7,680 research outputs found
Spiking Reasoning System
© 2017 IEEE. In this position paper the newel approach for the spiking reasoning system for the real-time processing of a robotic system represented. This is the development of the 'Robot dream' architecture presented earlier, specifically the real-time robotic management system. The main idea of the architecture is inherited from our previous works on machine cognition that have their roots in works of Marvin Minsky, specifically 'model of six' as six levels of the mental activity. The principal approach for the high-level architecture and provide examples of the data structures of the spiking reasoning system and robotic system management architecture was demonstrated
Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms
Fuzzy Reasoning Spiking Neural P systems (FRSN P systems,
for short) is a variant of Spiking Neural P systems incorporating
fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge
and reasoning required for fault diagnosis applications. In this sense,
several FRSN P system variants have been proposed, dealing with real
numbers, trapezoidal numbers, weights, etc. The model incorporating
real numbers was the first introduced [13], presenting promising applications
in the field of fault diagnosis of electrical systems. For this variant,
a matrix-based algorithm was provided which, when executed on parallel
computing platforms, fully exploits the model maximally parallel
capacities. In this paper we introduce a P-Lingua framework extension
to parse and simulate FRSN P systems with real numbers. Two simulators,
implementing a variant of the original matrix-based simulation
algorithm, are provided: a sequential one (written in Java), intended to
run on traditional CPUs, and a parallel one, intended to run on CUDAenabled
devices.Ministerio de Economía y Competitividad TIN2012-3743
Fuzzy reasoning spiking neural P system for fault diagnosis
Spiking neural P systems (SN P systems) have been well established as a novel class of distributed
parallel computing models. Some features that SN P systems possess are attractive
to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required
for many fault diagnosis applications. The lack of ability is a major problem of existing SN P
systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by
introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing
mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems).
The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis
knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning
algorithm based on FRSN P systems is developed according to neuron’s dynamic firing mechanism.
Besides, a practical example of transformer fault diagnosis is used to demonstrate the
feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
Fuzzy reasoning spiking neural P systems revisited: A formalization
Research interest within membrane computing is becoming increasingly interdisciplinary.In particular, one of the latest applications is fault diagnosis. The underlying mechanismwas conceived by bridging spiking neural P systems with fuzzy rule-based reasoning systems. Despite having a number of publications associated with it, this research line stilllacks a proper formalization of the foundations.National Natural Science Foundation of China No 61320106005National Natural Science Foundation of China No 6147232
Logic Negation with Spiking Neural P Systems
Nowadays, the success of neural networks as reasoning systems is doubtless.
Nonetheless, one of the drawbacks of such reasoning systems is that they work
as black-boxes and the acquired knowledge is not human readable. In this paper,
we present a new step in order to close the gap between connectionist and logic
based reasoning systems. We show that two of the most used inference rules for
obtaining negative information in rule based reasoning systems, the so-called
Closed World Assumption and Negation as Finite Failure can be characterized by
means of spiking neural P systems, a formal model of the third generation of
neural networks born in the framework of membrane computing.Comment: 25 pages, 1 figur
Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes
This research is a survey to determine the career chosen of form four student
in commerce streams. The important aspect of the career chosen has been divided
into three, first is information about career, type of career and factor that most
influence students in choosing a career. The study was conducted at Sekolah
Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was
chosen by using non-random sampling purpose method as respondent. All
information was gather by using questionnaire. Data collected has been analyzed in
form of frequency, percentage and mean. Results are performed in table and graph.
The finding show that information about career have been improved in students
career chosen and mass media is the main factor influencing students in choosing
their career
Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration
To reveal fault propagation paths is one of the most critical studies for the analysis of
power system security; however, it is rather dif cult. This paper proposes a new framework for the fault
propagation path modeling method of power systems based on membrane computing.We rst model the fault
propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter
concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models
and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability
and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP
system and make them suitable for large-scale power systems, we propose a model reduction method
for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output
neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE
14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the
SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise
investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction,
and the simulation results show a new success and promising approach to the engineering domain
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration
Turn-taking is essential to the structure of human teamwork. Humans are
typically aware of team members' intention to keep or relinquish their turn
before a turn switch, where the responsibility of working on a shared task is
shifted. Future co-robots are also expected to provide such competence. To that
end, this paper proposes the Cognitive Turn-taking Model (CTTM), which
leverages cognitive models (i.e., Spiking Neural Network) to achieve early
turn-taking prediction. The CTTM framework can process multimodal human
communication cues (both implicit and explicit) and predict human turn-taking
intentions in an early stage. The proposed framework is tested on a simulated
surgical procedure, where a robotic scrub nurse predicts the surgeon's
turn-taking intention. It was found that the proposed CTTM framework
outperforms the state-of-the-art turn-taking prediction algorithms by a large
margin. It also outperforms humans when presented with partial observations of
communication cues (i.e., less than 40% of full actions). This early prediction
capability enables robots to initiate turn-taking actions at an early stage,
which facilitates collaboration and increases overall efficiency.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 201
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