8041290
A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price
In this paper, a new hybrid approach is presented to analyze factors affecting crude oil price using rough set and wavelet neural network. Related factors that affect crude oil price are found using text mining technique and Brent oil price is chosen as the decision price because it plays an important role in world crude oil markets. The relevant subsets of the factors are discovered by rough set module and the main factors are got, and then the important degrees of these are measured using wavelet neural network. Based on the novel hybrid approach, the predictability of crude oil price is discussed.
20174343
An operator splitting method for variational inequalities with partially unknown mappings
In this paper, we propose a new operator splitting method for solving a class of variational inequality problems in which part of the underlying mappings are unknown. This class of problems arises frequently from engineering, economics and transportation equilibrium problems. At each iteration, by using the information observed from the system, the method solves a system of nonlinear equations, which is well-defined. Under mild assumptions, the global convergence of the method is proved, and its efficiency is demonstrated with numerical examples.
17665653
Sequential Experimental Approach for Congestion Pricing with Multiple Vehicle Types and Multiple Time Periods
In this paper, a sequential experimental approach is proposed for searching an optimal congestion pricing with multiple vehicle types and multiple time periods. The method explicitly considers the interaction among different types of vehicular flows as well as the inter-dependence of travel demands among various origin-destination (O-D) pairs and in different time periods in estimating the optimal link tolls. Considering that the demand functions are not easily available in practice, the developed iterative experimental pricing strategy updates link tolls from only observed link flows without requiring the explicit forms of demand functions. The detailed procedure of implementation is presented and examined with the numerical experiments.
19005326
A hybrid particle swarm optimization approach with prior crossover differential evolution
Particle swarm optimization (PSO) is population-based heuristic searching algorithm. PSO has excellent ability of global optimization. However, there are some shortcomings of prematurity, low convergence accuracy and speed, similarly to other evolutionary algorithms (EA). To improve its performance, a hybrid particle swarm optimization is proposed in the paper. Firstly, the average position and velocity of particles are incorporated into basic PSO for concerning with the effect of the evolution of the whole swarm. Then a differential evolution (DE) computation, which introduces an extra population for prior crossover, is hybridized with the improved PSO to form a novel optimization algorithm, PSOPDE. The role of prior crossover is to appropriately diversify the population and increase the probability of reaching better solutions. DE component takes into account the stochastic differential variation, and enhances the exploitation in the neighborhoods of current solutions. PSOPDE is implemented on five typical benchmark functions, and compared with six other algorithms. The results indicate that PSOPDE behaves better, and greatly improve the searching efficiency and quality.
9032052
Deriving Event Relevance from the Ontology Constructed with Formal Concept Analysis
In this paper, we present a novel approach to derive event relevance from event ontology constructed with Formal Concept Analysis (FCA), a mathematical approach to data analysis and knowledge representation. The ontology is built from a set of relevant documents and according to the named entities associated to the events. Various relevance measures are explored, from binary to scaled, and from symmetrical to asymmetrical associations. We then apply the derived event relevance to the task of multi-document summarization. The experiments on DUC 2004 data set show that the relevant-event-based approaches outperform the independent-event-based approach.
28123449
Zum Einsatzpotenzial von Wetterderivaten in der Landwirtschaft - dargestellt am Beispiel einer Niederschlagsoption
In diesem Beitrag wird am Beispiel eines getreideproduzierenden Betriebes in Brandenburg unter Verwendung von realen Wetterdaten der risikomindernde Effekt durch den Einsatz von Niederschlagsoptionen analysiert. Es zeigt sich, dass Niederschlagsoptionen nur dann effektiv eingesetzt werden konnen, wenn sich die Referenzwetterstation in unmittelbarer Nahe des Ortes der landwirtschaftlichen Produktion befindet.
2507502
An Analytical Performance Model of IP-SAN
In this paper we propose a component-based analytical performance model for IP-SAN. We model four key components within IP-SAN individually and they are IP network, cache, RAID and disks. We validate these component models and compose them to give an entire IP-SAN performance model. The entire IP-SAN model analyzes the performance of an IP-SAN configuration under multiple synchronous workloads by using closed queueing network model and under asynchronous workload by using open queueing network model. We also validate the entire IP-SAN performance model against a real IP-SAN system. The theoretical results are compared with the results from a real IP-SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. To our knowledge, this is the first exploration into building analytical performance model for entire IP-SAN system and validating against a real system. We also identify areas for future research in the performance analysis of real IP-SAN.
2171158
Detecting video events based on action recognition in complex scenes using spatio-temporal descriptor
Event detection plays an essential role in video content analysis and remains a challenging open problem. In particular, the study on detecting human-related video events in complex scenes with both a crowd of people and dynamic motion is still limited. In this paper, we investigate detecting video events that involve elementary human actions, e.g. making cellphone call, putting an object down, and pointing to something, in complex scenes using a novel spatio-temporal descriptor based approach. A new spatio-temporal descriptor, which temporally integrates the statistics of a set of response maps of low-level features, e.g. image gradients and optical flows, in a space-time cube, is proposed to capture the characteristics of actions in terms of their appearance and motion patterns. Based on this kind of descriptors, the bag-of-words method is utilized to describe a human figure as a concise feature vector. Then, these features are employed to train SVM classifiers at multiple spatial pyramid levels to distinguish different actions. Finally, a Gaussian kernel based temporal filtering is conducted to segment the sequences of events from a video stream taking account of the temporal consistency of actions. The proposed approach is capable of tolerating spatial layout variations and local deformations of human actions due to diverse view angles and rough human figure alignment in complex scenes. Extensive experiments on the 50-hour video dataset of TRECVid 2008 event detection task demonstrate that our approach outperforms the well-known SIFT descriptor based methods and effectively detects video events in challenging real-world conditions.