Information Feedback Appraoch for the Simulation of Service Quality in the Inter-Object Communications

2008 ◽  
pp. 175-198
Author(s):  
R. Manjunath
Keyword(s):  
Neural Networks ◽  
Real Life ◽  
System Level ◽  
New Class ◽  
Model Based ◽  
Multiple Flows ◽  
Life Problems ◽  
Reference Design ◽  
The Impact ◽  
Level Performance

Simulation of a system with limited data is challenging. It calls for a certain degree of intelligence built into the system. This chapter provides a new model-based simulation methodology that may be customized and used in the simulation of a wide variety of problems involving multiple source-destination flows with intermediate agents. It explains the model based on a new class of neural networks called differentially fed artificial neural networks and the system level performance of the same. Next, as an example, the impact of system level differential feedback on multiple flows and the application of the concept are presented, followed by the simulation results. The author hopes that a variety of real life problems that involve multiple flows may be mapped onto this simulation model and optimal performance may be obtained. The model serves as a reference design that may be fine-tuned based on the application.

scholarly journals Formulating Modes of Cooperative Leaning for Education for Sustainable Development

2021 ◽  
Vol 13 (6) ◽  
pp. 3465
Author(s):  
Jordi Colomer ◽  
Dolors Cañabate ◽  
Brigita Stanikūnienė ◽  
Remigijus Bubnys
Keyword(s):  
Sustainable Development ◽  
Real Life ◽  
Education For Sustainable Development ◽  
Professional Life ◽  
Global Challenges ◽  
Life Problems ◽  
The Face ◽  
Contemporary Education ◽  
The Impact

In the face of today’s global challenges, the practice and theory of contemporary education inevitably focuses on developing the competences that help individuals to find meaningfulness in their societal and professional life, to understand the impact of local actions on global processes and to enable them to solve real-life problems [...]

scholarly journals Hybrid Neural-global Minimization Method of Logical Rule Extraction

1999 ◽  
Vol 3 (5) ◽  
pp. 348-356 ◽  
Author(s):  
Wlodzislaw Duch ◽  
◽  
Rafal Adamczak ◽  
KrzysAof Grabczewski ◽  
Grzegorz Zal
Keyword(s):  
Neural Networks ◽  
Hybrid Approach ◽  
Real Life ◽  
Rule Extraction ◽  
Global Minimization ◽  
Minimization Method ◽  
Life Problems ◽  
Multilayered Perceptron ◽  
Logical Functions ◽  
Logical Rules

Methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multilayered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming MLPs into simpler networks performing logical functions. A constructive algorithm called CMLP2LN is proposed, in which rules of increasing specificity are generated consecutively by adding more nodes to the network. Neural rule extraction is followed by optimization of rules using global minimization techniques. Estimation of confidence of various sets of rules is discussed. The hybrid approach to rule extraction has been applied to a number of benchmark and real life problems with very good results.

scholarly journals The origin of compression influences geometric instabilities in bilayers

2018 ◽  
Vol 474 (2217) ◽  
pp. 20180267 ◽  
Author(s):  
Sebastian Andres ◽  
Paul Steinmann ◽  
Silvia Budday
Keyword(s):  
Film Growth ◽  
Numerical Study ◽  
Real Life ◽  
Critical Conditions ◽  
Pattern Selection ◽  
Smart Surfaces ◽  
Life Problems ◽  
Wide Range ◽  
Post Buckling ◽  
The Impact

Geometric instabilities in bilayered structures control the surface morphology in a wide range of biological and technical systems. Depending on the application, different mechanisms induce compressive stresses in the bilayer. However, the impact of the chosen origin of compression on the critical conditions, post-buckling evolution and higher-order pattern selection remains insufficiently understood. Here, we conduct a numerical study on a finite-element set-up and systematically vary well-known factors contributing to pattern selection under the four main origins of compression: film growth, substrate shrinkage and whole-domain compression with and without pre-stretch. We find that the origin of compression determines the substrate stretch state at the primary instability point and thus significantly affects the critical buckling conditions. Similarly, it leads to different post-buckling evolutions and secondary instability patterns when the load further increases. Our results emphasize that future phase diagrams of geometric instabilities should incorporate not only the film thickness but also the origin of compression. Thoroughly understanding the influence of the origin of compression on geometric instabilities is crucial to solving real-life problems such as the engineering of smart surfaces or the diagnosis of neuronal disorders, which typically involve temporally or spatially combined origins of compression.

Using Artificial Neural Networks to Forecast Market Response

2011 ◽  
pp. 23-46
Author(s):  
Leonard J. Parsons ◽  
Ashutosh Dixit
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sales Performance ◽  
Marketing Mix ◽  
Causal Modeling ◽  
Market Response ◽  
Model Based ◽  
Artificial Neural ◽  
Marketing Actions ◽  
The Impact

Marketing managers must quantify the effects of marketing actions on contemporaneous and future sales performance. This chapter examines forecasting with artificial neural networks in the context of model-based planning and forecasting. The emphasis here is on causal modeling; that is, forecasting the impact of marketing mix variables, such as price and advertising, on sales.

A Methodology to Manage System-level Uncertainty During Conceptual Design

2006 ◽  
Vol 128 (4) ◽  
pp. 959-968 ◽  
Author(s):  
Jay D. Martin ◽  
Timothy W. Simpson
Keyword(s):  
Decision Making ◽  
Conceptual Design ◽  
Kriging Model ◽  
Satellite System ◽  
System Level ◽  
Computationally Efficient ◽  
Design Decisions ◽  
Current Design ◽  
The Impact ◽  
Level Performance

Current design decisions must be made while considering uncertainty in both models of the design and inputs to the design. In most cases, high fidelity models are used with the assumption that the resulting model uncertainties are insignificant to the decision making process. This paper presents a methodology for managing uncertainty during system-level conceptual design of complex multidisciplinary systems. This methodology is based upon quantifying the information available in a set of observations of computationally expensive subsystem models with more computationally efficient kriging models. By using kriging models, the computational expense of a Monte Carlo simulation to assess the impact of the sources of uncertainty on system-level performance parameters becomes tractable. The use of a kriging model as an approximation to an original computer model introduces model uncertainty, which is included as part of the methodology. The methodology is demonstrated as a decision-making tool for the design of a satellite system.

Model-Based Method to Translate System Level Customer Need to Part Specification

2010 ◽  
Author(s):  
Qi D. Van Eikema Hommes
Keyword(s):  
Gasoline Engine ◽  
System Level ◽  
Fuel Injector ◽  
Fuel Injectors ◽  
Customer Needs ◽  
Model Based ◽  
Powertrain System ◽  
Hardware Component ◽  
The Impact ◽  
Customer Need

Products are successful because they meet customer needs. However, many customer needs are not expressed in measurable terms. In addition, when such needs are achieved by a complex system made of hardware parts and software, decomposing customer needs to part-level specification is not a trivial task. This paper presents a model-based approach to address such problems. In the case study, the customer need was the noise and vibration level of an unconventional gasoline engine system when running at idle. The hardware component whose performance tolerance needed to be specified was a new type of fuel injectors. These new fuel injectors had higher piece-to-piece performance variations than the conventional fuel injectors. It was unclear whether such variation was acceptable for customer perceived powertrain quality. A virtual powertrain system simulation model was used to analytically evaluate the impact of the fuel injector performance variability. Monte Carlo simulation was carried out to assess the impact of injector variability. The results from the simulation were further refined using engine hardware testing. This study made recommendations for the acceptable level of hardware tolerance, which was different from what the supplier of the injectors had suggested.

scholarly journals Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Stefano Brivio ◽  
Denys R. B. Ly ◽  
Elisa Vianello ◽  
Sabina Spiga
Keyword(s):  
Neural Networks ◽  
Classification Performance ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
System Level ◽  
Training Time ◽  
Background Theory ◽  
Non Linear ◽  
Synaptic Dynamics ◽  
The Impact

Spiking neural networks (SNNs) are a computational tool in which the information is coded into spikes, as in some parts of the brain, differently from conventional neural networks (NNs) that compute over real-numbers. Therefore, SNNs can implement intelligent information extraction in real-time at the edge of data acquisition and correspond to a complementary solution to conventional NNs working for cloud-computing. Both NN classes face hardware constraints due to limited computing parallelism and separation of logic and memory. Emerging memory devices, like resistive switching memories, phase change memories, or memristive devices in general are strong candidates to remove these hurdles for NN applications. The well-established training procedures of conventional NNs helped in defining the desiderata for memristive device dynamics implementing synaptic units. The generally agreed requirements are a linear evolution of memristive conductance upon stimulation with train of identical pulses and a symmetric conductance change for conductance increase and decrease. Conversely, little work has been done to understand the main properties of memristive devices supporting efficient SNN operation. The reason lies in the lack of a background theory for their training. As a consequence, requirements for NNs have been taken as a reference to develop memristive devices for SNNs. In the present work, we show that, for efficient CMOS/memristive SNNs, the requirements for synaptic memristive dynamics are very different from the needs of a conventional NN. System-level simulations of a SNN trained to classify hand-written digit images through a spike timing dependent plasticity protocol are performed considering various linear and non-linear plausible synaptic memristive dynamics. We consider memristive dynamics bounded by artificial hard conductance values and limited by the natural dynamics evolution toward asymptotic values (soft-boundaries). We quantitatively analyze the impact of resolution and non-linearity properties of the synapses on the network training and classification performance. Finally, we demonstrate that the non-linear synapses with hard boundary values enable higher classification performance and realize the best trade-off between classification accuracy and required training time. With reference to the obtained results, we discuss how memristive devices with non-linear dynamics constitute a technologically convenient solution for the development of on-line SNN training.

scholarly journals Applications and props: the impact on engagement and understanding

2018 ◽  
Vol 17 (1) ◽  
pp. 3
Author(s):  
Alexander S Corner ◽  
Claire Cornock
Keyword(s):  
Group Theory ◽  
Data Storage ◽  
Planar Graphs ◽  
Real Life ◽  
Euclidean Algorithm ◽  
Modular Arithmetic ◽  
First Year ◽  
Life Problems ◽  
Physical Objects ◽  
The Impact

Problems based on applications or objects were added into a first year pure module in gaps where real-life problems were missing. Physical props were incorporated within the teaching sessions where it was possible. The additions to the module were the utilities problem whilst studying planar graphs, data storage when looking at number bases, RSA encryption after modular arithmetic and the Euclidean algorithm, as well as molecules and the mattress problem when looking at group theory. The physical objects used were tori, molecule models and mini mattresses. Evaluation was carried out through a questionnaire to gain the students' opinions of these additions and their general views of applications. Particular attention was paid to the effect on engagement and understanding.

Model of Human Actions Recognition Based on 2D Kernel

2021 ◽  
Author(s):  
Bogdan Alexandru Radulescu ◽  
Victorita Radulescu
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Real Life ◽  
Sensor Data ◽  
Human Actions ◽  
Advantages And Disadvantages ◽  
Life Problems ◽  
Starting Point ◽  
Rgb Images ◽  
Human Actions Recognition

Abstract Action Recognition is a domain that gains interest along with the development of specific motion capture equipment, hardware and power of processing. Its many applications in domains such as national security and behavior analysis make it even more popular among the scientific community, especially considering the ascending trend of machine learning methods. Nowadays approaches necessary to solve real life problems through human actions recognition became more interesting. To solve this problem are mainly two approaches when attempting to build a classifier, either using RGB images or sensor data, or where possible a combination of these two. Both methods have advantages and disadvantages and domains of utilization in real life problems, solvable through actions recognition. Using RGB input makes it possible to adopt a classifier on almost any infrastructure without specialized equipment, whereas combining video with sensor data provides a higher accuracy, albeit at a higher cost. Neural networks and especially convolutional neural networks are the starting point for human action recognition. By their nature, they can recognize very well spatial and temporal features, making them ideal for RGB images or sequences of RGB images. In the present paper is proposed the convolutional neural network architecture based on 2D kernels. Its structure, along with metrics measuring the performance, advantages and disadvantages are here illustrated. This solution based on 2D convolutions is fast, but has lower performance compared to other known solutions. The main problem when dealing with videos is the context extraction from a sequence of frames. Video classification using 2D Convolutional Layers is realized either by the most significant frame or by frame to frame, applying a probability distribution over the partial classes to obtain the final prediction. To classify actions, especially when differences between them are subtle, and consists of only a small part of the overall image is difficult. When classifying via the key frames, the total accuracy obtained is around 10%. The other approach, classifying each frame individually, proved to be too computationally expensive with negligible gains.

A FRAMEWORK FOR INTEGRATING ARTIFICIAL NEURAL NETWORKS AND LOGIC PROGRAMMING

1995 ◽  
Vol 04 (01n02) ◽  
pp. 3-32 ◽  
Author(s):  
J.H.M. LEE ◽  
V.W.L. TAM
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Logic Programming ◽  
Operational Semantics ◽  
Real Life ◽  
Constraint Logic Programming ◽  
Constraint Satisfaction Problems ◽  
Problem Size ◽  
Life Problems ◽  
Artificial Neural

Many real-life problems belong to the class of constraint satisfaction problems (CSP’s), which are NP-complete, and some NP-hard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a general framework for integrating artificial neural networks and logic programming so as to provide an efficient and yet expressive programming environment for solving CSP’s. To realize this framework, we propose PROCLANN, a novel constraint logic programming language. The PROCLANN language retains the simple and elegant declarative semantics of constraint logic programming. Operationally, PROCLANN uses the standard goal reduction strategy in the frontend to generate constraints, and an efficient backend constraint-solver based on artificial neural networks. Its operational semantics is probabilistic in nature. We show that PROCLANN is sound and weakly complete. A novelty of PROCLANN is that while it is a committed-choice language, PROCLANN supports non-determinism, allowing the generation of multiple answers to a query. An initial prototype implementation of PROCLANN is constructed and demonstrates empirically that PROCLANN out-performs the state of art in constraint logic programming implementation on certain hard instances of CSP’s.

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