Generalized network improvement and packing problems
Holzhauser, Michael
2016-01-01
Michael Holzhauser discusses generalizations of well-known network flow and packing problems by additional or modified side constraints. By exploiting the inherent connection between the two problem classes, the author investigates the complexity and approximability of several novel network flow and packing problems and presents combinatorial solution and approximation algorithms. Contents Fractional Packing and Parametric Search Frameworks Budget-Constrained Minimum Cost Flows: The Continuous Case Budget-Constrained Minimum Cost Flows: The Discrete Case Generalized Processing Networks Convex Generalized Flows Target Groups Researchers and students in the fields of mathematics, computer science, and economics Practitioners in operations research and logistics The Author Dr. Michael Holzhauser studied computer science at the University of Kaiserslautern and is now a research fellow in the Optimization Research Group at the Department of Mathematics of the University of Kaiserslautern.
An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks
Directory of Open Access Journals (Sweden)
Naixue Xiong
2017-06-01
Full Text Available A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.
International Nuclear Information System (INIS)
Onomi, T; Nakajima, K
2014-01-01
We have proposed a superconducting Hopfield-type neural network for solving the N-Queens problem which is one of combinatorial optimization problems. The sigmoid-shape function of a neuron output is represented by the output of coupled SQUIDs gate consisting of a single-junction and a double-junction SQUIDs. One of the important factors for an improvement of the network performance is an improvement of a threshold characteristic of a neuron circuit. In this paper, we report an improved design of coupled SQUID gates for a superconducting neural network. A step-like function with a steep threshold at a rising edge is desirable for a neuron circuit to solve a combinatorial optimization problem. A neuron circuit is composed of two coupled SQUIDs gates with a cascade connection in order to obtain such characteristics. The designed neuron circuit is fabricated by a 2.5 kA/cm 2 Nb/AlOx/Nb process. The operation of a fabricated neuron circuit is experimentally demonstrated. Moreover, we discuss about the performance of the neural network using the improved neuron circuits and delayed negative self-connections.
Improving network management with Software Defined Networking
International Nuclear Information System (INIS)
Dzhunev, Pavel
2013-01-01
Software-defined networking (SDN) is developed as an alternative to closed networks in centers for data processing by providing a means to separate the control layer data layer switches, and routers. SDN introduces new possibilities for network management and configuration methods. In this article, we identify problems with the current state-of-the-art network configuration and management mechanisms and introduce mechanisms to improve various aspects of network management
vhv supply networks, problems of network structure
Energy Technology Data Exchange (ETDEWEB)
Raimbault, J
1966-04-01
The present and future power requirements of the Paris area and the structure of the existing networks are discussed. The various limitations that will have to be allowed for to lay down the structure of a regional transmission network leading in the power of the large national transmission network to within the Paris built up area are described. The theoretical solution that has been adopted, and the features of its final achievement, which is planned for about the year 2000, and the intermediate stages are given. The problem of the structure of the National Power Transmission network which is to supply the regional network was studied. To solve this problem, a 730 kV voltage network will have to be introduced.
Networks in social policy problems
Scotti, marco
2012-01-01
Network science is the key to managing social communities, designing the structure of efficient organizations and planning for sustainable development. This book applies network science to contemporary social policy problems. In the first part, tools of diffusion and team design are deployed to challenges in adoption of ideas and the management of creativity. Ideas, unlike information, are generated and adopted in networks of personal ties. Chapters in the second part tackle problems of power and malfeasance in political and business organizations, where mechanisms in accessing and controlling informal networks often outweigh formal processes. The third part uses ideas from biology and physics to understand global economic and financial crises, ecological depletion and challenges to energy security. Ideal for researchers and policy makers involved in social network analysis, business strategy and economic policy, it deals with issues ranging from what makes public advisories effective to how networks influenc...
NETWORKS AND QUALITY IMPROVEMENT
Directory of Open Access Journals (Sweden)
Miodrag Hadžistević
2009-12-01
Full Text Available Tools used in the past to analyze business value creation, such as value chain and process models, are simply too slow, inadequate, or inappropriate to address this new level of business complexity. In stead of that, company has to find way to create quality management system in a multi-layered supply chain. The problem can be solved by networking in the cluster. Cluster can be known as a competitive cooperation in the purpose to gain higher level of competitiveness and success. Bat there is another problem: Organization of the production process in a company is extremely complex process itself, and when we transfer it to the cluster level, we get a complex task which is difficult to solve. For that purpose, this paper analyses the conditions and possibilities that would enable those structures to adapt to changes in the surroundings - flexibility and management adequacy of production and organizational structures - by creating network value system.
Directory of Open Access Journals (Sweden)
Bisheng He
2014-01-01
Full Text Available A time-space network based optimization method is designed for high-speed rail train timetabling problem to improve the service level of the high-speed rail. The general time-space path cost is presented which considers both the train travel time and the high-speed rail operation requirements: (1 service frequency requirement; (2 stopping plan adjustment; and (3 priority of train types. Train timetabling problem based on time-space path aims to minimize the total general time-space path cost of all trains. An improved branch-and-price algorithm is applied to solve the large scale integer programming problem. When dealing with the algorithm, a rapid branching and node selection for branch-and-price tree and a heuristic train time-space path generation for column generation are adopted to speed up the algorithm computation time. The computational results of a set of experiments on China’s high-speed rail system are presented with the discussions about the model validation, the effectiveness of the general time-space path cost, and the improved branch-and-price algorithm.
Bayesian networks improve causal environmental ...
Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value
Networks in Social Policy Problems
Vedres, Balázs; Scotti, Marco
2012-08-01
1. Introduction M. Scotti and B. Vedres; Part I. Information, Collaboration, Innovation: The Creative Power of Networks: 2. Dissemination of health information within social networks C. Dhanjal, S. Blanchemanche, S. Clemençon, A. Rona-Tas and F. Rossi; 3. Scientific teams and networks change the face of knowledge creation S. Wuchty, J. Spiro, B. F. Jones and B. Uzzi; 4. Structural folds: the innovative potential of overlapping groups B. Vedres and D. Stark; 5. Team formation and performance on nanoHub: a network selection challenge in scientific communities D. Margolin, K. Ognyanova, M. Huang, Y. Huang and N. Contractor; Part II. Influence, Capture, Corruption: Networks Perspectives on Policy Institutions: 6. Modes of coordination of collective action: what actors in policy making? M. Diani; 7. Why skewed distributions of pay for executives is the cause of much grief: puzzles and few answers so far B. Kogut and J.-S. Yang; 8. Networks of institutional capture: a case of business in the State apparatus E. Lazega and L. Mounier; 9. The social and institutional structure of corruption: some typical network configurations of corruption transactions in Hungary Z. Szántó, I. J. Tóth and S. Varga; Part III. Crisis, Extinction, World System Change: Network Dynamics on a Large Scale: 10. How creative elements help the recovery of networks after crisis: lessons from biology A. Mihalik, A. S. Kaposi, I. A. Kovács, T. Nánási, R. Palotai, Á. Rák, M. S. Szalay-Beko and P. Csermely; 11. Networks and globalization policies D. R. White; 12. Network science in ecology: the structure of ecological communities and the biodiversity question A. Bodini, S. Allesina and C. Bondavalli; 13. Supply security in the European natural gas pipeline network M. Scotti and B. Vedres; 14. Conclusions and outlook A.-L. Barabási; Index.
Mapping social networks in software process improvement
DEFF Research Database (Denmark)
Tjørnehøj, Gitte; Nielsen, Peter Axel
2005-01-01
Software process improvement in small, agile organizations is often problematic. Model-based approaches seem to overlook problems. We have been seeking an alternative approach to overcome this through action research. Here we report on a piece of action research from which we developed an approach...... to map social networks and suggest how it can be used in software process improvement. We applied the mapping approach in a small software company to support the realization of new ways of improving software processes. The mapping approach was found useful in improving social networks, and thus furthers...... software process improvement....
Supply network configuration—A benchmarking problem
Brandenburg, Marcus
2018-03-01
Managing supply networks is a highly relevant task that strongly influences the competitiveness of firms from various industries. Designing supply networks is a strategic process that considerably affects the structure of the whole network. In contrast, supply networks for new products are configured without major adaptations of the existing structure, but the network has to be configured before the new product is actually launched in the marketplace. Due to dynamics and uncertainties, the resulting planning problem is highly complex. However, formal models and solution approaches that support supply network configuration decisions for new products are scant. The paper at hand aims at stimulating related model-based research. To formulate mathematical models and solution procedures, a benchmarking problem is introduced which is derived from a case study of a cosmetics manufacturer. Tasks, objectives, and constraints of the problem are described in great detail and numerical values and ranges of all problem parameters are given. In addition, several directions for future research are suggested.
Integrated network design and scheduling problems :
Energy Technology Data Exchange (ETDEWEB)
Nurre, Sarah G.; Carlson, Jeffrey J.
2014-01-01
We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.
Solving inversion problems with neural networks
Kamgar-Parsi, Behzad; Gualtieri, J. A.
1990-01-01
A class of inverse problems in remote sensing can be characterized by Q = F(x), where F is a nonlinear and noninvertible (or hard to invert) operator, and the objective is to infer the unknowns, x, from the observed quantities, Q. Since the number of observations is usually greater than the number of unknowns, these problems are formulated as optimization problems, which can be solved by a variety of techniques. The feasibility of neural networks for solving such problems is presently investigated. As an example, the problem of finding the atmospheric ozone profile from measured ultraviolet radiances is studied.
Artificial Astrocytes Improve Neural Network Performance
Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso
2011-01-01
Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157
Artificial astrocytes improve neural network performance.
Directory of Open Access Journals (Sweden)
Ana B Porto-Pazos
Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.
Artificial astrocytes improve neural network performance.
Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso
2011-04-19
Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao
2016-01-01
A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...
Solving Hub Network Problem Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Mursyid Hasan Basri
2012-01-01
Full Text Available This paper addresses a network problem that described as follows. There are n ports that interact, and p of those will be designated as hubs. All hubs are fully interconnected. Each spoke will be allocated to only one of available hubs. Direct connection between two spokes is allowed only if they are allocated to the same hub. The latter is a distinct characteristic that differs it from pure hub-and-spoke system. In case of pure hub-and-spoke system, direct connection between two spokes is not allowed. The problem is where to locate hub ports and to which hub a spoke should be allocated so that total transportation cost is minimum. In the first model, there are some additional aspects are taken into consideration in order to achieve a better representation of the problem. The first, weekly service should be accomplished. Secondly, various vessel types should be considered. The last, a concept of inter-hub discount factor is introduced. Regarding the last aspect, it represents cost reduction factor at hub ports due to economies of scale. In practice, it is common that the cost rate for inter-hub movement is less than the cost rate for movement between hub and origin/destination. In this first model, inter-hub discount factor is assumed independent with amount of flows on inter-hub links (denoted as flow-independent discount policy. The results indicated that the patterns of enlargement of container ship size, to some degree, are similar with those in Kurokawa study. However, with regard to hub locations, the results have not represented the real practice. In the proposed model, unsatisfactory result on hub locations is addressed. One aspect that could possibly be improved to find better hub locations is inter-hub discount factor. Then inter-hub discount factor is assumed to depend on amount of inter-hub flows (denoted as flow-dependent discount policy. There are two discount functions examined in this paper. Both functions are characterized by
SCM: A method to improve network service layout efficiency with network evolution
Zhao, Qi; Zhang, Chuanhao
2017-01-01
Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently. PMID:29267299
SCM: A method to improve network service layout efficiency with network evolution.
Zhao, Qi; Zhang, Chuanhao; Zhao, Zheng
2017-01-01
Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of "software defined network + network function virtualization" (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.
Hub location problems in transportation networks
DEFF Research Database (Denmark)
Gelareh, Shahin; Nickel, Stefan
2011-01-01
In this paper we propose a 4-index formulation for the uncapacitated multiple allocation hub location problem tailored for urban transport and liner shipping network design. This formulation is very tight and most of the tractable instances for MIP solvers are optimally solvable at the root node....... also introduce fixed cost values for Australian Post (AP) dataset....
Improving information filtering via network manipulation
Zhang, Fuguo; Zeng, An
2012-12-01
The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.
A matheuristic for the liner shipping network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy; Pisinger, David
We present a matheuristic, an integer programming based heuristic, for the liner shipping network design problem. This problem consists of finding a set of container shipping routes defining a capacitated network for cargo transport. The objective is to maximize the revenue of cargo transport...... the available fleet of container vessels. The cargo transports make extensive use of transshipments between routes and the number of transshipments of the cargo flow is decisive for network profitability. Computational results are reported for the benchmark suite LINER-LIB 2012 following the industry standard...... of weekly departures on every schedule. The heuristic shows overall good performance and is able to find high quality solutions within competitive execution times. The matheuristic can also be applied as a decision support tool to improve an existing network by optimizing on a designated subset...
Solving Dynamic Battlespace Movement Problems Using Dynamic Distributed Computer Networks
National Research Council Canada - National Science Library
Bradford, Robert
2000-01-01
.... The thesis designs a system using this architecture that invokes operations research network optimization algorithms to solve problems involving movement of people and equipment over dynamic road networks...
Network Performance Improvement under Epidemic Failures in Optical Transport Networks
DEFF Research Database (Denmark)
Fagertun, Anna Manolova; Ruepp, Sarah Renée
2013-01-01
In this paper we investigate epidemic failure spreading in large- scale GMPLS-controlled transport networks. By evaluating the effect of the epidemic failure spreading on the network, we design several strategies for cost-effective network performance improvement via differentiated repair times....... First we identify the most vulnerable and the most strategic nodes in the network. Then, via extensive simulations we show that strategic placement of resources for improved failure recovery has better performance than randomly assigning lower repair times among the network nodes. Our OPNET simulation...... model can be used during the network planning process for facilitating cost- effective network survivability design....
Evolving neural networks for strategic decision-making problems.
Kohl, Nate; Miikkulainen, Risto
2009-04-01
Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems-such as those involving strategic decision-making-have remained difficult for neuroevolution to solve. This paper evaluates the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. A method for measuring fracture using the concept of function variation is proposed and, based on this concept, two methods for dealing with fracture are examined: neurons with local receptive fields, and refinement based on a cascaded network architecture. Experiments in several benchmark domains are performed to evaluate how different levels of fracture affect the performance of neuroevolution methods, demonstrating that these two modifications improve performance significantly. These results form a promising starting point for expanding neuroevolution to strategic tasks.
Improvement on LEACH Agreement of Mine Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Yun-xiang Liu
2017-05-01
Full Text Available Based on the characteristics of wireless sensor network communication in mine, LEACH protocol clustering is optimized, and the factors of energy and distance are considered fully. The selection of cluster head nodes is optimized, and a routing algorithm based on K-means ++ clustering is proposed. The problem of uneven distribution of cluster head nodes, uneven energy consumption and network stability in LEACH algorithm is improved effectively. Simulation results show that the proposed algorithm can improve the energy consumption of the whole network and improve the energy utilization rate, extending the network life cycle effectively.
Problem Diagnosis in Software Process Improvement
DEFF Research Database (Denmark)
Iversen, Jakob; Nielsen, Peter Axel; Nørbjerg, Jacob
1998-01-01
This paper addresses software process improvement. In particular it reports on action research undertaken to understand the problems with software processes of a large Danish company. It is argued that in order to understand what the specific problems are we may, on the one hand, rely on process...... to enable process improvement to effectively take place. It is argued that problem diagnosis a useful approach and that it has advantages over model-based assessment....... models like CMM or Bootstrap. On the other hand, we may also see the specific and unique features of software processes in this company through what we call problem diagnosis. Problem diagnosis deals with eliciting problems perceived by software project managers and with forming commitment structures...
A Mathematical Model to Improve the Performance of Logistics Network
Directory of Open Access Journals (Sweden)
Muhammad Izman Herdiansyah
2012-01-01
Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization
A theory of intelligence: networked problem solving in animal societies
Shour, Robert
2009-01-01
A society's single emergent, increasing intelligence arises partly from the thermodynamic advantages of networking the innate intelligence of different individuals, and partly from the accumulation of solved problems. Economic growth is proportional to the square of the network entropy of a society's population times the network entropy of the number of the society's solved problems.
Game Theoretic Problems in Network Economics and Mechanism Design Solutions
Narahari, Y; Narayanam, Ramasuri; Prakash, Hastagiri
2009-01-01
Explores game theoretic modeling and mechanism design for problem solving in Internet and network economics. This monograph contains an exposition of representative game theoretic problems in three different network economics situations and a systematic exploration of mechanism design solutions to these problems.
Solving Minimum Cost Multi-Commodity Network Flow Problem ...
African Journals Online (AJOL)
ADOWIE PERE
2018-03-23
Mar 23, 2018 ... network-based modeling framework for integrated fixed and mobile ... Minimum Cost Network Flow Problem (MCNFP) and some ..... Unmanned Aerial Vehicle Routing in Traffic. Incident ... Ph.D. Thesis, Dept. of Surveying &.
Could HPS Improve Problem-Solving?
Coelho, Ricardo Lopes
2013-05-01
It is generally accepted nowadays that History and Philosophy of Science (HPS) is useful in understanding scientific concepts, theories and even some experiments. Problem-solving strategies are a significant topic, since students' careers depend on their skill to solve problems. These are the reasons for addressing the question of whether problem solving could be improved by means of HPS. Three typical problems in introductory courses of mechanics—the inclined plane, the simple pendulum and the Atwood machine—are taken as the object of the present study. The solving strategies of these problems in the eighteenth and nineteenth century constitute the historical component of the study. Its philosophical component stems from the foundations of mechanics research literature. The use of HPS leads us to see those problems in a different way. These different ways can be tested, for which experiments are proposed. The traditional solving strategies for the incline and pendulum problems are adequate for some situations but not in general. The recourse to apparent weights in the Atwood machine problem leads us to a new insight and a solving strategy for composed Atwood machines. Educational implications also concern the development of logical thinking by means of the variety of lines of thought provided by HPS.
Inverse kinematics problem in robotics using neural networks
Choi, Benjamin B.; Lawrence, Charles
1992-01-01
In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.
Solving network design problems via decomposition, aggregation and approximation
Bärmann, Andreas
2016-01-01
Andreas Bärmann develops novel approaches for the solution of network design problems as they arise in various contexts of applied optimization. At the example of an optimal expansion of the German railway network until 2030, the author derives a tailor-made decomposition technique for multi-period network design problems. Next, he develops a general framework for the solution of network design problems via aggregation of the underlying graph structure. This approach is shown to save much computation time as compared to standard techniques. Finally, the author devises a modelling framework for the approximation of the robust counterpart under ellipsoidal uncertainty, an often-studied case in the literature. Each of these three approaches opens up a fascinating branch of research which promises a better theoretical understanding of the problem and an increasing range of solvable application settings at the same time. Contents Decomposition for Multi-Period Network Design Solving Network Design Problems via Ag...
An Improved Crow Search Algorithm Applied to Energy Problems
Directory of Open Access Journals (Sweden)
Primitivo Díaz
2018-03-01
Full Text Available The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I the awareness probability (AP and (II the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.
Directory of Open Access Journals (Sweden)
M. A. Karakuts
2015-01-01
Full Text Available The basic problems of route network and aircraft fleet optimization and its role in airline strategic planning are considered. Measures to improve the methods of its implementation are proposed.
PRIVACY PROTECTION PROBLEMS IN SOCIAL NETWORKS
OKUR, M. Cudi
2011-01-01
Protecting privacy has become a major concern for most social network users because of increased difficulties of controlling the online data. This article presents an assessment of the common privacy related risks of social networking sites. Open and hidden privacy risks of active and passive online profiles are examined and increasing share of social networking in these phenomena is discussed. Inadequacy of available legal and institutional protection is demonstrated and the effectiveness of...
An Improved Harmony Search Algorithm for Power Distribution Network Planning
Directory of Open Access Journals (Sweden)
Wei Sun
2015-01-01
Full Text Available Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.
On the Update Problems for Software Defined Networks
Directory of Open Access Journals (Sweden)
V. A. Zakharov
2014-01-01
Full Text Available The designing of network update algorithms is urgent for the development of SDN control software. A particular case of Network Update Problem is that of restoring seamlessly a given network configuration after some packet forwarding rules have been disabled (say, at the expiry of their time-outs. We study this problem in the framework of a formal model of SDN, develop correct and safe network recovering algorithms, and show that in general case there is no way to restore network configuration seamlessly without referring to priorities of packet forwarding rules.
Secure Wireless Sensor Networks: Problems and Solutions
Directory of Open Access Journals (Sweden)
Fei Hu
2003-08-01
Full Text Available As sensor networks edge closer towards wide-spread deployment, security issues become a central concern. So far, the main research focus has been on making sensor networks feasible and useful, and less emphasis was placed on security. This paper analyzes security challenges in wireless sensor networks and summarizes key issues that should be solved for achieving the ad hoc security. It gives an overview of the current state of solutions on such key issues as secure routing, prevention of denial-of-service and key management service. We also present some secure methods to achieve security in wireless sensor networks. Finally we present our integrated approach to securing sensor networks.
A matheuristic for the liner shipping network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy
2012-01-01
for revenue and transshipment of cargo along with in/decrease of vessel- and operational cost for the current solution. The evaluation functions may be used by heuristics in general to evaluate changes to a network design without solving a large scale multicommodity flow problem.......We present a matheuristic, an integer programming based heuristic, for the Liner Shipping Network Design Problem. The heuristic applies a greedy construction heuristic based on an interpretation of the liner shipping network design problem as a multiple quadratic knapsack problem. The construction...
Network Monitoring as a Streaming Analytics Problem
Gupta, Arpit
2016-11-02
Programmable switches make it easier to perform flexible network monitoring queries at line rate, and scalable stream processors make it possible to fuse data streams to answer more sophisticated queries about the network in real-time. Unfortunately, processing such network monitoring queries at high traffic rates requires both the switches and the stream processors to filter the traffic iteratively and adaptively so as to extract only that traffic that is of interest to the query at hand. Others have network monitoring in the context of streaming; yet, previous work has not closed the loop in a way that allows network operators to perform streaming analytics for network monitoring applications at scale. To achieve this objective, Sonata allows operators to express a network monitoring query by considering each packet as a tuple and efficiently partitioning each query between the switches and the stream processor through iterative refinement. Sonata extracts only the traffic that pertains to each query, ensuring that the stream processor can scale traffic rates of several terabits per second. We show with a simple example query involving DNS reflection attacks and traffic traces from one of the world\\'s largest IXPs that Sonata can capture 95% of all traffic pertaining to the query, while reducing the overall data rate by a factor of about 400 and the number of required counters by four orders of magnitude. Copyright 2016 ACM.
Improvement on the Performance of Canal Network and Method of ...
African Journals Online (AJOL)
This paper presents the required improvement on the performance of canal network and method of on-farm water application systems at Tunga-Kawo irrigation scheme, Wushishi, Niger state. The problems of poor delivery of water to the farmland were identified to include erosion of canal embarkment, lack of water ...
Problem solving for wireless sensor networks
Garcia-Hernando, Ana-Belen; Lopez-Navarro, Juan-Manuel; Prayati, Aggeliki; Redondo-Lopez, Luis
2008-01-01
Wireless Sensor Networks (WSN) is an area of huge research interest, attracting substantial attention from industry and academia for its enormous potential and its inherent challenges. This reader-friendly text delivers a comprehensive review of the developments related to the important technological issues in WSN.
Network Monitoring as a Streaming Analytics Problem
Gupta, Arpit; Birkner, Rü diger; Canini, Marco; Feamster, Nick; Mac-Stoker, Chris; Willinger, Walter
2016-01-01
, processing such network monitoring queries at high traffic rates requires both the switches and the stream processors to filter the traffic iteratively and adaptively so as to extract only that traffic that is of interest to the query at hand. Others have
Bidding for surplus in network allocation problems
Slikker, M.
2007-01-01
In this paper we study non-cooperative foundations of network allocation rules. We focus on three allocation rules: the Myerson value, the position value and the component-wise egalitarian solution. For any of these three rules we provide a characterization based on component efficiency and some
Improving Earth/Prediction Models to Improve Network Processing
Wagner, G. S.
2017-12-01
The United States Atomic Energy Detection System (USAEDS) primaryseismic network consists of a relatively small number of arrays andthree-component stations. The relatively small number of stationsin the USAEDS primary network make it both necessary and feasibleto optimize both station and network processing.Station processing improvements include detector tuning effortsthat use Receiver Operator Characteristic (ROC) curves to helpjudiciously set acceptable Type 1 (false) vs. Type 2 (miss) errorrates. Other station processing improvements include the use ofempirical/historical observations and continuous background noisemeasurements to compute time-varying, maximum likelihood probabilityof detection thresholds.The USAEDS network processing software makes extensive use of theazimuth and slowness information provided by frequency-wavenumberanalysis at array sites, and polarization analysis at three-componentsites. Most of the improvements in USAEDS network processing aredue to improvements in the models used to predict azimuth, slowness,and probability of detection. Kriged travel-time, azimuth andslowness corrections-and associated uncertainties-are computedusing a ground truth database. Improvements in station processingand the use of improved models for azimuth, slowness, and probabilityof detection have led to significant improvements in USADES networkprocessing.
Problems of improving the investing process management in NPP construction
International Nuclear Information System (INIS)
Denisov, G.A.
1986-01-01
Problems of development of the optimal system for the investing process management in NPP construction are discussed. It includes 3 steps: design construction ( including building structure and equipment production ), and achievement of designed technical and economical indices, during reactor start-up. The method for estimating the interest of each participator of the intensing process and developing the optimal solution, that is capable to approach these interests, is suggested. The conclusion is made that it is necessary to develop and confirm the branch standard, which should include a complex amalgamated network of works to improve the organization of the investing process
Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.
Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang
2016-01-01
Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.
Network capacity auctions: promise and problems
International Nuclear Information System (INIS)
Newbery, David M.
2003-01-01
Well-designed auctions work favorably for allocating idiosyncratic properties efficiently. Auctions are used to allocate entry capacity for United Kingdom gas and inter-connector capacity for electricity in several European Union countries and can work well for allocating existing capacity, though careful auction design is needed to mitigate potential market power. Using auction prices to guide investment decisions in networks is problematic if bidders fear that sub-optimal investment will be compensated by regulatory fiat, lowering future capacity values. (Author)
SOCIAL NETWORK OPTIMIZATION A NEW METHAHEURISTIC FOR GENERAL OPTIMIZATION PROBLEMS
Directory of Open Access Journals (Sweden)
Hassan Sherafat
2017-12-01
Full Text Available In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.
Improving mathematical problem solving : A computerized approach
Harskamp, EG; Suhre, CJM
Mathematics teachers often experience difficulties in teaching students to become skilled problem solvers. This paper evaluates the effectiveness of two interactive computer programs for high school mathematics problem solving. Both programs present students with problems accompanied by instruction
Controlled neural network application in track-match problem
International Nuclear Information System (INIS)
Baginyan, S.A.; Ososkov, G.A.
1993-01-01
Track-match problem of high energy physics (HEP) data handling is formulated in terms of incidence matrices. The corresponding Hopfield neural network is developed to solve this type of constraint satisfaction problems (CSP). A special concept of the controlled neural network is proposed as a basis of an algorithm for the effective CSP solution. Results of comparable calculations show the very high performance of this algorithm against conventional search procedures. 8 refs.; 1 fig.; 1 tab
Cellular neural networks for the stereo matching problem
International Nuclear Information System (INIS)
Taraglio, S.; Zanela, A.
1997-03-01
The applicability of the Cellular Neural Network (CNN) paradigm to the problem of recovering information on the tridimensional structure of the environment is investigated. The approach proposed is the stereo matching of video images. The starting point of this work is the Zhou-Chellappa neural network implementation for the same problem. The CNN based system we present here yields the same results as the previous approach, but without the many existing drawbacks
Problems in the Deployment of Learning Networks In Small Organizations
Shankle, Dean E.; Shankle, Jeremy P.
2006-01-01
Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:
Solving constraint satisfaction problems with networks of spiking neurons
Directory of Open Access Journals (Sweden)
Zeno eJonke
2016-03-01
Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.
Improving Network Security with Watchguard UTM Firewall
Lehmonen, Harri
2017-01-01
After working many years in close contact with end customers, the author has noticed that Finnish small and mid-size businesses are not paying as much attention to network security threats as they should. Even though different kind of security threats are probably present and reported often in news, very basic security practices are discarded and no resources are spent advancing the issue. The topic of this thesis is Improving Network Security with Watchguard’s UTM Firewall. It focuses o...
Improving ecosystem service frameworks to address wicked problems
Directory of Open Access Journals (Sweden)
Kathryn K. Davies
2015-06-01
Full Text Available Complex problems often result from the multiple interactions between human activities and ecosystems. The interconnected nature of ecological and social systems should be considered if these "wicked problems" are to be addressed. Ecosystem service approaches provide an opportunity to link ecosystem function with social values, but in practice the essential role that social dynamics play in the delivery of outcomes remains largely unexplored. Social factors such as management regimes, power relationships, skills, and values, can dramatically affect the definition and delivery of ecosystem services. Input from a diverse group of stakeholders improves the capacity of ecosystem service approaches to address wicked problems by acknowledging diverse sets of values and accounting for conflicting world views. Participatory modeling can incorporate both social and ecological dynamics into decision making that involves stakeholders, but is itself a complex social undertaking that may not yield precise or predictable outcomes. We explore the efficacy of different types of participatory modeling in relation to the integration of social values into ecosystem services frameworks and the generation of four important elements of social capital needed to address wicked problems: enhancing social learning and capacity building; increasing transparency; mediating power; and building trust. Our findings indicate that mediated modeling, group mapping, and mental/conceptual modeling are likely to generate elements of social capital that can improve ecosystem service frameworks. Participatory simulation, system dynamic modeling, and Bayesian belief networks, if utilized in isolation, were found to have a low likelihood of generating the social capital needed to improve ecosystem services frameworks. Scenario planning, companion modeling, group model building, and participatory mapping all generate a moderate to high level of social capital elements that improve the
Algorithms for Scheduling and Network Problems
1991-09-01
time. We already know, by Lemma 2.2.1, that WOPT = O(log( mpU )), so if we could solve this integer program optimally we would be done. However, the...Folydirat, 15:177-191, 1982. [6] I.S. Belov and Ya. N. Stolin. An algorithm in a single path operations scheduling problem. In Mathematical Economics and
Analysing Stagecoach Network Problem Using Dynamic ...
African Journals Online (AJOL)
In this paper we present a recursive dynamic programming algorithm for solving the stagecoach problem. The algorithm is computationally more efficient than the first method as it obtains its minimum total cost using the suboptimal policies of the different stages without computing the cost of all the routes. By the dynamic ...
A neural network approach to the orienteering problem
Energy Technology Data Exchange (ETDEWEB)
Golden, B.; Wang, Q.; Sun, X.; Jia, J.
1994-12-31
In the orienteering problem, we are given a transportation network in which a start point and an end point are specified. Other points have associated scores. Given a fixed amount of time, the goal is to determine a path from start to end through a subset of locations in order to maximize the total path score. This problem has received a considerable amount of attention in the last ten years. The TSP is a variant of the orienteering problem. This paper applies a modified, continuous Hopfield neural network to attack this NP-hard optimization problem. In it, we design an effective energy function and learning algorithm. Unlike some applications of neural networks to optimization problems, this approach is shown to perform quite well.
Reconstructing the Hopfield network as an inverse Ising problem
International Nuclear Information System (INIS)
Huang Haiping
2010-01-01
We test four fast mean-field-type algorithms on Hopfield networks as an inverse Ising problem. The equilibrium behavior of Hopfield networks is simulated through Glauber dynamics. In the low-temperature regime, the simulated annealing technique is adopted. Although performances of these network reconstruction algorithms on the simulated network of spiking neurons are extensively studied recently, the analysis of Hopfield networks is lacking so far. For the Hopfield network, we found that, in the retrieval phase favored when the network wants to memory one of stored patterns, all the reconstruction algorithms fail to extract interactions within a desired accuracy, and the same failure occurs in the spin-glass phase where spurious minima show up, while in the paramagnetic phase, albeit unfavored during the retrieval dynamics, the algorithms work well to reconstruct the network itself. This implies that, as an inverse problem, the paramagnetic phase is conversely useful for reconstructing the network while the retrieval phase loses all the information about interactions in the network except for the case where only one pattern is stored. The performances of algorithms are studied with respect to the system size, memory load, and temperature; sample-to-sample fluctuations are also considered.
A improved Network Security Situation Awareness Model
Directory of Open Access Journals (Sweden)
Li Fangwei
2015-08-01
Full Text Available In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.
Improved Degree Search Algorithms in Unstructured P2P Networks
Directory of Open Access Journals (Sweden)
Guole Liu
2012-01-01
Full Text Available Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P networks. Breadth-first search (BFS and depth-first search (DFS are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD and memory function preference degree algorithm (PD. We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.
Networking to improve end of life care
2009-01-01
Network organisations are increasingly common in healthcare. This paper describes an example of clinically led networking, which improved end of life care (EOLC) in care homes, differentiating between a ‘network’ as a formal entity and the more informal process of ‘networking’. The paper begins with a brief discussion of networks and their development in healthcare, then an overview of EOLC policy, the case setting and methods. The paper describes four key features of this networking; (1) how it enabled discussions and implemented processes to help people address difficult taboos about dying; (2) how personal communication and ‘distributed leadership’ facilitated learning; (3) how EOLC occasionally lapsed during the handover of patient care, where personal relationship and communication were weaker; and (4) how successful learning and sharing of best practice was fragile and could be potentially undermined by wider financial pressures in the NHS. PMID:25949588
The Network Completion Problem: Inferring Missing Nodes and Edges in Networks
Energy Technology Data Exchange (ETDEWEB)
Kim, M; Leskovec, J
2011-11-14
Network structures, such as social networks, web graphs and networks from systems biology, play important roles in many areas of science and our everyday lives. In order to study the networks one needs to first collect reliable large scale network data. While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.
An Algorithm for the Mixed Transportation Network Design Problem.
Liu, Xinyu; Chen, Qun
2016-01-01
This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately.
An Algorithm for the Mixed Transportation Network Design Problem.
Directory of Open Access Journals (Sweden)
Xinyu Liu
Full Text Available This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA, for solving a mixed transportation network design problem (MNDP, which is generally expressed as a mathematical programming with equilibrium constraint (MPEC. The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE problem. The idea of the proposed solution algorithm (DDIA is to reduce the dimensions of the problem. A group of variables (discrete/continuous is fixed to optimize another group of variables (continuous/discrete alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems and DNDPs (discrete network design problems repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions. Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately.
Generative Adversarial Networks for Improving Face Classification
Natten, Jonas
2017-01-01
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Facial recognition can be applied in a wide variety of cases, including entertainment purposes and biometric security. In this thesis we take a look at improving the results of an existing facial recognition approach by utilizing generative adversarial networks to improve the existing dataset. The training data was taken from the LFW dataset[4] and was preprocessed using OpenCV[2] for...
Network information improves cancer outcome prediction.
Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael
2014-07-01
Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
Integrated Circuit Chip Improves Network Efficiency
2008-01-01
Prior to 1999 and the development of SpaceWire, a standard for high-speed links for computer networks managed by the European Space Agency (ESA), there was no high-speed communications protocol for flight electronics. Onboard computers, processing units, and other electronics had to be designed for individual projects and then redesigned for subsequent projects, which increased development periods, costs, and risks. After adopting the SpaceWire protocol in 2000, NASA implemented the standard on the Swift mission, a gamma ray burst-alert telescope launched in November 2004. Scientists and developers on the James Webb Space Telescope further developed the network version of SpaceWire. In essence, SpaceWire enables more science missions at a lower cost, because it provides a standard interface between flight electronics components; new systems need not be custom built to accommodate individual missions, so electronics can be reused. New protocols are helping to standardize higher layers of computer communication. Goddard Space Flight Center improved on the ESA-developed SpaceWire by enabling standard protocols, which included defining quality of service and supporting plug-and-play capabilities. Goddard upgraded SpaceWire to make the routers more efficient and reliable, with features including redundant cables, simultaneous discrete broadcast pulses, prevention of network blockage, and improved verification. Redundant cables simplify management because the user does not need to worry about which connection is available, and simultaneous broadcast signals allow multiple users to broadcast low-latency side-band signal pulses across the network using the same resources for data communication. Additional features have been added to the SpaceWire switch to prevent network blockage so that more robust networks can be designed. Goddard s verification environment for the link-and-switch implementation continuously randomizes and tests different parts, constantly anticipating
Neural network for solving convex quadratic bilevel programming problems.
He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie
2014-03-01
In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.
Second-order design problem in the Ancona geodetic network
International Nuclear Information System (INIS)
Baldi, P.; Ferrari, G.; Postpischl, D.; Unguendoli, M.
1980-01-01
In this note an examination is made of the control network installed in the Ancona area in 1975 for seismotectonic studies. From an analysis of the network there arises the possibility of achieving a considerable improvement in the results by considering a plan of work derived from the a priori analysis of the covariance matrix and improving the atmospheric data fo the correction of electronic distance measurements, by the use of meteorological balloons. (author)
Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization
Directory of Open Access Journals (Sweden)
Xing Xu
2014-04-01
Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.
Social networking sites: an adjunctive treatment modality for psychological problems.
Menon, Indu S; Sharma, Manoj Kumar; Chandra, Prabha S; Thennarasu, K
2014-07-01
Social networking is seen as a way to enhance social support and feeling of well-being. The present work explores the potentials of social networking sites as an adjunctive treatment modality for initiating treatment contact as well as for managing psychological problems. Interview schedule, Facebook intensity questionnaire were administered on 28 subjects with a combination of 18 males and 10 females. They were taken from the in-patient and out-patient psychiatry setting of the hospital. Facebook was the most popular sites and used to seek emotional support on the basis of the frequent updates of emotional content that users put in their profile; reconciliations, escape from the problems or to manage the loneliness; getting information about illness and its treatment and interaction with experts and also manifested as problematic use. It has implications for developing social networking based adjunctive treatment modality for psychological problems.
Observation and inverse problems in coupled cell networks
International Nuclear Information System (INIS)
Joly, Romain
2012-01-01
A coupled cell network is a model for many situations such as food webs in ecosystems, cellular metabolism and economic networks. It consists in a directed graph G, each node (or cell) representing an agent of the network and each directed arrow representing which agent acts on which. It yields a system of differential equations .x(t)=f(x(t)), where the component i of f depends only on the cells x j (t) for which the arrow j → i exists in G. In this paper, we investigate the observation problems in coupled cell networks: can one deduce the behaviour of the whole network (oscillations, stabilization, etc) by observing only one of the cells? We show that the natural observation properties hold for almost all the interactions f
CNNcon: improved protein contact maps prediction using cascaded neural networks.
Directory of Open Access Journals (Sweden)
Wang Ding
Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective
Pricing and Capacity Planning Problems in Energy Transmission Networks
DEFF Research Database (Denmark)
Villumsen, Jonas Christoffer
strategy. In the Nordic electricity system a market with zonal prices is adopted. We consider the problem of designing zones in an optimal way explicitly considering uncertainty. Finally, we formulate the integrated problem of pipeline capacity expansion planning and transmission pricing in natural gas...... necessitates a radical change in the way we plan and operate energy systems. Another paradigm change which began in the 1990’s for electricity systems is that of deregulation. This has led to a variety of different market structures implemented across the world. In this thesis we discuss capacity planning...... and transmission pricing problems in energy transmission networks. Although the modelling framework applies to energy networks in general, most of the applications discussed concern the transmission of electricity. A number of the problems presented involves transmission switching, which allows the operator...
Networking to Improve Nutrition Policy Research
Kim, Sonia A.; Blanck, Heidi M.; Cradock, Angie; Gortmaker, Steven
2015-01-01
Effective nutrition and obesity policies that improve the food environments in which Americans live, work, and play can have positive effects on the quality of human diets. The Centers for Disease Control and Prevention’s (CDC’s) Nutrition and Obesity Policy Research and Evaluation Network (NOPREN) conducts transdisciplinary practice-based policy research and evaluation to foster understanding of the effectiveness of nutrition policies. The articles in this special collection bring to light a...
An outer approximation method for the road network design problem.
Asadi Bagloee, Saeed; Sarvi, Majid
2018-01-01
Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well.
Networking to Improve Nutrition Policy Research.
Kim, Sonia A; Blanck, Heidi M; Cradock, Angie; Gortmaker, Steven
2015-09-10
Effective nutrition and obesity policies that improve the food environments in which Americans live, work, and play can have positive effects on the quality of human diets. The Centers for Disease Control and Prevention's (CDC's) Nutrition and Obesity Policy Research and Evaluation Network (NOPREN) conducts transdisciplinary practice-based policy research and evaluation to foster understanding of the effectiveness of nutrition policies. The articles in this special collection bring to light a set of policies that are being used across the United States. They add to the larger picture of policies that can work together over time to improve diet and health.
On generalizations of network design problems with degree bounds
Bansal, N.; Khandekar, R.; Könemann, J.; Nagarajan, V.; Peis, B.
2013-01-01
Iterative rounding and relaxation have arguably become the method of choice in dealing with unconstrained and constrained network design problems. In this paper we extend the scope of the iterative relaxation method in two directions: (1) by handling more complex degree constraints in the minimum
On generalizations of network design problems with degree bounds
N. Bansal (Nikhil); R. Khandekar; J. Könemann (Jochen); V. Nagarajan; B. Peis
2013-01-01
htmlabstractIterative rounding and relaxation have arguably become the method of choice in dealing with unconstrained and constrained network design problems. In this paper we extend the scope of the iterative relaxation method in two directions: (1) by handling more complex degree constraints in
Instruction Emphasizing Effort Improves Physics Problem Solving
Li, Daoquan
2012-01-01
Effectively using strategies to solve complex problems is an important educational goal and is implicated in successful academic performance. However, people often do not spontaneously use the effective strategies unless they are motivated to do so. The present study was designed to test whether educating students about the importance of effort in…
Network Analysis of Students' Use of Representations in Problem Solving
McPadden, Daryl; Brewe, Eric
2016-03-01
We present the preliminary results of a study on student use of representations in problem solving within the Modeling Instruction - Electricity and Magnetism (MI-E&M) course. Representational competence is a critical skill needed for students to develop a sophisticated understanding of college science topics and to succeed in their science courses. In this study, 70 students from the MI-E&M, calculus-based course were given a survey of 25 physics problem statements both pre- and post- instruction, covering both Newtonian Mechanics and Electricity and Magnetism (E&M). For each problem statement, students were asked which representations they would use in that given situation. We analyze the survey results through network analysis, identifying which representations are linked together in which contexts. We also compare the representation networks for those students who had already taken the first-semester Modeling Instruction Mechanics course and those students who had taken a non-Modeling Mechanics course.
Improved Extension Neural Network and Its Applications
Directory of Open Access Journals (Sweden)
Yu Zhou
2014-01-01
Full Text Available Extension neural network (ENN is a new neural network that is a combination of extension theory and artificial neural network (ANN. The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data and the borders between clusters (boundary data are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.
Augmented neural networks and problem structure-based heuristics for the bin-packing problem
Kasap, Nihat; Agarwal, Anurag
2012-08-01
In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 s per problem. We also discuss the computational complexity of our approach.
Mechler, G. E.
2002-05-01
Some television networks have displayed a propensity for producing specials of a pseudoscientific nature. The Fox Network has especially demonstrated this propensity. Its most notorious cases were ``Alien Autopsy" in the mid-90s and last Winter's ``Conspiracy Theory: Did we land on the moon?" Both have had effective critical responses from scientists and those responses are readily accessible on the Internet. But their existence is emblematic of the larger societal problem of large numbers of citizens not being able to discriminate between science and pseudoscience. Many educators hesitate to include critical examinations of pseudosciences because 1) They themselves are not well versed in these areas, and 2) they prefer to avoid possible controversy and upset with their credulous students. Fox Network's ``Conspiracy Theory: Did we land on the moon?" offers educators a rich example of televised pseudoscience that 1) can be rebutted in ways readily understandable by nonscience students and 2) will not result in throngs of offended students as this is not a particularly popular pseudoscience and few students will have an emotional investment in it. This oral presentation will cover the benefits of using this particular television program to demonstrate scientific critical examination of claims, raise their general level of informed skepticism, and make clear how susceptible people --they, themselves-- can be to pseudoscientific claims when one is not familiar with the relevant science. A computer-slide presentation of this critique is available to those interested. In addition, informal surveys were taken of two lab classes in which the program and critique were shown. Students' opinions of the moon-landings-were-a-hoax claim were taken before and after seeing the program and after the critique.
A Formal Model and Verification Problems for Software Defined Networks
Directory of Open Access Journals (Sweden)
V. A. Zakharov
2013-01-01
Full Text Available Software-defined networking (SDN is an approach to building computer networks that separate and abstract data planes and control planes of these systems. In a SDN a centralized controller manages a distributed set of switches. A set of open commands for packet forwarding and flow-table updating was defined in the form of a protocol known as OpenFlow. In this paper we describe an abstract formal model of SDN, introduce a tentative language for specification of SDN forwarding policies, and set up formally model-checking problems for SDN.
Some problems of NPP construction base improvement
International Nuclear Information System (INIS)
Movchan, S.V.
1984-01-01
NPP construction bases are characterized by high cost of construction and large area. Duration of base construction makes up 3-4 years, labour contents for their erection constitute 600-900 thousand man-days. Delays in organizing functional base services essentially decelerate construction rates of the main NPP buildings. Maximum joining of separate buildings by their functional assignment and structural peculiarities, wide application of container buildings, partial utilization of permanent buildings of production centre for construction needs; transition to new organizational form of construction based on industrial production of buildings; production of volumetric structural-technological cells with mounted equipment manufactured at specialized plants, mounting NPP components with stock produced cells, consideration of the problem of large power centre creation are necessary for reduction of construction centres, area reduction of cost and duration of their construction
Directory of Open Access Journals (Sweden)
MANAR Y. KASHMOLA
2012-06-01
Full Text Available The development of hybrid algorithms for solving complex optimization problems focuses on enhancing the strengths and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of these approaches to find superior solutions to solve optimization problems. Optimal routing in communication network is considering a complex optimization problem. In this paper we propose a hybrid Hopfield Neural Network (HNN and Tabu Search (TS algorithm, this algorithm called hybrid HNN-TS algorithm. The paradigm of this hybridization is embedded. We embed the short-term memory and tabu restriction features from TS algorithm in the HNN model. The short-term memory and tabu restriction control the neuron selection process in the HNN model in order to get around the local minima problem and find an optimal solution using the HNN model to solve complex optimization problem. The proposed algorithm is intended to find the optimal path for packet transmission in the network which is fills in the field of routing problem. The optimal path that will be selected is depending on 4-tuples (delay, cost, reliability and capacity. Test results show that the propose algorithm can find path with optimal cost and a reasonable number of iterations. It also shows that the complexity of the network model won’t be a problem since the neuron selection is done heuristically.
Improvability of assembly systems I: Problem formulation and performance evaluation
Directory of Open Access Journals (Sweden)
S.-Y. Chiang
2000-01-01
Full Text Available This work develops improvability theory for assembly systems. It consists of two parts. Part I includes the problem formulation and the analysis technique. Part II presents the so-called improvability indicators and a case study.
A simulated annealing approach for redesigning a warehouse network problem
Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia
2017-09-01
Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.
Some dynamic resource allocation problems in wireless networks
Berry, Randall
2001-07-01
We consider dynamic resource allocation problems that arise in wireless networking. Specifically transmission scheduling problems are studied in cases where a user can dynamically allocate communication resources such as transmission rate and power based on current channel knowledge as well as traffic variations. We assume that arriving data is stored in a transmission buffer, and investigate the trade-off between average transmission power and average buffer delay. A general characterization of this trade-off is given and the behavior of this trade-off in the regime of asymptotically large buffer delays is explored. An extension to a more general utility based quality of service definition is also discussed.
Address Translation Problems in IMS Based Next Generation Networks
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Balazs Godor
2006-01-01
Full Text Available The development of packed based multimedia networks reached a turning point when the ITU-T and the ETSIhave incorporated the IMS to the NGN. With the fast development of mobile communication more and more services andcontent are available. In contrast with fix network telephony both the services and the devices are personalized in the “mobileworld”. Services, known from the Internet - like e-mail, chat, browsing, presence, etc. – are already available via mobiledevices as well. The IMS originally wanted to exploit both the benefits of mobile networks and the fancy services of theInternet. But today it is already more than that. IMS is the core of the next generation telecommunication networks and abasis for fix-mobile convergent services. The fact however that IMS was originally a “mobile” standard, where IPv6 was notoddity generated some problems for the fix networks, where IPv4 is used. In this article I give an overview of these problemsand mention some solutions as well.
Complex network problems in physics, computer science and biology
Cojocaru, Radu Ionut
There is a close relation between physics and mathematics and the exchange of ideas between these two sciences are well established. However until few years ago there was no such a close relation between physics and computer science. Even more, only recently biologists started to use methods and tools from statistical physics in order to study the behavior of complex system. In this thesis we concentrate on applying and analyzing several methods borrowed from computer science to biology and also we use methods from statistical physics in solving hard problems from computer science. In recent years physicists have been interested in studying the behavior of complex networks. Physics is an experimental science in which theoretical predictions are compared to experiments. In this definition, the term prediction plays a very important role: although the system is complex, it is still possible to get predictions for its behavior, but these predictions are of a probabilistic nature. Spin glasses, lattice gases or the Potts model are a few examples of complex systems in physics. Spin glasses and many frustrated antiferromagnets map exactly to computer science problems in the NP-hard class defined in Chapter 1. In Chapter 1 we discuss a common result from artificial intelligence (AI) which shows that there are some problems which are NP-complete, with the implication that these problems are difficult to solve. We introduce a few well known hard problems from computer science (Satisfiability, Coloring, Vertex Cover together with Maximum Independent Set and Number Partitioning) and then discuss their mapping to problems from physics. In Chapter 2 we provide a short review of combinatorial optimization algorithms and their applications to ground state problems in disordered systems. We discuss the cavity method initially developed for studying the Sherrington-Kirkpatrick model of spin glasses. We extend this model to the study of a specific case of spin glass on the Bethe
Towards overcoming the Monte Carlo sign problem with tensor networks
Directory of Open Access Journals (Sweden)
Bañuls Mari Carmen
2017-01-01
Full Text Available The study of lattice gauge theories with Monte Carlo simulations is hindered by the infamous sign problem that appears under certain circumstances, in particular at non-zero chemical potential. So far, there is no universal method to overcome this problem. However, recent years brought a new class of non-perturbative Hamiltonian techniques named tensor networks, where the sign problem is absent. In previous work, we have demonstrated that this approach, in particular matrix product states in 1+1 dimensions, can be used to perform precise calculations in a lattice gauge theory, the massless and massive Schwinger model. We have computed the mass spectrum of this theory, its thermal properties and real-time dynamics. In this work, we review these results and we extend our calculations to the case of two flavours and non-zero chemical potential. We are able to reliably reproduce known analytical results for this model, thus demonstrating that tensor networks can tackle the sign problem of a lattice gauge theory at finite density.
Improved Ant Colony Optimization for Seafood Product Delivery Routing Problem
Directory of Open Access Journals (Sweden)
Baozhen Yao
2014-02-01
Full Text Available This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem.
Peer-to-Peer Enclaves for Improving Network Defence
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David W. Archer
2013-07-01
Full Text Available Information about cyberthreats within networks spreads slowly relative to the speed at which those threats spread. Typical "threat feeds" that are commercially available also disseminate information slowly relative to the propagation speed of attacks, and they often convey irrelevant information about imminent threats. As a result, hosts sharing a network may miss opportunities to improve their defence postures against imminent attack because needed information arrives too late or is lost in irrelevant noise. We envision timely, relevant peer-to-peer sharing of threat information – based on current technologies – as a solution to these problems and as a useful design pattern for defensive cyberwarfare. In our setting, network nodes form communities that we call enclaves, where each node defends itself while sharing information on imminent threats with peers that have similar threat exposure. In this article, we present our vision for this solution. We sketch the architecture of a typical node in such a network and how it might interact with a framework for sharing threat information; we explain why certain defensive countermeasures may work better in our setting; we discuss current tools that could be used as components in our vision; and we describe opportunities for future research and development.
Distance learning, problem based learning and dynamic knowledge networks.
Giani, U; Martone, P
1998-06-01
This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.
Application of neural networks and cellular automata to calorimetric problems
Energy Technology Data Exchange (ETDEWEB)
Brenton, V; Fonvieille, H; Guicheney, C; Jousset, J; Roblin, Y; Tamin, F; Grenier, P
1994-09-01
Computing techniques based on parallel processing have been used to treat the information from the electromagnetic calorimeters in SLAC experiments E142/E143. Cluster finding and separation of overlapping showers are performed by a cellular automaton, pion and electron identification is done by using a multilayered neural network. Both applications are presented and their resulting performances are shown to be improved compared to more standard approaches. (author). 9 refs.; Submitted to Nuclear Instruments and Methods (NL).
Application of neural networks and cellular automata to calorimetric problems
International Nuclear Information System (INIS)
Brenton, V.; Fonvieille, H.; Guicheney, C.; Jousset, J.; Roblin, Y.; Tamin, F.; Grenier, P.
1994-09-01
Computing techniques based on parallel processing have been used to treat the information from the electromagnetic calorimeters in SLAC experiments E142/E143. Cluster finding and separation of overlapping showers are performed by a cellular automaton, pion and electron identification is done by using a multilayered neural network. Both applications are presented and their resulting performances are shown to be improved compared to more standard approaches. (author)
A problem-solving routine for improving hospital operations.
Ghosh, Manimay; Sobek Ii, Durward K
2015-01-01
The purpose of this paper is to examine empirically why a systematic problem-solving routine can play an important role in the process improvement efforts of hospitals. Data on 18 process improvement cases were collected through semi-structured interviews, reports and other documents, and artifacts associated with the cases. The data were analyzed using a grounded theory approach. Adherence to all the steps of the problem-solving routine correlated to greater degrees of improvement across the sample. Analysis resulted in two models. The first partially explains why hospital workers tended to enact short-term solutions when faced with process-related problems; and tended not seek longer-term solutions that prevent problems from recurring. The second model highlights a set of self-reinforcing behaviors that are more likely to address problem recurrence and result in sustained process improvement. The study was conducted in one hospital setting. Hospital managers can improve patient care and increase operational efficiency by adopting and diffusing problem-solving routines that embody three key characteristics. This paper offers new insights on why caregivers adopt short-term approaches to problem solving. Three characteristics of an effective problem-solving routine in a healthcare setting are proposed.
Using crowdsourcing to prioritize bicycle network improvements : final report.
2016-04-01
Effort to improve the bicycle route network using crowdsourced data is a powerful means : of incorporating citizens in infrastructure improvement decisions, which will improve : livability by maximizing the benefit of the bicycle infrastructure fundi...
Wei, Chengying; Xiong, Cuilian; Liu, Huanlin
2017-12-01
Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.
A recurrent neural network for solving bilevel linear programming problem.
He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie; Huang, Junjian
2014-04-01
In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.
Self-affirmation improves problem-solving under stress.
Creswell, J David; Dutcher, Janine M; Klein, William M P; Harris, Peter R; Levine, John M
2013-01-01
High levels of acute and chronic stress are known to impair problem-solving and creativity on a broad range of tasks. Despite this evidence, we know little about protective factors for mitigating the deleterious effects of stress on problem-solving. Building on previous research showing that self-affirmation can buffer stress, we tested whether an experimental manipulation of self-affirmation improves problem-solving performance in chronically stressed participants. Eighty undergraduates indicated their perceived chronic stress over the previous month and were randomly assigned to either a self-affirmation or control condition. They then completed 30 difficult remote associate problem-solving items under time pressure in front of an evaluator. Results showed that self-affirmation improved problem-solving performance in underperforming chronically stressed individuals. This research suggests a novel means for boosting problem-solving under stress and may have important implications for understanding how self-affirmation boosts academic achievement in school settings.
Improving Latino disaster preparedness using social networks.
Eisenman, David P; Glik, Deborah; Gonzalez, Lupe; Maranon, Richard; Zhou, Qiong; Tseng, Chi-Hong; Asch, Steven M
2009-12-01
Culturally targeted, informal social networking approaches to improving disaster preparedness have not been empirically tested. In partnership with community health promoters and the Los Angeles County Department of Public Health, this study tested a disaster preparedness program for Latino households. This study had a community-based, randomized, longitudinal cohort design with two groups and was conducted during February-October 2007. Assessments were made at baseline and 3 months. Analyses were carried out January-October 2008. Community-based study of 231 Latinos living in Los Angeles County. Participants were randomly assigned to attending platicas (small-group discussions led by a health promoter/promotora de salud) or receiving "media" (a culturally tailored mailer). A total of 187 (81.0%) completed the 3-month follow-up. A self-reported disaster preparedness checklist was used. Among participants who did not have emergency water pre-intervention, 93.3% of those in the platica arm had it at follow-up, compared to 66.7% in the media arm (p=0.003). Among participants who did not have food pre-intervention, 91.7% in the platica arm reported it at follow-up, compared to 60.6% in the media arm (p=0.013). Finally, among participants who did not have a family communication plan pre-intervention, 70.4% in the platica arm reported one at follow-up, compared to 42.3% in the media arm (p=0.002). Although both arms improved in stockpiling water and food and creating a communication plan, the platica arm showed greater improvement than the media group.
Mericle, Amy A; Kaskutas, Lee A; Polcin, Doug L; Karriker-Jaffe, Katherine J
2018-01-01
Socioecological approaches to public health problems like addiction emphasize the importance of person-environment interactions. Neighborhood and social network characteristics may influence the likelihood of relapse among individuals in recovery, but these factors have been understudied, particularly with respect to conceptualizing social network characteristics as moderators of neighborhood disadvantage. Drawing from a larger prospective study of individuals recruited from outpatient treatment (N=451) and interviewed 1, 3, 5, and 7 years later, the aim of this study was to examine the independent and interactive effects of neighborhood and social network characteristics on continued problem drinking after treatment. Models using generalized estimating equations controlling for demographic and other risk factors found the number of heavy drinkers in one's network increases risk of relapse, with the effects being significantly stronger among those living in disadvantaged neighborhoods than among those in non-disadvantaged neighborhoods. No independent effects were found for neighborhood disadvantage or for the number of network members supporting reduced drinking. Future research is needed to examine potential protective factors in neighborhoods which may offset socioeconomic disadvantage as well as to investigate the functions that network members serve in helping to improve long-term treatment outcomes.
Middel, H.G.A.; Groen, Arend J.; Fisscher, O.A.M.
2004-01-01
More than ever, companies are challenged to improve their performance and respond quickly and accurately to changes within the market. As competitive battlefield is moving towards the level of networks of organisations, the individual firm is an inadequate entity for identifying improvements.
Mental Health, School Problems, and Social Networks: Modeling Urban Adolescent Substance Use
Mason, Michael J.
2010-01-01
This study tested a mediation model of the relationship with school problems, social network quality, and substance use with a primary care sample of 301 urban adolescents. It was theorized that social network quality (level of risk or protection in network) would mediate the effects of school problems, accounting for internalizing problems and…
Improved Recommendations Based on Trust Relationships in Social Networks
Directory of Open Access Journals (Sweden)
Hao Tian
2017-03-01
Full Text Available In order to alleviate the pressure of information overload and enhance consumer satisfaction, personalization recommendation has become increasingly popular in recent years. As a result, various approaches for recommendation have been proposed in the past few years. However, traditional recommendation methods are still troubled with typical issues such as cold start, sparsity, and low accuracy. To address these problems, this paper proposed an improved recommendation method based on trust relationships in social networks to improve the performance of recommendations. In particular, we define trust relationship afresh and consider several representative factors in the formalization of trust relationships. To verify the proposed approach comprehensively, this paper conducted experiments in three ways. The experimental results show that our proposed approach leads to a substantial increase in prediction accuracy and is very helpful in dealing with cold start and sparsity.
Route Selection Problem Based on Hopfield Neural Network
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N. Kojic
2013-12-01
Full Text Available Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc. and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems.
Application of Artificial Neural Networks to Complex Groundwater Management Problems
International Nuclear Information System (INIS)
Coppola, Emery; Poulton, Mary; Charles, Emmanuel; Dustman, John; Szidarovszky, Ferenc
2003-01-01
As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models
Algorithm for solving multicriteria problem of appointments on the networks
Directory of Open Access Journals (Sweden)
Yu. V. Bugaeev
2017-01-01
Full Text Available To describe complex projects or various jobs that make up a set of interrelated activities, use the network schedule. Several variants of network models are used. 1. For practical use, the Gantt chart is the most widely used - it is a graphical representation of consecutive intervals of time and the use of resources. 2. The network graph is represented as a graph, where the vertices are an event (or its state at a certain point in time, and the connecting arcs (or edges are works. The graph model is used in the work. In this case, the events (the fact of the completion or the beginning of the work correspond to the vertices of the graph, and the work to the arcs, the orientation of which corresponds to the technology of this process. An important role in the project management model is played by the optimal assignment of performers to the existing list of works. With this formulation of the problem, the total implementation time or the length of the critical path on the graph can be used as a criterion. In this case, the criterion is imposed a restriction on the deadline for the execution of work (or the project as a whole. Thus, the total time spent on the project and the length of the critical path are represented by equally important characteristics of the project implementation, and they should be considered as two equivalent criteria for the multicriteria project management task. We have proposed an algorithm, in general, an approximate determination of the set of Pareto-optimal solutions of a given problem.
Modeling Multilevel Supplier Selection Problem Based on Weighted-Directed Network and Its Solution
Directory of Open Access Journals (Sweden)
Chia-Te Wei
2017-01-01
Full Text Available With the rapid development of economy, the supplier network is becoming more and more complicated. It is important to choose the right suppliers for improving the efficiency of the supply chain, so how to choose the right ones is one of the important research directions of supply chain management. This paper studies the partner selection problem from the perspective of supplier network global optimization. Firstly, this paper discusses and forms the evaluation system to estimate the supplier from the two indicators of risk and greenness and then applies the value as the weight of the network between two nodes to build a weighted-directed supplier network; secondly, the study establishes the optimal combination model of supplier selection based on the global network perspective and solves the model by the dynamic programming-tabu search algorithm and the improved ant colony algorithm, respectively; finally, different scale simulation examples are given to testify the efficiency of the two algorithms. The results show that the ant colony algorithm is superior to the tabu search one as a whole, but the latter is slightly better than the former when network scale is small.
Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network
Jing Wang; Yourui Huang
2013-01-01
In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...
Method of Geometric Connected Disk Cover Problem for UAV realy network deployment
Directory of Open Access Journals (Sweden)
Chuang Liu
2017-01-01
Full Text Available Aiming at the problem of the effective connectivity of a large number of mobile combat units in the future aeronautic swarm operation, this paper proposes an idea of using UAV(Unmanned Aerial Vehicle to build, and studies the deployment of the network. User coverage and network connectivity are important for a relay network planning which are studied separately in traditional ways. In order to effectively combine these two factors while the network’s survivability is taken into account. Firstly, the concept of node aggregation degree is proposed. Secondly, a performance evaluation parameter for UAV relay network is proposed based on node aggregation degree, then analyzes the lack of deterministic deployment and presents one a PSO (VFA-PSO deployment algorithm based on virtual force. Finally, compared with the existing algorithms, the validity and stability of the algorithm are verified. The experimental results show that the VFA-PSO algorithm can effectively improve the network coverage and the survivability of the network under the premise of ensuring the network connectivity, and has better deployment effect.
Improved Maximum Parsimony Models for Phylogenetic Networks.
Van Iersel, Leo; Jones, Mark; Scornavacca, Celine
2018-05-01
Phylogenetic networks are well suited to represent evolutionary histories comprising reticulate evolution. Several methods aiming at reconstructing explicit phylogenetic networks have been developed in the last two decades. In this article, we propose a new definition of maximum parsimony for phylogenetic networks that permits to model biological scenarios that cannot be modeled by the definitions currently present in the literature (namely, the "hardwired" and "softwired" parsimony). Building on this new definition, we provide several algorithmic results that lay the foundations for new parsimony-based methods for phylogenetic network reconstruction.
Improvement of Networked Control Systems Performance Using a New Encryption Algorithm
Directory of Open Access Journals (Sweden)
Seyed Ali Mesbahifard
2014-07-01
Full Text Available Networked control systems are control systems which controllers and plants are connected via telecommunication network. One of the most important challenges in networked control systems is the problem of network time delay. Increasing of time delay may affect on control system performance extremely. Other important issue in networked control systems is the security problems. Since it is possible that unknown people access to network especially Internet, the probability of terrible attacks such as deception attacks is greater, therefore presentation of methods which could decrease time delay and increase system immunity are desired. In this paper a symmetric encryption with low data volume against deception attacks is proposed. This method has high security and low time delay rather than the other encryption algorithms and could improve the control system performance against deception attacks.
Face recognition based on improved BP neural network
Directory of Open Access Journals (Sweden)
Yue Gaili
2017-01-01
Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.
Facial expression recognition based on improved deep belief networks
Wu, Yao; Qiu, Weigen
2017-08-01
In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.
An Improved Walk Model for Train Movement on Railway Network
International Nuclear Information System (INIS)
Li Keping; Mao Bohua; Gao Ziyou
2009-01-01
In this paper, we propose an improved walk model for simulating the train movement on railway network. In the proposed method, walkers represent trains. The improved walk model is a kind of the network-based simulation analysis model. Using some management rules for walker movement, walker can dynamically determine its departure and arrival times at stations. In order to test the proposed method, we simulate the train movement on a part of railway network. The numerical simulation and analytical results demonstrate that the improved model is an effective tool for simulating the train movement on railway network. Moreover, it can well capture the characteristic behaviors of train scheduling in railway traffic. (general)
An improved sampling method of complex network
Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing
2014-12-01
Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.
Library Automation and Networking in India: Problems and Prospects.
Vyas, S. D.
1997-01-01
Examines the information infrastructure and the impact of information technology in India. Highlights include attempts toward automation; library networking at the national and local level; descriptions of four major networks; library software; and constraints of networking in academic libraries. (LRW)
A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.
Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J
2015-04-01
Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.
Improve Problem Solving Skills through Adapting Programming Tools
Shaykhian, Linda H.; Shaykhian, Gholam Ali
2007-01-01
There are numerous ways for engineers and students to become better problem-solvers. The use of command line and visual programming tools can help to model a problem and formulate a solution through visualization. The analysis of problem attributes and constraints provide insight into the scope and complexity of the problem. The visualization aspect of the problem-solving approach tends to make students and engineers more systematic in their thought process and help them catch errors before proceeding too far in the wrong direction. The problem-solver identifies and defines important terms, variables, rules, and procedures required for solving a problem. Every step required to construct the problem solution can be defined in program commands that produce intermediate output. This paper advocates improved problem solving skills through using a programming tool. MatLab created by MathWorks, is an interactive numerical computing environment and programming language. It is a matrix-based system that easily lends itself to matrix manipulation, and plotting of functions and data. MatLab can be used as an interactive command line or a sequence of commands that can be saved in a file as a script or named functions. Prior programming experience is not required to use MatLab commands. The GNU Octave, part of the GNU project, a free computer program for performing numerical computations, is comparable to MatLab. MatLab visual and command programming are presented here.
Sensitivity analysis of linear programming problem through a recurrent neural network
Das, Raja
2017-11-01
In this paper we study the recurrent neural network for solving linear programming problems. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate the sensitivity analysis of linear programming problem through the neural network. A detailed example is also presented to demonstrate the performance of the recurrent neural network.
Enhancement of a model for Large-scale Airline Network Planning Problems
Kölker, K.; Lopes dos Santos, Bruno F.; Lütjens, K.
2016-01-01
The main focus of this study is to solve the network planning problem based on passenger decision criteria including the preferred departure time and travel time for a real-sized airline network. For this purpose, a model of the integrated network planning problem is formulated including scheduling
Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Yunpeng Wang
2014-01-01
Full Text Available We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.
Unsupervised neural networks for solving Troesch's problem
International Nuclear Information System (INIS)
Raja Muhammad Asif Zahoor
2014-01-01
In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. (interdisciplinary physics and related areas of science and technology)
Managing BTSs to Solve Handover Problem in Mobile Network
Directory of Open Access Journals (Sweden)
Wael Etaiwi
2011-01-01
Full Text Available Handover is a key solution that improves the telecommunication services using GSM by assure the continual service delivery between two mobiles regardless of location's changes of the sender or receiver, and now GSM technology becomes applicable all over the world and the customers become more satisfied to the dealer's services delivery, But Handover suffers from a major problem refers to the limitation of hardware capacity of the BTS (Base Transfer Station. This approach consists of three schemes, the first one based on reserve an extra ports for handover purposes by implementing a software solution that control BTS ports. The second alternative scheme based on channel exchange between adjacent BTSs by shifting a chosen allocated signal to another adjacent free BTS and then allocating the new signal to the new free port. The third schema depends on carrying the Handover problem to another BTS to solve it if it didn't solved in the second schema.
Self-affirmation improves problem-solving under stress.
Directory of Open Access Journals (Sweden)
J David Creswell
Full Text Available High levels of acute and chronic stress are known to impair problem-solving and creativity on a broad range of tasks. Despite this evidence, we know little about protective factors for mitigating the deleterious effects of stress on problem-solving. Building on previous research showing that self-affirmation can buffer stress, we tested whether an experimental manipulation of self-affirmation improves problem-solving performance in chronically stressed participants. Eighty undergraduates indicated their perceived chronic stress over the previous month and were randomly assigned to either a self-affirmation or control condition. They then completed 30 difficult remote associate problem-solving items under time pressure in front of an evaluator. Results showed that self-affirmation improved problem-solving performance in underperforming chronically stressed individuals. This research suggests a novel means for boosting problem-solving under stress and may have important implications for understanding how self-affirmation boosts academic achievement in school settings.
An Improved Tarpit for Network Deception
2016-03-25
pollute network measurement studies, as well as note the negative impact that even small blocks of tarpit address spaces have on automated scanners...to ping Greasy and LaBrea hosts: a Mac OS X Version 10.10.5 machine on a home residential net- work in California, the CentOS Linux release 7.2.1511...attack analysis and democracy,” 2010. [18] C. Ruvalcaba, “ Smart IDS – Hybrid LaBrea Tarpit,” SANS Institute, Report, 2009. [19] V. Oppleman, “Network
DOE Network 2025: Network Research Problems and Challenges for DOE Scientists. Workshop Report
Energy Technology Data Exchange (ETDEWEB)
None, None
2016-02-01
The growing investments in large science instruments and supercomputers by the US Department of Energy (DOE) hold enormous promise for accelerating the scientific discovery process. They facilitate unprecedented collaborations of geographically dispersed teams of scientists that use these resources. These collaborations critically depend on the production, sharing, moving, and management of, as well as interactive access to, large, complex data sets at sites dispersed across the country and around the globe. In particular, they call for significant enhancements in network capacities to sustain large data volumes and, equally important, the capabilities to collaboratively access the data across computing, storage, and instrument facilities by science users and automated scripts and systems. Improvements in network backbone capacities of several orders of magnitude are essential to meet these challenges, in particular, to support exascale initiatives. Yet, raw network speed represents only a part of the solution. Indeed, the speed must be matched by network and transport layer protocols and higher layer tools that scale in ways that aggregate, compose, and integrate the disparate subsystems into a complete science ecosystem. Just as important, agile monitoring and management services need to be developed to operate the network at peak performance levels. Finally, these solutions must be made an integral part of the production facilities by using sound approaches to develop, deploy, diagnose, operate, and maintain them over the science infrastructure.
Involving young people with mental health problems in improving healthcare
Milnes, Linda; Kendal, Sarah
2017-01-01
Latif et al’s (2017) paper is a valuable addition to knowledge in this field. It highlights the need to improve the education of registered children’s nurses in the care of children and young people (CYP) with physical health problems related to self-harm.
An Improvement for Fuzzy Stochastic Goal Programming Problems
Directory of Open Access Journals (Sweden)
Shu-Cheng Lin
2017-01-01
Full Text Available We examined the solution process for linear programming problems under a fuzzy and random environment to transform fuzzy stochastic goal programming problems into standard linear programming problems. A previous paper that revised the solution process with the lower-side attainment index motivated our work. In this paper, we worked on a revision for both-side attainment index to amend its definition and theorems. Two previous examples were used to examine and demonstrate our improvement over previous results. Our findings not only improve the previous paper with both-side attainment index, but also provide a theoretical extension from lower-side attainment index to the both-side attainment index.
A note on the consensus finding problem in communication networks with switching topologies
Haskovec, Jan
2014-01-01
In this note, we discuss the problem of consensus finding in communication networks of agents with dynamically switching topologies. In particular, we consider the case of directed networks with unbalanced matrices of communication rates. We
Improved transformer protection using probabilistic neural network ...
African Journals Online (AJOL)
user
secure and dependable protection for power transformers. Owing to its superior learning and generalization capabilities Artificial. Neural Network (ANN) can considerably enhance the scope of WI method. ANN approach is faster, robust and easier to implement than the conventional waveform approach. The use of neural ...
Improved transformer protection using probabilistic neural network ...
African Journals Online (AJOL)
This article presents a novel technique to distinguish between magnetizing inrush current and internal fault current of power transformer. An algorithm has been developed around the theme of the conventional differential protection method in which parallel combination of Probabilistic Neural Network (PNN) and Power ...
Open Problems in Network-aware Data Management in Exa-scale Computing and Terabit Networking Era
Energy Technology Data Exchange (ETDEWEB)
Balman, Mehmet; Byna, Surendra
2011-12-06
Accessing and managing large amounts of data is a great challenge in collaborative computing environments where resources and users are geographically distributed. Recent advances in network technology led to next-generation high-performance networks, allowing high-bandwidth connectivity. Efficient use of the network infrastructure is necessary in order to address the increasing data and compute requirements of large-scale applications. We discuss several open problems, evaluate emerging trends, and articulate our perspectives in network-aware data management.
An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks
Directory of Open Access Journals (Sweden)
Vala Ali Rohani
2014-01-01
Full Text Available Abundance of information in recent years has become a serious challenge for web users. Recommender systems (RSs have been often utilized to alleviate this issue. RSs prune large information spaces to recommend the most relevant items to users by considering their preferences. Nonetheless, in situations where users or items have few opinions, the recommendations cannot be made properly. This notable shortcoming in practical RSs is called cold-start problem. In the present study, we propose a novel approach to address this problem by incorporating social networking features. Coined as enhanced content-based algorithm using social networking (ECSN, the proposed algorithm considers the submitted ratings of faculty mates and friends besides user’s own preferences. The effectiveness of ECSN algorithm was evaluated by implementing it in MyExpert, a newly designed academic social network (ASN for academics in Malaysia. Real feedbacks from live interactions of MyExpert users with the recommended items are recorded for 12 consecutive weeks in which four different algorithms, namely, random, collaborative, content-based, and ECSN were applied every three weeks. The empirical results show significant performance of ECSN in mitigating the cold-start problem besides improving the prediction accuracy of recommendations when compared with other studied recommender algorithms.
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Dan Zhang
2011-01-01
We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can ...
Design and Optimisation Problems in Wireless Sensor Networks
Indian Academy of Sciences (India)
Premkumar Karumbu,1.05 ECE,,+91-9448227167
2010-11-14
Nov 14, 2010 ... Wireless Networks of Multifunction Smart Sensors (WSNs). A smart sensor ... Energy and environment management networks in large buildings. Emerging ISA ... Monitoring mobile patients in hospitals and homes. Locating ...
Game Theoretic Solutions to Cyber Attack and Network Defense Problems
National Research Council Canada - National Science Library
Shen, Dan; Chen, Genshe; Cruz, Jr., , Jose B; Blasch, Erik; Kruger, Martin
2007-01-01
.... The protection and defense against cyber attacks to computer network is becoming inadequate as the hacker knowledge sophisticates and as the network and each computer system become more complex...
Improved Efficient Routing Strategy on Scale-Free Networks
Jiang, Zhong-Yuan; Liang, Man-Gui
Since the betweenness of nodes in complex networks can theoretically represent the traffic load of nodes under the currently used routing strategy, we propose an improved efficient (IE) routing strategy to enhance to the network traffic capacity based on the betweenness centrality. Any node with the highest betweenness is susceptible to traffic congestion. An efficient way to improve the network traffic capacity is to redistribute the heavy traffic load from these central nodes to non-central nodes, so in this paper, we firstly give a path cost function by considering the sum of node betweenness with a tunable parameter β along the actual path. Then, by minimizing the path cost, our IE routing strategy achieved obvious improvement on the network transport efficiency. Simulations on scale-free Barabási-Albert (BA) networks confirmed the effectiveness of our strategy, when compared with the efficient routing (ER) and the shortest path (SP) routing.
The transportation management division institutional program: Networking and problem solving
International Nuclear Information System (INIS)
McGinnis, K.A.; Peterson, J.M.
1989-06-01
The US Department of Energy (DOE) has several programs related to transportation. While these programs may have differing missions and legislative authority, the required activities are frequently similar. To ensure a DOE-wide perspective in developing transportation policies and procedures, a DOE Transportation Institutional Task Force (Task Force) has been formed, which is the primary focus of this paper. The Task Force, composed of representatives from each of the major DOE transportation programs, meets periodically to exchange experiences and insights on institutional issues related to Departmental shipping. The primary purpose of the group is to identify opportunities for productive interactions with the transportation community, including interested and affected members of the public. This paper will also focus sharply on the networking of DOE with the State, Tribal, and local officials in fostering better understanding and in solving problems. An example of such activity is the DOE's cooperative agreement with the Energy Task Force of the Urban Consortium. A major effort is to encourage cooperative action in identifying, addressing, and resolving issues that could impede the transportation of radioactive materials
Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang
2011-05-01
A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.
Visualization of protein interaction networks: problems and solutions
Directory of Open Access Journals (Sweden)
Agapito Giuseppe
2013-01-01
Full Text Available Abstract Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins and edges (interactions, the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i technology, i.e. availability/license of the software and supported OS (Operating System platforms; (ii interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the
An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
Directory of Open Access Journals (Sweden)
Wei He
2013-01-01
Full Text Available Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.
Weinstock, Jeremiah; Burton, Steve; Rash, Carla J; Moran, Sheila; Biller, Warren; Krudelbach, Norman; Phoenix, Natalie; Morasco, Benjamin J
2011-06-01
Gambling help-lines are an essential access point, or frontline resource, for treatment seeking. This study investigated treatment engagement after calling a gambling help-line. From 2000-2007 over 2,900 unique callers were offered an in-person assessment appointment. Logistic regression analyses assessed predictors of (a) accepting the referral to the in-person assessment appointment and (b) attending the in-person assessment appointment. Over 76% of callers accepted the referral and 55% of all callers attended the in-person assessment appointment. This treatment engagement rate is higher than typically found for other help-lines. Demographic factors and clinical factors such as gender, severity of gambling problems, amount of gambling debt, and coercion by legal and social networks predicted engagement in treatment. Programmatic factors such as offering an appointment within 72 hr also aided treatment engagement. Results suggest gambling help-lines can be a convenient and confidential way for many individuals with gambling problems to access gambling-specific treatment. Alternative services such as telephone counseling may be beneficial for those who do not engage in treatment. (PsycINFO Database Record (c) 2011 APA, all rights reserved).
Revenue Improvement Through Demand-Dependent Pricing of Network Services
National Research Council Canada - National Science Library
Sanders, David
2000-01-01
... of the expectation of rewards based upon variable demands. This work shows that revenue improvement can occur in this network environment when a dynamic pricing policy is applied as opposed to optimal static pricing...
Social network analysis in software process improvement
DEFF Research Database (Denmark)
Nielsen, Peter Axel; Tjørnehøj, Gitte
2010-01-01
Software process improvement in small organisation is often problematic and communication and knowledge sharing is more informal. To improve software processes we need to understand how they communicate and share knowledge. In this article have studied the company SmallSoft through action research...
Application of improved PSO-RBF neural network in the synthetic ammonia decarbonization
Directory of Open Access Journals (Sweden)
Yongwei LI
2017-12-01
Full Text Available The synthetic ammonia decarbonization is a typical complex industrial process, which has the characteristics of time variation, nonlinearity and uncertainty, and the on-line control model is difficult to be established. An improved PSO-RBF neural network control algorithm is proposed to solve the problems of low precision and poor robustness in the complex process of the synthetic ammonia decarbonization. The particle swarm optimization algorithm and RBF neural network are combined. The improved particle swarm algorithm is used to optimize the RBF neural network's hidden layer primary function center, width and the output layer's connection value to construct the RBF neural network model optimized by the improved PSO algorithm. The improved PSO-RBF neural network control model is applied to the key carbonization process and compared with the traditional fuzzy neural network. The simulation results show that the improved PSO-RBF neural network control method used in the synthetic ammonia decarbonization process has higher control accuracy and system robustness, which provides an effective way to solve the modeling and optimization control of a complex industrial process.
Wireless Sensor Networks - Node Localization for Various Industry Problems
International Nuclear Information System (INIS)
Derr, Kurt; Manic, Milos
2015-01-01
Fast, effective monitoring following airborne releases of toxic substances is critical to mitigate risks to threatened population areas. Wireless sensor nodes at fixed predetermined locations may monitor such airborne releases and provide early warnings to the public. A challenging algorithmic problem is determining the locations to place these sensor nodes while meeting several criteria: 1) provide complete coverage of the domain, and 2) create a topology with problem dependent node densities, while 3) minimizing the number of sensor nodes. This manuscript presents a novel approach to determining optimal sensor placement, Advancing Front mEsh generation with Constrained dElaunay Triangulation and Smoothing (AFECETS) that addresses these criteria. A unique aspect of AFECETS is the ability to determine wireless sensor node locations for areas of high interest (hospitals, schools, high population density areas) that require higher density of nodes for monitoring environmental conditions, a feature that is difficult to find in other research work. The AFECETS algorithm was tested on several arbitrary shaped domains. AFECETS simulation results show that the algorithm 1) provides significant reduction in the number of nodes, in some cases over 40%, compared to an advancing front mesh generation algorithm, 2) maintains and improves optimal spacing between nodes, and 3) produces simulation run times suitable for real-time applications
A new neural network model for solving random interval linear programming problems.
Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
2017-05-01
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.
Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints
Kmet', Tibor; Kmet'ová, Mária
2009-09-01
A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.
An Improved Car-Following Model in Vehicle Networking Based on Network Control
Directory of Open Access Journals (Sweden)
D. Y. Kong
2014-01-01
Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.
Critical cooperation range to improve spatial network robustness.
Directory of Open Access Journals (Sweden)
Vitor H P Louzada
Full Text Available A robust worldwide air-transportation network (WAN is one that minimizes the number of stranded passengers under a sequence of airport closures. Building on top of this realistic example, here we address how spatial network robustness can profit from cooperation between local actors. We swap a series of links within a certain distance, a cooperation range, while following typical constraints of spatially embedded networks. We find that the network robustness is only improved above a critical cooperation range. Such improvement can be described in the framework of a continuum transition, where the critical exponents depend on the spatial correlation of connected nodes. For the WAN we show that, except for Australia, all continental networks fall into the same universality class. Practical implications of this result are also discussed.
FCL: A solution to fault current problems in DC networks
International Nuclear Information System (INIS)
Cointe, Y; Tixador, P; Villard, C
2008-01-01
Within the context of the electric power market liberalization, DC networks have many interests compared to AC ones. New energy landscapes open the way of a diversified production. Innovative interconnection diagrams, in particular using DC buses, are under development. In this case it is not possible to defer the fault current interruption in the AC side. DC fault current cutting remains a difficult problem. FCLs (Fault Current Limiters) enable to limit the current to a preset value, lower than the theoretical short-circuit current. For this application Coated Conductors (CC) offer an excellent opportunity. Due to these promising characteristics we build a test bench and work on the implementation of these materials. The test bench is composed by 10 power amplifiers, to reach 4 kVA in many configurations of current and voltage. We carried out limiting experiments on DyBaCuO CC from EHTS, samples are about five centimeters long and many potential measuring points are pasted on the shunt to estimate the quench homogeneity. Thermal phenomena in FCLs are essential, numerical models are important to calculate the maximum temperatures. To validate these models we measure the CC temperature by depositing thermal sensors (Cu resistance) above the shunt layer and the substrate. An electrical insulation with a low thermal resistivity between the CC and the sensors is necessary. We use a thin layer of Parylene because of its good mechanical and electrical insulation properties at low temperature. The better quench behaviour of CC for temperatures close to the critical temperature has been confirmed. The measurements are in good agreement with simulations, this validates the thermal models
Problems in the design of multifunction meteor-radar networks
Nechitailenko, V. A.; Voloshchuk, Iu. I.
The design of meteor-radar networks is examined in connection with the need to conduct experiments on a mass scale in meteor geophysics and astronomy. Attention is given to network architecture features and procedures of communication-path selection in the organization of information transfer, with allowance for the features of the meteor communication link. The meteor link is considered as the main means to ensure traffic in the meteor-radar network.
Self-teaching neural network learns difficult reactor control problem
International Nuclear Information System (INIS)
Jouse, W.C.
1989-01-01
A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits
Research on the Wire Network Signal Prediction Based on the Improved NNARX Model
Zhang, Zipeng; Fan, Tao; Wang, Shuqing
It is difficult to obtain accurately the wire net signal of power system's high voltage power transmission lines in the process of monitoring and repairing. In order to solve this problem, the signal measured in remote substation or laboratory is employed to make multipoint prediction to gain the needed data. But, the obtained power grid frequency signal is delay. In order to solve the problem, an improved NNARX network which can predict frequency signal based on multi-point data collected by remote substation PMU is describes in this paper. As the error curved surface of the NNARX network is more complicated, this paper uses L-M algorithm to train the network. The result of the simulation shows that the NNARX network has preferable predication performance which provides accurate real time data for field testing and maintenance.
Performance analysis and improvement of WPAN MAC for home networks.
Mehta, Saurabh; Kwak, Kyung Sup
2010-01-01
The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking.
Performance Analysis and Improvement of WPAN MAC for Home Networks
Directory of Open Access Journals (Sweden)
Saurabh Mehta
2010-03-01
Full Text Available The wireless personal area network (WPAN is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3 MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking.
A new cut-based algorithm for the multi-state flow network reliability problem
International Nuclear Information System (INIS)
Yeh, Wei-Chang; Bae, Changseok; Huang, Chia-Ling
2015-01-01
Many real-world systems can be modeled as multi-state network systems in which reliability can be derived in terms of the lower bound points of level d, called d-minimal cuts (d-MCs). This study proposes a new method to find and verify obtained d-MCs with simple and useful found properties for the multi-state flow network reliability problem. The proposed algorithm runs in O(mσp) time, which represents a significant improvement over the previous O(mp 2 σ) time bound based on max-flow/min-cut, where p, σ and m denote the number of MCs, d-MC candidates and edges, respectively. The proposed algorithm also conquers the weakness of some existing methods, which failed to remove duplicate d-MCs in special cases. A step-by-step example is given to demonstrate how the proposed algorithm locates and verifies all d-MC candidates. As evidence of the utility of the proposed approach, we present extensive computational results on 20 benchmark networks in another example. The computational results compare favorably with a previously developed algorithm in the literature. - Highlights: • A new method is proposed to find all d-MCs for the multi-state flow networks. • The proposed method can prevent the generation of d-MC duplicates. • The proposed method is simpler and more efficient than the best-known algorithms
Dynamic shortest path problems : hybrid routing policies considering network disruptions
Sever, D.; Dellaert, N.P.; Woensel, van T.; Kok, de A.G.
2013-01-01
Traffic network disruptions lead to significant increases in transportation costs. We consider networks in which a number of links are vulnerable to these disruptions leading to a significantly higher travel time on these links. For these vulnerable links, we consider known link disruption
An improved scheduling algorithm for linear networks
Bader, Ahmed; Alouini, Mohamed-Slim; Ayadi, Yassin
2017-01-01
In accordance with the present disclosure, embodiments of an exemplary scheduling controller module or device implement an improved scheduling process such that the targeted reduction in schedule length can be achieve while incurring minimal energy penalty by allowing for a large rate (or duration) selection alphabet.
An improved scheduling algorithm for linear networks
Bader, Ahmed
2017-02-09
In accordance with the present disclosure, embodiments of an exemplary scheduling controller module or device implement an improved scheduling process such that the targeted reduction in schedule length can be achieve while incurring minimal energy penalty by allowing for a large rate (or duration) selection alphabet.
Interchange. Program Improvement Products Identified through Networking.
Ohio State Univ., Columbus. National Center for Research in Vocational Education.
This catalog lists exemplary field-based program improvement products identified by the Dissemination and Utilization Products and Services Program (D&U) at the National Center for Research in Vocational Education. It is designed to increase awareness of these products among vocational educators and to provide information about them that…
International Nuclear Information System (INIS)
Scheepers, M.J.J.; Jansen, J.C.; De Joode, J.; Bauknecht, D.; Gomez, T.; Pudjianto, D.; Strbac, G.; Ropenus, S.
2007-11-01
The growth of distributed electricity supply of renewable energy sources (RES-E) and combined heat and power (CHP) - so called distributed generation (DG) - can cause technical problems for electricity distribution networks. These integration problems can be overcome by reinforcing the network. Many European Member States apply network regulation that does not account for the impact of DG growth on the network costs. Passing on network integration costs to the DG-operator who is responsible for these extra costs may result in discrimination between different DG plants and between DG and large power generation. Therefore, in many regulatory systems distribution system operators (DSOs) are not being compensated for the DG integration costs. The DG-GRID project analysed technical and economical barriers for integration of distributed generation into electricity distribution networks. The project looked into the impact of a high DG deployment on the electricity distribution system costs and the impact on the financial position of the DSO. Several ways for improving network regulation in order to compensate DSOs for the increasing DG penetration were identified and tested. The DG-GRID project looked also into stimulating network innovations through economic regulation. The project was co-financed by the European Commission and carried out by nine European universities and research institutes. This report summarises the project results and is based on a number of DG-GRID reports that describe the conducted analyses and their results
Institute of Scientific and Technical Information of China (English)
ZHANG Yin; WEI Zhiyuan; ZHANG Yinping; WANG Xin
2017-01-01
Urban heating in northern China accounts for 40％ of total building energy usage.In central heating systems,heat is often transfened from heat source to users by the heat network where several heat exchangers arc installed at heat source,substations and terminals respectively.For given overall heating capacity and heat source temperarure,increasing the terminal fluid temperature is an effective way to improve the thermal performance of such cascade heat exchange network for energy saving.In this paper,the mathematical optimization model of the cascade heat exchange network with three-stage heat exchangers in series is established.Aim at maximizing the cold fluid temperature for given hot fluid temperature and overall heating capacity,the optimal heat exchange area distribution and the medium fluids' flow rates are determined through inverse problem and variation method.The preliminary results show that the heat exchange areas should be distributed equally for each heat exchanger.It also indicates that in order to improve the thernmal performance of the whole system,more heat exchange areas should be allocated to the heat exchanger where flow rate difference between two fluids is relatively small.This work is important for guiding the optimization design of practical cascade heating systems.
Directory of Open Access Journals (Sweden)
Mi Gan
2018-01-01
Full Text Available The rapid growth of logistics distribution highlights the problems including the imperfect infrastructure of logistics distribution network, the serious shortage of distribution capacity of each individual enterprise, and the high cost of distribution in China. While the development of sharing economy makes it possible to achieve the integration of whole social logistic resources, big data technology can grasp customer’s logistics demand accurately on the basis of analyzing the customer’s logistics distribution preference, which contributes to the integration and optimization of the whole logistics resources. This paper proposes a kind of intensive distribution logistics network considering sharing economy, which assumes that all the social logistics suppliers build a strategic alliance, and individual idle logistics resources are also used to deal with distribution needs. Analyzing customer shopping behavior by the big data technology to determine customer’s logistics preference on the basis of dividing the customer’s logistics preference into high speed, low cost, and low pollution and then constructing the corresponding objective function model according to different logistics preferences, we obtain the intensive distribution logistics network model and solve it with heuristic algorithm. Furthermore, this paper analyzes the mechanism of interest distribution of the participants in the distribution network and puts forward an improved interval Shapley value method considering both satisfaction and contribution, with case verifying the feasibility and effectiveness of the model. The results showed that, compared with the traditional Shapley method, distribution coefficient calculated by the improved model could be fairer, improve stakeholder satisfaction, and promote the sustainable development of the alliance as well.
Omega-Harmonic Functions and Inverse Conductivity Problems on Networks
National Research Council Canada - National Science Library
Berenstein, Carlos A; Chung, Soon-Yeong
2003-01-01
.... To do this, they introduce an elliptic operator DELTA omega and an omega-harmonic function on the graph, with its physical interpretation being the diffusion equation on the graph, which models an electric network...
Kim, Jeong-Nam
2018-01-01
This special issue of Health Communication compiles 10 articles to laud the promise and yet confront the problems in the digital networked information society related to public health. We present this anthology of symphony and cacophony of lay individuals' communicative actions in a digital networked information society. The collection of problems and promise of the new digital world may be a cornerstone joining two worlds-pre- and postdigital network society-and we hope this special issue will help better shape our future states of public health.
An improved partial bundle method for linearly constrained minimax problems
Directory of Open Access Journals (Sweden)
Chunming Tang
2016-02-01
Full Text Available In this paper, we propose an improved partial bundle method for solving linearly constrained minimax problems. In order to reduce the number of component function evaluations, we utilize a partial cutting-planes model to substitute for the traditional one. At each iteration, only one quadratic programming subproblem needs to be solved to obtain a new trial point. An improved descent test criterion is introduced to simplify the algorithm. The method produces a sequence of feasible trial points, and ensures that the objective function is monotonically decreasing on the sequence of stability centers. Global convergence of the algorithm is established. Moreover, we utilize the subgradient aggregation strategy to control the size of the bundle and therefore overcome the difficulty of computation and storage. Finally, some preliminary numerical results show that the proposed method is effective.
Improving Researcher-Patient Collaboration through Social Network Websites
Akindayo, Olayiwola; Dopgima, Cynthia
2012-01-01
Purpose: The main purpose of this study/thesis is to, through an interview with researchers in medical field in Jönköping, provide an empirical analysis of the link or relationship between medical researcher and patient through social networking sites specifically for collaboration in order to improve relationships, dissemination of information and knowledge sharing. Background: The importance of social networking websites as a means of interaction between groups of individuals cannot be und...
Process efficiency. Redesigning social networks to improve surgery patient flow.
Samarth, Chandrika N; Gloor, Peter A
2009-01-01
We propose a novel approach to improve throughput of the surgery patient flow process of a Boston area teaching hospital. A social network analysis was conducted in an effort to demonstrate that process efficiency gains could be achieved through redesign of social network patterns at the workplace; in conjunction with redesign of organization structure and the implementation of workflow over an integrated information technology system. Key knowledge experts and coordinators in times of crisis were identified and a new communication structure more conducive to trust and knowledge sharing was suggested. The new communication structure is scalable without compromising on coordination required among key roles in the network for achieving efficiency gains.
Directory of Open Access Journals (Sweden)
Zhaoyu Chen
2018-01-01
Full Text Available The network planning is a key factor that directly affects the performance of the wireless networks. Distributed antenna system (DAS is an effective strategy for the network planning. This paper investigates the antenna deployment in a DAS for the high-speed railway communication networks and formulates an optimization problem which is NP-hard for achieving the optimal deployment of the antennas in the DAS. To solve this problem, a scheme based on an improved cuckoo search based on dimension cells (ICSDC algorithm is proposed. ICSDC introduces the dimension cell mechanism to avoid the internal dimension interferences in order to improve the performance of the algorithm. Simulation results show that the proposed ICSDC-based scheme obtains a lower network cost compared with the uniform network planning method. Moreover, ICSDC algorithm has better performance in terms of the convergence rate and accuracy compared with the conventional cuckoo search algorithm, the particle swarm optimization, and the firefly algorithm.
An analogue of Morse theory for planar linear networks and the generalized Steiner problem
International Nuclear Information System (INIS)
Karpunin, G A
2000-01-01
A study is made of the generalized Steiner problem: the problem of finding all the locally minimal networks spanning a given boundary set (terminal set). It is proposed to solve this problem by using an analogue of Morse theory developed here for planar linear networks. The space K of all planar linear networks spanning a given boundary set is constructed. The concept of a critical point and its index is defined for the length function l of a planar linear network. It is shown that locally minimal networks are local minima of l on K and are critical points of index 1. The theorem is proved that the sum of the indices of all the critical points is equal to χ(K)=1. This theorem is used to find estimates for the number of locally minimal networks spanning a given boundary set
Bilevel programming problems theory, algorithms and applications to energy networks
Dempe, Stephan; Pérez-Valdés, Gerardo A; Kalashnykova, Nataliya; Kalashnikova, Nataliya
2015-01-01
This book describes recent theoretical findings relevant to bilevel programming in general, and in mixed-integer bilevel programming in particular. It describes recent applications in energy problems, such as the stochastic bilevel optimization approaches used in the natural gas industry. New algorithms for solving linear and mixed-integer bilevel programming problems are presented and explained.
A matheuristic for the liner shipping network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy; Pisinger, David
2014-01-01
, while minimizing the cost of operating the network. Liner shipping companies publish a set of routes with a time schedule, and it is an industry standard to have a weekly departure at each port call on a route. A weekly frequency is achieved by deploying several vessels to a single route, respecting...
Analyzing Human Communication Networks in Organizations: Applications to Management Problems.
Farace, Richard V.; Danowski, James A.
Investigating the networks of communication in organizations leads to an understanding of efficient and inefficient information dissemination as practiced in large systems. Most important in organizational communication is the role of the "liaison person"--the coordinator of intercommunication. When functioning efficiently, coordinators maintain…
Inverse problems in eddy current testing using neural network
Yusa, N.; Cheng, W.; Miya, K.
2000-05-01
Reconstruction of crack in conductive material is one of the most important issues in the field of eddy current testing. Although many attempts to reconstruct cracks have been made, most of them deal with only artificial cracks machined with electro-discharge. However, in the case of natural cracks like stress corrosion cracking or inter-granular attack, there must be contact region and therefore their conductivity is not necessarily zero. In this study, an attempt to reconstruct natural cracks using neural network is presented. The neural network was trained through numerical simulated data obtained by the fast forward solver that calculated unflawed potential data a priori to save computational time. The solver is based on A-φ method discretized by using FEM-BEM A natural crack was modeled as an area whose conductivity was less than that of a specimen. The distribution of conductivity in that area was reconstructed as well. It took much time to train the network, but the speed of reconstruction was extremely fast after once it was trained. Well-trained network gave good reconstruction result.
Directory of Open Access Journals (Sweden)
Stavros I. Dimitriadis
2017-12-01
Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node
Energy Technology Data Exchange (ETDEWEB)
Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; /SLAC; Grigoriev, Maxim; /Fermilab; Haro, Felipe; /Chile U., Catolica; Nazir, Fawad; /NUST, Rawalpindi; Sandford, Mark
2006-01-25
End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our
Parallel algorithms for network routing problems and recurrences
International Nuclear Information System (INIS)
Wisniewski, J.A.; Sameh, A.H.
1982-01-01
In this paper, we consider the parallel solution of recurrences, and linear systems in the regular algebra of Carre. These problems are equivalent to solving the shortest path problem in graph theory, and they also arise in the analysis of Fortran programs. Our methods for solving linear systems in the regular algebra are analogues of well-known methods for solving systems of linear algebraic equations. A parallel version of Dijkstra's method, which has no linear algebraic analogue, is presented. Considerations for choosing an algorithm when the problem is large and sparse are also discussed
Directory of Open Access Journals (Sweden)
Sen Tian
2014-01-01
Full Text Available With the development of mine industry, tailings storage facility (TSF, as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP and improved back-propagation (BP neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.
Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui
2017-10-01
Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.
DEFF Research Database (Denmark)
Cetin, Bilge Kartal; Prasad, Neeli R.; Prasad, Ramjee
2011-01-01
In wireless sensor networks, one of the key challenge is to achieve minimum energy consumption in order to maximize network lifetime. In fact, lifetime depends on many parameters: the topology of the sensor network, the data aggregation regime in the network, the channel access schemes, the routing...... protocols, and the energy model for transmission. In this paper, we tackle the routing challenge for maximum lifetime of the sensor network. We introduce a novel linear programming approach to the maximum lifetime routing problem. To the best of our knowledge, this is the first mathematical programming...
Reliability Improved Cooperative Communication over Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Zhuangbin Chen
2017-10-01
Full Text Available With the development of smart devices and connection technologies, Wireless Sensor Networks (WSNs are becoming increasingly intelligent. New or special functions can be obtained by receiving new versions of program codes to upgrade their software systems, forming the so-called smart Internet of Things (IoT. Due to the lossy property of wireless channels, data collection in WSNs still suffers from a long delay, high energy consumption, and many retransmissions. Thanks to wireless software-defined networks (WSDNs, software in sensors can now be updated to help them transmit data cooperatively, thereby achieving more reliable communication. In this paper, a Reliability Improved Cooperative Communication (RICC data collection scheme is proposed to improve the reliability of random-network-coding-based cooperative communications in multi-hop relay WSNs without reducing the network lifetime. In WSNs, sensors in different positions can have different numbers of packets to handle, resulting in the unbalanced energy consumption of the network. In particular, nodes in non-hotspot areas have up to 90% of their original energy remaining when the network dies. To efficiently use the residual energy, in RICC, high data transmission power is adopted in non-hotspot areas to achieve a higher reliability at the cost of large energy consumption, and relatively low transmission power is adopted in hotspot areas to maintain the long network lifetime. Therefore, high reliability and a long network lifetime can be obtained simultaneously. The simulation results show that compared with other scheme, RICC can reduce the end-to-end Message Fail delivering Ratio (MFR by 59.4%–62.8% under the same lifetime with a more balanced energy utilization.
Designing Networked Improvement in a Small-College Context
Rachford, Jennifer L.; Brown, Travis M.; Sambolin, Hector L., Jr.; Seligman, Lenny
2017-01-01
This chapter demonstrates the complexity of pedagogical and curricular change as it unfolds through several overlapping phases of increasingly coordinated reflection and action around STEM initiatives at Pomona College. It argues for a networked model of research and practice, drawing on theory and lessons from improvement science and highlighting…
Improving Artificial Neural Network Forecasts with Kalman Filtering ...
African Journals Online (AJOL)
In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies ...
Camera Network Coverage Improving by Particle Swarm Optimization
Xu, Y.C.; Lei, B.; Hendriks, E.A.
2011-01-01
This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm
Application of artificial neural networks to improve power transfer ...
African Journals Online (AJOL)
Application of artificial neural networks to improve power transfer capability through OLTC. ... International Journal of Engineering, Science and Technology ... Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and the ...
Analysis of multimedian problems on time dependent networks
Salman, F Sibel
1994-01-01
Ankara : The Department of Industrial Engineering and the Institute of Enginering and Science of Bilkent Univ., 1994. Thesis (Master's) -- Bilkent University, 1994. Includes bibliographical references leaves 81-85. Time dependency arises in transportation and computer-communication networks due to factors such as time varying demand, traffic intensity, and road conditions. This necessitates a locational decision to be based on an analysis involving a time horizon. In this st...
Multicast backup reprovisioning problem for Hamiltonian cycle-based protection on WDM networks
Din, Der-Rong; Huang, Jen-Shen
2014-03-01
As networks grow in size and complexity, the chance and the impact of failures increase dramatically. The pre-allocated backup resources cannot provide 100% protection guarantee when continuous failures occur in a network. In this paper, the multicast backup re-provisioning problem (MBRP) for Hamiltonian cycle (HC)-based protection on WDM networks for the link-failure case is studied. We focus on how to recover the protecting capabilities of Hamiltonian cycle against the subsequent link-failures on WDM networks for multicast transmissions, after recovering the multicast trees affected by the previous link-failure. Since this problem is a hard problem, an algorithm, which consists of several heuristics and a genetic algorithm (GA), is proposed to solve it. The simulation results of the proposed method are also given. Experimental results indicate that the proposed algorithm can solve this problem efficiently.
DEFF Research Database (Denmark)
Karsten, Christian Vad; Pisinger, David; Røpke, Stefan
2015-01-01
-commodity network flow problem with transit time constraints which puts limits on the duration of the transit of the commodities through the network. It is shown that for the particular application it does not increase the solution time to include the transit time constraints and that including the transit time...... is essential to offer customers a competitive product. © 2015 Elsevier Ltd. All rights reserved....
Interacting with Users in Social Networks: The Follow-back Problem
2016-05-02
These functions are known as network centrali- ties. They quantify how central a vertex is to the problem at hand, with the definition of centrality ...56 4.2.2 Twitter networks . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2.3 Network centrality policies...tool can hinder the free movement of alternative ideas and information and thus can be analyzed through the A2/AD paradigm. Non-state adversaries have
Symposium Connects Government Problems with State of the Art Network Science Research
2015-10-16
Symposium Connects Government Problems with State-of-the- Art Network Science Research By Rajmonda S. Caceres and Benjamin A. Miller Network...the US Gov- ernment, and match these with the state-of-the- art models and techniques developed in the network science research community. Since its... science has grown significantly in the last several years as a field at the intersec- tion of mathematics, computer science , social science , and engineering
Computation of optimal transport and related hedging problems via penalization and neural networks
Eckstein, Stephan; Kupper, Michael
2018-01-01
This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks. The core idea is to penalize the optimization problem in its dual formulation and reduce it to a finite dimensional one which corresponds to optimizing a neural network with smooth objective function. We present numerical examples from optimal transport, martingale optimal transport, portfolio optimization under uncertainty and generative adversa...
Improving the recommender algorithms with the detected communities in bipartite networks
Zhang, Peng; Wang, Duo; Xiao, Jinghua
2017-04-01
Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems.
Brands, Ties; van Berkum, Eric C.
2014-01-01
The optimization of infrastructure planning in a multimodal network is defined as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train
Improved personalized recommendation based on a similarity network
Wang, Ximeng; Liu, Yun; Xiong, Fei
2016-08-01
A recommender system helps individual users find the preferred items rapidly and has attracted extensive attention in recent years. Many successful recommendation algorithms are designed on bipartite networks, such as network-based inference or heat conduction. However, most of these algorithms define the resource-allocation methods for an average allocation. That is not reasonable because average allocation cannot indicate the user choice preference and the influence between users which leads to a series of non-personalized recommendation results. We propose a personalized recommendation approach that combines the similarity function and bipartite network to generate a similarity network that improves the resource-allocation process. Our model introduces user influence into the recommender system and states that the user influence can make the resource-allocation process more reasonable. We use four different metrics to evaluate our algorithms for three benchmark data sets. Experimental results show that the improved recommendation on a similarity network can obtain better accuracy and diversity than some competing approaches.
International Nuclear Information System (INIS)
Carlson, K.E.; Ransom, V.H.; Roth, P.A.
1987-03-01
The ATHENA (Advanced Thermal Hydraulic Energy Network Analyzer) code has been developed to perform transient simulation of the thermal hydraulic systems that may be found in fusion reactors, space reactors, and other advanced systems. As an assessment of current capability the code was applied to a number of physical problems, both conceptual and actual experiments. Results indicate that the numerical solution to the basic conservation equations is technically sound, and that generally good agreement can be obtained when modeling relevant hydrodynamic experiments. The assessment also demonstrates basic fusion system modeling capability and verifies compatibility of the code with both CDC and CRAY mainframes. Areas where improvements could be made include constitutive modeling, which describes the interfacial exchange term. 13 refs., 84 figs
Network-targeted cerebellar transcranial magnetic stimulation improves attentional control
Esterman, Michael; Thai, Michelle; Okabe, Hidefusa; DeGutis, Joseph; Saad, Elyana; Laganiere, Simon E.; Halko, Mark A.
2018-01-01
Developing non-invasive brain stimulation interventions to improve attentional control is extremely relevant to a variety of neurologic and psychiatric populations, yet few studies have identified reliable biomarkers that can be readily modified to improve attentional control. One potential biomarker of attention is functional connectivity in the core cortical network supporting attention - the dorsal attention network (DAN). We used a network-targeted cerebellar transcranial magnetic stimulation (TMS) procedure, intended to enhance cortical functional connectivity in the DAN. Specifically, in healthy young adults we administered intermittent theta burst TMS (iTBS) to the midline cerebellar node of the DAN and, as a control, the right cerebellar node of the default mode network (DMN). These cerebellar targets were localized using individual resting-state fMRI scans. Participants completed assessments of both sustained (gradual onset continuous performance task, gradCPT) and transient attentional control (attentional blink) immediately before and after stimulation, in two sessions (cerebellar DAN and DMN). Following cerebellar DAN stimulation, participants had significantly fewer attentional lapses (lower commission error rates) on the gradCPT. In contrast, stimulation to the cerebellar DMN did not affect gradCPT performance. Further, in the DAN condition, individuals with worse baseline gradCPT performance showed the greatest enhancement in gradCPT performance. These results suggest that temporarily increasing functional connectivity in the DAN via network-targeted cerebellar stimulation can enhance sustained attention, particularly in those with poor baseline performance. With regard to transient attention, TMS stimulation improved attentional blink performance across both stimulation sites, suggesting increasing functional connectivity in both networks can enhance this aspect of attention. These findings have important implications for intervention applications
Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks
Directory of Open Access Journals (Sweden)
D. Hämäläinen
2008-03-01
Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%Ã¢Â€Â“76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.
Comparison of neural networks for solving the travelling salesman problem
Maire, La B.F.J.; Mladenov, V.M.
2012-01-01
The TSP deals with finding a shortest path through a number of cities. This seemingly simple problem is hard to solve because of the amount of possible solutions. Which is why methods that give a good suboptimal solution in a reasonable time are generally used. In this paper three methods were
Adolescent problem behavior in school : the role of peer networks
Geven, S.A.J.
2016-01-01
Adolescence is a notable period during which a considerable share of students tends to engage in problem behavior in school. Students for example skip class, fail to do their best in school, or have serious arguments with their teachers. A student’s decision to engage in such behavior is not usually
On the Integrated Job Scheduling and Constrained Network Routing Problem
DEFF Research Database (Denmark)
Gamst, Mette
This paper examines the NP-hard problem of scheduling a number of jobs on a finite set of machines such that the overall profit of executed jobs is maximized. Each job demands a number of resources, which must be sent to the executing machine via constrained paths. Furthermore, two resource demand...
Improved result on stability analysis of discrete stochastic neural networks with time delay
International Nuclear Information System (INIS)
Wu Zhengguang; Su Hongye; Chu Jian; Zhou Wuneng
2009-01-01
This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.
Deep learning classification in asteroseismology using an improved neural network
DEFF Research Database (Denmark)
Hon, Marc; Stello, Dennis; Yu, Jie
2018-01-01
Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic...... frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower...
An Improved Convolutional Neural Network on Crowd Density Estimation
Directory of Open Access Journals (Sweden)
Pan Shao-Yun
2016-01-01
Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.
Kadir, Z. Abdul; Abdullah, N. H.; Anthony, E.; Salleh, B. Mohd; Kamarulzaman, R.
2016-01-01
Problem-based Learning (PBL) approach has been widely used in various disciplines since it is claimed to improve students' soft skills. However, empirical supports on the effect of PBL on problem solving skills have been lacking and anecdotal in nature. This study aimed to determine the effect of PBL approach on students' problem solving skills…
Panda, Satyasen
2018-05-01
This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.
Jordan, Katy; Weller, Martin
2018-01-01
The web has had a profound effect on the ways people interact, with online social networks arguably playing an important role in changing or augmenting how we connect with others. However, uptake of online social networking by the academic community varies, and needs to be understood. This paper presents an independent, novel analysis of a large-scale dataset published by Nature Publishing Group detailing the results of a survey about academics use of online social networking services. An ope...
Numerical Analysis of Modeling Based on Improved Elman Neural Network
Directory of Open Access Journals (Sweden)
Shao Jie
2014-01-01
Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.
Improving deep convolutional neural networks with mixed maxout units.
Directory of Open Access Journals (Sweden)
Hui-Zhen Zhao
Full Text Available Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.
Improving assessment of personality disorder traits through social network analysis.
Clifton, Allan; Turkheimer, Eric; Oltmanns, Thomas F
2007-10-01
When assessing personality disorder traits, not all judges make equally valid judgments of all targets. The present study uses social network analysis to investigate factors associated with reliability and validity in peer assessment. Participants were groups of military recruits (N=809) who acted as both targets and judges in a round-robin design. Participants completed self- and informant versions of the Multisource Assessment of Personality Pathology. Social network matrices were constructed based on reported acquaintance, and cohesive subgroups were identified. Judges who shared a mutual subgroup were more reliable and had higher self-peer agreement than those who did not. Partitioning networks into two subgroups achieved more consistent improvements than multiple subgroups. We discuss implications for multiple informant assessments.
Agribusiness networks in Bulgaria: Design and creative problem-solving
Directory of Open Access Journals (Sweden)
Doitchinova Julia
2017-01-01
Full Text Available With the increasing integration of the global economy and the complex challenges of the business environment it is becoming crucial to focus and gain a full understanding on the role of the value chains' structure and functioning. This particularly refers to the countries from the post-communist Europe and their transformation and progress achieved in marketization and democratization. The present paper is purposeful towards providing an overall framework for assessment of the different forms of network structures in the agricultural sector and to identify as well their capacity to counteract market restrictions, and to benefit form the opportunities of the agribusiness development. The methodological framework bases on the transaction costs economics and the 4C concept. The paper presents methodology at two stages, where results of expert assessment and evaluation at national and regional level led to selection of three case studies to be presented in certain sectors of agribusiness.
Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks.
Bai, Weigang; Wang, Haiyan; He, Ke; Zhao, Ruiqin
2018-04-23
The packets carried along a pre-defined route in underwater sensor networks are very vulnerble. Node mobility or intermittent channel availability easily leads to unreachable routing. Opportunistic routing has been proven to be a promising paradigm to design routing protocols for underwater sensor networks. It takes advantage of the broadcast nature of the wireless medium to combat packet losses and selects potential paths on the fly. Finding an appropriate forwarding candidate set is a key issue in opportunistic routing. Many existing solutions ignore the impact of candidates location distribution on packet forwarding. In this paper, a path diversity improved candidate selection strategy is applied in opportunistic routing to improve packet forwarding efficiency. It not only maximizes the packet forwarding advancements but also takes the candidate’s location distribution into account. Based on this strategy, we propose two effective routing protocols: position improved candidates selection (PICS) and position random candidates selection (PRCS). PICS employs two-hop neighbor information to make routing decisions. PRCS only uses one-hop neighbor information. Simulation results show that both PICS and PRCS can significantly improve network performance when compared with the previous solutions, in terms of packet delivery ratio, average energy consumption and end-to-end delay.
Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Weigang Bai
2018-04-01
Full Text Available The packets carried along a pre-defined route in underwater sensor networks are very vulnerble. Node mobility or intermittent channel availability easily leads to unreachable routing. Opportunistic routing has been proven to be a promising paradigm to design routing protocols for underwater sensor networks. It takes advantage of the broadcast nature of the wireless medium to combat packet losses and selects potential paths on the fly. Finding an appropriate forwarding candidate set is a key issue in opportunistic routing. Many existing solutions ignore the impact of candidates location distribution on packet forwarding. In this paper, a path diversity improved candidate selection strategy is applied in opportunistic routing to improve packet forwarding efficiency. It not only maximizes the packet forwarding advancements but also takes the candidate’s location distribution into account. Based on this strategy, we propose two effective routing protocols: position improved candidates selection (PICS and position random candidates selection (PRCS. PICS employs two-hop neighbor information to make routing decisions. PRCS only uses one-hop neighbor information. Simulation results show that both PICS and PRCS can significantly improve network performance when compared with the previous solutions, in terms of packet delivery ratio, average energy consumption and end-to-end delay.
Assessment of network inference methods: how to cope with an underdetermined problem.
Directory of Open Access Journals (Sweden)
Caroline Siegenthaler
Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.
A two-layer recurrent neural network for nonsmooth convex optimization problems.
Qin, Sitian; Xue, Xiaoping
2015-06-01
In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.
Converging Redundant Sensor Network Information for Improved Building Control
Energy Technology Data Exchange (ETDEWEB)
Dale Tiller; D. Phil; Gregor Henze; Xin Guo
2007-09-30
This project investigated the development and application of sensor networks to enhance building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, but current sensor technology and control algorithms limit the effectiveness of these systems. For example, most of these systems rely on single monitoring points to detect occupancy, when more than one monitoring point could improve system performance. Phase I of the project focused on instrumentation and data collection. During the initial project phase, a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Phase II of the project demonstrated that a network of several sensors provides a more accurate measure of occupancy than is possible using systems based on single monitoring points. This phase also established that analysis algorithms could be applied to the sensor network data stream to improve the accuracy of system performance in energy management and security applications. In Phase III of the project, the sensor network from Phase I was complemented by a control strategy developed based on the results from the first two project phases: this controller was implemented in a small sample of work areas, and applied to lighting control. Two additional technologies were developed in the course of completing the project. A prototype web-based display that portrays the current status of each detector in a sensor network monitoring building occupancy was designed and implemented. A new capability that enables occupancy sensors in a sensor network to dynamically set the 'time delay' interval based on ongoing occupant behavior in the space was also designed and implemented.
Consensus problem in directed networks of multi-agents via nonlinear protocols
International Nuclear Information System (INIS)
Liu Xiwei; Chen Tianping; Lu Wenlian
2009-01-01
In this Letter, the consensus problem via distributed nonlinear protocols for directed networks is investigated. Its dynamical behaviors are described by ordinary differential equations (ODEs). Based on graph theory, matrix theory and the Lyapunov direct method, some sufficient conditions of nonlinear protocols guaranteeing asymptotical or exponential consensus are presented and rigorously proved. The main contribution of this work is that for nonlinearly coupled networks, we generalize the results for undirected networks to directed networks. Consensus under pinning control technique is also developed here. Simulations are also given to show the validity of the theories.
A non-penalty recurrent neural network for solving a class of constrained optimization problems.
Hosseini, Alireza
2016-01-01
In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map satisfies some conditions, then solution trajectory of the differential inclusion converges to optimal solution set of its corresponding in optimization problem. Based on the obtained methodology, we introduce a new recurrent neural network for solving nonsmooth optimization problems. Objective function does not need to be convex on R(n) nor does the new neural network model require any penalty parameter. We compare our new method with some penalty-based and non-penalty based models. Moreover for differentiable cases, we implement circuit diagram of the new neural network. Copyright © 2015 Elsevier Ltd. All rights reserved.
Composite mechanisms for improving Bubble Rap in delay tolerant networks
Directory of Open Access Journals (Sweden)
Sweta Jain
2014-01-01
Full Text Available Delay tolerant networks (DTNs are a subset of mobile ad hoc networks where connections are sparse and intermittent. This often results in a network graph which is rarely connected which introduces a challenge in message forwarding because of a lack of end-to-end connectivity towards the destination. Recently, social-based forwarding algorithms are gaining popularity because of the social nature displayed by the node movements in a DTN, especially in application areas like the pocket switched networks. The social-based metrics like community, similarity, centrality etc. are used to determine the carrier to which a node has to forward its message. Composite methods are used to improve the performance of Bubble Rap social-based forwarding algorithm. In the proposed mechanism, a new social metric termed ‘friendship’ has been introduced along with a time-to-live (TTL-based ‘threshold’ and acknowledgement (ACK IDs. Real trace data and working day movement models are used for simulations in the opportunistic network environment simulator to demonstrate that the proposed algorithm gives better delivery ratio than the original Bubble Rap algorithm.
Improving mathematical problem solving skills through visual media
Widodo, S. A.; Darhim; Ikhwanudin, T.
2018-01-01
The purpose of this article was to find out the enhancement of students’ mathematical problem solving by using visual learning media. The ability to solve mathematical problems is the ability possessed by students to solve problems encountered, one of the problem-solving model of Polya. This preliminary study was not to make a model, but it only took a conceptual approach by comparing the various literature of problem-solving skills by linking visual learning media. The results of the study indicated that the use of learning media had not been appropriated so that the ability to solve mathematical problems was not optimal. The inappropriateness of media use was due to the instructional media that was not adapted to the characteristics of the learners. Suggestions that can be given is the need to develop visual media to increase the ability to solve problems.
Jordan, Katy; Weller, Martin
2018-01-01
The web has had a profound effect on the ways people interact, with online social networks arguably playing an important role in changing or augmenting how we connect with others. However, uptake of online social networking by the academic community varies, and needs to be understood. This paper presents an independent, novel analysis of a…
A polynomial time algorithm for solving the maximum flow problem in directed networks
International Nuclear Information System (INIS)
Tlas, M.
2015-01-01
An efficient polynomial time algorithm for solving maximum flow problems has been proposed in this paper. The algorithm is basically based on the binary representation of capacities; it solves the maximum flow problem as a sequence of O(m) shortest path problems on residual networks with nodes and m arcs. It runs in O(m"2r) time, where is the smallest integer greater than or equal to log B , and B is the largest arc capacity of the network. A numerical example has been illustrated using this proposed algorithm.(author)
A Branch and Cut algorithm for the container shipping network design problem
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Pisinger, David
2012-01-01
The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear...... programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependant capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers...
Network Model for The Problem of Integer Balancing of a Fourdimensional Matrix
Directory of Open Access Journals (Sweden)
A. V. Smirnov
2016-01-01
Full Text Available The problem of integer balancing of a four-dimensional matrix is studied. The elements of the inner part (all four indices are greater than zero of the given real matrix are summed in each direction and each two- and three-dimensional section of the matrix; the total sum is also found. These sums are placed into the elements where one or more indices are equal to zero (according to the summing directions. The problem is to find an integer matrix of the same structure, which can be produced from the initial one by replacing the elements with the largest previous or the smallest following integer. At the same time, the element with four zero indices should be produced with standard rules of rounding - off. In the article the problem of finding the maximum multiple flow in the network of any natural multiplicity is also studied. There are arcs of three types: ordinary arcs, multiple arcs and multi-arcs. Each multiple and multi-arc is a union of linked arcs, which are adjusted with each other. The network constructing rules are described. The definitions of a divisible network and some associated subjects are stated. There are defined the basic principles for reducing the integer balancing problem of an -dimensional matrix ( to the problem of finding the maximum flow in a divisible multiple network of multiplicity . There are stated the rules for reducing the four-dimensional balancing problem to the maximum flow problem in the network of multiplicity 5. The algorithm of finding the maximum flow, which meets the solvability conditions for the integer balancing problem, is formulated for such a network.
The simplest problem in the collective dynamics of neural networks: is synchrony stable?
International Nuclear Information System (INIS)
Timme, Marc; Wolf, Fred
2008-01-01
For spiking neural networks we consider the stability problem of global synchrony, arguably the simplest non-trivial collective dynamics in such networks. We find that even this simplest dynamical problem—local stability of synchrony—is non-trivial to solve and requires novel methods for its solution. In particular, the discrete mode of pulsed communication together with the complicated connectivity of neural interaction networks requires a non-standard approach. The dynamics in the vicinity of the synchronous state is determined by a multitude of linear operators, in contrast to a single stability matrix in conventional linear stability theory. This unusual property qualitatively depends on network topology and may be neglected for globally coupled homogeneous networks. For generic networks, however, the number of operators increases exponentially with the size of the network. We present methods to treat this multi-operator problem exactly. First, based on the Gershgorin and Perron–Frobenius theorems, we derive bounds on the eigenvalues that provide important information about the synchronization process but are not sufficient to establish the asymptotic stability or instability of the synchronous state. We then present a complete analysis of asymptotic stability for topologically strongly connected networks using simple graph-theoretical considerations. For inhibitory interactions between dissipative (leaky) oscillatory neurons the synchronous state is stable, independent of the parameters and the network connectivity. These results indicate that pulse-like interactions play a profound role in network dynamical systems, and in particular in the dynamics of biological synchronization, unless the coupling is homogeneous and all-to-all. The concepts introduced here are expected to also facilitate the exact analysis of more complicated dynamical network states, for instance the irregular balanced activity in cortical neural networks
Report: Improved Management Practices Needed to Increase Use of Exchange Network
Report #2007-P-00030, August 20, 2007. EPA established a partnership with the Exchange Network’s governance bodies to assist them with accomplishing Network initiatives, more improvements are needed to ensure Network partners fully utilize the Network.
Du, Tingsong; Hu, Yang; Ke, Xianting
2015-01-01
An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.
Some improvements to the solution of Stefan-like problems
International Nuclear Information System (INIS)
El-Genk, M.S.; Cronenberg, W.
1979-01-01
Two approximate analytical methods are developed for solving one-dimensional transient heat-conduction problems with phase transformation, where the growth rate of a frozen crust (layer) on a cold wall is sought. Both provide an accurate prediction of the instantaneous position of the moving boundary as applied to one dimensional melting and freezing problems. (author)
Solution Algorithm for a New Bi-Level Discrete Network Design Problem
Directory of Open Access Journals (Sweden)
Qun Chen
2013-12-01
Full Text Available A new discrete network design problem (DNDP was pro-posed in this paper, where the variables can be a series of integers rather than just 0-1. The new DNDP can determine both capacity improvement grades of reconstruction roads and locations and capacity grades of newly added roads, and thus complies with the practical projects where road capacity can only be some discrete levels corresponding to the number of lanes of roads. This paper designed a solution algorithm combining branch-and-bound with Hooke-Jeeves algorithm, where feasible integer solutions are recorded in searching the process of Hooke-Jeeves algorithm, lend -ing itself to determine the upper bound of the upper-level problem. The thresholds for branch cutting and ending were set for earlier convergence. Numerical examples are given to demonstrate the efficiency of the proposed algorithm.
Analysis of Power Network for Line Reactance Variation to Improve Total Transmission Capacity
Directory of Open Access Journals (Sweden)
Ikram Ullah
2016-11-01
Full Text Available The increasing growth in power demand and the penetration of renewable distributed generations in competitive electricity market demands large and flexible capacity from the transmission grid to reduce transmission bottlenecks. The bottlenecks cause transmission congestion, reliability problems, restrict competition, and limit the maximum dispatch of low cost generations in the network. The electricity system requires efficient utilization of the current transmission capability to improve the Available Transfer Capability (ATC. To improve the ATC, power flow among the lines can be managed by using Flexible AC Transmission System (FACTS devices as power flow controllers, which alter the parameters of power lines. It is important to place FACTS devices on suitable lines to vary the reactance for improving Total Transmission Capacity (TTC of the network and provide flexibility in the power flow. In this paper a transmission network is analyzed based on line parameters variation to improve TTC of the interconnected system. Lines are selected for placing FACTS devices based on real power flow Performance Index (PI sensitivity factors. TTC is computed using the Repeated Power Flow (RPF method using the constraints of lines thermal limits, bus voltage limits and generator limits. The reactance of suitable lines, selected on the basis of PI sensitivity factors are changed to divert the power flow to other lines with enough transfer capacity available. The improvement of TTC using line reactance variation is demonstrated with three IEEE test systems with multi-area networks. The results show the variation of the selected lines’ reactance in improving TTC for all the test networks with defined contingency cases.
Relevant problems in collaborative processes of non-hierarchical manufacturing networks
Directory of Open Access Journals (Sweden)
Beatriz Andrés
2013-07-01
Full Text Available Purpose: The purpose of this paper is to identify some of existing problems associated with collaboration among SMEs of the same network. Concretely, the problems are focused. The research objective is to identify the most relevant problems existing when SMEs have to deal with decentralized decisions (DDM. Design/methodology/approach: Through the literature review there have been collected collaborative problems caused by inter-organizational barriers. The approach taken is a qualitative study and analysis that classifies collaborative problems from less important to very important. In light of this, we are able to identify what are the most relevant problems to study in the NHN collaborative context. Findings and Originality/value: The developed methodology allows researchers to indentify amongst the collaborative problems those that are most relevant to solve in the NHN context, with the main aim of providing solutions in the future. The research aim is to provide the expert in the collaborative field a starting point to address the collaborative problems SMEs can find when belonging to collaborative networks. Research limitations/implications: Not all the problems that appear when an SME establish collaborative relationships, in a NHN, are considered. The identified problems have been arisen because there are discussed in the literature for addressing collaborative problems among networked partners. Identified problems are also considered because there are relevant to achieve collaboration among SMEs. Originality/value: The degree of coverage and the degree of significance is the taxonomy criteria used to identify the importance of solution degree of the encountered collaborative problems, in NHN context, in order to provide a future research of solutions to overcome them.
Improved Lower Bounds on the Price of Stability of Undirected Network Design Games
Bilò, Vittorio; Caragiannis, Ioannis; Fanelli, Angelo; Monaco, Gianpiero
Bounding the price of stability of undirected network design games with fair cost allocation is a challenging open problem in the Algorithmic Game Theory research agenda. Even though the generalization of such games in directed networks is well understood in terms of the price of stability (it is exactly H n , the n-th harmonic number, for games with n players), far less is known for network design games in undirected networks. The upper bound carries over to this case as well while the best known lower bound is 42/23 ≈ 1.826. For more restricted but interesting variants of such games such as broadcast and multicast games, sublogarithmic upper bounds are known while the best known lower bound is 12/7 ≈ 1.714. In the current paper, we improve the lower bounds as follows. We break the psychological barrier of 2 by showing that the price of stability of undirected network design games is at least 348/155 ≈ 2.245. Our proof uses a recursive construction of a network design game with a simple gadget as the main building block. For broadcast and multicast games, we present new lower bounds of 20/11 ≈ 1.818 and 1.862, respectively.
Decentralized coverage control problems for mobile robotic sensor and actuator networks
Savkin, A; Xi, Z; Javed, F; Matveev, A; Nguyen, H
2015-01-01
This book introduces various coverage control problems for mobile sensor networks including barrier, sweep and blanket. Unlike many existing algorithms, all of the robotic sensor and actuator motion algorithms developed in the book are fully decentralized or distributed, computationally efficient, easily implementable in engineering practice and based only on information on the closest neighbours of each mobile sensor and actuator and local information about the environment. Moreover, the mobile robotic sensors have no prior information about the environment in which they operation. These various types of coverage problems have never been covered before by a single book in a systematic way. Another topic of this book is the study of mobile robotic sensor and actuator networks. Many modern engineering applications include the use of sensor and actuator networks to provide efficient and effective monitoring and control of industrial and environmental processes. Such mobile sensor and actuator networks are abl...
The application of deep confidence network in the problem of image recognition
Directory of Open Access Journals (Sweden)
Chumachenko О.І.
2016-12-01
Full Text Available In order to study the concept of deep learning, in particular the substitution of multilayer perceptron on the corresponding network of deep confidence, computer simulations of the learning process to test voters was carried out. Multi-layer perceptron has been replaced by a network of deep confidence, consisting of successive limited Boltzmann machines. After training of a network of deep confidence algorithm of layer-wise training it was found that the use of networks of deep confidence greatly improves the accuracy of multilayer perceptron training by method of reverse distribution errors.
A Bio-Inspired Approach to Traffic Network Equilibrium Assignment Problem.
Zhang, Xiaoge; Mahadevan, Sankaran
2018-04-01
Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes. At last, we demonstrate that the Physarum solver converges to the user-optimized (Wardrop) equilibrium by dynamically updating the costs of links in the network. In addition, convergence of the developed algorithm is proved. Numerical examples are used to demonstrate the efficiency of the proposed algorithm. The superiority of the proposed algorithm is demonstrated in comparison with several other algorithms, including the Frank-Wolfe algorithm, conjugate Frank-Wolfe algorithm, biconjugate Frank-Wolfe algorithm, and gradient projection algorithm.
International Nuclear Information System (INIS)
Mohammadi, A.; Varahram, M.H.
2007-01-01
In this study, two methods for solving economic dispatch problems, namely Hopfield neural network and lambda iteration method are compared. Three sample of power system with 3, 6 and 20 units have been considered. The time required for CPU, for solving economic dispatch of these two systems has been calculated. It has been Shown that for on-line economic dispatch, Hopfield neural network is more efficient and the time required for Convergence is considerably smaller compared to classical methods. (author)
2012-09-13
46, 1989. [75] S. Melkote and M.S. Daskin . An integrated model of facility location and transportation network design. Transportation Research Part A ... a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT/DS/ENS/12-09 THE AVERAGE NETWORK FLOW PROBLEM...focused thinking (VFT) are used sparingly, as is the case across the entirety of the supply chain literature. We provide a VFT tutorial for supply chain
A problem of finding an acceptable variant in generalized project networks
Directory of Open Access Journals (Sweden)
David Blokh
2005-01-01
Full Text Available A project network often has some activities or groups of activities which can be performed at different stages of the project. Then, the problem of finding an optimal/acceptable time or/and optimal/acceptable order of such an activity or a group of activities arises. Such a problem emerges, in particular, in house-building management when the beginnings of some activities may vary in time or/and order. We consider a mathematical formulation of the problem, show its computational complexity, and describe an algorithm for solving the problem.
Phylogenetically informed logic relationships improve detection of biological network organization
2011-01-01
Background A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction. PMID:22172058
Problem-Solving Methods for the Prospective Development of Urban Power Distribution Network
Directory of Open Access Journals (Sweden)
A. P. Karpenko
2014-01-01
Full Text Available This article succeeds the former A. P. K nko’ and A. I. Kuzmina’ ubl t on titled "A mathematical model of urban distribution electro-network considering its future development" (electronic scientific and technical magazine "Science and education" No. 5, 2014.The article offers a model of urban power distribution network as a set of transformer and distribution substations and cable lines. All elements of the network and new consumers are determined owing to vectors of parameters consistent with them.A problem of the urban power distribution network design, taking into account a prospective development of the city, is presented as a problem of discrete programming. It is in deciding on the optimal option to connect new consumers to the power supply network, on the number and sites to build new substations, and on the option to include them in the power supply network.Two methods, namely a reduction method for a set the nested tasks of global minimization and a decomposition method are offered to solve the problem.In reduction method the problem of prospective development of power supply network breaks into three subtasks of smaller dimension: a subtask to define the number and sites of new transformer and distribution substations, a subtask to define the option to connect new consumers to the power supply network, and a subtask to include new substations in the power supply network. The vector of the varied parameters is broken into three subvectors consistent with the subtasks. Each subtask is solved using an area of admissible vector values of the varied parameters at the fixed components of the subvectors obtained when solving the higher subtasks.In decomposition method the task is presented as a set of three, similar to reduction method, reductions of subtasks and a problem of coordination. The problem of coordination specifies a sequence of the subtasks solution, defines the moment of calculation termination. Coordination is realized by
Nasertdinova, A. D.; Bochkarev, V. V.
2017-11-01
Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.
Directory of Open Access Journals (Sweden)
Haorui Liu
2016-01-01
Full Text Available In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF, longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects.
An improved recommended algorithm for network structure based on two partial graphs
Directory of Open Access Journals (Sweden)
Deng Song
2017-08-01
Full Text Available In this thesis,we introduce an improved algorithm based on network structure.Based on the standard material diffusion algorithm,considering the influence of the user's score on the recommendation,the adjustment factor of the initial resource allocation vector and the resource transfer matrix in the recommendation algorithm is improved.Using the practical data set from GroupLens webite to evaluate the performance of the proposed algorithm,we performed a series of experiments.The experimental results reveal that it can yield better recommendation accuracy and has higher hitting rate than collaborative filtering,network-based inference.It can solve the problem of cold start and scalability in the standard material diffusion algorithm.And it also can make the recommendation results diversified.
Identifying and localizing network problems using the PuNDIT project
International Nuclear Information System (INIS)
Batista, Jorge; McKee, Shawn; Dovrolis, Constantine; Lee, Danny
2015-01-01
In today's world of distributed collaborations of scientists, there are many challenges to providing effective infrastructures to couple these groups of scientists with their shared computing and storage resources. The Pythia Network Diagnostic InfrasTructure (PuNDIT[1]) project is integrating and scaling research tools and creating robust code suitable for operational needs addressing the difficult challenge of automating the detection and location of network problems.PuNDIT is building upon the de-facto standard perfSONAR[2] network measurement infrastructure deployed in Open Science Grid(OSG)[3] and the Worldwide LHC Computing Grid(WLCG)[4]to gather and analyze complex real-world network topologies coupled with their corresponding network metrics to identify possible signatures of network problems from a set of symptoms. The PuNDIT Team is working closely with the perfSONAR developers from ESnet and Internet2 to integrate PuNDIT components as part of the perfSONAR Toolkit. A primary goal for PuNDIT is to convert complex network metrics into easily understood diagnoses in an automated way. We will report on the project progress to-date in working with the OSG and WLCG communities, describe the current implementation including some initial results and discuss future plans and the project timeline. (paper)
Control room annunciation - problem assessment and selection of improvement priorities
International Nuclear Information System (INIS)
Hartley, P.; Yaraskavitch, E.; Davey, E.
1998-01-01
In 1997, Pickering B undertook a project to examine current annunciation practice and identify improvement opportunities and priorities. The objectives and scope of the study were to: document the deficiencies with control room annunciation and the subsequent operational and financial impacts to station operations, develop an operations-based definition of the requirements for annunciation to adequately support control room staff, propose annunciation improvements based on a comparison of the annunciation deficiencies identified and the operational needs to be met, assess the relative operational impact, and financial benefits and costs of the improvement initiatives proposed, and recommend annunciation improvement priorities that offer a mix of operational and financial return for improvement investment. This paper discusses the rationale for the project, outlines the approaches applied in achieving the assessment objectives, reviews the key assessment findings and describes the improvement initiatives recommended. (author)
A Branch and Cut algorithm for the container shipping network design problem
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Kallehauge, Brian; Pisinger, David
The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear...... programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependant capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers...... transhipment cost. The problem is solved with branch-and-cut using clover and transhipment inequalities. Computational results are reported for instances with up to 15 ports....
Sánchez-Pérez, J F; Marín, F; Morales, J L; Cánovas, M; Alhama, F
2018-01-01
Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model.
2018-01-01
Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model. PMID:29518121
Neural networks improve performance of coal-fired boilers
Energy Technology Data Exchange (ETDEWEB)
Radl, B.J. [Pegasus Technologies Ltd., Painesville, OH (United States)
1999-03-01
Work sponsored by the US Department of Energy through its NICE{sup 3} programme, and co-funded by industry partners First Energy Corp. (host organisation and co-funder) and Pegasus Technologies (inventor, developer and supplier), has resulted in the development of online, real-time neural networks which help coal-fired utility boilers to dynamically adjust combustion setpoints. The payoff is a system which helps reduce NOx emissions up to 60%, while improving heat rate up to 2% overall. The system has avoided or postponed large capacity expenditures while meeting environmental compliance requirements. 3 figs., 1 tab.
Definition of new 3D invariants. Applications to pattern recognition problems with neural networks
International Nuclear Information System (INIS)
Proriol, J.
1996-01-01
We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the defined 3D invariants which can be used with neural networks to solve pattern recognition problems. We describe some methods to use the 2 D approximants. This work is an extension of previous 3D invariants used to solve some high energy physics problems. (author)
Latorre, Vittorio
2014-01-01
We propose to solve large instances of the non-convex optimization problems reformulated with canonical duality theory. To this aim we propose an interior point potential reduction algorithm based on the solution of the primal-dual total complementarity (Lagrange) function. We establish the global convergence result for the algorithm under mild assumptions and demonstrate the method on instances of the Sensor Network Localization problem. Our numerical results are promising and show the possi...
Young doctors' problem solving strategies on call may be improved
DEFF Research Database (Denmark)
Michelsen, Jens; Malchow-Møller, Axel; Charles, Peder
2013-01-01
The first year following graduation from medical school is challenging as learning from books changes to workplace-based learning. Analysis and reflection on experience may ease this transition. We used Significant Event Analysis (SEA) as a tool to explore what pre-registration house officers (PR...... (PRHOs) consider successful and problematic events, and to identify what problem-solving strategies they employ....
Improving the psychometric properties of the Mooney problem ...
African Journals Online (AJOL)
This study aims to examine the psychometric characteristics of Mooney Problem Checklist (MPCL) items using the Rasch measurement model framework in the context of polytechnics. The MPCL with eleven dimensions was administered to 252 respondents who were selected from seven polytechnic institutions in Malaysia ...
Do Social Networks Improve Chinese Adults' Subjective Well-being?
Lei, Xiaoyan; Shen, Yan; Smith, James P; Zhou, Guangsu
2015-12-01
This paper studies relationships between social networks, health and subjective well-being (SWB) using nationally representative data of the Chinese Population-the Chinese Family Panel Studies (CFPS). Our data contain SWB indicators in two widely used variants-happiness and life-satisfaction. Social network variables used include kinship relationships measured by marital status, family size, and having a genealogy; ties with friends/relatives/neighbors measured by holiday visitation, frequency of contacts, and whether and value gifts given and received; total number and time spent in social activities, and engagement in organizations including the communist party, religious groups, and other types. We find that giving and receiving gifts has a larger impact on SWB than either just giving or receiving them. Similarly the number of friends is more important than number of relatives, and marriage is associated with higher levels of SWB. Time spent in social activities and varieties of activities both matter for SWB but varieties matter more. Participation in organizations is associated with higher SWB across such diverse groups as being a member of the communist party or a religious organization. China represents an interesting test since it is simultaneously a traditional society with long-established norms about appropriate social networks and a rapidly changing society due to substantial economic and demographic changes. We find that it is better to both give and receive, to engage in more types of social activities, and that participation in groups all improve well-being of Chinese people.
Bi and tri-objective optimization in the deterministic network interdiction problem
International Nuclear Information System (INIS)
Rocco S, Claudio M.; Emmanuel Ramirez-Marquez, Jose; Salazar A, Daniel E.
2010-01-01
Solution approaches to the deterministic network interdiction problem have previously been developed for optimizing a single figure-of-merit of the network configuration (i.e. flow that can be transmitted between a source node and a sink node for a fixed network design) under constraints related to limited amount of resources available to interdict network links. These approaches work under the assumption that: (1) nominal capacity of each link is completely reduced when interdicted and (2) there is a single criterion to optimize. This paper presents a newly developed evolutionary algorithm that for the first time allows solving multi-objective optimization models for the design of network interdiction strategies that take into account a variety of figures-of-merit. The algorithm provides an approximation to the optimal Pareto frontier using: (a) techniques in Monte Carlo simulation to generate potential network interdiction strategies, (b) graph theory to analyze strategies' maximum source-sink flow and (c) an evolutionary search that is driven by the probability that a link will belong to the optimal Pareto set. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate and validate the approach.
Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi
2017-03-01
Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Darma, I. K.
2018-01-01
This research is aimed at determining: 1) the differences of mathematical problem solving ability between the students facilitated with problem-based learning model and conventional learning model, 2) the differences of mathematical problem solving ability between the students facilitated with authentic and conventional assessment model, and 3) interaction effect between learning and assessment model on mathematical problem solving. The research was conducted in Bali State Polytechnic, using the 2x2 experiment factorial design. The samples of this research were 110 students. The data were collected using a theoretically and empirically-validated test. Instruments were validated by using Aiken’s approach of technique content validity and item analysis, and then analyzed using anova stylistic. The result of the analysis shows that the students facilitated with problem-based learning and authentic assessment models get the highest score average compared to the other students, both in the concept understanding and mathematical problem solving. The result of hypothesis test shows that, significantly: 1) there is difference of mathematical problem solving ability between the students facilitated with problem-based learning model and conventional learning model, 2) there is difference of mathematical problem solving ability between the students facilitated with authentic assessment model and conventional assessment model, and 3) there is interaction effect between learning model and assessment model on mathematical problem solving. In order to improve the effectiveness of mathematics learning, collaboration between problem-based learning model and authentic assessment model can be considered as one of learning models in class.
Quality assurance and improvement: the Pediatric Regional Anesthesia Network.
Polaner, David M; Martin, Lynn D
2012-01-01
Quality assurance and improvement (QA/QI) is a critical activity in medicine. The use of large-scale collaborative databases is increasingly essential to obtain enough reports with which to establish standards of practice and define the incidence of complications and risk/benefit ratios for rare events. Such projects can enhance local QA/QI endeavors by enabling institutions to obtain benchmark data against which to compare their performance and can be used for prospective analyses of inter-institutional differences to determine 'best practice'. The pediatric regional anesthesia network (PRAN) is such a project. The first data cohort is currently being analyzed and offers insight into how such data can be used to detect trends in adverse events and improve care. © 2011 Blackwell Publishing Ltd.
Directory of Open Access Journals (Sweden)
Yao Yevenyo Ziggah
Full Text Available Abstract: Geocentric translation model (GTM in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP and new parameters determined using the arithmetic mean (AM were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN, radial basis function neural network (RBFNN and generalized regression neural network (GRNN were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.
Directory of Open Access Journals (Sweden)
Masoud Rabbani
2017-02-01
Full Text Available Nowadays, fiber-optic due to having greater bandwidth and being more efficient compared with other similar technologies, are counted as one the most important tools for data transfer. In this article, an integrated mathematical model for a three-level fiber-optic distribution network with consideration of simultaneous backbone and local access networks is presented in which the backbone network is a ring and the access networks has a star-star topology. The aim of the model is to determine the location of the central offices and splitters, how connections are made between central offices, and allocation of each demand node to a splitter or central office in a way that the wiring cost of fiber optical and concentrator installation are minimized. Moreover, each user’s desired bandwidth should be provided efficiently. Then, the proposed model is validated by GAMS software in small-sized problems, afterwards the model is solved by two meta-heuristic methods including differential evolution (DE and genetic algorithm (GA in large-scaled problems and the results of two algorithms are compared with respect to computational time and objective function obtained value. Finally, a sensitivity analysis is provided. Keyword: Fiber-optic, telecommunication network, hub-location, passive splitter, three-level network.
An Improved Approach to the PageRank Problems
Directory of Open Access Journals (Sweden)
Yue Xie
2013-01-01
Full Text Available We introduce a partition of the web pages particularly suited to the PageRank problems in which the web link graph has a nested block structure. Based on the partition of the web pages, dangling nodes, common nodes, and general nodes, the hyperlink matrix can be reordered to be a more simple block structure. Then based on the parallel computation method, we propose an algorithm for the PageRank problems. In this algorithm, the dimension of the linear system becomes smaller, and the vector for general nodes in each block can be calculated separately in every iteration. Numerical experiments show that this approach speeds up the computation of PageRank.
Towards a Versatile Problem Diagnosis Infrastructure for LargeWireless Sensor Networks
Iwanicki, Konrad; Steen, van Maarten
2007-01-01
In this position paper, we address the issue of durable maintenance of a wireless sensor network, which will be crucial if the vision of large, long-lived sensornets is to become reality. Durable maintenance requires tools for diagnosing and fixing occurring problems, which can range from
On Unrooted and Root-Uncertain Variants of Several Well-Known Phylogenetic Network Problems
van Iersel, L.J.J.; Kelk, Steven; Stougie, Leen; Boes, Olivier
2017-01-01
The hybridization number problem requires us to embed a set of binary rooted phylogenetic trees into a binary rooted phylogenetic network such that the number of nodes with indegree two is minimized. However, from a biological point of view accurately inferring the root location in a phylogenetic
1991-06-01
Proceedings of The National Conference on Artificial Intelligence , pages 181-184, The American Association for Aritificial Intelligence , Pittsburgh...Intermediary Resource: Intelligent Executive Computer Communication John Lyman and Carla J. Conaway University of California at Los Angeles for Contracting...Include Security Classification) Interim Report: Distributed Problem Solving: Adaptive Networks With a Computer Intermediary Resource: Intelligent
A Branch-and-Price Approach to the Feeder Network Design Problem
DEFF Research Database (Denmark)
Santini, Alberto; Plum, Christian Edinger Munk; Røpke, Stefan
2017-01-01
In this paper we consider the problem of designing a container liner shipping feeder network. The designer has to choose which port to serve during many rotations that start and end at a central hub. Many operational characteristics are considered, such as variable leg-by-leg speeds and cargo...
Quadratic head loss approximations for optimisation problems in water supply networks
Pecci, Filippo; Abraham, E.; I, Stoianov
2017-01-01
This paper presents a novel analysis of the accuracy of quadratic approximations for the Hazen–Williams (HW) head loss formula, which enables the control of constraint violations in optimisation problems for water supply networks. The two smooth polynomial approximations considered here minimise the
Young doctors' problem solving strategies on call may be improved.
Michelsen, Jens; Malchow-Møller, Axel; Charles, Peder; Eika, Berit
2013-03-01
The first year following graduation from medical school is challenging as learning from books changes to workplace-based learning. Analysis and reflection on experience may ease this transition. We used Significant Event Analysis (SEA) as a tool to explore what pre-registration house officers (PRHOs) consider successful and problematic events, and to identify what problem-solving strategies they employ. A senior house officer systematically led the PRHO through the SEA of one successful and one problematic event following a night call. The PRHO wrote answers to questions about diagnosis, what happened, how he or she contributed and what knowledge-gaining activities the PRHO would prioritise before the next call. By using an inductive, thematic data analysis, we identified five problem-solving strategies: non-analytical reasoning, analytical reasoning, communication with patients, communication with colleagues and professional behaviour. On average, 1.5 strategies were used in the successful events and 1.2 strategies in the problematic events. Most PRHOs were unable to suggest activities other than reading textbooks. SEA was valuable for the identification of PRHOs' problem-solving strategies in a natural setting. PRHOs should be assisted in increasing their repertoire of strategies, and they should also be helped to "learn to learn" as they were largely unable to point to new learning strategies. not relevant. not relevant.
Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.
2012-01-01
The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480
Chen, Zhong; Liu, June; Li, Xiong
2017-01-01
A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP). The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization problem by using scalar approach, and then the whole efficient set of the BLBOP is derived by the proposed two-stage ANN for exploring the induced set. In order to illustrate the proposed method, seven numerical examples are tested and compared with results in the classical literature. Finally, a practical problem is solved by the proposed algorithm. PMID:29312446
Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach
Directory of Open Access Journals (Sweden)
Khalid Haddouch
2016-09-01
Full Text Available A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs. In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance, we use the continuous Hopfield network to solve the obtained model basing on original energy function. To validate our model, we solve several instance of benchmarking WCSP. In this regard, our approach recognizes the optimal solution of the said instances.
An L∞/L1-Constrained Quadratic Optimization Problem with Applications to Neural Networks
International Nuclear Information System (INIS)
Leizarowitz, Arie; Rubinstein, Jacob
2003-01-01
Pattern formation in associative neural networks is related to a quadratic optimization problem. Biological considerations imply that the functional is constrained in the L ∞ norm and in the L 1 norm. We consider such optimization problems. We derive the Euler-Lagrange equations, and construct basic properties of the maximizers. We study in some detail the case where the kernel of the quadratic functional is finite-dimensional. In this case the optimization problem can be fully characterized by the geometry of a certain convex and compact finite-dimensional set
Road network selection for small-scale maps using an improved centrality-based algorithm
Directory of Open Access Journals (Sweden)
Roy Weiss
2014-12-01
Full Text Available The road network is one of the key feature classes in topographic maps and databases. In the task of deriving road networks for products at smaller scales, road network selection forms a prerequisite for all other generalization operators, and is thus a fundamental operation in the overall process of topographic map and database production. The objective of this work was to develop an algorithm for automated road network selection from a large-scale (1:10,000 to a small-scale database (1:200,000. The project was pursued in collaboration with swisstopo, the national mapping agency of Switzerland, with generic mapping requirements in mind. Preliminary experiments suggested that a selection algorithm based on betweenness centrality performed best for this purpose, yet also exposed problems. The main contribution of this paper thus consists of four extensions that address deficiencies of the basic centrality-based algorithm and lead to a significant improvement of the results. The first two extensions improve the formation of strokes concatenating the road segments, which is crucial since strokes provide the foundation upon which the network centrality measure is computed. Thus, the first extension ensures that roundabouts are detected and collapsed, thus avoiding interruptions of strokes by roundabouts, while the second introduces additional semantics in the process of stroke formation, allowing longer and more plausible strokes to built. The third extension detects areas of high road density (i.e., urban areas using density-based clustering and then locally increases the threshold of the centrality measure used to select road segments, such that more thinning takes place in those areas. Finally, since the basic algorithm tends to create dead-ends—which however are not tolerated in small-scale maps—the fourth extension reconnects these dead-ends to the main network, searching for the best path in the main heading of the dead-end.
PRIMARY IMMUNODEFICIENCY: STATUS OF A PROBLEM TODAY. RUSSIAN NETWORK OF JMF-CENTERS
Directory of Open Access Journals (Sweden)
E. A. Latysheva
2013-01-01
Full Text Available The problems of primary immunodeficiency in Russia and the ways of solving of them are discussed in the article. Primary immunodeficiency is a group of rare diseases, so awareness of this pathology in the medical community and among patients is very low. This leads to late diagnosis and inadequate treatment of patients with such conditions. The result of the late beginning of treatment is early development of disability, and the high mortality rate of patients, as well as the high costs of the treatment of complications of primary immunodeficiency and sick-leave certificates for the government. Today in time and adequate therapy allows patients not only to reach adulthood without signs of disability, and to lead an active way of life, but to have healthy children. Given the high cost of therapy in many countries, the issue of providing patients with life-saving drugs remains unresolved. The global practice is to involve social organizations and funds. One of the foundations supporting educational programs, development of laboratories and research in the field of primary immunodeficiency is the Foundation of the Jeffrey Modell. A network of centres for primary immunodeficiency supported by the Jeffrey Modell Foundation (JMF-centers has started its functioning over the territory of the Russian Federation since 2011 in order to improve diagnostics and treatment of patients with primary immunodeficiency. A brief description of activity of these centers is presented in the article.
An Exploratory Application of Neural Networks to the Sortie Generation Forecasting Problem
1991-09-01
research of Dr. David A. Diener, Major, USAF. As the initial research increment to be improved upon by future researchers, this study (1) provides a... David A. Diener, Major, USAF, who virtually transformed my dream of exploring neural network techniques into concrete reality. His talents in...New York: John Wiley & Sons, 1978. Barron R. L., Gilstrap, L. 0., and Shrier , S. "Polynomial al and Neural Networks: Analogies and Engineering
Optimal improvement of graphs related to nuclear safeguards problems
International Nuclear Information System (INIS)
Jacobsen, S.E.
1977-08-01
This report develops the methodology for optimally improving graphs related to nuclear safeguards issues. In particular, given a fixed number of dollars, the report provides a method for optimally allocating such dollars over the arcs of a weighted graph (the weights vary as a function of dollars spent on arcs) so as to improve the system effectiveness measure which is the shortest of all shortest paths to several targets. Arc weights can be either clock times or detection probabilities and the algorithm does not explicitly consider all paths to the targets
A minimum resource neural network framework for solving multiconstraint shortest path problems.
Zhang, Junying; Zhao, Xiaoxue; He, Xiaotao
2014-08-01
Characterized by using minimum hard (structural) and soft (computational) resources, a novel parameter-free minimal resource neural network (MRNN) framework is proposed for solving a wide range of single-source shortest path (SP) problems for various graph types. The problems are the k-shortest time path problems with any combination of three constraints: time, hop, and label constraints, and the graphs can be directed, undirected, or bidirected with symmetric and/or asymmetric traversal time, which can be real and time dependent. Isomorphic to the graph where the SP is to be sought, the network is activated by generating autowave at source neuron and the autowave travels automatically along the paths with the speed of a hop in an iteration. Properties of the network are studied, algorithms are presented, and computation complexity is analyzed. The framework guarantees globally optimal solutions of a series of problems during the iteration process of the network, which provides insight into why even the SP is still too long to be satisfied. The network facilitates very large scale integrated circuit implementation and adapt to very large scale problems due to its massively parallel processing and minimum resource utilization. When implemented in a sequentially processing computer, experiments on synthetic graphs, road maps of cities of the USA, and vehicle routing with time windows indicate that the MRNN is especially efficient for large scale sparse graphs and even dense graphs with some constraints, e.g., the CPU time taken and the iteration number used for the road maps of cities of the USA is even less than ∼ 2% and 0.5% that of the Dijkstra's algorithm.
Directory of Open Access Journals (Sweden)
Botond Molnár
Full Text Available There has been a long history of using neural networks for combinatorial optimization and constraint satisfaction problems. Symmetric Hopfield networks and similar approaches use steepest descent dynamics, and they always converge to the closest local minimum of the energy landscape. For finding global minima additional parameter-sensitive techniques are used, such as classical simulated annealing or the so-called chaotic simulated annealing, which induces chaotic dynamics by addition of extra terms to the energy landscape. Here we show that asymmetric continuous-time neural networks can solve constraint satisfaction problems without getting trapped in non-solution attractors. We concentrate on a model solving Boolean satisfiability (k-SAT, which is a quintessential NP-complete problem. There is a one-to-one correspondence between the stable fixed points of the neural network and the k-SAT solutions and we present numerical evidence that limit cycles may also be avoided by appropriately choosing the parameters of the model. This optimal parameter region is fairly independent of the size and hardness of instances, this way parameters can be chosen independently of the properties of problems and no tuning is required during the dynamical process. The model is similar to cellular neural networks already used in CNN computers. On an analog device solving a SAT problem would take a single operation: the connection weights are determined by the k-SAT instance and starting from any initial condition the system searches until finding a solution. In this new approach transient chaotic behavior appears as a natural consequence of optimization hardness and not as an externally induced effect.
Facing a Problem of Electrical Energy Quality in Ship Networks-measurements, Estimation, Control
Institute of Scientific and Technical Information of China (English)
Tomasz Tarasiuk; Janusz Mindykowski; Xiaoyan Xu
2003-01-01
In this paper, electrical energy quality and its indices in ship electric networks are introduced, especially the meaning of electrical energy quality terms in voltage and active and reactive power distribution indices. Then methods of measurement of marine electrical energy indices are introduced in details and a microprocessor measurement-diagnosis system with the function of measurement and control is designed. Afterwards, estimation and control of electrical power quality of marine electrical power networks are introduced. And finally, according to the existing method of measurement and control of electrical power quality in ship power networks, the improvement of relative method is proposed.
QPS - analysis of main problems, areas to target, possible improvements
Denz, R
2012-01-01
The fault statistics of the LHC Quench Protection System QPS for the LHC run 2011 will be presented and the various fault types explained. Starting with teething and EMC related problems, the tune feedback compatibility, hardware faults of the quench heater power supplies and spurious openings of electrical circuit breakers will be described in more details. Hereby as well the necessary and potential consolidation measures will be discussed. The role of the MPE standby service for the system availability will be addressed. Finally radiation induced faults and the respective mitigation and consolidation measures will be treated as separate subject.
PROACTIVE APPROACH TO THE INCIDENT AND PROBLEM MANAGEMENT IN COMMUNICATION NETWORKS
Directory of Open Access Journals (Sweden)
Vjeran Strahonja
2007-06-01
Full Text Available Proactive approach to communication network maintenance has the capability of enhancing the integrity and reliability of communication networks, as well as of reducing maintenance costs and overall number of incidents. This paper presents approaches to problem and incident prevention with the help of root-cause analysis, aligning that with the goal to foresee software performance. Implementation of proactive approach requires recognition of enterprise's current level of maintenance better insights into available approaches and tools, as well as their comparison, interoperability, integration and further development. The approach we are proposing and elaborating in this paper lies on the construction of a metamodel of the problem management of information technology, particularly the proactive problem management. The metamodel is derived from the original ITIL specification and presented in an object-oriented fashion by using structure (class diagrams conform to UML notation. Based on current research, appropriate metrics based on the concept of Key Performance Indicators is suggested.
Cichocki, A; Unbehauen, R
1994-01-01
In this paper a new class of simplified low-cost analog artificial neural networks with on chip adaptive learning algorithms are proposed for solving linear systems of algebraic equations in real time. The proposed learning algorithms for linear least squares (LS), total least squares (TLS) and data least squares (DLS) problems can be considered as modifications and extensions of well known algorithms: the row-action projection-Kaczmarz algorithm and/or the LMS (Adaline) Widrow-Hoff algorithms. The algorithms can be applied to any problem which can be formulated as a linear regression problem. The correctness and high performance of the proposed neural networks are illustrated by extensive computer simulation results.
A review on application of neural networks and fuzzy logic to solve hydrothermal scheduling problem
International Nuclear Information System (INIS)
Haroon, S.; Malik, T.N.; Zafar, S.
2014-01-01
Electrical power system is highly complicated having hydro and thermal mix with large number of machines. To reduce power production cost, hydro and thermal resources are mixed. Hydrothermal scheduling is the optimal coordination of hydro and thermal plants to meet the system load demand at minimum possible operational cost while satisfying the system constraints. Hydrothermal scheduling is dynamic, large scale, non-linear and non-convex optimization problem. The classical techniques have failed in solving such problem. Artificial Intelligence Tools based techniques are used now a day to solve this complex optimization problem because of their no requirements on the nature of the problem. The aim of this research paper is to provide a comprehensive survey of literature related to both Artificial Neural Network (ANN) and Fuzzy Logic (FL) as effective optimization algorithms for the hydrothermal scheduling problem. The outcomes along with the merits and demerits of individual techniques are also discussed. (author)
DEFF Research Database (Denmark)
Gamst, M.
2014-01-01
problem. The methods are computationally evaluated on test instances arising from telecommunications with up to 500 jobs and 500 machines. Results show that solving the integrated job scheduling and constrained network routing problem to optimality is very difficult. The exact solution approach performs......This paper examines the problem of scheduling a number of jobs on a finite set of machines such that the overall profit of executed jobs is maximized. Each job has a certain demand, which must be sent to the executing machine via constrained paths. A job cannot start before all its demands have...... arrived at the machine. Furthermore, two resource demand transmissions cannot use the same edge in the same time period. The problem has application in grid computing, where a number of geographically distributed machines work together for solving large problems. The machines are connected through...
Improved Vehicular Information Network Architecture Using Fuzzy Based Named Data NetworkingNDN
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Kanwalpreet Kaur
2015-08-01
Full Text Available Vehicular Ad-hoc System VANETs is really a component with smart transport systems. It has ability to prevent accidents and the road congestion issues on highways but it suffers from the accomplishment and scalability issues. To handle these difficulties from the Inter Vehicular Communication IVC we apply Name Data Networking NDN. All though in NDN the users are only concerned about necessary data and give no attention on the number of locations from where the data is coming. The NDN layout is usually much more worthy for IVC circumstance getting the ordered material labeling design as well as amp64258exible material retrieval. In this report we propose vehicular network dependent on fuzzy membership function which offers the fundamental NDN style to improve support location dependent forwarding content aggregation and distributed mobility management. This paper finally winds up the several boundaries regarding earlier approaches.
The practical problem of improving quality in multicenter dialysis facilities.
Balter, Paul
2003-01-01
Multifacility dialysis groups are frequently interested in improving overall quality and find that there are major differences between individual units. Upper management must consider what strategy is needed for the whole company and what strategy must be formulated by individual facilities. To make substantive changes, management must decide to adopt a new culture of true teamwork, drive out fear, and emphasize leadership and education both at the management level and in the individual unit. Both at the corporate and unit levels, leaders must be chosen who are able to recognize people who have the ability, the educational background, the enthusiasm, and the time to direct change. Empowering the individual units and individual employees to make changes and be enthusiastic about improvement is the key to success.
New Eye Cleansing Product Improves Makeup-Related Ocular Problems.
Okura, Masako; Kawashima, Motoko; Katagiri, Mikiyuki; Shirasawa, Takuji; Tsubota, Kazuo
2015-01-01
Purpose. This study evaluated the effects of using a newly developed eye cleansing formulation (Eye Shampoo) to cleanse the eyelids for 4 weeks in a parallel-group comparative study in women with chronic eye discomfort caused by heavy use of eye makeup and poor eye hygiene habits. Methods. Twenty women participants who met the inclusion criteria were randomly allocated to 2 groups comprising 10 participants each. The participants were asked to use either artificial tears alone or artificial tears in conjunction with Eye Shampoo for 4 weeks. The participants answered the questionnaire again and were reexamined, and changes in symptoms within each group and variations of symptoms between the two groups were statistically analyzed. Results. In the group using only artificial tears, improvements in subjective symptoms but not in ophthalmologic examination results were found. In the group using Eye Shampoo together with artificial tears, significant improvements were observed in the subjective symptoms, meibomian orifice obstruction, meibum secretion, tear breakup time, and superficial punctate keratopathy. Conclusion. In patients with chronic eye discomfort thought to be caused by heavy eye makeup, maintaining eyelid hygiene using Eye Shampoo caused a marked improvement in meibomian gland blockage and dry eye symptoms.
New Eye Cleansing Product Improves Makeup-Related Ocular Problems
Directory of Open Access Journals (Sweden)
Masako Okura
2015-01-01
Full Text Available Purpose. This study evaluated the effects of using a newly developed eye cleansing formulation (Eye Shampoo to cleanse the eyelids for 4 weeks in a parallel-group comparative study in women with chronic eye discomfort caused by heavy use of eye makeup and poor eye hygiene habits. Methods. Twenty women participants who met the inclusion criteria were randomly allocated to 2 groups comprising 10 participants each. The participants were asked to use either artificial tears alone or artificial tears in conjunction with Eye Shampoo for 4 weeks. The participants answered the questionnaire again and were reexamined, and changes in symptoms within each group and variations of symptoms between the two groups were statistically analyzed. Results. In the group using only artificial tears, improvements in subjective symptoms but not in ophthalmologic examination results were found. In the group using Eye Shampoo together with artificial tears, significant improvements were observed in the subjective symptoms, meibomian orifice obstruction, meibum secretion, tear breakup time, and superficial punctate keratopathy. Conclusion. In patients with chronic eye discomfort thought to be caused by heavy eye makeup, maintaining eyelid hygiene using Eye Shampoo caused a marked improvement in meibomian gland blockage and dry eye symptoms.
An improved network layer protocol based on mobile IPv6
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The mobile [Pv6 proposed by the IETF aims at providing mobility support on the next generation Internet. First, the authors described the basic principle of mobile lPv6 in brief and analyzed the advantages and disadvantages of it, presented a new idea of allocating a specific address space for mobile node ( MN ) and developed a new extension header and two ICMP message types for mobile IPv6. Lastly the authors proposed an optimization strategy, for mobile IPv6 based on these extensions of protocol, which has the following advantages:1 ) It is more convenient to manage the MNs because MN can be judged from its IP address; 2) When the correspondent node (CN) is not actively communicating with a MN, the MN and its home agent (HA) need not send Binding Update to tire CN, and the CN need not send Binding Request to the MN. Only when the CN really wants to send a packet to the MN, will the CN voluntarily send a MN Discover Request message to acquire the MN's care-of address. In this way, the transmission of Binding Update and Binding Request is greatly reduced, consequently the network overhead is also decreased; 3) While sending packets, the CN simply uses a MN Home Address Extension Header without using IPinlP encapsulation and routing header, which can reduce the redundant information in the packet and the message delay; 4) All the packets sent by the CN can be directly routed to the MN and the triangle routing can be completely avoided. By using these protocol extensions, the overhead of the network is greatly reduced and the network quality of services (QoS) is improved.
Heuristic for solving capacitor allocation problems in electric energy radial distribution networks
Directory of Open Access Journals (Sweden)
Maria A. Biagio
2012-04-01
Full Text Available The goal of the capacitor allocation problem in radial distribution networks is to minimize technical losses with consequential positive impacts on economic and environmental areas. The main objective is to define the size and location of the capacitors while considering load variations in a given horizon. The mathematical formulation for this planning problem is given by an integer nonlinear mathematical programming model that demands great computational effort to be solved. With the goal of solving this problem, this paper proposes a methodology that is composed of heuristics and Tabu Search procedures. The methodology presented explores network system characteristics of the network system reactive loads for identifying regions where procedures of local and intensive searches should be performed. A description of the proposed methodology and an analysis of computational results obtained which are based on several test systems including actual systems are presented. The solutions reached are as good as or better than those indicated by well referenced methodologies. The technique proposed is simple in its use and does not require calibrating an excessive amount of parameters, making it an attractive alternative for companies involved in the planning of radial distribution networks.
A Very Large Area Network (VLAN) knowledge-base applied to space communication problems
Zander, Carol S.
1988-01-01
This paper first describes a hierarchical model for very large area networks (VLAN). Space communication problems whose solution could profit by the model are discussed and then an enhanced version of this model incorporating the knowledge needed for the missile detection-destruction problem is presented. A satellite network or VLAN is a network which includes at least one satellite. Due to the complexity, a compromise between fully centralized and fully distributed network management has been adopted. Network nodes are assigned to a physically localized group, called a partition. Partitions consist of groups of cell nodes with one cell node acting as the organizer or master, called the Group Master (GM). Coordinating the group masters is a Partition Master (PM). Knowledge is also distributed hierarchically existing in at least two nodes. Each satellite node has a back-up earth node. Knowledge must be distributed in such a way so as to minimize information loss when a node fails. Thus the model is hierarchical both physically and informationally.
Storage Solutions for Power Quality Problems in Cyprus Electricity Distribution Network
Directory of Open Access Journals (Sweden)
Andreas Poullikkas
2014-01-01
Full Text Available In this work, a prediction of the effects of introducing energy storage systems on the network stability of the distribution network of Cyprus and a comparison in terms of cost with a traditional solution is carried out. In particular, for solving possible overvoltage problems, several scenarios of storage units' installation are used and compared with the alternative solution of extra cable connection between the node with the lowest voltage and the node with the highest voltage of the distribution network. For the comparison, a case study of a typical LV distribution feeder in the power system of Cyprus is used. The results indicated that the performance indicator of each solution depends on the type, the size and the position of installation of the storage unit. Also, as more storage units are installed the better the performance indicator and the more attractive is the investment in storage units to solve power quality problems in the distribution network. In the case where the technical requirements in voltage limitations according to distribution regulations are satisfied with one storage unit, the installation of an additional storage unit will only increase the final cost. The best solution, however, still remains the alternative solution of extra cable connection between the node with the lowest voltage and the node with the highest voltage of the distribution network, due to the lower investment costs compared to that of the storage units.
A service flow model for the liner shipping network design problem
DEFF Research Database (Denmark)
Plum, Christian Edinger Munk; Pisinger, David; Sigurd, Mikkel M.
2014-01-01
. The formulation alleviates issues faced by arc flow formulations with regards to handling multiple calls to the same port. A problem which has not been fully dealt with earlier by LSNDP formulations. Multiple calls are handled by introducing service nodes, together with port nodes in a graph representation...... of the network and a penalty for not flowed cargo. The model can be used to design liner shipping networks to utilize a container carrier’s assets efficiently and to investigate possible scenarios of changed market conditions. The model is solved as a Mixed Integer Program. Results are presented for the two...
A note on the consensus finding problem in communication networks with switching topologies
Haskovec, Jan
2014-05-07
In this note, we discuss the problem of consensus finding in communication networks of agents with dynamically switching topologies. In particular, we consider the case of directed networks with unbalanced matrices of communication rates. We formulate sufficient conditions for consensus finding in terms of strong connectivity of the underlying directed graphs and prove that, given these conditions, consensus is found asymptotically. Moreover, we show that this consensus is an emergent property of the system, being encoded in its dynamics and not just an invariant of its initial configuration. © 2014 © 2014 Taylor & Francis.
Tight bounds on the size of neural networks for classification problems
Energy Technology Data Exchange (ETDEWEB)
Beiu, V. [Los Alamos National Lab., NM (United States); Pauw, T. de [Universite Catholique de Louvain, Louvain-la-Neuve (Belgium). Dept. de Mathematique
1997-06-01
This paper relies on the entropy of a data-set (i.e., number-of-bits) to prove tight bounds on the size of neural networks solving a classification problem. First, based on a sequence of geometrical steps, the authors constructively compute an upper bound of O(mn) on the number-of-bits for a given data-set - here m is the number of examples and n is the number of dimensions (i.e., R{sup n}). This result is used further in a nonconstructive way to bound the size of neural networks which correctly classify that data-set.
A dual exterior point simplex type algorithm for the minimum cost network flow problem
Directory of Open Access Journals (Sweden)
Geranis George
2009-01-01
Full Text Available A new dual simplex type algorithm for the Minimum Cost Network Flow Problem (MCNFP is presented. The proposed algorithm belongs to a special 'exterior- point simplex type' category. Similarly to the classical network dual simplex algorithm (NDSA, this algorithm starts with a dual feasible tree-solution and reduces the primal infeasibility, iteration by iteration. However, contrary to the NDSA, the new algorithm does not always maintain a dual feasible solution. Instead, the new algorithm might reach a basic point (tree-solution outside the dual feasible area (exterior point - dual infeasible tree.
Executive Leadership in School Improvement Networks: A Conceptual Framework and Agenda for Research
Peurach, Donald J.; Gumus, Emine
2011-01-01
The purpose of this analysis is to improve understanding of executive leadership in school improvement networks: for example, networks supported by comprehensive school reform providers, charter management organizations, and education management organizations. In this analysis, we review the literature on networks and executive leadership. We draw…
Fully convolutional neural networks improve abdominal organ segmentation
Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.
2018-03-01
Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1
Directory of Open Access Journals (Sweden)
Wen-Xiang Wu
2014-01-01
Full Text Available The cost-based system optimum problem in networks with continuously distributed value of time is formulated as a path-based form, which cannot be solved by the Frank-Wolfe algorithm. In light of magnitude improvement in the availability of computer memory in recent years, path-based algorithms have been regarded as a viable approach for traffic assignment problems with reasonably large network sizes. We develop a path-based gradient projection algorithm for solving the cost-based system optimum model, based on Goldstein-Levitin-Polyak method which has been successfully applied to solve standard user equilibrium and system optimum problems. The Sioux Falls network tested is used to verify the effectiveness of the algorithm.
Using Coaching to Improve the Teaching of Problem Solving to Year 8 Students in Mathematics
Kargas, Christine Anestis; Stephens, Max
2014-01-01
This study investigated how to improve the teaching of problem solving in a large Melbourne secondary school. Coaching was used to support and equip five teachers, some with limited experiences in teaching problem solving, with knowledge and strategies to build up students' problem solving and reasoning skills. The results showed increased…
2013-11-18
... consumers value overall network reliability and quality in selecting mobile wireless service providers, they...-125] Improving the Resiliency of Mobile Wireless Communications Networks; Reliability and Continuity... (Reliability NOI) in 2011 to ``initiate a comprehensive examination of issues regarding the reliability...
Improving the security of the Hwang-Su protocol for mobile networks
African Journals Online (AJOL)
user
Improving the security of the Hwang-Su protocol for mobile networks. Miloud Ait ... But, it is threatened by weak ... Wireless networks (IEEE standard 802.11 1996, Gast 2005) have allowed computer systems to exchange data without cable.
Nease, Beth M; Haney, Tina S
Astute observation, description, and problem identification skills provide the underpinning for nursing assessment, surveillance, and prevention of failure to rescue events. Art-based education has been effective in nursing schools for improving observation, description, and problem identification. The authors describe a randomized controlled pilot study testing the effectiveness of an art-based educational intervention aimed at improving these skills in practicing nurses.
Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems
Directory of Open Access Journals (Sweden)
Longhui Gang
2015-07-01
Full Text Available Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.
Fortuin, Janna; van Geel, Mitch; Vedder, Paul
2015-04-01
Adolescents who like each other may become more similar to each other with regard to internalizing and externalizing problems, though it is not yet clear which social mechanisms explain these similarities. In this longitudinal study, we analyzed four mechanisms that may explain similarity in adolescent peer networks with regard to externalizing and internalizing problems: selection, socialization, avoidance and withdrawal. At three moments during one school-year, we asked 542 adolescents (8th grade, M-age = 13.3 years, 51 % female) to report who they liked in their classroom, and their own internalizing and externalizing problems. Adolescents tend to prefer peers who have similar externalizing problem scores, but no significant selection effect was found for internalizing problems. Adolescents who share the same group of friends socialize each other and then become more similar with respect to externalizing problems, but not with respect to internalizing problems. We found no significant effects for avoidance or withdrawal. Adolescents may choose to belong to a peer group that is similar to them in terms of externalizing problem behaviors, and through peer group socialization (e.g., enticing, modelling, mimicking, and peer pressure) become more similar to that group over time.
Directory of Open Access Journals (Sweden)
Adamu Murtala Zungeru
2013-01-01
Full Text Available The main problem for event gathering in wireless sensor networks (WSNs is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR algorithm, based on the ant colony optimization (ACO metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1 a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2 intelligent update of routing tables in case of a node or link failure, and (3 reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC and Beesensor.
Directory of Open Access Journals (Sweden)
Nur Faziera Napis
2018-05-01
Full Text Available The presence of optimized distributed generation (DG with suitable distribution network reconfiguration (DNR in the electrical distribution network has an advantage for voltage support, power losses reduction, deferment of new transmission line and distribution structure and system stability improvement. However, installation of a DG unit at non-optimal size with non-optimal DNR may lead to higher power losses, power quality problem, voltage instability and incremental of operational cost. Thus, an appropriate DG and DNR planning are essential and are considered as an objective of this research. An effective heuristic optimization technique named as improved evolutionary particle swarm optimization (IEPSO is proposed in this research. The objective function is formulated to minimize the total power losses (TPL and to improve the voltage stability index (VSI. The voltage stability index is determined for three load demand levels namely light load, nominal load, and heavy load with proper optimal DNR and DG sizing. The performance of the proposed technique is compared with other optimization techniques, namely particle swarm optimization (PSO and iteration particle swarm optimization (IPSO. Four case studies on IEEE 33-bus and IEEE 69-bus distribution systems have been conducted to validate the effectiveness of the proposed IEPSO. The optimization results show that, the best achievement is done by IEPSO technique with power losses reduction up to 79.26%, and 58.41% improvement in the voltage stability index. Moreover, IEPSO has the fastest computational time for all load conditions as compared to other algorithms.
A PSO based Artificial Neural Network approach for short term unit commitment problem
Directory of Open Access Journals (Sweden)
AFTAB AHMAD
2010-10-01
Full Text Available Unit commitment (UC is a non-linear, large scale, complex, mixed-integer combinatorial constrained optimization problem. This paper proposes, a new hybrid approach for generating unit commitment schedules using swarm intelligence learning rule based neural network. The training data has been generated using dynamic programming for machines without valve point effects and using genetic algorithm for machines with valve point effects. A set of load patterns as inputs and the corresponding unit generation schedules as outputs are used to train the network. The neural network fine tunes the best results to the desired targets. The proposed approach has been validated for three thermal machines with valve point effects and without valve point effects. The results are compared with the approaches available in the literature. The PSO-ANN trained model gives better results which show the promise of the proposed methodology.
A Bayesian network approach to the database search problem in criminal proceedings
2012-01-01
Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions
The Bilevel Design Problem for Communication Networks on Trains: Model, Algorithm, and Verification
Directory of Open Access Journals (Sweden)
Yin Tian
2014-01-01
Full Text Available This paper proposes a novel method to solve the problem of train communication network design. Firstly, we put forward a general description of such problem. Then, taking advantage of the bilevel programming theory, we created the cost-reliability-delay model (CRD model that consisted of two parts: the physical topology part aimed at obtaining the networks with the maximum reliability under constrained cost, while the logical topology part focused on the communication paths yielding minimum delay based on the physical topology delivered from upper level. We also suggested a method to solve the CRD model, which combined the genetic algorithm and the Floyd-Warshall algorithm. Finally, we used a practical example to verify the accuracy and the effectiveness of the CRD model and further applied the novel method on a train with six carriages.
Shortest path problem on a grid network with unordered intermediate points
Saw, Veekeong; Rahman, Amirah; Eng Ong, Wen
2017-10-01
We consider a shortest path problem with single cost factor on a grid network with unordered intermediate points. A two stage heuristic algorithm is proposed to find a feasible solution path within a reasonable amount of time. To evaluate the performance of the proposed algorithm, computational experiments are performed on grid maps of varying size and number of intermediate points. Preliminary results for the problem are reported. Numerical comparisons against brute forcing show that the proposed algorithm consistently yields solutions that are within 10% of the optimal solution and uses significantly less computation time.
The Case of Web-Based Course on Taxation: Current Status, Problems and Future Improvement
Qin, Zhigang
This paper mainly introduces the case of the web-based course on taxation developed by Xiamen University. We analyze the current status, problems and future improvement of the web-based course. The web-based course has the basic contents and modules, but it has several problems including unclear object, lacking interaction, lacking examination module, lacking study management module, and the learning materials and the navigation are too simple. According to its problems, we put forward the measures to improve it.
Non-parametric Bayesian networks: Improving theory and reviewing applications
International Nuclear Information System (INIS)
Hanea, Anca; Morales Napoles, Oswaldo; Ababei, Dan
2015-01-01
Applications in various domains often lead to high dimensional dependence modelling. A Bayesian network (BN) is a probabilistic graphical model that provides an elegant way of expressing the joint distribution of a large number of interrelated variables. BNs have been successfully used to represent uncertain knowledge in a variety of fields. The majority of applications use discrete BNs, i.e. BNs whose nodes represent discrete variables. Integrating continuous variables in BNs is an area fraught with difficulty. Several methods that handle discrete-continuous BNs have been proposed in the literature. This paper concentrates only on one method called non-parametric BNs (NPBNs). NPBNs were introduced in 2004 and they have been or are currently being used in at least twelve professional applications. This paper provides a short introduction to NPBNs, a couple of theoretical advances, and an overview of applications. The aim of the paper is twofold: one is to present the latest improvements of the theory underlying NPBNs, and the other is to complement the existing overviews of BNs applications with the NPNBs applications. The latter opens the opportunity to discuss some difficulties that applications pose to the theoretical framework and in this way offers some NPBN modelling guidance to practitioners. - Highlights: • The paper gives an overview of the current NPBNs methodology. • We extend the NPBN methodology by relaxing the conditions of one of its fundamental theorems. • We propose improvements of the data mining algorithm for the NPBNs. • We review the professional applications of the NPBNs.
A neural-network approach to the problem of photon-pair combinatorics
International Nuclear Information System (INIS)
Awes, T.C.
1990-06-01
A recursive neural-network algorithm is applied to the problem of correctly pairing photons from π 0 , η, and higher resonance decays in the presence of a large background of photons resulting from many simultaneous decays. The method uses the full information of the multi-photon final state to suppress the selection of false photon pairs which arise from the many combinatorial possibilities. The method is demonstrated for simulated photon events under semirealistic experimental conditions. 3 refs., 3 figs
Problems With Deployment of Multi-Domained, Multi-Homed Mobile Networks
Ivancic, William D.
2008-01-01
This document describes numerous problems associated with deployment of multi-homed mobile platforms consisting of multiple networks and traversing large geographical areas. The purpose of this document is to provide insight to real-world deployment issues and provide information to groups that are addressing many issues related to multi-homing, policy-base routing, route optimization and mobile security - particularly those groups within the Internet Engineering Task Force.
A neural network approach to breast cancer diagnosis as a constraint satisfaction problem
International Nuclear Information System (INIS)
Tourassi, Georgia D.; Markey, Mia K.; Lo, Joseph Y.; Floyd, Carey E. Jr.
2001-01-01
A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84±0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs
Improving Protein Fold Recognition by Deep Learning Networks
Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin
2015-12-01
For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.
Improving Protein Fold Recognition by Deep Learning Networks.
Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin
2015-12-04
For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.
The application of neural network techniques to magnetic and optical inverse problems
International Nuclear Information System (INIS)
Jones, H.V.
2000-12-01
The processing power of the computer has increased at unimaginable rates over the last few decades. However, even today's fastest computer can take several hours to find solutions to some mathematical problems; and there are instances where a high powered supercomputer may be impractical, with the need for near instant solutions just as important (such as in an on-line testing system). This led us to believe that such complex problems could be solved using a novel approach, whereby the system would have prior knowledge about the expected solutions through a process of learning. One method of approaching this kind of problem is through the use of machine learning. Just as a human can be trained and is able to learn from past experiences, a machine is can do just the same. This is the concept of neural networks. The research which was conducted involves the investigation of various neural network techniques, and their applicability to solve some known complex inverse problems in the field of magnetic and optical recording. In some cases a comparison is also made to more conventional methods of solving the problems, from which it was possible to outline some key advantages of using a neural network approach. We initially investigated the application of neural networks to transverse susceptibility data in order to determine anisotropy distributions. This area of research is proving to be very important, as it gives us information about the switching field distribution, which then determines the minimum transition width achievable in a medium, and affects the overwrite characteristics of the media. Secondly, we investigated a similar situation, but applied to an optical problem. This involved the determination of important compact disc parameters from the diffraction pattern of a laser from a disc. This technique was then intended for use in an on-line testing system. Finally we investigated another area of neural networks with the analysis of magnetisation maps and
Guo, Y.; Fujinoki, H.
2006-10-01
The concept of a new tree-based architecture for networked multi-player games was proposed by Matuszek to improve scalability in network traffic at the same time to improve reliability. The architecture (we refer it as "Tree-Based Server- Middlemen-Client architecture") will solve the two major problems in ad-hoc wireless networks: frequent link failures and significance in battery power consumption at wireless transceivers by using two new techniques, recursive aggregation of client messages and subscription-based propagation of game state. However, the performance of the TBSMC architecture has never been quantitatively studied. In this paper, the TB-SMC architecture is compared with the client-server architecture using simulation experiments. We developed an event driven simulator to evaluate the performance of the TB-SMC architecture. In the network traffic scalability experiments, the TB-SMC architecture resulted in less than 1/14 of the network traffic load for 200 end users. In the reliability experiments, the TB-SMC architecture improved the number of successfully delivered players' votes by 31.6, 19.0, and 12.4% from the clientserver architecture at high (failure probability of 90%), moderate (50%) and low (10%) failure probability.
Li, Ming; Miao, Chunyan; Leung, Cyril
2015-12-04
Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.
A hopfield-like artificial neural network for solving inverse radiation transport problems
International Nuclear Information System (INIS)
Lee, Sang Hoon
1997-02-01
In this thesis, we solve inverse radiation transport problems by an Artificial Neural Network(ANN) approach. ANNs have many interesting properties such as nonlinear, parallel, and distributed processing. Some of the promising applications of ANNs are optimization, image and signal processing, system control, etc. In some optimization problems, Hopfield Neural Network(HNN) which has one-layered and fully interconnected neurons with feed-back topology showed that it worked well with acceptable fault tolerance and efficiency. The identification of radioactive source in a medium with a limited number of external detectors is treated as an inverse radiation transport problem in this work. This kind of inverse problem is usually ill-posed and severely under-determined; however, its applications are very useful in many fields including medical diagnosis and nondestructive assay of nuclear materials. Therefore, it is desired to develop efficient and robust solution algorithms. Firstly, we study a representative ANN model which has learning ability and fault tolerance, i.e., feed-forward neural network. It has an error backpropagation learning algorithm processed by reducing error in learning patterns that are usually results of test or calculation. Although it has enough fault tolerance and efficiency, a major obstacle is 'curse of dimensionality'--required number of learning patterns and learning time increase exponentially proportional to the problem size. Therefore, in this thesis, this type of ANN is used as benchmarking the reliability of the solution. Secondly, another approach for solving inverse problems, a modified version of HNN is proposed. When diagonal elements of the interconnection matrix are not zero, HNN may become unstable. However, most problems including this identification problem contain non-zero diagonal elements when programmed on neural networks. According to Soulie et al., discrete random iterations could produce the stable minimum state
A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm
Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu
Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.
The 3rd ATLAS Domestic Standard Problem for Improvement of Safety Analysis Technology
International Nuclear Information System (INIS)
Choi, Ki-Yong; Kang, Kyoung-Ho; Park, Yusun; Kim, Jongrok; Bae, Byoung-Uhn; Choi, Nam-Hyun
2014-01-01
The third ATLAS DSP (domestic standard problem exercise) was launched at the end of 2012 in response to the strong need for continuation of the ATLAS DSP. A guillotine break of a main steam line without LOOP at a zero power condition was selected as a target scenario, and it was successfully completed in the beginning of 2014. In the 3 rd ATLAS DSP, comprehensive utilization of the integral effect test data was made by dividing analysis with three topics; 1. scale-up where extrapolation of ATLAS IET data was investigated 2. 3D analysis where how much improvement can be obtained by 3D modeling was studied 3. 1D sensitivity analysis where the key phenomena affecting the SLB simulation were identified and the best modeling guideline was achieved. Through such DSP exercises, it has been possible to effectively utilize high-quality ATLAS experimental data of to enhance thermal-hydraulic understanding and to validate the safety analysis codes. A strong human network and technical expertise sharing among the various nuclear experts are also important outcomes from this program
DEFF Research Database (Denmark)
Quaglia, Alberto; Gargalo, Carina L.; Chairakwongsa, Siwanat
2015-01-01
when large problems are considered. In an earlier work, we proposed a computer-aided framework for synthesis and design of process networks. In this contribution, we expand the framework by including methods and tools developed to structure, automate and simplify the mathematical formulation......The developments obtained in recent years in the field of mathematical programming considerably reduced the computational time and resources needed to solve large and complex Mixed Integer Non Linear Programming (MINLP) problems. Nevertheless, the application of these methods in industrial practice...... is still limited by the complexity associated with the mathematical formulation of some problems. In particular, the tasks of design space definition and representation as superstructure, as well as the data collection, validation and handling may become too complex and cumbersome to execute, especially...
Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques
Stottler, D.
There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.
Improving the Robustness of Deep Neural Networks via Stability Training
Zheng, Stephan; Song, Yang; Leung, Thomas; Goodfellow, Ian
2016-01-01
In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such...
Academic Social Networking Sites: Improves Research Visibility and Impact
Ebrahim, Nader Ale
2017-01-01
Researchers needs to remove many traditional obstacles to disseminate and outreach their research outputs. Academic social networking allows you to connect with other researchers in your field, share your publications, and get feedback on your non-peer-reviewed work. The academic social networking, making your work more widely discoverable and easily available. The two best known academic social networking are ResearchGate and Academia.edu. These sites offer an instant technique to monitor wh...
Wade, Shari L; Walz, Nicolay C; Carey, JoAnne; McMullen, Kendra M; Cass, Jennifer; Mark, Erin; Yeates, Keith Owen
2012-11-01
To examine the results of a randomized clinical trial (RCT) of Teen Online Problem Solving (TOPS), an online problem solving therapy model, in increasing problem-solving skills and decreasing depressive symptoms and global distress for caregivers of adolescents with traumatic brain injury (TBI). Families of adolescents aged 11-18 who sustained a moderate to severe TBI between 3 and 19 months earlier were recruited from hospital trauma registries. Participants were assigned to receive a web-based, problem-solving intervention (TOPS, n = 20), or access to online resources pertaining to TBI (Internet Resource Comparison; IRC; n = 21). Parent report of problem solving skills, depressive symptoms, global distress, utilization, and satisfaction were assessed pre- and posttreatment. Groups were compared on follow-up scores after controlling for pretreatment levels. Family income was examined as a potential moderator of treatment efficacy. Improvement in problem solving was examined as a mediator of reductions in depression and distress. Forty-one participants provided consent and completed baseline assessments, with follow-up assessments completed on 35 participants (16 TOPS and 19 IRC). Parents in both groups reported a high level of satisfaction with both interventions. Improvements in problem solving skills and depression were moderated by family income, with caregivers of lower income in TOPS reporting greater improvements. Increases in problem solving partially mediated reductions in global distress. Findings suggest that TOPS may be effective in improving problem solving skills and reducing depressive symptoms for certain subsets of caregivers in families of adolescents with TBI.
Cunningham, Frances C; Ranmuthugala, Geetha; Plumb, Jennifer; Georgiou, Andrew; Westbrook, Johanna I; Braithwaite, Jeffrey
2012-03-01
While there is a considerable corpus of theoretical and empirical literature on networks within and outside of the health sector, multiple research questions are yet to be answered. To conduct a systematic review of studies of professionals' network structures, identifying factors associated with network effectiveness and sustainability, particularly in relation to quality of care and patient safety. The authors searched MEDLINE, CINAHL, EMBASE, Web of Science and Business Source Premier from January 1995 to December 2009. A majority of the 26 unique studies identified used social network analysis to examine structural relationships in networks: structural relationships within and between networks, health professionals and their social context, health collaboratives and partnerships, and knowledge sharing networks. Key aspects of networks explored were administrative and clinical exchanges, network performance, integration, stability and influences on the quality of healthcare. More recent studies show that cohesive and collaborative health professional networks can facilitate the coordination of care and contribute to improving quality and safety of care. Structural network vulnerabilities include cliques, professional and gender homophily, and over-reliance on central agencies or individuals. Effective professional networks employ natural structural network features (eg, bridges, brokers, density, centrality, degrees of separation, social capital, trust) in producing collaboratively oriented healthcare. This requires efficient transmission of information and social and professional interaction within and across networks. For those using networks to improve care, recurring success factors are understanding your network's characteristics, attending to its functioning and investing time in facilitating its improvement. Despite this, there is no guarantee that time spent on networks will necessarily improve patient care.
Statistical learning problem of artificial neural network to control roofing process
Directory of Open Access Journals (Sweden)
Lapidus Azariy
2017-01-01
Full Text Available Now software developed on the basis of artificial neural networks (ANN has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.
Improving Schools through Networks: A New Approach to Urban School Reform.
Wohlstetter, Priscilla; Malloy, Courtney L.; Chau, Derrick; Polhemus, Jennifer L.
2003-01-01
Data from an evaluation of the Annenberg Challenge in Los Angeles, a reform effort that experimented with school networks as a vehicle for improving schools, revealed that when school networks created structures that decentralized power and distributed organizational resources throughout the network, they also enhanced school capacity for reform.…
Directory of Open Access Journals (Sweden)
Sung-Woong Jo
2017-01-01
Full Text Available Video streaming service is one of the most popular applications for mobile users. However, mobile video streaming services consume a lot of energy, resulting in a reduced battery life. This is a critical problem that results in a degraded user’s quality of experience (QoE. Therefore, in this paper, a joint optimization scheme that controls both the central processing unit (CPU and wireless networking of the video streaming process for improved energy efficiency on mobile devices is proposed. For this purpose, the energy consumption of the network interface and CPU is analyzed, and based on the energy consumption profile a joint optimization problem is formulated to maximize the energy efficiency of the mobile device. The proposed algorithm adaptively adjusts the number of chunks to be downloaded and decoded in each packet. Simulation results show that the proposed algorithm can effectively improve the energy efficiency when compared with the existing algorithms.
Improving TCP Performance over Wireless Ad Hoc Networks with Busy Tone Assisted Scheme
Directory of Open Access Journals (Sweden)
Cai Lin
2006-01-01
Full Text Available It is well known that transmission control protocol (TCP performance degrades severely in IEEE 802.11-based wireless ad hoc networks. We first identify two critical issues leading to the TCP performance degradation: (1 unreliable broadcast, since broadcast frames are transmitted without the request-to-send and clear-to-send (RTS/CTS dialog and Data/ACK handshake, so they are vulnerable to the hidden terminal problem; and (2 false link failure which occurs when a node cannot successfully transmit data temporarily due to medium contention. We then propose a scheme to use a narrow-bandwidth, out-of-band busy tone channel to make reservation for broadcast and link error detection frames only. The proposed scheme is simple and power efficient, because only the sender needs to transmit two short messages in the busy tone channel before sending broadcast or link error detection frames in the data channel. Analytical results show that the proposed scheme can dramatically reduce the collision probability of broadcast and link error detection frames. Extensive simulations with different network topologies further demonstrate that the proposed scheme can improve TCP throughput by 23% to 150%, depending on user mobility, and effectively enhance both short-term and long-term fairness among coexisting TCP flows in multihop wireless ad hoc networks.
Directory of Open Access Journals (Sweden)
Tingsong Du
2015-01-01
Full Text Available An improved quantum artificial fish swarm algorithm (IQAFSA for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA, the basic artificial fish swarm algorithm (BAFSA, and the global edition artificial fish swarm algorithm (GAFSA to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.
A Novel Prioritization Scheme to Improve QoS in IEEE 802.11e Networks
Directory of Open Access Journals (Sweden)
Navid Tadayon
2010-01-01
Full Text Available IEEE 802.11 WLAN utilizes a distributed function at its MAC layer, namely, DCF to access the wireless medium. Due to its distributed nature, DCF is able to guarantee working stability in a wireless medium while maintaining the assembling and maintenance cost in a low level. However, DCF is inefficient in dealing with real-time traffics due to its incapability on providing QoS. IEEE 802.11e was introduced as a supplementary standard to cope with this problem. This standard introduces an Enhanced Distributed Coordination Function (EDCF that works based on diff-Serve model and can serve multiple classes of traffics (by using different prioritizations schemes. With the emergence of new time-sensitive applications, EDCF has proved to be yet inefficient in dealing with these kinds of traffics because it could not provide network with well-differentiated QoS. In this study, we propose a novel prioritization scheme to improve QoS level in IEEE 802.11e network. In this scheme, we replace Uniform PDF with Gamma PDF, which has salient differentiating properties. We investigate the suitability and superiority of this scheme on furnishing network with well-differentiated QoS using probabilistic analysis. We strengthen our claims by extensive simulation runs.
Mikami, Amori Yee; Szwedo, David E; Allen, Joseph P; Evans, Meredyth A; Hare, Amanda L
2010-01-01
This study examined online communication on social networking web pages in a longitudinal sample of 92 youths (39 male, 53 female). Participants' social and behavioral adjustment was assessed when they were ages 13-14 years and again at ages 20-22 years. At ages 20-22 years, participants' social networking website use and indicators of friendship quality on their web pages were coded by observers. Results suggested that youths who had been better adjusted at ages 13-14 years were more likely to be using social networking web pages at ages 20-22 years, after statistically controlling for age, gender, ethnicity, and parental income. Overall, youths' patterns of peer relationships, friendship quality, and behavioral adjustment at ages 13-14 years and at ages 20-22 years predicted similar qualities of interaction and problem behavior on their social networking websites at ages 20-22 years. Findings are consistent with developmental theory asserting that youths display cross-situational continuity in their social behaviors and suggest that the conceptualization of continuity may be extended into the online domain. Copyright 2009 APA, all rights reserved.
Improvement of the Hopfield Neural Network by MC-Adaptation Rule
Zhou, Zhen; Zhao, Hong
2006-06-01
We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.
Directory of Open Access Journals (Sweden)
D. V. Mironov
2014-12-01
Full Text Available Purpose. The new methods development for improving the maintenance service for equipment of traction networks in order to increase its efficiency and quality. Methodology. In world practice of solving problems related to the quality of products and services is usually achieved by introducing quality management system in to the enterprises. The provisions of quality management system were used for solving the problem. The technologies of process engineering were used for describing the main stages of maintenance service. Findings. The development of high-speed movement and growth of its intensity, the use of electric rolling stock of a new generation require the introduction of new methods diagnostics of equipment technical state and improvement of the existing maintenance system and repair of power supply. Developing a model of business-processes, their optimization with using techniques of process engineering and system management is needed for the transition to the management system based on the process approach. From the standpoint of the process approach and in accordance with the requirements of the quality management system (ISO 9001-2009, the operation of the E (Department of electrification and power supply infrastructure sector is represented as a scheme of business-processes in which the guaranteed supply with electricity of railway and third-party consumers is defined as the main business-process of management. Each of the sub-process of power supply for consumers is described in details. The use methods and main stages of process approach for sample management system reorganization were investigated. The methodology and the application method of PDCA (Plan-Do-Check-Act closed loop to the equipment maintenance system were described. The monitoring process of traction networks maintenance using the process approach was divided into components after investigations. The technical documentation of maintenance service was investigated in
Dinger, Ulrike; Zilcha-Mano, Sigal; McCarthy, Kevin S; Barrett, Marna S; Barber, Jacques P
2013-11-01
Previous studies reported inconsistent findings regarding the association of interpersonal problems with therapy outcome. The current study investigates if interpersonal problems predict process and outcome of three different treatments for depression. The data originate from a randomized clinical trial comparing supportive-expressive psychotherapy, antidepressant medication and pill-placebo for treatment of depression. Interpersonal problems were used as predictors of alliance, symptomatic improvement and premature termination of treatment. Interpersonal problems related to communion predicted better alliances, but slower symptomatic improvement. Low agency predicted slower symptomatic improvement in supportive-expressive psychotherapy, but not in the medication or placebo condition. Lower interpersonal distress was associated with an increased likelihood to terminate treatment prematurely. The sample size did not allow the detection of small effects within the treatment groups. Interpersonal problems are influential for the treatment of depression, but parts of their effects depend on the type of treatment. © 2013 Elsevier B.V. All rights reserved.
Improving cloud network security using tree-rule firewall
He, Xiangjian; Chomsiri, Thawatchai; Nanda, Priyadarsi; Tan, Zhiyuan
This study proposes a new model of firewall called the ‘Tree-Rule Firewall’, which offers various benefits and is applicable for large networks such as ‘cloud’ networks. The recently available firewalls (i.e., Listed-Rule firewalls) have their limitations in performing the tasks and are inapplicable
Directory of Open Access Journals (Sweden)
Wahyudi Sutopo
2016-12-01
Full Text Available In recent years, the rising competitive environment with shorter product life cycles and high customization forces industries to increase their flexibility, speed up their response, and enhance concurrent engineering designs. To integrate these prospects, supply chain collaboration becomes a pertinent strategy for industries to strengthen their competitiveness. The network design problem is used to implement supply chain collaboration. In the buying and selling process, sharing information between buyer and supplier are important to obtain a transaction decision. The optimimum supply chain profit can be identified by mathematical model of network design problem. The Mathematical Model takes into consideration the uncertainity in negotiation of supply chain, transportation problems, and locationallocation of products from supplier to buyer in the planning based on the time value of money. The results show that the model can be used to optimize the supply chain profit. The supplier gets a profit because income were received in the initial contract, while the buyer profit comes from lower pay.
A parallel algorithm for solving linear equations arising from one-dimensional network problems
International Nuclear Information System (INIS)
Mesina, G.L.
1991-01-01
One-dimensional (1-D) network problems, such as those arising from 1- D fluid simulations and electrical circuitry, produce systems of sparse linear equations which are nearly tridiagonal and contain a few non-zero entries outside the tridiagonal. Most direct solution techniques for such problems either do not take advantage of the special structure of the matrix or do not fully utilize parallel computer architectures. We describe a new parallel direct linear equation solution algorithm, called TRBR, which is especially designed to take advantage of this structure on MIMD shared memory machines. The new method belongs to a family of methods which split the coefficient matrix into the sum of a tridiagonal matrix T and a matrix comprised of the remaining coefficients R. Efficient tridiagonal methods are used to algebraically simplify the linear system. A smaller auxiliary subsystem is created and solved and its solution is used to calculate the solution of the original system. The newly devised BR method solves the subsystem. The serial and parallel operation counts are given for the new method and related earlier methods. TRBR is shown to have the smallest operation count in this class of direct methods. Numerical results are given. Although the algorithm is designed for one-dimensional networks, it has been applied successfully to three-dimensional problems as well. 20 refs., 2 figs., 4 tabs
Directory of Open Access Journals (Sweden)
R. Rajakumar
2017-01-01
Full Text Available Seyedali Mirjalili et al. (2014 introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO. This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO and modified bat algorithm (MBA. The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.
INTERCONNECTING NETWORKS WITH DIFFERENT LEVELS OF SECURITY – A PRESENT NATO PROBLEM
Directory of Open Access Journals (Sweden)
LIVIU TATOMIR
2016-07-01
Full Text Available A situation often met in the Romanian Armed Forces in recent years is the need for interconnecting two networks (domains with different levels of classification. Considering that the Romanian armed troops are involved in numerous missions with NATO partners, solutions, already implemented across the organization, are considered to be applied in domestic systems, also. This paper presents the solutions adopted by NATO in order to solve the problem of cross -domains interconnections. We present the maturity level reached by these solutions and the possibility of implementing these solutions in the Romanian Armed Forces, with or without specific adaptation to our own rules and regulations. The goal is to use a NATO already proved solution to our national classified networks.
An Integrated Approach for Reliable Facility Location/Network Design Problem with Link Disruption
Directory of Open Access Journals (Sweden)
Davood Shishebori
2015-05-01
Full Text Available Proposing a robust designed facility location is one of the most effective ways to hedge against unexpected disruptions and failures in a transportation network system. This paper considers the combined facility location/network design problem with regard to transportation link disruptions and develops a mixed integer linear programming formulation to model it. With respect to the probability of link disruptions, the objective function of the model minimizes the total costs, including location costs, link construction costs and also the expected transportation costs. An efficient hybrid algorithm based on LP relaxation and variable neighbourhood search metaheuristic is developed in order to solve the mathematical model. Numerical results demonstrate that the proposed hybrid algorithm has suitable efficiency in terms of duration of solution time and determining excellent solution quality.
Directory of Open Access Journals (Sweden)
Mario A. de Oliveira
2018-01-01
Full Text Available A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1 Savitzky–Golay (SG filter, using both first and second derivatives; (2 Probabilistic Neural Network (PNN; and, (3 Simplified Fuzzy ARTMAP Network (SFAN. Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.
de Oliveira, Mario A; Araujo, Nelcileno V S; da Silva, Rodolfo N; da Silva, Tony I; Epaarachchi, Jayantha
2018-01-08
A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky-Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.
Araujo, Nelcileno V. S.; da Silva, Rodolfo N.; da Silva, Tony I.; Epaarachchi, Jayantha
2018-01-01
A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario. PMID:29316693
Trust, Privacy, and Frame Problems in Social and Business E-Networks, Part 1
Directory of Open Access Journals (Sweden)
Jeff Buechner
2011-03-01
Full Text Available Privacy issues in social and business e-networks are daunting in complexity—private information about oneself might be routed through countless artificial agents. For each such agent, in that context, two questions about trust are raised: Where an agent must access (or store personal information, can one trust that artificial agent with that information and, where an agent does not need to either access or store personal information, can one trust that agent not to either access or store that information? It would be an infeasible task for any human being to explicitly determine, for each artificial agent, whether it can be trusted. That is, no human being has the computational resources to make such an explicit determination. There is a well-known class of problems in the artificial intelligence literature, known as frame problems, where explicit solutions to them are computationally infeasible. Human common sense reasoning solves frame problems, though the mechanisms employed are largely unknown. I will argue that the trust relation between two agents (human or artificial functions, in some respects, is a frame problem solution. That is, a problem is solved without the need for a computationally infeasible explicit solution. This is an aspect of the trust relation that has remained unexplored in the literature. Moreover, there is a formal, iterative structure to agent-agent trust interactions that serves to establish the trust relation non-circularly, to reinforce it, and to “bootstrap” its strength.
Quality Education Improvement: Yemen and the Problem of the "Brain Drain"
Muthanna, Abdulghani
2015-01-01
This paper presents an overview of the problems that hinder improvement of the quality of education in Yemen, with a particular focus on higher education institutions. It discusses in particular the problem of the brain drain and why this phenomenon is occurring in Yemen. Semi-structured interviews with three professors at higher education…
Application of neural network technology to nuclear plant thermal efficiency improvement
International Nuclear Information System (INIS)
Doremus, Rick; Allen Ho, S.; Bailey, James V.; Roman, Harry
2004-01-01
Due to the tremendous cost of building new nuclear power plants, it has become increasingly attractive to increase the power output from the existing operating power plants. There are two options that may be available to accomplish this goal. One option is to uprate the plant through licensing modification for a comfortably achievable goal of 4% to 6%. However, the licensing efforts required are no small task, vary from plant to plant, and may take years to accomplish. Some nuclear power plants may not have this option because of design, environmental, political, or geographical limitations. A second option exists that is simpler and more immediate. It focuses on improving the plant operating conditions using adaptive software that could increase the total plant output by approximately one-half percent by adjusting certain key operating parameters. No design basis analyses, hardware modifications, or licensing changes are required. In fact, this technique can be used on a plant that has already obtained licensing modification to obtain an additional one-half percent on top of the 4% to 6% increase. Public Service Electric and Gas and ARD Corporation are jointly investigating the creation of a Plant Optimization System, called POSITIVE. POSITIVE is an adaptive software tool that enables a user to analyze current plant data to identify potential problem areas and to obtain recommendations for increasing the plant's electric output. POSITIVE uses a combination of expert systems and adaptive software to analyze the thermal performance of a nuclear power plant. Historical data, obtained while the plant was above 93% power, is used to train neural networks to determine the current electric output of the plant. Once sufficiently trained, new data can be processed through the neural network. The neural network first determines the electric output associated with the current data. If the actual power matches the power predicted by the network, the neural network can be used
The Improvement of Simple Explanation and Inferencetion Skills with Problem Solving
Dewanti, Fransiska Olivia; Diawati, Chansyanah; Fadiawati, Noor
2013-01-01
The learning process is strongly influenced by the ability and accuracy of teachers in selecting and applying the learning model. The model can be applied to improve of Â simple explanation and inferencetion skill is a model of problem solving. The purpose of this study was to describe the model of problem solving that are effective in improving simple explanation and inferencetion skills on the material electrolyte and non-electrolyte solution. This research use a quasi-experimental methods ...
Erdley-Kass, Shiloh D; Kass, Darrin S; Gellis, Zvi D; Bogner, Hillary A; Berger, Andrea; Perkins, Robert M
2017-08-24
To determine the effectiveness of Problem-Solving Therapy (PST) in older hemodialysis (HD) patients by assessing changes in health-related quality of life and problem-solving skills. 33 HD patients in an outpatient hemodialysis center without active medical and psychiatric illness were enrolled. The intervention group (n = 15) received PST from a licensed social worker for 6 weeks, whereas the control group (n = 18) received usual care treatment. In comparison to the control group, patients receiving PST intervention reported improved perceptions of mental health, were more likely to view their problems with a positive orientation and were more likely to use functional problem-solving methods. Furthermore, this group was also more likely to view their overall health, activity limits, social activities and ability to accomplish desired tasks with a more positive mindset. The results demonstrate that PST may positively impact mental health components of quality of life and problem-solving coping among older HD patients. PST is an effective, efficient, and easy to implement intervention that can benefit problem-solving abilities and mental health-related quality of life in older HD patients. In turn, this will help patients manage their daily living activities related to their medical condition and reduce daily stressors.
National Research Council Canada - National Science Library
Dunklee, David R
2007-01-01
.... The research then presents four recommendations to improve DCC operations. These include: Transition or improve the current signature-based IDS systems to include the capability to query and visualize network flows to detect malicious traffic...
Improving Network Performance with Affinity based Mobility Model in Opportunistic Network
Suvadip Batabyal; Parama Bhaumik
2012-01-01
Opportunistic network is a type of Delay Tolerant Network which is characterized by intermittent connectivity amongst the nodes and communication largely depends upon the mobility of the participating nodes. The network being highly dynamic, traditional MANET protocols cannot be applied and the nodes must adhere to store-carry-forward mechanism. Nodes do not have the information about the network topology, number of participating nodes and the location of the destination node. Hence, message ...
Directory of Open Access Journals (Sweden)
Vrinda Gupta
2016-06-01
Full Text Available In this paper, an improved version of the energy aware distributed unequal clustering protocol (EADUC is projected. The EADUC protocol is commonly used for solving energy hole problem in multi-hop wireless sensor networks. In the EADUC, location of base station and residual energy are given importance as clustering parameters. Based on these parameters, different competition radii are assigned to nodes. Herein, a new approach has been proposed to improve the working of EADUC, by electing cluster heads considering number of nodes in the neighborhood in addition to the above two parameters. The inclusion of the neighborhood information for computation of the competition radii provides better balancing of energy in comparison with the existing approach. Furthermore, for the selection of next hop node, the relay metric is defined directly in terms of energy expense instead of only the distance information used in the EADUC and the data transmission phase has been extended in every round by performing the data collection number of times through use of major slots and mini-slots. The methodology used is of retaining the same clusters for a few rounds and is effective in reducing the clustering overhead. The performance of the proposed protocol has been evaluated under three different scenarios and compared with existing protocols through simulations. The results show that the proposed scheme outperforms the existing protocols in terms of network lifetime in all the scenarios.
Inverse problems in 1D hemodynamics on systemic networks: a sequential approach.
Lombardi, D
2014-02-01
In this work, a sequential approach based on the unscented Kalman filter is applied to solve inverse problems in 1D hemodynamics, on a systemic network. For instance, the arterial stiffness is estimated by exploiting cross-sectional area and mean speed observations in several locations of the arteries. The results are compared with those ones obtained by estimating the pulse wave velocity and the Moens-Korteweg formula. In the last section, a perspective concerning the identification of the terminal models parameters and peripheral circulation (modeled by a Windkessel circuit) is presented. Copyright © 2013 John Wiley & Sons, Ltd.
Improved Local Weather Forecasts Using Artificial Neural Networks
DEFF Research Database (Denmark)
Wollsen, Morten Gill; Jørgensen, Bo Nørregaard
2015-01-01
Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...
Idm@ti Network: An Innovative Proposal for Improving Teaching and Learning in Spanish Universities
Salan, Nuria; Cabedo, Luis; Segarra, Mercedes; Guraya, Teresa; Lopez, Pascal; Sales, David; Gamez, Jose
2017-01-01
IdM@ti network members concurred in the diagnosis of the difficulties and opportunities arising from Bologna process implementation and teaching methodologies improvement in Materials Science and Engineering (MSE) teaching. This network has been created with the aim of improving efficiency of underway and future collaborations.The main objectives…
Thematic network for a Phebus FPT1 international standard problem (THENPHEBISP)
International Nuclear Information System (INIS)
Clement, B.; Haste, T.; Krausmann, E.; Dickinson, S.; Gyenes, G.; Duspiva, J.; Rosa, F. de; Paci, S.; Martin-Fuertes, F.; Scholytssek, W.; Allelein, H.-J.; Guentay, S.; Arien, B.; Marguet, S.; Leskovar, M.; Sartmadjiev, A.
2005-01-01
The THENPHEBISP 2-year thematic network started in December 2001, and was concerned with OECD/CSNI International Standard Problem 46, itself based on the Phebus FPT1 core degradation/source term experiment. The aim was to assess the capability of computer codes to model in an integrated way the physical processes taking place during a severe accident in a pressurised water reactor, from the initial stages of core degradation, the fission product transport through the primary circuit and the behaviour of the released fission products in the containment. ISP-46, coordinated by IRSN/DRS Cadarache, attracted 33 participating organisations, from 23 countries and international bodies, who submitted 47 base case calculations and 21 best-estimate calculations, using 15 different codes. The thermal behaviour of the fuel bundle and the hydrogen production were generally well captured, and good agreement for the core final state could be obtained with a suitable choice of bulk fuel relocation temperature, however this is unlikely to be representative of all plant studies so sensitivity calculations are needed with the modelling in its current state. Total volatile fission product release was simulated, but its kinetics, and the overall modelling of semi-volatile, low-volatile and structural material release (Ag/In/Cd, Sn) needs improvement. Overall retention in the circuit is well predicted, but calculations underestimate deposits in the upper plenum and overestimate those in the steam generator, also the volatility of some elements could be better predicted. Containment thermal hydraulics and depletion rate of aerosols are well calculated, but with difficulties related to partition amongst the deposition mechanisms. Calculation of iodine chemistry in the containment turned out to be more difficult. Its quality strongly depends of the calculation of release and transport in the integral codes. The major difficulties are related to the existence of gaseous iodine in the
On Throughput Improvement of Wireless Ad Hoc Networks with Hidden Nodes
Choi, Hong-Seok; Lim, Jong-Tae
In this letter, we present the throughput analysis of the wireless ad hoc networks based on the IEEE 802.11 MAC (Medium Access Control). Especially, our analysis includes the case with the hidden node problem so that it can be applied to the multi-hop networks. In addition, we suggest a new channel access control algorithm to maximize the network throughput and show the usefulness of the proposed algorithm through simulations.
The Problem of Privacy in Capitalism and the Alternative Social Networking Site Diaspora*
Directory of Open Access Journals (Sweden)
Sebastian Sevignani
2012-05-01
Full Text Available In this paper, l examine the alternative social networking site Diaspora* from a Marxist standpoint. The investigation focuses on privacy, and contributes to a better understanding of this issue within the context of capitalism in general. First, I describe Diaspora*’s way of production by pointing out its alternative character as part of the free software and copyleft movement. Second, dominant theories of privacy related to individual control, exclusion, and property are introduced. Third, the problem of privacy in capitalism is described wherein dominant concepts of privacy will be contextualised on behalf of a critical political economy analysis that refers to the Marxian concept of ideology critique, Marx’s differentiation between a societal sphere of production and a societal sphere of circulation, and his analysis of capitalist fetishisms. Fourth, taking into account the problem of privacy in capitalism, the alternative potential of Diaspora* is evaluated. Finally, a brief outline of a Marxist theory of privacy is proposed.
Institute of Scientific and Technical Information of China (English)
石玺
2013-01-01
目前国内农业企业普遍存在着网络营销观念意识淡薄、人才缺乏、网络营销水平低下、网站推广不力等诸多问题；而要解决这些问题的就必须加强农业企业管理人员培训,树立正确网络营销意识；建设专业化网络营销队伍,提升服务水平；采用信息发布、第三方网络营销平台、Email营销等方法提高网络营销水平；建设农业企业网站并搭建企业自己的网络品牌.%At present, it has widespread problems in domestic agricultural enterprises, such as laek of Internet marketing concept consciousness and human resource, the low level of network marketing, ineffective website promotion; And to solve these problems, we must strengthen the training management personnel of agricultural enterprise, sets up the correct network marketing consciousness; build professional network marketing team, improve the service level; adopt to information release, the third party network marketing platform, Email marketing and other methods to enhance the level of network marketing; set up agricultural enterprise web site and build own network brand.
An improved algorithm for connectivity analysis of distribution networks
International Nuclear Information System (INIS)
Kansal, M.L.; Devi, Sunita
2007-01-01
In the present paper, an efficient algorithm for connectivity analysis of moderately sized distribution networks has been suggested. Algorithm is based on generation of all possible minimal system cutsets. The algorithm is efficient as it identifies only the necessary and sufficient conditions of system failure conditions in n-out-of-n type of distribution networks. The proposed algorithm is demonstrated with the help of saturated and unsaturated distribution networks. The computational efficiency of the algorithm is justified by comparing the computational efforts with the previously suggested appended spanning tree (AST) algorithm. The proposed technique has the added advantage as it can be utilized for generation of system inequalities which is useful in reliability estimation of capacitated networks
an improved voltage regulation of a distribution network using facts
African Journals Online (AJOL)
OMEJE CO
2013-07-02
Jul 2, 2013 ... operational point of view, SVC behaves like a shunt-connected .... During normal operations, a small amount of active power must ..... Company of Nigeria. The bus with ... network with the line contingency control variables is ...
Web server's reliability improvements using recurrent neural networks
DEFF Research Database (Denmark)
Madsen, Henrik; Albu, Rǎzvan-Daniel; Felea, Ioan
2012-01-01
In this paper we describe an interesting approach to error prediction illustrated by experimental results. The application consists of monitoring the activity for the web servers in order to collect the specific data. Predicting an error with severe consequences for the performance of a server (t...... usage, network usage and memory usage. We collect different data sets from monitoring the web server's activity and for each one we predict the server's reliability with the proposed recurrent neural network. © 2012 Taylor & Francis Group...
Using biological networks to improve our understanding of infectious diseases
Directory of Open Access Journals (Sweden)
Nicola J. Mulder
2014-08-01
Full Text Available Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.
Reorganizing Complex Network to Improve Large-Scale Multiagent Teamwork
Directory of Open Access Journals (Sweden)
Yang Xu
2014-01-01
Full Text Available Large-scale multiagent teamwork has been popular in various domains. Similar to human society infrastructure, agents only coordinate with some of the others, with a peer-to-peer complex network structure. Their organization has been proven as a key factor to influence their performance. To expedite team performance, we have analyzed that there are three key factors. First, complex network effects may be able to promote team performance. Second, coordination interactions coming from their sources are always trying to be routed to capable agents. Although they could be transferred across the network via different paths, their sources and sinks depend on the intrinsic nature of the team which is irrelevant to the network connections. In addition, the agents involved in the same plan often form a subteam and communicate with each other more frequently. Therefore, if the interactions between agents can be statistically recorded, we are able to set up an integrated network adjustment algorithm by combining the three key factors. Based on our abstracted teamwork simulations and the coordination statistics, we implemented the adaptive reorganization algorithm. The experimental results briefly support our design that the reorganized network is more capable of coordinating heterogeneous agents.
Probabilistic diffusion tractography reveals improvement of structural network in musicians.
Directory of Open Access Journals (Sweden)
Jianfu Li
Full Text Available PURPOSE: Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. METHODS: Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. RESULTS: Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. CONCLUSIONS: We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.
Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks
Directory of Open Access Journals (Sweden)
José Raúl Machado-Fernández
2016-12-01
Full Text Available The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE. The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.
International Nuclear Information System (INIS)
Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi
2012-01-01
The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches
Charpiat, B; Mille, F; Fombeur, P; Machon, J; Zawadzki, E; Bobay-Madic, A
2018-05-21
The development of information systems in French hospitals is mandatory. The aim of this work was to analyze the content of exchanges carried out within social networks, dealing with problems encountered with hospital pharmacies information systems. Messages exchanged via the mailing list of the Association pour le Digital et l'Information en Pharmacie and abstracts of communications presented at hospital pharmacists trade union congresses were analyzed. Those referring to information systems used in hospital pharmacies were selected. From March 2015 to June 2016, 122 e-mails sent by 80 pharmacists concerned information systems. From 2002 to 2016, 45 abstracts dealt with this topic. Problems most often addressed in these 167 documents were "parameterization and/or functionalities" (n=116), interfaces and complexity of the hospital information systems (n=52), relationship with health information technologies vendors and poor reactivity (n=32), additional workload (n=32), ergonomics (n=30), insufficient user training (n=22). These problems are interdependent, lead to errors and in order to mitigate their consequences, they compel pharmacy professionals to divert a significant amount of working hours to the detriment of pharmaceutical care and dispensing and preparing drugs. Hospital pharmacists are faced with many problems of insecurity and inefficiency generated by information systems. Researches are warranted to determine their cost, specify their deleterious effects on care and identify the safest information systems. Copyright © 2018 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.
Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems
Directory of Open Access Journals (Sweden)
Gabriel A. Fonseca Guerra
2017-12-01
Full Text Available Constraint satisfaction problems (CSP are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems. The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs, and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.
Fernández Caballero, Juan Carlos; Martínez, Francisco José; Hervás, César; Gutiérrez, Pedro Antonio
2010-05-01
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.
Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems.
Fonseca Guerra, Gabriel A; Furber, Steve B
2017-01-01
Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs), and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.
A Location-Inventory-Routing Problem in Forward and Reverse Logistics Network Design
Directory of Open Access Journals (Sweden)
Qunli Yuchi
2016-01-01
Full Text Available We study a new problem of location-inventory-routing in forward and reverse logistic (LIRP-FRL network design, which simultaneously integrates the location decisions of distribution centers (DCs, the inventory policies of opened DCs, and the vehicle routing decision in serving customers, in which new goods are produced and damaged goods are repaired by a manufacturer and then returned to the market to satisfy customers’ demands as new ones. Our objective is to minimize the total costs of manufacturing and remanufacturing goods, building DCs, shipping goods (new or recovered between the manufacturer and opened DCs, and distributing new or recovered goods to customers and ordering and storage costs of goods. A nonlinear integer programming model is proposed to formulate the LIRP-FRL. A new tabu search (NTS algorithm is developed to achieve near optimal solution of the problem. Numerical experiments on the benchmark instances of a simplified version of the LIRP-FRL, the capacitated location routing problem, and the randomly generated LIRP-FRL instances demonstrate the effectiveness and efficiency of the proposed NTS algorithm in problem resolution.
International Nuclear Information System (INIS)
Alsumait, J.S.; Qasem, M.; Sykulski, J.K.; Al-Othman, A.K.
2010-01-01
In this paper, an improved algorithm based on Pattern Search method (PS) to solve the Dynamic Economic Dispatch is proposed. The algorithm maintains the essential unit ramp rate constraint, along with all other necessary constraints, not only for the time horizon of operation (24 h), but it preserves these constraints through the transaction period to the next time horizon (next day) in order to avoid the discontinuity of the power system operation. The Dynamic Economic and Emission Dispatch problem (DEED) is also considered. The load balance constraints, operating limits, valve-point loading and network losses are included in the models of both DED and DEED. The numerical results clarify the significance of the improved algorithm and verify its performance.
A Bi-Level Programming Model for the Railway Express Cargo Service Network Design Problem
Directory of Open Access Journals (Sweden)
Boliang Lin
2018-06-01
Full Text Available Service network design is fundamentally crucial for railway express cargo transportation. The main challenge is to strike a balance between two conflicting objectives: low network setup costs and high expected operational incomes. Different configurations of these objectives will have different impacts on the quality of freight transportation services. In this paper, a bi-level programming model for the railway express cargo service network design problem is proposed. The upper-level model forms the optimal decisions in terms of the service characteristics, and the low-level model selects the service arcs for each commodity. The rail express cargo is strictly subject to the service commitment, the capacity restriction, flow balance constraints, and logical relationship constraints among the decisions variables. Moreover, linearization techniques are used to convert the lower-level model to a linear one so that it can be directly solved by a standard optimization solver. Finally, a real-world case study based on the Beijing–Guangzhou Railway Line is carried out to demonstrate the effectiveness and efficiency of the proposed solution approach.
AN EVOLUTIONARY ALGORITHM FOR CHANNEL ASSIGNMENT PROBLEM IN WIRELESS MOBILE NETWORKS
Directory of Open Access Journals (Sweden)
Yee Shin Chia
2012-12-01
Full Text Available The channel assignment problem in wireless mobile network is the assignment of appropriate frequency spectrum to incoming calls while maintaining a satisfactory level of electromagnetic compatibility (EMC constraints. An effective channel assignment strategy is important due to the limited capacity of frequency spectrum in wireless mobile network. Most of the existing channel assignment strategies are based on deterministic methods. In this paper, an adaptive genetic algorithm (GA based channel assignment strategy is introduced for resource management and to reduce the effect of EMC interferences. The most significant advantage of the proposed optimization method is its capability to handle both the reassignment of channels for existing calls as well as the allocation of channel to a new incoming call in an adaptive process to maximize the utility of the limited resources. It is capable to adapt the population size to the number of eligible channels for a particular cell upon new call arrivals to achieve reasonable convergence speed. The MATLAB simulation on a 49-cells network model for both uniform and nonuniform call traffic distributions showed that the proposed channel optimization method can always achieve a lower average new incoming call blocking probability compared to the deterministic based channel assignment strategy.
An improved pulse coupled neural network with spectral residual for infrared pedestrian segmentation
He, Fuliang; Guo, Yongcai; Gao, Chao
2017-12-01
Pulse coupled neural network (PCNN) has become a significant tool for the infrared pedestrian segmentation, and a variety of relevant methods have been developed at present. However, these existing models commonly have several problems of the poor adaptability of infrared noise, the inaccuracy of segmentation results, and the fairly complex determination of parameters in current methods. This paper presents an improved PCNN model that integrates the simplified framework and spectral residual to alleviate the above problem. In this model, firstly, the weight matrix of the feeding input field is designed by the anisotropic Gaussian kernels (ANGKs), in order to suppress the infrared noise effectively. Secondly, the normalized spectral residual saliency is introduced as linking coefficient to enhance the edges and structural characteristics of segmented pedestrians remarkably. Finally, the improved dynamic threshold based on the average gray values of the iterative segmentation is employed to simplify the original PCNN model. Experiments on the IEEE OTCBVS benchmark and the infrared pedestrian image database built by our laboratory, demonstrate that the superiority of both subjective visual effects and objective quantitative evaluations in information differences and segmentation errors in our model, compared with other classic segmentation methods.
Sampasa-Kanyinga, H; Hamilton, H A
2015-11-01
Previous research has suggested an association between the use of social networking sites (SNSs) and mental health problems such as psychological distress, suicidal ideation and attempts in adolescents. However, little is known about the factors that might mediate these relationships. The present study examined the link between the use of social networking sites and psychological distress, suicidal ideation and suicide attempts, and tested the mediating role of cyberbullying victimization on these associations in adolescents. The sample consisted of a group of 11-to-20-year-old individuals (n=5126, 48% females; mean±SD age: 15.2±1.9 years) who completed the mental health portion of the Ontario Student Drug Use and Health Survey (OSDUHS) in 2013. Multiple logistic regression analyses were used to test the mediation models. After adjustment for age, sex, ethnicity, subjective socioeconomic status (SES), and parental education, use of SNSs was associated with psychological distress (adjusted odds ratio, 95% confidence interval=2.03, 1.22-3.37), suicidal ideation (3.44, 1.54-7.66) and attempts (5.10, 1.45-17.88). Cyberbullying victimization was found to fully mediate the relationships between the use of SNSs with psychological distress and attempts; whereas, it partially mediated the link between the use of SNSs and suicidal ideation. Findings provide supporting evidence that addressing cyberbullying victimization and the use of SNSs among adolescents may help reduce the risk of mental health problems. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
An MPCC Formulation and Its Smooth Solution Algorithm for Continuous Network Design Problem
Directory of Open Access Journals (Sweden)
Guangmin Wang
2017-12-01
Full Text Available Continuous network design problem (CNDP is searching for a transportation network configuration to minimize the sum of the total system travel time and the investment cost of link capacity expansions by considering that the travellers follow a traditional Wardrop user equilibrium (UE to choose their routes. In this paper, the CNDP model can be formulated as mathematical programs with complementarity constraints (MPCC by describing UE as a non-linear complementarity problem (NCP. To address the difficulty resulting from complementarity constraints in MPCC, they are substituted by the Fischer-Burmeister (FB function, which can be smoothed by the introduction of the smoothing parameter. Therefore, the MPCC can be transformed into a well-behaved non-linear program (NLP by replacing the complementarity constraints with a smooth equation. Consequently, the solver such as LINDOGLOBAL in GAMS can be used to solve the smooth approximate NLP to obtain the solution to MPCC for modelling CNDP. The numerical experiments on the example from the literature demonstrate that the proposed algorithm is feasible.
The Location-Routing Problem with Full Truckloads in Low-Carbon Supply Chain Network Designing
Directory of Open Access Journals (Sweden)
Cheng Chen
2018-01-01
Full Text Available In recent years, low-carbon supply chain network design has been the focus of studies as the development of low-carbon economy. The location-routing problem with full truckloads (LRPFT is investigated in this paper, which extends the existing studies on the LRP to full truckloads problem within the regional many-to-many raw material supply network. A mathematical model with dual objectives of minimizing total cost and environmental effects simultaneously is developed to determine the number and locations of facilities and optimize the flows among different kinds of nodes and routes of trucks as well. A novel multiobjective hybrid approach named NSGA-II-TS is proposed by combining a known multiobjective algorithm, NSGA-II, and a known heuristics, Tabu Search (TS. A chromosome presentation based on natural number and modified partially mapping crossover operator for the LRPFT are designed. Finally, the computational effectiveness of the hybrid approach is validated by the numerical results and a practical case study is applied to demonstrate the tradeoff between total cost and CO2 emission in the LRPFT.
Scientific Approach to Improve Mathematical Problem Solving Skills Students of Grade V
Roheni; Herman, T.; Jupri, A.
2017-09-01
This study investigates the skills of elementary school students’ in problem solving through the Scientific Approach. The purpose of this study is to determine mathematical problem solving skills of students by using Scientific Approach is better than mathematical problem solving skills of students by using Direct Instruction. This study is using quasi-experimental method. Subject of this study is students in grade V in one of state elementary school in Cirebon Regency. Instrument that used in this study is mathematical problem solving skills. The result of this study showed that mathematical problem solving skills of students who learn by using Scientific Approach is more significant than using Direct Instruction. Base on result and analysis, the conclusion is that Scientific Approach can improve students’ mathematical problem solving skills.
The role of problem solving method on the improvement of mathematical learning
Directory of Open Access Journals (Sweden)
Saeed Mokhtari-Hassanabad
2012-10-01
Full Text Available In history of education, problem solving is one of the important educational goals and teachers or parents have intended that their students have capacity of problem solving. In present research, it is tried that study the problem solving method for mathematical learning. This research is implemented via quasi-experimental method on 49 boy students at high school. The results of Leven test and T-test indicated that problem solving method has more effective on the improvement of mathematical learning than traditional instruction method. Therefore it seems that teachers of mathematics must apply the problem solving method in educational systems till students became self-efficiency in mathematical problem solving.
International Nuclear Information System (INIS)
Sauget, M.
2007-12-01
This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)
Directory of Open Access Journals (Sweden)
Xiaohui Lin
2017-01-01
Full Text Available Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies: (1 no control strategy, (2 boundary control, and (3 boundary control with limiting queue strategy. The results show that our proposed algorithm is better than the other two.
pn: A Tool for Improved Derivation of Process Networks
Directory of Open Access Journals (Sweden)
Sven Verdoolaege
2007-04-01
Full Text Available Current emerging embedded System-on-Chip platforms are increasingly becoming multiprocessor architectures. System designers experience significant difficulties in programming these platforms. The applications are typically specified as sequential programs that do not reveal the available parallelism in an application, thereby hindering the efficient mapping of an application onto a parallel multiprocessor platform. In this paper, we present our compiler techniques for facilitating the migration from a sequential application specification to a parallel application specification using the process network model of computation. Our work is inspired by a previous research project called Compaan. With our techniques we address optimization issues such as the generation of process networks with simplified topology and communication without sacrificing the process networks' performance. Moreover, we describe a technique for compile-time memory requirement estimation which we consider as an important contribution of this paper. We demonstrate the usefulness of our techniques on several examples.
Improving attitudes toward mathematics learning with problem posing in class VIII
Vionita, Alfha; Purboningsih, Dyah
2017-08-01
This research is classroom action research which is collaborated to improve student's behavior toward math and mathematics learning at class VIII by using problem posing approach. The subject of research is all of students grade VIIIA which consist of 32 students. This research has been held on two period, first period is about 3 times meeting, and second period is about 4 times meeting. The instrument of this research is implementation of learning observation's guidance by using problem posing approach. Cycle test has been used to measure cognitive competence, and questionnaire to measure the students' behavior in mathematics learning process. The result of research shows the students' behavior has been improving after using problem posing approach. It is showed by the behavior's criteria of students that has increasing result from the average in first period to high in second period. Furthermore, the percentage of test result is also improve from 68,75% in first period to 78,13% in second period. On the other hand, the implementation of learning observation by using problem posing approach has also improving and it is showed by the average percentage of teacher's achievement in first period is 89,2% and student's achievement 85,8%. These results get increase in second period for both teacher and students' achievement which are 94,4% and 91,11%. As a result, students' behavior toward math learning process in class VIII has been improving by using problem posing approach.
The Land Transport Network in the Post-Soviet Space- Problems and Prospective Development
Directory of Open Access Journals (Sweden)
Sergej Schlichter
2012-10-01
Full Text Available Road and rail networks in the post-Soviet space are analysedin view of the demands in transportation to be expected inthe 2 I st centwy. The road system is found te1ribly underdel'elopedin terms of density and canying capacity. It widely fails tofulfil the necessary feeder function for the rail system. Both railand road ~ystems need substantial improvements to allow forthe wgent economic recove1y of that lQige area between thosevital and dynamic regions in east (China, south (Middle Eastund west (Europe.
Improving a Computer Networks Course Using the Partov Simulation Engine
Momeni, B.; Kharrazi, M.
2012-01-01
Computer networks courses are hard to teach as there are many details in the protocols and techniques involved that are difficult to grasp. Employing programming assignments as part of the course helps students to obtain a better understanding and gain further insight into the theoretical lectures. In this paper, the Partov simulation engine and…
An Improved Voltage Regulation of a Distribution Network Using ...
African Journals Online (AJOL)
The Newton-Raphson Load flow equation modeling was a veritable tool applied in this analysis to determine the convergence points for the voltage magnitude, power (load) angle, power losses along the lines, sending end and receiving end power values at the various buses that make up the thirteen bus network.
Improving Family Forest Knowledge Transfer through Social Network Analysis
Gorczyca, Erika L.; Lyons, Patrick W.; Leahy, Jessica E.; Johnson, Teresa R.; Straub, Crista L.
2012-01-01
To better engage Maine's family forest landowners our study used social network analysis: a computational social science method for identifying stakeholders, evaluating models of engagement, and targeting areas for enhanced partnerships. Interviews with researchers associated with a research center were conducted to identify how social network…
Streaming-aware channel utilization improvement for wireless home networks
Aslam, W.; Lukkien, J.J.
2012-01-01
A wireless network of consumer electronic (CE) devices in a modern home, is typically running streaming services with heterogeneous bandwidth demands. Satisfying these demands offers the challenge of mapping them efficiently onto scarce wireless channel bandwidth. This mapping is supported by the
An improved anonymous authentication scheme for roaming in ubiquitous networks.
Lee, Hakjun; Lee, Donghoon; Moon, Jongho; Jung, Jaewook; Kang, Dongwoo; Kim, Hyoungshick; Won, Dongho
2018-01-01
With the evolution of communication technology and the exponential increase of mobile devices, the ubiquitous networking allows people to use our data and computing resources anytime and everywhere. However, numerous security concerns and complicated requirements arise as these ubiquitous networks are deployed throughout people's lives. To meet the challenge, the user authentication schemes in ubiquitous networks should ensure the essential security properties for the preservation of the privacy with low computational cost. In 2017, Chaudhry et al. proposed a password-based authentication scheme for the roaming in ubiquitous networks to enhance the security. Unfortunately, we found that their scheme remains insecure in its protection of the user privacy. In this paper, we prove that Chaudhry et al.'s scheme is vulnerable to the stolen-mobile device and user impersonation attacks, and its drawbacks comprise the absence of the incorrect login-input detection, the incorrectness of the password change phase, and the absence of the revocation provision. Moreover, we suggest a possible way to fix the security flaw in Chaudhry et al's scheme by using the biometric-based authentication for which the bio-hash is applied in the implementation of a three-factor authentication. We prove the security of the proposed scheme with the random oracle model and formally verify its security properties using a tool named ProVerif, and analyze it in terms of the computational and communication cost. The analysis result shows that the proposed scheme is suitable for resource-constrained ubiquitous environments.
Software defined networking to improve mobility management performance
Karimzadeh Motallebi Azar, Morteza; Sperotto, Anna; Pras, Aiko; Sperotto, Anna; Doyen, Guillaume; Latré, Steven; Charalambides, Marinos; Stiller, Burkhard
2014-01-01
n mobile networks, efficient IP mobility management is a crucial issue for the mobile users changing their mobility anchor points during handover. In this regard several mobility management methods have been proposed. However, those are insufficient for the future mobile Internet in terms of
An improved anonymous authentication scheme for roaming in ubiquitous networks.
Directory of Open Access Journals (Sweden)
Hakjun Lee
Full Text Available With the evolution of communication technology and the exponential increase of mobile devices, the ubiquitous networking allows people to use our data and computing resources anytime and everywhere. However, numerous security concerns and complicated requirements arise as these ubiquitous networks are deployed throughout people's lives. To meet the challenge, the user authentication schemes in ubiquitous networks should ensure the essential security properties for the preservation of the privacy with low computational cost. In 2017, Chaudhry et al. proposed a password-based authentication scheme for the roaming in ubiquitous networks to enhance the security. Unfortunately, we found that their scheme remains insecure in its protection of the user privacy. In this paper, we prove that Chaudhry et al.'s scheme is vulnerable to the stolen-mobile device and user impersonation attacks, and its drawbacks comprise the absence of the incorrect login-input detection, the incorrectness of the password change phase, and the absence of the revocation provision. Moreover, we suggest a possible way to fix the security flaw in Chaudhry et al's scheme by using the biometric-based authentication for which the bio-hash is applied in the implementation of a three-factor authentication. We prove the security of the proposed scheme with the random oracle model and formally verify its security properties using a tool named ProVerif, and analyze it in terms of the computational and communication cost. The analysis result shows that the proposed scheme is suitable for resource-constrained ubiquitous environments.
[Training of institutional research networks as a strategy of improvement].
Galván-Plata, María Eugenia; Almeida-Gutiérrez, Eduardo; Salamanca-Gómez, Fabio Abdel
2017-01-01
The Instituto Mexicano del Seguro Social (IMSS) through the Coordinación de Investigación en Salud (Health Research Council) has promoted a strong link between the generation of scientific knowledge and the clinical care through the program Redes Institucionales de Investigación (Institutional Research Network Program), whose main aim is to promote and generate collaborative research between clinical, basic, epidemiologic, educational, economic and health services researchers, seeking direct benefits for patients, as well as to generate a positive impact on institutional processes. All of these research lines have focused on high-priority health issues in Mexico. The IMSS internal structure, as well as the sufficient health services coverage, allows the integration of researchers at the three levels of health care into these networks. A few years after their creation, these networks have already generated significant results, and these are currently applied in the institutional regulations in diseases that represent a high burden to health care. Two examples are the National Health Care Program for Patients with Acute Myocardial Infarction "Código Infarto", and the Early Detection Program on Chronic Kidney Disease; another result is the generation of multiple scientific publications, and the promotion of training of human resources in research from the same members of our Research Networks. There is no doubt that the Coordinación de Investigación en Salud advances steadily implementing the translational research, which will keep being fruitful to the benefit of our patients, and of our own institution.
Improvement of the ID model for quantitative network data
DEFF Research Database (Denmark)
Sørensen, Peter Borgen; Damgaard, Christian Frølund; Dupont, Yoko Luise
2015-01-01
Many interactions are often poorly registered or even unobserved in empirical quantitative networks. Hence, the output of the statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. Such artefa......Many interactions are often poorly registered or even unobserved in empirical quantitative networks. Hence, the output of the statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks......)1. This presentation will illustrate the application of the ID method based on a data set which consists of counts of visits by 152 pollinator species to 16 plant species. The method is based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi...... reproduce the high number of zero valued cells in the data set and mimic the sampling distribution. 1 Sørensen et al, Journal of Pollination Ecology, 6(18), 2011, pp129-139...
One Improvement Method of Reducing Duration Directly to Solve Time-Cost Tradeoff Problem
Jian-xun, Qi; Dedong, Sun
Time and cost are two of the most important factors for project plan and schedule management, and specially, time-cost tradeoff problem is one classical problem in project scheduling, which is also a difficult problem. Methods of solving the problem mainly contain method of network flow and method of mending the minimal cost. Thereinto, for the method of mending the minimal cost is intuitionistic, convenient and lesser computation, these advantages make the method being used widely in practice. But disadvantage of the method is that the result of each step is optimal but the terminal result maybe not optimal. In this paper, firstly, method of confirming the maximal effective quantity of reducing duration is designed; secondly, on the basis of above method and the method of mending the minimal cost, the main method of reducing duration directly is designed to solve time-cost tradeoff problem, and by analyzing validity of the method, the method could obtain more optimal result for the problem.
Directory of Open Access Journals (Sweden)
Chunhua Ju
2012-01-01
Full Text Available Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM and an improved artificial immune network algorithm (aiNet are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA, simulated annealing algorithm (SA, and ant colony optimization (ACO.
Cerebellum-inspired neural network solution of the inverse kinematics problem.
Asadi-Eydivand, Mitra; Ebadzadeh, Mohammad Mehdi; Solati-Hashjin, Mehran; Darlot, Christian; Abu Osman, Noor Azuan
2015-12-01
The demand today for more complex robots that have manipulators with higher degrees of freedom is increasing because of technological advances. Obtaining the precise movement for a desired trajectory or a sequence of arm and positions requires the computation of the inverse kinematic (IK) function, which is a major problem in robotics. The solution of the IK problem leads robots to the precise position and orientation of their end-effector. We developed a bioinspired solution comparable with the cerebellar anatomy and function to solve the said problem. The proposed model is stable under all conditions merely by parameter determination, in contrast to recursive model-based solutions, which remain stable only under certain conditions. We modified the proposed model for the simple two-segmented arm to prove the feasibility of the model under a basic condition. A fuzzy neural network through its learning method was used to compute the parameters of the system. Simulation results show the practical feasibility and efficiency of the proposed model in robotics. The main advantage of the proposed model is its generalizability and potential use in any robot.
Directory of Open Access Journals (Sweden)
G. M. Behery
2009-01-01
Full Text Available This paper presents an automatic system of neural networks (NNs that has the ability to simulate and predict many of applied problems. The system architectures are automatically reorganized and the experimental process starts again, if the required performance is not reached. This processing is continued until the performance obtained. This system is first applied and tested on the two spiral problem; it shows that excellent generalization performance obtained by classifying all points of the two-spirals correctly. After that, it is applied and tested on the shear stress and the pressure drop problem across the short orifice die as a function of shear rate at different mean pressures for linear low-density polyethylene copolymer (LLDPE at 190∘C. The system shows a better agreement with an experimental data of the two cases: shear stress and pressure drop. The proposed system has been also designed to simulate other distributions not presented in the training set (predicted and matched them effectively.
An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem
Directory of Open Access Journals (Sweden)
Tao Zhang
2012-01-01
Full Text Available An improved particle swarm optimization (PSO algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP. For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.
An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems
Directory of Open Access Journals (Sweden)
Chandramouli Anandaraman
2011-10-01
Full Text Available Job Shop Scheduling Problem (JSSP and Flow Shop Scheduling Problem (FSSP are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.
An improved algorithm for searching all minimal cuts in modified networks
International Nuclear Information System (INIS)
Yeh, W.-C.
2008-01-01
A modified network is an updated network after inserting a branch string (a special path) between two nodes in the original network. Modifications are common for network expansion or reinforcement evaluation and planning. The problem of searching all minimal cuts (MCs) in a modified network is discussed and solved in this study. The existing best-known methods for solving this problem either needed extensive comparison and verification or failed to solve some special but important cases. Therefore, a more efficient, intuitive and generalized method for searching all MCs without an extensive research procedure is proposed. In this study, we first develop an intuitive algorithm based upon the reformation of all MCs in the original network to search for all MCs in a modified network. Next, the correctness of the proposed algorithm will be analyzed and proven. The computational complexity of the proposed algorithm is analyzed and compared with the existing best-known methods. Finally, two examples illustrate how all MCs are generated in a modified network using the information of all of the MCs in the corresponding original network
Improved merit order and augmented Lagrange Hopfield network for short term hydrothermal scheduling
International Nuclear Information System (INIS)
Vo Ngoc Dieu; Ongsakul, Weerakorn
2009-01-01
This paper proposes an improved merit order (IMO) combined with an augmented Lagrangian Hopfield network (ALHN) for solving short term hydrothermal scheduling (HTS) with pumped-storage hydro plants. The proposed IMO-ALHN consists of a merit order based on the average production cost of generating units enhanced by heuristic search algorithm for finding unit scheduling and a continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation for solving constrained economic dispatch (CED). The proposed method is applied to solve the HTS problem in five stages including thermal, hydro and pumped-storage unit commitment by IMO and heuristic search, constraint violations repairing by heuristic search and CED by ALHN. The proposed method is tested on the 24-bus IEEE RTS with 32 units including 4 fuel-constrained, 4-hydro, and 2 pumped-storage units scheduled over a 24-h period. Test results indicate that the proposed IMO-ALHN is efficient for hydrothermal systems with various constraints.
Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
Directory of Open Access Journals (Sweden)
Manuel Fogue
2018-01-01
Full Text Available Vehicular networks make use of the Roadside Units (RSUs to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures, in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations and to improve vehicular communication capabilities within different density scenarios and complexity layouts.
An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks.
Raza, Waseem; Arshad, Farzana; Ahmed, Imran; Abdul, Wadood; Ghouzali, Sanaa; Niaz, Iftikhar Azim; Javaid, Nadeem
2016-11-04
In this paper, a novel routing strategy to cater the energy consumption and delay sensitivity issues in deep underwater wireless sensor networks is proposed. This strategy is named as ESDR: Event Segregation based Delay sensitive Routing. In this strategy sensed events are segregated on the basis of their criticality and, are forwarded to their respective destinations based on forwarding functions. These functions depend on different routing metrics like: Signal Quality Index, Localization free Signal to Noise Ratio, Energy Cost Function and Depth Dependent Function. The problem of incomparable values of previously defined forwarding functions causes uneven delays in forwarding process. Hence forwarding functions are redefined to ensure their comparable values in different depth regions. Packet forwarding strategy is based on the event segregation approach which forwards one third of the generated events (delay sensitive) to surface sinks and two third events (normal events) are forwarded to mobile sinks. Motion of mobile sinks is influenced by the relative distribution of normal nodes. We have also incorporated two different mobility patterns named as; adaptive mobility and uniform mobility for mobile sinks. The later one is implemented for collecting the packets generated by the normal nodes. These improvements ensure optimum holding time, uniform delay and in-time reporting of delay sensitive events. This scheme is compared with the existing ones and outperforms the existing schemes in terms of network lifetime, delay and throughput.
A framework to approach problems of forensic anthropology using complex networks
Caridi, Inés; Dorso, Claudio O.; Gallo, Pablo; Somigliana, Carlos
2011-05-01
We have developed a method to analyze and interpret emerging structures in a set of data which lacks some information. It has been conceived to be applied to the problem of getting information about people who disappeared in the Argentine state of Tucumán from 1974 to 1981. Even if the military dictatorship formally started in Argentina had begun in 1976 and lasted until 1983, the disappearance and assassination of people began some months earlier. During this period several circuits of Illegal Detention Centres (IDC) were set up in different locations all over the country. In these secret centres, disappeared people were illegally kept without any sort of constitutional guarantees, and later assassinated. Even today, the final destination of most of the disappeared people’s remains is still unknown. The fundamental hypothesis in this work is that a group of people with the same political affiliation whose disappearances were closely related in time and space shared the same place of captivity (the same IDC or circuit of IDCs). This hypothesis makes sense when applied to the systematic method of repression and disappearances which was actually launched in Tucumán, Argentina (2007) [11]. In this work, the missing individuals are identified as nodes on a network and connections are established among them based on the individuals’ attributes while they were alive, by using rules to link them. In order to determine which rules are the most effective in defining the network, we use other kind of knowledge available in this problem: previous results from the anthropological point of view (based on other sources of information, both oral and written, historical and anthropological data, etc.); and information about the place (one or more IDCs) where some people were kept during their captivity. For these best rules, a prediction about these people’s possible destination is assigned (one or more IDCs where they could have been kept), and the success of the
Leveraging socially networked mobile ICT platforms for the last-mile delivery problem.
Suh, Kyo; Smith, Timothy; Linhoff, Michelle
2012-09-04
Increasing numbers of people are managing their social networks on mobile information and communication technology (ICT) platforms. This study materializes these social relationships by leveraging spatial and networked information for sharing excess capacity to reduce the environmental impacts associated with "last-mile" package delivery systems from online purchases, particularly in low population density settings. Alternative package pickup location systems (PLS), such as a kiosk on a public transit platform or in a grocery store, have been suggested as effective strategies for reducing package travel miles and greenhouse gas emissions, compared to current door-to-door delivery models (CDS). However, our results suggest that a pickup location delivery system operating in a suburban setting may actually increase travel miles and emissions. Only once a social network is employed to assist in package pickup (SPLS) are significant reductions in the last-mile delivery distance and carbon emissions observed across both urban and suburban settings. Implications for logistics management's decades-long focus on improving efficiencies of dedicated distribution systems through specialization, as well as for public policy targeting carbon emissions of the transport sector are discussed.
The Improvement of Basic Support and Advance Clarification Skill with Problem Solving
Safira, Novi Ayu; Diawati, Chansyanah; Rosilawati, Ila
2013-01-01
The low-creative critical thinking skill of the student is because many schools use low-level abilities in learning. The use of problem solving model in the learning is one of the efforts for practice the critical thinking skill students. This research aimed to describe the problem solving model that are effective in improving the basic support and advance clarification skill. This research using a quasi-experimental methods with Non Equivalent Control Group Design. The sampling technique use...
maisarera, yunita; diawati, chansyanah; fadiawati, noor
2012-01-01
The aim of this research is to describe the effectiveness of problem solving learning in improving communication and inference skills in colloid system material.Â Subjects in this research were students of XIIPA1 and XI IPA2 classrooms in Persada Junior High School in Bandar Lampung in academic year 2011-2012 where students of both classrooms had the same characteristics. This research used quasi experiment method and pretest-posttest control group design. Effectiveness of problem solving le...
Analysis of the Efficacy of an Intervention to Improve Parent-Adolescent Problem Solving
Semeniuk, Yulia Yuriyivna; Brown, Roger L.; Riesch, Susan K.
2016-01-01
We conducted a two-group longitudinal partially nested randomized controlled trial to examine whether young adolescent youth-parent dyads participating in Mission Possible: Parents and Kids Who Listen, in contrast to a comparison group, would demonstrate improved problem solving skill. The intervention is based on the Circumplex Model and Social Problem Solving Theory. The Circumplex Model posits that families who are balanced, that is characterized by high cohesion and flexibility and open c...
The admissible portfolio selection problem with transaction costs and an improved PSO algorithm
Chen, Wei; Zhang, Wei-Guo
2010-05-01
In this paper, we discuss the portfolio selection problem with transaction costs under the assumption that there exist admissible errors on expected returns and risks of assets. We propose a new admissible efficient portfolio selection model and design an improved particle swarm optimization (PSO) algorithm because traditional optimization algorithms fail to work efficiently for our proposed problem. Finally, we offer a numerical example to illustrate the proposed effective approaches and compare the admissible portfolio efficient frontiers under different constraints.
A Network Traffic Control Enhancement Approach over Bluetooth Networks
DEFF Research Database (Denmark)
Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun
2003-01-01
This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated solu...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well......This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...
Analysis of the Efficacy of an Intervention to Improve Parent-Adolescent Problem Solving.
Semeniuk, Yulia Yuriyivna; Brown, Roger L; Riesch, Susan K
2016-07-01
We conducted a two-group longitudinal partially nested randomized controlled trial to examine whether young adolescent youth-parent dyads participating in Mission Possible: Parents and Kids Who Listen, in contrast to a comparison group, would demonstrate improved problem-solving skill. The intervention is based on the Circumplex Model and Social Problem-Solving Theory. The Circumplex Model posits that families who are balanced, that is characterized by high cohesion and flexibility and open communication, function best. Social Problem-Solving Theory informs the process and skills of problem solving. The Conditional Latent Growth Modeling analysis revealed no statistically significant differences in problem solving among the final sample of 127 dyads in the intervention and comparison groups. Analyses of effect sizes indicated large magnitude group effects for selected scales for youth and dyads portraying a potential for efficacy and identifying for whom the intervention may be efficacious if study limitations and lessons learned were addressed. © The Author(s) 2016.
Energy Technology Data Exchange (ETDEWEB)
Castro, Paulo Alexandre de; Souza, Thaianne Lopes de [Universidade Federal de Goias (UFG), Catalao, GO (Brazil)
2011-07-01
Full text. What the Brazilian soccer championship, Hollywood actors, the network of the Internet, the spread of viruses and electric distribution network have in common? Until less than two decade ago, the answer would be 'nothing' or 'almost nothing'. However, the answer today to this same question is 'all' or 'almost all'. The answer to these questions and more can be found through a sub-area of statistical physics | called science of complex networks that has been used to approach and study the most diverse natural and non-natural systems, such as systems/social networks, information, technological or biological. In this work we study the distribution network of electric power in Brazil (DEEB), from a perspective of complex networks, where we associate stations and/or substations with a network of vertices and the links between the vertices we associate with the transmission lines. We are doing too a comparative study with the best-known models of complex networks, such as Erdoes-Renyi, Configuration Model and Barabasi-Albert, and then we compare with results obtained in real electrical distribution networks. Based on this information, we do a comparative analysis using the following variables: connectivity distribution, diameter, clustering coefficient, which are frequently used in studies of complex networks. We emphasize that the main objective of this study is to analyze the robustness of the network DEEB, and then propose alternatives for network connectivity, which may contribute to the increase of robustness in maintenance projects and/or expansion of the network, in other words our goal is to make the network to proof the blackouts or improve the endurance the network against the blackouts. For this purpose, we use information from the structural properties of networks, computer modeling and simulation. (author)
Zagoruyko, Sergey; Komodakis, Nikos
2016-01-01
Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcin...
Improving Students’ Scientific Reasoning and Problem-Solving Skills by The 5E Learning Model
Directory of Open Access Journals (Sweden)
Sri Mulyani Endang Susilowati
2017-12-01
Full Text Available Biology learning in MA (Madrasah Aliyah Khas Kempek was still dominated by teacher with low students’ involvement. This study would analyze the effectiveness of the 5E (Engagement, Exploration, Explanation, Elaboration, Evaluation learning model in improving scientific knowledge and problems solving. It also explained the relationship between students’ scientific reasoning with their problem-solving abilities. This was a pre-experimental research with one group pre-test post-test. Sixty students of MA Khas Kempek from XI MIA 3 and XI MIA 4 involved in this study. The learning outcome of the students was collected by the test of reasoning and problem-solving. The results showed that the rises of students’ scientific reasoning ability were 69.77% for XI MIA 3 and 66.27% for XI MIA 4, in the medium category. The problem-solving skills were 63.40% for XI MIA 3, 61.67% for XI MIA 4, and classified in the moderate category. The simple regression test found a linear correlation between students’ scientific reasoning and problem-solving ability. This study affirms that reasoning ability is needed in problem-solving. It is found that application of 5E learning model was effective to improve scientific reasoning and problem-solving ability of students.
Improving insight and non-insight problem solving with brief interventions.
Wen, Ming-Ching; Butler, Laurie T; Koutstaal, Wilma
2013-02-01
Developing brief training interventions that benefit different forms of problem solving is challenging. In earlier research, Chrysikou (2006) showed that engaging in a task requiring generation of alternative uses of common objects improved subsequent insight problem solving. These benefits were attributed to a form of implicit transfer of processing involving enhanced construction of impromptu, on-the-spot or 'ad hoc' goal-directed categorizations of the problem elements. Following this, it is predicted that the alternative uses exercise should benefit abilities that govern goal-directed behaviour, such as fluid intelligence and executive functions. Similarly, an indirect intervention - self-affirmation (SA) - that has been shown to enhance cognitive and executive performance after self-regulation challenge and when under stereotype threat, may also increase adaptive goal-directed thinking and likewise should bolster problem-solving performance. In Experiment 1, brief single-session interventions, involving either alternative uses generation or SA, significantly enhanced both subsequent insight and visual-spatial fluid reasoning problem solving. In Experiment 2, we replicated the finding of benefits of both alternative uses generation and SA on subsequent insight problem-solving performance, and demonstrated that the underlying mechanism likely involves improved executive functioning. Even brief cognitive- and social-psychological interventions may substantially bolster different types of problem solving and may exert largely similar facilitatory effects on goal-directed behaviours. © 2012 The British Psychological Society.
Hull, Sally; Chowdhury, Tahseen A; Mathur, Rohini; Robson, John
2014-02-01
Structured diabetes care can improve outcomes and reduce risk of complications, but improving care in a deprived, ethnically diverse area can prove challenging. This report evaluates a system change to enhance diabetes care delivery in a primary care setting. All 35 practices in one inner London Primary Care Trust were geographically grouped into eight networks of four to five practices, each supported by a network manager, clerical staff and an educational budget. A multidisciplinary team developed a 'care package' for type 2 diabetes management, with financial incentives based on network achievement of targets. Monthly electronic performance dashboards enabled networks to track and improve performance. Network multidisciplinary team meetings including the diabetic specialist team supported case management and education. Key measures for improvement included the number of diabetes care plans completed, proportion of patients attending for digital retinal screen and proportions of patients achieving a number of biomedical indices (blood pressure, cholesterol, glycated haemoglobin). Between 2009 and 2012, completed care plans rose from 10% to 88%. The proportion of patients attending for digital retinal screen rose from 72% to 82.8%. The proportion of patients achieving a combination of blood pressure ≤ 140/80 mm Hg and cholesterol ≤ 4 mmol/L rose from 35.3% to 46.1%. Mean glycated haemoglobin dropped from 7.80% to 7.66% (62-60 mmol/mol). Investment of financial, organisational and education resources into primary care practice networks can achieve clinically important improvements in diabetes care in deprived, ethnically diverse communities. This success is predicated on collaborative working between practices, purposively designed high-quality information on network performance and engagement between primary and secondary care clinicians.
CONVERGING REDUNDANT SENSOR NETWORK INFORMATION FOR IMPROVED BUILDING CONTROL
Energy Technology Data Exchange (ETDEWEB)
Dale K. Tiller; Gregor P. Henze
2004-11-01
Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Analysis tools based on Bayesian probability theory were applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.
Services Sector Development and Improving Production Network in ASEAN
Yose Rizal Damuri
2014-01-01
While the importance of services sector in creating value added and employment has been recognized, the role of services as providers of major inputs to production sector are often forgotten and overlooked. This paper stresses the importance of services sector in supporting economic activities in general; the role that has become increasingly more critical in the wake of global production network. It argues that development of the services sector is crucial to supporting an economyâ€™s partic...
An Improved Ant Colony Broadcasting Algorithm for Wireless Sensor Networks
Nan Jiang; Rigui Zhou; Shuqun Yang; Qiulin Ding
2009-01-01
Recent advances in nano-technology have made it possible to develop a large variety of Micro Electro-Mechanical Systems (MEMS)-miniaturized low-power devices that integrate sensing, special-purpose computing, and wireless communications capabilities. A sensor network is a collection of many small devices, each with sensing, computation, and communication capability. It has many potential applications, such as building surveillance and environmental monitoring. Broadcasting is defined that com...
An improved anonymous authentication scheme for roaming in ubiquitous networks
Lee, Hakjun; Lee, Donghoon; Moon, Jongho; Jung, Jaewook; Kang, Dongwoo; Kim, Hyoungshick
2018-01-01
With the evolution of communication technology and the exponential increase of mobile devices, the ubiquitous networking allows people to use our data and computing resources anytime and everywhere. However, numerous security concerns and complicated requirements arise as these ubiquitous networks are deployed throughout people’s lives. To meet the challenge, the user authentication schemes in ubiquitous networks should ensure the essential security properties for the preservation of the privacy with low computational cost. In 2017, Chaudhry et al. proposed a password-based authentication scheme for the roaming in ubiquitous networks to enhance the security. Unfortunately, we found that their scheme remains insecure in its protection of the user privacy. In this paper, we prove that Chaudhry et al.’s scheme is vulnerable to the stolen-mobile device and user impersonation attacks, and its drawbacks comprise the absence of the incorrect login-input detection, the incorrectness of the password change phase, and the absence of the revocation provision. Moreover, we suggest a possible way to fix the security flaw in Chaudhry et al’s scheme by using the biometric-based authentication for which the bio-hash is applied in the implementation of a three-factor authentication. We prove the security of the proposed scheme with the random oracle model and formally verify its security properties using a tool named ProVerif, and analyze it in terms of the computational and communication cost. The analysis result shows that the proposed scheme is suitable for resource-constrained ubiquitous environments. PMID:29505575
Data identification for improving gene network inference using computational algebra.
Dimitrova, Elena; Stigler, Brandilyn
2014-11-01
Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.
Possible improvements of the reactor safety via neural networks
International Nuclear Information System (INIS)
Marseguerra, M.; Padovani, E.
1996-01-01
The artificial neural networks (ANN) represent one of the fastest developing methodologies which has been extensively applied in the past decade to quite different areas such as pattern recognition and dynamic control. In the present paper we consider two possible application of the ANNs to the safe operation of a reactor. The first one concerns the possibility of using an ANN as a fast reactivity meter only based on previously measured values of the neutron flux. Since the network parameters are experimentally estimated during the training phase, this approach looks promising in comparison with others currently adopted which, besides the experimental data, also depend on theoretical assumptions such as the use of the inverse kinetic equations together with effective parameters. The second application concerns the BWRs and aims at obtaining an early indicator of a variation of the coolant velocity through the fuel elements. This velocity may be routinely measured by correlating the signals coming from two vertically displaced in-core detectors. The variation of the coolant velocity represents an indication of instabilities of the two-phase flow and therefore its on line measurements is of importance for the plant safety. Numerical results of simulated experiments concerning the two mentioned applications are reported. It appears that the neural network approach is suitable for obtaining fast and accurate results. (authors)
Selected Problems of Determining the Course of Railway Routes by Use of GPS Network Solution
Koc, Władysław; Specht, Cezary
2011-09-01
The main problem related to railroad surveying design and its maintenance is the necessity to operate in local geodetic reference systems caused by the long rail sections with straight lines and curvatures of the running edge. Due to that reason the geodetic railroad classical surveying methods requires to divide all track for a short measurement section and that caused additional errors. Development of the Global Navigational Satellite Systems (GNSS) positioning methods operating in the standardized World Geodetic System (WGS-84) allowed verification of capability of utilization GPS measurements for railroad surveying. It can be stated that implemented satellite measurement techniques opens a whole new perspective on applied research and enables very precise determination of data for railway line determining, modernization and design. The research works focused on implementation GNSS multi-receivers measurement positioning platform for projecting and stock-taking working based on polish active geodesic network ASG-EUPOS, as a reference frame. In order to eliminate the influence of random measurement errors and to obtain the coordinates representing the actual shape of the track few campaigns were realized in 2009 and 2010. Leica GPS Total station system 1200 SmartRover (with ATX1230 GG antennas) receivers were located in the diameter of the measurement platform. Polish Active Geodetic Network ASG-EUPOS was used as a reference network transmitted Real Time Kinematic Positioning Service according to RTCM 3.1 standard. Optimum time period were selected for GNSS campaign and testing area was chosen without large obstructions. The article presents some surveying results of the measurement campaigns and also discusses the accuracy of the course determination. Analyzes and implementation of results in railroad design process are also discussed.
Jayawickreme, Nuwan; Mootoo, Candace; Fountain, Christine; Rasmussen, Andrew; Jayawickreme, Eranda; Bertuccio, Rebecca F
2017-10-01
A growing body of literature indicates that the mental distress experienced by survivors of war is a function of both experienced trauma and stressful life events. However, the majority of these studies are limited in that they 1) employ models of psychological distress that emphasize underlying latent constructs and do not allow researchers to examine the unique associations between particular symptoms and various stressors; and 2) use one or more measures that were not developed for that particular context and thus may exclude key traumas, stressful life events and symptoms of psychopathology. The current study addresses both these limitations by 1) using a novel conceptual model, network analysis, which assumes that symptoms covary with each other not because they stem from a latent construct, but rather because they represent meaningful relationships between the symptoms; and 2) employing a locally developed measure of experienced trauma, stressful life problems and symptoms of psychopathology. Over the course of 2009-2011, 337 survivors of the Sri Lankan civil war were administered the Penn-RESIST-Peradeniya War Problems Questionnaire (PRPWPQ). Network analysis revealed that symptoms of psychopathology, problems pertaining to lack of basic needs, and social problems were central to the network relative to experienced trauma and other types of problems. After controlling for shared associations, social problems in particular were the most central, significantly more so than traumatic events and family problems. Several particular traumatic events, stressful life events and symptoms of psychopathology that were central to the network were also identified. Discussion emphasizes the utility of such network models to researchers and practitioners determining how to spend limited resources in the most impactful way possible. Copyright © 2017 Elsevier Ltd. All rights reserved.
Robust transient stabilisation problem for a synchronous generator in a power network
Verrelli, C. M.; Damm, G.
2010-04-01
The robust transient stabilisation problem (with stability proof) of a synchronous generator in an uncertain power network with transfer conductances is rigorously formulated and solved. The generator angular speed and electrical power are required to be kept close, when mechanical and electrical perturbations occur, to the synchronous speed and mechanical input power, respectively, while the generator terminal voltage is to be regulated, when perturbations are removed, to its pre-fault reference constant value. A robust adaptive nonlinear feedback control algorithm is designed on the basis of a third-order model of the synchronous machine: only two system parameters (synchronous machine damping and inertia constants) along with upper and lower bounds on the remaining uncertain ones are supposed to be known. The conditions to be satisfied by the remote network dynamics for guaranteeing ℒ2 and ℒ∞ robustness and asymptotic relative speed and voltage regulation to zero are weaker than those required by the single machine-infinite bus approximation: dynamic interactions between the local deviations of the generator states from the corresponding equilibrium values and the remote generators states are allowed.
Tucker, Jalie A; Foushee, H Russell; Simpson, Cathy A
2009-01-01
A large gap exists in the United States between population need and the utilization of treatment services for substance-related problems. Surveying consumer preferences may provide valuable information for developing more attractive services with greater reach and impact on population health. A state-level telephone survey using random digit dialling sampling methods assessed preferences for available professional, mutual help, and lay resources, as well as innovative computerized and self-help resources that enhance anonymity (N=439 households in Alabama). Respondents preferred help that involved personal contact compared to computerized help or self-help, but were indifferent whether personalized help was dispensed by professional or lay providers. Attractive service features included lower cost, insurance coverage, confidentiality, rapid and convenient appointments, and addressing functional problems and risks of substance misuse. Respondents in households with a member who misused substances rated services more negatively, especially if services had been used. The findings highlight the utility of viewing substance misusers and their social networks as consumers, and the implications for improving the system of care and for designing and marketing services that are responsive to user preferences are discussed.
Enhancing memory and imagination improves problem solving among individuals with depression.
McFarland, Craig P; Primosch, Mark; Maxson, Chelsey M; Stewart, Brandon T
2017-08-01
Recent work has revealed links between memory, imagination, and problem solving, and suggests that increasing access to detailed memories can lead to improved imagination and problem-solving performance. Depression is often associated with overgeneral memory and imagination, along with problem-solving deficits. In this study, we tested the hypothesis that an interview designed to elicit detailed recollections would enhance imagination and problem solving among both depressed and nondepressed participants. In a within-subjects design, participants completed a control interview or an episodic specificity induction prior to completing memory, imagination, and problem-solving tasks. Results revealed that compared to the control interview, the episodic specificity induction fostered increased detail generation in memory and imagination and more relevant steps on the problem-solving task among depressed and nondepressed participants. This study builds on previous work by demonstrating that a brief interview can enhance problem solving among individuals with depression and supports the notion that episodic memory plays a key role in problem solving. It should be noted, however, that the results of the interview are relatively short-lived.
Pedagogy and/or technology: Making difference in improving students' problem solving skills
Hrepic, Zdeslav; Lodder, Katherine; Shaw, Kimberly A.
2013-01-01
Pen input computers combined with interactive software may have substantial potential for promoting active instructional methodologies and for facilitating students' problem solving ability. An excellent example is a study in which introductory physics students improved retention, conceptual understanding and problem solving abilities when one of three weekly lectures was replaced with group problem solving sessions facilitated with Tablet PCs and DyKnow software [1,2]. The research goal of the present study was to isolate the effect of the methodology itself (using additional time to teach problem solving) from that of the involved technology. In Fall 2011 we compared the performance of students taking the same introductory physics lecture course while enrolled in two separate problem-solving sections. One section used pen-based computing to facilitate group problem solving while the other section used low-tech methods for one third of the semester (covering Kinematics), and then traded technologies for the middle third of the term (covering Dynamics). Analysis of quiz, exam and standardized pre-post test results indicated no significant difference in scores of the two groups. Combining this result with those of previous studies implies primacy of pedagogy (collaborative problem solving itself) over technology for student learning in problem solving recitations.
Walker, Sandra; Kennedy, Anne; Vassilev, Ivaylo; Rogers, Anne
2018-02-01
Social network processes impact on the genesis and management of mental health problems. There is currently less understanding of the way people negotiate networked relationships in times of crisis compared to how they manage at other times. This paper explores the patterns and nature of personal network involvement at times of crises and how these may differ from day-to-day networks of recovery and maintenance. Semi-structured interviews with 25 participants with a diagnosis of long-term mental health (MH) problems drawn from recovery settings in the south of England. Interviews centred on personal network mapping of members and resources providing support. The mapping interviews explored the work of network members and changes in times of crisis. Interviews were recorded, transcribed and analysed using a framework analysis. Three key themes were identified: the fluidity of network relationality between crisis and recovery; isolation as a means of crises management; leaning towards peer support. Personal network input retreated at times of crisis often as result of "ejection" from the network by participants who used self-isolation as a personal management strategy in an attempt to deal with crises. Peer support is considered useful during a crisis, whilst the role of services was viewed with some ambiguity. Social networks membership, and type and depth of involvement, is subject to change between times of crisis and everyday support. This has implications for managing mental health in terms of engaging with network support differently in times of crises versus recovery and everyday living. © 2017 The Authors Health Expectations Published by John Wiley & Sons Ltd.
Directory of Open Access Journals (Sweden)
Syarifah Fadillah
2017-03-01
Full Text Available The problem in this research is to know how the process of developing mathematics physics instructional book based on inquiry approach and its supporting documents to improve students' mathematical problem-solving ability. The purpose of this research is to provide mathematical physics instruction based on inquiry approach and its supporting documents (semester learning activity plan, lesson plan and mathematical problem-solving test to improve students' mathematical problem-solving ability. The development of textbook refers to the ADDIE model, including analysis, design, development, implementation, and evaluation. The validation result from the expert team shows that the textbook and its supporting documents are valid. The test results of the mathematical problem-solving skills show that all test questions are valid and reliable. The result of the incorporation of the textbook in teaching and learning process revealed that students' mathematical problem-solving ability using mathematical physics instruction based on inquiry approach book was better than the students who use the regular book.
Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng
2018-02-01
Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.
Improving Stochastic Communication Network Performance: Reliability vs. Throughput
1991-12-01
increased to one. 2) arc survivabil.. ities will be increased in increments of one tenths. and 3) the costs to increase- arc si’rvivabilities were equal and...This reliability value is leni used to maximize the associated expected flow. For Net work A. a bIdget of (8)() pro(duces a tradcoff point at (.58.37...Network B for a buidgel of 2000 which allows a nel \\\\ork relial)ilitv of one to be achieved and a bidget of 1200 which allows for ;, maximum 57
Traveling salesman problems with PageRank Distance on complex networks reveal community structure
Jiang, Zhongzhou; Liu, Jing; Wang, Shuai
2016-12-01
In this paper, we propose a new algorithm for community detection problems (CDPs) based on traveling salesman problems (TSPs), labeled as TSP-CDA. Since TSPs need to find a tour with minimum cost, cities close to each other are usually clustered in the tour. This inspired us to model CDPs as TSPs by taking each vertex as a city. Then, in the final tour, the vertices in the same community tend to cluster together, and the community structure can be obtained by cutting the tour into a couple of paths. There are two challenges. The first is to define a suitable distance between each pair of vertices which can reflect the probability that they belong to the same community. The second is to design a suitable strategy to cut the final tour into paths which can form communities. In TSP-CDA, we deal with these two challenges by defining a PageRank Distance and an automatic threshold-based cutting strategy. The PageRank Distance is designed with the intrinsic properties of CDPs in mind, and can be calculated efficiently. In the experiments, benchmark networks with 1000-10,000 nodes and varying structures are used to test the performance of TSP-CDA. A comparison is also made between TSP-CDA and two well-established community detection algorithms. The results show that TSP-CDA can find accurate community structure efficiently and outperforms the two existing algorithms.
DEFF Research Database (Denmark)
Kaul, Manohar; Khosla, Rajiv; Mitsukura, Y
2003-01-01
of this intelligent traffic-shaping algorithm on the underlying network real time packet traffic and the eradication of unwanted abruption in the streaming video qualiy. This paper concluded from the end results of the simulation that neural networks are a very superior means of modeling real-time traffic......This paper proposes a traffic shaping algorithm based on neural networks, which adapts to a network over which streaming video is being transmitted. The purpose of this intelligent shaper is to eradicate all traffic congestion and improve the end-user's video quality. It possesses the capability...
A new way to improve the robustness of complex communication networks by allocating redundancy links
International Nuclear Information System (INIS)
Shi Chunhui; Zhuo Yue; Tang Jieying; Long Keping; Peng Yunfeng
2012-01-01
We investigate the robustness of complex communication networks on allocating redundancy links. The protecting key nodes (PKN) strategy is proposed to improve the robustness of complex communication networks against intentional attack. Our numerical simulations show that allocating a few redundant links among key nodes using the PKN strategy will significantly increase the robustness of scale-free complex networks. We have also theoretically proved and demonstrated the effectiveness of the PKN strategy. We expect that our work will help achieve a better understanding of communication networks. (paper)
Hooshyar, D.; Ahmad, R. B.; Yousefi, M.; Yusop, F. D.; Horng, S.-J.
2015-01-01
Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem-solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one-to-one personalized and…
Students Use Graphic Organizers to Improve Mathematical Problem-Solving Communications
Zollman, Alan
2009-01-01
Improving students' problem-solving abilities is a major, if not the major, goal of middle grades mathematics. To address this goal, the author, who is a university mathematics educator, and nine inner-city middle school teachers developed a math/science action research project. This article describes their unique approach to mathematical problem…
Problems of Implementation of Strategic Plans for Secondary Schools' Improvement in Anambra State
Chukwumah, Fides Okwukweka; Ezeugbor, Carol Obiageli
2015-01-01
This study investigated the extent of problems of strategic plans implementation for secondary schools' improvement in Anambra State, Nigeria for quality education provision. The study used a descriptive survey design paradigm. Respondents comprised 217 principals. There was no sampling. All the principals were used. Data were collected using…
Sawyer, Alyssa C. P.; Miller-Lewis, Lauren R.; Searle, Amelia K.; Sawyer, Michael G.; Lynch, John W.
2015-01-01
The aim of this study was to determine whether the extent of improvement in self-regulation achieved between ages 4 and 6 years is associated with the level of behavioral problems later in childhood. Participants were 4-year-old children (n = 510) attending preschools in South Australia. Children's level of self-regulation was assessed using the…
The status quo, problems and improvements pertaining to radiation source management in China
International Nuclear Information System (INIS)
Jin Jiaqi
1998-01-01
Early in 1930s, radiation sources were used in medicine in China, and since then their application has been widely extended in a variety of fields. This paper presents a brief outline of the status quo, problems on management for radiation sources, and some relevant improvements as recommended by author are also included in it. (author)
Software Defined Networking for Improved Wireless Sensor Network Management: A Survey.
Ndiaye, Musa; Hancke, Gerhard P; Abu-Mahfouz, Adnan M
2017-05-04
Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management.
Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
Directory of Open Access Journals (Sweden)
Musa Ndiaye
2017-05-01
Full Text Available Wireless sensor networks (WSNs are becoming increasingly popular with the advent of the Internet of things (IoT. Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management.
Azami, Hamed; Escudero, Javier
2015-08-01
Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.
Dynamically Partitionable Autoassociative Networks as a Solution to the Neural Binding Problem
Directory of Open Access Journals (Sweden)
Kenneth Jeffrey Hayworth
2012-09-01
Full Text Available An outstanding question in theoretical neuroscience is how the brain solves the neural binding problem. In vision, binding can be summarized as the ability to represent that certain properties belong to one object while other properties belong to a different object. I review the binding problem in visual and other domains, and review its simplest proposed solution – the anatomical binding hypothesis. This hypothesis has traditionally been rejected as a true solution because it seems to require a type of one-to-one wiring of neurons that would be impossible in a biological system (as opposed to an engineered system like a computer. I show that this requirement for one-to-one wiring can be loosened by carefully considering how the neural representation is actually put to use by the rest of the brain. This leads to a solution where a symbol is represented not as a particular pattern of neural activation but instead as a piece of a global stable attractor state. I introduce the Dynamically Partitionable AutoAssociative Network (DPAAN as an implementation of this solution and show how DPANNs can be used in systems which perform perceptual binding and in systems that implement syntax-sensitive rules. Finally I show how the core parts of the cognitive architecture ACT-R can be neurally implemented using a DPAAN as ACT-R’s global workspace. Because the DPAAN solution to the binding problem requires only ‘flat’ neural representations (as opposed to the phase encoded representation hypothesized in neural synchrony solutions it is directly compatible with the most well developed neural models of learning, memory, and pattern recognition.
Directory of Open Access Journals (Sweden)
Bayu Kanigoro
2012-05-01
Full Text Available This research describes the improvement of computer network performance with bandwidth management in Kemurnian II Senior High School. The main issue of this research is the absence of bandwidth division on computer, which makes user who is downloading data, the provided bandwidth will be absorbed by the user. It leads other users do not get the bandwidth. Besides that, it has been done IP address division on each room, such as computer, teacher and administration room for supporting learning process in Kemurnian II Senior High School, so wireless network is needed. The method is location observation and interview with related parties in Kemurnian II Senior High School, the network analysis has run and designed a new topology network including the wireless network along with its configuration and separation bandwidth on microtic router and its limitation. The result is network traffic on Kemurnian II Senior High School can be shared evenly to each user; IX and IIX traffic are separated, which improve the speed on network access at school and the implementation of wireless network.Keywords: Bandwidth Management; Wireless Network
AN IMPROVEMENT ON GEOMETRY-BASED METHODS FOR GENERATION OF NETWORK PATHS FROM POINTS
Directory of Open Access Journals (Sweden)
Z. Akbari
2014-10-01
Full Text Available Determining network path is important for different purposes such as determination of road traffic, the average speed of vehicles, and other network analysis. One of the required input data is information about network path. Nevertheless, the data collected by the positioning systems often lead to the discrete points. Conversion of these points to the network path have become one of the challenges which different researchers, presents many ways for solving it. This study aims at investigating geometry-based methods to estimate the network paths from the obtained points and improve an existing point to curve method. To this end, some geometry-based methods have been studied and an improved method has been proposed by applying conditions on the best method after describing and illustrating weaknesses of them.
Use of social networks to improve radiation protection
International Nuclear Information System (INIS)
Medina Gironzini, E.
2013-01-01
The development of information and communication technologies has improved relations between the specialists in radiation protection worldwide. Takes advantage of these media to exchange experiences on issues of common interest and technical concerns are resolved in very short times. (Author)
Bilal, Muhammad; Asfand-e-Yar, Mockford, Steve; Khan, Wasiq; Awan, Irfan
2012-11-01
Mobile technology is among the fastest growing technologies in today's world with low cost and highly effective benefits. Most important and entertaining areas in mobile technology development and usage are location based services, user friendly networked applications and gaming applications. However, concern towards network operator service provision and improvement has been very low. The portable applications available for a range of mobile operating systems which help improve the network operator services are desirable by the mobile operators. This paper proposes a state of the art mobile application Tracesaver, which provides a great achievement over the barriers in gathering device and network related information, for network operators to improve their network service provision. Tracesaver is available for a broad range of mobile devices with different mobile operating systems and computational capabilities. The availability of Tracesaver in market has proliferated over the last year since it was published. The survey and results show that Tracesaver is being used by millions of mobile users and provides novel ways of network service improvement with its highly user friendly interface.
Improving publication rates in a collaborative clinical trials research network
Archer, Stephanie Wilson; Carlo, Waldemar A.; Truog, William E.; Stevenson, David K.; Van Meurs, Krisa P.; Sánchez, Pablo J.; Das, Abhik; Devaskar, Uday; Nelin, Leif D.; Petrie Huitema, Carolyn M.; Crawford, Margaret M.; Higgins, Rosemary D.
2016-01-01
Unpublished results can bias biomedical literature, favoring positive over negative findings, primary over secondary analyses, and can lead to duplicate studies that unnecessarily endanger subjects and waste resources. The Neonatal Research Network’s (NRN) publication policies for approving, reviewing, and tracking abstracts and papers work to combat these problems. In 2003, the NRN restricted investigators with unfinished manuscripts from proposing new ones and in 2010, urged authors to comp...
Improved Parallel Three-List Algorithm for the Knapsack Problem without Memory Conflicts
Institute of Scientific and Technical Information of China (English)
Pan Jun; Li Kenli; Li Qinghua
2006-01-01
Based on the two-list algorithm and the parallel three-list algorithm, an improved parallel three-list algorithm for knapsack problem is proposed, in which the method of divide and conquer, and parallel merging without memory conflicts are adopted. To find a solution for the n-element knapsack problem, the proposed algorithm needs O(23n/8) time when O(23n/8) shared memory units and O(2n/4) processors are available. The comparisons between the proposed algorithm and 10 existing algorithms show that the improved parallel three-list algorithm is the first exclusive-read exclusive-write (EREW) parallel algorithm that can solve the knapsack instances in less than O(2n/2) time when the available hardware resource is smaller than O(2n/2), and hence is an improved result over the past researches.
An effective method to improve the robustness of small-world networks under attack
International Nuclear Information System (INIS)
Zhang Zheng-Zhen; Xu Wen-Jun; Lin Jia-Ru; Zeng Shang-You
2014-01-01
In this study, the robustness of small-world networks to three types of attack is investigated. Global efficiency is introduced as the network coefficient to measure the robustness of a small-world network. The simulation results prove that an increase in rewiring probability or average degree can enhance the robustness of the small-world network under all three types of attack. The effectiveness of simultaneously increasing both rewiring probability and average degree is also studied, and the combined increase is found to significantly improve the robustness of the small-world network. Furthermore, the combined effect of rewiring probability and average degree on network robustness is shown to be several times greater than that of rewiring probability or average degree individually. This means that small-world networks with a relatively high rewiring probability and average degree have advantages both in network communications and in good robustness to attacks. Therefore, simultaneously increasing rewiring probability and average degree is an effective method of constructing realistic networks. Consequently, the proposed method is useful to construct efficient and robust networks in a realistic scenario. (interdisciplinary physics and related areas of science and technology)
The improved degree of urban road traffic network: A case study of Xiamen, China
Wang, Shiguang; Zheng, Lili; Yu, Dexin
2017-03-01
The complex network theory is applied to the study of urban road traffic network topology, and we constructed a new measure to characterize an urban road network. It is inspiring to quantify the interaction more appropriately between nodes in complex networks, especially in the field of traffic. The measure takes into account properties of lanes (e.g. number of lanes, width, traffic direction). As much, it is a more comprehensive measure in comparison to previous network measures. It can be used to grasp the features of urban street network more clearly. We applied this measure to the road network in Xiamen, China. Based on a standard method from statistical physics, we examined in more detail the distribution of this new measure and found that (1) due to the limitation of space geographic attributes, traditional research conclusions acquired by using the original definition of degree to study the primal approach modeled urban street network are not very persuasive; (2) both of the direction of the network connection and the degree's odd or even classifications need to be analyzed specifically; (3) the improved degree distribution presents obvious hierarchy, and hierarchical values conform to the power-law distribution, and correlation of our new measure shows some significant segmentation of the urban road network.
Improved asymptotic stability analysis for uncertain delayed state neural networks
International Nuclear Information System (INIS)
Souza, Fernando O.; Palhares, Reinaldo M.; Ekel, Petr Ya.
2009-01-01
This paper presents a new linear matrix inequality (LMI) based approach to the stability analysis of artificial neural networks (ANN) subject to time-delay and polytope-bounded uncertainties in the parameters. The main objective is to propose a less conservative condition to the stability analysis using the Gu's discretized Lyapunov-Krasovskii functional theory and an alternative strategy to introduce slack matrices. Two computer simulations examples are performed to support the theoretical predictions. Particularly, in the first example, the Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability. The second example is presented to illustrate how the proposed approach can provide better stability performance when compared to other ones in the literature
Liu, Mengting; Amey, Rachel C; Forbes, Chad E
2017-12-01
When individuals are placed in stressful situations, they are likely to exhibit deficits in cognitive capacity over and above situational demands. Despite this, individuals may still persevere and ultimately succeed in these situations. Little is known, however, about neural network properties that instantiate success or failure in both neutral and stressful situations, particularly with respect to regions integral for problem-solving processes that are necessary for optimal performance on more complex tasks. In this study, we outline how hidden Markov modeling based on multivoxel pattern analysis can be used to quantify unique brain states underlying complex network interactions that yield either successful or unsuccessful problem solving in more neutral or stressful situations. We provide evidence that brain network stability and states underlying synchronous interactions in regions integral for problem-solving processes are key predictors of whether individuals succeed or fail in stressful situations. Findings also suggested that individuals utilize discriminate neural patterns in successfully solving problems in stressful or neutral situations. Findings overall highlight how hidden Markov modeling can provide myriad possibilities for quantifying and better understanding the role of global network interactions in the problem-solving process and how the said interactions predict success or failure in different contexts.
Gemark Simonsen, Jenny; Gard, Gunvor
2016-09-15
Sonographers' perceptions of ergonomic and work-related pain problems at work have so far mostly been researched in quantitative studies by questionnaires. There is a need of experience-based research to deepen the knowledge about how sonographers perceive ergonomic problems at work. Therefore, the aim of this qualitative study was to describe sonographers' perceptions of ergonomic problems at work, and their suggestions for improvement strategies. Twenty-two female sonographers were individually interviewed regarding different aspects of their physical working environment. Content analysis was applied. The sonographers perceived different ergonomic problems in their working environment, but to offer patient comfort and to obtain the best possible images were often prioritized over working posture. Echocardiography was considered demanding as the examination is performed with little variation in posture. Ergonomic improvements included reducing the manual handling of the transducer, optimizing the adjustability of equipment, and taking the patient's physique and health into account. As some examinations were perceived to be more ergonomically demanding, variation between examinations was suggested, however, this requires broader skills. Sonography, especially echocardiography is ergonomically demanding but the improvement strategies suggested were perceived useful and applicable.
Software defined networking for improved wireless sensor network management: a survey
CSIR Research Space (South Africa)
Ndiaye, M
2017-05-01
Full Text Available Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment...
Directory of Open Access Journals (Sweden)
Faridah Hani Mohamed Salleh
2017-01-01
Full Text Available Gene regulatory network (GRN reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C as a direct interaction (A → C. Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Salleh, Faridah Hani Mohamed; Zainudin, Suhaila; Arif, Shereena M
2017-01-01
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Energy Technology Data Exchange (ETDEWEB)
Hsieh, Chien-Te, E-mail: cthsieh@saturn.yzu.edu.tw [Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan 320, Taiwan (China); Liu, Juan-Ru [Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan 320, Taiwan (China); Juang, Ruey-Shin [Department of Chemical and Materials Engineering, Chang Gung University, Taoyuan 333, Taiwan (China); Lee, Cheng-En; Chen, Yu-Fu [Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan 320, Taiwan (China)
2015-03-01
Herein reported is an efficient microwave-assisted (MA) approach for growing Cu network onto LiFePO{sub 4} (LFP) powders as cathode materials for high-performance Li-ion batteries. The MA approach is capable of depositing highly-porous Cu network, fully covered the LFP powders. The electrochemical performance of Cu-coated LFP cathodes are well characterized by charge/discharge cycling and electrochemical impedance spectroscopy (EIS). The Cu network acts as the key role in improving the specific capacity, rate capability, electrode polarization, as compared to fresh LFP cathode without the Cu coating. The EIS incorporated with equivalent circuit reveals that the completed Cu network obviously suppresses the charge transfer resistance. This result can be attributed to the fact that the Cu network ensures the LFP crystals to get electron easily, alleviating the electrode polarization in view of one-dimensional Li{sup +} ion mobility in the olivine crystals. Based on the analysis of Randles plots, the relatively higher Li{sup +} diffusion coefficient reflects the more efficient Li{sup +} pathway in the LFP powders through the aid of porous Cu network. - Highlights: • An efficient route was used to prepare Cu/LiFePO{sub 4} (LFP) hybrid as cathode material. • The Cu/LFP cathodes exhibit an improved performance as compared to fresh LFP one. • The microwave approach can deposit Cu network, fully covered the LFP powders. • The Cu network ensures LFP to get electrons, alleviating electrode polarization.
International Nuclear Information System (INIS)
Hsieh, Chien-Te; Liu, Juan-Ru; Juang, Ruey-Shin; Lee, Cheng-En; Chen, Yu-Fu
2015-01-01
Herein reported is an efficient microwave-assisted (MA) approach for growing Cu network onto LiFePO 4 (LFP) powders as cathode materials for high-performance Li-ion batteries. The MA approach is capable of depositing highly-porous Cu network, fully covered the LFP powders. The electrochemical performance of Cu-coated LFP cathodes are well characterized by charge/discharge cycling and electrochemical impedance spectroscopy (EIS). The Cu network acts as the key role in improving the specific capacity, rate capability, electrode polarization, as compared to fresh LFP cathode without the Cu coating. The EIS incorporated with equivalent circuit reveals that the completed Cu network obviously suppresses the charge transfer resistance. This result can be attributed to the fact that the Cu network ensures the LFP crystals to get electron easily, alleviating the electrode polarization in view of one-dimensional Li + ion mobility in the olivine crystals. Based on the analysis of Randles plots, the relatively higher Li + diffusion coefficient reflects the more efficient Li + pathway in the LFP powders through the aid of porous Cu network. - Highlights: • An efficient route was used to prepare Cu/LiFePO 4 (LFP) hybrid as cathode material. • The Cu/LFP cathodes exhibit an improved performance as compared to fresh LFP one. • The microwave approach can deposit Cu network, fully covered the LFP powders. • The Cu network ensures LFP to get electrons, alleviating electrode polarization
Directory of Open Access Journals (Sweden)
Ajay Arunachalam
2016-02-01
Full Text Available Resource Searching is one of the key functional tasks in large complex networks. With the P2P architecture, millions of peers connect together instantly building a communication pattern. Searching in mobile networks faces additional limitations and challenges. Flooding technique can cope up with the churn and searches aggressively by visiting almost all the nodes. But it exponentially increases the network traffic and thus does not scale well. Further the duplicated query messages consume extra battery power and network bandwidth. The blind flooding also suffers from long delay problem in P2P networks. In this paper, we propose optimal density based flooding resource discovery schemes. Our first model takes into account local graph topology information to supplement the resource discovery process while in our extended version we also consider the neighboring node topology information along with the local node information to further effectively use the mobile and network resources. Our proposed method reduces collision at the same time minimizes effect of redundant messages and failures. Overall the methods reduce network overhead, battery power consumption, query delay, routing load, MAC load and bandwidth usage while also achieving good success rate in comparison to the other techniques. We also perform a comprehensive analysis of the resource discovery schemes to verify the impact of varying node speed and different network conditions.
Wireless Sensor Network Quality of Service Improvement on Flooding Attack Condition
Hartono, R.; Widyawan; Wibowo, S. B.; Purnomo, A.; Hartatik
2018-03-01
There are two methods of building communication using wireless media. The first method is building a base infrastructure as an intermediary between users. Problems that arise on this type of network infrastructure is limited space to build any network physical infrastructure and also the cost factor. The second method is to build an ad hoc network between users who will communicate. On ad hoc network, each user must be willing to send data from source to destination for the occurrence of a communication. One of network protocol in Ad Hoc, Ad hoc on demand Distance Vector (AODV), has the smallest overhead value, easier to adapt to dynamic network and has small control message. One AODV protocol’s drawback is route finding process’ security for sending the data. In this research, AODV protocol is optimized by determining Expanding Ring Search (ERS) best value. Random topology is used with variation in the number of nodes: 25, 50, 75, 100, 125 and 150 with node’s speed of 10m/s in the area of 1000m x 1000m on flooding network condition. Parameters measured are Throughput, Packet Delivery Ratio, Average Delay and Normalized Routing Load. From the test results of AODV protocol optimization with best value of Expanding Ring Search (ERS), throughput increased by 5.67%, packet delivery ratio increased by 5.73%, and as for Normalized Routing Load decreased by 4.66%. ERS optimal value for each node’s condition depending on the number of nodes on the network.
Buddala, Raviteja; Mahapatra, Siba Sankar
2017-11-01
Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.
The SmartOR: a distributed sensor network to improve operating room efficiency.
Huang, Albert Y; Joerger, Guillaume; Fikfak, Vid; Salmon, Remi; Dunkin, Brian J; Bass, Barbara L; Garbey, Marc
2017-09-01
Despite the significant expense of OR time, best practice achieves only 70% efficiency. Compounding this problem is a lack of real-time data. Most current OR utilization programs require manual data entry. Automated systems require installation and maintenance of expensive tracking hardware throughout the institution. This study developed an inexpensive, automated OR utilization system and analyzed data from multiple operating rooms. OR activity was deconstructed into four room states. A sensor network was then developed to automatically capture these states using only three sensors, a local wireless network, and a data capture computer. Two systems were then installed into two ORs, recordings captured 24/7. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time of preoperative patient identification by the surgeon. From November 2014 to December 2015, data on 1003 cases were collected. The mean turnover time was 36 min, and 38% of cases met the institutional goal of ≤30 min. Data analysis also identified outlier cases (>1 SD from mean) in the domains of time from patient entry into the OR to intubation (11% of cases) and time from extubation to patient exiting the OR (11% of cases). Time from surgeon identification of patient to scheduled procedure start time was 11 min (institution bylaws require 20 min before scheduled start time), yet OR teams required 22 min on average to bring a patient into the room after surgeon identification. The SmartOR automatically and reliably captures data on OR room state and, in real time, identifies outlier cases that may be examined closer to improve efficiency. As no manual entry is required, the data are indisputable and allow OR teams to maintain a patient-centric focus.
Modification of Propellant Binder Network for Improvement of Mechanical Properties
1984-12-01
skeletal atoms) is more beneficial than 22 mole % of the shorter PEG 3350 (230 skeletal atoms) or 25 mole % of shorter PCP 0260 (180 skeletal atoms). One...of the reasons may be that a higher degree of strain-induced crystalliza- tion of PEG 8000 occurs compared with PEG 3350 or PCP 0260. 4.8 Effect of...prepolymer rubbers. Also, the stress capability of the cured rubbers is improved compared to the long chain prepolymer rubbers. Polyethylene glycol 8000 ( PEG
Directory of Open Access Journals (Sweden)
Arun Vasanaperumal
2015-11-01
Full Text Available There are number of potential applications of Wireless Sensor Networks (WSNs like wild habitat monitoring, forest fire detection, military surveillance etc. All these applications are constrained for power from a stand along battery power source. So it becomes of paramount importance to conserve the energy utilized from this power source. A lot of efforts have gone into this area recently and it remains as one of the hot research areas. In order to improve network lifetime and reduce average power consumption, this study proposes a novel cluster head selection algorithm. Clustering is the preferred architecture when the numbers of nodes are larger because it results in considerable power savings for large networks as compared to other ones like tree or star. Since majority of the applications generally involve more than 30 nodes, clustering has gained widespread importance and is most used network architecture. The optimum number of clusters is first selected based on the number of nodes in the network. When the network is in operation the cluster heads in a cluster are rotated periodically based on the proposed cluster head selection algorithm to increase the network lifetime. Throughout the network single-hop communication methodology is assumed. This work will serve as an encouragement for further advances in the low power techniques for implementing Wireless Sensor Networks (WSNs.
National Research Council Canada - National Science Library
Narayanan, N. H
2007-01-01
.... Results showed that various display strategies for augmenting information presented based on knowledge about both the viewer's gaze patterns and the problem solving procedure he or she is employing could indeed improve problem-solving performance.
National Research Council Canada - National Science Library
Narayanan, N. H
2007-01-01
.... Results showed that various display strategies for augmenting information presented based on knowledge about both the viewer's gaze patterns and the problem solving procedure he or she is employing could indeed improve problem-solving performance.
Shorikov, A. F.; Butsenko, E. V.
2017-10-01
This paper discusses the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. On the basis of network modeling proposed a new economic and mathematical model and a method for solving the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. Network economic and mathematical modeling allows you to determine the optimal time and calendar schedule for the implementation of the investment project and serves as an instrument to increase the economic potential and competitiveness of the enterprise. On a meaningful practical example, the processes of forming network models are shown, including the definition of the sequence of actions of a particular investment projecting process, the network-based work schedules are constructed. The calculation of the parameters of network models is carried out. Optimal (critical) paths have been formed and the optimal time for implementing the chosen technologies of the investment project has been calculated. It also shows the selection of the optimal technology from a set of possible technologies for project implementation, taking into account the time and cost of the work. The proposed model and method for solving the problem of managing investment projects can serve as a basis for the development, creation and application of appropriate computer information systems to support the adoption of managerial decisions by business people.
Coburn, Cynthia E.; Penuel, William R.; Geil, Kimberly E.
2015-01-01
The Carnegie Foundation for the Advancement of Teaching is a nonprofit, operating foundation with a long tradition of developing and studying ways to improve teaching practice. For the past three years, the Carnegie Foundation has initiated three different Networked Improvement Communities (NICs). The first, Quantway, is addressing the high…
Mental disorders as networks of problems : A review of recent insights
Fried, Eiko I.; van Borkulo, Claudia D.; Cramer, Angelique O. J.; Boschloo, Lynn; Schoevers, Robert A.; Borsboom, Denny
The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years.
A path based model for a green liner shipping network design problem
DEFF Research Database (Denmark)
Jepsen, Mads Kehlet; Brouer, Berit Dangaard; Plum, Christian Edinger Munk
2011-01-01
Liner shipping networks are the backbone of international trade providing low transportation cost, which is a major driver of globalization. These networks are under constant pressure to deliver capacity, cost effectiveness and environmentally conscious transport solutions. This article proposes...
Wang, Fengyu
Traditional deterministic reserve requirements rely on ad-hoc, rule of thumb methods to determine adequate reserve in order to ensure a reliable unit commitment. Since congestion and uncertainties exist in the system, both the quantity and the location of reserves are essential to ensure system reliability and market efficiency. The modeling of operating reserves in the existing deterministic reserve requirements acquire the operating reserves on a zonal basis and do not fully capture the impact of congestion. The purpose of a reserve zone is to ensure that operating reserves are spread across the network. Operating reserves are shared inside each reserve zone, but intra-zonal congestion may block the deliverability of operating reserves within a zone. Thus, improving reserve policies such as reserve zones may improve the location and deliverability of reserve. As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With existing deterministic reserve requirements unable to ensure optimal reserve locations, the importance of reserve location and reserve deliverability will increase. While stochastic programming can be used to determine reserve by explicitly modelling uncertainties, there are still scalability as well as pricing issues. Therefore, new methods to improve existing deterministic reserve requirements are desired. One key barrier of improving existing deterministic reserve requirements is its potential market impacts. A metric, quality of service, is proposed in this thesis to evaluate the price signal and market impacts of proposed hourly reserve zones. Three main goals of this thesis are: 1) to develop a theoretical and mathematical model to better locate reserve while maintaining the deterministic unit commitment and economic dispatch
McCullough, J Mac; Eisen-Cohen, Eileen; Salas, S Bianca
2016-07-13
Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when implementing a CHIP. The purpose of this study was to identify characteristics of the network involved in implementing the CHIP in one large community. The aims of this analysis are to: 1) identify essential network partners (and thereby highlight potential network gaps), 2) gauge current levels of partner involvement, 3) understand and effectively leverage network resources, and 4) enable a data-driven approach for future collaborative network improvements. We collected primary data via survey from n = 41 organizations involved in the Health Improvement Partnership of Maricopa County (HIPMC), in Arizona. Using the previously validated Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER) tool, organizations provided information on existing ties with other coalition members, including frequency and depth of partnership and eight categories of perceived value/trust of each current partner organization. The coalition's overall network had a density score of 30 %, degree centralization score of 73 %, and trust score of 81 %. Network maps are presented to identify existing relationships between HIPMC members according to partnership frequency and intensity, duration of involvement in the coalition, and self-reported contributions to the coalition. Overall, number of ties and other partnership measures were positively correlated with an organization's perceived value and trustworthiness as rated by other coalition members. Our study presents a novel use of social network analysis methods to evaluate the coalition of organizations involved in implementing a CHIP in an urban community. The large coalition had relatively low network density but high
Problems and Prospects in Automation and Networking in Libraries in India
Pradip, Joshi; Nikose, S.M.
2010-01-01
This article presents Scenario of Automation and the networking of academic libraries are still in their formative stages. The reasons for, prerequisites of, and benefits of networking are given. Networking systems at the national and local levels are described, as are the salient features of INFLIBNET, which has been functioning since 1988. There are also three metropolitan networks, viz., DELNET, CALIBNET, and BONET. The libraries of the three metropolitan cities are already reaping the ben...
Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.
Al-Saggaf, Yeslam; Islam, Md Zahidul
2015-08-01
This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.
Non-intrusive reduced order modeling of nonlinear problems using neural networks
Hesthaven, J. S.; Ubbiali, S.
2018-06-01
We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and employs artificial neural networks (ANNs), particularly multi-layer perceptrons (MLPs), to accurately approximate the coefficients of the reduced model. The search for the optimal number of neurons and the minimum amount of training samples to avoid overfitting is carried out in the offline phase through an automatic routine, relying upon a joint use of the Latin hypercube sampling (LHS) and the Levenberg-Marquardt (LM) training algorithm. This guarantees a complete offline-online decoupling, leading to an efficient RB method - referred to as POD-NN - suitable also for general nonlinear problems with a non-affine parametric dependence. Numerical studies are presented for the nonlinear Poisson equation and for driven cavity viscous flows, modeled through the steady incompressible Navier-Stokes equations. Both physical and geometrical parametrizations are considered. Several results confirm the accuracy of the POD-NN method and show the substantial speed-up enabled at the online stage as compared to a traditional RB strategy.
International Nuclear Information System (INIS)
Balino, Jorge L.; Larreteguy, Axel E.; Andrade Lima, Fernando R.
1995-01-01
The differential method was applied to the sensitivity analysis for water hammer problems in hydraulic networks. Starting from the classical water hammer equations in a single-phase liquid with friction, the state vector comprising the piezometric head and the velocity was defined. Applying the differential method the adjoint operator, the adjoint equations with the general form of their boundary conditions, and the general form of the bilinear concomitant were calculated. The discretized adjoint equations and the corresponding boundary conditions were programmed and solved by using the so called method of characteristics. As an example, a constant-level tank connected through a pipe to a valve discharging to atmosphere was considered. The bilinear concomitant was calculated for this particular case. The corresponding sensitivity coefficients due to the variation of different parameters by using both the differential method and the response surface generated by the computer code WHAT were also calculated. The results obtained with these methods show excellent agreement. (author). 11 refs, 2 figs, 2 tabs
Thomas, James C; Reynolds, Heidi W; Alterescu, Xavier; Bevc, Christine; Tsegaye, Ademe
2016-04-01
The service needs of people with human immunodeficiency virus (HIV) in low-income settings are wide-ranging. Service provision in a community is often disjointed among a variety of providers. We sought to reduce unmet patient needs by increasing referral coordination for HIV and family planning, measured as network density, with an organizational network approach. We conducted organizational network analysis on two networks in sub-cities of Addis Ababa, Ethiopia. There were 25 organizations in one sub-city network and 26 in the other. In one of them we sought to increase referrals through three network strengthening meetings. We then conducted the network analysis again in both sub-cities to measure any changes since baseline. We also quantitatively measured reported client service needs in both sub-cities before and after the intervention with two cross-sectional samples of face-to-face interviews with clients (459 at baseline and 587 at follow-up). In the sub-city with the intervention, the number of referral connections between organizations, measured as network density, increased 55%. In the control community, the density decreased over the same period. Reported unmet client service needs declined more consistently across services in the intervention community. This quasi experiment demonstrated that (1) an organizational network analysis can inform an intervention, (2) a modest network strengthening intervention can enhance client referrals in the network, (3) improvement in client referrals was accompanied by a decrease in atient-reported unmet needs and (4) a series of network analyses can be a useful evaluation tool. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Mushkin, I.; Solomon, S.
2017-10-01
We study the inverse contagion problem (ICP). As opposed to the direct contagion problem, in which the network structure is known and the question is when each node will be contaminated, in the inverse problem the links of the network are unknown but a sequence of contagion histories (the times when each node was contaminated) is observed. We consider two versions of the ICP: The strong problem (SICP), which is the reconstruction of the network and has been studied before, and the weak problem (WICP), which requires "only" the prediction (at each time step) of the nodes that will be contaminated at the next time step (this is often the real life situation in which a contagion is observed and predictions are made in real time). Moreover, our focus is on analyzing the increasing accuracy of the solution, as a function of the number of contagion histories already observed. For simplicity, we discuss the simplest (deterministic and synchronous) contagion dynamics and the simplest solution algorithm, which we have applied to different network types. The main result of this paper is that the complex problem of the convergence of the ICP for a network can be reduced to an individual property of pairs of nodes: the "false link difficulty". By definition, given a pair of unlinked nodes i and j, the difficulty of the false link (i,j) is the probability that in a random contagion history, the nodes i and j are not contaminated at the same time step (or at consecutive time steps). In other words, the "false link difficulty" of a non-existing network link is the probability that the observations during a random contagion history would not rule out that link. This probability is relatively straightforward to calculate, and in most instances relies only on the relative positions of the two nodes (i,j) and not on the entire network structure. We have observed the distribution of false link difficulty for various network types, estimated it theoretically and confronted it
Group relationships in early and late sessions and improvement in interpersonal problems.
Lo Coco, Gianluca; Gullo, Salvatore; Di Fratello, Carla; Giordano, Cecilia; Kivlighan, Dennis M
2016-07-01
Groups are more effective when positive bonds are established and interpersonal conflicts resolved in early sessions and work is accomplished in later sessions. Previous research has provided mixed support for this group development model. We performed a test of this theoretical perspective using group members' (actors) and aggregated group members' (partners) perceptions of positive bonding, positive working, and negative group relationships measured early and late in interpersonal growth groups. Participants were 325 Italian graduate students randomly (within semester) assigned to 1 of 16 interpersonal growth groups. Groups met for 9 weeks with experienced psychologists using Yalom and Leszcz's (2005) interpersonal process model. Outcome was assessed pre- and posttreatment using the Inventory of Interpersonal Problems, and group relationships were measured at Sessions 3 and 6 using the Group Questionnaire. As hypothesized, early measures of positive bonding and late measures of positive working, for both actors and partners, were positively related to improved interpersonal problems. Also as hypothesized, late measures of positive bonding and early measures of positive working, for both actors and partners, were negatively related to improved interpersonal problems. We also found that early actor and partner positive bonding and negative relationships interacted to predict changes in interpersonal problems. The findings are consistent with group development theory and suggest that group therapists focus on group-as-a-whole positive bonding relationships in early group sessions and on group-as-a-whole positive working relationships in later group sessions. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Lei Wang
2017-01-01
Full Text Available As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.
An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
Directory of Open Access Journals (Sweden)
Zhi Yang
2016-01-01
Full Text Available Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II.