18783473
A multi-objective-based non-stationary UAV assignment model for constraints handling using PSO
An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.
3020798
Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.
8588212
Temporal Aggregates in OWL-Time
In this paper, we describe our approach for representing temporal aggregates in OWL-Time, and a systematic way of mapping iCalendar recurrence sets to OWL-Time temporal sequences. We present several examples of natural language expressions for temporal aggregates and how they would be represented in OWL-Time; some examples can be described in both iCalendar and OWL-Time, while others can only be represented in OWL-Time. We also illustrate how we can translate from a natural language sentence to our representation.
16039054
Feature selection for ranking using boosted trees
Modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. The set of features useful for search ranking functions, though, continues to grow, making feature computation a latency bottleneck. As a result, not all available features can be used for ranking, and in fact, much of the time, only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. To this end, we explore different feature selection methods using boosted regression trees, including both greedy approaches (selecting the features with highest relative importance as computed by boosted trees; discounting importance by feature similarity and a randomized approach. We evaluate and compare these approaches using data from a commercial search engine. The experimental results show that the proposed randomized feature selection with feature-importance-based backward elimination outperforms greedy approaches and achieves a comparable relevance with 30 features to a full-feature model trained with 419 features and the same modeling parameters.
16312564
A new hybrid model for time-series prediction
This paper proposed a new hybrid model in order to increase time series prediction accuracy. This hybrid model considers the routine time prediction technique like AR, ANN or any others as atomic building block. A linear hybrid technique is used to combine their forecast result into the final result. The hybrid algorithm was tested against three different kinds of time series data. Experiments results showed the effectiveness of the proposed hybrid model.
7351388
An ontology of time for the semantic web
In connection with the DAML project for bringing about the Semantic Web, an ontology of time is being developed for describing the temporal content of Web pages and the temporal properties of Web services. This ontology covers topological properties of instants and intervals, measures of duration, and the meanings of clock and calendar terms.
9260921
Complexity adaptive quantization for intra-frames in very low bit rate video coding
Conventional rate control schemes focus on the problem of finding an optimal quantization value for P- and B-frames. No rate control is available for the encoding of I-frames. This could pose big problems due to the large number of bits an I-frame could generate, and due to the fact that the number of bits varies drastically from sequence to sequence, depending on their image complexity. This problem becomes severe especially at very low bit rate. Therefore, a mechanism to allocate data bits to an I-frame according to its complexity is indispensable in order to have constant coding quality. This work presents a mechanism to establish for I-frames a generic relationship between the quantization value, data bits, and their image complexity. Experimental results show that this generic relationship provides a fairly accurate estimation of quantization value for an I-frame at given data bits and image complexity, and is very useful in controlling the data bits generated by an I-frame.