Search results for "feature"
showing 10 items of 4091 documents
Saules aktīvo apgabalu īpatnību pētījumi, izmantojot novērojumus mikroviļņu diapazonā
2013
Elektroniskā versija nesatur pielikumus
Dissipative scaling of step-pool features
2021
Abstract This paper focuses on the dissipative similarity of step-pool units at rill, flume and stream scale. This investigation is carried out using recent advances in open channel flow resistance, applications of close-range photogrammetry to rill erosion, available published data on step-pool features in flumes and streams and a new dataset of measurements in fixed bed step-pool rills. A theoretically-based equation for calculating the Darcy-Weisbach friction factor obtained by integration of a power velocity profile is presented. The scale factor Γ of this power velocity profile, which is included in the flow resistance equation, was previously calibrated (Eq. 10) for mobile bed rills w…
Scale detection via keypoint density maps in regular or near-regular textures
2013
In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture ''scale'' as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A ''mode'' vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as th…
Comparative study of techniques for large-scale feature selection* *This work was suported by a SERC grant GR/E 97549. The first author was also supp…
1994
The combinatorial search problem arising in feature selection in high dimensional spaces is considered. Recently developed techniques based on the classical sequential methods and the (l, r) search called Floating search algorithms are compared against the Genetic approach to feature subset search. Both approaches have been designed with the view to give a good compromise between efficiency and effectiveness for large problems. The purpose of this paper is to investigate the applicability of these techniques to high dimensional problems of feature selection. The aim is to establish whether the properties inferred for these techniques from medium scale experiments involving up to a few tens …
Reflection scanning microscopy.
1992
To image nontransparent samples we have utilized a special type of scanning-probe microscope that is referred to here as a reflection scanning microscope. The reflection scanning microscope provides a method for producing a scanned point light source as well as a system for collecting the light that is reflected by the sample. The system, which uses an optical fiber coupler, is easily installed on an existing photon scanning tunneling microscope. A calculation of the coupling coefficient between the natural propagation mode of the optical fiber and the light that is reflected by the sample is presented along with a comparison between calculated and measured values of the intensity of the li…
A gray-level 2D feature detector using circular statistics
1997
Abstract This paper presents a new method for corner and circular feature detection in gray-level images. It is based on the application of standard statistical techniques to the distribution of gradient orientations in a circular neighborhood of the prospective feature point. An evaluation using standard procedures and a comparison with other approaches is presented. Results show the robustness of this method as compared to the other corner detectors analyzed. The main novelties are the possibility of detecting points that are centers of circular symmetries, and discriminating between junctions, which are classified into corners (two-edge junctions) and multiple edge junctions.
A Neural Network model for the Evaluation of Text Complexity in Italian Language: a Representation Point of View
2018
Abstract The goal of a text simplification system (TS) is to create a new text suited to the characteristics of a reader, with the final goal of making it more understandable.The building of an Automatic Text Simplification System (ATS) cannot be separated from a correct evaluation of the text complexity. In fact the ATS must be capable of understanding if a text should be simplified for the target reader or not. In a previous work we have presented a model capable of classifying Italian sentences based on their complexity level. Our model is a Long Short Term Memory (LSTM) Neural Network capable of learning the features of easy-to-read and complex-to-read sentences autonomously from a anno…
Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains
2021
This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the…
Elliptic Curve-Based Secure Multidimensional Aggregation for Smart Grid Communications
2017
In smart grid, data aggregation is considered as an essential paradigm in assessing information about current energy usage. To achieve the privacy-preserving goal, several homomorphic-based solutions have been proposed. However, these solutions either consider one-dimensional information or use costly pairing computation in order to ensure source authentication. In fact, smart grid data are likely to be multidimensional (e.g., time, purpose, and so on) for more accurate control. In addition, the aggregation node in smart grid needs to verify data that come from several smart meters in a residential area; hence, the verification must be cost-efficient. In this paper, we propose a scheme that…
Multi-cloud privacy preserving schemes for linear data mining
2015
This paper presents an approach to privacy-preserving data mining that relies upon a relatively simple secret sharing scheme. Its main feature is that users, sensitive data owners, are engaged in the secret sharing operations that protect their privacy. They are grouped in independent clouds connected to a central unit, the data miner, that only manages the aggregated data of each cloud, therefore avoiding the disclosure of information belonging to single nodes. We propose two privacy preserving schemes, with different privacy levels and communication costs. When designing them, we assume that some users' data might become inaccessible during the operation of the privacy preserving protocol…