We investigate the characteristics of constant-amplitude input shapers for robust time-optimal maneuvers of exi-ble structures. Trends in the timing of the shaper impulses are shown as a function of the system damping constant and move distance. The insensitivity to modeling errors, time-optimality, amount of vibration caused during maneuvers, and high frequency excitation of these shapers are ...

From the viewpoint of multilingual generation, the common underlying knowledge base should be kept clear of language-speciic concepts. This goal presupposes that lexical items of various languages cannot map one-to-one onto concepts all the time. We propose a more exi-ble way of attaching lexical items to conngurations of concepts and roles, and a lexical option nder that determines the set of ...

Building energy efficiency is vital, due to the substantial amount of consumed in buildings and associated adverse effects. A high-accuracy prediction model considered as one most effective ways understand building efficiency. In several studies, various machine learning models have been proposed for However, existing are based on classical approaches small datasets. Using a dataset inefficient...

climate is the most important factor which control desertification .in order to detect climate changes in desert zones, time trend analysis is applied to extreme indices in kashan station using extreme climate index software (ecis). results show significant trends in extreme indices during the past decade 1995-2004 and the pronounced warming is associated with a negative trend in cold extremes....

The description of the object shape is an important characteristic of the image; several different shape descriptors are used. This paper presents a novel shape descriptor which is robust with respect to noise, scale and orientation changes of the objects. It is based on the multi scale space approach to identify shapes. The descriptor of a shape is created by tracking the position of extreme c...

In extreme classification problems, learning algorithms are required to map instances to labels from an extremely large label set. We build on a recent extreme classification framework with logarithmic time and space [15], and on a general approach for error correcting output coding (ECOC [1]), and introduce a flexible and efficient approach accompanied by bounds. Our framework employs output c...

This paper presents an intrusion detection technique based on online sequential extreme learning machine. For performance evaluation, KDDCUP99 dataset is used. In this paper, we use three feature selection techniques – filtered subset evaluation, CFS subset evaluation and consistency subset evaluation to eliminate redundant features. Two network traffic profiling techniques are used. Alpha prof...