web Service negotiation process is proposed and solutions of the multi-participant and dynamic game model are gave. Then, we obtained the effective method of SLA generation. Experiments show that our method will ensure the common interests both service requesters and service providers.

The amount of direct runoff present in a watershed indicates the hydrological condition of the area, reflecting the characteristics of land use, soil and rainfall distribution. This study aims to measure the hydrological condition of Vientiane city by examining the runoff potential. This study obtained direct runoff from storm rainfall for the city area which covered the Mark-Hiao River watershed. The rainfall-runoff calculation is useful for future design of drainage networks within a watershed to support flooding management. The Geographical Information System (GIS) software is the main technology used in this study for the purposes of assigning the estimating process, watershed delineation, extracting hydrologic parameters and mapping. There are two main parts discussed with useful results in this study, delineating the river watershed and estimating weighted direct runoff for a watershed area. The result of delineating river watershed process, showing the changing elevation data achieved by combining the two elevation data, Shuttle Radar Topography Mission 30m Digital Elevation Model (STRM 30m DEM), and digital contour data (Vientiane database), performed well in terms of representing surface flow networks and their topography. The calculated average of direct runoff in each small sub-watershed estimated by using Weigh-Q method of the National Resources Conservation Service (NRCS) very well showed the poor to good hydrologic condition in each specific area in 1-to5-day durations of storm events.

The morphology of magnetic equipotential lines in axisymmetric magnetic mirror field was analyzed. Based on the magnetic fluid theory of electrons, an electron cloud model formed by magnetic mirror field confined electrons was built in this paper. And two-dimension differential potential equations were solved by using Runge-Kutta method. The calculation gave the Boltzmann distribution and potential distribution of electron density. The principle of both electron escape and confinement were analyzed in magnetic mirror field environment, the runaway electron current situation of the electronic cloud boundary was analyzed. Based on the leakage current of electron boundary, the balance of ionization electrons and escape electrons was used to deprive the confined region leakage current factor relationship equation in magnetic mirror environment. The relationship between the runaway electron current and the runaway factor, the effects electron temperature, electron velocity, and magnetic field lines on runaway factor were given. Furthermore, the minimum confined magnetic field needed to build electron cloud in magnetic mirror field was also given in this paper.

Load forecasting plays a major role in planning and operation of a power system. Many techniques are available in the literature among these neural networks, linear multiple regression, regression trees, curve fitting and averaging models are the most popular because these models gives accurate solutions with very less tolerable Least Mean Absolute Percent Error(MAPE). In this paper a comparative study was made between these forecasting models and it was found that when compared to the four independent models, the averaging model i.e. combination of Curve Fitting, Regression Trees & Neural Network gives less MAPE. MATLAB programming results validates that averaging model gives better performance than individual models.

There’re huge numbers of users and various tasks need to be handled in the cloud computing environment, the high effective task scheduling algorithm is one of the crucial problems that the cloud computing need to solve. Aiming to the model structure of cloud computing, in this article it introduces the Particle Swarm Optimization algorithm (PSO) and Ant Colony Optimization algorithm (ACO) to combine with optimized task scheduling algorithm. First it takes the particle swarm optimization algorithm to generate the initial scheduling results, and introduces the random inertia weight to improve the scheduling ability of the algorithm, then to take the generated results of improved particle swarm optimization algorithm as the initial pheromones of the ant colony algorithm to find out the optimal scheduling scheme, and use the elitist strategy and crossover operator in the genetic algorithm to improve the ant colony algorithm, among the algorithms to use multistage optimization algorithm to improve the operating efficiency. The experimental results show that under the same conditions, the total task completion time of improved algorithm has been reduced and its performance advantage are getting more obvious with the increased task measures.

In vehicular ad hoc network (VANETs), the applications mainly focused on the road and making their journey safe and comfortable to fulfill the users’ requirements. It is a challenging subclass of Mobile Ad-hoc Network, which empowers smart transmission among vehicles and between vehicles and roadside frameworks. It is an optimistic methodology for the Intelligent Transport System (ITS). There are numerous difficulties to be tended while utilizing VANET. It is flexible and has a high dynamic topology and flexibility which makes the conventional MANET protocols unacceptable for VANET. Trust is a principal element which is used to create a trustful environment to improve the security in vehicular networks. Trust can be calculated by observing the human behavior which creates a trusted communicating environment. In this paper, we define the architecture of VANET, then the communication establishment and various challenges. Some trust models had been defined in this paper to enforce honest information sharing between communicating nodes. Trust establishment is compulsory among communicating vehicles to build a secure messages exchange and reliability.