Potential applications of neural networks in construction

1992 ◽  
Vol 19 (3) ◽  
pp. 521-529 ◽  
Author(s):  
Osama Moselhi ◽  
Tarek Hegazy ◽  
Paul Fazio
Keyword(s):  
Neural Networks ◽  
Problem Solving ◽  
Expert Systems ◽  
Large Scale ◽  
Computer Applications ◽  
Productivity Level ◽  
The Past ◽  
Intelligent Decision Support Systems ◽  
Potential Applications ◽  
Underlying Mechanisms

During the past decade, several engineering disciplines, including construction, have embarked on developing “intelligent” decision support systems based on artificial intelligence (AI) techniques, including expert systems, symbolic knowledge representation, and logic programming. These systems attempt to capture the domain experts' intelligent behaviour and reasoning process utilized in decision-making, without regard to the underlying mechanisms producing that behaviour. This approach involves describing behaviours, usually with rules and symbols. In contrast, neural networks (NN), another AI-based technique that has been pursued on a large scale during the past few years, does not describe behaviours but rather imitate them. Neural networks are particularly superior to traditional expert systems in providing timely solutions based primarily on analogy with previous experience, rather than reasoning or computation. As such, neural networks have a great potential to work either as a supplement or as a complement to algorithmic and (or) other AI-based systems, providing more suitable tools for solving the industry ill-structured problems.This paper describes several characteristics of neural networks and outlines the advantages and limitations of commonly used NN paradigms. Potential applications of each paradigm in construction are identified. Two example applications are provided to demonstrate the problem-solving capabilities of neural networks: (i) estimation of hourly production rate of an excavation equipment; and (ii) estimation of productivity level for a construction trade. Future possibilities of integrating neural networks with other problem-solving techniques are described. Key words: construction, management techniques, neural networks, expert systems, pattern recognition, computer applications.

Quasiperiodic-Chaotic Neural Networks and Short-Term Analog Memory

2021 ◽  
Vol 31 (01) ◽  
pp. 2130003
Author(s):  
Natsuhiro Ichinose
Keyword(s):  
Neural Networks ◽  
Visual Memory ◽  
Large Scale ◽  
Quasiperiodic Solution ◽  
Short Term ◽  
Chaotic Neural Networks ◽  
Analog Memory ◽  
Short Term Visual Memory ◽  
Potential Applications ◽  
Chaotic Neuron

A model of quasiperiodic-chaotic neural networks is proposed on the basis of chaotic neural networks. A quasiperiodic-chaotic neuron exhibits quasiperiodic dynamics that an original chaotic neuron does not have. Quasiperiodic and chaotic solutions are exclusively isolated in the parameter space. The chaotic domain can be identified by the presence of a folding structure of an invariant closed curve. Using the property that the influence of perturbation is conserved in the quasiperiodic solution, we demonstrate short-term visual memory in which real numbers are acceptable for representing colors. The quasiperiodic solution is sensitive to dynamical noise when images are restored. However, the quasiperiodic synchronization among neurons can reduce the influence of noise. Short-term analog memory using quasiperiodicity is important in that it can directly store analog quantities. The quasiperiodic-chaotic neural networks are shown to work as large-scale analog storage arrays. This type of analog memory has potential applications to analog computation such as deep learning.

Task Characteristics as Source of Difficulty and Moderators of the Effect of Time-on-Task in Digital Problem-Solving

2020 ◽  
Vol 58 (8) ◽  
pp. 1494-1514
Author(s):  
Zsófia Vörös ◽  
Dániel Kehl ◽  
Jean-François Rouet
Keyword(s):  
Problem Solving ◽  
Large Scale ◽  
Task Complexity ◽  
International Study ◽  
Computer Applications ◽  
Task Characteristics ◽  
Problem Solver ◽  
Time On Task ◽  
Information Problem Solving ◽  
Technology Users

To be able to solve complex information problems in a digital environment is a key 21st century skill. Technology users usually expect to achieve their goals in a fast and accurate way. However, the actual relationship between time-on-task and task outcome is currently not well understood. We analyzed data from a large-scale international study in which representative samples of adults had to solve more or less complex problems using standard computer applications. Our results indicate that different task characteristics influence the relationship between problem-solving performance and time-on-task in specific ways. Spending more time on a task is more likely to compensate an average problem solver when task complexity can be attributed to intrinsic task and technology drivers than when complexity stems from the cognitive/metacognitive activities belonging to information problem-solving processes per se, especially acquiring and evaluating information. Thus, the interpretation of time-on-task should take the source of difficulty into consideration. Implications for personal and professional development are discussed.

Knowledge-acquisition tools with explicit problem-solving models

1993 ◽  
Vol 8 (1) ◽  
pp. 5-25 ◽  
Author(s):  
William Birmingham ◽  
Georg Klinker
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Code Generation ◽  
System Development ◽  
Generalization Error ◽  
Domain Experts ◽  
The Past

AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

INTEGRATING DISTRIBUTED EXPERTISE

1992 ◽  
Vol 01 (03n04) ◽  
pp. 393-410 ◽  
Author(s):  
EVANGELOS SIMOUDIS ◽  
MARK ADLER
Keyword(s):  
Problem Solving ◽  
Expert Systems ◽  
Real World ◽  
Computational Models ◽  
Knowledge Based Systems ◽  
Computing Environment ◽  
The Past ◽  
Knowledge Based ◽  
Narrow Scope ◽  
Distributed Expertise

Over the past ten years a myriad of knowledge-based expert systems have been developed and deployed. These systems have a narrow scope and usually operate in stand-alone mode. They also follow different implementation philosophies and use a variety of reasoning methods. To address problems of wider scope, researchers have developed systems that utilize either centralized or distributed computational models. Each of these systems is homogeneous, and due to the way developed, prohibitively expensive for real-world settings. In this paper we present OMNI, a framework for integrating existing knowledge-based systems in a way that they can cooperate during problem-solving while they remain distributed over a computing environment.

Cerebral Organization of Music Processing

2018 ◽  
pp. 87-121
Author(s):  
Thenille Braun Janzen ◽  
Michael H. Thaut
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Current Knowledge ◽  
Brain Networks ◽  
Music Listening ◽  
Functional Basis ◽  
Functional Interactions ◽  
The Past ◽  
Neuroscience Research ◽  
New Generation

This chapter presents a broad panorama of the current knowledge concerning the anatomical and functional basis of music processing in the healthy brain. Neuroimaging studies developed over the past 20 years provide evidence that music processing takes place in widely distributed neural networks. Here, attention is focused on core brain networks implicated in music processing, emphasizing the anatomical and functional interactions between cortical and subcortical areas within auditory-frontal networks, auditory-motor networks, and auditory-limbic networks. Finally, the authors review recent studies investigating how brain networks organize themselves in a naturalistic music listening context. Collectively, this robust body of literature demonstrates that music processing requires timely coordination of large-scale cognitive, motor, and limbic brain networks, setting the stage for a new generation of music neuroscience research on the dynamic organization of brain networks underlying music processing.

New Premises and New Tools for Medical Care and Medical Education

1989 ◽  
Vol 28 (04) ◽  
pp. 207-214 ◽  
Author(s):  
L. Weed
Keyword(s):  
Medical Education ◽  
Problem Solving ◽  
Expert Systems ◽  
Medical Care ◽  
Medical Record ◽  
The Past ◽  
Progress Notes ◽  
Problem Lists

Abstract:Medical care and medical education can be supported more than in the past by using new tools and new premises for the effective linkage between bodies of knowledge and the use of that knowledge. The medical record can be converted from a source-oriented record to a problem-oriented record, enabling to trace not only what was done, but why it was done. These possiblities reveal new insights in the use of databases, problem lists, problem-oriented plans, and problem-oriented progress notes and flowsheets. It brings about a neW behavior in teaching which replaces memorizing facts, new possibilities for medical care, and new responsibilities both for physicians and patients. We now have knowledge-coupling tools that can be used directly with the patients at the time of problem solving. Patients are becoming active participants in this process, bringing about new roles for experts as well as expert systems.

Artificial Intelligence in Chemistry

1992 ◽  
Vol 57 (12) ◽  
pp. 2413-2451 ◽  
Author(s):  
Vladimír Jakuš
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Problem Solving ◽  
Expert Systems ◽  
Intelligent Systems ◽  
Structure Elucidation ◽  
Chromatographic Retention ◽  
Natural Languages ◽  
Synthesis Planning ◽  
Intelligent Instrumentation

The definition of artificial intelligence and the associated tasks of this branch of science are discussed. The tasks include pattern recognition, adaptation and learning, problem solving by means of expert systems or neural networks, and understanding the natural language and communication with a machine in it. The principles of problem solving are analyzed. It is demonstrated how artificial intelligence-based computer programs in which chemical expertise is encoded assist in structure elucidation, in the investigation of relations between structure and biological activity or chromatographic retention, etc.; problems emerging in the synthesis planning with a retrosynthetic analysis, or in the planning of experiments and intelligent consultations are dealt with. Several models used for structure elucidation and synthesis planning are evaluated. An overview is presented of additional expert systems which, along with artificial intelligence-based robotics, are used in intelligent instrumentation. Also discussed is the role of neural networks, which begin to be successfully employed in structure elucidation, synthesis planning, in intelligent instrumentation and in the treatment of natural languages. They are expected to be an important tool in the implementation of intelligent systems for the classification of chemical databases and prediction of properties of molecules.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

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