Web information may currently be acquired by activating search engines. However, our daily experience is not only that web pages are often either redundant or missing but also that there is a mismatch between information needs and the web's responses. If we wish to satisfy more complex requests, we need to extract part of the information and transform it into new interactive knowledge. This transformation may either be performed by hand or automatically. In this article we describe an experimental agent-based framework skilled to help the user both in managing achieved information and in personalizing web searching activity. The first process is supported by a query-formulation facility and by a friendly structured representation of the searching results. On the other hand, the system provides a proactive support to the searching on the web by suggesting pages, which are selected according to the user's behavior shown in his navigation activity. A basic role is played by an extension of a classical fuzzy-clustering algorithm that provides a prototype-based representation of the knowledge extracted from the web. These prototypes lead both the proactive suggestion of new pages, mined through web spidering, and the structured representation of the searching results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1101–1122, 2007.
To improve the localisation accuracy of a DV-Hop localisation algorithm in the wireless sensor networks (WSNs) with irregular network topologies, an improved DV-Hop algorithm based on differential simulated annealing evolution (DSAE) is proposed.The distance between the unknown node and anchor are main element in the phase of calculating the coordinate of the unknown node, this is calculated by average hop distance (HD).So, the key of improving error will calculate HD.We innovatively divide the hop distance into the global single-hop average HD between beacon nodes, the corrected average HD between anchor nodes and the local single-hop average HD between anchor nodes.Combining three kinds of HDs, DSAE based on the HC threshold is used to estimate the average HD.The simulation results show that the improved DV-Hop algorithm can decrease error and significantly outperforms state of the art localisation algorithm by 37.38% in terms of localisation error.
In the next generation of robots, there are no motors and construction is from simple everyday materials. One of the stars of this evolution, is a wire which contracts when heated and is known as muscle wire. A simple soda straw provides a strong structure from which segmented limbs can be constructed. A flex sensor is used at the joint to measure the actual bend angle. A microcontroller controls the limb based on input from a higher level controller. The article covers the construction of a limb, characterizes it and presents some control solutions.
Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.
Fuzzy rule-based models form a commonly encountered category of fuzzy models. As such they have enjoyed a great deal of conceptual and algorithmic developments followed by numerous case studies. This paper contributes to this area by bringing forward a two-phase design of fuzzy rules completed on the basis of experimental data. This design directly reflects upon the nature of the rules vis-à-vis the data used in their construction. First, information granules (fuzzy sets) standing in condition and conclusion parts of the individual rules are formed following a commonly used clustering technique of Fuzzy C-Means (FCM). The results of fuzzy clustering are directly used to build a collection of fuzzy sets of conditions and conclusions forming the individual rules. Some optimization aspects are raised in this context by expressing the performance of the condition and conclusion fuzzy sets in terms of the reconstruction abilities of the data captured by the rules. Second, fuzzy sets present in the rules (which are typically described by membership functions having infinite support) are transformed into interval-valued information granules of finite support that capture the essential (core) relationships between the regions in the input and output spaces strongly supported by the experimental data. In this way, the proposed rule-based model exhibits a two-tier architecture built in two successive phases. Subsequently, the proposed architecture invokes two fundamentally different modes of reasoning: 1) a recall mode in case when a new datum is positioned within the interval-valued information granules and 2) approximation mode where we invoke an aggregation of the individual rules given their activation levels in case a new datum does not belong to the core structure of the rules. These two modes produce granular results (represented as intervals). A way of assessing the quality of the obtained results is provided. Along with these two modes, we offer a characterization of the quality of results as well as the quality of the rules (expressed in terms of coverage, specificity of condition space and specificity of conclusion space). Experimental results are reported to illustrate the design process and the performance of the constructed model.
Linz Donawitz converter gas (LDG) is one of the most important sources of fuel energy in steel industry, whose reasonable use plays a crucial role in energy saving and environment protection. In practice, online prediction of variation of gas holder level and gas demand by users is fundamental to gas utilization and scheduling activities. In this study, a least square support vector machine-based prediction model combined with the parallel strategies is proposed, in which parameter optimization is realized online by a parallel particle swarm optimization and a parallelized validation method, both being implemented with the use of a graphic processing unit. The experiments demonstrate that the online parameter optimization based model greatly improves the prediction quality compared to the version with the fixed modeling parameters. Furthermore, the parallelized strategies largely reduce the computational cost thus guaranteeing the real-time effectiveness of the practical application.
