31,681 research outputs found
Modeling of Social Transitions Using Intelligent Systems
In this study, we reproduce two new hybrid intelligent systems, involve three
prominent intelligent computing and approximate reasoning methods: Self
Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory
(RST),called SONFIS and SORST. We show how our algorithms can be construed as a
linkage of government-society interactions, where government catches various
states of behaviors: solid (absolute) or flexible. So, transition of society,
by changing of connectivity parameters (noise) from order to disorder is
inferred
Soft computing techniques applied to finance
Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad
Using fuzzy logic to handle the semantic descriptions of music in a content-based retrieval system
This paper explores the potential use of fuzzy logic for semantic music recommendation. We show that a set of affective/emotive, structural and kinaesthetic descriptors can be used to formulate a query which allows the retrieval of intended music. A semantic music recommendation system was built, based on an elaborate study of potential users and an analysis of the semantic descriptors that best characterize the user’s understanding of music. Significant relationships between expressive and structural semantic descriptions of music were found. Fuzzy logic was then applied to handle the
quality ratings associated with the semantic descriptions. A working semantic music recommendation system was tested and evaluated. Real-world testing revealed high user satisfaction
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future
Robust and fully automated segmentation of mandible from CT scans
Mandible bone segmentation from computed tomography (CT) scans is challenging
due to mandible's structural irregularities, complex shape patterns, and lack
of contrast in joints. Furthermore, connections of teeth to mandible and
mandible to remaining parts of the skull make it extremely difficult to
identify mandible boundary automatically. This study addresses these challenges
by proposing a novel framework where we define the segmentation as two
complementary tasks: recognition and delineation. For recognition, we use
random forest regression to localize mandible in 3D. For delineation, we
propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation
algorithm, operating on the recognized mandible sub-volume. Despite heavy CT
artifacts and dental fillings, consisting half of the CT image data in our
experiments, we have achieved highly accurate detection and delineation
results. Specifically, detection accuracy more than 96% (measured by union of
intersection (UoI)), the delineation accuracy of 91% (measured by dice
similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff
Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical
Imaging (ISBI) 201
Active Clothing Material Perception using Tactile Sensing and Deep Learning
Humans represent and discriminate the objects in the same category using
their properties, and an intelligent robot should be able to do the same. In
this paper, we build a robot system that can autonomously perceive the object
properties through touch. We work on the common object category of clothing.
The robot moves under the guidance of an external Kinect sensor, and squeezes
the clothes with a GelSight tactile sensor, then it recognizes the 11
properties of the clothing according to the tactile data. Those properties
include the physical properties, like thickness, fuzziness, softness and
durability, and semantic properties, like wearing season and preferred washing
methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616
robot exploring iterations on them. To extract the useful information from the
high-dimensional sensory output, we applied Convolutional Neural Networks (CNN)
on the tactile data for recognizing the clothing properties, and on the Kinect
depth images for selecting exploration locations. Experiments show that using
the trained neural networks, the robot can autonomously explore the unknown
clothes and learn their properties. This work proposes a new framework for
active tactile perception system with vision-touch system, and has potential to
enable robots to help humans with varied clothing related housework.Comment: ICRA 2018 accepte
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